fix(celery): Guardrail for User File Processing (#8633)

Co-authored-by: Jamison Lahman <jamison@lahman.dev>

.git-blame-ignore-revs

@@ -0,0 +1,8 @@
+# Exclude these commits from git blame (e.g. mass reformatting).
+# These are ignored by GitHub automatically.
+# To enable this locally, run:
+#
+#    git config blame.ignoreRevsFile .git-blame-ignore-revs
+
+3134e5f840c12c8f32613ce520101a047c89dcc2  # refactor(whitespace): rm temporary react fragments (#7161)
+ed3f72bc75f3e3a9ae9e4d8cd38278f9c97e78b4  # refactor(whitespace): rm react fragment #7190

.github/CODEOWNERS

@@ -1,3 +1,10 @@
 * @onyx-dot-app/onyx-core-team
 # Helm charts Owners
 /helm/ @justin-tahara
+
+# Web standards updates
+/web/STANDARDS.md @raunakab @Weves
+
+# Agent context files
+/CLAUDE.md.template @Weves
+/AGENTS.md.template @Weves

.github/actions/setup-python-and-install-dependencies/action.yml

@@ -7,14 +7,6 @@ inputs:
 runs:
   using: "composite"
   steps:
-    - name: Setup uv
-      uses: astral-sh/setup-uv@ed21f2f24f8dd64503750218de024bcf64c7250a # ratchet:astral-sh/setup-uv@v7
-      with:
-        version: "0.9.9"
-      # TODO: Enable caching once there is a uv.lock file checked in.
-      # with:
-      #   enable-cache: true
-
     - name: Compute requirements hash
       id: req-hash
       shell: bash
@@ -30,6 +22,8 @@ runs:
         done <<< "$REQUIREMENTS"
         echo "hash=$(echo "$hash" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
+    # NOTE: This comes before Setup uv since clean-ups run in reverse chronological order
+    # such that Setup uv's prune-cache is able to prune the cache before we upload.
     - name: Cache uv cache directory
       uses: runs-on/cache@50350ad4242587b6c8c2baa2e740b1bc11285ff4 # ratchet:runs-on/cache@v4
       with:
@@ -38,6 +32,14 @@ runs:
         restore-keys: |
           ${{ runner.os }}-uv-
 
+    - name: Setup uv
+      uses: astral-sh/setup-uv@ed21f2f24f8dd64503750218de024bcf64c7250a # ratchet:astral-sh/setup-uv@v7
+      with:
+        version: "0.9.9"
+      # TODO: Enable caching once there is a uv.lock file checked in.
+      # with:
+      #   enable-cache: true
+
     - name: Setup Python
       uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # ratchet:actions/setup-python@v5
       with:

.github/workflows/nightly-close-stale-issues.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 45
     steps:
-      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # ratchet:actions/stale@v10
+      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # ratchet:actions/stale@v10
         with:
           stale-issue-message: 'This issue is stale because it has been open 75 days with no activity. Remove stale label or comment or this will be closed in 15 days.'
           stale-pr-message: 'This PR is stale because it has been open 75 days with no activity. Remove stale label or comment or this will be closed in 15 days.'

.github/workflows/pr-external-dependency-unit-tests.yml

@@ -38,6 +38,8 @@ env:
   # LLMs
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+  VERTEX_CREDENTIALS: ${{ secrets.VERTEX_CREDENTIALS }}
+  VERTEX_LOCATION: ${{ vars.VERTEX_LOCATION }}
 
   # Code Interpreter
   # TODO: debug why this is failing and enable
@@ -170,7 +172,7 @@ jobs:
 
       - name: Upload Docker logs
         if: failure()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v5
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-logs-${{ matrix.test-dir }}
           path: docker-logs/

.github/workflows/pr-helm-chart-testing.yml

@@ -6,11 +6,11 @@ concurrency:
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches: [main]
   push:
     tags:
       - "v*.*.*"
-  workflow_dispatch:  # Allows manual triggering
+  workflow_dispatch: # Allows manual triggering
 
 permissions:
   contents: read
@@ -18,225 +18,233 @@ permissions:
 jobs:
   helm-chart-check:
     # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=8cpu-linux-x64,hdd=256,"run-id=${{ github.run_id }}-helm-chart-check"]
+    runs-on:
+      [
+        runs-on,
+        runner=8cpu-linux-x64,
+        hdd=256,
+        "run-id=${{ github.run_id }}-helm-chart-check",
+      ]
     timeout-minutes: 45
 
     # fetch-depth 0 is required for helm/chart-testing-action
     steps:
-    - name: Checkout code
-      uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
-      with:
-        fetch-depth: 0
-        persist-credentials: false
+      - name: Checkout code
+        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
-    - name: Set up Helm
-      uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # ratchet:azure/setup-helm@v4.3.1
-      with:
-        version: v3.19.0
+      - name: Set up Helm
+        uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # ratchet:azure/setup-helm@v4.3.1
+        with:
+          version: v3.19.0
 
-    - name: Set up chart-testing
-      uses: helm/chart-testing-action@6ec842c01de15ebb84c8627d2744a0c2f2755c9f # ratchet:helm/chart-testing-action@v2.8.0
+      - name: Set up chart-testing
+        # NOTE: This is Jamison's patch from https://github.com/helm/chart-testing-action/pull/194
+        uses: helm/chart-testing-action@8958a6ac472cbd8ee9a8fbb6f1acbc1b0e966e44 # zizmor: ignore[impostor-commit]
+        with:
+          uv_version: "0.9.9"
 
-    # even though we specify chart-dirs in ct.yaml, it isn't used by ct for the list-changed command...
-    - name: Run chart-testing (list-changed)
-      id: list-changed
-      env:
-        DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
-      run: |
-        echo "default_branch: ${DEFAULT_BRANCH}"
-        changed=$(ct list-changed --remote origin --target-branch ${DEFAULT_BRANCH} --chart-dirs deployment/helm/charts)
-        echo "list-changed output: $changed"
-        if [[ -n "$changed" ]]; then
-          echo "changed=true" >> "$GITHUB_OUTPUT"
-        fi
-
-    # uncomment to force run chart-testing
-#     - name: Force run chart-testing (list-changed)
-#       id: list-changed
-#       run: echo "changed=true" >> $GITHUB_OUTPUT
-
-    # lint all charts if any changes were detected
-    - name: Run chart-testing (lint)
-      if: steps.list-changed.outputs.changed == 'true'
-      run: ct lint --config ct.yaml --all
-      # the following would lint only changed charts, but linting isn't expensive
-      # run: ct lint --config ct.yaml --target-branch ${{ github.event.repository.default_branch }}
-
-    - name: Create kind cluster
-      if: steps.list-changed.outputs.changed == 'true'
-      uses: helm/kind-action@92086f6be054225fa813e0a4b13787fc9088faab # ratchet:helm/kind-action@v1.13.0
-
-    - name: Pre-install cluster status check
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Pre-install Cluster Status ==="
-        kubectl get nodes -o wide
-        kubectl get pods --all-namespaces
-        kubectl get storageclass
-
-    - name: Add Helm repositories and update
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Adding Helm repositories ==="
-        helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
-        helm repo add vespa https://onyx-dot-app.github.io/vespa-helm-charts
-        helm repo add cloudnative-pg https://cloudnative-pg.github.io/charts
-        helm repo add ot-container-kit https://ot-container-kit.github.io/helm-charts
-        helm repo add minio https://charts.min.io/
-        helm repo add code-interpreter https://onyx-dot-app.github.io/code-interpreter/
-        helm repo update
-
-    - name: Install Redis operator
-      if: steps.list-changed.outputs.changed == 'true'
-      shell: bash
-      run: |
-        echo "=== Installing redis-operator CRDs ==="
-        helm upgrade --install redis-operator ot-container-kit/redis-operator \
-          --namespace redis-operator --create-namespace --wait --timeout 300s
-
-    - name: Pre-pull required images
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Pre-pulling required images to avoid timeout ==="
-        KIND_CLUSTER=$(kubectl config current-context | sed 's/kind-//')
-        echo "Kind cluster: $KIND_CLUSTER"
-
-        IMAGES=(
-          "ghcr.io/cloudnative-pg/cloudnative-pg:1.27.0"
-          "quay.io/opstree/redis:v7.0.15"
-          "docker.io/onyxdotapp/onyx-web-server:latest"
-        )
-
-        for image in "${IMAGES[@]}"; do
-          echo "Pre-pulling $image"
-          if docker pull "$image"; then
-            kind load docker-image "$image" --name "$KIND_CLUSTER" || echo "Failed to load $image into kind"
-          else
-            echo "Failed to pull $image"
+      # even though we specify chart-dirs in ct.yaml, it isn't used by ct for the list-changed command...
+      - name: Run chart-testing (list-changed)
+        id: list-changed
+        env:
+          DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
+        run: |
+          echo "default_branch: ${DEFAULT_BRANCH}"
+          changed=$(ct list-changed --remote origin --target-branch ${DEFAULT_BRANCH} --chart-dirs deployment/helm/charts)
+          echo "list-changed output: $changed"
+          if [[ -n "$changed" ]]; then
+            echo "changed=true" >> "$GITHUB_OUTPUT"
           fi
-        done
 
-        echo "=== Images loaded into Kind cluster ==="
-        docker exec "$KIND_CLUSTER"-control-plane crictl images | grep -E "(cloudnative-pg|redis|onyx)" || echo "Some images may still be loading..."
+      # uncomment to force run chart-testing
+      #     - name: Force run chart-testing (list-changed)
+      #       id: list-changed
+      #       run: echo "changed=true" >> $GITHUB_OUTPUT
+      # lint all charts if any changes were detected
+      - name: Run chart-testing (lint)
+        if: steps.list-changed.outputs.changed == 'true'
+        run: ct lint --config ct.yaml --all
+        # the following would lint only changed charts, but linting isn't expensive
+        # run: ct lint --config ct.yaml --target-branch ${{ github.event.repository.default_branch }}
 
-    - name: Validate chart dependencies
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Validating chart dependencies ==="
-        cd deployment/helm/charts/onyx
-        helm dependency update
-        helm lint .
+      - name: Create kind cluster
+        if: steps.list-changed.outputs.changed == 'true'
+        uses: helm/kind-action@92086f6be054225fa813e0a4b13787fc9088faab # ratchet:helm/kind-action@v1.13.0
 
-    - name: Run chart-testing (install) with enhanced monitoring
-      timeout-minutes: 25
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Starting chart installation with monitoring ==="
+      - name: Pre-install cluster status check
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Pre-install Cluster Status ==="
+          kubectl get nodes -o wide
+          kubectl get pods --all-namespaces
+          kubectl get storageclass
 
-        # Function to monitor cluster state
-        monitor_cluster() {
-          while true; do
-            echo "=== Cluster Status Check at $(date) ==="
-            # Only show non-running pods to reduce noise
-            NON_RUNNING_PODS=$(kubectl get pods --all-namespaces --field-selector=status.phase!=Running,status.phase!=Succeeded --no-headers 2>/dev/null | wc -l)
-            if [ "$NON_RUNNING_PODS" -gt 0 ]; then
-              echo "Non-running pods:"
-              kubectl get pods --all-namespaces --field-selector=status.phase!=Running,status.phase!=Succeeded
+      - name: Add Helm repositories and update
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Adding Helm repositories ==="
+          helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
+          helm repo add vespa https://onyx-dot-app.github.io/vespa-helm-charts
+          helm repo add cloudnative-pg https://cloudnative-pg.github.io/charts
+          helm repo add ot-container-kit https://ot-container-kit.github.io/helm-charts
+          helm repo add minio https://charts.min.io/
+          helm repo add code-interpreter https://onyx-dot-app.github.io/code-interpreter/
+          helm repo update
+
+      - name: Install Redis operator
+        if: steps.list-changed.outputs.changed == 'true'
+        shell: bash
+        run: |
+          echo "=== Installing redis-operator CRDs ==="
+          helm upgrade --install redis-operator ot-container-kit/redis-operator \
+            --namespace redis-operator --create-namespace --wait --timeout 300s
+
+      - name: Pre-pull required images
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Pre-pulling required images to avoid timeout ==="
+          KIND_CLUSTER=$(kubectl config current-context | sed 's/kind-//')
+          echo "Kind cluster: $KIND_CLUSTER"
+
+          IMAGES=(
+            "ghcr.io/cloudnative-pg/cloudnative-pg:1.27.0"
+            "quay.io/opstree/redis:v7.0.15"
+            "docker.io/onyxdotapp/onyx-web-server:latest"
+          )
+
+          for image in "${IMAGES[@]}"; do
+            echo "Pre-pulling $image"
+            if docker pull "$image"; then
+              kind load docker-image "$image" --name "$KIND_CLUSTER" || echo "Failed to load $image into kind"
             else
-              echo "All pods running successfully"
+              echo "Failed to pull $image"
             fi
-            # Only show recent events if there are issues
-            RECENT_EVENTS=$(kubectl get events --sort-by=.lastTimestamp --all-namespaces --field-selector=type!=Normal 2>/dev/null | tail -5)
-            if [ -n "$RECENT_EVENTS" ]; then
-              echo "Recent warnings/errors:"
-              echo "$RECENT_EVENTS"
-            fi
-            sleep 60
           done
-        }
 
-        # Start monitoring in background
-        monitor_cluster &
-        MONITOR_PID=$!
+          echo "=== Images loaded into Kind cluster ==="
+          docker exec "$KIND_CLUSTER"-control-plane crictl images | grep -E "(cloudnative-pg|redis|onyx)" || echo "Some images may still be loading..."
 
-        # Set up cleanup
-        cleanup() {
-          echo "=== Cleaning up monitoring process ==="
-          kill $MONITOR_PID 2>/dev/null || true
+      - name: Validate chart dependencies
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Validating chart dependencies ==="
+          cd deployment/helm/charts/onyx
+          helm dependency update
+          helm lint .
+
+      - name: Run chart-testing (install) with enhanced monitoring
+        timeout-minutes: 25
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Starting chart installation with monitoring ==="
+
+          # Function to monitor cluster state
+          monitor_cluster() {
+            while true; do
+              echo "=== Cluster Status Check at $(date) ==="
+              # Only show non-running pods to reduce noise
+              NON_RUNNING_PODS=$(kubectl get pods --all-namespaces --field-selector=status.phase!=Running,status.phase!=Succeeded --no-headers 2>/dev/null | wc -l)
+              if [ "$NON_RUNNING_PODS" -gt 0 ]; then
+                echo "Non-running pods:"
+                kubectl get pods --all-namespaces --field-selector=status.phase!=Running,status.phase!=Succeeded
+              else
+                echo "All pods running successfully"
+              fi
+              # Only show recent events if there are issues
+              RECENT_EVENTS=$(kubectl get events --sort-by=.lastTimestamp --all-namespaces --field-selector=type!=Normal 2>/dev/null | tail -5)
+              if [ -n "$RECENT_EVENTS" ]; then
+                echo "Recent warnings/errors:"
+                echo "$RECENT_EVENTS"
+              fi
+              sleep 60
+            done
+          }
+
+          # Start monitoring in background
+          monitor_cluster &
+          MONITOR_PID=$!
+
+          # Set up cleanup
+          cleanup() {
+            echo "=== Cleaning up monitoring process ==="
+            kill $MONITOR_PID 2>/dev/null || true
+            echo "=== Final cluster state ==="
+            kubectl get pods --all-namespaces
+            kubectl get events --all-namespaces --sort-by=.lastTimestamp | tail -20
+          }
+
+          # Trap cleanup on exit
+          trap cleanup EXIT
+
+          # Run the actual installation with detailed logging
+          echo "=== Starting ct install ==="
+          set +e
+          ct install --all \
+            --helm-extra-set-args="\
+              --set=nginx.enabled=false \
+              --set=minio.enabled=false \
+              --set=vespa.enabled=false \
+              --set=slackbot.enabled=false \
+              --set=postgresql.enabled=true \
+              --set=postgresql.nameOverride=cloudnative-pg \
+              --set=postgresql.cluster.storage.storageClass=standard \
+              --set=redis.enabled=true \
+              --set=redis.storageSpec.volumeClaimTemplate.spec.storageClassName=standard \
+              --set=webserver.replicaCount=1 \
+              --set=api.replicaCount=0 \
+              --set=inferenceCapability.replicaCount=0 \
+              --set=indexCapability.replicaCount=0 \
+              --set=celery_beat.replicaCount=0 \
+              --set=celery_worker_heavy.replicaCount=0 \
+              --set=celery_worker_docfetching.replicaCount=0 \
+              --set=celery_worker_docprocessing.replicaCount=0 \
+              --set=celery_worker_light.replicaCount=0 \
+              --set=celery_worker_monitoring.replicaCount=0 \
+              --set=celery_worker_primary.replicaCount=0 \
+              --set=celery_worker_user_file_processing.replicaCount=0 \
+              --set=celery_worker_user_files_indexing.replicaCount=0" \
+            --helm-extra-args="--timeout 900s --debug" \
+            --debug --config ct.yaml
+          CT_EXIT=$?
+          set -e
+
+          if [[ $CT_EXIT -ne 0 ]]; then
+            echo "ct install failed with exit code $CT_EXIT"
+            exit $CT_EXIT
+          else
+            echo "=== Installation completed successfully ==="
+          fi
+
+          kubectl get pods --all-namespaces
+
+      - name: Post-install verification
+        if: steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Post-install verification ==="
+          kubectl get pods --all-namespaces
+          kubectl get services --all-namespaces
+          # Only show issues if they exist
+          kubectl describe pods --all-namespaces | grep -A 5 -B 2 "Failed\|Error\|Warning" || echo "No pod issues found"
+
+      - name: Cleanup on failure
+        if: failure() && steps.list-changed.outputs.changed == 'true'
+        run: |
+          echo "=== Cleanup on failure ==="
           echo "=== Final cluster state ==="
           kubectl get pods --all-namespaces
-          kubectl get events --all-namespaces --sort-by=.lastTimestamp | tail -20
-        }
+          kubectl get events --all-namespaces --sort-by=.lastTimestamp | tail -10
 
-        # Trap cleanup on exit
-        trap cleanup EXIT
+          echo "=== Pod descriptions for debugging ==="
+          kubectl describe pods --all-namespaces | grep -A 10 -B 3 "Failed\|Error\|Warning\|Pending" || echo "No problematic pods found"
 
-        # Run the actual installation with detailed logging
-        echo "=== Starting ct install ==="
-        set +e
-        ct install --all \
-          --helm-extra-set-args="\
-            --set=nginx.enabled=false \
-            --set=minio.enabled=false \
-            --set=vespa.enabled=false \
-            --set=slackbot.enabled=false \
-            --set=postgresql.enabled=true \
-            --set=postgresql.nameOverride=cloudnative-pg \
-            --set=postgresql.cluster.storage.storageClass=standard \
-            --set=redis.enabled=true \
-            --set=redis.storageSpec.volumeClaimTemplate.spec.storageClassName=standard \
-            --set=webserver.replicaCount=1 \
-            --set=api.replicaCount=0 \
-            --set=inferenceCapability.replicaCount=0 \
-            --set=indexCapability.replicaCount=0 \
-            --set=celery_beat.replicaCount=0 \
-            --set=celery_worker_heavy.replicaCount=0 \
-            --set=celery_worker_docfetching.replicaCount=0 \
-            --set=celery_worker_docprocessing.replicaCount=0 \
-            --set=celery_worker_light.replicaCount=0 \
-            --set=celery_worker_monitoring.replicaCount=0 \
-            --set=celery_worker_primary.replicaCount=0 \
-            --set=celery_worker_user_file_processing.replicaCount=0 \
-            --set=celery_worker_user_files_indexing.replicaCount=0" \
-          --helm-extra-args="--timeout 900s --debug" \
-          --debug --config ct.yaml
-        CT_EXIT=$?
-        set -e
+          echo "=== Recent logs for debugging ==="
+          kubectl logs --all-namespaces --tail=50 | grep -i "error\|timeout\|failed\|pull" || echo "No error logs found"
 
-        if [[ $CT_EXIT -ne 0 ]]; then
-          echo "ct install failed with exit code $CT_EXIT"
-          exit $CT_EXIT
-        else
-          echo "=== Installation completed successfully ==="
-        fi
-
-        kubectl get pods --all-namespaces
-
-    - name: Post-install verification
-      if: steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Post-install verification ==="
-        kubectl get pods --all-namespaces
-        kubectl get services --all-namespaces
-        # Only show issues if they exist
-        kubectl describe pods --all-namespaces | grep -A 5 -B 2 "Failed\|Error\|Warning" || echo "No pod issues found"
-
-    - name: Cleanup on failure
-      if: failure() && steps.list-changed.outputs.changed == 'true'
-      run: |
-        echo "=== Cleanup on failure ==="
-        echo "=== Final cluster state ==="
-        kubectl get pods --all-namespaces
-        kubectl get events --all-namespaces --sort-by=.lastTimestamp | tail -10
-
-        echo "=== Pod descriptions for debugging ==="
-        kubectl describe pods --all-namespaces | grep -A 10 -B 3 "Failed\|Error\|Warning\|Pending" || echo "No problematic pods found"
-
-        echo "=== Recent logs for debugging ==="
-        kubectl logs --all-namespaces --tail=50 | grep -i "error\|timeout\|failed\|pull" || echo "No error logs found"
-
-        echo "=== Helm releases ==="
-        helm list --all-namespaces
-      # the following would install only changed charts, but we only have one chart so
-      # don't worry about that for now
-      # run: ct install --target-branch ${{ github.event.repository.default_branch }}
+          echo "=== Helm releases ==="
+          helm list --all-namespaces
+        # the following would install only changed charts, but we only have one chart so
+        # don't worry about that for now
+        # run: ct install --target-branch ${{ github.event.repository.default_branch }}

.github/workflows/pr-integration-tests.yml

@@ -56,7 +56,7 @@ jobs:
         id: set-matrix
         run: |
           # Find all leaf-level directories in both test directories
-          tests_dirs=$(find backend/tests/integration/tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" -exec basename {} \; | sort)
+          tests_dirs=$(find backend/tests/integration/tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" ! -name "mcp" -exec basename {} \; | sort)
           connector_dirs=$(find backend/tests/integration/connector_job_tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" -exec basename {} \; | sort)
 
           # Create JSON array with directory info
@@ -310,7 +310,9 @@ jobs:
           ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID}
           INTEGRATION_TESTS_MODE=true
           CHECK_TTL_MANAGEMENT_TASK_FREQUENCY_IN_HOURS=0.001
+          AUTO_LLM_UPDATE_INTERVAL_SECONDS=10
           MCP_SERVER_ENABLED=true
+          USE_LIGHTWEIGHT_BACKGROUND_WORKER=false
           EOF
 
       - name: Start Docker containers
@@ -324,7 +326,6 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker
@@ -367,12 +368,6 @@ jobs:
           }
 
           wait_for_service "http://localhost:8080/health" "API server"
-          test_dir="${{ matrix.test-dir.path }}"
-          if [ "$test_dir" = "tests/mcp" ]; then
-            wait_for_service "http://localhost:8090/health" "MCP server"
-          else
-            echo "Skipping MCP server wait for non-MCP suite: $test_dir"
-          fi
           echo "Finished waiting for services."
 
       - name: Start Mock Services
@@ -402,8 +397,6 @@ jobs:
               -e VESPA_HOST=index \
               -e REDIS_HOST=cache \
               -e API_SERVER_HOST=api_server \
-              -e MCP_SERVER_HOST=mcp_server \
-              -e MCP_SERVER_PORT=8090 \
               -e OPENAI_API_KEY=${OPENAI_API_KEY} \
               -e EXA_API_KEY=${EXA_API_KEY} \
               -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
@@ -446,7 +439,7 @@ jobs:
 
       - name: Upload logs
         if: always()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-all-logs-${{ matrix.test-dir.name }}
           path: ${{ github.workspace }}/docker-compose.log
@@ -488,10 +481,10 @@ jobs:
           AUTH_TYPE=cloud \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
+          OPENAI_DEFAULT_API_KEY=${OPENAI_API_KEY} \
           ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID} \
           ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID} \
           DEV_MODE=true \
-          MCP_SERVER_ENABLED=true \
           docker compose -f docker-compose.multitenant-dev.yml up \
             relational_db \
             index \
@@ -500,7 +493,6 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker_multi_tenant
@@ -549,8 +541,6 @@ jobs:
             -e VESPA_HOST=index \
             -e REDIS_HOST=cache \
             -e API_SERVER_HOST=api_server \
-            -e MCP_SERVER_HOST=mcp_server \
-            -e MCP_SERVER_PORT=8090 \
             -e OPENAI_API_KEY=${OPENAI_API_KEY} \
             -e EXA_API_KEY=${EXA_API_KEY} \
             -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
@@ -578,7 +568,7 @@ jobs:
 
       - name: Upload logs (multi-tenant)
         if: always()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-all-logs-multitenant
           path: ${{ github.workspace }}/docker-compose-multitenant.log

.github/workflows/pr-jest-tests.yml

@@ -44,7 +44,7 @@ jobs:
 
       - name: Upload coverage reports
         if: always()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: jest-coverage-${{ github.run_id }}
           path: ./web/coverage

.github/workflows/pr-mit-integration-tests.yml

@@ -48,7 +48,7 @@ jobs:
         id: set-matrix
         run: |
           # Find all leaf-level directories in both test directories
-          tests_dirs=$(find backend/tests/integration/tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" -exec basename {} \; | sort)
+          tests_dirs=$(find backend/tests/integration/tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" ! -name "mcp" -exec basename {} \; | sort)
           connector_dirs=$(find backend/tests/integration/connector_job_tests -mindepth 1 -maxdepth 1 -type d ! -name "__pycache__" -exec basename {} \; | sort)
 
           # Create JSON array with directory info
@@ -301,6 +301,7 @@ jobs:
           ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID}
           INTEGRATION_TESTS_MODE=true
           MCP_SERVER_ENABLED=true
+          AUTO_LLM_UPDATE_INTERVAL_SECONDS=10
           EOF
 
       - name: Start Docker containers
@@ -314,7 +315,6 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker
@@ -357,12 +357,6 @@ jobs:
           }
 
           wait_for_service "http://localhost:8080/health" "API server"
-          test_dir="${{ matrix.test-dir.path }}"
-          if [ "$test_dir" = "tests/mcp" ]; then
-            wait_for_service "http://localhost:8090/health" "MCP server"
-          else
-            echo "Skipping MCP server wait for non-MCP suite: $test_dir"
-          fi
           echo "Finished waiting for services."
 
       - name: Start Mock Services
@@ -393,8 +387,6 @@ jobs:
               -e VESPA_HOST=index \
               -e REDIS_HOST=cache \
               -e API_SERVER_HOST=api_server \
-              -e MCP_SERVER_HOST=mcp_server \
-              -e MCP_SERVER_PORT=8090 \
               -e OPENAI_API_KEY=${OPENAI_API_KEY} \
               -e EXA_API_KEY=${EXA_API_KEY} \
               -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
@@ -432,7 +424,7 @@ jobs:
 
       - name: Upload logs
         if: always()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-all-logs-${{ matrix.test-dir.name }}
           path: ${{ github.workspace }}/docker-compose.log

.github/workflows/pr-playwright-tests.yml

@@ -435,7 +435,7 @@ jobs:
           fi
           npx playwright test --project ${PROJECT}
 
-      - uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+      - uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         if: always()
         with:
           # Includes test results and trace.zip files
@@ -455,7 +455,7 @@ jobs:
 
       - name: Upload logs
         if: success() || failure()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-logs-${{ matrix.project }}-${{ github.run_id }}
           path: ${{ github.workspace }}/docker-compose.log

.github/workflows/pr-python-checks.yml

@@ -50,8 +50,9 @@ jobs:
         uses: runs-on/cache@50350ad4242587b6c8c2baa2e740b1bc11285ff4 # ratchet:runs-on/cache@v4
         with:
           path: backend/.mypy_cache
-          key: mypy-${{ runner.os }}-${{ hashFiles('**/*.py', '**/*.pyi', 'backend/pyproject.toml') }}
+          key: mypy-${{ runner.os }}-${{ github.base_ref || github.event.merge_group.base_ref || 'main' }}-${{ hashFiles('**/*.py', '**/*.pyi', 'backend/pyproject.toml') }}
           restore-keys: |
+            mypy-${{ runner.os }}-${{ github.base_ref || github.event.merge_group.base_ref || 'main' }}-
             mypy-${{ runner.os }}-
 
       - name: Run MyPy

.github/workflows/pr-python-model-tests.yml

@@ -5,11 +5,6 @@ on:
     # This cron expression runs the job daily at 16:00 UTC (9am PT)
     - cron: "0 16 * * *"
   workflow_dispatch:
-    inputs:
-      branch:
-        description: 'Branch to run the workflow on'
-        required: false
-        default: 'main'
 
 permissions:
   contents: read
@@ -31,7 +26,11 @@ env:
 jobs:
   model-check:
     # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}-model-check"]
+    runs-on:
+      - runs-on
+      - runner=4cpu-linux-arm64
+      - "run-id=${{ github.run_id }}-model-check"
+      - "extras=ecr-cache"
     timeout-minutes: 45
 
     env:
@@ -43,108 +42,87 @@ jobs:
         with:
           persist-credentials: false
 
+      - name: Setup Python and Install Dependencies
+        uses: ./.github/actions/setup-python-and-install-dependencies
+        with:
+          requirements: |
+            backend/requirements/default.txt
+            backend/requirements/dev.txt
+
+      - name: Format branch name for cache
+        id: format-branch
+        env:
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          REF_NAME: ${{ github.ref_name }}
+        run: |
+          if [ -n "${PR_NUMBER}" ]; then
+            CACHE_SUFFIX="${PR_NUMBER}"
+          else
+            # shellcheck disable=SC2001
+            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
+          fi
+          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
+
       - name: Login to Docker Hub
-        uses: docker/login-action@5e57cd118135c172c3672efd75eb46360885c0ef # ratchet:docker/login-action@v3
+        uses: docker/login-action@5e57cd118135c172c3672efd75eb46360885c0ef
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
-      # tag every docker image with "test" so that we can spin up the correct set
-      # of images during testing
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435
 
-      # We don't need to build the Web Docker image since it's not yet used
-      # in the integration tests. We have a separate action to verify that it builds
-      # successfully.
-      - name: Pull Model Server Docker image
-        run: |
-          docker pull onyxdotapp/onyx-model-server:latest
-          docker tag onyxdotapp/onyx-model-server:latest onyxdotapp/onyx-model-server:test
-
-      - name: Set up Python
-        uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # ratchet:actions/setup-python@v6
+      - name: Build and load
+        uses: docker/bake-action@5be5f02ff8819ecd3092ea6b2e6261c31774f2b4 # ratchet:docker/bake-action@v6
+        env:
+          TAG: model-server-${{ github.run_id }}
         with:
-          python-version: "3.11"
-          cache: "pip"
-          cache-dependency-path: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
-
-      - name: Install Dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
-          pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
+          load: true
+          targets: model-server
+          set: |
+            model-server.cache-from=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }}
+            model-server.cache-from=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }}
+            model-server.cache-from=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache
+            model-server.cache-from=type=registry,ref=onyxdotapp/onyx-model-server:latest
+            model-server.cache-to=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
+            model-server.cache-to=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
+            model-server.cache-to=type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
 
       - name: Start Docker containers
+        id: start_docker
+        env:
+          IMAGE_TAG: model-server-${{ github.run_id }}
         run: |
           cd deployment/docker_compose
-          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
-          AUTH_TYPE=basic \
-          REQUIRE_EMAIL_VERIFICATION=false \
-          DISABLE_TELEMETRY=true \
-          IMAGE_TAG=test \
-          docker compose -f docker-compose.model-server-test.yml up -d indexing_model_server
-        id: start_docker
-
-      - name: Wait for service to be ready
-        run: |
-          echo "Starting wait-for-service script..."
-
-          start_time=$(date +%s)
-          timeout=300  # 5 minutes in seconds
-
-          while true; do
-            current_time=$(date +%s)
-            elapsed_time=$((current_time - start_time))
-
-            if [ $elapsed_time -ge $timeout ]; then
-              echo "Timeout reached. Service did not become ready in 5 minutes."
-              exit 1
-            fi
-
-            # Use curl with error handling to ignore specific exit code 56
-            response=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:9000/api/health || echo "curl_error")
-
-            if [ "$response" = "200" ]; then
-              echo "Service is ready!"
-              break
-            elif [ "$response" = "curl_error" ]; then
-              echo "Curl encountered an error, possibly exit code 56. Continuing to retry..."
-            else
-              echo "Service not ready yet (HTTP status $response). Retrying in 5 seconds..."
-            fi
-
-            sleep 5
-          done
-          echo "Finished waiting for service."
+          docker compose \
+            -f docker-compose.yml \
+            -f docker-compose.dev.yml \
+            up -d --wait \
+            inference_model_server
 
       - name: Run Tests
-        shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"
         run: |
           py.test -o junit_family=xunit2 -xv --ff backend/tests/daily/llm
           py.test -o junit_family=xunit2 -xv --ff backend/tests/daily/embedding
 
       - name: Alert on Failure
         if: failure() && github.event_name == 'schedule'
-        env:
-          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
-          REPO: ${{ github.repository }}
-          RUN_ID: ${{ github.run_id }}
-        run: |
-          curl -X POST \
-            -H 'Content-type: application/json' \
-            --data "{\"text\":\"Scheduled Model Tests failed! Check the run at: https://github.com/${REPO}/actions/runs/${RUN_ID}\"}" \
-            $SLACK_WEBHOOK
+        uses: ./.github/actions/slack-notify
+        with:
+          webhook-url: ${{ secrets.SLACK_WEBHOOK }}
+          failed-jobs: model-check
+          title: "🚨 Scheduled Model Tests failed!"
+          ref-name: ${{ github.ref_name }}
 
       - name: Dump all-container logs (optional)
         if: always()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.model-server-test.yml logs --no-color > $GITHUB_WORKSPACE/docker-compose.log || true
+          docker compose logs --no-color > $GITHUB_WORKSPACE/docker-compose.log || true
 
       - name: Upload logs
         if: always()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v4
+        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f
         with:
           name: docker-all-logs
           path: ${{ github.workspace }}/docker-compose.log

.github/workflows/zizmor.yml

@@ -21,18 +21,29 @@ jobs:
         with:
           persist-credentials: false
 
+      - name: Detect changes
+        id: filter
+        uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # ratchet:dorny/paths-filter@v3
+        with:
+          filters: |
+            zizmor:
+              - '.github/**'
+
       - name: Install the latest version of uv
+        if: steps.filter.outputs.zizmor == 'true' || github.ref_name == 'main'
         uses: astral-sh/setup-uv@ed21f2f24f8dd64503750218de024bcf64c7250a # ratchet:astral-sh/setup-uv@v7
         with:
           enable-cache: false
           version: "0.9.9"
 
       - name: Run zizmor
+        if: steps.filter.outputs.zizmor == 'true' || github.ref_name == 'main'
         run: uv run --no-sync --with zizmor zizmor --format=sarif . > results.sarif
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Upload SARIF file
+        if: steps.filter.outputs.zizmor == 'true' || github.ref_name == 'main'
         uses: github/codeql-action/upload-sarif@ba454b8ab46733eb6145342877cd148270bb77ab # ratchet:github/codeql-action/upload-sarif@codeql-bundle-v2.23.5
         with:
           sarif_file: results.sarif

.gitignore

@@ -21,6 +21,7 @@ backend/tests/regression/search_quality/*.json
 backend/onyx/evals/data/
 backend/onyx/evals/one_off/*.json
 *.log
+*.csv
 
 # secret files
 .env

.pre-commit-config.yaml

@@ -9,9 +9,8 @@ repos:
     rev: d30b4298e4fb63ce8609e29acdbcf4c9018a483c
     hooks:
       - id: uv-sync
-        args: ["--active", "--locked", "--all-extras"]
+        args: ["--locked", "--all-extras"]
       - id: uv-lock
-        files: ^pyproject\.toml$
       - id: uv-export
         name: uv-export default.txt
         args:
@@ -147,6 +146,22 @@ repos:
         pass_filenames: false
         files: \.tf$
 
+      - id: npm-install
+        name: npm install
+        description: "Automatically run 'npm install' after a checkout, pull or rebase"
+        language: system
+        entry: bash -c 'cd web && npm install --no-save'
+        pass_filenames: false
+        files: ^web/package(-lock)?\.json$
+        stages: [post-checkout, post-merge, post-rewrite]
+      - id: npm-install-check
+        name: npm install --package-lock-only
+        description: "Check the 'web/package-lock.json' is updated"
+        language: system
+        entry: bash -c 'cd web && npm install --package-lock-only'
+        pass_filenames: false
+        files: ^web/package(-lock)?\.json$
+
       # Uses tsgo (TypeScript's native Go compiler) for ~10x faster type checking.
       # This is a preview package - if it breaks:
       #   1. Try updating: cd web && npm update @typescript/native-preview

.vscode/env_template.txt

@@ -1,36 +1,39 @@
-# Copy this file to .env in the .vscode folder
-# Fill in the <REPLACE THIS> values as needed, it is recommended to set the GEN_AI_API_KEY value to avoid having to set up an LLM in the UI
-# Also check out onyx/backend/scripts/restart_containers.sh for a script to restart the containers which Onyx relies on outside of VSCode/Cursor processes
+# Copy this file to .env in the .vscode folder.
+# Fill in the <REPLACE THIS> values as needed; it is recommended to set the
+# GEN_AI_API_KEY value to avoid having to set up an LLM in the UI.
+# Also check out onyx/backend/scripts/restart_containers.sh for a script to
+# restart the containers which Onyx relies on outside of VSCode/Cursor
+# processes.
 
-# For local dev, often user Authentication is not needed
+
+# For local dev, often user Authentication is not needed.
 AUTH_TYPE=disabled
 
-# Always keep these on for Dev
-# Logs model prompts, reasoning, and answer to stdout
+
+# Always keep these on for Dev.
+# Logs model prompts, reasoning, and answer to stdout.
 LOG_ONYX_MODEL_INTERACTIONS=True
 # More verbose logging
 LOG_LEVEL=debug
 
 
-# This passes top N results to LLM an additional time for reranking prior to answer generation
-# This step is quite heavy on token usage so we disable it for dev generally
-DISABLE_LLM_DOC_RELEVANCE=False
-
-
-# Useful if you want to toggle auth on/off (google_oauth/OIDC specifically)
+# Useful if you want to toggle auth on/off (google_oauth/OIDC specifically).
 OAUTH_CLIENT_ID=<REPLACE THIS>
 OAUTH_CLIENT_SECRET=<REPLACE THIS>
 OPENID_CONFIG_URL=<REPLACE THIS>
 SAML_CONF_DIR=/<ABSOLUTE PATH TO ONYX>/onyx/backend/ee/onyx/configs/saml_config
 
-# Generally not useful for dev, we don't generally want to set up an SMTP server for dev
+
+# Generally not useful for dev, we don't generally want to set up an SMTP server
+# for dev.
 REQUIRE_EMAIL_VERIFICATION=False
 
 
-# Set these so if you wipe the DB, you don't end up having to go through the UI every time
+# Set these so if you wipe the DB, you don't end up having to go through the UI
+# every time.
 GEN_AI_API_KEY=<REPLACE THIS>
 OPENAI_API_KEY=<REPLACE THIS>
-# If answer quality isn't important for dev, use gpt-4o-mini since it's cheaper
+# If answer quality isn't important for dev, use gpt-4o-mini since it's cheaper.
 GEN_AI_MODEL_VERSION=gpt-4o
 FAST_GEN_AI_MODEL_VERSION=gpt-4o
 
@@ -40,26 +43,36 @@ PYTHONPATH=../backend
 PYTHONUNBUFFERED=1
 
 
-# Enable the full set of Danswer Enterprise Edition features
-# NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
+# Enable the full set of Danswer Enterprise Edition features.
+# NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you
+# are using this for local testing/development).
 ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
 
+
 # S3 File Store Configuration (MinIO for local development)
 S3_ENDPOINT_URL=http://localhost:9004
 S3_FILE_STORE_BUCKET_NAME=onyx-file-store-bucket
 S3_AWS_ACCESS_KEY_ID=minioadmin
 S3_AWS_SECRET_ACCESS_KEY=minioadmin
 
-# Show extra/uncommon connectors
+
+# Show extra/uncommon connectors.
 SHOW_EXTRA_CONNECTORS=True
 
+
 # Local langsmith tracing
 LANGSMITH_TRACING="true"
 LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
 LANGSMITH_API_KEY=<REPLACE_THIS>
 LANGSMITH_PROJECT=<REPLACE_THIS>
 
+
 # Local Confluence OAuth testing
 # OAUTH_CONFLUENCE_CLOUD_CLIENT_ID=<REPLACE_THIS>
 # OAUTH_CONFLUENCE_CLOUD_CLIENT_SECRET=<REPLACE_THIS>
-# NEXT_PUBLIC_TEST_ENV=True
+# NEXT_PUBLIC_TEST_ENV=True
+
+
+# OpenSearch
+# Arbitrary password is fine for local development.
+OPENSEARCH_INITIAL_ADMIN_PASSWORD=<REPLACE THIS>

.vscode/launch.template.jsonc

@@ -151,6 +151,24 @@
       },
       "consoleTitle": "Slack Bot Console"
     },
+    {
+      "name": "Discord Bot",
+      "consoleName": "Discord Bot",
+      "type": "debugpy",
+      "request": "launch",
+      "program": "onyx/onyxbot/discord/client.py",
+      "cwd": "${workspaceFolder}/backend",
+      "envFile": "${workspaceFolder}/.vscode/.env",
+      "env": {
+        "LOG_LEVEL": "DEBUG",
+        "PYTHONUNBUFFERED": "1",
+        "PYTHONPATH": "."
+      },
+      "presentation": {
+        "group": "2"
+      },
+      "consoleTitle": "Discord Bot Console"
+    },
     {
       "name": "MCP Server",
       "consoleName": "MCP Server",
@@ -512,6 +530,21 @@
         "group": "3"
       }
     },
+    {
+      "name": "Clear and Restart OpenSearch Container",
+      // Generic debugger type, required arg but has no bearing on bash.
+      "type": "node",
+      "request": "launch",
+      "runtimeExecutable": "bash",
+      "runtimeArgs": [
+        "${workspaceFolder}/backend/scripts/restart_opensearch_container.sh"
+      ],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal",
+      "presentation": {
+        "group": "3"
+      }
+    },
     {
       "name": "Eval CLI",
       "type": "debugpy",

AGENTS.md.template

@@ -1,13 +1,13 @@
 # AGENTS.md
 
-This file provides guidance to Codex when working with code in this repository.
+This file provides guidance to AI agents when working with code in this repository.
 
 ## KEY NOTES
 
-- If you run into any missing python dependency errors, try running your command with `source backend/.venv/bin/activate` \
+- If you run into any missing python dependency errors, try running your command with `source .venv/bin/activate` \
 to assume the python venv.
 - To make tests work, check the `.env` file at the root of the project to find an OpenAI key.
-- If using `playwright` to explore the frontend, you can usually log in with username `a@test.com` and password
+- If using `playwright` to explore the frontend, you can usually log in with username `a@example.com` and password
 `a`. The app can be accessed at `http://localhost:3000`.
 - You should assume that all Onyx services are running. To verify, you can check the `backend/log` directory to
 make sure we see logs coming out from the relevant service.
@@ -181,6 +181,286 @@ web/
 └── src/lib/                     # Utilities & business logic
 ```
 
+## Frontend Standards
+
+### 1. Import Standards
+
+**Always use absolute imports with the `@` prefix.**
+
+**Reason:** Moving files around becomes easier since you don't also have to update those import statements. This makes modifications to the codebase much nicer.
+
+```typescript
+// ✅ Good
+import { Button } from "@/components/ui/button";
+import { useAuth } from "@/hooks/useAuth";
+import { Text } from "@/refresh-components/texts/Text";
+
+// ❌ Bad
+import { Button } from "../../../components/ui/button";
+import { useAuth } from "./hooks/useAuth";
+```
+
+### 2. React Component Functions
+
+**Prefer regular functions over arrow functions for React components.**
+
+**Reason:** Functions just become easier to read.
+
+```typescript
+// ✅ Good
+function UserProfile({ userId }: UserProfileProps) {
+  return <div>User Profile</div>
+}
+
+// ❌ Bad
+const UserProfile = ({ userId }: UserProfileProps) => {
+  return <div>User Profile</div>
+}
+```
+
+### 3. Props Interface Extraction
+
+**Extract prop types into their own interface definitions.**
+
+**Reason:** Functions just become easier to read.
+
+```typescript
+// ✅ Good
+interface UserCardProps {
+  user: User
+  showActions?: boolean
+  onEdit?: (userId: string) => void
+}
+
+function UserCard({ user, showActions = false, onEdit }: UserCardProps) {
+  return <div>User Card</div>
+}
+
+// ❌ Bad
+function UserCard({
+  user,
+  showActions = false,
+  onEdit
+}: {
+  user: User
+  showActions?: boolean
+  onEdit?: (userId: string) => void
+}) {
+  return <div>User Card</div>
+}
+```
+
+### 4. Spacing Guidelines
+
+**Prefer padding over margins for spacing.**
+
+**Reason:** We want to consolidate usage to paddings instead of margins.
+
+```typescript
+// ✅ Good
+<div className="p-4 space-y-2">
+  <div className="p-2">Content</div>
+</div>
+
+// ❌ Bad
+<div className="m-4 space-y-2">
+  <div className="m-2">Content</div>
+</div>
+```
+
+### 5. Tailwind Dark Mode
+
+**Strictly forbid using the `dark:` modifier in Tailwind classes, except for logo icon handling.**
+
+**Reason:** The `colors.css` file already, VERY CAREFULLY, defines what the exact opposite colour of each light-mode colour is. Overriding this behaviour is VERY bad and will lead to horrible UI breakages.
+
+**Exception:** The `createLogoIcon` helper in `web/src/components/icons/icons.tsx` uses `dark:` modifiers (`dark:invert`, `dark:hidden`, `dark:block`) to handle third-party logo icons that cannot automatically adapt through `colors.css`. This is the ONLY acceptable use of dark mode modifiers.
+
+```typescript
+// ✅ Good - Standard components use `web/tailwind-themes/tailwind.config.js` / `web/src/app/css/colors.css`
+<div className="bg-background-neutral-03 text-text-02">
+  Content
+</div>
+
+// ✅ Good - Logo icons with dark mode handling via createLogoIcon
+export const GithubIcon = createLogoIcon(githubLightIcon, {
+  monochromatic: true,  // Will apply dark:invert internally
+});
+
+export const GitbookIcon = createLogoIcon(gitbookLightIcon, {
+  darkSrc: gitbookDarkIcon,  // Will use dark:hidden/dark:block internally
+});
+
+// ❌ Bad - Manual dark mode overrides
+<div className="bg-white dark:bg-black text-black dark:text-white">
+  Content
+</div>
+```
+
+### 6. Class Name Utilities
+
+**Use the `cn` utility instead of raw string formatting for classNames.**
+
+**Reason:** `cn`s are easier to read. They also allow for more complex types (i.e., string-arrays) to get formatted properly (it flattens each element in that string array down). As a result, it can allow things such as conditionals (i.e., `myCondition && "some-tailwind-class"`, which evaluates to `false` when `myCondition` is `false`) to get filtered out.
+
+```typescript
+import { cn } from '@/lib/utils'
+
+// ✅ Good
+<div className={cn(
+  'base-class',
+  isActive && 'active-class',
+  className
+)}>
+  Content
+</div>
+
+// ❌ Bad
+<div className={`base-class ${isActive ? 'active-class' : ''} ${className}`}>
+  Content
+</div>
+```
+
+### 7. Custom Hooks Organization
+
+**Follow a "hook-per-file" layout. Each hook should live in its own file within `web/src/hooks`.**
+
+**Reason:** This is just a layout preference. Keeps code clean.
+
+```typescript
+// web/src/hooks/useUserData.ts
+export function useUserData(userId: string) {
+  // hook implementation
+}
+
+// web/src/hooks/useLocalStorage.ts
+export function useLocalStorage<T>(key: string, initialValue: T) {
+  // hook implementation
+}
+```
+
+### 8. Icon Usage
+
+**ONLY use icons from the `web/src/icons` directory. Do NOT use icons from `react-icons`, `lucide`, or other external libraries.**
+
+**Reason:** We have a very carefully curated selection of icons that match our Onyx guidelines. We do NOT want to muddy those up with different aesthetic stylings.
+
+```typescript
+// ✅ Good
+import SvgX from "@/icons/x";
+import SvgMoreHorizontal from "@/icons/more-horizontal";
+
+// ❌ Bad
+import { User } from "lucide-react";
+import { FiSearch } from "react-icons/fi";
+```
+
+**Missing Icons**: If an icon is needed but doesn't exist in the `web/src/icons` directory, import it from Figma using the Figma MCP tool and add it to the icons directory.
+If you need help with this step, reach out to `raunak@onyx.app`.
+
+### 9. Text Rendering
+
+**Prefer using the `refresh-components/texts/Text` component for all text rendering. Avoid "naked" text nodes.**
+
+**Reason:** The `Text` component is fully compliant with the stylings provided in Figma. It provides easy utilities to specify the text-colour and font-size in the form of flags. Super duper easy.
+
+```typescript
+// ✅ Good
+import { Text } from '@/refresh-components/texts/Text'
+
+function UserCard({ name }: { name: string }) {
+  return (
+    <Text
+      {/* The `text03` flag makes the text it renders to be coloured the 3rd-scale grey */}
+      text03
+      {/* The `mainAction` flag makes the text it renders to be "main-action" font + line-height + weightage, as described in the Figma */}
+      mainAction
+    >
+      {name}
+    </Text>
+  )
+}
+
+// ❌ Bad
+function UserCard({ name }: { name: string }) {
+  return (
+    <div>
+      <h2>{name}</h2>
+      <p>User details</p>
+    </div>
+  )
+}
+```
+
+### 10. Component Usage
+
+**Heavily avoid raw HTML input components. Always use components from the `web/src/refresh-components` or `web/lib/opal/src` directory.**
+
+**Reason:** We've put in a lot of effort to unify the components that are rendered in the Onyx app. Using raw components breaks the entire UI of the application, and leaves it in a muddier state than before.
+
+```typescript
+// ✅ Good
+import Button from '@/refresh-components/buttons/Button'
+import InputTypeIn from '@/refresh-components/inputs/InputTypeIn'
+import SvgPlusCircle from '@/icons/plus-circle'
+
+function ContactForm() {
+  return (
+    <form>
+      <InputTypeIn placeholder="Search..." />
+      <Button type="submit" leftIcon={SvgPlusCircle}>Submit</Button>
+    </form>
+  )
+}
+
+// ❌ Bad
+function ContactForm() {
+  return (
+    <form>
+      <input placeholder="Name" />
+      <textarea placeholder="Message" />
+      <button type="submit">Submit</button>
+    </form>
+  )
+}
+```
+
+### 11. Colors
+
+**Always use custom overrides for colors and borders rather than built in Tailwind CSS colors. These overrides live in `web/tailwind-themes/tailwind.config.js`.**
+
+**Reason:** Our custom color system uses CSS variables that automatically handle dark mode and maintain design consistency across the app. Standard Tailwind colors bypass this system.
+
+**Available color categories:**
+- **Text:** `text-01` through `text-05`, `text-inverted-XX`
+- **Backgrounds:** `background-neutral-XX`, `background-tint-XX` (and inverted variants)
+- **Borders:** `border-01` through `border-05`, `border-inverted-XX`
+- **Actions:** `action-link-XX`, `action-danger-XX`
+- **Status:** `status-info-XX`, `status-success-XX`, `status-warning-XX`, `status-error-XX`
+- **Theme:** `theme-primary-XX`, `theme-red-XX`, `theme-blue-XX`, etc.
+
+```typescript
+// ✅ Good - Use custom Onyx color classes
+<div className="bg-background-neutral-01 border border-border-02" />
+<div className="bg-background-tint-02 border border-border-01" />
+<div className="bg-status-success-01" />
+<div className="bg-action-link-01" />
+<div className="bg-theme-primary-05" />
+
+// ❌ Bad - Do NOT use standard Tailwind colors
+<div className="bg-gray-100 border border-gray-300 text-gray-600" />
+<div className="bg-white border border-slate-200" />
+<div className="bg-green-100 text-green-700" />
+<div className="bg-blue-100 text-blue-600" />
+<div className="bg-indigo-500" />
+```
+
+### 12. Data Fetching
+
+**Prefer using `useSWR` for data fetching. Data should generally be fetched on the client side. Components that need data should display a loader / placeholder while waiting for that data. Prefer loading data within the component that needs it rather than at the top level and passing it down.**
+
+**Reason:** Client side fetching allows us to load the skeleton of the page without waiting for data to load, leading to a snappier UX. Loading data where needed reduces dependencies between a component and its parent component(s).
+
 ## Database & Migrations
 
 ### Running Migrations
@@ -295,14 +575,6 @@ will be tailing their logs to this file.
 - Token management and rate limiting
 - Custom prompts and agent actions
 
-## UI/UX Patterns
-
-- Tailwind CSS with design system in `web/src/components/ui/`
-- Radix UI and Headless UI for accessible components
-- SWR for data fetching and caching
-- Form validation with react-hook-form
-- Error handling with popup notifications
-
 ## Creating a Plan
 When creating a plan in the `plans` directory, make sure to include at least these elements:
 

CLAUDE.md.template

@@ -7,7 +7,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 - If you run into any missing python dependency errors, try running your command with `source .venv/bin/activate` \
 to assume the python venv.
 - To make tests work, check the `.env` file at the root of the project to find an OpenAI key.
-- If using `playwright` to explore the frontend, you can usually log in with username `a@test.com` and password
+- If using `playwright` to explore the frontend, you can usually log in with username `a@example.com` and password
 `a`. The app can be accessed at `http://localhost:3000`.
 - You should assume that all Onyx services are running. To verify, you can check the `backend/log` directory to
 make sure we see logs coming out from the relevant service.
@@ -184,6 +184,286 @@ web/
 └── src/lib/                     # Utilities & business logic
 ```
 
+## Frontend Standards
+
+### 1. Import Standards
+
+**Always use absolute imports with the `@` prefix.**
+
+**Reason:** Moving files around becomes easier since you don't also have to update those import statements. This makes modifications to the codebase much nicer.
+
+```typescript
+// ✅ Good
+import { Button } from "@/components/ui/button";
+import { useAuth } from "@/hooks/useAuth";
+import { Text } from "@/refresh-components/texts/Text";
+
+// ❌ Bad
+import { Button } from "../../../components/ui/button";
+import { useAuth } from "./hooks/useAuth";
+```
+
+### 2. React Component Functions
+
+**Prefer regular functions over arrow functions for React components.**
+
+**Reason:** Functions just become easier to read.
+
+```typescript
+// ✅ Good
+function UserProfile({ userId }: UserProfileProps) {
+  return <div>User Profile</div>
+}
+
+// ❌ Bad
+const UserProfile = ({ userId }: UserProfileProps) => {
+  return <div>User Profile</div>
+}
+```
+
+### 3. Props Interface Extraction
+
+**Extract prop types into their own interface definitions.**
+
+**Reason:** Functions just become easier to read.
+
+```typescript
+// ✅ Good
+interface UserCardProps {
+  user: User
+  showActions?: boolean
+  onEdit?: (userId: string) => void
+}
+
+function UserCard({ user, showActions = false, onEdit }: UserCardProps) {
+  return <div>User Card</div>
+}
+
+// ❌ Bad
+function UserCard({
+  user,
+  showActions = false,
+  onEdit
+}: {
+  user: User
+  showActions?: boolean
+  onEdit?: (userId: string) => void
+}) {
+  return <div>User Card</div>
+}
+```
+
+### 4. Spacing Guidelines
+
+**Prefer padding over margins for spacing.**
+
+**Reason:** We want to consolidate usage to paddings instead of margins.
+
+```typescript
+// ✅ Good
+<div className="p-4 space-y-2">
+  <div className="p-2">Content</div>
+</div>
+
+// ❌ Bad
+<div className="m-4 space-y-2">
+  <div className="m-2">Content</div>
+</div>
+```
+
+### 5. Tailwind Dark Mode
+
+**Strictly forbid using the `dark:` modifier in Tailwind classes, except for logo icon handling.**
+
+**Reason:** The `colors.css` file already, VERY CAREFULLY, defines what the exact opposite colour of each light-mode colour is. Overriding this behaviour is VERY bad and will lead to horrible UI breakages.
+
+**Exception:** The `createLogoIcon` helper in `web/src/components/icons/icons.tsx` uses `dark:` modifiers (`dark:invert`, `dark:hidden`, `dark:block`) to handle third-party logo icons that cannot automatically adapt through `colors.css`. This is the ONLY acceptable use of dark mode modifiers.
+
+```typescript
+// ✅ Good - Standard components use `tailwind-themes/tailwind.config.js` / `src/app/css/colors.css`
+<div className="bg-background-neutral-03 text-text-02">
+  Content
+</div>
+
+// ✅ Good - Logo icons with dark mode handling via createLogoIcon
+export const GithubIcon = createLogoIcon(githubLightIcon, {
+  monochromatic: true,  // Will apply dark:invert internally
+});
+
+export const GitbookIcon = createLogoIcon(gitbookLightIcon, {
+  darkSrc: gitbookDarkIcon,  // Will use dark:hidden/dark:block internally
+});
+
+// ❌ Bad - Manual dark mode overrides
+<div className="bg-white dark:bg-black text-black dark:text-white">
+  Content
+</div>
+```
+
+### 6. Class Name Utilities
+
+**Use the `cn` utility instead of raw string formatting for classNames.**
+
+**Reason:** `cn`s are easier to read. They also allow for more complex types (i.e., string-arrays) to get formatted properly (it flattens each element in that string array down). As a result, it can allow things such as conditionals (i.e., `myCondition && "some-tailwind-class"`, which evaluates to `false` when `myCondition` is `false`) to get filtered out.
+
+```typescript
+import { cn } from '@/lib/utils'
+
+// ✅ Good
+<div className={cn(
+  'base-class',
+  isActive && 'active-class',
+  className
+)}>
+  Content
+</div>
+
+// ❌ Bad
+<div className={`base-class ${isActive ? 'active-class' : ''} ${className}`}>
+  Content
+</div>
+```
+
+### 7. Custom Hooks Organization
+
+**Follow a "hook-per-file" layout. Each hook should live in its own file within `web/src/hooks`.**
+
+**Reason:** This is just a layout preference. Keeps code clean.
+
+```typescript
+// web/src/hooks/useUserData.ts
+export function useUserData(userId: string) {
+  // hook implementation
+}
+
+// web/src/hooks/useLocalStorage.ts
+export function useLocalStorage<T>(key: string, initialValue: T) {
+  // hook implementation
+}
+```
+
+### 8. Icon Usage
+
+**ONLY use icons from the `web/src/icons` directory. Do NOT use icons from `react-icons`, `lucide`, or other external libraries.**
+
+**Reason:** We have a very carefully curated selection of icons that match our Onyx guidelines. We do NOT want to muddy those up with different aesthetic stylings.
+
+```typescript
+// ✅ Good
+import SvgX from "@/icons/x";
+import SvgMoreHorizontal from "@/icons/more-horizontal";
+
+// ❌ Bad
+import { User } from "lucide-react";
+import { FiSearch } from "react-icons/fi";
+```
+
+**Missing Icons**: If an icon is needed but doesn't exist in the `web/src/icons` directory, import it from Figma using the Figma MCP tool and add it to the icons directory.
+If you need help with this step, reach out to `raunak@onyx.app`.
+
+### 9. Text Rendering
+
+**Prefer using the `refresh-components/texts/Text` component for all text rendering. Avoid "naked" text nodes.**
+
+**Reason:** The `Text` component is fully compliant with the stylings provided in Figma. It provides easy utilities to specify the text-colour and font-size in the form of flags. Super duper easy.
+
+```typescript
+// ✅ Good
+import { Text } from '@/refresh-components/texts/Text'
+
+function UserCard({ name }: { name: string }) {
+  return (
+    <Text
+      {/* The `text03` flag makes the text it renders to be coloured the 3rd-scale grey */}
+      text03
+      {/* The `mainAction` flag makes the text it renders to be "main-action" font + line-height + weightage, as described in the Figma */}
+      mainAction
+    >
+      {name}
+    </Text>
+  )
+}
+
+// ❌ Bad
+function UserCard({ name }: { name: string }) {
+  return (
+    <div>
+      <h2>{name}</h2>
+      <p>User details</p>
+    </div>
+  )
+}
+```
+
+### 10. Component Usage
+
+**Heavily avoid raw HTML input components. Always use components from the `web/src/refresh-components` or `web/lib/opal/src` directory.**
+
+**Reason:** We've put in a lot of effort to unify the components that are rendered in the Onyx app. Using raw components breaks the entire UI of the application, and leaves it in a muddier state than before.
+
+```typescript
+// ✅ Good
+import Button from '@/refresh-components/buttons/Button'
+import InputTypeIn from '@/refresh-components/inputs/InputTypeIn'
+import SvgPlusCircle from '@/icons/plus-circle'
+
+function ContactForm() {
+  return (
+    <form>
+      <InputTypeIn placeholder="Search..." />
+      <Button type="submit" leftIcon={SvgPlusCircle}>Submit</Button>
+    </form>
+  )
+}
+
+// ❌ Bad
+function ContactForm() {
+  return (
+    <form>
+      <input placeholder="Name" />
+      <textarea placeholder="Message" />
+      <button type="submit">Submit</button>
+    </form>
+  )
+}
+```
+
+### 11. Colors
+
+**Always use custom overrides for colors and borders rather than built in Tailwind CSS colors. These overrides live in `web/tailwind-themes/tailwind.config.js`.**
+
+**Reason:** Our custom color system uses CSS variables that automatically handle dark mode and maintain design consistency across the app. Standard Tailwind colors bypass this system.
+
+**Available color categories:**
+- **Text:** `text-01` through `text-05`, `text-inverted-XX`
+- **Backgrounds:** `background-neutral-XX`, `background-tint-XX` (and inverted variants)
+- **Borders:** `border-01` through `border-05`, `border-inverted-XX`
+- **Actions:** `action-link-XX`, `action-danger-XX`
+- **Status:** `status-info-XX`, `status-success-XX`, `status-warning-XX`, `status-error-XX`
+- **Theme:** `theme-primary-XX`, `theme-red-XX`, `theme-blue-XX`, etc.
+
+```typescript
+// ✅ Good - Use custom Onyx color classes
+<div className="bg-background-neutral-01 border border-border-02" />
+<div className="bg-background-tint-02 border border-border-01" />
+<div className="bg-status-success-01" />
+<div className="bg-action-link-01" />
+<div className="bg-theme-primary-05" />
+
+// ❌ Bad - Do NOT use standard Tailwind colors
+<div className="bg-gray-100 border border-gray-300 text-gray-600" />
+<div className="bg-white border border-slate-200" />
+<div className="bg-green-100 text-green-700" />
+<div className="bg-blue-100 text-blue-600" />
+<div className="bg-indigo-500" />
+```
+
+### 12. Data Fetching
+
+**Prefer using `useSWR` for data fetching. Data should generally be fetched on the client side. Components that need data should display a loader / placeholder while waiting for that data. Prefer loading data within the component that needs it rather than at the top level and passing it down.**
+
+**Reason:** Client side fetching allows us to load the skeleton of the page without waiting for data to load, leading to a snappier UX. Loading data where needed reduces dependencies between a component and its parent component(s).
+
 ## Database & Migrations
 
 ### Running Migrations
@@ -300,14 +580,6 @@ will be tailing their logs to this file.
 - Token management and rate limiting
 - Custom prompts and agent actions
 
-## UI/UX Patterns
-
-- Tailwind CSS with design system in `web/src/components/ui/`
-- Radix UI and Headless UI for accessible components
-- SWR for data fetching and caching
-- Form validation with react-hook-form
-- Error handling with popup notifications
-
 ## Creating a Plan
 When creating a plan in the `plans` directory, make sure to include at least these elements:
 

CONTRIBUTING.md

@@ -1,262 +1,31 @@
-<!-- ONYX_METADATA={"link": "https://github.com/onyx-dot-app/onyx/blob/main/CONTRIBUTING.md"} -->
-
 # Contributing to Onyx
-
 Hey there! We are so excited that you're interested in Onyx.
 
-As an open source project in a rapidly changing space, we welcome all contributions.
 
-## 💃 Guidelines
+## Contribution Opportunities
+The [GitHub Issues](https://github.com/onyx-dot-app/onyx/issues) page is a great place to look for and share contribution ideas.
 
-### Contribution Opportunities
+If you have your own feature that you would like to build please create an issue and community members can provide feedback and
+thumb it up if they feel a common need. 
 
-The [GitHub Issues](https://github.com/onyx-dot-app/onyx/issues) page is a great place to start for contribution ideas.
 
-To ensure that your contribution is aligned with the project's direction, please reach out to any maintainer on the Onyx team
-via [Discord](https://discord.gg/4NA5SbzrWb) or [email](mailto:hello@onyx.app).
+## Contributing Code
+Please reference the documents in contributing_guides folder to ensure that the code base is kept to a high standard.
+1. dev_setup.md (start here): gives you a guide to setting up a local development environment.
+2. contribution_process.md: how to ensure you are building valuable features that will get reviewed and merged.
+3. best_practices.md: before asking for reviews, ensure your changes meet the repo code quality standards.
 
-Issues that have been explicitly approved by the maintainers (aligned with the direction of the project)
-will be marked with the `approved by maintainers` label.
-Issues marked `good first issue` are an especially great place to start.
-
-**Connectors** to other tools are another great place to contribute. For details on how, refer to this
-[README.md](https://github.com/onyx-dot-app/onyx/blob/main/backend/onyx/connectors/README.md).
-
-If you have a new/different contribution in mind, we'd love to hear about it!
-Your input is vital to making sure that Onyx moves in the right direction.
-Before starting on implementation, please raise a GitHub issue.
-
-Also, always feel free to message the founders (Chris Weaver / Yuhong Sun) on
-[Discord](https://discord.gg/4NA5SbzrWb) directly about anything at all.
-
-### Contributing Code
-
-To contribute to this project, please follow the
+To contribute, please follow the
 ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
-When opening a pull request, mention related issues and feel free to tag relevant maintainers.
 
-Before creating a pull request please make sure that the new changes conform to the formatting and linting requirements.
-See the [Formatting and Linting](#formatting-and-linting) section for how to run these checks locally.
 
-### Getting Help 🙋
+## Getting Help 🙋
+We have support channels and generally interesting discussions on our [Discord](https://discord.gg/4NA5SbzrWb).
 
-Our goal is to make contributing as easy as possible. If you run into any issues please don't hesitate to reach out.
-That way we can help future contributors and users can avoid the same issue.
+See you there!
 
-We also have support channels and generally interesting discussions on our
-[Discord](https://discord.gg/4NA5SbzrWb).
-
-We would love to see you there!
-
-## Get Started 🚀
-
-Onyx being a fully functional app, relies on some external software, specifically:
-
-- [Postgres](https://www.postgresql.org/) (Relational DB)
-- [Vespa](https://vespa.ai/) (Vector DB/Search Engine)
-- [Redis](https://redis.io/) (Cache)
-- [MinIO](https://min.io/) (File Store)
-- [Nginx](https://nginx.org/) (Not needed for development flows generally)
-
-> **Note:**
-> This guide provides instructions to build and run Onyx locally from source with Docker containers providing the above external software. We believe this combination is easier for
-> development purposes. If you prefer to use pre-built container images, we provide instructions on running the full Onyx stack within Docker below.
-
-### Local Set Up
-
-Be sure to use Python version 3.11. For instructions on installing Python 3.11 on macOS, refer to the [CONTRIBUTING_MACOS.md](./CONTRIBUTING_MACOS.md) readme.
-
-If using a lower version, modifications will have to be made to the code.
-If using a higher version, sometimes some libraries will not be available (i.e. we had problems with Tensorflow in the past with higher versions of python).
-
-#### Backend: Python requirements
-
-Currently, we use [uv](https://docs.astral.sh/uv/) and recommend creating a [virtual environment](https://docs.astral.sh/uv/pip/environments/#using-a-virtual-environment).
-
-For convenience here's a command for it:
-
-```bash
-uv venv .venv --python 3.11
-source .venv/bin/activate
-```
-
-_For Windows, activate the virtual environment using Command Prompt:_
-
-```bash
-.venv\Scripts\activate
-```
-
-If using PowerShell, the command slightly differs:
-
-```powershell
-.venv\Scripts\Activate.ps1
-```
-
-Install the required python dependencies:
-
-```bash
-uv sync --all-extras
-```
-
-Install Playwright for Python (headless browser required by the Web Connector):
-
-```bash
-uv run playwright install
-```
-
-#### Frontend: Node dependencies
-
-Onyx uses Node v22.20.0. We highly recommend you use [Node Version Manager (nvm)](https://github.com/nvm-sh/nvm)
-to manage your Node installations. Once installed, you can run
-
-```bash
-nvm install 22 && nvm use 22
-node -v # verify your active version
-```
-
-Navigate to `onyx/web` and run:
-
-```bash
-npm i
-```
-
-## Formatting and Linting
-
-### Backend
-
-For the backend, you'll need to setup pre-commit hooks (black / reorder-python-imports).
-
-Then run:
-
-```bash
-uv run pre-commit install
-```
-
-Additionally, we use `mypy` for static type checking.
-Onyx is fully type-annotated, and we want to keep it that way!
-To run the mypy checks manually, run `uv run mypy .` from the `onyx/backend` directory.
-
-### Web
-
-We use `prettier` for formatting. The desired version will be installed via a `npm i` from the `onyx/web` directory.
-To run the formatter, use `npx prettier --write .` from the `onyx/web` directory.
-
-Pre-commit will also run prettier automatically on files you've recently touched. If re-formatted, your commit will fail.
-Re-stage your changes and commit again.
-
-# Running the application for development
-
-## Developing using VSCode Debugger (recommended)
-
-**We highly recommend using VSCode debugger for development.**
-See [CONTRIBUTING_VSCODE.md](./CONTRIBUTING_VSCODE.md) for more details.
-
-Otherwise, you can follow the instructions below to run the application for development.
-
-## Manually running the application for development
-### Docker containers for external software
-
-You will need Docker installed to run these containers.
-
-First navigate to `onyx/deployment/docker_compose`, then start up Postgres/Vespa/Redis/MinIO with:
-
-```bash
-docker compose -f docker-compose.yml -f docker-compose.dev.yml up -d index relational_db cache minio
-```
-
-(index refers to Vespa, relational_db refers to Postgres, and cache refers to Redis)
-
-### Running Onyx locally
-
-To start the frontend, navigate to `onyx/web` and run:
-
-```bash
-npm run dev
-```
-
-Next, start the model server which runs the local NLP models.
-Navigate to `onyx/backend` and run:
-
-```bash
-uvicorn model_server.main:app --reload --port 9000
-```
-
-_For Windows (for compatibility with both PowerShell and Command Prompt):_
-
-```bash
-powershell -Command "uvicorn model_server.main:app --reload --port 9000"
-```
-
-The first time running Onyx, you will need to run the DB migrations for Postgres.
-After the first time, this is no longer required unless the DB models change.
-
-Navigate to `onyx/backend` and with the venv active, run:
-
-```bash
-alembic upgrade head
-```
-
-Next, start the task queue which orchestrates the background jobs.
-Jobs that take more time are run async from the API server.
-
-Still in `onyx/backend`, run:
-
-```bash
-python ./scripts/dev_run_background_jobs.py
-```
-
-To run the backend API server, navigate back to `onyx/backend` and run:
-
-```bash
-AUTH_TYPE=disabled uvicorn onyx.main:app --reload --port 8080
-```
-
-_For Windows (for compatibility with both PowerShell and Command Prompt):_
-
-```bash
-powershell -Command "
-    $env:AUTH_TYPE='disabled'
-    uvicorn onyx.main:app --reload --port 8080
-"
-```
-
-> **Note:**
-> If you need finer logging, add the additional environment variable `LOG_LEVEL=DEBUG` to the relevant services.
-
-#### Wrapping up
-
-You should now have 4 servers running:
-
-- Web server
-- Backend API
-- Model server
-- Background jobs
-
-Now, visit `http://localhost:3000` in your browser. You should see the Onyx onboarding wizard where you can connect your external LLM provider to Onyx.
-
-You've successfully set up a local Onyx instance! 🏁
-
-#### Running the Onyx application in a container
-
-You can run the full Onyx application stack from pre-built images including all external software dependencies.
-
-Navigate to `onyx/deployment/docker_compose` and run:
-
-```bash
-docker compose up -d
-```
-
-After Docker pulls and starts these containers, navigate to `http://localhost:3000` to use Onyx.
-
-If you want to make changes to Onyx and run those changes in Docker, you can also build a local version of the Onyx container images that incorporates your changes like so:
-
-```bash
-docker compose up -d --build
-```
-
-
-### Release Process
 
+## Release Process
 Onyx loosely follows the SemVer versioning standard.
 Major changes are released with a "minor" version bump. Currently we use patch release versions to indicate small feature changes.
 A set of Docker containers will be pushed automatically to DockerHub with every tag.

backend/alembic/env.py

@@ -225,7 +225,6 @@ def do_run_migrations(
 ) -> None:
     if create_schema:
         connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{schema_name}"'))
-        connection.execute(text("COMMIT"))
 
     connection.execute(text(f'SET search_path TO "{schema_name}"'))
 
@@ -309,6 +308,7 @@ async def run_async_migrations() -> None:
                         schema_name=schema,
                         create_schema=create_schema,
                     )
+                    await connection.commit()
             except Exception as e:
                 logger.error(f"Error migrating schema {schema}: {e}")
                 if not continue_on_error:
@@ -346,6 +346,7 @@ async def run_async_migrations() -> None:
                         schema_name=schema,
                         create_schema=create_schema,
                     )
+                    await connection.commit()
             except Exception as e:
                 logger.error(f"Error migrating schema {schema}: {e}")
                 if not continue_on_error:

backend/alembic/versions/2b90f3af54b8_usage_limits.py

@@ -0,0 +1,46 @@
+"""usage_limits
+
+Revision ID: 2b90f3af54b8
+Revises: 9a0296d7421e
+Create Date: 2026-01-03 16:55:30.449692
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "2b90f3af54b8"
+down_revision = "9a0296d7421e"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "tenant_usage",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column(
+            "window_start", sa.DateTime(timezone=True), nullable=False, index=True
+        ),
+        sa.Column("llm_cost_cents", sa.Float(), nullable=False, server_default="0.0"),
+        sa.Column("chunks_indexed", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("api_calls", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column(
+            "non_streaming_api_calls", sa.Integer(), nullable=False, server_default="0"
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=True,
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint("window_start", name="uq_tenant_usage_window"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_tenant_usage_window_start", table_name="tenant_usage")
+    op.drop_table("tenant_usage")

backend/alembic/versions/47a07e1a38f1_fix_invalid_model_configurations_state.py

@@ -11,7 +11,7 @@ from pydantic import BaseModel, ConfigDict
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 
-from onyx.llm.llm_provider_options import (
+from onyx.llm.well_known_providers.llm_provider_options import (
     fetch_model_names_for_provider_as_set,
     fetch_visible_model_names_for_provider_as_set,
 )

backend/alembic/versions/505c488f6662_merge_default_assistants_into_unified.py

@@ -85,103 +85,122 @@ class UserRow(NamedTuple):
 def upgrade() -> None:
     conn = op.get_bind()
 
-    # Start transaction
-    conn.execute(sa.text("BEGIN"))
+    # Step 1: Create or update the unified assistant (ID 0)
+    search_assistant = conn.execute(
+        sa.text("SELECT * FROM persona WHERE id = 0")
+    ).fetchone()
 
-    try:
-        # Step 1: Create or update the unified assistant (ID 0)
-        search_assistant = conn.execute(
-            sa.text("SELECT * FROM persona WHERE id = 0")
-        ).fetchone()
-
-        if search_assistant:
-            # Update existing Search assistant to be the unified assistant
-            conn.execute(
-                sa.text(
-                    """
-                    UPDATE persona
-                    SET name = :name,
-                        description = :description,
-                        system_prompt = :system_prompt,
-                        num_chunks = :num_chunks,
-                        is_default_persona = true,
-                        is_visible = true,
-                        deleted = false,
-                        display_priority = :display_priority,
-                        llm_filter_extraction = :llm_filter_extraction,
-                        llm_relevance_filter = :llm_relevance_filter,
-                        recency_bias = :recency_bias,
-                        chunks_above = :chunks_above,
-                        chunks_below = :chunks_below,
-                        datetime_aware = :datetime_aware,
-                        starter_messages = null
-                    WHERE id = 0
-                """
-                ),
-                INSERT_DICT,
-            )
-        else:
-            # Create new unified assistant with ID 0
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO persona (
-                        id, name, description, system_prompt, num_chunks,
-                        is_default_persona, is_visible, deleted, display_priority,
-                        llm_filter_extraction, llm_relevance_filter, recency_bias,
-                        chunks_above, chunks_below, datetime_aware, starter_messages,
-                        builtin_persona
-                    ) VALUES (
-                        0, :name, :description, :system_prompt, :num_chunks,
-                        true, true, false, :display_priority, :llm_filter_extraction,
-                        :llm_relevance_filter, :recency_bias, :chunks_above, :chunks_below,
-                        :datetime_aware, null, true
-                    )
-                """
-                ),
-                INSERT_DICT,
-            )
-
-        # Step 2: Mark ALL builtin assistants as deleted (except the unified assistant ID 0)
+    if search_assistant:
+        # Update existing Search assistant to be the unified assistant
         conn.execute(
             sa.text(
                 """
                 UPDATE persona
-                SET deleted = true, is_visible = false, is_default_persona = false
-                WHERE builtin_persona = true AND id != 0
+                SET name = :name,
+                    description = :description,
+                    system_prompt = :system_prompt,
+                    num_chunks = :num_chunks,
+                    is_default_persona = true,
+                    is_visible = true,
+                    deleted = false,
+                    display_priority = :display_priority,
+                    llm_filter_extraction = :llm_filter_extraction,
+                    llm_relevance_filter = :llm_relevance_filter,
+                    recency_bias = :recency_bias,
+                    chunks_above = :chunks_above,
+                    chunks_below = :chunks_below,
+                    datetime_aware = :datetime_aware,
+                    starter_messages = null
+                WHERE id = 0
             """
-            )
+            ),
+            INSERT_DICT,
+        )
+    else:
+        # Create new unified assistant with ID 0
+        conn.execute(
+            sa.text(
+                """
+                INSERT INTO persona (
+                    id, name, description, system_prompt, num_chunks,
+                    is_default_persona, is_visible, deleted, display_priority,
+                    llm_filter_extraction, llm_relevance_filter, recency_bias,
+                    chunks_above, chunks_below, datetime_aware, starter_messages,
+                    builtin_persona
+                ) VALUES (
+                    0, :name, :description, :system_prompt, :num_chunks,
+                    true, true, false, :display_priority, :llm_filter_extraction,
+                    :llm_relevance_filter, :recency_bias, :chunks_above, :chunks_below,
+                    :datetime_aware, null, true
+                )
+            """
+            ),
+            INSERT_DICT,
         )
 
-        # Step 3: Add all built-in tools to the unified assistant
-        # First, get the tool IDs for SearchTool, ImageGenerationTool, and WebSearchTool
-        search_tool = conn.execute(
-            sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'SearchTool'")
-        ).fetchone()
+    # Step 2: Mark ALL builtin assistants as deleted (except the unified assistant ID 0)
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET deleted = true, is_visible = false, is_default_persona = false
+            WHERE builtin_persona = true AND id != 0
+        """
+        )
+    )
 
-        if not search_tool:
-            raise ValueError(
-                "SearchTool not found in database. Ensure tools migration has run first."
-            )
+    # Step 3: Add all built-in tools to the unified assistant
+    # First, get the tool IDs for SearchTool, ImageGenerationTool, and WebSearchTool
+    search_tool = conn.execute(
+        sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'SearchTool'")
+    ).fetchone()
 
-        image_gen_tool = conn.execute(
-            sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'ImageGenerationTool'")
-        ).fetchone()
+    if not search_tool:
+        raise ValueError(
+            "SearchTool not found in database. Ensure tools migration has run first."
+        )
 
-        if not image_gen_tool:
-            raise ValueError(
-                "ImageGenerationTool not found in database. Ensure tools migration has run first."
-            )
+    image_gen_tool = conn.execute(
+        sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'ImageGenerationTool'")
+    ).fetchone()
 
-        # WebSearchTool is optional - may not be configured
-        web_search_tool = conn.execute(
-            sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'WebSearchTool'")
-        ).fetchone()
+    if not image_gen_tool:
+        raise ValueError(
+            "ImageGenerationTool not found in database. Ensure tools migration has run first."
+        )
 
-        # Clear existing tool associations for persona 0
-        conn.execute(sa.text("DELETE FROM persona__tool WHERE persona_id = 0"))
+    # WebSearchTool is optional - may not be configured
+    web_search_tool = conn.execute(
+        sa.text("SELECT id FROM tool WHERE in_code_tool_id = 'WebSearchTool'")
+    ).fetchone()
 
-        # Add tools to the unified assistant
+    # Clear existing tool associations for persona 0
+    conn.execute(sa.text("DELETE FROM persona__tool WHERE persona_id = 0"))
+
+    # Add tools to the unified assistant
+    conn.execute(
+        sa.text(
+            """
+            INSERT INTO persona__tool (persona_id, tool_id)
+            VALUES (0, :tool_id)
+            ON CONFLICT DO NOTHING
+        """
+        ),
+        {"tool_id": search_tool[0]},
+    )
+
+    conn.execute(
+        sa.text(
+            """
+            INSERT INTO persona__tool (persona_id, tool_id)
+            VALUES (0, :tool_id)
+            ON CONFLICT DO NOTHING
+        """
+        ),
+        {"tool_id": image_gen_tool[0]},
+    )
+
+    if web_search_tool:
         conn.execute(
             sa.text(
                 """
@@ -190,191 +209,148 @@ def upgrade() -> None:
                 ON CONFLICT DO NOTHING
             """
             ),
-            {"tool_id": search_tool[0]},
+            {"tool_id": web_search_tool[0]},
         )
 
-        conn.execute(
-            sa.text(
-                """
-                INSERT INTO persona__tool (persona_id, tool_id)
-                VALUES (0, :tool_id)
-                ON CONFLICT DO NOTHING
+    # Step 4: Migrate existing chat sessions from all builtin assistants to unified assistant
+    conn.execute(
+        sa.text(
             """
-            ),
-            {"tool_id": image_gen_tool[0]},
+            UPDATE chat_session
+            SET persona_id = 0
+            WHERE persona_id IN (
+                SELECT id FROM persona WHERE builtin_persona = true AND id != 0
+            )
+        """
         )
+    )
 
-        if web_search_tool:
+    # Step 5: Migrate user preferences - remove references to all builtin assistants
+    # First, get all builtin assistant IDs (except 0)
+    builtin_assistants_result = conn.execute(
+        sa.text(
+            """
+            SELECT id FROM persona
+            WHERE builtin_persona = true AND id != 0
+        """
+        )
+    ).fetchall()
+    builtin_assistant_ids = [row[0] for row in builtin_assistants_result]
+
+    # Get all users with preferences
+    users_result = conn.execute(
+        sa.text(
+            """
+            SELECT id, chosen_assistants, visible_assistants,
+                   hidden_assistants, pinned_assistants
+            FROM "user"
+        """
+        )
+    ).fetchall()
+
+    for user_row in users_result:
+        user = UserRow(*user_row)
+        user_id: UUID = user.id
+        updates: dict[str, Any] = {}
+
+        # Remove all builtin assistants from chosen_assistants
+        if user.chosen_assistants:
+            new_chosen: list[int] = [
+                assistant_id
+                for assistant_id in user.chosen_assistants
+                if assistant_id not in builtin_assistant_ids
+            ]
+            if new_chosen != user.chosen_assistants:
+                updates["chosen_assistants"] = json.dumps(new_chosen)
+
+        # Remove all builtin assistants from visible_assistants
+        if user.visible_assistants:
+            new_visible: list[int] = [
+                assistant_id
+                for assistant_id in user.visible_assistants
+                if assistant_id not in builtin_assistant_ids
+            ]
+            if new_visible != user.visible_assistants:
+                updates["visible_assistants"] = json.dumps(new_visible)
+
+        # Add all builtin assistants to hidden_assistants
+        if user.hidden_assistants:
+            new_hidden: list[int] = list(user.hidden_assistants)
+            for old_id in builtin_assistant_ids:
+                if old_id not in new_hidden:
+                    new_hidden.append(old_id)
+            if new_hidden != user.hidden_assistants:
+                updates["hidden_assistants"] = json.dumps(new_hidden)
+        else:
+            updates["hidden_assistants"] = json.dumps(builtin_assistant_ids)
+
+        # Remove all builtin assistants from pinned_assistants
+        if user.pinned_assistants:
+            new_pinned: list[int] = [
+                assistant_id
+                for assistant_id in user.pinned_assistants
+                if assistant_id not in builtin_assistant_ids
+            ]
+            if new_pinned != user.pinned_assistants:
+                updates["pinned_assistants"] = json.dumps(new_pinned)
+
+        # Apply updates if any
+        if updates:
+            set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
+            updates["user_id"] = str(user_id)  # Convert UUID to string for SQL
             conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO persona__tool (persona_id, tool_id)
-                    VALUES (0, :tool_id)
-                    ON CONFLICT DO NOTHING
-                """
-                ),
-                {"tool_id": web_search_tool[0]},
+                sa.text(f'UPDATE "user" SET {set_clause} WHERE id = :user_id'),
+                updates,
             )
 
-        # Step 4: Migrate existing chat sessions from all builtin assistants to unified assistant
-        conn.execute(
-            sa.text(
-                """
-                UPDATE chat_session
-                SET persona_id = 0
-                WHERE persona_id IN (
-                    SELECT id FROM persona WHERE builtin_persona = true AND id != 0
-                )
-            """
-            )
-        )
-
-        # Step 5: Migrate user preferences - remove references to all builtin assistants
-        # First, get all builtin assistant IDs (except 0)
-        builtin_assistants_result = conn.execute(
-            sa.text(
-                """
-                SELECT id FROM persona
-                WHERE builtin_persona = true AND id != 0
-            """
-            )
-        ).fetchall()
-        builtin_assistant_ids = [row[0] for row in builtin_assistants_result]
-
-        # Get all users with preferences
-        users_result = conn.execute(
-            sa.text(
-                """
-                SELECT id, chosen_assistants, visible_assistants,
-                       hidden_assistants, pinned_assistants
-                FROM "user"
-            """
-            )
-        ).fetchall()
-
-        for user_row in users_result:
-            user = UserRow(*user_row)
-            user_id: UUID = user.id
-            updates: dict[str, Any] = {}
-
-            # Remove all builtin assistants from chosen_assistants
-            if user.chosen_assistants:
-                new_chosen: list[int] = [
-                    assistant_id
-                    for assistant_id in user.chosen_assistants
-                    if assistant_id not in builtin_assistant_ids
-                ]
-                if new_chosen != user.chosen_assistants:
-                    updates["chosen_assistants"] = json.dumps(new_chosen)
-
-            # Remove all builtin assistants from visible_assistants
-            if user.visible_assistants:
-                new_visible: list[int] = [
-                    assistant_id
-                    for assistant_id in user.visible_assistants
-                    if assistant_id not in builtin_assistant_ids
-                ]
-                if new_visible != user.visible_assistants:
-                    updates["visible_assistants"] = json.dumps(new_visible)
-
-            # Add all builtin assistants to hidden_assistants
-            if user.hidden_assistants:
-                new_hidden: list[int] = list(user.hidden_assistants)
-                for old_id in builtin_assistant_ids:
-                    if old_id not in new_hidden:
-                        new_hidden.append(old_id)
-                if new_hidden != user.hidden_assistants:
-                    updates["hidden_assistants"] = json.dumps(new_hidden)
-            else:
-                updates["hidden_assistants"] = json.dumps(builtin_assistant_ids)
-
-            # Remove all builtin assistants from pinned_assistants
-            if user.pinned_assistants:
-                new_pinned: list[int] = [
-                    assistant_id
-                    for assistant_id in user.pinned_assistants
-                    if assistant_id not in builtin_assistant_ids
-                ]
-                if new_pinned != user.pinned_assistants:
-                    updates["pinned_assistants"] = json.dumps(new_pinned)
-
-            # Apply updates if any
-            if updates:
-                set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
-                updates["user_id"] = str(user_id)  # Convert UUID to string for SQL
-                conn.execute(
-                    sa.text(f'UPDATE "user" SET {set_clause} WHERE id = :user_id'),
-                    updates,
-                )
-
-        # Commit transaction
-        conn.execute(sa.text("COMMIT"))
-
-    except Exception as e:
-        # Rollback on error
-        conn.execute(sa.text("ROLLBACK"))
-        raise e
-
 
 def downgrade() -> None:
     conn = op.get_bind()
 
-    # Start transaction
-    conn.execute(sa.text("BEGIN"))
-
-    try:
-        # Only restore General (ID -1) and Art (ID -3) assistants
-        # Step 1: Keep Search assistant (ID 0) as default but restore original state
-        conn.execute(
-            sa.text(
-                """
-                UPDATE persona
-                SET is_default_persona = true,
-                    is_visible = true,
-                    deleted = false
-                WHERE id = 0
+    # Only restore General (ID -1) and Art (ID -3) assistants
+    # Step 1: Keep Search assistant (ID 0) as default but restore original state
+    conn.execute(
+        sa.text(
             """
-            )
+            UPDATE persona
+            SET is_default_persona = true,
+                is_visible = true,
+                deleted = false
+            WHERE id = 0
+        """
         )
+    )
 
-        # Step 2: Restore General assistant (ID -1)
-        conn.execute(
-            sa.text(
-                """
-                UPDATE persona
-                SET deleted = false,
-                    is_visible = true,
-                    is_default_persona = true
-                WHERE id = :general_assistant_id
+    # Step 2: Restore General assistant (ID -1)
+    conn.execute(
+        sa.text(
             """
-            ),
-            {"general_assistant_id": GENERAL_ASSISTANT_ID},
-        )
+            UPDATE persona
+            SET deleted = false,
+                is_visible = true,
+                is_default_persona = true
+            WHERE id = :general_assistant_id
+        """
+        ),
+        {"general_assistant_id": GENERAL_ASSISTANT_ID},
+    )
 
-        # Step 3: Restore Art assistant (ID -3)
-        conn.execute(
-            sa.text(
-                """
-                UPDATE persona
-                SET deleted = false,
-                    is_visible = true,
-                    is_default_persona = true
-                WHERE id = :art_assistant_id
+    # Step 3: Restore Art assistant (ID -3)
+    conn.execute(
+        sa.text(
             """
-            ),
-            {"art_assistant_id": ART_ASSISTANT_ID},
-        )
+            UPDATE persona
+            SET deleted = false,
+                is_visible = true,
+                is_default_persona = true
+            WHERE id = :art_assistant_id
+        """
+        ),
+        {"art_assistant_id": ART_ASSISTANT_ID},
+    )
 
-        # Note: We don't restore the original tool associations, names, or descriptions
-        # as those would require more complex logic to determine original state.
-        # We also cannot restore original chat session persona_ids as we don't
-        # have the original mappings.
-        # Other builtin assistants remain deleted as per the requirement.
-
-        # Commit transaction
-        conn.execute(sa.text("COMMIT"))
-
-    except Exception as e:
-        # Rollback on error
-        conn.execute(sa.text("ROLLBACK"))
-        raise e
+    # Note: We don't restore the original tool associations, names, or descriptions
+    # as those would require more complex logic to determine original state.
+    # We also cannot restore original chat session persona_ids as we don't
+    # have the original mappings.
+    # Other builtin assistants remain deleted as per the requirement.

backend/alembic/versions/5c3dca366b35_backend_driven_notification_details.py

@@ -0,0 +1,35 @@
+"""backend driven notification details
+
+Revision ID: 5c3dca366b35
+Revises: 9087b548dd69
+Create Date: 2026-01-06 16:03:11.413724
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "5c3dca366b35"
+down_revision = "9087b548dd69"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "notification",
+        sa.Column(
+            "title", sa.String(), nullable=False, server_default="New Notification"
+        ),
+    )
+    op.add_column(
+        "notification",
+        sa.Column("description", sa.String(), nullable=True, server_default=""),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("notification", "title")
+    op.drop_column("notification", "description")

backend/alembic/versions/699221885109_nullify_default_task_prompt.py

@@ -0,0 +1,75 @@
+"""nullify_default_task_prompt
+
+Revision ID: 699221885109
+Revises: 7e490836d179
+Create Date: 2025-12-30 10:00:00.000000
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "699221885109"
+down_revision = "7e490836d179"
+branch_labels = None
+depends_on = None
+
+DEFAULT_PERSONA_ID = 0
+
+
+def upgrade() -> None:
+    # Make task_prompt column nullable
+    # Note: The model had nullable=True but the DB column was NOT NULL until this point
+    op.alter_column(
+        "persona",
+        "task_prompt",
+        nullable=True,
+    )
+
+    # Set task_prompt to NULL for the default persona
+    conn = op.get_bind()
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET task_prompt = NULL
+            WHERE id = :persona_id
+            """
+        ),
+        {"persona_id": DEFAULT_PERSONA_ID},
+    )
+
+
+def downgrade() -> None:
+    # Restore task_prompt to empty string for the default persona
+    conn = op.get_bind()
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET task_prompt = ''
+            WHERE id = :persona_id AND task_prompt IS NULL
+            """
+        ),
+        {"persona_id": DEFAULT_PERSONA_ID},
+    )
+
+    # Set any remaining NULL task_prompts to empty string before making non-nullable
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET task_prompt = ''
+            WHERE task_prompt IS NULL
+            """
+        )
+    )
+
+    # Revert task_prompt column to not nullable
+    op.alter_column(
+        "persona",
+        "task_prompt",
+        nullable=False,
+    )

backend/alembic/versions/7206234e012a_add_image_generation_config_table.py

@@ -0,0 +1,54 @@
+"""add image generation config table
+
+Revision ID: 7206234e012a
+Revises: 699221885109
+Create Date: 2025-12-21 00:00:00.000000
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "7206234e012a"
+down_revision = "699221885109"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "image_generation_config",
+        sa.Column("image_provider_id", sa.String(), primary_key=True),
+        sa.Column("model_configuration_id", sa.Integer(), nullable=False),
+        sa.Column("is_default", sa.Boolean(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["model_configuration_id"],
+            ["model_configuration.id"],
+            ondelete="CASCADE",
+        ),
+    )
+    op.create_index(
+        "ix_image_generation_config_is_default",
+        "image_generation_config",
+        ["is_default"],
+        unique=False,
+    )
+    op.create_index(
+        "ix_image_generation_config_model_configuration_id",
+        "image_generation_config",
+        ["model_configuration_id"],
+        unique=False,
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "ix_image_generation_config_model_configuration_id",
+        table_name="image_generation_config",
+    )
+    op.drop_index(
+        "ix_image_generation_config_is_default", table_name="image_generation_config"
+    )
+    op.drop_table("image_generation_config")

backend/alembic/versions/73e9983e5091_add_search_query_table.py

@@ -0,0 +1,47 @@
+"""add_search_query_table
+
+Revision ID: 73e9983e5091
+Revises: d1b637d7050a
+Create Date: 2026-01-14 14:16:52.837489
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "73e9983e5091"
+down_revision = "d1b637d7050a"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "search_query",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True),
+        sa.Column(
+            "user_id",
+            postgresql.UUID(as_uuid=True),
+            sa.ForeignKey("user.id"),
+            nullable=False,
+        ),
+        sa.Column("query", sa.String(), nullable=False),
+        sa.Column("query_expansions", postgresql.ARRAY(sa.String()), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )
+
+    op.create_index("ix_search_query_user_id", "search_query", ["user_id"])
+    op.create_index("ix_search_query_created_at", "search_query", ["created_at"])
+
+
+def downgrade() -> None:
+    op.drop_index("ix_search_query_created_at", table_name="search_query")
+    op.drop_index("ix_search_query_user_id", table_name="search_query")
+    op.drop_table("search_query")

backend/alembic/versions/776b3bbe9092_remove_remaining_enums.py

@@ -10,8 +10,7 @@ from alembic import op
 import sqlalchemy as sa
 
 from onyx.db.models import IndexModelStatus
-from onyx.context.search.enums import RecencyBiasSetting
-from onyx.context.search.enums import SearchType
+from onyx.context.search.enums import RecencyBiasSetting, SearchType
 
 # revision identifiers, used by Alembic.
 revision = "776b3bbe9092"

backend/alembic/versions/7a70b7664e37_add_model_configuration_table.py

@@ -10,7 +10,7 @@ from alembic import op
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 
-from onyx.llm.llm_provider_options import (
+from onyx.llm.well_known_providers.llm_provider_options import (
     fetch_model_names_for_provider_as_set,
     fetch_visible_model_names_for_provider_as_set,
 )

backend/alembic/versions/7e490836d179_nullify_default_system_prompt.py

@@ -0,0 +1,80 @@
+"""nullify_default_system_prompt
+
+Revision ID: 7e490836d179
+Revises: c1d2e3f4a5b6
+Create Date: 2025-12-29 16:54:36.635574
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "7e490836d179"
+down_revision = "c1d2e3f4a5b6"
+branch_labels = None
+depends_on = None
+
+
+# This is the default system prompt from the previous migration (87c52ec39f84)
+# ruff: noqa: E501, W605 start
+PREVIOUS_DEFAULT_SYSTEM_PROMPT = """
+You are a highly capable, thoughtful, and precise assistant. Your goal is to deeply understand the user's intent, ask clarifying questions when needed, think step-by-step through complex problems, provide clear and accurate answers, and proactively anticipate helpful follow-up information. Always prioritize being truthful, nuanced, insightful, and efficient.
+
+The current date is [[CURRENT_DATETIME]].[[CITATION_GUIDANCE]]
+
+# Response Style
+You use different text styles, bolding, emojis (sparingly), block quotes, and other formatting to make your responses more readable and engaging.
+You use proper Markdown and LaTeX to format your responses for math, scientific, and chemical formulas, symbols, etc.: '$$\\n[expression]\\n$$' for standalone cases and '\\( [expression] \\)' when inline.
+For code you prefer to use Markdown and specify the language.
+You can use horizontal rules (---) to separate sections of your responses.
+You can use Markdown tables to format your responses for data, lists, and other structured information.
+""".lstrip()
+# ruff: noqa: E501, W605 end
+
+
+def upgrade() -> None:
+    # Make system_prompt column nullable (model already has nullable=True but DB doesn't)
+    op.alter_column(
+        "persona",
+        "system_prompt",
+        nullable=True,
+    )
+
+    # Set system_prompt to NULL where it matches the previous default
+    conn = op.get_bind()
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET system_prompt = NULL
+            WHERE system_prompt = :previous_default
+            """
+        ),
+        {"previous_default": PREVIOUS_DEFAULT_SYSTEM_PROMPT},
+    )
+
+
+def downgrade() -> None:
+    # Restore the default system prompt for personas that have NULL
+    # Note: This may restore the prompt to personas that originally had NULL
+    # before this migration, but there's no way to distinguish them
+    conn = op.get_bind()
+    conn.execute(
+        sa.text(
+            """
+            UPDATE persona
+            SET system_prompt = :previous_default
+            WHERE system_prompt IS NULL
+            """
+        ),
+        {"previous_default": PREVIOUS_DEFAULT_SYSTEM_PROMPT},
+    )
+
+    # Revert system_prompt column to not nullable
+    op.alter_column(
+        "persona",
+        "system_prompt",
+        nullable=False,
+    )

backend/alembic/versions/8405ca81cc83_notifications_constraint.py

@@ -0,0 +1,49 @@
+"""notifications constraint, sort index, and cleanup old notifications
+
+Revision ID: 8405ca81cc83
+Revises: a3c1a7904cd0
+Create Date: 2026-01-07 16:43:44.855156
+
+"""
+
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "8405ca81cc83"
+down_revision = "a3c1a7904cd0"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Create unique index for notification deduplication.
+    # This enables atomic ON CONFLICT DO NOTHING inserts in batch_create_notifications.
+    #
+    # Uses COALESCE to handle NULL additional_data (NULLs are normally distinct
+    # in unique constraints, but we want NULL == NULL for deduplication).
+    # The '{}' represents an empty JSONB object as the NULL replacement.
+
+    # Clean up legacy notifications first
+    op.execute("DELETE FROM notification WHERE title = 'New Notification'")
+
+    op.execute(
+        """
+        CREATE UNIQUE INDEX IF NOT EXISTS ix_notification_user_type_data
+        ON notification (user_id, notif_type, COALESCE(additional_data, '{}'::jsonb))
+        """
+    )
+
+    # Create index for efficient notification sorting by user
+    # Covers: WHERE user_id = ? ORDER BY dismissed, first_shown DESC
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_notification_user_sort
+        ON notification (user_id, dismissed, first_shown DESC)
+        """
+    )
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS ix_notification_user_type_data")
+    op.execute("DROP INDEX IF EXISTS ix_notification_user_sort")

backend/alembic/versions/8b5ce697290e_add_discord_bot_tables.py

@@ -0,0 +1,116 @@
+"""Add Discord bot tables
+
+Revision ID: 8b5ce697290e
+Revises: a1b2c3d4e5f7
+Create Date: 2025-01-14
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "8b5ce697290e"
+down_revision = "a1b2c3d4e5f7"
+branch_labels: None = None
+depends_on: None = None
+
+
+def upgrade() -> None:
+    # DiscordBotConfig (singleton table - one per tenant)
+    op.create_table(
+        "discord_bot_config",
+        sa.Column(
+            "id",
+            sa.String(),
+            primary_key=True,
+            server_default=sa.text("'SINGLETON'"),
+        ),
+        sa.Column("bot_token", sa.LargeBinary(), nullable=False),  # EncryptedString
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=False,
+        ),
+        sa.CheckConstraint("id = 'SINGLETON'", name="ck_discord_bot_config_singleton"),
+    )
+
+    # DiscordGuildConfig
+    op.create_table(
+        "discord_guild_config",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("guild_id", sa.BigInteger(), nullable=True, unique=True),
+        sa.Column("guild_name", sa.String(), nullable=True),
+        sa.Column("registration_key", sa.String(), nullable=False, unique=True),
+        sa.Column("registered_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column(
+            "default_persona_id",
+            sa.Integer(),
+            sa.ForeignKey("persona.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column(
+            "enabled", sa.Boolean(), server_default=sa.text("true"), nullable=False
+        ),
+    )
+
+    # DiscordChannelConfig
+    op.create_table(
+        "discord_channel_config",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column(
+            "guild_config_id",
+            sa.Integer(),
+            sa.ForeignKey("discord_guild_config.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("channel_id", sa.BigInteger(), nullable=False),
+        sa.Column("channel_name", sa.String(), nullable=False),
+        sa.Column(
+            "channel_type",
+            sa.String(20),
+            server_default=sa.text("'text'"),
+            nullable=False,
+        ),
+        sa.Column(
+            "is_private",
+            sa.Boolean(),
+            server_default=sa.text("false"),
+            nullable=False,
+        ),
+        sa.Column(
+            "thread_only_mode",
+            sa.Boolean(),
+            server_default=sa.text("false"),
+            nullable=False,
+        ),
+        sa.Column(
+            "require_bot_invocation",
+            sa.Boolean(),
+            server_default=sa.text("true"),
+            nullable=False,
+        ),
+        sa.Column(
+            "persona_override_id",
+            sa.Integer(),
+            sa.ForeignKey("persona.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column(
+            "enabled", sa.Boolean(), server_default=sa.text("false"), nullable=False
+        ),
+    )
+
+    # Unique constraint: one config per channel per guild
+    op.create_unique_constraint(
+        "uq_discord_channel_guild_channel",
+        "discord_channel_config",
+        ["guild_config_id", "channel_id"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("discord_channel_config")
+    op.drop_table("discord_guild_config")
+    op.drop_table("discord_bot_config")

backend/alembic/versions/9087b548dd69_seed_default_image_gen_config.py

@@ -0,0 +1,136 @@
+"""seed_default_image_gen_config
+
+Revision ID: 9087b548dd69
+Revises: 2b90f3af54b8
+Create Date: 2026-01-05 00:00:00.000000
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "9087b548dd69"
+down_revision = "2b90f3af54b8"
+branch_labels = None
+depends_on = None
+
+# Constants for default image generation config
+# Source: web/src/app/admin/configuration/image-generation/constants.ts
+IMAGE_PROVIDER_ID = "openai_gpt_image_1"
+MODEL_NAME = "gpt-image-1"
+PROVIDER_NAME = "openai"
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    # Check if image_generation_config table already has records
+    existing_configs = (
+        conn.execute(sa.text("SELECT COUNT(*) FROM image_generation_config")).scalar()
+        or 0
+    )
+
+    if existing_configs > 0:
+        # Skip if configs already exist - user may have configured manually
+        return
+
+    # Find the first OpenAI LLM provider
+    openai_provider = conn.execute(
+        sa.text(
+            """
+            SELECT id, api_key
+            FROM llm_provider
+            WHERE provider = :provider
+            ORDER BY id
+            LIMIT 1
+            """
+        ),
+        {"provider": PROVIDER_NAME},
+    ).fetchone()
+
+    if not openai_provider:
+        # No OpenAI provider found - nothing to do
+        return
+
+    source_provider_id, api_key = openai_provider
+
+    # Create new LLM provider for image generation (clone only api_key)
+    result = conn.execute(
+        sa.text(
+            """
+            INSERT INTO llm_provider (
+                name, provider, api_key, api_base, api_version,
+                deployment_name, default_model_name, is_public,
+                is_default_provider, is_default_vision_provider, is_auto_mode
+            )
+            VALUES (
+                :name, :provider, :api_key, NULL, NULL,
+                NULL, :default_model_name, :is_public,
+                NULL, NULL, :is_auto_mode
+            )
+            RETURNING id
+            """
+        ),
+        {
+            "name": f"Image Gen - {IMAGE_PROVIDER_ID}",
+            "provider": PROVIDER_NAME,
+            "api_key": api_key,
+            "default_model_name": MODEL_NAME,
+            "is_public": True,
+            "is_auto_mode": False,
+        },
+    )
+    new_provider_id = result.scalar()
+
+    # Create model configuration
+    result = conn.execute(
+        sa.text(
+            """
+            INSERT INTO model_configuration (
+                llm_provider_id, name, is_visible, max_input_tokens,
+                supports_image_input, display_name
+            )
+            VALUES (
+                :llm_provider_id, :name, :is_visible, :max_input_tokens,
+                :supports_image_input, :display_name
+            )
+            RETURNING id
+            """
+        ),
+        {
+            "llm_provider_id": new_provider_id,
+            "name": MODEL_NAME,
+            "is_visible": True,
+            "max_input_tokens": None,
+            "supports_image_input": False,
+            "display_name": None,
+        },
+    )
+    model_config_id = result.scalar()
+
+    # Create image generation config
+    conn.execute(
+        sa.text(
+            """
+            INSERT INTO image_generation_config (
+                image_provider_id, model_configuration_id, is_default
+            )
+            VALUES (
+                :image_provider_id, :model_configuration_id, :is_default
+            )
+            """
+        ),
+        {
+            "image_provider_id": IMAGE_PROVIDER_ID,
+            "model_configuration_id": model_config_id,
+            "is_default": True,
+        },
+    )
+
+
+def downgrade() -> None:
+    # We don't remove the config on downgrade since it's safe to keep around
+    # If we upgrade again, it will be a no-op due to the existing records check
+    pass

backend/alembic/versions/9a0296d7421e_add_is_auto_mode_to_llm_provider.py

@@ -0,0 +1,33 @@
+"""add_is_auto_mode_to_llm_provider
+
+Revision ID: 9a0296d7421e
+Revises: 7206234e012a
+Create Date: 2025-12-17 18:14:29.620981
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "9a0296d7421e"
+down_revision = "7206234e012a"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "llm_provider",
+        sa.Column(
+            "is_auto_mode",
+            sa.Boolean(),
+            nullable=False,
+            server_default="false",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("llm_provider", "is_auto_mode")

backend/alembic/versions/9b66d3156fc6_user_file_schema_additions.py

@@ -234,6 +234,8 @@ def downgrade() -> None:
         if "instructions" in columns:
             op.drop_column("user_project", "instructions")
         op.execute("ALTER TABLE user_project RENAME TO user_folder")
+        # Update NULL descriptions to empty string before setting NOT NULL constraint
+        op.execute("UPDATE user_folder SET description = '' WHERE description IS NULL")
         op.alter_column("user_folder", "description", nullable=False)
         logger.info("Renamed user_project back to user_folder")
 

backend/alembic/versions/a01bf2971c5d_update_default_tool_descriptions.py

@@ -42,20 +42,13 @@ TOOL_DESCRIPTIONS = {
 
 def upgrade() -> None:
     conn = op.get_bind()
-    conn.execute(sa.text("BEGIN"))
-
-    try:
-        for tool_id, description in TOOL_DESCRIPTIONS.items():
-            conn.execute(
-                sa.text(
-                    "UPDATE tool SET description = :description WHERE in_code_tool_id = :tool_id"
-                ),
-                {"description": description, "tool_id": tool_id},
-            )
-        conn.execute(sa.text("COMMIT"))
-    except Exception as e:
-        conn.execute(sa.text("ROLLBACK"))
-        raise e
+    for tool_id, description in TOOL_DESCRIPTIONS.items():
+        conn.execute(
+            sa.text(
+                "UPDATE tool SET description = :description WHERE in_code_tool_id = :tool_id"
+            ),
+            {"description": description, "tool_id": tool_id},
+        )
 
 
 def downgrade() -> None:

backend/alembic/versions/a1b2c3d4e5f7_drop_agent_search_metrics_table.py

@@ -0,0 +1,47 @@
+"""drop agent_search_metrics table
+
+Revision ID: a1b2c3d4e5f7
+Revises: 73e9983e5091
+Create Date: 2026-01-17
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "a1b2c3d4e5f7"
+down_revision = "73e9983e5091"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_table("agent__search_metrics")
+
+
+def downgrade() -> None:
+    op.create_table(
+        "agent__search_metrics",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("user_id", sa.UUID(), nullable=True),
+        sa.Column("persona_id", sa.Integer(), nullable=True),
+        sa.Column("agent_type", sa.String(), nullable=False),
+        sa.Column("start_time", sa.DateTime(timezone=True), nullable=False),
+        sa.Column("base_duration_s", sa.Float(), nullable=False),
+        sa.Column("full_duration_s", sa.Float(), nullable=False),
+        sa.Column("base_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("refined_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("all_metrics", postgresql.JSONB(), nullable=True),
+        sa.ForeignKeyConstraint(
+            ["user_id"],
+            ["user.id"],
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )

backend/alembic/versions/a3c1a7904cd0_remove_userfile_related_deprecated_.py

@@ -0,0 +1,39 @@
+"""remove userfile related deprecated fields
+
+Revision ID: a3c1a7904cd0
+Revises: 5c3dca366b35
+Create Date: 2026-01-06 13:00:30.634396
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "a3c1a7904cd0"
+down_revision = "5c3dca366b35"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_column("user_file", "document_id")
+    op.drop_column("user_file", "document_id_migrated")
+    op.drop_column("connector_credential_pair", "is_user_file")
+
+
+def downgrade() -> None:
+    op.add_column(
+        "connector_credential_pair",
+        sa.Column("is_user_file", sa.Boolean(), nullable=False, server_default="false"),
+    )
+    op.add_column(
+        "user_file",
+        sa.Column("document_id", sa.String(), nullable=True),
+    )
+    op.add_column(
+        "user_file",
+        sa.Column(
+            "document_id_migrated", sa.Boolean(), nullable=False, server_default="true"
+        ),
+    )

backend/alembic/versions/c1d2e3f4a5b6_add_deep_research_tool.py

@@ -7,7 +7,6 @@ Create Date: 2025-12-18 16:00:00.000000
 """
 
 from alembic import op
-from onyx.deep_research.dr_mock_tools import RESEARCH_AGENT_DB_NAME
 import sqlalchemy as sa
 
 
@@ -19,7 +18,7 @@ depends_on = None
 
 
 DEEP_RESEARCH_TOOL = {
-    "name": RESEARCH_AGENT_DB_NAME,
+    "name": "ResearchAgent",
     "display_name": "Research Agent",
     "description": "The Research Agent is a sub-agent that conducts research on a specific topic.",
     "in_code_tool_id": "ResearchAgent",

backend/alembic/versions/d09fc20a3c66_seed_builtin_tools.py

@@ -70,80 +70,66 @@ BUILT_IN_TOOLS = [
 def upgrade() -> None:
     conn = op.get_bind()
 
-    # Start transaction
-    conn.execute(sa.text("BEGIN"))
+    # Get existing tools to check what already exists
+    existing_tools = conn.execute(
+        sa.text("SELECT in_code_tool_id FROM tool WHERE in_code_tool_id IS NOT NULL")
+    ).fetchall()
+    existing_tool_ids = {row[0] for row in existing_tools}
 
-    try:
-        # Get existing tools to check what already exists
-        existing_tools = conn.execute(
-            sa.text(
-                "SELECT in_code_tool_id FROM tool WHERE in_code_tool_id IS NOT NULL"
+    # Insert or update built-in tools
+    for tool in BUILT_IN_TOOLS:
+        in_code_id = tool["in_code_tool_id"]
+
+        # Handle historical rename: InternetSearchTool -> WebSearchTool
+        if (
+            in_code_id == "WebSearchTool"
+            and "WebSearchTool" not in existing_tool_ids
+            and "InternetSearchTool" in existing_tool_ids
+        ):
+            # Rename the existing InternetSearchTool row in place and update fields
+            conn.execute(
+                sa.text(
+                    """
+                    UPDATE tool
+                    SET name = :name,
+                        display_name = :display_name,
+                        description = :description,
+                        in_code_tool_id = :in_code_tool_id
+                    WHERE in_code_tool_id = 'InternetSearchTool'
+                    """
+                ),
+                tool,
             )
-        ).fetchall()
-        existing_tool_ids = {row[0] for row in existing_tools}
+            # Keep the local view of existing ids in sync to avoid duplicate insert
+            existing_tool_ids.discard("InternetSearchTool")
+            existing_tool_ids.add("WebSearchTool")
+            continue
 
-        # Insert or update built-in tools
-        for tool in BUILT_IN_TOOLS:
-            in_code_id = tool["in_code_tool_id"]
-
-            # Handle historical rename: InternetSearchTool -> WebSearchTool
-            if (
-                in_code_id == "WebSearchTool"
-                and "WebSearchTool" not in existing_tool_ids
-                and "InternetSearchTool" in existing_tool_ids
-            ):
-                # Rename the existing InternetSearchTool row in place and update fields
-                conn.execute(
-                    sa.text(
-                        """
-                        UPDATE tool
-                        SET name = :name,
-                            display_name = :display_name,
-                            description = :description,
-                            in_code_tool_id = :in_code_tool_id
-                        WHERE in_code_tool_id = 'InternetSearchTool'
-                        """
-                    ),
-                    tool,
-                )
-                # Keep the local view of existing ids in sync to avoid duplicate insert
-                existing_tool_ids.discard("InternetSearchTool")
-                existing_tool_ids.add("WebSearchTool")
-                continue
-
-            if in_code_id in existing_tool_ids:
-                # Update existing tool
-                conn.execute(
-                    sa.text(
-                        """
-                        UPDATE tool
-                        SET name = :name,
-                            display_name = :display_name,
-                            description = :description
-                        WHERE in_code_tool_id = :in_code_tool_id
-                        """
-                    ),
-                    tool,
-                )
-            else:
-                # Insert new tool
-                conn.execute(
-                    sa.text(
-                        """
-                        INSERT INTO tool (name, display_name, description, in_code_tool_id)
-                        VALUES (:name, :display_name, :description, :in_code_tool_id)
-                        """
-                    ),
-                    tool,
-                )
-
-        # Commit transaction
-        conn.execute(sa.text("COMMIT"))
-
-    except Exception as e:
-        # Rollback on error
-        conn.execute(sa.text("ROLLBACK"))
-        raise e
+        if in_code_id in existing_tool_ids:
+            # Update existing tool
+            conn.execute(
+                sa.text(
+                    """
+                    UPDATE tool
+                    SET name = :name,
+                        display_name = :display_name,
+                        description = :description
+                    WHERE in_code_tool_id = :in_code_tool_id
+                    """
+                ),
+                tool,
+            )
+        else:
+            # Insert new tool
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO tool (name, display_name, description, in_code_tool_id)
+                    VALUES (:name, :display_name, :description, :in_code_tool_id)
+                    """
+                ),
+                tool,
+            )
 
 
 def downgrade() -> None:

backend/alembic/versions/d1b637d7050a_sync_exa_api_key_to_content_provider.py

@@ -0,0 +1,64 @@
+"""sync_exa_api_key_to_content_provider
+
+Revision ID: d1b637d7050a
+Revises: d25168c2beee
+Create Date: 2026-01-09 15:54:15.646249
+
+"""
+
+from alembic import op
+from sqlalchemy import text
+
+
+# revision identifiers, used by Alembic.
+revision = "d1b637d7050a"
+down_revision = "d25168c2beee"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Exa uses a shared API key between search and content providers.
+    # For existing Exa search providers with API keys, create the corresponding
+    # content provider if it doesn't exist yet.
+    connection = op.get_bind()
+
+    # Check if Exa search provider exists with an API key
+    result = connection.execute(
+        text(
+            """
+            SELECT api_key FROM internet_search_provider
+            WHERE provider_type = 'exa' AND api_key IS NOT NULL
+            LIMIT 1
+            """
+        )
+    )
+    row = result.fetchone()
+
+    if row:
+        api_key = row[0]
+        # Create Exa content provider with the shared key
+        connection.execute(
+            text(
+                """
+                INSERT INTO internet_content_provider
+                (name, provider_type, api_key, is_active)
+                VALUES ('Exa', 'exa', :api_key, false)
+                ON CONFLICT (name) DO NOTHING
+                """
+            ),
+            {"api_key": api_key},
+        )
+
+
+def downgrade() -> None:
+    # Remove the Exa content provider that was created by this migration
+    connection = op.get_bind()
+    connection.execute(
+        text(
+            """
+            DELETE FROM internet_content_provider
+            WHERE provider_type = 'exa'
+            """
+        )
+    )

backend/alembic/versions/d25168c2beee_tool_name_consistency.py

@@ -0,0 +1,86 @@
+"""tool_name_consistency
+
+Revision ID: d25168c2beee
+Revises: 8405ca81cc83
+Create Date: 2026-01-11 17:54:40.135777
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "d25168c2beee"
+down_revision = "8405ca81cc83"
+branch_labels = None
+depends_on = None
+
+
+# Currently the seeded tools have the in_code_tool_id == name
+CURRENT_TOOL_NAME_MAPPING = [
+    "SearchTool",
+    "WebSearchTool",
+    "ImageGenerationTool",
+    "PythonTool",
+    "OpenURLTool",
+    "KnowledgeGraphTool",
+    "ResearchAgent",
+]
+
+# Mapping of in_code_tool_id -> name
+# These are the expected names that we want in the database
+EXPECTED_TOOL_NAME_MAPPING = {
+    "SearchTool": "internal_search",
+    "WebSearchTool": "web_search",
+    "ImageGenerationTool": "generate_image",
+    "PythonTool": "python",
+    "OpenURLTool": "open_url",
+    "KnowledgeGraphTool": "run_kg_search",
+    "ResearchAgent": "research_agent",
+}
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    # Mapping of in_code_tool_id to the NAME constant from each tool class
+    # These match the .name property of each tool implementation
+    tool_name_mapping = EXPECTED_TOOL_NAME_MAPPING
+
+    # Update the name column for each tool based on its in_code_tool_id
+    for in_code_tool_id, expected_name in tool_name_mapping.items():
+        conn.execute(
+            sa.text(
+                """
+                UPDATE tool
+                SET name = :expected_name
+                WHERE in_code_tool_id = :in_code_tool_id
+                """
+            ),
+            {
+                "expected_name": expected_name,
+                "in_code_tool_id": in_code_tool_id,
+            },
+        )
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+
+    # Reverse the migration by setting name back to in_code_tool_id
+    # This matches the original pattern where name was the class name
+    for in_code_tool_id in CURRENT_TOOL_NAME_MAPPING:
+        conn.execute(
+            sa.text(
+                """
+                UPDATE tool
+                SET name = :current_name
+                WHERE in_code_tool_id = :in_code_tool_id
+                """
+            ),
+            {
+                "current_name": in_code_tool_id,
+                "in_code_tool_id": in_code_tool_id,
+            },
+        )

backend/ee/onyx/configs/app_configs.py

@@ -109,11 +109,6 @@ CHECK_TTL_MANAGEMENT_TASK_FREQUENCY_IN_HOURS = float(
 
 
 STRIPE_SECRET_KEY = os.environ.get("STRIPE_SECRET_KEY")
-STRIPE_PRICE_ID = os.environ.get("STRIPE_PRICE")
-
-OPENAI_DEFAULT_API_KEY = os.environ.get("OPENAI_DEFAULT_API_KEY")
-ANTHROPIC_DEFAULT_API_KEY = os.environ.get("ANTHROPIC_DEFAULT_API_KEY")
-COHERE_DEFAULT_API_KEY = os.environ.get("COHERE_DEFAULT_API_KEY")
 
 # JWT Public Key URL
 JWT_PUBLIC_KEY_URL: str | None = os.getenv("JWT_PUBLIC_KEY_URL", None)
@@ -133,3 +128,8 @@ MARKETING_POSTHOG_API_KEY = os.environ.get("MARKETING_POSTHOG_API_KEY")
 HUBSPOT_TRACKING_URL = os.environ.get("HUBSPOT_TRACKING_URL")
 
 GATED_TENANTS_KEY = "gated_tenants"
+
+# License enforcement - when True, blocks API access for gated/expired licenses
+LICENSE_ENFORCEMENT_ENABLED = (
+    os.environ.get("LICENSE_ENFORCEMENT_ENABLED", "").lower() == "true"
+)

backend/ee/onyx/db/persona.py

@@ -3,30 +3,42 @@ from uuid import UUID
 from sqlalchemy.orm import Session
 
 from onyx.configs.constants import NotificationType
+from onyx.db.models import Persona
 from onyx.db.models import Persona__User
 from onyx.db.models import Persona__UserGroup
 from onyx.db.notification import create_notification
 from onyx.server.features.persona.models import PersonaSharedNotificationData
 
 
-def make_persona_private(
+def update_persona_access(
     persona_id: int,
     creator_user_id: UUID | None,
-    user_ids: list[UUID] | None,
-    group_ids: list[int] | None,
     db_session: Session,
+    is_public: bool | None = None,
+    user_ids: list[UUID] | None = None,
+    group_ids: list[int] | None = None,
 ) -> None:
-    """NOTE(rkuo): This function batches all updates into a single commit. If we don't
-    dedupe the inputs, the commit will exception."""
+    """Updates the access settings for a persona including public status, user shares,
+    and group shares.
 
-    db_session.query(Persona__User).filter(
-        Persona__User.persona_id == persona_id
-    ).delete(synchronize_session="fetch")
-    db_session.query(Persona__UserGroup).filter(
-        Persona__UserGroup.persona_id == persona_id
-    ).delete(synchronize_session="fetch")
+    NOTE: This function batches all updates. If we don't dedupe the inputs,
+    the commit will exception.
+
+    NOTE: Callers are responsible for committing."""
+
+    if is_public is not None:
+        persona = db_session.query(Persona).filter(Persona.id == persona_id).first()
+        if persona:
+            persona.is_public = is_public
+
+    # NOTE: For user-ids and group-ids, `None` means "leave unchanged", `[]` means "clear all shares",
+    # and a non-empty list means "replace with these shares".
+
+    if user_ids is not None:
+        db_session.query(Persona__User).filter(
+            Persona__User.persona_id == persona_id
+        ).delete(synchronize_session="fetch")
 
-    if user_ids:
         user_ids_set = set(user_ids)
         for user_id in user_ids_set:
             db_session.add(Persona__User(persona_id=persona_id, user_id=user_id))
@@ -34,17 +46,20 @@ def make_persona_private(
                 create_notification(
                     user_id=user_id,
                     notif_type=NotificationType.PERSONA_SHARED,
+                    title="A new agent was shared with you!",
                     db_session=db_session,
                     additional_data=PersonaSharedNotificationData(
                         persona_id=persona_id,
                     ).model_dump(),
                 )
 
-    if group_ids:
+    if group_ids is not None:
+        db_session.query(Persona__UserGroup).filter(
+            Persona__UserGroup.persona_id == persona_id
+        ).delete(synchronize_session="fetch")
+
         group_ids_set = set(group_ids)
         for group_id in group_ids_set:
             db_session.add(
                 Persona__UserGroup(persona_id=persona_id, user_group_id=group_id)
             )
-
-    db_session.commit()

backend/ee/onyx/db/search.py

@@ -0,0 +1,64 @@
+import uuid
+from datetime import timedelta
+from uuid import UUID
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from onyx.db.engine.time_utils import get_db_current_time
+from onyx.db.models import SearchQuery
+
+
+def create_search_query(
+    db_session: Session,
+    user_id: UUID,
+    query: str,
+    query_expansions: list[str] | None = None,
+) -> SearchQuery:
+    """Create and persist a `SearchQuery` row.
+
+    Notes:
+    - `SearchQuery.id` is a UUID PK without a server-side default, so we generate it.
+    - `created_at` is filled by the DB (server_default=now()).
+    """
+    search_query = SearchQuery(
+        id=uuid.uuid4(),
+        user_id=user_id,
+        query=query,
+        query_expansions=query_expansions,
+    )
+    db_session.add(search_query)
+    db_session.commit()
+    db_session.refresh(search_query)
+    return search_query
+
+
+def fetch_search_queries_for_user(
+    db_session: Session,
+    user_id: UUID,
+    filter_days: int | None = None,
+    limit: int | None = None,
+) -> list[SearchQuery]:
+    """Fetch `SearchQuery` rows for a user.
+
+    Args:
+        user_id: User UUID.
+        filter_days: Optional time filter. If provided, only rows created within
+            the last `filter_days` days are returned.
+        limit: Optional max number of rows to return.
+    """
+    if filter_days is not None and filter_days <= 0:
+        raise ValueError("filter_days must be > 0")
+
+    stmt = select(SearchQuery).where(SearchQuery.user_id == user_id)
+
+    if filter_days is not None and filter_days > 0:
+        cutoff = get_db_current_time(db_session) - timedelta(days=filter_days)
+        stmt = stmt.where(SearchQuery.created_at >= cutoff)
+
+    stmt = stmt.order_by(SearchQuery.created_at.desc())
+
+    if limit is not None:
+        stmt = stmt.limit(limit)
+
+    return list(db_session.scalars(stmt).all())

backend/ee/onyx/main.py

@@ -16,16 +16,17 @@ from ee.onyx.server.enterprise_settings.api import (
 from ee.onyx.server.evals.api import router as evals_router
 from ee.onyx.server.license.api import router as license_router
 from ee.onyx.server.manage.standard_answer import router as standard_answer_router
+from ee.onyx.server.middleware.license_enforcement import (
+    add_license_enforcement_middleware,
+)
 from ee.onyx.server.middleware.tenant_tracking import (
     add_api_server_tenant_id_middleware,
 )
 from ee.onyx.server.oauth.api import router as ee_oauth_router
-from ee.onyx.server.query_and_chat.chat_backend import (
-    router as chat_router,
-)
 from ee.onyx.server.query_and_chat.query_backend import (
     basic_router as ee_query_router,
 )
+from ee.onyx.server.query_and_chat.search_backend import router as search_router
 from ee.onyx.server.query_history.api import router as query_history_router
 from ee.onyx.server.reporting.usage_export_api import router as usage_export_router
 from ee.onyx.server.seeding import seed_db
@@ -85,6 +86,10 @@ def get_application() -> FastAPI:
     if MULTI_TENANT:
         add_api_server_tenant_id_middleware(application, logger)
 
+    # Add license enforcement middleware (runs after tenant tracking)
+    # This blocks access when license is expired/gated
+    add_license_enforcement_middleware(application, logger)
+
     if AUTH_TYPE == AuthType.CLOUD:
         # For Google OAuth, refresh tokens are requested by:
         # 1. Adding the right scopes
@@ -124,7 +129,7 @@ def get_application() -> FastAPI:
     # EE only backend APIs
     include_router_with_global_prefix_prepended(application, query_router)
     include_router_with_global_prefix_prepended(application, ee_query_router)
-    include_router_with_global_prefix_prepended(application, chat_router)
+    include_router_with_global_prefix_prepended(application, search_router)
     include_router_with_global_prefix_prepended(application, standard_answer_router)
     include_router_with_global_prefix_prepended(application, ee_oauth_router)
     include_router_with_global_prefix_prepended(application, ee_document_cc_pair_router)

backend/ee/onyx/prompts/query_expansion.py

@@ -0,0 +1,27 @@
+# Single message is likely most reliable and generally better for this task
+# No final reminders at the end since the user query is expected to be short
+# If it is not short, it should go into the chat flow so we do not need to account for this.
+KEYWORD_EXPANSION_PROMPT = """
+Generate a set of keyword-only queries to help find relevant documents for the provided query. \
+These queries will be passed to a bm25-based keyword search engine. \
+Provide a single query per line (where each query consists of one or more keywords). \
+The queries must be purely keywords and not contain any filler natural language. \
+The each query should have as few keywords as necessary to represent the user's search intent. \
+If there are no useful expansions, simply return the original query with no additional keyword queries. \
+CRITICAL: Do not include any additional formatting, comments, or anything aside from the keyword queries.
+
+The user query is:
+{user_query}
+""".strip()
+
+
+QUERY_TYPE_PROMPT = """
+Determine if the provided query is better suited for a keyword search or a semantic search.
+Respond with "keyword" or "semantic" literally and nothing else.
+Do not provide any additional text or reasoning to your response.
+
+CRITICAL: It must only be 1 single word - EITHER "keyword" or "semantic".
+
+The user query is:
+{user_query}
+""".strip()

backend/ee/onyx/prompts/search_flow_classification.py

@@ -0,0 +1,42 @@
+# ruff: noqa: E501, W605 start
+SEARCH_CLASS = "search"
+CHAT_CLASS = "chat"
+
+# Will note that with many larger LLMs the latency on running this prompt via third party APIs is as high as 2 seconds which is too slow for many
+# use cases.
+SEARCH_CHAT_PROMPT = f"""
+Determine if the following query is better suited for a search UI or a chat UI. Respond with "{SEARCH_CLASS}" or "{CHAT_CLASS}" literally and nothing else. \
+Do not provide any additional text or reasoning to your response. CRITICAL, IT MUST ONLY BE 1 SINGLE WORD - EITHER "{SEARCH_CLASS}" or "{CHAT_CLASS}".
+
+# Classification Guidelines:
+## {SEARCH_CLASS}
+- If the query consists entirely of keywords or query doesn't require any answer from the AI
+- If the query is a short statement that seems like a search query rather than a question
+- If the query feels nonsensical or is a short phrase that possibly describes a document or information that could be found in a internal document
+
+### Examples of {SEARCH_CLASS} queries:
+- Find me the document that goes over the onboarding process for a new hire
+- Pull requests since last week
+- Sales Runbook AMEA Region
+- Procurement process
+- Retrieve the PRD for project X
+
+## {CHAT_CLASS}
+- If the query is asking a question that requires an answer rather than a document
+- If the query is asking for a solution, suggestion, or general help
+- If the query is seeking information that is on the web and likely not in a company internal document
+- If the query should be answered without any context from additional documents or searches
+
+### Examples of {CHAT_CLASS} queries:
+- What led us to win the deal with company X? (seeking answer)
+- Google Drive not sync-ing files to my computer (seeking solution)
+- Review my email: <whatever the email is> (general help)
+- Write me a script to... (general help)
+- Cheap flights Europe to Tokyo (information likely found on the web, not internal)
+
+# User Query:
+{{user_query}}
+
+REMEMBER TO ONLY RESPOND WITH "{SEARCH_CLASS}" OR "{CHAT_CLASS}" AND NOTHING ELSE.
+""".strip()
+# ruff: noqa: E501, W605 end

backend/ee/onyx/search/process_search_query.py

@@ -0,0 +1,270 @@
+from collections.abc import Generator
+
+from sqlalchemy.orm import Session
+
+from ee.onyx.db.search import create_search_query
+from ee.onyx.secondary_llm_flows.query_expansion import expand_keywords
+from ee.onyx.server.query_and_chat.models import SearchDocWithContent
+from ee.onyx.server.query_and_chat.models import SearchFullResponse
+from ee.onyx.server.query_and_chat.models import SendSearchQueryRequest
+from ee.onyx.server.query_and_chat.streaming_models import LLMSelectedDocsPacket
+from ee.onyx.server.query_and_chat.streaming_models import SearchDocsPacket
+from ee.onyx.server.query_and_chat.streaming_models import SearchErrorPacket
+from ee.onyx.server.query_and_chat.streaming_models import SearchQueriesPacket
+from onyx.context.search.models import BaseFilters
+from onyx.context.search.models import ChunkSearchRequest
+from onyx.context.search.models import InferenceChunk
+from onyx.context.search.pipeline import merge_individual_chunks
+from onyx.context.search.pipeline import search_pipeline
+from onyx.db.models import User
+from onyx.document_index.factory import get_current_primary_default_document_index
+from onyx.document_index.interfaces import DocumentIndex
+from onyx.llm.factory import get_default_llm
+from onyx.secondary_llm_flows.document_filter import select_sections_for_expansion
+from onyx.tools.tool_implementations.search.search_utils import (
+    weighted_reciprocal_rank_fusion,
+)
+from onyx.utils.logger import setup_logger
+from onyx.utils.threadpool_concurrency import run_functions_tuples_in_parallel
+
+logger = setup_logger()
+
+
+# This is just a heuristic that also happens to work well for the UI/UX
+# Users would not find it useful to see a huge list of suggested docs
+# but more than 1 is also likely good as many questions may target more than 1 doc.
+TARGET_NUM_SECTIONS_FOR_LLM_SELECTION = 3
+
+
+def _run_single_search(
+    query: str,
+    filters: BaseFilters | None,
+    document_index: DocumentIndex,
+    user: User | None,
+    db_session: Session,
+) -> list[InferenceChunk]:
+    """Execute a single search query and return chunks."""
+    chunk_search_request = ChunkSearchRequest(
+        query=query,
+        user_selected_filters=filters,
+    )
+
+    return search_pipeline(
+        chunk_search_request=chunk_search_request,
+        document_index=document_index,
+        user=user,
+        persona=None,  # No persona for direct search
+        db_session=db_session,
+    )
+
+
+def stream_search_query(
+    request: SendSearchQueryRequest,
+    user: User | None,
+    db_session: Session,
+) -> Generator[
+    SearchQueriesPacket | SearchDocsPacket | LLMSelectedDocsPacket | SearchErrorPacket,
+    None,
+    None,
+]:
+    """
+    Core search function that yields streaming packets.
+    Used by both streaming and non-streaming endpoints.
+    """
+    # Get document index
+    document_index = get_current_primary_default_document_index(db_session)
+
+    # Determine queries to execute
+    original_query = request.search_query
+    keyword_expansions: list[str] = []
+
+    if request.run_query_expansion:
+        try:
+            llm = get_default_llm()
+            keyword_expansions = expand_keywords(
+                user_query=original_query,
+                llm=llm,
+            )
+            if keyword_expansions:
+                logger.debug(
+                    f"Query expansion generated {len(keyword_expansions)} keyword queries"
+                )
+        except Exception as e:
+            logger.warning(f"Query expansion failed: {e}; using original query only.")
+            keyword_expansions = []
+
+    # Build list of all executed queries for tracking
+    all_executed_queries = [original_query] + keyword_expansions
+
+    # TODO remove this check, user should not be None
+    if user is not None:
+        create_search_query(
+            db_session=db_session,
+            user_id=user.id,
+            query=request.search_query,
+            query_expansions=keyword_expansions if keyword_expansions else None,
+        )
+
+    # Execute search(es)
+    if not keyword_expansions:
+        # Single query (original only) - no threading needed
+        chunks = _run_single_search(
+            query=original_query,
+            filters=request.filters,
+            document_index=document_index,
+            user=user,
+            db_session=db_session,
+        )
+    else:
+        # Multiple queries - run in parallel and merge with RRF
+        # First query is the original (semantic), rest are keyword expansions
+        search_functions = [
+            (
+                _run_single_search,
+                (query, request.filters, document_index, user, db_session),
+            )
+            for query in all_executed_queries
+        ]
+
+        # Run all searches in parallel
+        all_search_results: list[list[InferenceChunk]] = (
+            run_functions_tuples_in_parallel(
+                search_functions,
+                allow_failures=True,
+            )
+        )
+
+        # Separate original query results from keyword expansion results
+        # Note that in rare cases, the original query may have failed and so we may be
+        # just overweighting one set of keyword results, should be not a big deal though.
+        original_result = all_search_results[0] if all_search_results else []
+        keyword_results = all_search_results[1:] if len(all_search_results) > 1 else []
+
+        # Build valid results and weights
+        # Original query (semantic): weight 2.0
+        # Keyword expansions: weight 1.0 each
+        valid_results: list[list[InferenceChunk]] = []
+        weights: list[float] = []
+
+        if original_result:
+            valid_results.append(original_result)
+            weights.append(2.0)
+
+        for keyword_result in keyword_results:
+            if keyword_result:
+                valid_results.append(keyword_result)
+                weights.append(1.0)
+
+        if not valid_results:
+            logger.warning("All parallel searches returned empty results")
+            chunks = []
+        else:
+            chunks = weighted_reciprocal_rank_fusion(
+                ranked_results=valid_results,
+                weights=weights,
+                id_extractor=lambda chunk: f"{chunk.document_id}_{chunk.chunk_id}",
+            )
+
+    # Merge chunks into sections
+    sections = merge_individual_chunks(chunks)
+
+    # Apply LLM document selection if requested
+    # num_docs_fed_to_llm_selection specifies how many sections to feed to the LLM for selection
+    # The LLM will always try to select TARGET_NUM_SECTIONS_FOR_LLM_SELECTION sections from those fed to it
+    # llm_selected_doc_ids will be:
+    #   - None if LLM selection was not requested or failed
+    #   - Empty list if LLM selection ran but selected nothing
+    #   - List of doc IDs if LLM selection succeeded
+    run_llm_selection = (
+        request.num_docs_fed_to_llm_selection is not None
+        and request.num_docs_fed_to_llm_selection >= 1
+    )
+    llm_selected_doc_ids: list[str] | None = None
+    llm_selection_failed = False
+    if run_llm_selection and sections:
+        try:
+            llm = get_default_llm()
+            sections_to_evaluate = sections[: request.num_docs_fed_to_llm_selection]
+            selected_sections, _ = select_sections_for_expansion(
+                sections=sections_to_evaluate,
+                user_query=original_query,
+                llm=llm,
+                max_sections=TARGET_NUM_SECTIONS_FOR_LLM_SELECTION,
+                try_to_fill_to_max=True,
+            )
+            # Extract unique document IDs from selected sections (may be empty)
+            llm_selected_doc_ids = list(
+                dict.fromkeys(
+                    section.center_chunk.document_id for section in selected_sections
+                )
+            )
+            logger.debug(
+                f"LLM document selection evaluated {len(sections_to_evaluate)} sections, "
+                f"selected {len(selected_sections)} sections with doc IDs: {llm_selected_doc_ids}"
+            )
+        except Exception as e:
+            # Allowing a blanket exception here as this step is not critical and the rest of the results are still valid
+            logger.warning(f"LLM document selection failed: {e}")
+            llm_selection_failed = True
+    elif run_llm_selection and not sections:
+        # LLM selection requested but no sections to evaluate
+        llm_selected_doc_ids = []
+
+    # Convert to SearchDocWithContent list, optionally including content
+    search_docs = SearchDocWithContent.from_inference_sections(
+        sections,
+        include_content=request.include_content,
+        is_internet=False,
+    )
+
+    # Yield queries packet
+    yield SearchQueriesPacket(all_executed_queries=all_executed_queries)
+
+    # Yield docs packet
+    yield SearchDocsPacket(search_docs=search_docs)
+
+    # Yield LLM selected docs packet if LLM selection was requested
+    # - llm_selected_doc_ids is None if selection failed
+    # - llm_selected_doc_ids is empty list if no docs were selected
+    # - llm_selected_doc_ids is list of IDs if docs were selected
+    if run_llm_selection:
+        yield LLMSelectedDocsPacket(
+            llm_selected_doc_ids=None if llm_selection_failed else llm_selected_doc_ids
+        )
+
+
+def gather_search_stream(
+    packets: Generator[
+        SearchQueriesPacket
+        | SearchDocsPacket
+        | LLMSelectedDocsPacket
+        | SearchErrorPacket,
+        None,
+        None,
+    ],
+) -> SearchFullResponse:
+    """
+    Aggregate all streaming packets into SearchFullResponse.
+    """
+    all_executed_queries: list[str] = []
+    search_docs: list[SearchDocWithContent] = []
+    llm_selected_doc_ids: list[str] | None = None
+    error: str | None = None
+
+    for packet in packets:
+        if isinstance(packet, SearchQueriesPacket):
+            all_executed_queries = packet.all_executed_queries
+        elif isinstance(packet, SearchDocsPacket):
+            search_docs = packet.search_docs
+        elif isinstance(packet, LLMSelectedDocsPacket):
+            llm_selected_doc_ids = packet.llm_selected_doc_ids
+        elif isinstance(packet, SearchErrorPacket):
+            error = packet.error
+
+    return SearchFullResponse(
+        all_executed_queries=all_executed_queries,
+        search_docs=search_docs,
+        doc_selection_reasoning=None,
+        llm_selected_doc_ids=llm_selected_doc_ids,
+        error=error,
+    )

backend/ee/onyx/secondary_llm_flows/query_expansion.py

@@ -0,0 +1,92 @@
+import re
+
+from ee.onyx.prompts.query_expansion import KEYWORD_EXPANSION_PROMPT
+from onyx.llm.interfaces import LLM
+from onyx.llm.models import LanguageModelInput
+from onyx.llm.models import ReasoningEffort
+from onyx.llm.models import UserMessage
+from onyx.llm.utils import llm_response_to_string
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+# Pattern to remove common LLM artifacts: brackets, quotes, list markers, etc.
+CLEANUP_PATTERN = re.compile(r'[\[\]"\'`]')
+
+
+def _clean_keyword_line(line: str) -> str:
+    """Clean a keyword line by removing common LLM artifacts.
+
+    Removes brackets, quotes, and other characters that LLMs may accidentally
+    include in their output.
+    """
+    # Remove common artifacts
+    cleaned = CLEANUP_PATTERN.sub("", line)
+    # Remove leading list markers like "1.", "2.", "-", "*"
+    cleaned = re.sub(r"^\s*(?:\d+[\.\)]\s*|[-*]\s*)", "", cleaned)
+    return cleaned.strip()
+
+
+def expand_keywords(
+    user_query: str,
+    llm: LLM,
+) -> list[str]:
+    """Expand a user query into multiple keyword-only queries for BM25 search.
+
+    Uses an LLM to generate keyword-based search queries that capture different
+    aspects of the user's search intent. Returns only the expanded queries,
+    not the original query.
+
+    Args:
+        user_query: The original search query from the user
+        llm: Language model to use for keyword expansion
+
+    Returns:
+        List of expanded keyword queries (excluding the original query).
+        Returns empty list if expansion fails or produces no useful expansions.
+    """
+    messages: LanguageModelInput = [
+        UserMessage(content=KEYWORD_EXPANSION_PROMPT.format(user_query=user_query))
+    ]
+
+    try:
+        response = llm.invoke(
+            prompt=messages,
+            reasoning_effort=ReasoningEffort.OFF,
+            # Limit output - we only expect a few short keyword queries
+            max_tokens=150,
+        )
+
+        content = llm_response_to_string(response).strip()
+
+        if not content:
+            logger.warning("Keyword expansion returned empty response.")
+            return []
+
+        # Parse response - each line is a separate keyword query
+        # Clean each line to remove LLM artifacts and drop empty lines
+        parsed_queries = []
+        for line in content.strip().split("\n"):
+            cleaned = _clean_keyword_line(line)
+            if cleaned:
+                parsed_queries.append(cleaned)
+
+        if not parsed_queries:
+            logger.warning("Keyword expansion parsing returned no queries.")
+            return []
+
+        # Filter out duplicates and queries that match the original
+        expanded_queries: list[str] = []
+        seen_lower: set[str] = {user_query.lower()}
+        for query in parsed_queries:
+            query_lower = query.lower()
+            if query_lower not in seen_lower:
+                seen_lower.add(query_lower)
+                expanded_queries.append(query)
+
+        logger.debug(f"Keyword expansion generated {len(expanded_queries)} queries")
+        return expanded_queries
+
+    except Exception as e:
+        logger.warning(f"Keyword expansion failed: {e}")
+        return []

backend/ee/onyx/secondary_llm_flows/search_flow_classification.py

@@ -0,0 +1,50 @@
+from ee.onyx.prompts.search_flow_classification import CHAT_CLASS
+from ee.onyx.prompts.search_flow_classification import SEARCH_CHAT_PROMPT
+from ee.onyx.prompts.search_flow_classification import SEARCH_CLASS
+from onyx.llm.interfaces import LLM
+from onyx.llm.models import LanguageModelInput
+from onyx.llm.models import ReasoningEffort
+from onyx.llm.models import UserMessage
+from onyx.llm.utils import llm_response_to_string
+from onyx.utils.logger import setup_logger
+from onyx.utils.timing import log_function_time
+
+logger = setup_logger()
+
+
+@log_function_time(print_only=True)
+def classify_is_search_flow(
+    query: str,
+    llm: LLM,
+) -> bool:
+    messages: LanguageModelInput = [
+        UserMessage(content=SEARCH_CHAT_PROMPT.format(user_query=query))
+    ]
+    response = llm.invoke(
+        prompt=messages,
+        reasoning_effort=ReasoningEffort.OFF,
+        # Nothing can happen in the UI until this call finishes so we need to be aggressive with the timeout
+        timeout_override=2,
+        # Well more than necessary but just to ensure completion and in case it succeeds with classifying but
+        # ends up rambling
+        max_tokens=20,
+    )
+
+    content = llm_response_to_string(response).strip().lower()
+    if not content:
+        logger.warning(
+            "Search flow classification returned empty response; defaulting to chat flow."
+        )
+        return False
+
+    # Prefer chat if both appear.
+    if CHAT_CLASS in content:
+        return False
+    if SEARCH_CLASS in content:
+        return True
+
+    logger.warning(
+        "Search flow classification returned unexpected response; defaulting to chat flow. Response=%r",
+        content,
+    )
+    return False

backend/ee/onyx/server/analytics/api.py

@@ -19,10 +19,11 @@ from ee.onyx.db.analytics import fetch_query_analytics
 from ee.onyx.db.analytics import user_can_view_assistant_stats
 from onyx.auth.users import current_admin_user
 from onyx.auth.users import current_user
+from onyx.configs.constants import PUBLIC_API_TAGS
 from onyx.db.engine.sql_engine import get_session
 from onyx.db.models import User
 
-router = APIRouter(prefix="/analytics")
+router = APIRouter(prefix="/analytics", tags=PUBLIC_API_TAGS)
 
 
 _DEFAULT_LOOKBACK_DAYS = 30

backend/ee/onyx/server/enterprise_settings/models.py

@@ -1,3 +1,4 @@
+from enum import Enum
 from typing import Any
 from typing import List
 
@@ -23,6 +24,12 @@ class NavigationItem(BaseModel):
         return instance
 
 
+class LogoDisplayStyle(str, Enum):
+    LOGO_AND_NAME = "logo_and_name"
+    LOGO_ONLY = "logo_only"
+    NAME_ONLY = "name_only"
+
+
 class EnterpriseSettings(BaseModel):
     """General settings that only apply to the Enterprise Edition of Onyx
 
@@ -31,6 +38,7 @@ class EnterpriseSettings(BaseModel):
     application_name: str | None = None
     use_custom_logo: bool = False
     use_custom_logotype: bool = False
+    logo_display_style: LogoDisplayStyle | None = None
 
     # custom navigation
     custom_nav_items: List[NavigationItem] = Field(default_factory=list)
@@ -42,6 +50,9 @@ class EnterpriseSettings(BaseModel):
     custom_popup_header: str | None = None
     custom_popup_content: str | None = None
     enable_consent_screen: bool | None = None
+    consent_screen_prompt: str | None = None
+    show_first_visit_notice: bool | None = None
+    custom_greeting_message: str | None = None
 
     def check_validity(self) -> None:
         return

backend/ee/onyx/server/middleware/license_enforcement.py

@@ -0,0 +1,102 @@
+"""Middleware to enforce license status application-wide."""
+
+import logging
+from collections.abc import Awaitable
+from collections.abc import Callable
+
+from fastapi import FastAPI
+from fastapi import Request
+from fastapi import Response
+from fastapi.responses import JSONResponse
+from redis.exceptions import RedisError
+
+from ee.onyx.configs.app_configs import LICENSE_ENFORCEMENT_ENABLED
+from ee.onyx.db.license import get_cached_license_metadata
+from ee.onyx.server.tenants.product_gating import is_tenant_gated
+from onyx.server.settings.models import ApplicationStatus
+from shared_configs.configs import MULTI_TENANT
+from shared_configs.contextvars import get_current_tenant_id
+
+# Paths that are ALWAYS accessible, even when license is expired/gated.
+# These enable users to:
+#   /auth - Log in/out (users can't fix billing if locked out of auth)
+#   /license - Fetch, upload, or check license status
+#   /health - Health checks for load balancers/orchestrators
+#   /me - Basic user info needed for UI rendering
+#   /settings, /enterprise-settings - View app status and branding
+#   /tenants/billing-* - Manage subscription to resolve gating
+ALLOWED_PATH_PREFIXES = {
+    "/auth",
+    "/license",
+    "/health",
+    "/me",
+    "/settings",
+    "/enterprise-settings",
+    "/tenants/billing-information",
+    "/tenants/create-customer-portal-session",
+    "/tenants/create-subscription-session",
+}
+
+
+def _is_path_allowed(path: str) -> bool:
+    """Check if path is in allowlist (prefix match)."""
+    return any(path.startswith(prefix) for prefix in ALLOWED_PATH_PREFIXES)
+
+
+def add_license_enforcement_middleware(
+    app: FastAPI, logger: logging.LoggerAdapter
+) -> None:
+    logger.info("License enforcement middleware registered")
+
+    @app.middleware("http")
+    async def enforce_license(
+        request: Request, call_next: Callable[[Request], Awaitable[Response]]
+    ) -> Response:
+        """Block requests when license is expired/gated."""
+        if not LICENSE_ENFORCEMENT_ENABLED:
+            return await call_next(request)
+
+        path = request.url.path
+        if path.startswith("/api"):
+            path = path[4:]
+
+        if _is_path_allowed(path):
+            return await call_next(request)
+
+        is_gated = False
+        tenant_id = get_current_tenant_id()
+
+        if MULTI_TENANT:
+            try:
+                is_gated = is_tenant_gated(tenant_id)
+            except RedisError as e:
+                logger.warning(f"Failed to check tenant gating status: {e}")
+                # Fail open - don't block users due to Redis connectivity issues
+                is_gated = False
+        else:
+            try:
+                metadata = get_cached_license_metadata(tenant_id)
+                if metadata:
+                    if metadata.status == ApplicationStatus.GATED_ACCESS:
+                        is_gated = True
+                else:
+                    # No license metadata = gated for self-hosted EE
+                    is_gated = True
+            except RedisError as e:
+                logger.warning(f"Failed to check license metadata: {e}")
+                # Fail open - don't block users due to Redis connectivity issues
+                is_gated = False
+
+        if is_gated:
+            logger.info(f"Blocking request for gated tenant: {tenant_id}, path={path}")
+            return JSONResponse(
+                status_code=402,
+                content={
+                    "detail": {
+                        "error": "license_expired",
+                        "message": "Your subscription has expired. Please update your billing.",
+                    }
+                },
+            )
+
+        return await call_next(request)

backend/ee/onyx/server/query_and_chat/chat_backend.py

@@ -1,216 +0,0 @@
-from fastapi import APIRouter
-from fastapi import Depends
-from fastapi import HTTPException
-from sqlalchemy.orm import Session
-
-from ee.onyx.server.query_and_chat.models import BasicCreateChatMessageRequest
-from ee.onyx.server.query_and_chat.models import (
-    BasicCreateChatMessageWithHistoryRequest,
-)
-from onyx.auth.users import current_user
-from onyx.chat.chat_utils import create_chat_history_chain
-from onyx.chat.models import ChatBasicResponse
-from onyx.chat.process_message import gather_stream
-from onyx.chat.process_message import stream_chat_message_objects
-from onyx.configs.constants import MessageType
-from onyx.context.search.models import OptionalSearchSetting
-from onyx.context.search.models import RetrievalDetails
-from onyx.db.chat import create_chat_session
-from onyx.db.chat import create_new_chat_message
-from onyx.db.chat import get_or_create_root_message
-from onyx.db.engine.sql_engine import get_session
-from onyx.db.models import User
-from onyx.llm.factory import get_llm_for_persona
-from onyx.natural_language_processing.utils import get_tokenizer
-from onyx.server.query_and_chat.models import CreateChatMessageRequest
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-router = APIRouter(prefix="/chat")
-
-
-@router.post("/send-message-simple-api")
-def handle_simplified_chat_message(
-    chat_message_req: BasicCreateChatMessageRequest,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> ChatBasicResponse:
-    """This is a Non-Streaming version that only gives back a minimal set of information"""
-    logger.notice(f"Received new simple api chat message: {chat_message_req.message}")
-
-    if not chat_message_req.message:
-        raise HTTPException(status_code=400, detail="Empty chat message is invalid")
-
-    # Handle chat session creation if chat_session_id is not provided
-    if chat_message_req.chat_session_id is None:
-        if chat_message_req.persona_id is None:
-            raise HTTPException(
-                status_code=400,
-                detail="Either chat_session_id or persona_id must be provided",
-            )
-
-        # Create a new chat session with the provided persona_id
-        try:
-            new_chat_session = create_chat_session(
-                db_session=db_session,
-                description="",  # Leave empty for simple API
-                user_id=user.id if user else None,
-                persona_id=chat_message_req.persona_id,
-            )
-            chat_session_id = new_chat_session.id
-        except Exception as e:
-            logger.exception(e)
-            raise HTTPException(status_code=400, detail="Invalid Persona provided.")
-    else:
-        chat_session_id = chat_message_req.chat_session_id
-
-    try:
-        parent_message = create_chat_history_chain(
-            chat_session_id=chat_session_id, db_session=db_session
-        )[-1]
-    except Exception:
-        parent_message = get_or_create_root_message(
-            chat_session_id=chat_session_id, db_session=db_session
-        )
-
-    if (
-        chat_message_req.retrieval_options is None
-        and chat_message_req.search_doc_ids is None
-    ):
-        retrieval_options: RetrievalDetails | None = RetrievalDetails(
-            run_search=OptionalSearchSetting.ALWAYS,
-            real_time=False,
-        )
-    else:
-        retrieval_options = chat_message_req.retrieval_options
-
-    full_chat_msg_info = CreateChatMessageRequest(
-        chat_session_id=chat_session_id,
-        parent_message_id=parent_message.id,
-        message=chat_message_req.message,
-        file_descriptors=[],
-        search_doc_ids=chat_message_req.search_doc_ids,
-        retrieval_options=retrieval_options,
-        # Simple API does not support reranking, hide complexity from user
-        rerank_settings=None,
-        query_override=chat_message_req.query_override,
-        # Currently only applies to search flow not chat
-        chunks_above=0,
-        chunks_below=0,
-        full_doc=chat_message_req.full_doc,
-        structured_response_format=chat_message_req.structured_response_format,
-    )
-
-    packets = stream_chat_message_objects(
-        new_msg_req=full_chat_msg_info,
-        user=user,
-        db_session=db_session,
-        enforce_chat_session_id_for_search_docs=False,
-    )
-
-    return gather_stream(packets)
-
-
-@router.post("/send-message-simple-with-history")
-def handle_send_message_simple_with_history(
-    req: BasicCreateChatMessageWithHistoryRequest,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> ChatBasicResponse:
-    """This is a Non-Streaming version that only gives back a minimal set of information.
-    takes in chat history maintained by the caller
-    and does query rephrasing similar to answer-with-quote"""
-
-    if len(req.messages) == 0:
-        raise HTTPException(status_code=400, detail="Messages cannot be zero length")
-
-    # This is a sanity check to make sure the chat history is valid
-    # It must start with a user message and alternate beteen user and assistant
-    expected_role = MessageType.USER
-    for msg in req.messages:
-        if not msg.message:
-            raise HTTPException(
-                status_code=400, detail="One or more chat messages were empty"
-            )
-
-        if msg.role != expected_role:
-            raise HTTPException(
-                status_code=400,
-                detail="Message roles must start and end with MessageType.USER and alternate in-between.",
-            )
-        if expected_role == MessageType.USER:
-            expected_role = MessageType.ASSISTANT
-        else:
-            expected_role = MessageType.USER
-
-    query = req.messages[-1].message
-    msg_history = req.messages[:-1]
-
-    logger.notice(f"Received new simple with history chat message: {query}")
-
-    user_id = user.id if user is not None else None
-    chat_session = create_chat_session(
-        db_session=db_session,
-        description="handle_send_message_simple_with_history",
-        user_id=user_id,
-        persona_id=req.persona_id,
-    )
-
-    llm = get_llm_for_persona(persona=chat_session.persona, user=user)
-
-    llm_tokenizer = get_tokenizer(
-        model_name=llm.config.model_name,
-        provider_type=llm.config.model_provider,
-    )
-
-    # Every chat Session begins with an empty root message
-    root_message = get_or_create_root_message(
-        chat_session_id=chat_session.id, db_session=db_session
-    )
-
-    chat_message = root_message
-    for msg in msg_history:
-        chat_message = create_new_chat_message(
-            chat_session_id=chat_session.id,
-            parent_message=chat_message,
-            message=msg.message,
-            token_count=len(llm_tokenizer.encode(msg.message)),
-            message_type=msg.role,
-            db_session=db_session,
-            commit=False,
-        )
-    db_session.commit()
-
-    if req.retrieval_options is None and req.search_doc_ids is None:
-        retrieval_options: RetrievalDetails | None = RetrievalDetails(
-            run_search=OptionalSearchSetting.ALWAYS,
-            real_time=False,
-        )
-    else:
-        retrieval_options = req.retrieval_options
-
-    full_chat_msg_info = CreateChatMessageRequest(
-        chat_session_id=chat_session.id,
-        parent_message_id=chat_message.id,
-        message=query,
-        file_descriptors=[],
-        search_doc_ids=req.search_doc_ids,
-        retrieval_options=retrieval_options,
-        # Simple API does not support reranking, hide complexity from user
-        rerank_settings=None,
-        query_override=None,
-        chunks_above=0,
-        chunks_below=0,
-        full_doc=req.full_doc,
-        structured_response_format=req.structured_response_format,
-    )
-
-    packets = stream_chat_message_objects(
-        new_msg_req=full_chat_msg_info,
-        user=user,
-        db_session=db_session,
-        enforce_chat_session_id_for_search_docs=False,
-    )
-
-    return gather_stream(packets)

backend/ee/onyx/server/query_and_chat/models.py

@@ -1,18 +1,12 @@
-from collections import OrderedDict
-from typing import Literal
-from uuid import UUID
+from collections.abc import Sequence
+from datetime import datetime
 
 from pydantic import BaseModel
 from pydantic import Field
-from pydantic import model_validator
 
-from onyx.chat.models import ThreadMessage
-from onyx.configs.constants import DocumentSource
 from onyx.context.search.models import BaseFilters
-from onyx.context.search.models import BasicChunkRequest
-from onyx.context.search.models import ChunkContext
-from onyx.context.search.models import InferenceChunk
-from onyx.context.search.models import RetrievalDetails
+from onyx.context.search.models import InferenceSection
+from onyx.context.search.models import SearchDoc
 from onyx.server.manage.models import StandardAnswer
 
 
@@ -25,119 +19,88 @@ class StandardAnswerResponse(BaseModel):
     standard_answers: list[StandardAnswer] = Field(default_factory=list)
 
 
-class DocumentSearchRequest(BasicChunkRequest):
-    user_selected_filters: BaseFilters | None = None
+class SearchFlowClassificationRequest(BaseModel):
+    user_query: str
 
 
-class DocumentSearchResponse(BaseModel):
-    top_documents: list[InferenceChunk]
+class SearchFlowClassificationResponse(BaseModel):
+    is_search_flow: bool
 
 
-class BasicCreateChatMessageRequest(ChunkContext):
-    """If a chat_session_id is not provided, a persona_id must be provided to automatically create a new chat session
-    Note, for simplicity this option only allows for a single linear chain of messages
-    """
+class SendSearchQueryRequest(BaseModel):
+    search_query: str
+    filters: BaseFilters | None = None
+    num_docs_fed_to_llm_selection: int | None = None
+    run_query_expansion: bool = False
 
-    chat_session_id: UUID | None = None
-    # Optional persona_id to create a new chat session if chat_session_id is not provided
-    persona_id: int | None = None
-    # New message contents
-    message: str
-    # Defaults to using retrieval with no additional filters
-    retrieval_options: RetrievalDetails | None = None
-    # Allows the caller to specify the exact search query they want to use
-    # will disable Query Rewording if specified
-    query_override: str | None = None
-    # If search_doc_ids provided, then retrieval options are unused
-    search_doc_ids: list[int] | None = None
-    # only works if using an OpenAI model. See the following for more details:
-    # https://platform.openai.com/docs/guides/structured-outputs/introduction
-    structured_response_format: dict | None = None
-
-    @model_validator(mode="after")
-    def validate_chat_session_or_persona(self) -> "BasicCreateChatMessageRequest":
-        if self.chat_session_id is None and self.persona_id is None:
-            raise ValueError("Either chat_session_id or persona_id must be provided")
-        return self
+    include_content: bool = False
+    stream: bool = False
 
 
-class BasicCreateChatMessageWithHistoryRequest(ChunkContext):
-    # Last element is the new query. All previous elements are historical context
-    messages: list[ThreadMessage]
-    persona_id: int
-    retrieval_options: RetrievalDetails | None = None
-    query_override: str | None = None
-    skip_rerank: bool | None = None
-    # If search_doc_ids provided, then retrieval options are unused
-    search_doc_ids: list[int] | None = None
-    # only works if using an OpenAI model. See the following for more details:
-    # https://platform.openai.com/docs/guides/structured-outputs/introduction
-    structured_response_format: dict | None = None
+class SearchDocWithContent(SearchDoc):
+    # Allows None because this is determined by a flag but the object used in code
+    # of the search path uses this type
+    content: str | None
 
+    @classmethod
+    def from_inference_sections(
+        cls,
+        sections: Sequence[InferenceSection],
+        include_content: bool = False,
+        is_internet: bool = False,
+    ) -> list["SearchDocWithContent"]:
+        """Convert InferenceSections to SearchDocWithContent objects.
 
-class SimpleDoc(BaseModel):
-    id: str
-    semantic_identifier: str
-    link: str | None
-    blurb: str
-    match_highlights: list[str]
-    source_type: DocumentSource
-    metadata: dict | None
+        Args:
+            sections: Sequence of InferenceSection objects
+            include_content: If True, populate content field with combined_content
+            is_internet: Whether these are internet search results
 
-
-class AgentSubQuestion(BaseModel):
-    sub_question: str
-    document_ids: list[str]
-
-
-class AgentAnswer(BaseModel):
-    answer: str
-    answer_type: Literal["agent_sub_answer", "agent_level_answer"]
-
-
-class AgentSubQuery(BaseModel):
-    sub_query: str
-    query_id: int
-
-    @staticmethod
-    def make_dict_by_level_and_question_index(
-        original_dict: dict[tuple[int, int, int], "AgentSubQuery"],
-    ) -> dict[int, dict[int, list["AgentSubQuery"]]]:
-        """Takes a dict of tuple(level, question num, query_id) to sub queries.
-
-        returns a dict of level to dict[question num to list of query_id's]
-        Ordering is asc for readability.
+        Returns:
+            List of SearchDocWithContent with optional content
         """
-        # In this function, when we sort int | None, we deliberately push None to the end
+        if not sections:
+            return []
 
-        # map entries to the level_question_dict
-        level_question_dict: dict[int, dict[int, list["AgentSubQuery"]]] = {}
-        for k1, obj in original_dict.items():
-            level = k1[0]
-            question = k1[1]
-
-            if level not in level_question_dict:
-                level_question_dict[level] = {}
-
-            if question not in level_question_dict[level]:
-                level_question_dict[level][question] = []
-
-            level_question_dict[level][question].append(obj)
-
-        # sort each query_id list and question_index
-        for key1, obj1 in level_question_dict.items():
-            for key2, value2 in obj1.items():
-                # sort the query_id list of each question_index
-                level_question_dict[key1][key2] = sorted(
-                    value2, key=lambda o: o.query_id
-                )
-            # sort the question_index dict of level
-            level_question_dict[key1] = OrderedDict(
-                sorted(level_question_dict[key1].items(), key=lambda x: (x is None, x))
+        return [
+            cls(
+                document_id=(chunk := section.center_chunk).document_id,
+                chunk_ind=chunk.chunk_id,
+                semantic_identifier=chunk.semantic_identifier or "Unknown",
+                link=chunk.source_links[0] if chunk.source_links else None,
+                blurb=chunk.blurb,
+                source_type=chunk.source_type,
+                boost=chunk.boost,
+                hidden=chunk.hidden,
+                metadata=chunk.metadata,
+                score=chunk.score,
+                match_highlights=chunk.match_highlights,
+                updated_at=chunk.updated_at,
+                primary_owners=chunk.primary_owners,
+                secondary_owners=chunk.secondary_owners,
+                is_internet=is_internet,
+                content=section.combined_content if include_content else None,
             )
+            for section in sections
+        ]
 
-        # sort the top dict of levels
-        sorted_dict = OrderedDict(
-            sorted(level_question_dict.items(), key=lambda x: (x is None, x))
-        )
-        return sorted_dict
+
+class SearchFullResponse(BaseModel):
+    all_executed_queries: list[str]
+    search_docs: list[SearchDocWithContent]
+    # Reasoning tokens output by the LLM for the document selection
+    doc_selection_reasoning: str | None = None
+    # This a list of document ids that are in the search_docs list
+    llm_selected_doc_ids: list[str] | None = None
+    # Error message if the search failed partway through
+    error: str | None = None
+
+
+class SearchQueryResponse(BaseModel):
+    query: str
+    query_expansions: list[str] | None
+    created_at: datetime
+
+
+class SearchHistoryResponse(BaseModel):
+    search_queries: list[SearchQueryResponse]

backend/ee/onyx/server/query_and_chat/search_backend.py

@@ -0,0 +1,170 @@
+from collections.abc import Generator
+
+from fastapi import APIRouter
+from fastapi import Depends
+from fastapi import HTTPException
+from fastapi.responses import StreamingResponse
+from sqlalchemy.orm import Session
+
+from ee.onyx.db.search import fetch_search_queries_for_user
+from ee.onyx.search.process_search_query import gather_search_stream
+from ee.onyx.search.process_search_query import stream_search_query
+from ee.onyx.secondary_llm_flows.search_flow_classification import (
+    classify_is_search_flow,
+)
+from ee.onyx.server.query_and_chat.models import SearchFlowClassificationRequest
+from ee.onyx.server.query_and_chat.models import SearchFlowClassificationResponse
+from ee.onyx.server.query_and_chat.models import SearchFullResponse
+from ee.onyx.server.query_and_chat.models import SearchHistoryResponse
+from ee.onyx.server.query_and_chat.models import SearchQueryResponse
+from ee.onyx.server.query_and_chat.models import SendSearchQueryRequest
+from ee.onyx.server.query_and_chat.streaming_models import SearchErrorPacket
+from onyx.auth.users import current_user
+from onyx.db.engine.sql_engine import get_session
+from onyx.db.engine.sql_engine import get_session_with_current_tenant
+from onyx.db.models import User
+from onyx.llm.factory import get_default_llm
+from onyx.server.usage_limits import check_llm_cost_limit_for_provider
+from onyx.server.utils import get_json_line
+from onyx.utils.logger import setup_logger
+from shared_configs.contextvars import get_current_tenant_id
+
+logger = setup_logger()
+
+router = APIRouter(prefix="/search")
+
+
+@router.post("/search-flow-classification")
+def search_flow_classification(
+    request: SearchFlowClassificationRequest,
+    # This is added just to ensure this endpoint isn't spammed by non-authorized users since there's an LLM call underneath it
+    _: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> SearchFlowClassificationResponse:
+    query = request.user_query
+    # This is a heuristic that if the user is typing a lot of text, it's unlikely they're looking for some specific document
+    # Most likely something needs to be done with the text included so we'll just classify it as a chat flow
+    if len(query) > 200:
+        return SearchFlowClassificationResponse(is_search_flow=False)
+
+    llm = get_default_llm()
+
+    check_llm_cost_limit_for_provider(
+        db_session=db_session,
+        tenant_id=get_current_tenant_id(),
+        llm_provider_api_key=llm.config.api_key,
+    )
+
+    try:
+        is_search_flow = classify_is_search_flow(query=query, llm=llm)
+    except Exception as e:
+        logger.exception(
+            "Search flow classification failed; defaulting to chat flow",
+            exc_info=e,
+        )
+        is_search_flow = False
+
+    return SearchFlowClassificationResponse(is_search_flow=is_search_flow)
+
+
+@router.post("/send-search-message", response_model=None)
+def handle_send_search_message(
+    request: SendSearchQueryRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> StreamingResponse | SearchFullResponse:
+    """
+    Execute a search query with optional streaming.
+
+    When stream=True: Returns StreamingResponse with SSE
+    When stream=False: Returns SearchFullResponse
+    """
+    logger.debug(f"Received search query: {request.search_query}")
+
+    # Non-streaming path
+    if not request.stream:
+        try:
+            packets = stream_search_query(request, user, db_session)
+            return gather_search_stream(packets)
+        except NotImplementedError as e:
+            return SearchFullResponse(
+                all_executed_queries=[],
+                search_docs=[],
+                error=str(e),
+            )
+
+    # Streaming path
+    def stream_generator() -> Generator[str, None, None]:
+        try:
+            with get_session_with_current_tenant() as streaming_db_session:
+                for packet in stream_search_query(request, user, streaming_db_session):
+                    yield get_json_line(packet.model_dump())
+        except NotImplementedError as e:
+            yield get_json_line(SearchErrorPacket(error=str(e)).model_dump())
+        except HTTPException:
+            raise
+        except Exception as e:
+            logger.exception("Error in search streaming")
+            yield get_json_line(SearchErrorPacket(error=str(e)).model_dump())
+
+    return StreamingResponse(stream_generator(), media_type="text/event-stream")
+
+
+@router.get("/search-history")
+def get_search_history(
+    limit: int = 100,
+    filter_days: int | None = None,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> SearchHistoryResponse:
+    """
+    Fetch past search queries for the authenticated user.
+
+    Args:
+        limit: Maximum number of queries to return (default 100)
+        filter_days: Only return queries from the last N days (optional)
+
+    Returns:
+        SearchHistoryResponse with list of search queries, ordered by most recent first.
+    """
+    # Validate limit
+    if limit <= 0:
+        raise HTTPException(
+            status_code=400,
+            detail="limit must be greater than 0",
+        )
+    if limit > 1000:
+        raise HTTPException(
+            status_code=400,
+            detail="limit must be at most 1000",
+        )
+
+    # Validate filter_days
+    if filter_days is not None and filter_days <= 0:
+        raise HTTPException(
+            status_code=400,
+            detail="filter_days must be greater than 0",
+        )
+
+    # TODO(yuhong) remove this
+    if user is None:
+        # Return empty list for unauthenticated users
+        return SearchHistoryResponse(search_queries=[])
+
+    search_queries = fetch_search_queries_for_user(
+        db_session=db_session,
+        user_id=user.id,
+        filter_days=filter_days,
+        limit=limit,
+    )
+
+    return SearchHistoryResponse(
+        search_queries=[
+            SearchQueryResponse(
+                query=sq.query,
+                query_expansions=sq.query_expansions,
+                created_at=sq.created_at,
+            )
+            for sq in search_queries
+        ]
+    )

backend/ee/onyx/server/query_and_chat/streaming_models.py

@@ -0,0 +1,35 @@
+from typing import Literal
+
+from pydantic import BaseModel
+from pydantic import ConfigDict
+
+from ee.onyx.server.query_and_chat.models import SearchDocWithContent
+
+
+class SearchQueriesPacket(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    type: Literal["search_queries"] = "search_queries"
+    all_executed_queries: list[str]
+
+
+class SearchDocsPacket(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    type: Literal["search_docs"] = "search_docs"
+    search_docs: list[SearchDocWithContent]
+
+
+class SearchErrorPacket(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    type: Literal["search_error"] = "search_error"
+    error: str
+
+
+class LLMSelectedDocsPacket(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    type: Literal["llm_selected_docs"] = "llm_selected_docs"
+    # None if LLM selection failed, empty list if no docs selected, list of IDs otherwise
+    llm_selected_doc_ids: list[str] | None

backend/ee/onyx/server/query_history/api.py

@@ -32,6 +32,7 @@ from onyx.configs.constants import MessageType
 from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryQueues
 from onyx.configs.constants import OnyxCeleryTask
+from onyx.configs.constants import PUBLIC_API_TAGS
 from onyx.configs.constants import QAFeedbackType
 from onyx.configs.constants import QueryHistoryType
 from onyx.configs.constants import SessionType
@@ -294,7 +295,7 @@ def list_all_query_history_exports(
         )
 
 
-@router.post("/admin/query-history/start-export")
+@router.post("/admin/query-history/start-export", tags=PUBLIC_API_TAGS)
 def start_query_history_export(
     _: User | None = Depends(current_admin_user),
     db_session: Session = Depends(get_session),
@@ -340,7 +341,7 @@ def start_query_history_export(
     return {"request_id": task_id}
 
 
-@router.get("/admin/query-history/export-status")
+@router.get("/admin/query-history/export-status", tags=PUBLIC_API_TAGS)
 def get_query_history_export_status(
     request_id: str,
     _: User | None = Depends(current_admin_user),
@@ -374,7 +375,7 @@ def get_query_history_export_status(
     return {"status": TaskStatus.SUCCESS}
 
 
-@router.get("/admin/query-history/download")
+@router.get("/admin/query-history/download", tags=PUBLIC_API_TAGS)
 def download_query_history_csv(
     request_id: str,
     _: User | None = Depends(current_admin_user),

backend/ee/onyx/server/settings/api.py

@@ -0,0 +1,54 @@
+"""EE Settings API - provides license-aware settings override."""
+
+from redis.exceptions import RedisError
+
+from ee.onyx.configs.app_configs import LICENSE_ENFORCEMENT_ENABLED
+from ee.onyx.db.license import get_cached_license_metadata
+from onyx.server.settings.models import ApplicationStatus
+from onyx.server.settings.models import Settings
+from onyx.utils.logger import setup_logger
+from shared_configs.configs import MULTI_TENANT
+from shared_configs.contextvars import get_current_tenant_id
+
+logger = setup_logger()
+
+# Statuses that indicate a billing/license problem - propagate these to settings
+_GATED_STATUSES = frozenset(
+    {
+        ApplicationStatus.GATED_ACCESS,
+        ApplicationStatus.GRACE_PERIOD,
+        ApplicationStatus.PAYMENT_REMINDER,
+    }
+)
+
+
+def apply_license_status_to_settings(settings: Settings) -> Settings:
+    """EE version: checks license status for self-hosted deployments.
+
+    For self-hosted, looks up license metadata and overrides application_status
+    if the license is missing or indicates a problem (expired, grace period, etc.).
+
+    For multi-tenant (cloud), the settings already have the correct status
+    from the control plane, so no override is needed.
+
+    If LICENSE_ENFORCEMENT_ENABLED is false, settings are returned unchanged,
+    allowing the product to function normally without license checks.
+    """
+    if not LICENSE_ENFORCEMENT_ENABLED:
+        return settings
+
+    if MULTI_TENANT:
+        return settings
+
+    tenant_id = get_current_tenant_id()
+    try:
+        metadata = get_cached_license_metadata(tenant_id)
+        if metadata and metadata.status in _GATED_STATUSES:
+            settings.application_status = metadata.status
+        elif not metadata:
+            # No license = gated access for self-hosted EE
+            settings.application_status = ApplicationStatus.GATED_ACCESS
+    except RedisError as e:
+        logger.warning(f"Failed to check license metadata for settings: {e}")
+
+    return settings

backend/ee/onyx/server/tenant_usage_limits.py

@@ -0,0 +1,133 @@
+"""Tenant-specific usage limit overrides from the control plane (EE version)."""
+
+import time
+
+import requests
+
+from ee.onyx.server.tenants.access import generate_data_plane_token
+from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
+from onyx.configs.app_configs import DEV_MODE
+from onyx.server.tenant_usage_limits import TenantUsageLimitOverrides
+from onyx.server.usage_limits import NO_LIMIT
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+# In-memory storage for tenant overrides (populated at startup)
+_tenant_usage_limit_overrides: dict[str, TenantUsageLimitOverrides] | None = None
+_last_fetch_time: float = 0.0
+_FETCH_INTERVAL = 60 * 60 * 24  # 24 hours
+_ERROR_FETCH_INTERVAL = 30 * 60  # 30 minutes (if the last fetch failed)
+
+
+def fetch_usage_limit_overrides() -> dict[str, TenantUsageLimitOverrides] | None:
+    """
+    Fetch tenant-specific usage limit overrides from the control plane.
+
+    Returns:
+        Dictionary mapping tenant_id to their specific limit overrides.
+        Returns empty dict on any error (falls back to defaults).
+    """
+    try:
+        token = generate_data_plane_token()
+        headers = {
+            "Authorization": f"Bearer {token}",
+            "Content-Type": "application/json",
+        }
+        url = f"{CONTROL_PLANE_API_BASE_URL}/usage-limit-overrides"
+        response = requests.get(url, headers=headers, timeout=30)
+        response.raise_for_status()
+
+        tenant_overrides = response.json()
+
+        # Parse each tenant's overrides
+        result: dict[str, TenantUsageLimitOverrides] = {}
+        for override_data in tenant_overrides:
+            tenant_id = override_data["tenant_id"]
+            try:
+                result[tenant_id] = TenantUsageLimitOverrides(**override_data)
+            except Exception as e:
+                logger.warning(
+                    f"Failed to parse usage limit overrides for tenant {tenant_id}: {e}"
+                )
+
+        return (
+            result or None
+        )  # if empty dictionary, something went wrong and we shouldn't enforce limits
+
+    except requests.exceptions.RequestException as e:
+        logger.warning(f"Failed to fetch usage limit overrides from control plane: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Error parsing usage limit overrides: {e}")
+        return None
+
+
+def load_usage_limit_overrides() -> None:
+    """
+    Load tenant usage limit overrides from the control plane.
+    """
+    global _tenant_usage_limit_overrides
+    global _last_fetch_time
+
+    logger.info("Loading tenant usage limit overrides from control plane...")
+    overrides = fetch_usage_limit_overrides()
+
+    _last_fetch_time = time.time()
+
+    # use the new result if it exists, otherwise use the old result
+    # (prevents us from updating to a failed fetch result)
+    _tenant_usage_limit_overrides = overrides or _tenant_usage_limit_overrides
+
+    if overrides:
+        logger.info(f"Loaded usage limit overrides for {len(overrides)} tenants")
+    else:
+        logger.info("No tenant-specific usage limit overrides found")
+
+
+def unlimited(tenant_id: str) -> TenantUsageLimitOverrides:
+    return TenantUsageLimitOverrides(
+        tenant_id=tenant_id,
+        llm_cost_cents_trial=NO_LIMIT,
+        llm_cost_cents_paid=NO_LIMIT,
+        chunks_indexed_trial=NO_LIMIT,
+        chunks_indexed_paid=NO_LIMIT,
+        api_calls_trial=NO_LIMIT,
+        api_calls_paid=NO_LIMIT,
+        non_streaming_calls_trial=NO_LIMIT,
+        non_streaming_calls_paid=NO_LIMIT,
+    )
+
+
+def get_tenant_usage_limit_overrides(
+    tenant_id: str,
+) -> TenantUsageLimitOverrides | None:
+    """
+    Get the usage limit overrides for a specific tenant.
+
+    Args:
+        tenant_id: The tenant ID to look up
+
+    Returns:
+        TenantUsageLimitOverrides if the tenant has overrides, None otherwise.
+    """
+
+    if DEV_MODE:  # in dev mode, we return unlimited limits for all tenants
+        return unlimited(tenant_id)
+
+    global _tenant_usage_limit_overrides
+    time_since = time.time() - _last_fetch_time
+    if (
+        _tenant_usage_limit_overrides is None and time_since > _ERROR_FETCH_INTERVAL
+    ) or (time_since > _FETCH_INTERVAL):
+        logger.debug(
+            f"Last fetch time: {_last_fetch_time}, time since last fetch: {time_since}"
+        )
+
+        load_usage_limit_overrides()
+
+    # If we have failed to fetch from the control plane or we're in dev mode, don't usage limit anyone.
+    if _tenant_usage_limit_overrides is None or DEV_MODE:
+        return unlimited(tenant_id)
+    return _tenant_usage_limit_overrides.get(tenant_id)

backend/ee/onyx/server/tenants/billing.py

@@ -1,9 +1,9 @@
 from typing import cast
+from typing import Literal
 
 import requests
 import stripe
 
-from ee.onyx.configs.app_configs import STRIPE_PRICE_ID
 from ee.onyx.configs.app_configs import STRIPE_SECRET_KEY
 from ee.onyx.server.tenants.access import generate_data_plane_token
 from ee.onyx.server.tenants.models import BillingInformation
@@ -16,15 +16,21 @@ stripe.api_key = STRIPE_SECRET_KEY
 logger = setup_logger()
 
 
-def fetch_stripe_checkout_session(tenant_id: str) -> str:
+def fetch_stripe_checkout_session(
+    tenant_id: str,
+    billing_period: Literal["monthly", "annual"] = "monthly",
+) -> str:
     token = generate_data_plane_token()
     headers = {
         "Authorization": f"Bearer {token}",
         "Content-Type": "application/json",
     }
     url = f"{CONTROL_PLANE_API_BASE_URL}/create-checkout-session"
-    params = {"tenant_id": tenant_id}
-    response = requests.post(url, headers=headers, params=params)
+    payload = {
+        "tenant_id": tenant_id,
+        "billing_period": billing_period,
+    }
+    response = requests.post(url, headers=headers, json=payload)
     response.raise_for_status()
     return response.json()["sessionId"]
 
@@ -70,24 +76,46 @@ def fetch_billing_information(
     return BillingInformation(**response_data)
 
 
+def fetch_customer_portal_session(tenant_id: str, return_url: str | None = None) -> str:
+    """
+    Fetch a Stripe customer portal session URL from the control plane.
+    NOTE: This is currently only used for multi-tenant (cloud) deployments.
+    Self-hosted proxy endpoints will be added in a future phase.
+    """
+    token = generate_data_plane_token()
+    headers = {
+        "Authorization": f"Bearer {token}",
+        "Content-Type": "application/json",
+    }
+    url = f"{CONTROL_PLANE_API_BASE_URL}/create-customer-portal-session"
+    payload = {"tenant_id": tenant_id}
+    if return_url:
+        payload["return_url"] = return_url
+    response = requests.post(url, headers=headers, json=payload)
+    response.raise_for_status()
+    return response.json()["url"]
+
+
 def register_tenant_users(tenant_id: str, number_of_users: int) -> stripe.Subscription:
     """
-    Send a request to the control service to register the number of users for a tenant.
+    Update the number of seats for a tenant's subscription.
+    Preserves the existing price (monthly, annual, or grandfathered).
     """
-
-    if not STRIPE_PRICE_ID:
-        raise Exception("STRIPE_PRICE_ID is not set")
-
     response = fetch_tenant_stripe_information(tenant_id)
     stripe_subscription_id = cast(str, response.get("stripe_subscription_id"))
 
     subscription = stripe.Subscription.retrieve(stripe_subscription_id)
+    subscription_item = subscription["items"]["data"][0]
+
+    # Use existing price to preserve the customer's current plan
+    current_price_id = subscription_item.price.id
+
     updated_subscription = stripe.Subscription.modify(
         stripe_subscription_id,
         items=[
             {
-                "id": subscription["items"]["data"][0].id,
-                "price": STRIPE_PRICE_ID,
+                "id": subscription_item.id,
+                "price": current_price_id,
                 "quantity": number_of_users,
             }
         ],

backend/ee/onyx/server/tenants/billing_api.py

@@ -1,15 +1,14 @@
-import stripe
 from fastapi import APIRouter
 from fastapi import Depends
 from fastapi import HTTPException
 
 from ee.onyx.auth.users import current_admin_user
-from ee.onyx.configs.app_configs import STRIPE_SECRET_KEY
 from ee.onyx.server.tenants.access import control_plane_dep
 from ee.onyx.server.tenants.billing import fetch_billing_information
+from ee.onyx.server.tenants.billing import fetch_customer_portal_session
 from ee.onyx.server.tenants.billing import fetch_stripe_checkout_session
-from ee.onyx.server.tenants.billing import fetch_tenant_stripe_information
 from ee.onyx.server.tenants.models import BillingInformation
+from ee.onyx.server.tenants.models import CreateSubscriptionSessionRequest
 from ee.onyx.server.tenants.models import ProductGatingFullSyncRequest
 from ee.onyx.server.tenants.models import ProductGatingRequest
 from ee.onyx.server.tenants.models import ProductGatingResponse
@@ -23,7 +22,6 @@ from onyx.utils.logger import setup_logger
 from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 from shared_configs.contextvars import get_current_tenant_id
 
-stripe.api_key = STRIPE_SECRET_KEY
 logger = setup_logger()
 
 router = APIRouter(prefix="/tenants")
@@ -82,21 +80,17 @@ async def billing_information(
 async def create_customer_portal_session(
     _: User = Depends(current_admin_user),
 ) -> dict:
+    """
+    Create a Stripe customer portal session via the control plane.
+    NOTE: This is currently only used for multi-tenant (cloud) deployments.
+    Self-hosted proxy endpoints will be added in a future phase.
+    """
     tenant_id = get_current_tenant_id()
+    return_url = f"{WEB_DOMAIN}/admin/billing"
 
     try:
-        stripe_info = fetch_tenant_stripe_information(tenant_id)
-        stripe_customer_id = stripe_info.get("stripe_customer_id")
-        if not stripe_customer_id:
-            raise HTTPException(status_code=400, detail="Stripe customer ID not found")
-        logger.info(stripe_customer_id)
-
-        portal_session = stripe.billing_portal.Session.create(
-            customer=stripe_customer_id,
-            return_url=f"{WEB_DOMAIN}/admin/billing",
-        )
-        logger.info(portal_session)
-        return {"url": portal_session.url}
+        portal_url = fetch_customer_portal_session(tenant_id, return_url)
+        return {"url": portal_url}
     except Exception as e:
         logger.exception("Failed to create customer portal session")
         raise HTTPException(status_code=500, detail=str(e))
@@ -104,15 +98,18 @@ async def create_customer_portal_session(
 
 @router.post("/create-subscription-session")
 async def create_subscription_session(
+    request: CreateSubscriptionSessionRequest | None = None,
     _: User = Depends(current_admin_user),
 ) -> SubscriptionSessionResponse:
     try:
         tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
         if not tenant_id:
             raise HTTPException(status_code=400, detail="Tenant ID not found")
-        session_id = fetch_stripe_checkout_session(tenant_id)
+
+        billing_period = request.billing_period if request else "monthly"
+        session_id = fetch_stripe_checkout_session(tenant_id, billing_period)
         return SubscriptionSessionResponse(sessionId=session_id)
 
     except Exception as e:
-        logger.exception("Failed to create resubscription session")
+        logger.exception("Failed to create subscription session")
         raise HTTPException(status_code=500, detail=str(e))

backend/ee/onyx/server/tenants/models.py

@@ -1,4 +1,5 @@
 from datetime import datetime
+from typing import Literal
 
 from pydantic import BaseModel
 
@@ -73,6 +74,12 @@ class SubscriptionSessionResponse(BaseModel):
     sessionId: str
 
 
+class CreateSubscriptionSessionRequest(BaseModel):
+    """Request to create a subscription checkout session."""
+
+    billing_period: Literal["monthly", "annual"] = "monthly"
+
+
 class TenantByDomainResponse(BaseModel):
     tenant_id: str
     number_of_users: int

backend/ee/onyx/server/tenants/product_gating.py

@@ -65,3 +65,9 @@ def get_gated_tenants() -> set[str]:
     redis_client = get_redis_replica_client(tenant_id=ONYX_CLOUD_TENANT_ID)
     gated_tenants_bytes = cast(set[bytes], redis_client.smembers(GATED_TENANTS_KEY))
     return {tenant_id.decode("utf-8") for tenant_id in gated_tenants_bytes}
+
+
+def is_tenant_gated(tenant_id: str) -> bool:
+    """Fast O(1) check if tenant is in gated set (multi-tenant only)."""
+    redis_client = get_redis_replica_client(tenant_id=ONYX_CLOUD_TENANT_ID)
+    return bool(redis_client.sismember(GATED_TENANTS_KEY, tenant_id))

backend/ee/onyx/server/tenants/provisioning.py

@@ -1,5 +1,4 @@
 import asyncio
-import logging
 import uuid
 
 import aiohttp  # Async HTTP client
@@ -10,10 +9,7 @@ from fastapi import Request
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from ee.onyx.configs.app_configs import ANTHROPIC_DEFAULT_API_KEY
-from ee.onyx.configs.app_configs import COHERE_DEFAULT_API_KEY
 from ee.onyx.configs.app_configs import HUBSPOT_TRACKING_URL
-from ee.onyx.configs.app_configs import OPENAI_DEFAULT_API_KEY
 from ee.onyx.server.tenants.access import generate_data_plane_token
 from ee.onyx.server.tenants.models import TenantByDomainResponse
 from ee.onyx.server.tenants.models import TenantCreationPayload
@@ -25,11 +21,18 @@ from ee.onyx.server.tenants.user_mapping import add_users_to_tenant
 from ee.onyx.server.tenants.user_mapping import get_tenant_id_for_email
 from ee.onyx.server.tenants.user_mapping import user_owns_a_tenant
 from onyx.auth.users import exceptions
+from onyx.configs.app_configs import ANTHROPIC_DEFAULT_API_KEY
+from onyx.configs.app_configs import COHERE_DEFAULT_API_KEY
 from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
 from onyx.configs.app_configs import DEV_MODE
+from onyx.configs.app_configs import OPENAI_DEFAULT_API_KEY
+from onyx.configs.app_configs import OPENROUTER_DEFAULT_API_KEY
+from onyx.configs.app_configs import VERTEXAI_DEFAULT_CREDENTIALS
+from onyx.configs.app_configs import VERTEXAI_DEFAULT_LOCATION
 from onyx.configs.constants import MilestoneRecordType
 from onyx.db.engine.sql_engine import get_session_with_shared_schema
 from onyx.db.engine.sql_engine import get_session_with_tenant
+from onyx.db.image_generation import create_default_image_gen_config_from_api_key
 from onyx.db.llm import update_default_provider
 from onyx.db.llm import upsert_cloud_embedding_provider
 from onyx.db.llm import upsert_llm_provider
@@ -37,13 +40,24 @@ from onyx.db.models import AvailableTenant
 from onyx.db.models import IndexModelStatus
 from onyx.db.models import SearchSettings
 from onyx.db.models import UserTenantMapping
-from onyx.llm.constants import LlmProviderNames
-from onyx.llm.llm_provider_options import get_anthropic_model_names
-from onyx.llm.llm_provider_options import get_openai_model_names
+from onyx.llm.well_known_providers.auto_update_models import LLMRecommendations
+from onyx.llm.well_known_providers.constants import ANTHROPIC_PROVIDER_NAME
+from onyx.llm.well_known_providers.constants import OPENAI_PROVIDER_NAME
+from onyx.llm.well_known_providers.constants import OPENROUTER_PROVIDER_NAME
+from onyx.llm.well_known_providers.constants import VERTEX_CREDENTIALS_FILE_KWARG
+from onyx.llm.well_known_providers.constants import VERTEX_LOCATION_KWARG
+from onyx.llm.well_known_providers.constants import VERTEXAI_PROVIDER_NAME
+from onyx.llm.well_known_providers.llm_provider_options import (
+    get_recommendations,
+)
+from onyx.llm.well_known_providers.llm_provider_options import (
+    model_configurations_for_provider,
+)
 from onyx.server.manage.embedding.models import CloudEmbeddingProviderCreationRequest
 from onyx.server.manage.llm.models import LLMProviderUpsertRequest
 from onyx.server.manage.llm.models import ModelConfigurationUpsertRequest
 from onyx.setup import setup_onyx
+from onyx.utils.logger import setup_logger
 from onyx.utils.telemetry import mt_cloud_telemetry
 from shared_configs.configs import MULTI_TENANT
 from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
@@ -52,7 +66,7 @@ from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 from shared_configs.enums import EmbeddingProvider
 
 
-logger = logging.getLogger(__name__)
+logger = setup_logger()
 
 
 async def get_or_provision_tenant(
@@ -261,59 +275,173 @@ async def rollback_tenant_provisioning(tenant_id: str) -> None:
         logger.info(f"Tenant rollback completed successfully for tenant {tenant_id}")
 
 
-def configure_default_api_keys(db_session: Session) -> None:
-    if ANTHROPIC_DEFAULT_API_KEY:
-        anthropic_provider = LLMProviderUpsertRequest(
-            name="Anthropic",
-            provider=LlmProviderNames.ANTHROPIC,
-            api_key=ANTHROPIC_DEFAULT_API_KEY,
-            default_model_name="claude-3-7-sonnet-20250219",
-            model_configurations=[
-                ModelConfigurationUpsertRequest(
-                    name=name,
-                    is_visible=False,
-                    max_input_tokens=None,
-                )
-                for name in get_anthropic_model_names()
-            ],
-            api_key_changed=True,
-        )
-        try:
-            full_provider = upsert_llm_provider(anthropic_provider, db_session)
-            update_default_provider(full_provider.id, db_session)
-        except Exception as e:
-            logger.error(f"Failed to configure Anthropic provider: {e}")
-    else:
-        logger.error(
-            "ANTHROPIC_DEFAULT_API_KEY not set, skipping Anthropic provider configuration"
+def _build_model_configuration_upsert_requests(
+    provider_name: str,
+    recommendations: LLMRecommendations,
+) -> list[ModelConfigurationUpsertRequest]:
+    model_configurations = model_configurations_for_provider(
+        provider_name, recommendations
+    )
+    return [
+        ModelConfigurationUpsertRequest(
+            name=model_configuration.name,
+            is_visible=model_configuration.is_visible,
+            max_input_tokens=model_configuration.max_input_tokens,
+            supports_image_input=model_configuration.supports_image_input,
         )
+        for model_configuration in model_configurations
+    ]
 
+
+def configure_default_api_keys(db_session: Session) -> None:
+    """Configure default LLM providers using recommended-models.json for model selection."""
+    # Load recommendations from JSON config
+    recommendations = get_recommendations()
+
+    has_set_default_provider = False
+
+    def _upsert(request: LLMProviderUpsertRequest) -> None:
+        nonlocal has_set_default_provider
+        try:
+            provider = upsert_llm_provider(request, db_session)
+            if not has_set_default_provider:
+                update_default_provider(provider.id, db_session)
+                has_set_default_provider = True
+        except Exception as e:
+            logger.error(f"Failed to configure {request.provider} provider: {e}")
+
+    # Configure OpenAI provider
     if OPENAI_DEFAULT_API_KEY:
+        default_model = recommendations.get_default_model(OPENAI_PROVIDER_NAME)
+        if default_model is None:
+            logger.error(
+                f"No default model found for {OPENAI_PROVIDER_NAME} in recommendations"
+            )
+        default_model_name = default_model.name if default_model else "gpt-5.2"
+
         openai_provider = LLMProviderUpsertRequest(
             name="OpenAI",
-            provider=LlmProviderNames.OPENAI,
+            provider=OPENAI_PROVIDER_NAME,
             api_key=OPENAI_DEFAULT_API_KEY,
-            default_model_name="gpt-4o",
-            model_configurations=[
-                ModelConfigurationUpsertRequest(
-                    name=model_name,
-                    is_visible=False,
-                    max_input_tokens=None,
-                )
-                for model_name in get_openai_model_names()
-            ],
+            default_model_name=default_model_name,
+            model_configurations=_build_model_configuration_upsert_requests(
+                OPENAI_PROVIDER_NAME, recommendations
+            ),
             api_key_changed=True,
+            is_auto_mode=True,
         )
+        _upsert(openai_provider)
+
+        # Create default image generation config using the OpenAI API key
         try:
-            full_provider = upsert_llm_provider(openai_provider, db_session)
-            update_default_provider(full_provider.id, db_session)
+            create_default_image_gen_config_from_api_key(
+                db_session, OPENAI_DEFAULT_API_KEY
+            )
         except Exception as e:
-            logger.error(f"Failed to configure OpenAI provider: {e}")
+            logger.error(f"Failed to create default image gen config: {e}")
     else:
-        logger.error(
+        logger.info(
             "OPENAI_DEFAULT_API_KEY not set, skipping OpenAI provider configuration"
         )
 
+    # Configure Anthropic provider
+    if ANTHROPIC_DEFAULT_API_KEY:
+        default_model = recommendations.get_default_model(ANTHROPIC_PROVIDER_NAME)
+        if default_model is None:
+            logger.error(
+                f"No default model found for {ANTHROPIC_PROVIDER_NAME} in recommendations"
+            )
+        default_model_name = (
+            default_model.name if default_model else "claude-sonnet-4-5"
+        )
+
+        anthropic_provider = LLMProviderUpsertRequest(
+            name="Anthropic",
+            provider=ANTHROPIC_PROVIDER_NAME,
+            api_key=ANTHROPIC_DEFAULT_API_KEY,
+            default_model_name=default_model_name,
+            model_configurations=_build_model_configuration_upsert_requests(
+                ANTHROPIC_PROVIDER_NAME, recommendations
+            ),
+            api_key_changed=True,
+            is_auto_mode=True,
+        )
+        _upsert(anthropic_provider)
+    else:
+        logger.info(
+            "ANTHROPIC_DEFAULT_API_KEY not set, skipping Anthropic provider configuration"
+        )
+
+    # Configure Vertex AI provider
+    if VERTEXAI_DEFAULT_CREDENTIALS:
+        default_model = recommendations.get_default_model(VERTEXAI_PROVIDER_NAME)
+        if default_model is None:
+            logger.error(
+                f"No default model found for {VERTEXAI_PROVIDER_NAME} in recommendations"
+            )
+        default_model_name = default_model.name if default_model else "gemini-2.5-pro"
+
+        # Vertex AI uses custom_config for credentials and location
+        custom_config = {
+            VERTEX_CREDENTIALS_FILE_KWARG: VERTEXAI_DEFAULT_CREDENTIALS,
+            VERTEX_LOCATION_KWARG: VERTEXAI_DEFAULT_LOCATION,
+        }
+
+        vertexai_provider = LLMProviderUpsertRequest(
+            name="Google Vertex AI",
+            provider=VERTEXAI_PROVIDER_NAME,
+            custom_config=custom_config,
+            default_model_name=default_model_name,
+            model_configurations=_build_model_configuration_upsert_requests(
+                VERTEXAI_PROVIDER_NAME, recommendations
+            ),
+            api_key_changed=True,
+            is_auto_mode=True,
+        )
+        _upsert(vertexai_provider)
+    else:
+        logger.info(
+            "VERTEXAI_DEFAULT_CREDENTIALS not set, skipping Vertex AI provider configuration"
+        )
+
+    # Configure OpenRouter provider
+    if OPENROUTER_DEFAULT_API_KEY:
+        default_model = recommendations.get_default_model(OPENROUTER_PROVIDER_NAME)
+        if default_model is None:
+            logger.error(
+                f"No default model found for {OPENROUTER_PROVIDER_NAME} in recommendations"
+            )
+        default_model_name = default_model.name if default_model else "z-ai/glm-4.7"
+
+        # For OpenRouter, we use the visible models from recommendations as model_configurations
+        # since OpenRouter models are dynamic (fetched from their API)
+        visible_models = recommendations.get_visible_models(OPENROUTER_PROVIDER_NAME)
+        model_configurations = [
+            ModelConfigurationUpsertRequest(
+                name=model.name,
+                is_visible=True,
+                max_input_tokens=None,
+                display_name=model.display_name,
+            )
+            for model in visible_models
+        ]
+
+        openrouter_provider = LLMProviderUpsertRequest(
+            name="OpenRouter",
+            provider=OPENROUTER_PROVIDER_NAME,
+            api_key=OPENROUTER_DEFAULT_API_KEY,
+            default_model_name=default_model_name,
+            model_configurations=model_configurations,
+            api_key_changed=True,
+            is_auto_mode=True,
+        )
+        _upsert(openrouter_provider)
+    else:
+        logger.info(
+            "OPENROUTER_DEFAULT_API_KEY not set, skipping OpenRouter provider configuration"
+        )
+
+    # Configure Cohere embedding provider
     if COHERE_DEFAULT_API_KEY:
         cloud_embedding_provider = CloudEmbeddingProviderCreationRequest(
             provider_type=EmbeddingProvider.COHERE,

backend/ee/onyx/server/token_rate_limits/api.py

@@ -9,6 +9,7 @@ from ee.onyx.db.token_limit import fetch_user_group_token_rate_limits_for_user
 from ee.onyx.db.token_limit import insert_user_group_token_rate_limit
 from onyx.auth.users import current_admin_user
 from onyx.auth.users import current_curator_or_admin_user
+from onyx.configs.constants import PUBLIC_API_TAGS
 from onyx.db.engine.sql_engine import get_session
 from onyx.db.models import User
 from onyx.db.token_limit import fetch_all_user_token_rate_limits
@@ -17,7 +18,7 @@ from onyx.server.query_and_chat.token_limit import any_rate_limit_exists
 from onyx.server.token_rate_limits.models import TokenRateLimitArgs
 from onyx.server.token_rate_limits.models import TokenRateLimitDisplay
 
-router = APIRouter(prefix="/admin/token-rate-limits")
+router = APIRouter(prefix="/admin/token-rate-limits", tags=PUBLIC_API_TAGS)
 
 
 """

backend/ee/onyx/server/usage_limits.py

@@ -0,0 +1,38 @@
+"""EE Usage limits - trial detection via billing information."""
+
+from ee.onyx.server.tenants.billing import fetch_billing_information
+from ee.onyx.server.tenants.models import BillingInformation
+from ee.onyx.server.tenants.models import SubscriptionStatusResponse
+from onyx.utils.logger import setup_logger
+from shared_configs.configs import MULTI_TENANT
+
+logger = setup_logger()
+
+
+def is_tenant_on_trial(tenant_id: str) -> bool:
+    """
+    Determine if a tenant is currently on a trial subscription.
+
+    In multi-tenant mode, we fetch billing information from the control plane
+    to determine if the tenant has an active trial.
+    """
+    if not MULTI_TENANT:
+        return False
+
+    try:
+        billing_info = fetch_billing_information(tenant_id)
+
+        # If not subscribed at all, check if we have trial information
+        if isinstance(billing_info, SubscriptionStatusResponse):
+            # No subscription means they're likely on trial (new tenant)
+            return True
+
+        if isinstance(billing_info, BillingInformation):
+            return billing_info.status == "trialing"
+
+        return False
+
+    except Exception as e:
+        logger.warning(f"Failed to fetch billing info for trial check: {e}")
+        # Default to trial limits on error (more restrictive = safer)
+        return True

backend/ee/onyx/server/user_group/api.py

@@ -18,6 +18,7 @@ from ee.onyx.server.user_group.models import UserGroupCreate
 from ee.onyx.server.user_group.models import UserGroupUpdate
 from onyx.auth.users import current_admin_user
 from onyx.auth.users import current_curator_or_admin_user
+from onyx.configs.constants import PUBLIC_API_TAGS
 from onyx.db.engine.sql_engine import get_session
 from onyx.db.models import User
 from onyx.db.models import UserRole
@@ -25,7 +26,7 @@ from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
-router = APIRouter(prefix="/manage")
+router = APIRouter(prefix="/manage", tags=PUBLIC_API_TAGS)
 
 
 @router.get("/admin/user-group")

backend/onyx/access/models.py

@@ -105,6 +105,8 @@ class DocExternalAccess:
         )
 
 
+# TODO(andrei): First refactor this into a pydantic model, then get rid of
+# duplicate fields.
 @dataclass(frozen=True, init=False)
 class DocumentAccess(ExternalAccess):
     # User emails for Onyx users, None indicates admin

backend/onyx/auth/captcha.py

@@ -0,0 +1,107 @@
+"""Captcha verification for user registration."""
+
+import httpx
+from pydantic import BaseModel
+from pydantic import Field
+
+from onyx.configs.app_configs import CAPTCHA_ENABLED
+from onyx.configs.app_configs import RECAPTCHA_SCORE_THRESHOLD
+from onyx.configs.app_configs import RECAPTCHA_SECRET_KEY
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+RECAPTCHA_VERIFY_URL = "https://www.google.com/recaptcha/api/siteverify"
+
+
+class CaptchaVerificationError(Exception):
+    """Raised when captcha verification fails."""
+
+
+class RecaptchaResponse(BaseModel):
+    """Response from Google reCAPTCHA verification API."""
+
+    success: bool
+    score: float | None = None  # Only present for reCAPTCHA v3
+    action: str | None = None
+    challenge_ts: str | None = None
+    hostname: str | None = None
+    error_codes: list[str] | None = Field(default=None, alias="error-codes")
+
+
+def is_captcha_enabled() -> bool:
+    """Check if captcha verification is enabled."""
+    return CAPTCHA_ENABLED and bool(RECAPTCHA_SECRET_KEY)
+
+
+async def verify_captcha_token(
+    token: str,
+    expected_action: str = "signup",
+) -> None:
+    """
+    Verify a reCAPTCHA token with Google's API.
+
+    Args:
+        token: The reCAPTCHA response token from the client
+        expected_action: Expected action name for v3 verification
+
+    Raises:
+        CaptchaVerificationError: If verification fails
+    """
+    if not is_captcha_enabled():
+        return
+
+    if not token:
+        raise CaptchaVerificationError("Captcha token is required")
+
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                RECAPTCHA_VERIFY_URL,
+                data={
+                    "secret": RECAPTCHA_SECRET_KEY,
+                    "response": token,
+                },
+                timeout=10.0,
+            )
+            response.raise_for_status()
+
+            data = response.json()
+            result = RecaptchaResponse(**data)
+
+            if not result.success:
+                error_codes = result.error_codes or ["unknown-error"]
+                logger.warning(f"Captcha verification failed: {error_codes}")
+                raise CaptchaVerificationError(
+                    f"Captcha verification failed: {', '.join(error_codes)}"
+                )
+
+            # For reCAPTCHA v3, also check the score
+            if result.score is not None:
+                if result.score < RECAPTCHA_SCORE_THRESHOLD:
+                    logger.warning(
+                        f"Captcha score too low: {result.score} < {RECAPTCHA_SCORE_THRESHOLD}"
+                    )
+                    raise CaptchaVerificationError(
+                        "Captcha verification failed: suspicious activity detected"
+                    )
+
+                # Optionally verify the action matches
+                if result.action and result.action != expected_action:
+                    logger.warning(
+                        f"Captcha action mismatch: {result.action} != {expected_action}"
+                    )
+                    raise CaptchaVerificationError(
+                        "Captcha verification failed: action mismatch"
+                    )
+
+            logger.debug(
+                f"Captcha verification passed: score={result.score}, "
+                f"action={result.action}"
+            )
+
+    except httpx.HTTPError as e:
+        logger.error(f"Captcha API request failed: {e}")
+        # In case of API errors, we might want to allow registration
+        # to prevent blocking legitimate users. This is a policy decision.
+        raise CaptchaVerificationError("Captcha verification service unavailable")

backend/onyx/auth/disposable_email_validator.py

@@ -0,0 +1,192 @@
+"""
+Utility to validate and block disposable/temporary email addresses.
+
+This module fetches a list of known disposable email domains from a remote source
+and caches them for performance. It's used during user registration to prevent
+abuse from temporary email services.
+"""
+
+import threading
+import time
+from typing import Set
+
+import httpx
+
+from onyx.configs.app_configs import DISPOSABLE_EMAIL_DOMAINS_URL
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+class DisposableEmailValidator:
+    """
+    Thread-safe singleton validator for disposable email domains.
+
+    Fetches and caches the list of disposable domains, with periodic refresh.
+    """
+
+    _instance: "DisposableEmailValidator | None" = None
+    _lock = threading.Lock()
+
+    def __new__(cls) -> "DisposableEmailValidator":
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self) -> None:
+        # Check if already initialized using a try/except to avoid type issues
+        try:
+            if self._initialized:
+                return
+        except AttributeError:
+            pass
+
+        self._domains: Set[str] = set()
+        self._last_fetch_time: float = 0
+        self._fetch_lock = threading.Lock()
+        # Cache for 1 hour
+        self._cache_duration = 3600
+        # Hardcoded fallback list of common disposable domains
+        # This ensures we block at least these even if the remote fetch fails
+        self._fallback_domains = {
+            "trashlify.com",
+            "10minutemail.com",
+            "guerrillamail.com",
+            "mailinator.com",
+            "tempmail.com",
+            "throwaway.email",
+            "yopmail.com",
+            "temp-mail.org",
+            "getnada.com",
+            "maildrop.cc",
+        }
+        # Set initialized flag last to prevent race conditions
+        self._initialized: bool = True
+
+    def _should_refresh(self) -> bool:
+        """Check if the cached domains should be refreshed."""
+        return (time.time() - self._last_fetch_time) > self._cache_duration
+
+    def _fetch_domains(self) -> Set[str]:
+        """
+        Fetch disposable email domains from the configured URL.
+
+        Returns:
+            Set of domain strings (lowercased)
+        """
+        if not DISPOSABLE_EMAIL_DOMAINS_URL:
+            logger.debug("DISPOSABLE_EMAIL_DOMAINS_URL not configured")
+            return self._fallback_domains.copy()
+
+        try:
+            logger.info(
+                f"Fetching disposable email domains from {DISPOSABLE_EMAIL_DOMAINS_URL}"
+            )
+            with httpx.Client(timeout=10.0) as client:
+                response = client.get(DISPOSABLE_EMAIL_DOMAINS_URL)
+                response.raise_for_status()
+
+                domains_list = response.json()
+
+                if not isinstance(domains_list, list):
+                    logger.error(
+                        f"Expected list from disposable domains URL, got {type(domains_list)}"
+                    )
+                    return self._fallback_domains.copy()
+
+                # Convert all to lowercase and create set
+                domains = {domain.lower().strip() for domain in domains_list if domain}
+
+                # Always include fallback domains
+                domains.update(self._fallback_domains)
+
+                logger.info(
+                    f"Successfully fetched {len(domains)} disposable email domains"
+                )
+                return domains
+
+        except httpx.HTTPError as e:
+            logger.warning(f"Failed to fetch disposable domains (HTTP error): {e}")
+        except Exception as e:
+            logger.warning(f"Failed to fetch disposable domains: {e}")
+
+        # On error, return fallback domains
+        return self._fallback_domains.copy()
+
+    def get_domains(self) -> Set[str]:
+        """
+        Get the cached set of disposable email domains.
+        Refreshes the cache if needed.
+
+        Returns:
+            Set of disposable domain strings (lowercased)
+        """
+        # Fast path: return cached domains if still fresh
+        if self._domains and not self._should_refresh():
+            return self._domains.copy()
+
+        # Slow path: need to refresh
+        with self._fetch_lock:
+            # Double-check after acquiring lock
+            if self._domains and not self._should_refresh():
+                return self._domains.copy()
+
+            self._domains = self._fetch_domains()
+            self._last_fetch_time = time.time()
+            return self._domains.copy()
+
+    def is_disposable(self, email: str) -> bool:
+        """
+        Check if an email address uses a disposable domain.
+
+        Args:
+            email: The email address to check
+
+        Returns:
+            True if the email domain is disposable, False otherwise
+        """
+        if not email or "@" not in email:
+            return False
+
+        parts = email.split("@")
+        if len(parts) != 2 or not parts[0]:  # Must have user@domain with non-empty user
+            return False
+
+        domain = parts[1].lower().strip()
+        if not domain:  # Domain part must not be empty
+            return False
+
+        disposable_domains = self.get_domains()
+        return domain in disposable_domains
+
+
+# Global singleton instance
+_validator = DisposableEmailValidator()
+
+
+def is_disposable_email(email: str) -> bool:
+    """
+    Check if an email address uses a disposable/temporary domain.
+
+    This is a convenience function that uses the global validator instance.
+
+    Args:
+        email: The email address to check
+
+    Returns:
+        True if the email uses a disposable domain, False otherwise
+    """
+    return _validator.is_disposable(email)
+
+
+def refresh_disposable_domains() -> None:
+    """
+    Force a refresh of the disposable domains list.
+
+    This can be called manually if you want to update the list
+    without waiting for the cache to expire.
+    """
+    _validator._last_fetch_time = 0
+    _validator.get_domains()

backend/onyx/auth/schemas.py

@@ -40,6 +40,8 @@ class UserRead(schemas.BaseUser[uuid.UUID]):
 class UserCreate(schemas.BaseUserCreate):
     role: UserRole = UserRole.BASIC
     tenant_id: str | None = None
+    # Captcha token for cloud signup protection (optional, only used when captcha is enabled)
+    captcha_token: str | None = None
 
 
 class UserUpdateWithRole(schemas.BaseUserUpdate):

backend/onyx/auth/users.py

@@ -60,6 +60,7 @@ from sqlalchemy.exc import IntegrityError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from onyx.auth.api_key import get_hashed_api_key_from_request
+from onyx.auth.disposable_email_validator import is_disposable_email
 from onyx.auth.email_utils import send_forgot_password_email
 from onyx.auth.email_utils import send_user_verification_email
 from onyx.auth.invited_users import get_invited_users
@@ -248,13 +249,23 @@ def verify_email_in_whitelist(email: str, tenant_id: str) -> None:
 
 
 def verify_email_domain(email: str) -> None:
+    if email.count("@") != 1:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Email is not valid",
+        )
+
+    domain = email.split("@")[-1].lower()
+
+    # Check if email uses a disposable/temporary domain
+    if is_disposable_email(email):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Disposable email addresses are not allowed. Please use a permanent email address.",
+        )
+
+    # Check domain whitelist if configured
     if VALID_EMAIL_DOMAINS:
-        if email.count("@") != 1:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail="Email is not valid",
-            )
-        domain = email.split("@")[-1].lower()
         if domain not in VALID_EMAIL_DOMAINS:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
@@ -292,11 +303,57 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         safe: bool = False,
         request: Optional[Request] = None,
     ) -> User:
+        # Verify captcha if enabled (for cloud signup protection)
+        from onyx.auth.captcha import CaptchaVerificationError
+        from onyx.auth.captcha import is_captcha_enabled
+        from onyx.auth.captcha import verify_captcha_token
+
+        if is_captcha_enabled() and request is not None:
+            # Get captcha token from request body or headers
+            captcha_token = None
+            if hasattr(user_create, "captcha_token"):
+                captcha_token = getattr(user_create, "captcha_token", None)
+
+            # Also check headers as a fallback
+            if not captcha_token:
+                captcha_token = request.headers.get("X-Captcha-Token")
+
+            try:
+                await verify_captcha_token(
+                    captcha_token or "", expected_action="signup"
+                )
+            except CaptchaVerificationError as e:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail={"reason": str(e)},
+                )
+
         # We verify the password here to make sure it's valid before we proceed
         await self.validate_password(
             user_create.password, cast(schemas.UC, user_create)
         )
 
+        # Check for disposable emails BEFORE provisioning tenant
+        # This prevents creating tenants for throwaway email addresses
+        try:
+            verify_email_domain(user_create.email)
+        except HTTPException as e:
+            # Log blocked disposable email attempts
+            if (
+                e.status_code == status.HTTP_400_BAD_REQUEST
+                and "Disposable email" in str(e.detail)
+            ):
+                domain = (
+                    user_create.email.split("@")[-1]
+                    if "@" in user_create.email
+                    else "unknown"
+                )
+                logger.warning(
+                    f"Blocked disposable email registration attempt: {domain}",
+                    extra={"email_domain": domain},
+                )
+            raise
+
         user_count: int | None = None
         referral_source = (
             request.cookies.get("referral_source", None)
@@ -318,8 +375,17 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
         try:
             async with get_async_session_context_manager(tenant_id) as db_session:
-                verify_email_is_invited(user_create.email)
-                verify_email_domain(user_create.email)
+                # Check invite list based on deployment mode
+                if MULTI_TENANT:
+                    # Multi-tenant: Only require invite for existing tenants
+                    # New tenant creation (first user) doesn't require an invite
+                    user_count = await get_user_count()
+                    if user_count > 0:
+                        # Tenant already has users - require invite for new users
+                        verify_email_is_invited(user_create.email)
+                else:
+                    # Single-tenant: Check invite list (skips if SAML/OIDC or no list configured)
+                    verify_email_is_invited(user_create.email)
                 if MULTI_TENANT:
                     tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
                         db_session, User, OAuthAccount

backend/onyx/background/celery/apps/app_base.py

@@ -26,6 +26,7 @@ from onyx.background.celery.celery_utils import celery_is_worker_primary
 from onyx.background.celery.celery_utils import make_probe_path
 from onyx.background.celery.tasks.vespa.document_sync import DOCUMENT_SYNC_PREFIX
 from onyx.background.celery.tasks.vespa.document_sync import DOCUMENT_SYNC_TASKSET_KEY
+from onyx.configs.app_configs import ENABLE_OPENSEARCH_FOR_ONYX
 from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import OnyxRedisLocks
 from onyx.db.engine.sql_engine import get_sqlalchemy_engine
@@ -515,6 +516,10 @@ def wait_for_vespa_or_shutdown(sender: Any, **kwargs: Any) -> None:
     """Waits for Vespa to become ready subject to a timeout.
     Raises WorkerShutdown if the timeout is reached."""
 
+    if ENABLE_OPENSEARCH_FOR_ONYX:
+        # TODO(andrei): Do some similar liveness checking for OpenSearch.
+        return
+
     if not wait_for_vespa_with_timeout():
         msg = "Vespa: Readiness probe did not succeed within the timeout. Exiting..."
         logger.error(msg)

backend/onyx/background/celery/apps/background.py

@@ -124,6 +124,7 @@ celery_app.autodiscover_tasks(
         "onyx.background.celery.tasks.kg_processing",
         "onyx.background.celery.tasks.monitoring",
         "onyx.background.celery.tasks.user_file_processing",
+        "onyx.background.celery.tasks.llm_model_update",
         # Light worker tasks
         "onyx.background.celery.tasks.shared",
         "onyx.background.celery.tasks.vespa",

backend/onyx/background/celery/apps/docfetching.py

@@ -98,8 +98,5 @@ for bootstep in base_bootsteps:
 celery_app.autodiscover_tasks(
     [
         "onyx.background.celery.tasks.docfetching",
-        # Ensure the user files indexing worker registers the doc_id migration task
-        # TODO(subash): remove this once the doc_id migration is complete
-        "onyx.background.celery.tasks.user_file_processing",
     ]
 )

backend/onyx/background/celery/tasks/beat_schedule.py

@@ -2,8 +2,12 @@ import copy
 from datetime import timedelta
 from typing import Any
 
+from celery.schedules import crontab
+
+from onyx.configs.app_configs import AUTO_LLM_CONFIG_URL
+from onyx.configs.app_configs import AUTO_LLM_UPDATE_INTERVAL_SECONDS
 from onyx.configs.app_configs import ENTERPRISE_EDITION_ENABLED
-from onyx.configs.app_configs import LLM_MODEL_UPDATE_API_URL
+from onyx.configs.app_configs import SCHEDULED_EVAL_DATASET_NAMES
 from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryQueues
@@ -53,16 +57,6 @@ beat_task_templates: list[dict] = [
             "expires": BEAT_EXPIRES_DEFAULT,
         },
     },
-    {
-        "name": "user-file-docid-migration",
-        "task": OnyxCeleryTask.USER_FILE_DOCID_MIGRATION,
-        "schedule": timedelta(minutes=10),
-        "options": {
-            "priority": OnyxCeleryPriority.HIGH,
-            "expires": BEAT_EXPIRES_DEFAULT,
-            "queue": OnyxCeleryQueues.USER_FILES_INDEXING,
-        },
-    },
     {
         "name": "check-for-kg-processing",
         "task": OnyxCeleryTask.CHECK_KG_PROCESSING,
@@ -171,13 +165,32 @@ if ENTERPRISE_EDITION_ENABLED:
         ]
     )
 
-# Only add the LLM model update task if the API URL is configured
-if LLM_MODEL_UPDATE_API_URL:
+# Add the Auto LLM update task if the config URL is set (has a default)
+if AUTO_LLM_CONFIG_URL:
     beat_task_templates.append(
         {
-            "name": "check-for-llm-model-update",
-            "task": OnyxCeleryTask.CHECK_FOR_LLM_MODEL_UPDATE,
-            "schedule": timedelta(hours=1),  # Check every hour
+            "name": "check-for-auto-llm-update",
+            "task": OnyxCeleryTask.CHECK_FOR_AUTO_LLM_UPDATE,
+            "schedule": timedelta(seconds=AUTO_LLM_UPDATE_INTERVAL_SECONDS),
+            "options": {
+                "priority": OnyxCeleryPriority.LOW,
+                "expires": BEAT_EXPIRES_DEFAULT,
+            },
+        }
+    )
+
+# Add scheduled eval task if datasets are configured
+if SCHEDULED_EVAL_DATASET_NAMES:
+    beat_task_templates.append(
+        {
+            "name": "scheduled-eval-pipeline",
+            "task": OnyxCeleryTask.SCHEDULED_EVAL_TASK,
+            # run every Sunday at midnight UTC
+            "schedule": crontab(
+                hour=0,
+                minute=0,
+                day_of_week=0,
+            ),
             "options": {
                 "priority": OnyxCeleryPriority.LOW,
                 "expires": BEAT_EXPIRES_DEFAULT,

backend/onyx/background/celery/tasks/docfetching/task_creation_utils.py

@@ -72,15 +72,6 @@ def try_creating_docfetching_task(
             # Another indexing attempt is already running
             return None
 
-        # Determine which queue to use based on whether this is a user file
-        # TODO: at the moment the indexing pipeline is
-        # shared between user files and connectors
-        queue = (
-            OnyxCeleryQueues.USER_FILES_INDEXING
-            if cc_pair.is_user_file
-            else OnyxCeleryQueues.CONNECTOR_DOC_FETCHING
-        )
-
         # Use higher priority for first-time indexing to ensure new connectors
         # get processed before re-indexing of existing connectors
         has_successful_attempt = cc_pair.last_successful_index_time is not None
@@ -99,7 +90,7 @@ def try_creating_docfetching_task(
                 search_settings_id=search_settings.id,
                 tenant_id=tenant_id,
             ),
-            queue=queue,
+            queue=OnyxCeleryQueues.CONNECTOR_DOC_FETCHING,
             task_id=custom_task_id,
             priority=priority,
         )

backend/onyx/background/celery/tasks/docprocessing/tasks.py

@@ -12,6 +12,7 @@ from celery import Celery
 from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
+from fastapi import HTTPException
 from pydantic import BaseModel
 from redis import Redis
 from redis.lock import Lock as RedisLock
@@ -40,9 +41,11 @@ from onyx.background.indexing.checkpointing_utils import (
 )
 from onyx.background.indexing.index_attempt_utils import cleanup_index_attempts
 from onyx.background.indexing.index_attempt_utils import get_old_index_attempts
+from onyx.configs.app_configs import AUTH_TYPE
 from onyx.configs.app_configs import MANAGED_VESPA
 from onyx.configs.app_configs import VESPA_CLOUD_CERT_PATH
 from onyx.configs.app_configs import VESPA_CLOUD_KEY_PATH
+from onyx.configs.constants import AuthType
 from onyx.configs.constants import CELERY_GENERIC_BEAT_LOCK_TIMEOUT
 from onyx.configs.constants import CELERY_INDEXING_LOCK_TIMEOUT
 from onyx.configs.constants import MilestoneRecordType
@@ -59,11 +62,9 @@ from onyx.db.connector import mark_ccpair_with_indexing_trigger
 from onyx.db.connector_credential_pair import (
     fetch_indexable_standard_connector_credential_pair_ids,
 )
-from onyx.db.connector_credential_pair import (
-    fetch_indexable_user_file_connector_credential_pair_ids,
-)
 from onyx.db.connector_credential_pair import get_connector_credential_pair_from_id
 from onyx.db.connector_credential_pair import set_cc_pair_repeated_error_state
+from onyx.db.connector_credential_pair import update_connector_credential_pair_from_id
 from onyx.db.engine.sql_engine import get_session_with_current_tenant
 from onyx.db.engine.time_utils import get_db_current_time
 from onyx.db.enums import ConnectorCredentialPairStatus
@@ -112,6 +113,7 @@ from onyx.utils.telemetry import RecordType
 from shared_configs.configs import INDEXING_MODEL_SERVER_HOST
 from shared_configs.configs import INDEXING_MODEL_SERVER_PORT
 from shared_configs.configs import MULTI_TENANT
+from shared_configs.configs import USAGE_LIMITS_ENABLED
 from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 from shared_configs.contextvars import INDEX_ATTEMPT_INFO_CONTEXTVAR
 
@@ -538,12 +540,7 @@ def check_indexing_completion(
             ]:
                 # User file connectors must be paused on success
                 # NOTE: _run_indexing doesn't update connectors if the index attempt is the future embedding model
-                # TODO: figure out why this doesn't pause connectors during swap
-                cc_pair.status = (
-                    ConnectorCredentialPairStatus.PAUSED
-                    if cc_pair.is_user_file
-                    else ConnectorCredentialPairStatus.ACTIVE
-                )
+                cc_pair.status = ConnectorCredentialPairStatus.ACTIVE
                 db_session.commit()
 
             mt_cloud_telemetry(
@@ -809,13 +806,8 @@ def check_for_indexing(self: Task, *, tenant_id: str) -> int | None:
                     db_session, active_cc_pairs_only=True
                 )
             )
-            user_file_cc_pair_ids = (
-                fetch_indexable_user_file_connector_credential_pair_ids(
-                    db_session, search_settings_id=current_search_settings.id
-                )
-            )
 
-            primary_cc_pair_ids = standard_cc_pair_ids + user_file_cc_pair_ids
+            primary_cc_pair_ids = standard_cc_pair_ids
 
             # Get CC pairs for secondary search settings
             secondary_cc_pair_ids: list[int] = []
@@ -831,30 +823,47 @@ def check_for_indexing(self: Task, *, tenant_id: str) -> int | None:
                         db_session, active_cc_pairs_only=not include_paused
                     )
                 )
-                user_file_cc_pair_ids = (
-                    fetch_indexable_user_file_connector_credential_pair_ids(
-                        db_session, search_settings_id=secondary_search_settings.id
-                    )
-                    or []
-                )
 
-                secondary_cc_pair_ids = standard_cc_pair_ids + user_file_cc_pair_ids
+                secondary_cc_pair_ids = standard_cc_pair_ids
 
         # Flag CC pairs in repeated error state for primary/current search settings
         with get_session_with_current_tenant() as db_session:
             for cc_pair_id in primary_cc_pair_ids:
                 lock_beat.reacquire()
 
-                if is_in_repeated_error_state(
-                    cc_pair_id=cc_pair_id,
-                    search_settings_id=current_search_settings.id,
+                cc_pair = get_connector_credential_pair_from_id(
                     db_session=db_session,
+                    cc_pair_id=cc_pair_id,
+                )
+
+                # if already in repeated error state, don't do anything
+                # this is important so that we don't keep pausing the connector
+                # immediately upon a user un-pausing it to manually re-trigger and
+                # recover.
+                if (
+                    cc_pair
+                    and not cc_pair.in_repeated_error_state
+                    and is_in_repeated_error_state(
+                        cc_pair=cc_pair,
+                        search_settings_id=current_search_settings.id,
+                        db_session=db_session,
+                    )
                 ):
                     set_cc_pair_repeated_error_state(
                         db_session=db_session,
                         cc_pair_id=cc_pair_id,
                         in_repeated_error_state=True,
                     )
+                    # When entering repeated error state, also pause the connector
+                    # to prevent continued indexing retry attempts burning through embedding credits.
+                    # NOTE: only for Cloud, since most self-hosted users use self-hosted embedding
+                    # models. Also, they are more prone to repeated failures -> eventual success.
+                    if AUTH_TYPE == AuthType.CLOUD:
+                        update_connector_credential_pair_from_id(
+                            db_session=db_session,
+                            cc_pair_id=cc_pair.id,
+                            status=ConnectorCredentialPairStatus.PAUSED,
+                        )
 
         # NOTE: At this point, we haven't done heavy checks on whether or not the CC pairs should actually be indexed
         # Heavy check, should_index(), is called in _kickoff_indexing_tasks
@@ -1279,6 +1288,26 @@ def docprocessing_task(
         INDEX_ATTEMPT_INFO_CONTEXTVAR.reset(token)
 
 
+def _check_chunk_usage_limit(tenant_id: str) -> None:
+    """Check if chunk indexing usage limit has been exceeded.
+
+    Raises UsageLimitExceededError if the limit is exceeded.
+    """
+    if not USAGE_LIMITS_ENABLED:
+        return
+
+    from onyx.db.usage import UsageType
+    from onyx.server.usage_limits import check_usage_and_raise
+
+    with get_session_with_current_tenant() as db_session:
+        check_usage_and_raise(
+            db_session=db_session,
+            usage_type=UsageType.CHUNKS_INDEXED,
+            tenant_id=tenant_id,
+            pending_amount=0,  # Just check current usage
+        )
+
+
 def _docprocessing_task(
     index_attempt_id: int,
     cc_pair_id: int,
@@ -1290,6 +1319,25 @@ def _docprocessing_task(
     if tenant_id:
         CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
 
+    # Check if chunk indexing usage limit has been exceeded before processing
+    if USAGE_LIMITS_ENABLED:
+        try:
+            _check_chunk_usage_limit(tenant_id)
+        except HTTPException as e:
+            # Log the error and fail the indexing attempt
+            task_logger.error(
+                f"Chunk indexing usage limit exceeded for tenant {tenant_id}: {e}"
+            )
+            with get_session_with_current_tenant() as db_session:
+                from onyx.db.index_attempt import mark_attempt_failed
+
+                mark_attempt_failed(
+                    index_attempt_id=index_attempt_id,
+                    db_session=db_session,
+                    failure_reason=str(e),
+                )
+            raise
+
     task_logger.info(
         f"Processing document batch: "
         f"attempt={index_attempt_id} "
@@ -1434,6 +1482,23 @@ def _docprocessing_task(
                 adapter=adapter,
             )
 
+        # Track chunk indexing usage for cloud usage limits
+        if USAGE_LIMITS_ENABLED and index_pipeline_result.total_chunks > 0:
+            try:
+                from onyx.db.usage import increment_usage
+                from onyx.db.usage import UsageType
+
+                with get_session_with_current_tenant() as usage_db_session:
+                    increment_usage(
+                        db_session=usage_db_session,
+                        usage_type=UsageType.CHUNKS_INDEXED,
+                        amount=index_pipeline_result.total_chunks,
+                    )
+                    usage_db_session.commit()
+            except Exception as e:
+                # Log but don't fail indexing if usage tracking fails
+                task_logger.warning(f"Failed to track chunk indexing usage: {e}")
+
         # Update batch completion and document counts atomically using database coordination
 
         with get_session_with_current_tenant() as db_session, cross_batch_db_lock:

backend/onyx/background/celery/tasks/docprocessing/utils.py

@@ -10,7 +10,6 @@ from sqlalchemy.orm import Session
 from onyx.configs.app_configs import DISABLE_INDEX_UPDATE_ON_SWAP
 from onyx.configs.constants import CELERY_GENERIC_BEAT_LOCK_TIMEOUT
 from onyx.configs.constants import DocumentSource
-from onyx.db.connector_credential_pair import get_connector_credential_pair_from_id
 from onyx.db.engine.time_utils import get_db_current_time
 from onyx.db.enums import ConnectorCredentialPairStatus
 from onyx.db.enums import IndexingStatus
@@ -126,18 +125,9 @@ class IndexingCallback(IndexingHeartbeatInterface):
 
 
 def is_in_repeated_error_state(
-    cc_pair_id: int, search_settings_id: int, db_session: Session
+    cc_pair: ConnectorCredentialPair, search_settings_id: int, db_session: Session
 ) -> bool:
     """Checks if the cc pair / search setting combination is in a repeated error state."""
-    cc_pair = get_connector_credential_pair_from_id(
-        db_session=db_session,
-        cc_pair_id=cc_pair_id,
-    )
-    if not cc_pair:
-        raise RuntimeError(
-            f"is_in_repeated_error_state - could not find cc_pair with id={cc_pair_id}"
-        )
-
     # if the connector doesn't have a refresh_freq, a single failed attempt is enough
     number_of_failed_attempts_in_a_row_needed = (
         NUM_REPEAT_ERRORS_BEFORE_REPEATED_ERROR_STATE
@@ -146,7 +136,7 @@ def is_in_repeated_error_state(
     )
 
     most_recent_index_attempts = get_recent_attempts_for_cc_pair(
-        cc_pair_id=cc_pair_id,
+        cc_pair_id=cc_pair.id,
         search_settings_id=search_settings_id,
         limit=number_of_failed_attempts_in_a_row_needed,
         db_session=db_session,
@@ -180,7 +170,7 @@ def should_index(
         db_session=db_session,
     )
     all_recent_errored = is_in_repeated_error_state(
-        cc_pair_id=cc_pair.id,
+        cc_pair=cc_pair,
         search_settings_id=search_settings_instance.id,
         db_session=db_session,
     )

backend/onyx/background/celery/tasks/evals/tasks.py

@@ -1,9 +1,15 @@
+from datetime import datetime
+from datetime import timezone
 from typing import Any
 
 from celery import shared_task
 from celery import Task
 
+from onyx.configs.app_configs import BRAINTRUST_API_KEY
 from onyx.configs.app_configs import JOB_TIMEOUT
+from onyx.configs.app_configs import SCHEDULED_EVAL_DATASET_NAMES
+from onyx.configs.app_configs import SCHEDULED_EVAL_PERMISSIONS_EMAIL
+from onyx.configs.app_configs import SCHEDULED_EVAL_PROJECT
 from onyx.configs.constants import OnyxCeleryTask
 from onyx.evals.eval import run_eval
 from onyx.evals.models import EvalConfigurationOptions
@@ -33,3 +39,109 @@ def eval_run_task(
     except Exception:
         logger.error("Failed to run eval task")
         raise
+
+
+@shared_task(
+    name=OnyxCeleryTask.SCHEDULED_EVAL_TASK,
+    ignore_result=True,
+    soft_time_limit=JOB_TIMEOUT * 5,  # Allow more time for multiple datasets
+    bind=True,
+    trail=False,
+)
+def scheduled_eval_task(self: Task, **kwargs: Any) -> None:
+    """
+    Scheduled task to run evaluations on configured datasets.
+    Runs weekly on Sunday at midnight UTC.
+
+    Configure via environment variables (with defaults):
+    - SCHEDULED_EVAL_DATASET_NAMES: Comma-separated list of Braintrust dataset names
+    - SCHEDULED_EVAL_PERMISSIONS_EMAIL: Email for search permissions (default: roshan@onyx.app)
+    - SCHEDULED_EVAL_PROJECT: Braintrust project name
+    """
+    if not BRAINTRUST_API_KEY:
+        logger.error("BRAINTRUST_API_KEY is not configured, cannot run scheduled evals")
+        return
+
+    if not SCHEDULED_EVAL_PROJECT:
+        logger.error(
+            "SCHEDULED_EVAL_PROJECT is not configured, cannot run scheduled evals"
+        )
+        return
+
+    if not SCHEDULED_EVAL_DATASET_NAMES:
+        logger.info("No scheduled eval datasets configured, skipping")
+        return
+
+    if not SCHEDULED_EVAL_PERMISSIONS_EMAIL:
+        logger.error("SCHEDULED_EVAL_PERMISSIONS_EMAIL not configured")
+        return
+
+    project_name = SCHEDULED_EVAL_PROJECT
+    dataset_names = SCHEDULED_EVAL_DATASET_NAMES
+    permissions_email = SCHEDULED_EVAL_PERMISSIONS_EMAIL
+
+    # Create a timestamp for the scheduled run
+    run_timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+
+    logger.info(
+        f"Starting scheduled eval pipeline for project '{project_name}' "
+        f"with {len(dataset_names)} dataset(s): {dataset_names}"
+    )
+
+    pipeline_start = datetime.now(timezone.utc)
+    results: list[dict[str, Any]] = []
+
+    for dataset_name in dataset_names:
+        start_time = datetime.now(timezone.utc)
+        error_message: str | None = None
+        success = False
+
+        # Create informative experiment name for scheduled runs
+        experiment_name = f"{dataset_name} - {run_timestamp}"
+
+        try:
+            logger.info(
+                f"Running scheduled eval for dataset: {dataset_name} "
+                f"(project: {project_name})"
+            )
+
+            configuration = EvalConfigurationOptions(
+                search_permissions_email=permissions_email,
+                dataset_name=dataset_name,
+                no_send_logs=False,
+                braintrust_project=project_name,
+                experiment_name=experiment_name,
+            )
+
+            result = run_eval(
+                configuration=configuration,
+                remote_dataset_name=dataset_name,
+            )
+            success = result.success
+            logger.info(f"Completed eval for {dataset_name}: success={success}")
+
+        except Exception as e:
+            logger.exception(f"Failed to run scheduled eval for {dataset_name}")
+            error_message = str(e)
+            success = False
+
+        end_time = datetime.now(timezone.utc)
+
+        results.append(
+            {
+                "dataset_name": dataset_name,
+                "success": success,
+                "start_time": start_time,
+                "end_time": end_time,
+                "error_message": error_message,
+            }
+        )
+
+    pipeline_end = datetime.now(timezone.utc)
+    total_duration = (pipeline_end - pipeline_start).total_seconds()
+
+    passed_count = sum(1 for r in results if r["success"])
+    logger.info(
+        f"Scheduled eval pipeline completed: {passed_count}/{len(results)} passed "
+        f"in {total_duration:.1f}s"
+    )

backend/onyx/background/celery/tasks/llm_model_update/tasks.py

@@ -1,135 +1,45 @@
-from typing import Any
-
-import requests
 from celery import shared_task
 from celery import Task
 
 from onyx.background.celery.apps.app_base import task_logger
-from onyx.configs.app_configs import JOB_TIMEOUT
-from onyx.configs.app_configs import LLM_MODEL_UPDATE_API_URL
+from onyx.configs.app_configs import AUTO_LLM_CONFIG_URL
 from onyx.configs.constants import OnyxCeleryTask
 from onyx.db.engine.sql_engine import get_session_with_current_tenant
-from onyx.db.models import LLMProvider
-from onyx.db.models import ModelConfiguration
-
-
-def _process_model_list_response(model_list_json: Any) -> list[str]:
-    # Handle case where response is wrapped in a "data" field
-    if isinstance(model_list_json, dict):
-        if "data" in model_list_json:
-            model_list_json = model_list_json["data"]
-        elif "models" in model_list_json:
-            model_list_json = model_list_json["models"]
-        else:
-            raise ValueError(
-                "Invalid response from API - expected dict with 'data' or "
-                f"'models' field, got {type(model_list_json)}"
-            )
-
-    if not isinstance(model_list_json, list):
-        raise ValueError(
-            f"Invalid response from API - expected list, got {type(model_list_json)}"
-        )
-
-    # Handle both string list and object list cases
-    model_names: list[str] = []
-    for item in model_list_json:
-        if isinstance(item, str):
-            model_names.append(item)
-        elif isinstance(item, dict):
-            if "model_name" in item:
-                model_names.append(item["model_name"])
-            elif "id" in item:
-                model_names.append(item["id"])
-            else:
-                raise ValueError(
-                    f"Invalid item in model list - expected dict with model_name or id, got {type(item)}"
-                )
-        else:
-            raise ValueError(
-                f"Invalid item in model list - expected string or dict, got {type(item)}"
-            )
-
-    return model_names
+from onyx.llm.well_known_providers.auto_update_service import (
+    sync_llm_models_from_github,
+)
 
 
 @shared_task(
-    name=OnyxCeleryTask.CHECK_FOR_LLM_MODEL_UPDATE,
+    name=OnyxCeleryTask.CHECK_FOR_AUTO_LLM_UPDATE,
     ignore_result=True,
-    soft_time_limit=JOB_TIMEOUT,
+    soft_time_limit=300,  # 5 minute timeout
     trail=False,
     bind=True,
 )
-def check_for_llm_model_update(self: Task, *, tenant_id: str) -> bool | None:
-    if not LLM_MODEL_UPDATE_API_URL:
-        raise ValueError("LLM model update API URL not configured")
+def check_for_auto_llm_updates(self: Task, *, tenant_id: str) -> bool | None:
+    """Periodic task to fetch LLM model updates from GitHub
+    and sync them to providers in Auto mode.
 
-    # First fetch the models from the API
-    try:
-        response = requests.get(LLM_MODEL_UPDATE_API_URL)
-        response.raise_for_status()
-        available_models = _process_model_list_response(response.json())
-        task_logger.info(f"Found available models: {available_models}")
-
-    except Exception:
-        task_logger.exception("Failed to fetch models from API.")
+    This task checks the GitHub-hosted config file and updates all
+    providers that have is_auto_mode=True.
+    """
+    if not AUTO_LLM_CONFIG_URL:
+        task_logger.debug("AUTO_LLM_CONFIG_URL not configured, skipping")
         return None
 
-    # Then update the database with the fetched models
-    with get_session_with_current_tenant() as db_session:
-        # Get the default LLM provider
-        default_provider = (
-            db_session.query(LLMProvider)
-            .filter(LLMProvider.is_default_provider.is_(True))
-            .first()
-        )
+    try:
+        # Sync to database
+        with get_session_with_current_tenant() as db_session:
+            results = sync_llm_models_from_github(db_session)
 
-        if not default_provider:
-            task_logger.warning("No default LLM provider found")
-            return None
+            if results:
+                task_logger.info(f"Auto mode sync results: {results}")
+            else:
+                task_logger.debug("No model updates applied")
 
-        # log change if any
-        old_models = set(
-            model_configuration.name
-            for model_configuration in default_provider.model_configurations
-        )
-        new_models = set(available_models)
-        added_models = new_models - old_models
-        removed_models = old_models - new_models
-
-        if added_models:
-            task_logger.info(f"Adding models: {sorted(added_models)}")
-        if removed_models:
-            task_logger.info(f"Removing models: {sorted(removed_models)}")
-
-        # Update the provider's model list
-        # Remove models that are no longer available
-        db_session.query(ModelConfiguration).filter(
-            ModelConfiguration.llm_provider_id == default_provider.id,
-            ModelConfiguration.name.notin_(available_models),
-        ).delete(synchronize_session=False)
-
-        # Add new models
-        for available_model_name in available_models:
-            db_session.merge(
-                ModelConfiguration(
-                    llm_provider_id=default_provider.id,
-                    name=available_model_name,
-                    is_visible=False,
-                    max_input_tokens=None,
-                )
-            )
-
-        # if the default model is no longer available, set it to the first model in the list
-        if default_provider.default_model_name not in available_models:
-            task_logger.info(
-                f"Default model {default_provider.default_model_name} not "
-                f"available, setting to first model in list: {available_models[0]}"
-            )
-            default_provider.default_model_name = available_models[0]
-        db_session.commit()
-
-        if added_models or removed_models:
-            task_logger.info("Updated model list for default provider.")
+    except Exception:
+        task_logger.exception("Error in auto LLM update task")
+        raise
 
     return True

backend/onyx/background/celery/tasks/monitoring/tasks.py

@@ -886,9 +886,7 @@ def monitor_celery_queues_helper(
         OnyxCeleryQueues.CONNECTOR_DOC_FETCHING, r_celery
     )
     n_docprocessing = celery_get_queue_length(OnyxCeleryQueues.DOCPROCESSING, r_celery)
-    n_user_files_indexing = celery_get_queue_length(
-        OnyxCeleryQueues.USER_FILES_INDEXING, r_celery
-    )
+
     n_user_file_processing = celery_get_queue_length(
         OnyxCeleryQueues.USER_FILE_PROCESSING, r_celery
     )
@@ -924,7 +922,6 @@ def monitor_celery_queues_helper(
         f"docfetching_prefetched={len(n_docfetching_prefetched)} "
         f"docprocessing={n_docprocessing} "
         f"docprocessing_prefetched={len(n_docprocessing_prefetched)} "
-        f"user_files_indexing={n_user_files_indexing} "
         f"user_file_processing={n_user_file_processing} "
         f"user_file_project_sync={n_user_file_project_sync} "
         f"user_file_delete={n_user_file_delete} "

backend/onyx/background/celery/tasks/user_file_processing/tasks.py

@@ -1,6 +1,5 @@
 import datetime
 import time
-from collections.abc import Sequence
 from typing import Any
 from uuid import UUID
 
@@ -13,39 +12,33 @@ from retry import retry
 from sqlalchemy import select
 
 from onyx.background.celery.apps.app_base import task_logger
+from onyx.background.celery.celery_redis import celery_get_queue_length
 from onyx.background.celery.celery_utils import httpx_init_vespa_pool
 from onyx.background.celery.tasks.shared.RetryDocumentIndex import RetryDocumentIndex
 from onyx.configs.app_configs import MANAGED_VESPA
 from onyx.configs.app_configs import VESPA_CLOUD_CERT_PATH
 from onyx.configs.app_configs import VESPA_CLOUD_KEY_PATH
 from onyx.configs.constants import CELERY_GENERIC_BEAT_LOCK_TIMEOUT
-from onyx.configs.constants import CELERY_USER_FILE_DOCID_MIGRATION_LOCK_TIMEOUT
 from onyx.configs.constants import CELERY_USER_FILE_PROCESSING_LOCK_TIMEOUT
+from onyx.configs.constants import CELERY_USER_FILE_PROCESSING_TASK_EXPIRES
 from onyx.configs.constants import CELERY_USER_FILE_PROJECT_SYNC_LOCK_TIMEOUT
 from onyx.configs.constants import DocumentSource
-from onyx.configs.constants import FileOrigin
 from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryQueues
 from onyx.configs.constants import OnyxCeleryTask
 from onyx.configs.constants import OnyxRedisLocks
+from onyx.configs.constants import USER_FILE_PROCESSING_MAX_QUEUE_DEPTH
 from onyx.connectors.file.connector import LocalFileConnector
 from onyx.connectors.models import Document
 from onyx.db.engine.sql_engine import get_session_with_current_tenant
 from onyx.db.enums import UserFileStatus
-from onyx.db.models import FileRecord
-from onyx.db.models import SearchDoc
 from onyx.db.models import UserFile
 from onyx.db.search_settings import get_active_search_settings
 from onyx.db.search_settings import get_active_search_settings_list
 from onyx.document_index.factory import get_default_document_index
-from onyx.document_index.interfaces import VespaDocumentFields
 from onyx.document_index.interfaces import VespaDocumentUserFields
-from onyx.document_index.vespa.shared_utils.utils import (
-    replace_invalid_doc_id_characters,
-)
 from onyx.document_index.vespa_constants import DOCUMENT_ID_ENDPOINT
 from onyx.file_store.file_store import get_default_file_store
-from onyx.file_store.file_store import S3BackedFileStore
 from onyx.file_store.utils import user_file_id_to_plaintext_file_name
 from onyx.httpx.httpx_pool import HttpxPool
 from onyx.indexing.adapters.user_file_indexing_adapter import UserFileIndexingAdapter
@@ -63,6 +56,17 @@ def _user_file_lock_key(user_file_id: str | UUID) -> str:
     return f"{OnyxRedisLocks.USER_FILE_PROCESSING_LOCK_PREFIX}:{user_file_id}"
 
 
+def _user_file_queued_key(user_file_id: str | UUID) -> str:
+    """Key that exists while a process_single_user_file task is sitting in the queue.
+
+    The beat generator sets this with a TTL equal to CELERY_USER_FILE_PROCESSING_TASK_EXPIRES
+    before enqueuing and the worker deletes it as its first action.  This prevents
+    the beat from adding duplicate tasks for files that already have a live task
+    in flight.
+    """
+    return f"{OnyxRedisLocks.USER_FILE_QUEUED_PREFIX}:{user_file_id}"
+
+
 def _user_file_project_sync_lock_key(user_file_id: str | UUID) -> str:
     return f"{OnyxRedisLocks.USER_FILE_PROJECT_SYNC_LOCK_PREFIX}:{user_file_id}"
 
@@ -126,7 +130,24 @@ def _get_document_chunk_count(
 def check_user_file_processing(self: Task, *, tenant_id: str) -> None:
     """Scan for user files with PROCESSING status and enqueue per-file tasks.
 
-    Uses direct Redis locks to avoid overlapping runs.
+    Three mechanisms prevent queue runaway:
+
+    1. **Queue depth backpressure** – if the broker queue already has more than
+       USER_FILE_PROCESSING_MAX_QUEUE_DEPTH items we skip this beat cycle
+       entirely.  Workers are clearly behind; adding more tasks would only make
+       the backlog worse.
+
+    2. **Per-file queued guard** – before enqueuing a task we set a short-lived
+       Redis key (TTL = CELERY_USER_FILE_PROCESSING_TASK_EXPIRES).  If that key
+       already exists the file already has a live task in the queue, so we skip
+       it.  The worker deletes the key the moment it picks up the task so the
+       next beat cycle can re-enqueue if the file is still PROCESSING.
+
+    3. **Task expiry** – every enqueued task carries an `expires` value equal to
+       CELERY_USER_FILE_PROCESSING_TASK_EXPIRES.  If a task is still sitting in
+       the queue after that deadline, Celery discards it without touching the DB.
+       This is a belt-and-suspenders defence: even if the guard key is lost (e.g.
+       Redis restart), stale tasks evict themselves rather than piling up forever.
     """
     task_logger.info("check_user_file_processing - Starting")
 
@@ -141,7 +162,21 @@ def check_user_file_processing(self: Task, *, tenant_id: str) -> None:
         return None
 
     enqueued = 0
+    skipped_guard = 0
     try:
+        # --- Protection 1: queue depth backpressure ---
+        r_celery = self.app.broker_connection().channel().client  # type: ignore
+        queue_len = celery_get_queue_length(
+            OnyxCeleryQueues.USER_FILE_PROCESSING, r_celery
+        )
+        if queue_len > USER_FILE_PROCESSING_MAX_QUEUE_DEPTH:
+            task_logger.warning(
+                f"check_user_file_processing - Queue depth {queue_len} exceeds "
+                f"{USER_FILE_PROCESSING_MAX_QUEUE_DEPTH}, skipping enqueue for "
+                f"tenant={tenant_id}"
+            )
+            return None
+
         with get_session_with_current_tenant() as db_session:
             user_file_ids = (
                 db_session.execute(
@@ -154,12 +189,35 @@ def check_user_file_processing(self: Task, *, tenant_id: str) -> None:
             )
 
             for user_file_id in user_file_ids:
-                self.app.send_task(
-                    OnyxCeleryTask.PROCESS_SINGLE_USER_FILE,
-                    kwargs={"user_file_id": str(user_file_id), "tenant_id": tenant_id},
-                    queue=OnyxCeleryQueues.USER_FILE_PROCESSING,
-                    priority=OnyxCeleryPriority.HIGH,
+                # --- Protection 2: per-file queued guard ---
+                queued_key = _user_file_queued_key(user_file_id)
+                guard_set = redis_client.set(
+                    queued_key,
+                    1,
+                    ex=CELERY_USER_FILE_PROCESSING_TASK_EXPIRES,
+                    nx=True,
                 )
+                if not guard_set:
+                    skipped_guard += 1
+                    continue
+
+                # --- Protection 3: task expiry ---
+                # If task submission fails, clear the guard immediately so the
+                # next beat cycle can retry enqueuing this file.
+                try:
+                    self.app.send_task(
+                        OnyxCeleryTask.PROCESS_SINGLE_USER_FILE,
+                        kwargs={
+                            "user_file_id": str(user_file_id),
+                            "tenant_id": tenant_id,
+                        },
+                        queue=OnyxCeleryQueues.USER_FILE_PROCESSING,
+                        priority=OnyxCeleryPriority.HIGH,
+                        expires=CELERY_USER_FILE_PROCESSING_TASK_EXPIRES,
+                    )
+                except Exception:
+                    redis_client.delete(queued_key)
+                    raise
                 enqueued += 1
 
     finally:
@@ -167,7 +225,8 @@ def check_user_file_processing(self: Task, *, tenant_id: str) -> None:
             lock.release()
 
     task_logger.info(
-        f"check_user_file_processing - Enqueued {enqueued} tasks for tenant={tenant_id}"
+        f"check_user_file_processing - Enqueued {enqueued} skipped_guard={skipped_guard} "
+        f"tasks for tenant={tenant_id}"
     )
     return None
 
@@ -182,6 +241,12 @@ def process_single_user_file(self: Task, *, user_file_id: str, tenant_id: str) -
     start = time.monotonic()
 
     redis_client = get_redis_client(tenant_id=tenant_id)
+
+    # Clear the "queued" guard set by the beat generator so that the next beat
+    # cycle can re-enqueue this file if it is still in PROCESSING state after
+    # this task completes or fails.
+    redis_client.delete(_user_file_queued_key(user_file_id))
+
     file_lock: RedisLock = redis_client.lock(
         _user_file_lock_key(user_file_id),
         timeout=CELERY_USER_FILE_PROCESSING_LOCK_TIMEOUT,
@@ -618,315 +683,3 @@ def process_single_user_file_project_sync(
             file_lock.release()
 
     return None
-
-
-def _normalize_legacy_user_file_doc_id(old_id: str) -> str:
-    # Convert USER_FILE_CONNECTOR__<uuid> -> FILE_CONNECTOR__<uuid> for legacy values
-    user_prefix = "USER_FILE_CONNECTOR__"
-    file_prefix = "FILE_CONNECTOR__"
-    if old_id.startswith(user_prefix):
-        remainder = old_id[len(user_prefix) :]
-        return file_prefix + remainder
-    return old_id
-
-
-def update_legacy_plaintext_file_records() -> None:
-    """Migrate legacy plaintext cache objects from int-based keys to UUID-based
-    keys. Copies each S3 object to its expected UUID key and updates DB.
-
-    Examples:
-    - Old key: bucket/schema/plaintext_<int>
-    - New key: bucket/schema/plaintext_<uuid>
-    """
-
-    task_logger.info("update_legacy_plaintext_file_records - Starting")
-
-    with get_session_with_current_tenant() as db_session:
-        store = get_default_file_store()
-
-        if not isinstance(store, S3BackedFileStore):
-            task_logger.info(
-                "update_legacy_plaintext_file_records - Skipping non-S3 store"
-            )
-            return
-
-        s3_client = store._get_s3_client()
-        bucket_name = store._get_bucket_name()
-
-        # Select PLAINTEXT_CACHE records whose object_key ends with 'plaintext_' + non-hyphen chars
-        # Example: 'some/path/plaintext_abc123' matches; '.../plaintext_foo-bar' does not
-        plaintext_records: Sequence[FileRecord] = (
-            db_session.execute(
-                sa.select(FileRecord).where(
-                    FileRecord.file_origin == FileOrigin.PLAINTEXT_CACHE,
-                    FileRecord.object_key.op("~")(r"plaintext_[^-]+$"),
-                )
-            )
-            .scalars()
-            .all()
-        )
-
-        task_logger.info(
-            f"update_legacy_plaintext_file_records - Found {len(plaintext_records)} plaintext records to update"
-        )
-
-        normalized = 0
-        for fr in plaintext_records:
-            try:
-                expected_key = store._get_s3_key(fr.file_id)
-                if fr.object_key == expected_key:
-                    continue
-
-                if fr.bucket_name is None:
-                    task_logger.warning(f"id={fr.file_id} - Bucket name is None")
-                    continue
-
-                if fr.object_key is None:
-                    task_logger.warning(f"id={fr.file_id} - Object key is None")
-                    continue
-
-                # Copy old object to new key
-                copy_source = f"{fr.bucket_name}/{fr.object_key}"
-                s3_client.copy_object(
-                    CopySource=copy_source,
-                    Bucket=bucket_name,
-                    Key=expected_key,
-                    MetadataDirective="COPY",
-                )
-
-                # Delete old object (best-effort)
-                try:
-                    s3_client.delete_object(Bucket=fr.bucket_name, Key=fr.object_key)
-                except Exception:
-                    pass
-
-                # Update DB record with new key
-                fr.object_key = expected_key
-                db_session.add(fr)
-                normalized += 1
-            except Exception as e:
-                task_logger.warning(f"id={fr.file_id} - {e.__class__.__name__}")
-
-        if normalized:
-            db_session.commit()
-            task_logger.info(
-                f"user_file_docid_migration_task normalized {normalized} plaintext objects"
-            )
-
-
-@shared_task(
-    name=OnyxCeleryTask.USER_FILE_DOCID_MIGRATION,
-    ignore_result=True,
-    bind=True,
-)
-def user_file_docid_migration_task(self: Task, *, tenant_id: str) -> bool:
-
-    task_logger.info(
-        f"user_file_docid_migration_task - Starting for tenant={tenant_id}"
-    )
-
-    redis_client = get_redis_client(tenant_id=tenant_id)
-    lock: RedisLock = redis_client.lock(
-        OnyxRedisLocks.USER_FILE_DOCID_MIGRATION_LOCK,
-        timeout=CELERY_USER_FILE_DOCID_MIGRATION_LOCK_TIMEOUT,
-    )
-
-    if not lock.acquire(blocking=False):
-        task_logger.info(
-            f"user_file_docid_migration_task - Lock held, skipping tenant={tenant_id}"
-        )
-        return False
-
-    updated_count = 0
-    try:
-        update_legacy_plaintext_file_records()
-        # Track lock renewal
-        last_lock_time = time.monotonic()
-        with get_session_with_current_tenant() as db_session:
-
-            # 20 is the documented default for httpx max_keepalive_connections
-            if MANAGED_VESPA:
-                httpx_init_vespa_pool(
-                    20, ssl_cert=VESPA_CLOUD_CERT_PATH, ssl_key=VESPA_CLOUD_KEY_PATH
-                )
-            else:
-                httpx_init_vespa_pool(20)
-
-            active_settings = get_active_search_settings(db_session)
-            document_index = get_default_document_index(
-                search_settings=active_settings.primary,
-                secondary_search_settings=active_settings.secondary,
-                httpx_client=HttpxPool.get("vespa"),
-            )
-
-            retry_index = RetryDocumentIndex(document_index)
-
-            # Select user files with a legacy doc id that have not been migrated
-            user_files = (
-                db_session.execute(
-                    sa.select(UserFile).where(
-                        sa.and_(
-                            UserFile.document_id.is_not(None),
-                            UserFile.document_id_migrated.is_(False),
-                        )
-                    )
-                )
-                .scalars()
-                .all()
-            )
-
-            task_logger.info(
-                f"user_file_docid_migration_task - Found {len(user_files)} user files to migrate"
-            )
-
-            # Query all SearchDocs that need updating
-            search_docs = (
-                db_session.execute(
-                    sa.select(SearchDoc).where(
-                        SearchDoc.document_id.like("%FILE_CONNECTOR__%")
-                    )
-                )
-                .scalars()
-                .all()
-            )
-            task_logger.info(
-                f"user_file_docid_migration_task - Found {len(search_docs)} search docs to update"
-            )
-
-            # Build a map of normalized doc IDs to SearchDocs
-            search_doc_map: dict[str, list[SearchDoc]] = {}
-            for sd in search_docs:
-                doc_id = sd.document_id
-                if search_doc_map.get(doc_id) is None:
-                    search_doc_map[doc_id] = []
-                search_doc_map[doc_id].append(sd)
-
-            task_logger.debug(
-                f"user_file_docid_migration_task - Built search doc map with {len(search_doc_map)} entries"
-            )
-
-            ids_preview = list(search_doc_map.keys())[:5]
-            task_logger.debug(
-                f"user_file_docid_migration_task - First few search_doc_map ids: {ids_preview if ids_preview else 'No ids found'}"
-            )
-            task_logger.debug(
-                f"user_file_docid_migration_task - search_doc_map total items: "
-                f"{sum(len(docs) for docs in search_doc_map.values())}"
-            )
-            for user_file in user_files:
-                # Periodically renew the Redis lock to prevent expiry mid-run
-                current_time = time.monotonic()
-                if current_time - last_lock_time >= (
-                    CELERY_USER_FILE_DOCID_MIGRATION_LOCK_TIMEOUT / 4
-                ):
-                    renewed = False
-                    try:
-                        # extend lock ttl to full timeout window
-                        lock.extend(CELERY_USER_FILE_DOCID_MIGRATION_LOCK_TIMEOUT)
-                        renewed = True
-                    except Exception:
-                        # if extend fails, best-effort reacquire as a fallback
-                        try:
-                            lock.reacquire()
-                            renewed = True
-                        except Exception:
-                            renewed = False
-                    last_lock_time = current_time
-                    if not renewed or not lock.owned():
-                        task_logger.error(
-                            "user_file_docid_migration_task - Lost lock ownership or failed to renew; aborting for safety"
-                        )
-                        return False
-
-                try:
-                    clean_old_doc_id = replace_invalid_doc_id_characters(
-                        user_file.document_id
-                    )
-                    normalized_doc_id = _normalize_legacy_user_file_doc_id(
-                        clean_old_doc_id
-                    )
-                    user_project_ids = [project.id for project in user_file.projects]
-                    task_logger.info(
-                        f"user_file_docid_migration_task - Migrating user file {user_file.id} with doc_id {normalized_doc_id}"
-                    )
-
-                    index_name = active_settings.primary.index_name
-
-                    # First find the chunks count using direct Vespa query
-                    selection = f"{index_name}.document_id=='{normalized_doc_id}'"
-
-                    # Count all chunks for this document
-                    chunk_count = _get_document_chunk_count(
-                        index_name=index_name,
-                        selection=selection,
-                    )
-
-                    task_logger.info(
-                        f"Found {chunk_count} chunks for document {normalized_doc_id}"
-                    )
-
-                    # Now update Vespa chunks with the found chunk count using retry_index
-                    # WARNING: In the future this will error; we no longer want
-                    # to support changing document ID.
-                    # TODO(andrei): Delete soon.
-                    retry_index.update_single(
-                        doc_id=str(normalized_doc_id),
-                        tenant_id=tenant_id,
-                        chunk_count=chunk_count,
-                        fields=VespaDocumentFields(document_id=str(user_file.id)),
-                        user_fields=VespaDocumentUserFields(
-                            user_projects=user_project_ids
-                        ),
-                    )
-                    user_file.chunk_count = chunk_count
-
-                    # Update the SearchDocs
-                    actual_doc_id = str(user_file.document_id)
-                    normalized_actual_doc_id = _normalize_legacy_user_file_doc_id(
-                        actual_doc_id
-                    )
-                    if (
-                        normalized_doc_id in search_doc_map
-                        or normalized_actual_doc_id in search_doc_map
-                    ):
-                        to_update = (
-                            search_doc_map[normalized_doc_id]
-                            if normalized_doc_id in search_doc_map
-                            else search_doc_map[normalized_actual_doc_id]
-                        )
-                        task_logger.debug(
-                            f"user_file_docid_migration_task - Updating {len(to_update)} search docs for user file {user_file.id}"
-                        )
-                        for search_doc in to_update:
-                            search_doc.document_id = str(user_file.id)
-                            db_session.add(search_doc)
-
-                    user_file.document_id_migrated = True
-                    db_session.add(user_file)
-                    db_session.commit()
-                    updated_count += 1
-                except Exception as per_file_exc:
-                    # Rollback the current transaction and continue with the next file
-                    db_session.rollback()
-                    task_logger.exception(
-                        f"user_file_docid_migration_task - Error migrating user file {user_file.id} - "
-                        f"{per_file_exc.__class__.__name__}"
-                    )
-
-            task_logger.info(
-                f"user_file_docid_migration_task - Updated {updated_count} user files"
-            )
-
-        task_logger.info(
-            f"user_file_docid_migration_task - Completed for tenant={tenant_id} (updated={updated_count})"
-        )
-        return True
-    except Exception as e:
-        task_logger.exception(
-            f"user_file_docid_migration_task - Error during execution for tenant={tenant_id} "
-            f"(updated={updated_count}) exception={e.__class__.__name__}"
-        )
-        return False
-    finally:
-        if lock.owned():
-            lock.release()

backend/onyx/chat/README.md

@@ -63,7 +63,7 @@ To ensure the LLM follows certain specific instructions, instructions are added
 tool is used, a citation reminder is always added. Otherwise, by default there is no reminder. If the user configures reminders, those are added to the
 final message. If a search related tool just ran and the user has reminders, both appear in a single message.
 
-If a search related tool is called at any point during the turn, the reminder will remain at the end until the turn is over and the agent as responded.
+If a search related tool is called at any point during the turn, the reminder will remain at the end until the turn is over and the agent has responded.
 
 
 ## Tool Calls
@@ -145,9 +145,83 @@ attention despite having global access.
 In a similar concept, LLM instructions in the system prompt are structured specifically so that there are coherent sections for the LLM to attend to. This is
 fairly surprising actually but if there is a line of instructions effectively saying "If you try to use some tools and find that you need more information or
 need to call additional tools, you are encouraged to do this", having this in the Tool section of the System prompt makes all the LLMs follow it well but if it's
-even just a paragraph away like near the beginning of the prompt, it is often often ignored. The difference is as drastic as a 30% follow rate to a 90% follow
+even just a paragraph away like near the beginning of the prompt, it is often ignored. The difference is as drastic as a 30% follow rate to a 90% follow
 rate even just moving the same statement a few sentences.
 
 
 ## Other related pointers
 - How messages, files, images are stored can be found in backend/onyx/db/models.py, there is also a README.md under that directory that may be helpful.
+
+---
+
+# Overview of LLM flow architecture
+
+**Concepts:**
+Turn: User sends a message and AI does some set of things and responds
+Step/Cycle: 1 single LLM inference given some context and some tools
+
+
+## 1. Top Level (process_message function):
+This function can be thought of as the set-up and validation layer. It ensures that the database is in a valid state, reads the
+messages in the session and sets up all the necessary items to run the chat loop and state containers. The major things it does
+are:
+- Validates the request
+- Builds the chat history for the session
+- Fetches any additional context such as files and images
+- Prepares all of the tools for the LLM
+- Creates the state container objects for use in the loop
+
+### Wrapper (run_chat_loop_with_state_containers function):
+This wrapper is used to run the LLM flow in a background thread and monitor the emitter for stop signals. This means the top
+level is as isolated from the LLM flow as possible and can continue to yield packets as soon as they are available from the lower
+levels. This also means that if the lower levels fail, the top level will still guarantee a reasonable response to the user.
+All of the saving and database operations are abstracted away from the lower levels.
+
+### Emitter
+The emitter is designed to be an object queue so that lower levels do not need to yield objects all the way back to the top.
+This way the functions can be better designed (not everything as a generator) and more easily tested. The wrapper around the
+LLM flow (run_chat_loop_with_state_containers) is used to monitor the emitter and handle packets as soon as they are available
+from the lower levels. Both the emitter and the state container are mutating state objects and only used to accumulate state.
+There should be no logic dependent on the states of these objects, especially in the lower levels. The emitter should only take
+packets and should not be used for other things.
+
+### State Container
+The state container is used to accumulate state during the LLM flow. Similar to the emitter, it should not be used for logic,
+only for accumulating state. It is used to gather all of the necessary information for saving the chat turn into the database.
+So it will accumulate answer tokens, reasoning tokens, tool calls, citation info, etc. This is used at the end of the flow once
+the lower level is completed whether on its own or stopped by the user. At that point, all of the state is read and stored into
+the database. The state container can be added to by any of the underlying layers, this is fine.
+
+### Stopping Generation
+A stop signal is checked every 300ms by the wrapper around the LLM flow. The signal itself
+is stored in Redis and is set by the user calling the stop endpoint. The wrapper ensures that no matter what the lower level is
+doing at the time, the thread can be killed by the top level. It does not require a cooperative cancellation from the lower level
+and in fact the lower level does not know about the stop signal at all.
+
+
+## 2. LLM Loop (run_llm_loop function)
+This function handles the logic of the Turn. It's essentially a while loop where context is added and modified (according what
+is outlined in the first half of this doc). Its main functionality is:
+- Translate and truncate the context for the LLM inference
+- Add context modifiers like reminders, updates to the system prompts, etc.
+- Run tool calls and gather results
+- Build some of the objects stored in the state container.
+
+
+## 3. LLM Step (run_llm_step function)
+This function is a single inference of the LLM. It's a wrapper around the LLM stream function which handles packet translations
+so that the Emitter can emit individual tokens as soon as they arrive. It also keeps track of the different sections since they
+do not all come at once (reasoning, answers, tool calls are all built up token by token). This layer also tracks the different
+tool calls and returns that to the LLM Loop to execute.
+
+
+## Things to know
+- Packets are labeled with a "turn_index" field as part of the Placement of the packet. This is not the same as the backend
+concept of a turn. The turn_index for the frontend is which block does this packet belong to. So while a reasoning + tool call
+comes from the same LLM inference (same backend LLM step), they are 2 turns to the frontend because that's how it's rendered.
+
+- There are 3 representations of "message". The first is the database model ChatMessage, this one should be translated away and
+not used deep into the flow. The second is ChatMessageSimple which is the data model which should be used throughout the code
+as much as possible. If modifications/additions are needed, it should be to this object. This is the rich representation of a
+message for the code. Finally there is the LanguageModelInput representation of a message. This one is for the LLM interface
+layer and is as stripped down as possible so that the LLM interface can be clean and easy to maintain/extend.

backend/onyx/chat/chat_processing_checker.py

@@ -0,0 +1,57 @@
+from uuid import UUID
+
+from redis.client import Redis
+
+# Redis key prefixes for chat message processing
+PREFIX = "chatprocessing"
+FENCE_PREFIX = f"{PREFIX}_fence"
+FENCE_TTL = 30 * 60  # 30 minutes
+
+
+def _get_fence_key(chat_session_id: UUID) -> str:
+    """
+    Generate the Redis key for a chat session processing a message.
+
+    Args:
+        chat_session_id: The UUID of the chat session
+
+    Returns:
+        The fence key string (tenant_id is automatically added by the Redis client)
+    """
+    return f"{FENCE_PREFIX}_{chat_session_id}"
+
+
+def set_processing_status(
+    chat_session_id: UUID, redis_client: Redis, value: bool
+) -> None:
+    """
+    Set or clear the fence for a chat session processing a message.
+
+    If the key exists, we are processing a message. If the key does not exist, we are not processing a message.
+
+    Args:
+        chat_session_id: The UUID of the chat session
+        redis_client: The Redis client to use
+        value: True to set the fence, False to clear it
+    """
+    fence_key = _get_fence_key(chat_session_id)
+
+    if value:
+        redis_client.set(fence_key, 0, ex=FENCE_TTL)
+    else:
+        redis_client.delete(fence_key)
+
+
+def is_chat_session_processing(chat_session_id: UUID, redis_client: Redis) -> bool:
+    """
+    Check if the chat session is processing a message.
+
+    Args:
+        chat_session_id: The UUID of the chat session
+        redis_client: The Redis client to use
+
+    Returns:
+        True if the chat session is processing a message, False otherwise
+    """
+    fence_key = _get_fence_key(chat_session_id)
+    return bool(redis_client.exists(fence_key))

backend/onyx/chat/chat_state.py

@@ -1,4 +1,5 @@
 import threading
+import time
 from collections.abc import Callable
 from collections.abc import Generator
 from queue import Empty
@@ -38,6 +39,7 @@ class ChatStateContainer:
         self.citation_to_doc: CitationMapping = {}
         # True if this turn is a clarification question (deep research flow)
         self.is_clarification: bool = False
+        # Note: LLM cost tracking is now handled in multi_llm.py
 
     def add_tool_call(self, tool_call: ToolCallInfo) -> None:
         """Add a tool call to the accumulated state."""
@@ -92,6 +94,7 @@ class ChatStateContainer:
 
 def run_chat_loop_with_state_containers(
     func: Callable[..., None],
+    completion_callback: Callable[[ChatStateContainer], None],
     is_connected: Callable[[], bool],
     emitter: Emitter,
     state_container: ChatStateContainer,
@@ -144,6 +147,9 @@ def run_chat_loop_with_state_containers(
     thread = run_in_background(run_with_exception_capture)
 
     pkt: Packet | None = None
+    last_turn_index = 0  # Track the highest turn_index seen for stop packet
+    last_cancel_check = time.monotonic()
+    cancel_check_interval = 0.3  # Check for cancellation every 300ms
     try:
         while True:
             # Poll queue with 300ms timeout for natural stop signal checking
@@ -152,20 +158,51 @@ def run_chat_loop_with_state_containers(
                 pkt = emitter.bus.get(timeout=0.3)
             except Empty:
                 if not is_connected():
-                    # Stop signal detected, kill the thread
+                    # Stop signal detected
+                    yield Packet(
+                        placement=Placement(turn_index=last_turn_index + 1),
+                        obj=OverallStop(type="stop", stop_reason="user_cancelled"),
+                    )
                     break
+                last_cancel_check = time.monotonic()
                 continue
 
             if pkt is not None:
-                if pkt.obj == OverallStop(type="stop"):
+                # Track the highest turn_index for the stop packet
+                if pkt.placement and pkt.placement.turn_index > last_turn_index:
+                    last_turn_index = pkt.placement.turn_index
+
+                if isinstance(pkt.obj, OverallStop):
                     yield pkt
                     break
                 elif isinstance(pkt.obj, PacketException):
                     raise pkt.obj.exception
                 else:
                     yield pkt
+
+                # Check for cancellation periodically even when packets are flowing
+                # This ensures stop signal is checked during active streaming
+                current_time = time.monotonic()
+                if current_time - last_cancel_check >= cancel_check_interval:
+                    if not is_connected():
+                        # Stop signal detected during streaming
+                        yield Packet(
+                            placement=Placement(turn_index=last_turn_index + 1),
+                            obj=OverallStop(type="stop", stop_reason="user_cancelled"),
+                        )
+                        break
+                    last_cancel_check = current_time
     finally:
         # Wait for thread to complete on normal exit to propagate exceptions and ensure cleanup.
         # Skip waiting if user disconnected to exit quickly.
         if is_connected():
             wait_on_background(thread)
+        try:
+            completion_callback(state_container)
+        except Exception as e:
+            emitter.emit(
+                Packet(
+                    placement=Placement(turn_index=last_turn_index + 1),
+                    obj=PacketException(type="error", exception=e),
+                )
+            )

backend/onyx/chat/chat_utils.py

@@ -18,14 +18,13 @@ from onyx.background.celery.tasks.kg_processing.kg_indexing import (
 from onyx.chat.models import ChatLoadedFile
 from onyx.chat.models import ChatMessageSimple
 from onyx.chat.models import PersonaOverrideConfig
-from onyx.chat.models import ThreadMessage
 from onyx.configs.constants import DEFAULT_PERSONA_ID
 from onyx.configs.constants import MessageType
 from onyx.configs.constants import TMP_DRALPHA_PERSONA_NAME
-from onyx.context.search.models import RerankingDetails
-from onyx.context.search.models import RetrievalDetails
+from onyx.context.search.enums import RecencyBiasSetting
 from onyx.db.chat import create_chat_session
 from onyx.db.chat import get_chat_messages_by_session
+from onyx.db.chat import get_or_create_root_message
 from onyx.db.kg_config import get_kg_config_settings
 from onyx.db.kg_config import is_kg_config_settings_enabled_valid
 from onyx.db.llm import fetch_existing_doc_sets
@@ -37,6 +36,7 @@ from onyx.db.models import SearchDoc as DbSearchDoc
 from onyx.db.models import Tool
 from onyx.db.models import User
 from onyx.db.models import UserFile
+from onyx.db.projects import check_project_ownership
 from onyx.db.search_settings import get_current_search_settings
 from onyx.file_processing.extract_file_text import extract_file_text
 from onyx.file_store.file_store import get_default_file_store
@@ -46,12 +46,10 @@ from onyx.kg.models import KGException
 from onyx.kg.setup.kg_default_entity_definitions import (
     populate_missing_default_entity_types__commit,
 )
-from onyx.llm.override_models import LLMOverride
-from onyx.natural_language_processing.utils import BaseTokenizer
 from onyx.prompts.chat_prompts import ADDITIONAL_CONTEXT_PROMPT
 from onyx.prompts.chat_prompts import TOOL_CALL_RESPONSE_CROSS_MESSAGE
 from onyx.prompts.tool_prompts import TOOL_CALL_FAILURE_PROMPT
-from onyx.server.query_and_chat.models import CreateChatMessageRequest
+from onyx.server.query_and_chat.models import ChatSessionCreationRequest
 from onyx.server.query_and_chat.streaming_models import CitationInfo
 from onyx.tools.models import ToolCallKickoff
 from onyx.tools.tool_implementations.custom.custom_tool import (
@@ -61,89 +59,44 @@ from onyx.utils.logger import setup_logger
 from onyx.utils.threadpool_concurrency import run_functions_tuples_in_parallel
 from onyx.utils.timing import log_function_time
 
+
 logger = setup_logger()
 
 
-def prepare_chat_message_request(
-    message_text: str,
-    user: User | None,
-    persona_id: int | None,
-    # Does the question need to have a persona override
-    persona_override_config: PersonaOverrideConfig | None,
-    message_ts_to_respond_to: str | None,
-    retrieval_details: RetrievalDetails | None,
-    rerank_settings: RerankingDetails | None,
+def create_chat_session_from_request(
+    chat_session_request: ChatSessionCreationRequest,
+    user_id: UUID | None,
     db_session: Session,
-    skip_gen_ai_answer_generation: bool = False,
-    llm_override: LLMOverride | None = None,
-    allowed_tool_ids: list[int] | None = None,
-) -> CreateChatMessageRequest:
-    # Typically used for one shot flows like SlackBot or non-chat API endpoint use cases
-    new_chat_session = create_chat_session(
+) -> ChatSession:
+    """Create a chat session from a ChatSessionCreationRequest.
+
+    Includes project ownership validation when project_id is provided.
+
+    Args:
+        chat_session_request: The request containing persona_id, description, and project_id
+        user_id: The ID of the user creating the session (can be None for anonymous)
+        db_session: The database session
+
+    Returns:
+        The newly created ChatSession
+
+    Raises:
+        ValueError: If user lacks access to the specified project
+        Exception: If the persona is invalid
+    """
+    project_id = chat_session_request.project_id
+    if project_id:
+        if not check_project_ownership(project_id, user_id, db_session):
+            raise ValueError("User does not have access to project")
+
+    return create_chat_session(
         db_session=db_session,
-        description=None,
-        user_id=user.id if user else None,
-        # If using an override, this id will be ignored later on
-        persona_id=persona_id or DEFAULT_PERSONA_ID,
-        onyxbot_flow=True,
-        slack_thread_id=message_ts_to_respond_to,
+        description=chat_session_request.description or "",
+        user_id=user_id,
+        persona_id=chat_session_request.persona_id,
+        project_id=chat_session_request.project_id,
     )
 
-    return CreateChatMessageRequest(
-        chat_session_id=new_chat_session.id,
-        parent_message_id=None,  # It's a standalone chat session each time
-        message=message_text,
-        file_descriptors=[],  # Currently SlackBot/answer api do not support files in the context
-        # Can always override the persona for the single query, if it's a normal persona
-        # then it will be treated the same
-        persona_override_config=persona_override_config,
-        search_doc_ids=None,
-        retrieval_options=retrieval_details,
-        rerank_settings=rerank_settings,
-        skip_gen_ai_answer_generation=skip_gen_ai_answer_generation,
-        llm_override=llm_override,
-        allowed_tool_ids=allowed_tool_ids,
-    )
-
-
-def combine_message_thread(
-    messages: list[ThreadMessage],
-    max_tokens: int | None,
-    llm_tokenizer: BaseTokenizer,
-) -> str:
-    """Used to create a single combined message context from threads"""
-    if not messages:
-        return ""
-
-    message_strs: list[str] = []
-    total_token_count = 0
-
-    for message in reversed(messages):
-        if message.role == MessageType.USER:
-            role_str = message.role.value.upper()
-            if message.sender:
-                role_str += " " + message.sender
-            else:
-                # Since other messages might have the user identifying information
-                # better to use Unknown for symmetry
-                role_str += " Unknown"
-        else:
-            role_str = message.role.value.upper()
-
-        msg_str = f"{role_str}:\n{message.message}"
-        message_token_count = len(llm_tokenizer.encode(msg_str))
-
-        if (
-            max_tokens is not None
-            and total_token_count + message_token_count > max_tokens
-        ):
-            break
-
-        message_strs.insert(0, msg_str)
-        total_token_count += message_token_count
-
-    return "\n\n".join(message_strs)
-
 
 def create_chat_history_chain(
     chat_session_id: UUID,
@@ -164,13 +117,15 @@ def create_chat_history_chain(
     )
 
     if not all_chat_messages:
-        raise RuntimeError("No messages in Chat Session")
-
-    root_message = all_chat_messages[0]
-    if root_message.parent_message is not None:
-        raise RuntimeError(
-            "Invalid root message, unable to fetch valid chat message sequence"
+        root_message = get_or_create_root_message(
+            chat_session_id=chat_session_id, db_session=db_session
         )
+    else:
+        root_message = all_chat_messages[0]
+        if root_message.parent_message is not None:
+            raise RuntimeError(
+                "Invalid root message, unable to fetch valid chat message sequence"
+            )
 
     current_message: ChatMessage | None = root_message
     previous_message: ChatMessage | None = None
@@ -201,37 +156,9 @@ def create_chat_history_chain(
 
         previous_message = current_message
 
-    if not mainline_messages:
-        raise RuntimeError("Could not trace chat message history")
-
     return mainline_messages
 
 
-def combine_message_chain(
-    messages: list[ChatMessage],
-    token_limit: int,
-    msg_limit: int | None = None,
-) -> str:
-    """Used for secondary LLM flows that require the chat history,"""
-    message_strs: list[str] = []
-    total_token_count = 0
-
-    if msg_limit is not None:
-        messages = messages[-msg_limit:]
-
-    for message in cast(list[ChatMessage], reversed(messages)):
-        message_token_count = message.token_count
-
-        if total_token_count + message_token_count > token_limit:
-            break
-
-        role = message.message_type.value.upper()
-        message_strs.insert(0, f"{role}:\n{message.message}")
-        total_token_count += message_token_count
-
-    return "\n\n".join(message_strs)
-
-
 def reorganize_citations(
     answer: str, citations: list[CitationInfo]
 ) -> tuple[str, list[CitationInfo]]:
@@ -372,7 +299,7 @@ def create_temporary_persona(
         num_chunks=persona_config.num_chunks,
         llm_relevance_filter=persona_config.llm_relevance_filter,
         llm_filter_extraction=persona_config.llm_filter_extraction,
-        recency_bias=persona_config.recency_bias,
+        recency_bias=RecencyBiasSetting.BASE_DECAY,
         llm_model_provider_override=persona_config.llm_model_provider_override,
         llm_model_version_override=persona_config.llm_model_version_override,
     )
@@ -542,6 +469,71 @@ def load_all_chat_files(
     return files
 
 
+def convert_chat_history_basic(
+    chat_history: list[ChatMessage],
+    token_counter: Callable[[str], int],
+    max_individual_message_tokens: int | None = None,
+    max_total_tokens: int | None = None,
+) -> list[ChatMessageSimple]:
+    """Convert ChatMessage history to ChatMessageSimple format with no tool calls or files included.
+
+    Args:
+        chat_history: List of ChatMessage objects to convert
+        token_counter: Function to count tokens in a message string
+        max_individual_message_tokens: If set, messages exceeding this number of tokens are dropped.
+            If None, no messages are dropped based on individual token count.
+        max_total_tokens: If set, maximum number of tokens allowed for the entire history.
+            If None, the history is not trimmed based on total token count.
+
+    Returns:
+        List of ChatMessageSimple objects
+    """
+    # Defensive: treat a non-positive total budget as "no history".
+    if max_total_tokens is not None and max_total_tokens <= 0:
+        return []
+
+    # Convert only the core USER/ASSISTANT messages; omit files and tool calls.
+    converted: list[ChatMessageSimple] = []
+    for chat_message in chat_history:
+        if chat_message.message_type not in (MessageType.USER, MessageType.ASSISTANT):
+            continue
+
+        message = chat_message.message or ""
+        token_count = getattr(chat_message, "token_count", None)
+        if token_count is None:
+            token_count = token_counter(message)
+
+        # Drop any single message that would dominate the context window.
+        if (
+            max_individual_message_tokens is not None
+            and token_count > max_individual_message_tokens
+        ):
+            continue
+
+        converted.append(
+            ChatMessageSimple(
+                message=message,
+                token_count=token_count,
+                message_type=chat_message.message_type,
+                image_files=None,
+            )
+        )
+
+    if max_total_tokens is None:
+        return converted
+
+    # Enforce a max total budget by keeping a contiguous suffix of the conversation.
+    trimmed_reversed: list[ChatMessageSimple] = []
+    total_tokens = 0
+    for msg in reversed(converted):
+        if total_tokens + msg.token_count > max_total_tokens:
+            break
+        trimmed_reversed.append(msg)
+        total_tokens += msg.token_count
+
+    return list(reversed(trimmed_reversed))
+
+
 def convert_chat_history(
     chat_history: list[ChatMessage],
     files: list[ChatLoadedFile],

backend/onyx/chat/citation_processor.py

@@ -4,14 +4,15 @@ Dynamic Citation Processor for LLM Responses
 This module provides a citation processor that can:
 - Accept citation number to SearchDoc mappings dynamically
 - Process token streams from LLMs to extract citations
-- Optionally replace citation markers with formatted markdown links
-- Emit CitationInfo objects for detected citations (when replacing)
-- Track all seen citations regardless of replacement mode
+- Handle citations in three modes: REMOVE, KEEP_MARKERS, or HYPERLINK
+- Emit CitationInfo objects for detected citations (in HYPERLINK mode)
+- Track all seen citations regardless of mode
 - Maintain a list of cited documents in order of first citation
 """
 
 import re
 from collections.abc import Generator
+from enum import Enum
 from typing import TypeAlias
 
 from onyx.configs.chat_configs import STOP_STREAM_PAT
@@ -23,6 +24,29 @@ from onyx.utils.logger import setup_logger
 logger = setup_logger()
 
 
+class CitationMode(Enum):
+    """Defines how citations should be handled in the output.
+
+    REMOVE: Citations are completely removed from output text.
+            No CitationInfo objects are emitted.
+            Use case: When you need to remove citations from the output if they are not shared with the user
+            (e.g. in discord bot, public slack bot).
+
+    KEEP_MARKERS: Original citation markers like [1], [2] are preserved unchanged.
+                  No CitationInfo objects are emitted.
+                  Use case: When you need to track citations in research agent and later process
+                  them with collapse_citations() to renumber.
+
+    HYPERLINK: Citations are replaced with markdown links like [[1]](url).
+               CitationInfo objects are emitted for UI tracking.
+               Use case: Final reports shown to users with clickable links.
+    """
+
+    REMOVE = "remove"
+    KEEP_MARKERS = "keep_markers"
+    HYPERLINK = "hyperlink"
+
+
 CitationMapping: TypeAlias = dict[int, SearchDoc]
 
 
@@ -48,29 +72,37 @@ class DynamicCitationProcessor:
 
     This processor is designed for multi-turn conversations where the citation
     number to document mapping is provided externally. It processes streaming
-    tokens from an LLM, detects citations (e.g., [1], [2,3], [[4]]), and based
-    on the `replace_citation_tokens` setting:
+    tokens from an LLM, detects citations (e.g., [1], [2,3], [[4]]), and handles
+    them according to the configured CitationMode:
 
-    When replace_citation_tokens=True (default):
+    CitationMode.HYPERLINK (default):
         1. Replaces citation markers with formatted markdown links (e.g., [[1]](url))
         2. Emits CitationInfo objects for tracking
         3. Maintains the order in which documents were first cited
+        Use case: Final reports shown to users with clickable links.
 
-    When replace_citation_tokens=False:
-        1. Preserves original citation markers in the output text
+    CitationMode.KEEP_MARKERS:
+        1. Preserves original citation markers like [1], [2] unchanged
         2. Does NOT emit CitationInfo objects
         3. Still tracks all seen citations via get_seen_citations()
+        Use case: When citations need later processing (e.g., renumbering).
+
+    CitationMode.REMOVE:
+        1. Removes citation markers entirely from the output text
+        2. Does NOT emit CitationInfo objects
+        3. Still tracks all seen citations via get_seen_citations()
+        Use case: Research agent intermediate reports.
 
     Features:
         - Accepts citation number → SearchDoc mapping via update_citation_mapping()
-        - Configurable citation replacement behavior at initialization
-        - Always tracks seen citations regardless of replacement mode
+        - Configurable citation mode at initialization
+        - Always tracks seen citations regardless of mode
         - Holds back tokens that might be partial citations
         - Maintains list of cited SearchDocs in order of first citation
         - Handles unicode bracket variants (【】, ［］)
         - Skips citation processing inside code blocks
 
-    Example (with citation replacement - default):
+    Example (HYPERLINK mode - default):
         processor = DynamicCitationProcessor()
 
         # Set up citation mapping
@@ -87,8 +119,8 @@ class DynamicCitationProcessor:
         # Get cited documents at the end
         cited_docs = processor.get_cited_documents()
 
-    Example (without citation replacement):
-        processor = DynamicCitationProcessor(replace_citation_tokens=False)
+    Example (KEEP_MARKERS mode):
+        processor = DynamicCitationProcessor(citation_mode=CitationMode.KEEP_MARKERS)
         processor.update_citation_mapping({1: search_doc1, 2: search_doc2})
 
         # Process tokens from LLM
@@ -99,26 +131,42 @@ class DynamicCitationProcessor:
 
         # Get all seen citations after processing
         seen_citations = processor.get_seen_citations()  # {1: search_doc1, ...}
+
+    Example (REMOVE mode):
+        processor = DynamicCitationProcessor(citation_mode=CitationMode.REMOVE)
+        processor.update_citation_mapping({1: search_doc1, 2: search_doc2})
+
+        # Process tokens - citations are removed but tracked
+        for token in llm_stream:
+            for result in processor.process_token(token):
+                print(result)  # Text without any citation markers
+
+        # Citations are still tracked
+        seen_citations = processor.get_seen_citations()
     """
 
     def __init__(
         self,
-        replace_citation_tokens: bool = True,
+        citation_mode: CitationMode = CitationMode.HYPERLINK,
         stop_stream: str | None = STOP_STREAM_PAT,
     ):
         """
         Initialize the citation processor.
 
         Args:
-            replace_citation_tokens: If True (default), citations like [1] are replaced
-                with formatted markdown links like [[1]](url) and CitationInfo objects
-                are emitted. If False, original citation text is preserved in output
-                and no CitationInfo objects are emitted. Regardless of this setting,
-                all seen citations are tracked and available via get_seen_citations().
+            citation_mode: How to handle citations in the output. One of:
+                - CitationMode.HYPERLINK (default): Replace [1] with [[1]](url)
+                  and emit CitationInfo objects.
+                - CitationMode.KEEP_MARKERS: Keep original [1] markers unchanged,
+                  no CitationInfo objects emitted.
+                - CitationMode.REMOVE: Remove citations entirely from output,
+                  no CitationInfo objects emitted.
+                All modes track seen citations via get_seen_citations().
             stop_stream: Optional stop token pattern to halt processing early.
                 When this pattern is detected in the token stream, processing stops.
                 Defaults to STOP_STREAM_PAT from chat configs.
         """
+
         # Citation mapping from citation number to SearchDoc
         self.citation_to_doc: CitationMapping = {}
         self.seen_citations: CitationMapping = {}  # citation num -> SearchDoc
@@ -128,7 +176,7 @@ class DynamicCitationProcessor:
         self.curr_segment = ""  # tokens held for citation processing
         self.hold = ""  # tokens held for stop token processing
         self.stop_stream = stop_stream
-        self.replace_citation_tokens = replace_citation_tokens
+        self.citation_mode = citation_mode
 
         # Citation tracking
         self.cited_documents_in_order: list[SearchDoc] = (
@@ -199,19 +247,21 @@ class DynamicCitationProcessor:
         5. Handles stop tokens
         6. Always tracks seen citations in self.seen_citations
 
-        Behavior depends on the `replace_citation_tokens` setting from __init__:
-        - If True: Citations are replaced with [[n]](url) format and CitationInfo
+        Behavior depends on the `citation_mode` setting from __init__:
+        - HYPERLINK: Citations are replaced with [[n]](url) format and CitationInfo
           objects are yielded before each formatted citation
-        - If False: Original citation text (e.g., [1]) is preserved in output
-          and no CitationInfo objects are yielded
+        - KEEP_MARKERS: Original citation markers like [1] are preserved unchanged,
+          no CitationInfo objects are yielded
+        - REMOVE: Citations are removed entirely from output,
+          no CitationInfo objects are yielded
 
         Args:
             token: The next token from the LLM stream, or None to signal end of stream.
                 Pass None to flush any remaining buffered text at end of stream.
 
         Yields:
-            str: Text chunks to display. Citation format depends on replace_citation_tokens.
-            CitationInfo: Citation metadata (only when replace_citation_tokens=True)
+            str: Text chunks to display. Citation format depends on citation_mode.
+            CitationInfo: Citation metadata (only when citation_mode=HYPERLINK)
         """
         # None -> end of stream, flush remaining segment
         if token is None:
@@ -299,17 +349,17 @@ class DynamicCitationProcessor:
                 if self.non_citation_count > 5:
                     self.recent_cited_documents.clear()
 
-                # Yield text before citation FIRST (preserve order)
-                if intermatch_str:
-                    yield intermatch_str
-
                 # Process the citation (returns formatted citation text and CitationInfo objects)
-                # Always tracks seen citations regardless of strip_citations flag
+                # Always tracks seen citations regardless of citation_mode
                 citation_text, citation_info_list = self._process_citation(
-                    match, has_leading_space, self.replace_citation_tokens
+                    match, has_leading_space
                 )
 
-                if self.replace_citation_tokens:
+                if self.citation_mode == CitationMode.HYPERLINK:
+                    # HYPERLINK mode: Replace citations with markdown links [[n]](url)
+                    # Yield text before citation FIRST (preserve order)
+                    if intermatch_str:
+                        yield intermatch_str
                     # Yield CitationInfo objects BEFORE the citation text
                     # This allows the frontend to receive citation metadata before the token
                     # that contains [[n]](link), enabling immediate rendering
@@ -318,10 +368,34 @@ class DynamicCitationProcessor:
                     # Then yield the formatted citation text
                     if citation_text:
                         yield citation_text
-                else:
-                    # When not stripping, yield the original citation text unchanged
+
+                elif self.citation_mode == CitationMode.KEEP_MARKERS:
+                    # KEEP_MARKERS mode: Preserve original citation markers unchanged
+                    # Yield text before citation
+                    if intermatch_str:
+                        yield intermatch_str
+                    # Yield the original citation marker as-is
                     yield match.group()
 
+                else:  # CitationMode.REMOVE
+                    # REMOVE mode: Remove citations entirely from output
+                    # This strips citation markers like [1], [2], 【1】 from the output text
+                    # When removing citations, we need to handle spacing to avoid issues like:
+                    # - "text [1] more" -> "text  more" (double space)
+                    # - "text [1]." -> "text ." (space before punctuation)
+                    if intermatch_str:
+                        remaining_text = self.curr_segment[match_span[1] :]
+                        # Strip trailing space from intermatch if:
+                        # 1. Remaining text starts with space (avoids double space)
+                        # 2. Remaining text starts with punctuation (avoids space before punctuation)
+                        if intermatch_str[-1].isspace() and remaining_text:
+                            first_char = remaining_text[0]
+                            # Check if next char is space or common punctuation
+                            if first_char.isspace() or first_char in ".,;:!?)]}":
+                                intermatch_str = intermatch_str.rstrip()
+                        if intermatch_str:
+                            yield intermatch_str
+
                 self.non_citation_count = 0
 
             # Leftover text could be part of next citation
@@ -338,7 +412,7 @@ class DynamicCitationProcessor:
             yield result
 
     def _process_citation(
-        self, match: re.Match, has_leading_space: bool, replace_tokens: bool = True
+        self, match: re.Match, has_leading_space: bool
     ) -> tuple[str, list[CitationInfo]]:
         """
         Process a single citation match and return formatted citation text and citation info objects.
@@ -349,31 +423,28 @@ class DynamicCitationProcessor:
         This method always:
         1. Extracts citation numbers from the match
         2. Looks up the corresponding SearchDoc from the mapping
-        3. Tracks seen citations in self.seen_citations (regardless of replace_tokens)
+        3. Tracks seen citations in self.seen_citations (regardless of citation_mode)
 
-        When replace_tokens=True (controlled by self.replace_citation_tokens):
+        When citation_mode is HYPERLINK:
         4. Creates formatted citation text as [[n]](url)
         5. Creates CitationInfo objects for new citations
         6. Handles deduplication of recently cited documents
 
-        When replace_tokens=False:
-        4. Returns empty string and empty list (caller yields original match text)
+        When citation_mode is REMOVE or KEEP_MARKERS:
+        4. Returns empty string and empty list (caller handles output based on mode)
 
         Args:
             match: Regex match object containing the citation pattern
             has_leading_space: Whether the text immediately before this citation
                 ends with whitespace. Used to determine if a leading space should
                 be added to the formatted output.
-            replace_tokens: If True, return formatted text and CitationInfo objects.
-                If False, only track seen citations and return empty results.
-                This is passed from self.replace_citation_tokens by the caller.
 
         Returns:
             Tuple of (formatted_citation_text, citation_info_list):
             - formatted_citation_text: Markdown-formatted citation text like
-              "[[1]](https://example.com)" or empty string if replace_tokens=False
+              "[[1]](https://example.com)" or empty string if not in HYPERLINK mode
             - citation_info_list: List of CitationInfo objects for newly cited
-              documents, or empty list if replace_tokens=False
+              documents, or empty list if not in HYPERLINK mode
         """
         citation_str: str = match.group()  # e.g., '[1]', '[1, 2, 3]', '[[1]]', '【1】'
         formatted = (
@@ -411,11 +482,11 @@ class DynamicCitationProcessor:
             doc_id = search_doc.document_id
             link = search_doc.link or ""
 
-            # Always track seen citations regardless of replace_tokens setting
+            # Always track seen citations regardless of citation_mode setting
             self.seen_citations[num] = search_doc
 
-            # When not replacing citation tokens, skip the rest of the processing
-            if not replace_tokens:
+            # Only generate formatted citations and CitationInfo in HYPERLINK mode
+            if self.citation_mode != CitationMode.HYPERLINK:
                 continue
 
             # Format the citation text as [[n]](link)
@@ -450,14 +521,14 @@ class DynamicCitationProcessor:
         """
         Get the list of cited SearchDoc objects in the order they were first cited.
 
-        Note: This list is only populated when `replace_citation_tokens=True`.
-        When `replace_citation_tokens=False`, this will return an empty list.
+        Note: This list is only populated when `citation_mode=HYPERLINK`.
+        When using REMOVE or KEEP_MARKERS mode, this will return an empty list.
         Use get_seen_citations() instead if you need to track citations without
-        replacing them.
+        emitting CitationInfo objects.
 
         Returns:
             List of SearchDoc objects in the order they were first cited.
-            Empty list if replace_citation_tokens=False.
+            Empty list if citation_mode is not HYPERLINK.
         """
         return self.cited_documents_in_order
 
@@ -465,14 +536,14 @@ class DynamicCitationProcessor:
         """
         Get the list of cited document IDs in the order they were first cited.
 
-        Note: This list is only populated when `replace_citation_tokens=True`.
-        When `replace_citation_tokens=False`, this will return an empty list.
+        Note: This list is only populated when `citation_mode=HYPERLINK`.
+        When using REMOVE or KEEP_MARKERS mode, this will return an empty list.
         Use get_seen_citations() instead if you need to track citations without
-        replacing them.
+        emitting CitationInfo objects.
 
         Returns:
             List of document IDs (strings) in the order they were first cited.
-            Empty list if replace_citation_tokens=False.
+            Empty list if citation_mode is not HYPERLINK.
         """
         return [doc.document_id for doc in self.cited_documents_in_order]
 
@@ -481,12 +552,12 @@ class DynamicCitationProcessor:
         Get all seen citations as a mapping from citation number to SearchDoc.
 
         This returns all citations that have been encountered during processing,
-        regardless of the `replace_citation_tokens` setting. Citations are tracked
+        regardless of the `citation_mode` setting. Citations are tracked
         whenever they are parsed, making this useful for cases where you need to
-        know which citations appeared in the text without replacing them.
+        know which citations appeared in the text without emitting CitationInfo objects.
 
-        This is particularly useful when `replace_citation_tokens=False`, as
-        get_cited_documents() will be empty in that case, but get_seen_citations()
+        This is particularly useful when using REMOVE or KEEP_MARKERS mode, as
+        get_cited_documents() will be empty in those cases, but get_seen_citations()
         will still contain all the citations that were found.
 
         Returns:
@@ -501,13 +572,13 @@ class DynamicCitationProcessor:
         """
         Get the number of unique documents that have been cited.
 
-        Note: This count is only updated when `replace_citation_tokens=True`.
-        When `replace_citation_tokens=False`, this will always return 0.
+        Note: This count is only updated when `citation_mode=HYPERLINK`.
+        When using REMOVE or KEEP_MARKERS mode, this will always return 0.
         Use len(get_seen_citations()) instead if you need to count citations
-        without replacing them.
+        without emitting CitationInfo objects.
 
         Returns:
-            Number of unique documents cited. 0 if replace_citation_tokens=False.
+            Number of unique documents cited. 0 if citation_mode is not HYPERLINK.
         """
         return len(self.cited_document_ids)
 
@@ -519,9 +590,9 @@ class DynamicCitationProcessor:
         CitationInfo objects for the same document when it's cited multiple times
         in close succession. This method clears that tracker.
 
-        This is primarily useful when `replace_citation_tokens=True` to allow
+        This is primarily useful when `citation_mode=HYPERLINK` to allow
         previously cited documents to emit CitationInfo objects again. Has no
-        effect when `replace_citation_tokens=False`.
+        effect when using REMOVE or KEEP_MARKERS mode.
 
         The recent citation tracker is also automatically cleared when more than
         5 non-citation characters are processed between citations.

backend/onyx/chat/llm_loop.py

@@ -5,9 +5,11 @@ from sqlalchemy.orm import Session
 from onyx.chat.chat_state import ChatStateContainer
 from onyx.chat.chat_utils import create_tool_call_failure_messages
 from onyx.chat.citation_processor import CitationMapping
+from onyx.chat.citation_processor import CitationMode
 from onyx.chat.citation_processor import DynamicCitationProcessor
 from onyx.chat.citation_utils import update_citation_processor_from_tool_response
 from onyx.chat.emitter import Emitter
+from onyx.chat.llm_step import extract_tool_calls_from_response_text
 from onyx.chat.llm_step import run_llm_step
 from onyx.chat.models import ChatMessageSimple
 from onyx.chat.models import ExtractedProjectFiles
@@ -37,6 +39,7 @@ from onyx.tools.built_in_tools import CITEABLE_TOOLS_NAMES
 from onyx.tools.built_in_tools import STOPPING_TOOLS_NAMES
 from onyx.tools.interface import Tool
 from onyx.tools.models import ToolCallInfo
+from onyx.tools.models import ToolCallKickoff
 from onyx.tools.models import ToolResponse
 from onyx.tools.tool_implementations.images.models import (
     FinalImageGenerationResponse,
@@ -50,6 +53,78 @@ from shared_configs.contextvars import get_current_tenant_id
 
 logger = setup_logger()
 
+
+def _try_fallback_tool_extraction(
+    llm_step_result: LlmStepResult,
+    tool_choice: ToolChoiceOptions,
+    fallback_extraction_attempted: bool,
+    tool_defs: list[dict],
+    turn_index: int,
+) -> tuple[LlmStepResult, bool]:
+    """Attempt to extract tool calls from response text as a fallback.
+
+    This is a last resort fallback for low quality LLMs or those that don't have
+    tool calling from the serving layer. Also triggers if there's reasoning but
+    no answer and no tool calls.
+
+    Args:
+        llm_step_result: The result from the LLM step
+        tool_choice: The tool choice option used for this step
+        fallback_extraction_attempted: Whether fallback extraction was already attempted
+        tool_defs: List of tool definitions
+        turn_index: The current turn index for placement
+
+    Returns:
+        Tuple of (possibly updated LlmStepResult, whether fallback was attempted this call)
+    """
+    if fallback_extraction_attempted:
+        return llm_step_result, False
+
+    no_tool_calls = (
+        not llm_step_result.tool_calls or len(llm_step_result.tool_calls) == 0
+    )
+    reasoning_but_no_answer_or_tools = (
+        llm_step_result.reasoning and not llm_step_result.answer and no_tool_calls
+    )
+    should_try_fallback = (
+        tool_choice == ToolChoiceOptions.REQUIRED and no_tool_calls
+    ) or reasoning_but_no_answer_or_tools
+
+    if not should_try_fallback:
+        return llm_step_result, False
+
+    # Try to extract from answer first, then fall back to reasoning
+    extracted_tool_calls: list[ToolCallKickoff] = []
+    if llm_step_result.answer:
+        extracted_tool_calls = extract_tool_calls_from_response_text(
+            response_text=llm_step_result.answer,
+            tool_definitions=tool_defs,
+            placement=Placement(turn_index=turn_index),
+        )
+    if not extracted_tool_calls and llm_step_result.reasoning:
+        extracted_tool_calls = extract_tool_calls_from_response_text(
+            response_text=llm_step_result.reasoning,
+            tool_definitions=tool_defs,
+            placement=Placement(turn_index=turn_index),
+        )
+
+    if extracted_tool_calls:
+        logger.info(
+            f"Extracted {len(extracted_tool_calls)} tool call(s) from response text "
+            f"as fallback (tool_choice was REQUIRED but no tool calls returned)"
+        )
+        return (
+            LlmStepResult(
+                reasoning=llm_step_result.reasoning,
+                answer=llm_step_result.answer,
+                tool_calls=extracted_tool_calls,
+            ),
+            True,
+        )
+
+    return llm_step_result, True
+
+
 # Hardcoded oppinionated value, might breaks down to something like:
 # Cycle 1: Calls web_search for something
 # Cycle 2: Calls open_url for some results
@@ -99,7 +174,7 @@ def _build_project_file_citation_mapping(
 
 
 def construct_message_history(
-    system_prompt: ChatMessageSimple,
+    system_prompt: ChatMessageSimple | None,
     custom_agent_prompt: ChatMessageSimple | None,
     simple_chat_history: list[ChatMessageSimple],
     reminder_message: ChatMessageSimple | None,
@@ -114,7 +189,7 @@ def construct_message_history(
             )
 
     history_token_budget = available_tokens
-    history_token_budget -= system_prompt.token_count
+    history_token_budget -= system_prompt.token_count if system_prompt else 0
     history_token_budget -= (
         custom_agent_prompt.token_count if custom_agent_prompt else 0
     )
@@ -125,9 +200,12 @@ def construct_message_history(
     if history_token_budget < 0:
         raise ValueError("Not enough tokens available to construct message history")
 
+    if system_prompt:
+        system_prompt.should_cache = True
+
     # If no history, build minimal context
     if not simple_chat_history:
-        result = [system_prompt]
+        result = [system_prompt] if system_prompt else []
         if custom_agent_prompt:
             result.append(custom_agent_prompt)
         if project_files and project_files.project_file_texts:
@@ -199,6 +277,7 @@ def construct_message_history(
 
     for msg in reversed(history_before_last_user):
         if current_token_count + msg.token_count <= remaining_budget:
+            msg.should_cache = True
             truncated_history_before.insert(0, msg)
             current_token_count += msg.token_count
         else:
@@ -218,7 +297,7 @@ def construct_message_history(
     # Build the final message list according to README ordering:
     # [system], [history_before_last_user], [custom_agent], [project_files],
     # [last_user_message], [messages_after_last_user], [reminder]
-    result = [system_prompt]
+    result = [system_prompt] if system_prompt else []
 
     # 1. Add truncated history before last user message
     result.extend(truncated_history_before)
@@ -293,6 +372,7 @@ def run_llm_loop(
     forced_tool_id: int | None = None,
     user_identity: LLMUserIdentity | None = None,
     chat_session_id: str | None = None,
+    include_citations: bool = True,
 ) -> None:
     with trace(
         "run_llm_loop",
@@ -310,7 +390,13 @@ def run_llm_loop(
         initialize_litellm()
 
         # Initialize citation processor for handling citations dynamically
-        citation_processor = DynamicCitationProcessor()
+        # When include_citations is True, use HYPERLINK mode to format citations as [[1]](url)
+        # When include_citations is False, use REMOVE mode to strip citations from output
+        citation_processor = DynamicCitationProcessor(
+            citation_mode=(
+                CitationMode.HYPERLINK if include_citations else CitationMode.REMOVE
+            )
+        )
 
         # Add project file citation mappings if project files are present
         project_citation_mapping: CitationMapping = {}
@@ -340,10 +426,16 @@ def run_llm_loop(
         ran_image_gen: bool = False
         just_ran_web_search: bool = False
         has_called_search_tool: bool = False
+        fallback_extraction_attempted: bool = False
         citation_mapping: dict[int, str] = {}  # Maps citation_num -> document_id/URL
 
+        default_base_system_prompt: str = get_default_base_system_prompt(db_session)
+        system_prompt = None
+        custom_agent_prompt_msg = None
+
         reasoning_cycles = 0
         for llm_cycle_count in range(MAX_LLM_CYCLES):
+            out_of_cycles = llm_cycle_count == MAX_LLM_CYCLES - 1
             if forced_tool_id:
                 # Needs to be just the single one because the "required" currently doesn't have a specified tool, just a binary
                 final_tools = [tool for tool in tools if tool.id == forced_tool_id]
@@ -351,7 +443,7 @@ def run_llm_loop(
                     raise ValueError(f"Tool {forced_tool_id} not found in tools")
                 tool_choice = ToolChoiceOptions.REQUIRED
                 forced_tool_id = None
-            elif llm_cycle_count == MAX_LLM_CYCLES - 1 or ran_image_gen:
+            elif out_of_cycles or ran_image_gen:
                 # Last cycle, no tools allowed, just answer!
                 tool_choice = ToolChoiceOptions.NONE
                 final_tools = []
@@ -370,35 +462,47 @@ def run_llm_loop(
                 )
                 custom_agent_prompt_msg = None
             else:
-                # System message and custom agent message are both included.
-                open_ai_formatting_enabled = model_needs_formatting_reenabled(
-                    llm.config.model_name
-                )
-
-                system_prompt_str = build_system_prompt(
-                    base_system_prompt=get_default_base_system_prompt(db_session),
-                    datetime_aware=persona.datetime_aware if persona else True,
-                    memories=memories,
-                    tools=tools,
-                    should_cite_documents=should_cite_documents
-                    or always_cite_documents,
-                    open_ai_formatting_enabled=open_ai_formatting_enabled,
-                )
-                system_prompt = ChatMessageSimple(
-                    message=system_prompt_str,
-                    token_count=token_counter(system_prompt_str),
-                    message_type=MessageType.SYSTEM,
-                )
-
-                custom_agent_prompt_msg = (
-                    ChatMessageSimple(
-                        message=custom_agent_prompt,
-                        token_count=token_counter(custom_agent_prompt),
-                        message_type=MessageType.USER,
+                # If it's an empty string, we assume the user does not want to include it as an empty System message
+                if default_base_system_prompt:
+                    open_ai_formatting_enabled = model_needs_formatting_reenabled(
+                        llm.config.model_name
                     )
-                    if custom_agent_prompt
-                    else None
-                )
+
+                    system_prompt_str = build_system_prompt(
+                        base_system_prompt=default_base_system_prompt,
+                        datetime_aware=persona.datetime_aware if persona else True,
+                        memories=memories,
+                        tools=tools,
+                        should_cite_documents=should_cite_documents
+                        or always_cite_documents,
+                        open_ai_formatting_enabled=open_ai_formatting_enabled,
+                    )
+                    system_prompt = ChatMessageSimple(
+                        message=system_prompt_str,
+                        token_count=token_counter(system_prompt_str),
+                        message_type=MessageType.SYSTEM,
+                    )
+                    custom_agent_prompt_msg = (
+                        ChatMessageSimple(
+                            message=custom_agent_prompt,
+                            token_count=token_counter(custom_agent_prompt),
+                            message_type=MessageType.USER,
+                        )
+                        if custom_agent_prompt
+                        else None
+                    )
+                else:
+                    # If there is a custom agent prompt, it replaces the system prompt when the default system prompt is empty
+                    system_prompt = (
+                        ChatMessageSimple(
+                            message=custom_agent_prompt,
+                            token_count=token_counter(custom_agent_prompt),
+                            message_type=MessageType.SYSTEM,
+                        )
+                        if custom_agent_prompt
+                        else None
+                    )
+                    custom_agent_prompt_msg = None
 
             reminder_message_text: str | None
             if ran_image_gen:
@@ -406,7 +510,7 @@ def run_llm_loop(
                 # This is to prevent it generating things like:
                 # [Cute Cat](attachment://a_cute_cat_sitting_playfully.png)
                 reminder_message_text = IMAGE_GEN_REMINDER
-            elif just_ran_web_search:
+            elif just_ran_web_search and not out_of_cycles:
                 reminder_message_text = OPEN_URL_REMINDER
             else:
                 # This is the default case, the LLM at this point may answer so it is important
@@ -417,6 +521,7 @@ def run_llm_loop(
                     ),
                     include_citation_reminder=should_cite_documents
                     or always_cite_documents,
+                    is_last_cycle=out_of_cycles,
                 )
 
             reminder_msg = (
@@ -440,10 +545,11 @@ def run_llm_loop(
 
             # This calls the LLM, yields packets (reasoning, answers, etc.) and returns the result
             # It also pre-processes the tool calls in preparation for running them
+            tool_defs = [tool.tool_definition() for tool in final_tools]
             llm_step_result, has_reasoned = run_llm_step(
                 emitter=emitter,
                 history=truncated_message_history,
-                tool_definitions=[tool.tool_definition() for tool in final_tools],
+                tool_definitions=tool_defs,
                 tool_choice=tool_choice,
                 llm=llm,
                 placement=Placement(turn_index=llm_cycle_count + reasoning_cycles),
@@ -458,6 +564,19 @@ def run_llm_loop(
             if has_reasoned:
                 reasoning_cycles += 1
 
+            # Fallback extraction for LLMs that don't support tool calling natively or are lower quality
+            # and might incorrectly output tool calls in other channels
+            llm_step_result, attempted = _try_fallback_tool_extraction(
+                llm_step_result=llm_step_result,
+                tool_choice=tool_choice,
+                fallback_extraction_attempted=fallback_extraction_attempted,
+                tool_defs=tool_defs,
+                turn_index=llm_cycle_count + reasoning_cycles,
+            )
+            if attempted:
+                # To prevent the case of excessive looping with bad models, we only allow one fallback attempt
+                fallback_extraction_attempted = True
+
             # Save citation mapping after each LLM step for incremental state updates
             state_container.set_citation_mapping(citation_processor.citation_to_doc)
 
@@ -483,7 +602,7 @@ def run_llm_loop(
             # in-flight citations
             # It can be cleaned up but not super trivial or worthwhile right now
             just_ran_web_search = False
-            tool_responses, citation_mapping = run_tool_calls(
+            parallel_tool_call_results = run_tool_calls(
                 tool_calls=tool_calls,
                 tools=final_tools,
                 message_history=truncated_message_history,
@@ -491,8 +610,11 @@ def run_llm_loop(
                 user_info=None,  # TODO, this is part of memories right now, might want to separate it out
                 citation_mapping=citation_mapping,
                 next_citation_num=citation_processor.get_next_citation_number(),
+                max_concurrent_tools=None,
                 skip_search_query_expansion=has_called_search_tool,
             )
+            tool_responses = parallel_tool_call_results.tool_responses
+            citation_mapping = parallel_tool_call_results.updated_citation_mapping
 
             # Failure case, give something reasonable to the LLM to try again
             if tool_calls and not tool_responses:
@@ -547,6 +669,12 @@ def run_llm_loop(
                 ):
                     generated_images = tool_response.rich_response.generated_images
 
+                saved_response = (
+                    tool_response.rich_response
+                    if isinstance(tool_response.rich_response, str)
+                    else tool_response.llm_facing_response
+                )
+
                 tool_call_info = ToolCallInfo(
                     parent_tool_call_id=None,  # Top-level tool calls are attached to the chat message
                     turn_index=llm_cycle_count + reasoning_cycles,
@@ -556,7 +684,7 @@ def run_llm_loop(
                     tool_id=tool.id,
                     reasoning_tokens=llm_step_result.reasoning,  # All tool calls from this loop share the same reasoning
                     tool_call_arguments=tool_call.tool_args,
-                    tool_call_response=tool_response.llm_facing_response,
+                    tool_call_response=saved_response,
                     search_docs=search_docs,
                     generated_images=generated_images,
                 )
@@ -612,7 +740,12 @@ def run_llm_loop(
                 should_cite_documents = True
 
         if not llm_step_result or not llm_step_result.answer:
-            raise RuntimeError("LLM did not return an answer.")
+            raise RuntimeError(
+                "The LLM did not return an answer. "
+                "Typically this is an issue with LLMs that do not support tool calling natively, "
+                "or the model serving API is not configured correctly. "
+                "This may also happen with models that are lower quality outputting invalid tool calls."
+            )
 
         emitter.emit(
             Packet(

backend/onyx/chat/llm_step.py

@@ -1,4 +1,6 @@
 import json
+import time
+import uuid
 from collections.abc import Callable
 from collections.abc import Generator
 from collections.abc import Mapping
@@ -17,6 +19,7 @@ from onyx.context.search.models import SearchDoc
 from onyx.file_store.models import ChatFileType
 from onyx.llm.interfaces import LanguageModelInput
 from onyx.llm.interfaces import LLM
+from onyx.llm.interfaces import LLMConfig
 from onyx.llm.interfaces import LLMUserIdentity
 from onyx.llm.interfaces import ToolChoiceOptions
 from onyx.llm.model_response import Delta
@@ -31,6 +34,7 @@ from onyx.llm.models import TextContentPart
 from onyx.llm.models import ToolCall
 from onyx.llm.models import ToolMessage
 from onyx.llm.models import UserMessage
+from onyx.llm.prompt_cache.processor import process_with_prompt_cache
 from onyx.server.query_and_chat.placement import Placement
 from onyx.server.query_and_chat.streaming_models import AgentResponseDelta
 from onyx.server.query_and_chat.streaming_models import AgentResponseStart
@@ -45,7 +49,7 @@ from onyx.tools.models import ToolCallKickoff
 from onyx.tracing.framework.create import generation_span
 from onyx.utils.b64 import get_image_type_from_bytes
 from onyx.utils.logger import setup_logger
-
+from onyx.utils.text_processing import find_all_json_objects
 
 logger = setup_logger()
 
@@ -134,12 +138,11 @@ def _format_message_history_for_logging(
 
     separator = "================================================"
 
-    # Handle string input
-    if isinstance(message_history, str):
-        formatted_lines.append("Message [string]:")
-        formatted_lines.append(separator)
-        formatted_lines.append(f"{message_history}")
-        return "\n".join(formatted_lines)
+    # Handle single ChatCompletionMessage - wrap in list for uniform processing
+    if isinstance(
+        message_history, (SystemMessage, UserMessage, AssistantMessage, ToolMessage)
+    ):
+        message_history = [message_history]
 
     # Handle sequence of messages
     for i, msg in enumerate(message_history):
@@ -209,7 +212,8 @@ def _update_tool_call_with_delta(
 
     if index not in tool_calls_in_progress:
         tool_calls_in_progress[index] = {
-            "id": None,
+            # Fallback ID in case the provider never sends one via deltas.
+            "id": f"fallback_{uuid.uuid4().hex}",
             "name": None,
             "arguments": "",
         }
@@ -275,8 +279,147 @@ def _extract_tool_call_kickoffs(
     return tool_calls
 
 
+def extract_tool_calls_from_response_text(
+    response_text: str | None,
+    tool_definitions: list[dict],
+    placement: Placement,
+) -> list[ToolCallKickoff]:
+    """Extract tool calls from LLM response text by matching JSON against tool definitions.
+
+    This is a fallback mechanism for when the LLM was expected to return tool calls
+    but didn't use the proper tool call format. It searches for JSON objects in the
+    response text that match the structure of available tools.
+
+    Args:
+        response_text: The LLM's text response to search for tool calls
+        tool_definitions: List of tool definitions to match against
+        placement: Placement information for the tool calls
+
+    Returns:
+        List of ToolCallKickoff objects for any matched tool calls
+    """
+    if not response_text or not tool_definitions:
+        return []
+
+    # Build a map of tool names to their definitions
+    tool_name_to_def: dict[str, dict] = {}
+    for tool_def in tool_definitions:
+        if tool_def.get("type") == "function" and "function" in tool_def:
+            func_def = tool_def["function"]
+            tool_name = func_def.get("name")
+            if tool_name:
+                tool_name_to_def[tool_name] = func_def
+
+    if not tool_name_to_def:
+        return []
+
+    # Find all JSON objects in the response text
+    json_objects = find_all_json_objects(response_text)
+
+    tool_calls: list[ToolCallKickoff] = []
+    tab_index = 0
+
+    for json_obj in json_objects:
+        matched_tool_call = _try_match_json_to_tool(json_obj, tool_name_to_def)
+        if matched_tool_call:
+            tool_name, tool_args = matched_tool_call
+            tool_calls.append(
+                ToolCallKickoff(
+                    tool_call_id=f"extracted_{uuid.uuid4().hex[:8]}",
+                    tool_name=tool_name,
+                    tool_args=tool_args,
+                    placement=Placement(
+                        turn_index=placement.turn_index,
+                        tab_index=tab_index,
+                        sub_turn_index=placement.sub_turn_index,
+                    ),
+                )
+            )
+            tab_index += 1
+
+    logger.info(
+        f"Extracted {len(tool_calls)} tool call(s) from response text as fallback"
+    )
+
+    return tool_calls
+
+
+def _try_match_json_to_tool(
+    json_obj: dict[str, Any],
+    tool_name_to_def: dict[str, dict],
+) -> tuple[str, dict[str, Any]] | None:
+    """Try to match a JSON object to a tool definition.
+
+    Supports several formats:
+    1. Direct tool call format: {"name": "tool_name", "arguments": {...}}
+    2. Function call format: {"function": {"name": "tool_name", "arguments": {...}}}
+    3. Tool name as key: {"tool_name": {...arguments...}}
+    4. Arguments matching a tool's parameter schema
+
+    Args:
+        json_obj: The JSON object to match
+        tool_name_to_def: Map of tool names to their function definitions
+
+    Returns:
+        Tuple of (tool_name, tool_args) if matched, None otherwise
+    """
+    # Format 1: Direct tool call format {"name": "...", "arguments": {...}}
+    if "name" in json_obj and json_obj["name"] in tool_name_to_def:
+        tool_name = json_obj["name"]
+        arguments = json_obj.get("arguments", json_obj.get("parameters", {}))
+        if isinstance(arguments, str):
+            try:
+                arguments = json.loads(arguments)
+            except json.JSONDecodeError:
+                arguments = {}
+        if isinstance(arguments, dict):
+            return (tool_name, arguments)
+
+    # Format 2: Function call format {"function": {"name": "...", "arguments": {...}}}
+    if "function" in json_obj and isinstance(json_obj["function"], dict):
+        func_obj = json_obj["function"]
+        if "name" in func_obj and func_obj["name"] in tool_name_to_def:
+            tool_name = func_obj["name"]
+            arguments = func_obj.get("arguments", func_obj.get("parameters", {}))
+            if isinstance(arguments, str):
+                try:
+                    arguments = json.loads(arguments)
+                except json.JSONDecodeError:
+                    arguments = {}
+            if isinstance(arguments, dict):
+                return (tool_name, arguments)
+
+    # Format 3: Tool name as key {"tool_name": {...arguments...}}
+    for tool_name in tool_name_to_def:
+        if tool_name in json_obj:
+            arguments = json_obj[tool_name]
+            if isinstance(arguments, dict):
+                return (tool_name, arguments)
+
+    # Format 4: Check if the JSON object matches a tool's parameter schema
+    for tool_name, func_def in tool_name_to_def.items():
+        params = func_def.get("parameters", {})
+        properties = params.get("properties", {})
+        required = params.get("required", [])
+
+        if not properties:
+            continue
+
+        # Check if all required parameters are present (empty required = all optional)
+        if all(req in json_obj for req in required):
+            # Check if any of the tool's properties are in the JSON object
+            matching_props = [prop for prop in properties if prop in json_obj]
+            if matching_props:
+                # Filter to only include known properties
+                filtered_args = {k: v for k, v in json_obj.items() if k in properties}
+                return (tool_name, filtered_args)
+
+    return None
+
+
 def translate_history_to_llm_format(
     history: list[ChatMessageSimple],
+    llm_config: LLMConfig,
 ) -> LanguageModelInput:
     """Convert a list of ChatMessageSimple to LanguageModelInput format.
 
@@ -284,8 +427,23 @@ def translate_history_to_llm_format(
     handling different message types and image files for multimodal support.
     """
     messages: list[ChatCompletionMessage] = []
+    last_cacheable_msg_idx = -1
+    all_previous_msgs_cacheable = True
+
+    for idx, msg in enumerate(history):
+        # if the message is being added to the history
+        if msg.message_type in [
+            MessageType.SYSTEM,
+            MessageType.USER,
+            MessageType.ASSISTANT,
+            MessageType.TOOL_CALL_RESPONSE,
+        ]:
+            all_previous_msgs_cacheable = (
+                all_previous_msgs_cacheable and msg.should_cache
+            )
+            if all_previous_msgs_cacheable:
+                last_cacheable_msg_idx = idx
 
-    for msg in history:
         if msg.message_type == MessageType.SYSTEM:
             system_msg = SystemMessage(
                 role="system",
@@ -395,7 +553,7 @@ def translate_history_to_llm_format(
             assistant_msg_with_tool = AssistantMessage(
                 role="assistant",
                 content=None,  # The tool call is parsed, doesn't need to be duplicated in the content
-                tool_calls=tool_calls if tool_calls else None,
+                tool_calls=tool_calls or None,
             )
             messages.append(assistant_msg_with_tool)
 
@@ -417,6 +575,18 @@ def translate_history_to_llm_format(
                 f"Unknown message type {msg.message_type} in history. Skipping message."
             )
 
+    # prompt caching: rely on should_cache in ChatMessageSimple to
+    # pick the split point for the cacheable prefix and suffix
+    if last_cacheable_msg_idx != -1:
+        processed_messages, _ = process_with_prompt_cache(
+            llm_config=llm_config,
+            cacheable_prefix=messages[: last_cacheable_msg_idx + 1],
+            suffix=messages[last_cacheable_msg_idx + 1 :],
+            continuation=False,
+        )
+        assert isinstance(processed_messages, list)  # for mypy
+        messages = processed_messages
+
     return messages
 
 
@@ -429,6 +599,10 @@ def _increment_turns(
         return turn_index, sub_turn_index + 1
 
 
+def _delta_has_action(delta: Delta) -> bool:
+    return bool(delta.content or delta.reasoning_content or delta.tool_calls)
+
+
 def run_llm_step_pkt_generator(
     history: list[ChatMessageSimple],
     tool_definitions: list[dict],
@@ -499,7 +673,7 @@ def run_llm_step_pkt_generator(
     tab_index = placement.tab_index
     sub_turn_index = placement.sub_turn_index
 
-    llm_msg_history = translate_history_to_llm_format(history)
+    llm_msg_history = translate_history_to_llm_format(history, llm.config)
     has_reasoned = 0
 
     # Uncomment the line below to log the entire message history to the console
@@ -526,6 +700,8 @@ def run_llm_step_pkt_generator(
         span_generation.span_data.input = cast(
             Sequence[Mapping[str, Any]], llm_msg_history
         )
+        stream_start_time = time.monotonic()
+        first_action_recorded = False
         for packet in llm.stream(
             prompt=llm_msg_history,
             tools=tool_definitions,
@@ -543,8 +719,26 @@ def run_llm_step_pkt_generator(
                     "cache_read_input_tokens": usage.cache_read_input_tokens,
                     "cache_creation_input_tokens": usage.cache_creation_input_tokens,
                 }
+                # Note: LLM cost tracking is now handled in multi_llm.py
             delta = packet.choice.delta
 
+            # Weird behavior from some model providers, just log and ignore for now
+            if (
+                delta.content is None
+                and delta.reasoning_content is None
+                and delta.tool_calls is None
+            ):
+                logger.warning(
+                    f"LLM packet is empty (no contents, reasoning or tool calls). Skipping: {packet}"
+                )
+                continue
+
+            if not first_action_recorded and _delta_has_action(delta):
+                span_generation.span_data.time_to_first_action_seconds = (
+                    time.monotonic() - stream_start_time
+                )
+                first_action_recorded = True
+
             if custom_token_processor:
                 # The custom token processor can modify the deltas for specific custom logic
                 # It can also return a state so that it can handle aggregated delta logic etc.
@@ -702,6 +896,15 @@ def run_llm_step_pkt_generator(
         # Flush custom token processor to get any final tool calls
         if custom_token_processor:
             flush_delta, processor_state = custom_token_processor(None, processor_state)
+            if (
+                not first_action_recorded
+                and flush_delta is not None
+                and _delta_has_action(flush_delta)
+            ):
+                span_generation.span_data.time_to_first_action_seconds = (
+                    time.monotonic() - stream_start_time
+                )
+                first_action_recorded = True
             if flush_delta and flush_delta.tool_calls:
                 for tool_call_delta in flush_delta.tool_calls:
                     _update_tool_call_with_delta(id_to_tool_call_map, tool_call_delta)
@@ -789,14 +992,14 @@ def run_llm_step_pkt_generator(
         logger.debug(f"Accumulated reasoning: {accumulated_reasoning}")
         logger.debug(f"Accumulated answer: {accumulated_answer}")
 
-    if tool_calls:
-        tool_calls_str = "\n".join(
-            f"  - {tc.tool_name}: {json.dumps(tc.tool_args, indent=4)}"
-            for tc in tool_calls
-        )
-        logger.debug(f"Tool calls:\n{tool_calls_str}")
-    else:
-        logger.debug("Tool calls: []")
+        if tool_calls:
+            tool_calls_str = "\n".join(
+                f"  - {tc.tool_name}: {json.dumps(tc.tool_args, indent=4)}"
+                for tc in tool_calls
+            )
+            logger.debug(f"Tool calls:\n{tool_calls_str}")
+        else:
+            logger.debug("Tool calls: []")
 
     return (
         LlmStepResult(

backend/onyx/chat/models.py

@@ -1,45 +1,25 @@
 from collections.abc import Callable
 from collections.abc import Iterator
-from datetime import datetime
 from enum import Enum
 from typing import Any
+from uuid import UUID
 
 from pydantic import BaseModel
 from pydantic import Field
 
-from onyx.configs.constants import DocumentSource
 from onyx.configs.constants import MessageType
-from onyx.context.search.enums import QueryFlow
-from onyx.context.search.enums import RecencyBiasSetting
 from onyx.context.search.enums import SearchType
 from onyx.context.search.models import SearchDoc
 from onyx.file_store.models import FileDescriptor
 from onyx.file_store.models import InMemoryChatFile
 from onyx.server.query_and_chat.streaming_models import CitationInfo
+from onyx.server.query_and_chat.streaming_models import GeneratedImage
 from onyx.server.query_and_chat.streaming_models import Packet
+from onyx.tools.models import SearchToolUsage
 from onyx.tools.models import ToolCallKickoff
 from onyx.tools.tool_implementations.custom.base_tool_types import ToolResultType
 
 
-# First chunk of info for streaming QA
-class QADocsResponse(BaseModel):
-    top_documents: list[SearchDoc]
-    rephrased_query: str | None = None
-    predicted_flow: QueryFlow | None
-    predicted_search: SearchType | None
-    applied_source_filters: list[DocumentSource] | None
-    applied_time_cutoff: datetime | None
-    recency_bias_multiplier: float
-
-    def model_dump(self, *args: list, **kwargs: dict[str, Any]) -> dict[str, Any]:  # type: ignore
-        initial_dict = super().model_dump(mode="json", *args, **kwargs)  # type: ignore
-        initial_dict["applied_time_cutoff"] = (
-            self.applied_time_cutoff.isoformat() if self.applied_time_cutoff else None
-        )
-
-        return initial_dict
-
-
 class StreamStopReason(Enum):
     CONTEXT_LENGTH = "context_length"
     CANCELLED = "cancelled"
@@ -67,22 +47,11 @@ class UserKnowledgeFilePacket(BaseModel):
     user_files: list[FileDescriptor]
 
 
-class LLMRelevanceFilterResponse(BaseModel):
-    llm_selected_doc_indices: list[int]
-
-
 class RelevanceAnalysis(BaseModel):
     relevant: bool
     content: str | None = None
 
 
-class SectionRelevancePiece(RelevanceAnalysis):
-    """LLM analysis mapped to an Inference Section"""
-
-    document_id: str
-    chunk_id: int  # ID of the center chunk for a given inference section
-
-
 class DocumentRelevance(BaseModel):
     """Contains all relevance information for a given search"""
 
@@ -113,12 +82,6 @@ class OnyxAnswer(BaseModel):
     answer: str | None
 
 
-class ThreadMessage(BaseModel):
-    message: str
-    sender: str | None = None
-    role: MessageType = MessageType.USER
-
-
 class FileChatDisplay(BaseModel):
     file_ids: list[str]
 
@@ -132,6 +95,13 @@ class ToolConfig(BaseModel):
     id: int
 
 
+class ProjectSearchConfig(BaseModel):
+    """Configuration for search tool availability in project context."""
+
+    search_usage: SearchToolUsage
+    disable_forced_tool: bool
+
+
 class PromptOverrideConfig(BaseModel):
     name: str
     description: str = ""
@@ -148,7 +118,6 @@ class PersonaOverrideConfig(BaseModel):
     num_chunks: float | None = None
     llm_relevance_filter: bool = False
     llm_filter_extraction: bool = False
-    recency_bias: RecencyBiasSetting = RecencyBiasSetting.AUTO
     llm_model_provider_override: str | None = None
     llm_model_version_override: str | None = None
 
@@ -171,6 +140,10 @@ AnswerQuestionPossibleReturn = (
 )
 
 
+class CreateChatSessionID(BaseModel):
+    chat_session_id: UUID
+
+
 AnswerQuestionStreamReturn = Iterator[AnswerQuestionPossibleReturn]
 
 
@@ -181,12 +154,14 @@ class LLMMetricsContainer(BaseModel):
 
 StreamProcessor = Callable[[Iterator[str]], AnswerQuestionStreamReturn]
 
+
 AnswerStreamPart = (
     Packet
     | StreamStopInfo
     | MessageResponseIDInfo
     | StreamingError
     | UserKnowledgeFilePacket
+    | CreateChatSessionID
 )
 
 AnswerStream = Iterator[AnswerStreamPart]
@@ -204,6 +179,37 @@ class ChatBasicResponse(BaseModel):
     citation_info: list[CitationInfo]
 
 
+class ToolCallResponse(BaseModel):
+    """Tool call with full details for non-streaming response."""
+
+    tool_name: str
+    tool_arguments: dict[str, Any]
+    tool_result: str
+    search_docs: list[SearchDoc] | None = None
+    generated_images: list[GeneratedImage] | None = None
+    # Reasoning that led to the tool call
+    pre_reasoning: str | None = None
+
+
+class ChatFullResponse(BaseModel):
+    """Complete non-streaming response with all available data."""
+
+    # Core response fields
+    answer: str
+    answer_citationless: str
+    pre_answer_reasoning: str | None = None
+    tool_calls: list[ToolCallResponse] = []
+
+    # Documents & citations
+    top_documents: list[SearchDoc]
+    citation_info: list[CitationInfo]
+
+    # Metadata
+    message_id: int
+    chat_session_id: UUID | None = None
+    error_msg: str | None = None
+
+
 class ChatLoadedFile(InMemoryChatFile):
     content_text: str | None
     token_count: int
@@ -217,6 +223,12 @@ class ChatMessageSimple(BaseModel):
     image_files: list[ChatLoadedFile] | None = None
     # Only for TOOL_CALL_RESPONSE type messages
     tool_call_id: str | None = None
+    # The last message for which this is true
+    # AND is true for all previous messages
+    # (counting from the start of the history)
+    # represents the end of the cacheable prefix
+    # used for prompt caching
+    should_cache: bool = False
 
 
 class ProjectFileMetadata(BaseModel):
@@ -234,6 +246,8 @@ class ExtractedProjectFiles(BaseModel):
     total_token_count: int
     # Metadata for project files to enable citations
     project_file_metadata: list[ProjectFileMetadata]
+    # None if not a project
+    project_uncapped_token_count: int | None
 
 
 class LlmStepResult(BaseModel):

backend/onyx/chat/process_message.py

@@ -1,15 +1,22 @@
+"""
+IMPORTANT: familiarize yourself with the design concepts prior to contributing to this file.
+An overview can be found in the README.md file in this directory.
+"""
+
 import re
 import traceback
 from collections.abc import Callable
-from collections.abc import Iterator
 from uuid import UUID
 
+from redis.client import Redis
 from sqlalchemy.orm import Session
 
+from onyx.chat.chat_processing_checker import set_processing_status
 from onyx.chat.chat_state import ChatStateContainer
 from onyx.chat.chat_state import run_chat_loop_with_state_containers
 from onyx.chat.chat_utils import convert_chat_history
 from onyx.chat.chat_utils import create_chat_history_chain
+from onyx.chat.chat_utils import create_chat_session_from_request
 from onyx.chat.chat_utils import get_custom_agent_prompt
 from onyx.chat.chat_utils import is_last_assistant_message_clarification
 from onyx.chat.chat_utils import load_all_chat_files
@@ -17,37 +24,40 @@ from onyx.chat.emitter import get_default_emitter
 from onyx.chat.llm_loop import run_llm_loop
 from onyx.chat.models import AnswerStream
 from onyx.chat.models import ChatBasicResponse
+from onyx.chat.models import ChatFullResponse
 from onyx.chat.models import ChatLoadedFile
+from onyx.chat.models import CreateChatSessionID
 from onyx.chat.models import ExtractedProjectFiles
 from onyx.chat.models import MessageResponseIDInfo
 from onyx.chat.models import ProjectFileMetadata
+from onyx.chat.models import ProjectSearchConfig
 from onyx.chat.models import StreamingError
+from onyx.chat.models import ToolCallResponse
 from onyx.chat.prompt_utils import calculate_reserved_tokens
 from onyx.chat.save_chat import save_chat_turn
 from onyx.chat.stop_signal_checker import is_connected as check_stop_signal
 from onyx.chat.stop_signal_checker import reset_cancel_status
-from onyx.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
-from onyx.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from onyx.configs.constants import DEFAULT_PERSONA_ID
+from onyx.configs.constants import DocumentSource
 from onyx.configs.constants import MessageType
 from onyx.configs.constants import MilestoneRecordType
+from onyx.context.search.models import BaseFilters
 from onyx.context.search.models import CitationDocInfo
 from onyx.context.search.models import SearchDoc
 from onyx.db.chat import create_new_chat_message
-from onyx.db.chat import get_chat_message
 from onyx.db.chat import get_chat_session_by_id
 from onyx.db.chat import get_or_create_root_message
 from onyx.db.chat import reserve_message_id
-from onyx.db.engine.sql_engine import get_session_with_current_tenant
 from onyx.db.memory import get_memories
 from onyx.db.models import ChatMessage
+from onyx.db.models import ChatSession
+from onyx.db.models import Persona
 from onyx.db.models import User
 from onyx.db.projects import get_project_token_count
 from onyx.db.projects import get_user_files_from_project
 from onyx.db.tools import get_tools
 from onyx.deep_research.dr_loop import run_deep_research_llm_loop
 from onyx.file_store.models import ChatFileType
-from onyx.file_store.models import FileDescriptor
 from onyx.file_store.utils import load_in_memory_chat_files
 from onyx.file_store.utils import verify_user_files
 from onyx.llm.factory import get_llm_for_persona
@@ -57,35 +67,45 @@ from onyx.llm.interfaces import LLMUserIdentity
 from onyx.llm.utils import litellm_exception_to_error_msg
 from onyx.onyxbot.slack.models import SlackContext
 from onyx.redis.redis_pool import get_redis_client
+from onyx.server.query_and_chat.models import AUTO_PLACE_AFTER_LATEST_MESSAGE
 from onyx.server.query_and_chat.models import CreateChatMessageRequest
+from onyx.server.query_and_chat.models import OptionalSearchSetting
+from onyx.server.query_and_chat.models import SendMessageRequest
 from onyx.server.query_and_chat.streaming_models import AgentResponseDelta
 from onyx.server.query_and_chat.streaming_models import AgentResponseStart
 from onyx.server.query_and_chat.streaming_models import CitationInfo
 from onyx.server.query_and_chat.streaming_models import Packet
-from onyx.server.utils import get_json_line
+from onyx.server.usage_limits import check_llm_cost_limit_for_provider
 from onyx.tools.constants import SEARCH_TOOL_ID
 from onyx.tools.interface import Tool
+from onyx.tools.models import SearchToolUsage
 from onyx.tools.tool_constructor import construct_tools
 from onyx.tools.tool_constructor import CustomToolConfig
 from onyx.tools.tool_constructor import SearchToolConfig
-from onyx.tools.tool_constructor import SearchToolUsage
 from onyx.utils.logger import setup_logger
 from onyx.utils.long_term_log import LongTermLogger
 from onyx.utils.telemetry import mt_cloud_telemetry
 from onyx.utils.timing import log_function_time
-from onyx.utils.timing import log_generator_function_time
 from shared_configs.contextvars import get_current_tenant_id
 
 logger = setup_logger()
 ERROR_TYPE_CANCELLED = "cancelled"
 
 
-class ToolCallException(Exception):
-    """Exception raised for errors during tool calls."""
+def _should_enable_slack_search(
+    persona: Persona,
+    filters: BaseFilters | None,
+) -> bool:
+    """Determine if Slack search should be enabled.
 
-    def __init__(self, message: str, tool_name: str | None = None):
-        super().__init__(message)
-        self.tool_name = tool_name
+    Returns True if:
+    - Source type filter exists and includes Slack, OR
+    - Default persona with no source type filter
+    """
+    source_types = filters.source_type if filters else None
+    return (source_types is not None and DocumentSource.SLACK in source_types) or (
+        persona.id == DEFAULT_PERSONA_ID and source_types is None
+    )
 
 
 def _extract_project_file_texts_and_images(
@@ -126,6 +146,7 @@ def _extract_project_file_texts_and_images(
             project_as_filter=False,
             total_token_count=0,
             project_file_metadata=[],
+            project_uncapped_token_count=None,
         )
 
     max_actual_tokens = (
@@ -211,159 +232,123 @@ def _extract_project_file_texts_and_images(
         project_as_filter=project_as_filter,
         total_token_count=total_token_count,
         project_file_metadata=project_file_metadata,
+        project_uncapped_token_count=project_tokens,
     )
 
 
 def _get_project_search_availability(
     project_id: int | None,
     persona_id: int | None,
-    has_project_file_texts: bool,
-    forced_tool_ids: list[int] | None,
+    loaded_project_files: bool,
+    project_has_files: bool,
+    forced_tool_id: int | None,
     search_tool_id: int | None,
-) -> SearchToolUsage:
+) -> ProjectSearchConfig:
     """Determine search tool availability based on project context.
 
-    Args:
-        project_id: The project ID if the user is in a project
-        persona_id: The persona ID to check if it's the default persona
-        has_project_file_texts: Whether project files are loaded in context
-        forced_tool_ids: List of forced tool IDs (may be mutated to remove search tool)
-        search_tool_id: The search tool ID to check against
+    Search is disabled when ALL of the following are true:
+    - User is in a project
+    - Using the default persona (not a custom agent)
+    - Project files are already loaded in context
 
-    Returns:
-        SearchToolUsage setting indicating how search should be used
+    When search is disabled and the user tried to force the search tool,
+    that forcing is also disabled.
+
+    Returns AUTO (follow persona config) in all other cases.
     """
-    # There are cases where the internal search tool should be disabled
-    # If the user is in a project, it should not use other sources / generic search
-    # If they are in a project but using a custom agent, it should use the agent setup
-    # (which means it can use search)
-    # However if in a project and there are more files than can fit in the context,
-    # it should use the search tool with the project filter on
-    # If no files are uploaded, search should remain enabled
-    search_usage_forcing_setting = SearchToolUsage.AUTO
-    if project_id:
-        if bool(persona_id is DEFAULT_PERSONA_ID and has_project_file_texts):
-            search_usage_forcing_setting = SearchToolUsage.DISABLED
-            # Remove search tool from forced_tool_ids if it's present
-            if forced_tool_ids and search_tool_id and search_tool_id in forced_tool_ids:
-                forced_tool_ids[:] = [
-                    tool_id for tool_id in forced_tool_ids if tool_id != search_tool_id
-                ]
-        elif forced_tool_ids and search_tool_id and search_tool_id in forced_tool_ids:
-            search_usage_forcing_setting = SearchToolUsage.ENABLED
-    return search_usage_forcing_setting
-
-
-def _initialize_chat_session(
-    message_text: str,
-    files: list[FileDescriptor],
-    token_counter: Callable[[str], int],
-    parent_id: int | None,
-    user_id: UUID | None,
-    chat_session_id: UUID,
-    db_session: Session,
-    use_existing_user_message: bool = False,
-) -> ChatMessage:
-    root_message = get_or_create_root_message(
-        chat_session_id=chat_session_id, db_session=db_session
-    )
-
-    if parent_id is None:
-        parent_message = root_message
-    else:
-        parent_message = get_chat_message(
-            chat_message_id=parent_id,
-            user_id=user_id,
-            db_session=db_session,
+    # Not in a project, this should have no impact on search tool availability
+    if not project_id:
+        return ProjectSearchConfig(
+            search_usage=SearchToolUsage.AUTO, disable_forced_tool=False
         )
 
-    # For seeding, the parent message points to the message that is supposed to be the last
-    # user message.
-    if use_existing_user_message:
-        if parent_message.parent_message is None:
-            raise RuntimeError("No parent message found for seeding")
-        if parent_message.message_type != MessageType.USER:
-            raise RuntimeError(
-                "Parent message is not a user message, needed for seeded flow."
-            )
-        message_text = parent_message.message
-        token_count = parent_message.token_count
-        parent_message = parent_message.parent_message
-    else:
-        token_count = token_counter(message_text)
+    # Custom persona in project - let persona config decide
+    # Even if there are no files in the project, it's still guided by the persona config.
+    if persona_id != DEFAULT_PERSONA_ID:
+        return ProjectSearchConfig(
+            search_usage=SearchToolUsage.AUTO, disable_forced_tool=False
+        )
 
-    # Flushed for ID but not committed yet
-    user_message = create_new_chat_message(
-        chat_session_id=chat_session_id,
-        parent_message=parent_message,
-        message=message_text,
-        token_count=token_count,
-        message_type=MessageType.USER,
-        files=files,
-        db_session=db_session,
-        commit=False,
+    # If in a project with the default persona and the files have been already loaded into the context or
+    # there are no files in the project, disable search as there is nothing to search for.
+    if loaded_project_files or not project_has_files:
+        user_forced_search = (
+            forced_tool_id is not None
+            and search_tool_id is not None
+            and forced_tool_id == search_tool_id
+        )
+        return ProjectSearchConfig(
+            search_usage=SearchToolUsage.DISABLED,
+            disable_forced_tool=user_forced_search,
+        )
+
+    # Default persona in a project with files, but also the files have not been loaded into the context already.
+    return ProjectSearchConfig(
+        search_usage=SearchToolUsage.ENABLED, disable_forced_tool=False
     )
-    return user_message
 
 
-def stream_chat_message_objects(
-    new_msg_req: CreateChatMessageRequest,
+def handle_stream_message_objects(
+    new_msg_req: SendMessageRequest,
     user: User | None,
     db_session: Session,
-    # Needed to translate persona num_chunks to tokens to the LLM
-    default_num_chunks: float = MAX_CHUNKS_FED_TO_CHAT,
-    # For flow with search, don't include as many chunks as possible since we need to leave space
-    # for the chat history, for smaller models, we likely won't get MAX_CHUNKS_FED_TO_CHAT chunks
-    max_document_percentage: float = CHAT_TARGET_CHUNK_PERCENTAGE,
     # if specified, uses the last user message and does not create a new user message based
     # on the `new_msg_req.message`. Currently, requires a state where the last message is a
     litellm_additional_headers: dict[str, str] | None = None,
     custom_tool_additional_headers: dict[str, str] | None = None,
-    is_connected: Callable[[], bool] | None = None,
-    enforce_chat_session_id_for_search_docs: bool = True,
+    mcp_headers: dict[str, str] | None = None,
     bypass_acl: bool = False,
     # Additional context that should be included in the chat history, for example:
     # Slack threads where the conversation cannot be represented by a chain of User/Assistant
-    # messages.
+    # messages. Both of the below are used for Slack
     # NOTE: is not stored in the database, only passed in to the LLM as context
     additional_context: str | None = None,
     # Slack context for federated Slack search
     slack_context: SlackContext | None = None,
+    # Optional external state container for non-streaming access to accumulated state
+    external_state_container: ChatStateContainer | None = None,
 ) -> AnswerStream:
     tenant_id = get_current_tenant_id()
-    use_existing_user_message = new_msg_req.use_existing_user_message
 
     llm: LLM | None = None
+    chat_session: ChatSession | None = None
+    redis_client: Redis | None = None
 
+    user_id = user.id if user is not None else None
+    llm_user_identifier = (
+        user.email
+        if user is not None and getattr(user, "email", None)
+        else (str(user_id) if user_id else "anonymous_user")
+    )
     try:
-        user_id = user.id if user is not None else None
-        llm_user_identifier = (
-            user.email
-            if user is not None and getattr(user, "email", None)
-            else (str(user_id) if user_id else "anonymous_user")
-        )
+        if not new_msg_req.chat_session_id:
+            if not new_msg_req.chat_session_info:
+                raise RuntimeError(
+                    "Must specify a chat session id or chat session info"
+                )
+            chat_session = create_chat_session_from_request(
+                chat_session_request=new_msg_req.chat_session_info,
+                user_id=user_id,
+                db_session=db_session,
+            )
+            yield CreateChatSessionID(chat_session_id=chat_session.id)
+        else:
+            chat_session = get_chat_session_by_id(
+                chat_session_id=new_msg_req.chat_session_id,
+                user_id=user_id,
+                db_session=db_session,
+            )
 
-        chat_session = get_chat_session_by_id(
-            chat_session_id=new_msg_req.chat_session_id,
-            user_id=user_id,
-            db_session=db_session,
-        )
         persona = chat_session.persona
 
         message_text = new_msg_req.message
-        chat_session_id = new_msg_req.chat_session_id
         user_identity = LLMUserIdentity(
-            user_id=llm_user_identifier, session_id=str(chat_session_id)
+            user_id=llm_user_identifier, session_id=str(chat_session.id)
         )
-        parent_id = new_msg_req.parent_message_id
-        reference_doc_ids = new_msg_req.search_doc_ids
-        retrieval_options = new_msg_req.retrieval_options
-        new_msg_req.alternate_assistant_id
-        user_selected_filters = retrieval_options.filters if retrieval_options else None
 
         # permanent "log" store, used primarily for debugging
         long_term_logger = LongTermLogger(
-            metadata={"user_id": str(user_id), "chat_session_id": str(chat_session_id)}
+            metadata={"user_id": str(user_id), "chat_session_id": str(chat_session.id)}
         )
 
         # Milestone tracking, most devs using the API don't need to understand this
@@ -373,10 +358,22 @@ def stream_chat_message_objects(
             event=MilestoneRecordType.MULTIPLE_ASSISTANTS,
         )
 
-        if reference_doc_ids is None and retrieval_options is None:
-            raise RuntimeError(
-                "Must specify a set of documents for chat or specify search options"
-            )
+        mt_cloud_telemetry(
+            tenant_id=tenant_id,
+            distinct_id=(
+                user.email
+                if user and not getattr(user, "is_anonymous", False)
+                else tenant_id
+            ),
+            event=MilestoneRecordType.USER_MESSAGE_SENT,
+            properties={
+                "origin": new_msg_req.origin.value,
+                "has_files": len(new_msg_req.file_descriptors) > 0,
+                "has_project": chat_session.project_id is not None,
+                "has_persona": persona is not None and persona.id != DEFAULT_PERSONA_ID,
+                "deep_research": new_msg_req.deep_research,
+            },
+        )
 
         llm = get_llm_for_persona(
             persona=persona,
@@ -387,6 +384,14 @@ def stream_chat_message_objects(
         )
         token_counter = get_llm_token_counter(llm)
 
+        # Check LLM cost limits before using the LLM (only for Onyx-managed keys)
+
+        check_llm_cost_limit_for_provider(
+            db_session=db_session,
+            tenant_id=tenant_id,
+            llm_provider_api_key=llm.config.api_key,
+        )
+
         # Verify that the user specified files actually belong to the user
         verify_user_files(
             user_files=new_msg_req.file_descriptors,
@@ -395,35 +400,61 @@ def stream_chat_message_objects(
             project_id=chat_session.project_id,
         )
 
-        # Makes sure that the chat session has the right message nodes
-        # and that the latest user message is created (not yet committed)
-        user_message = _initialize_chat_session(
-            message_text=message_text,
-            files=new_msg_req.file_descriptors,
-            token_counter=token_counter,
-            parent_id=parent_id,
-            user_id=user_id,
-            chat_session_id=chat_session_id,
-            db_session=db_session,
-            use_existing_user_message=use_existing_user_message,
-        )
-
         # re-create linear history of messages
         chat_history = create_chat_history_chain(
-            chat_session_id=chat_session_id, db_session=db_session
+            chat_session_id=chat_session.id, db_session=db_session
         )
 
-        last_chat_message = chat_history[-1]
+        # Determine the parent message based on the request:
+        # - -1: auto-place after latest message in chain
+        # - None: regeneration from root (first message)
+        # - positive int: place after that specific parent message
+        root_message = get_or_create_root_message(
+            chat_session_id=chat_session.id, db_session=db_session
+        )
 
-        if last_chat_message.id != user_message.id:
-            db_session.rollback()
-            raise RuntimeError(
-                "The new message was not on the mainline. "
-                "Chat message history tree is not correctly built."
+        if new_msg_req.parent_message_id == AUTO_PLACE_AFTER_LATEST_MESSAGE:
+            # Auto-place after the latest message in the chain
+            parent_message = chat_history[-1] if chat_history else root_message
+        elif (
+            new_msg_req.parent_message_id is None
+            or new_msg_req.parent_message_id == root_message.id
+        ):
+            # None = regeneration from root
+            parent_message = root_message
+            # Truncate history since we're starting from root
+            chat_history = []
+        else:
+            # Specific parent message ID provided, find parent in chat_history
+            parent_message = None
+            for i in range(len(chat_history) - 1, -1, -1):
+                if chat_history[i].id == new_msg_req.parent_message_id:
+                    parent_message = chat_history[i]
+                    # Truncate history to only include messages up to and including parent
+                    chat_history = chat_history[: i + 1]
+                    break
+
+        if parent_message is None:
+            raise ValueError(
+                "The new message sent is not on the latest mainline of messages"
             )
 
-        # At this point we can save the user message as it's validated and final
-        db_session.commit()
+        # If the parent message is a user message, it's a regeneration and we use the existing user message.
+        if parent_message.message_type == MessageType.USER:
+            user_message = parent_message
+        else:
+            user_message = create_new_chat_message(
+                chat_session_id=chat_session.id,
+                parent_message=parent_message,
+                message=message_text,
+                token_count=token_counter(message_text),
+                message_type=MessageType.USER,
+                files=new_msg_req.file_descriptors,
+                db_session=db_session,
+                commit=True,
+            )
+
+            chat_history.append(user_message)
 
         memories = get_memories(user, db_session)
 
@@ -433,7 +464,7 @@ def stream_chat_message_objects(
             db_session=db_session,
             persona_system_prompt=custom_agent_prompt or "",
             token_counter=token_counter,
-            files=last_chat_message.files,
+            files=new_msg_req.file_descriptors,
             memories=memories,
         )
 
@@ -455,15 +486,20 @@ def stream_chat_message_objects(
             None,
         )
 
-        # This may also mutate the new_msg_req.forced_tool_ids
-        # This logic is specifically for projects
-        search_usage_forcing_setting = _get_project_search_availability(
+        # Determine if search should be disabled for this project context
+        forced_tool_id = new_msg_req.forced_tool_id
+        project_search_config = _get_project_search_availability(
             project_id=chat_session.project_id,
             persona_id=persona.id,
-            has_project_file_texts=bool(extracted_project_files.project_file_texts),
-            forced_tool_ids=new_msg_req.forced_tool_ids,
+            loaded_project_files=bool(extracted_project_files.project_file_texts),
+            project_has_files=bool(
+                extracted_project_files.project_uncapped_token_count
+            ),
+            forced_tool_id=new_msg_req.forced_tool_id,
             search_tool_id=search_tool_id,
         )
+        if project_search_config.disable_forced_tool:
+            forced_tool_id = None
 
         emitter = get_default_emitter()
 
@@ -475,7 +511,7 @@ def stream_chat_message_objects(
             user=user,
             llm=llm,
             search_tool_config=SearchToolConfig(
-                user_selected_filters=user_selected_filters,
+                user_selected_filters=new_msg_req.internal_search_filters,
                 project_id=(
                     chat_session.project_id
                     if extracted_project_files.project_as_filter
@@ -483,19 +519,26 @@ def stream_chat_message_objects(
                 ),
                 bypass_acl=bypass_acl,
                 slack_context=slack_context,
+                enable_slack_search=_should_enable_slack_search(
+                    persona, new_msg_req.internal_search_filters
+                ),
             ),
             custom_tool_config=CustomToolConfig(
-                chat_session_id=chat_session_id,
+                chat_session_id=chat_session.id,
                 message_id=user_message.id if user_message else None,
                 additional_headers=custom_tool_additional_headers,
+                mcp_headers=mcp_headers,
             ),
             allowed_tool_ids=new_msg_req.allowed_tool_ids,
-            search_usage_forcing_setting=search_usage_forcing_setting,
+            search_usage_forcing_setting=project_search_config.search_usage,
         )
         tools: list[Tool] = []
         for tool_list in tool_dict.values():
             tools.extend(tool_list)
 
+        if forced_tool_id and forced_tool_id not in [tool.id for tool in tools]:
+            raise ValueError(f"Forced tool {forced_tool_id} not found in tools")
+
         # TODO Once summarization is done, we don't need to load all the files from the beginning anymore.
         # load all files needed for this chat chain in memory
         files = load_all_chat_files(chat_history, db_session)
@@ -505,7 +548,7 @@ def stream_chat_message_objects(
         # Reserve a message id for the assistant response for frontend to track packets
         assistant_response = reserve_message_id(
             db_session=db_session,
-            chat_session_id=chat_session_id,
+            chat_session_id=chat_session.id,
             parent_message=user_message.id,
             message_type=MessageType.ASSISTANT,
         )
@@ -529,15 +572,33 @@ def stream_chat_message_objects(
         redis_client = get_redis_client()
 
         reset_cancel_status(
-            chat_session_id,
+            chat_session.id,
             redis_client,
         )
 
         def check_is_connected() -> bool:
-            return check_stop_signal(chat_session_id, redis_client)
+            return check_stop_signal(chat_session.id, redis_client)
 
-        # Create state container for accumulating partial results
-        state_container = ChatStateContainer()
+        set_processing_status(
+            chat_session_id=chat_session.id,
+            redis_client=redis_client,
+            value=True,
+        )
+
+        # Use external state container if provided, otherwise create internal one
+        # External container allows non-streaming callers to access accumulated state
+        state_container = external_state_container or ChatStateContainer()
+
+        def llm_loop_completion_callback(
+            state_container: ChatStateContainer,
+        ) -> None:
+            llm_loop_completion_handle(
+                state_container=state_container,
+                db_session=db_session,
+                chat_session_id=str(chat_session.id),
+                is_connected=check_is_connected,
+                assistant_message=assistant_response,
+            )
 
         # Run the LLM loop with explicit wrapper for stop signal handling
         # The wrapper runs run_llm_loop in a background thread and polls every 300ms
@@ -554,6 +615,7 @@ def stream_chat_message_objects(
 
             yield from run_chat_loop_with_state_containers(
                 run_deep_research_llm_loop,
+                llm_loop_completion_callback,
                 is_connected=check_is_connected,
                 emitter=emitter,
                 state_container=state_container,
@@ -565,11 +627,12 @@ def stream_chat_message_objects(
                 db_session=db_session,
                 skip_clarification=skip_clarification,
                 user_identity=user_identity,
-                chat_session_id=str(chat_session_id),
+                chat_session_id=str(chat_session.id),
             )
         else:
             yield from run_chat_loop_with_state_containers(
                 run_llm_loop,
+                llm_loop_completion_callback,
                 is_connected=check_is_connected,  # Not passed through to run_llm_loop
                 emitter=emitter,
                 state_container=state_container,
@@ -582,60 +645,12 @@ def stream_chat_message_objects(
                 llm=llm,
                 token_counter=token_counter,
                 db_session=db_session,
-                forced_tool_id=(
-                    new_msg_req.forced_tool_ids[0]
-                    if new_msg_req.forced_tool_ids
-                    else None
-                ),
+                forced_tool_id=forced_tool_id,
                 user_identity=user_identity,
-                chat_session_id=str(chat_session_id),
+                chat_session_id=str(chat_session.id),
+                include_citations=new_msg_req.include_citations,
             )
 
-        # Determine if stopped by user
-        completed_normally = check_is_connected()
-        if not completed_normally:
-            logger.debug(f"Chat session {chat_session_id} stopped by user")
-
-        # Build final answer based on completion status
-        if completed_normally:
-            if state_container.answer_tokens is None:
-                raise RuntimeError(
-                    "LLM run completed normally but did not return an answer."
-                )
-            final_answer = state_container.answer_tokens
-        else:
-            # Stopped by user - append stop message
-            if state_container.answer_tokens:
-                final_answer = (
-                    state_container.answer_tokens
-                    + " ... The generation was stopped by the user here."
-                )
-            else:
-                final_answer = "The generation was stopped by the user."
-
-        # Build citation_docs_info from accumulated citations in state container
-        citation_docs_info: list[CitationDocInfo] = []
-        seen_citation_nums: set[int] = set()
-        for citation_num, search_doc in state_container.citation_to_doc.items():
-            if citation_num not in seen_citation_nums:
-                seen_citation_nums.add(citation_num)
-                citation_docs_info.append(
-                    CitationDocInfo(
-                        search_doc=search_doc,
-                        citation_number=citation_num,
-                    )
-                )
-
-        save_chat_turn(
-            message_text=final_answer,
-            reasoning_tokens=state_container.reasoning_tokens,
-            citation_docs_info=citation_docs_info,
-            tool_calls=state_container.tool_calls,
-            db_session=db_session,
-            assistant_message=assistant_response,
-            is_clarification=state_container.is_clarification,
-        )
-
     except ValueError as e:
         logger.exception("Failed to process chat message.")
 
@@ -653,15 +668,7 @@ def stream_chat_message_objects(
         error_msg = str(e)
         stack_trace = traceback.format_exc()
 
-        if isinstance(e, ToolCallException):
-            yield StreamingError(
-                error=error_msg,
-                stack_trace=stack_trace,
-                error_code="TOOL_CALL_FAILED",
-                is_retryable=True,
-                details={"tool_name": e.tool_name} if e.tool_name else None,
-            )
-        elif llm:
+        if llm:
             client_error_msg, error_code, is_retryable = litellm_exception_to_error_msg(
                 e, llm
             )
@@ -693,26 +700,128 @@ def stream_chat_message_objects(
             )
 
         db_session.rollback()
-        return
+    finally:
+        try:
+            if redis_client is not None and chat_session is not None:
+                set_processing_status(
+                    chat_session_id=chat_session.id,
+                    redis_client=redis_client,
+                    value=False,
+                )
+        except Exception:
+            logger.exception("Error in setting processing status")
 
 
-@log_generator_function_time()
-def stream_chat_message(
+def llm_loop_completion_handle(
+    state_container: ChatStateContainer,
+    is_connected: Callable[[], bool],
+    db_session: Session,
+    chat_session_id: str,
+    assistant_message: ChatMessage,
+) -> None:
+    # Determine if stopped by user
+    completed_normally = is_connected()
+    # Build final answer based on completion status
+    if completed_normally:
+        if state_container.answer_tokens is None:
+            raise RuntimeError(
+                "LLM run completed normally but did not return an answer."
+            )
+        final_answer = state_container.answer_tokens
+    else:
+        # Stopped by user - append stop message
+        logger.debug(f"Chat session {chat_session_id} stopped by user")
+        if state_container.answer_tokens:
+            final_answer = (
+                state_container.answer_tokens
+                + " ... \n\nGeneration was stopped by the user."
+            )
+        else:
+            final_answer = "The generation was stopped by the user."
+
+    # Build citation_docs_info from accumulated citations in state container
+    citation_docs_info: list[CitationDocInfo] = []
+    seen_citation_nums: set[int] = set()
+    for citation_num, search_doc in state_container.citation_to_doc.items():
+        if citation_num not in seen_citation_nums:
+            seen_citation_nums.add(citation_num)
+            citation_docs_info.append(
+                CitationDocInfo(
+                    search_doc=search_doc,
+                    citation_number=citation_num,
+                )
+            )
+
+    save_chat_turn(
+        message_text=final_answer,
+        reasoning_tokens=state_container.reasoning_tokens,
+        citation_docs_info=citation_docs_info,
+        tool_calls=state_container.tool_calls,
+        db_session=db_session,
+        assistant_message=assistant_message,
+        is_clarification=state_container.is_clarification,
+    )
+
+
+def stream_chat_message_objects(
     new_msg_req: CreateChatMessageRequest,
     user: User | None,
+    db_session: Session,
+    # if specified, uses the last user message and does not create a new user message based
+    # on the `new_msg_req.message`. Currently, requires a state where the last message is a
     litellm_additional_headers: dict[str, str] | None = None,
     custom_tool_additional_headers: dict[str, str] | None = None,
-) -> Iterator[str]:
-    with get_session_with_current_tenant() as db_session:
-        objects = stream_chat_message_objects(
-            new_msg_req=new_msg_req,
-            user=user,
-            db_session=db_session,
-            litellm_additional_headers=litellm_additional_headers,
-            custom_tool_additional_headers=custom_tool_additional_headers,
+    bypass_acl: bool = False,
+    # Additional context that should be included in the chat history, for example:
+    # Slack threads where the conversation cannot be represented by a chain of User/Assistant
+    # messages. Both of the below are used for Slack
+    # NOTE: is not stored in the database, only passed in to the LLM as context
+    additional_context: str | None = None,
+    # Slack context for federated Slack search
+    slack_context: SlackContext | None = None,
+) -> AnswerStream:
+    forced_tool_id = (
+        new_msg_req.forced_tool_ids[0] if new_msg_req.forced_tool_ids else None
+    )
+    if (
+        new_msg_req.retrieval_options
+        and new_msg_req.retrieval_options.run_search == OptionalSearchSetting.ALWAYS
+    ):
+        all_tools = get_tools(db_session)
+
+        search_tool_id = next(
+            (tool.id for tool in all_tools if tool.in_code_tool_id == SEARCH_TOOL_ID),
+            None,
         )
-        for obj in objects:
-            yield get_json_line(obj.model_dump())
+        forced_tool_id = search_tool_id
+
+    translated_new_msg_req = SendMessageRequest(
+        message=new_msg_req.message,
+        llm_override=new_msg_req.llm_override,
+        allowed_tool_ids=new_msg_req.allowed_tool_ids,
+        forced_tool_id=forced_tool_id,
+        file_descriptors=new_msg_req.file_descriptors,
+        internal_search_filters=(
+            new_msg_req.retrieval_options.filters
+            if new_msg_req.retrieval_options
+            else None
+        ),
+        deep_research=new_msg_req.deep_research,
+        parent_message_id=new_msg_req.parent_message_id,
+        chat_session_id=new_msg_req.chat_session_id,
+        origin=new_msg_req.origin,
+        include_citations=new_msg_req.include_citations,
+    )
+    return handle_stream_message_objects(
+        new_msg_req=translated_new_msg_req,
+        user=user,
+        db_session=db_session,
+        litellm_additional_headers=litellm_additional_headers,
+        custom_tool_additional_headers=custom_tool_additional_headers,
+        bypass_acl=bypass_acl,
+        additional_context=additional_context,
+        slack_context=slack_context,
+    )
 
 
 def remove_answer_citations(answer: str) -> str:
@@ -767,3 +876,83 @@ def gather_stream(
         error_msg=error_msg,
         top_documents=top_documents,
     )
+
+
+@log_function_time()
+def gather_stream_full(
+    packets: AnswerStream,
+    state_container: ChatStateContainer,
+) -> ChatFullResponse:
+    """
+    Aggregate streaming packets and state container into a complete ChatFullResponse.
+
+    This function consumes all packets from the stream and combines them with
+    the accumulated state from the ChatStateContainer to build a complete response
+    including answer, reasoning, citations, and tool calls.
+
+    Args:
+        packets: The stream of packets from handle_stream_message_objects
+        state_container: The state container that accumulates tool calls, reasoning, etc.
+
+    Returns:
+        ChatFullResponse with all available data
+    """
+    answer: str | None = None
+    citations: list[CitationInfo] = []
+    error_msg: str | None = None
+    message_id: int | None = None
+    top_documents: list[SearchDoc] = []
+    chat_session_id: UUID | None = None
+
+    for packet in packets:
+        if isinstance(packet, Packet):
+            if isinstance(packet.obj, AgentResponseStart):
+                if packet.obj.final_documents:
+                    top_documents = packet.obj.final_documents
+            elif isinstance(packet.obj, AgentResponseDelta):
+                if answer is None:
+                    answer = ""
+                if packet.obj.content:
+                    answer += packet.obj.content
+            elif isinstance(packet.obj, CitationInfo):
+                citations.append(packet.obj)
+        elif isinstance(packet, StreamingError):
+            error_msg = packet.error
+        elif isinstance(packet, MessageResponseIDInfo):
+            message_id = packet.reserved_assistant_message_id
+        elif isinstance(packet, CreateChatSessionID):
+            chat_session_id = packet.chat_session_id
+
+    if message_id is None:
+        raise ValueError("Message ID is required")
+
+    # Use state_container for complete answer (handles edge cases gracefully)
+    final_answer = state_container.get_answer_tokens() or answer or ""
+
+    # Get reasoning from state container (None when model doesn't produce reasoning)
+    reasoning = state_container.get_reasoning_tokens()
+
+    # Convert ToolCallInfo list to ToolCallResponse list
+    tool_call_responses = [
+        ToolCallResponse(
+            tool_name=tc.tool_name,
+            tool_arguments=tc.tool_call_arguments,
+            tool_result=tc.tool_call_response,
+            search_docs=tc.search_docs,
+            generated_images=tc.generated_images,
+            pre_reasoning=tc.reasoning_tokens,
+        )
+        for tc in state_container.get_tool_calls()
+    ]
+
+    return ChatFullResponse(
+        answer=final_answer,
+        answer_citationless=remove_answer_citations(final_answer),
+        pre_answer_reasoning=reasoning,
+        tool_calls=tool_call_responses,
+        top_documents=top_documents,
+        citation_info=citations,
+        message_id=message_id,
+        chat_session_id=chat_session_id,
+        error_msg=error_msg,
+    )

backend/onyx/chat/prompt_utils.py

@@ -10,6 +10,7 @@ from onyx.file_store.models import FileDescriptor
 from onyx.prompts.chat_prompts import CITATION_REMINDER
 from onyx.prompts.chat_prompts import CODE_BLOCK_MARKDOWN
 from onyx.prompts.chat_prompts import DEFAULT_SYSTEM_PROMPT
+from onyx.prompts.chat_prompts import LAST_CYCLE_CITATION_REMINDER
 from onyx.prompts.chat_prompts import REQUIRE_CITATION_GUIDANCE
 from onyx.prompts.chat_prompts import USER_INFO_HEADER
 from onyx.prompts.prompt_utils import get_company_context
@@ -17,15 +18,18 @@ from onyx.prompts.prompt_utils import handle_onyx_date_awareness
 from onyx.prompts.prompt_utils import replace_citation_guidance_tag
 from onyx.prompts.tool_prompts import GENERATE_IMAGE_GUIDANCE
 from onyx.prompts.tool_prompts import INTERNAL_SEARCH_GUIDANCE
+from onyx.prompts.tool_prompts import MEMORY_GUIDANCE
 from onyx.prompts.tool_prompts import OPEN_URLS_GUIDANCE
 from onyx.prompts.tool_prompts import PYTHON_TOOL_GUIDANCE
 from onyx.prompts.tool_prompts import TOOL_DESCRIPTION_SEARCH_GUIDANCE
 from onyx.prompts.tool_prompts import TOOL_SECTION_HEADER
 from onyx.prompts.tool_prompts import WEB_SEARCH_GUIDANCE
+from onyx.prompts.tool_prompts import WEB_SEARCH_SITE_DISABLED_GUIDANCE
 from onyx.tools.interface import Tool
 from onyx.tools.tool_implementations.images.image_generation_tool import (
     ImageGenerationTool,
 )
+from onyx.tools.tool_implementations.memory.memory_tool import MemoryTool
 from onyx.tools.tool_implementations.open_url.open_url_tool import OpenURLTool
 from onyx.tools.tool_implementations.python.python_tool import PythonTool
 from onyx.tools.tool_implementations.search.search_tool import SearchTool
@@ -37,7 +41,7 @@ def get_default_base_system_prompt(db_session: Session) -> str:
     default_persona = get_default_behavior_persona(db_session)
     return (
         default_persona.system_prompt
-        if default_persona and default_persona.system_prompt
+        if default_persona and default_persona.system_prompt is not None
         else DEFAULT_SYSTEM_PROMPT
     )
 
@@ -115,8 +119,11 @@ def calculate_reserved_tokens(
 def build_reminder_message(
     reminder_text: str | None,
     include_citation_reminder: bool,
+    is_last_cycle: bool,
 ) -> str | None:
     reminder = reminder_text.strip() if reminder_text else ""
+    if is_last_cycle:
+        reminder += "\n\n" + LAST_CYCLE_CITATION_REMINDER
     if include_citation_reminder:
         reminder += "\n\n" + CITATION_REMINDER
     reminder = reminder.strip()
@@ -169,10 +176,13 @@ def build_system_prompt(
             TOOL_SECTION_HEADER
             + TOOL_DESCRIPTION_SEARCH_GUIDANCE
             + INTERNAL_SEARCH_GUIDANCE
-            + WEB_SEARCH_GUIDANCE
+            + WEB_SEARCH_GUIDANCE.format(
+                site_colon_disabled=WEB_SEARCH_SITE_DISABLED_GUIDANCE
+            )
             + OPEN_URLS_GUIDANCE
-            + GENERATE_IMAGE_GUIDANCE
             + PYTHON_TOOL_GUIDANCE
+            + GENERATE_IMAGE_GUIDANCE
+            + MEMORY_GUIDANCE
         )
         return system_prompt
 
@@ -186,6 +196,7 @@ def build_system_prompt(
         has_generate_image = any(
             isinstance(tool, ImageGenerationTool) for tool in tools
         )
+        has_memory = any(isinstance(tool, MemoryTool) for tool in tools)
 
         if has_web_search or has_internal_search or include_all_guidance:
             system_prompt += TOOL_DESCRIPTION_SEARCH_GUIDANCE
@@ -195,7 +206,16 @@ def build_system_prompt(
             system_prompt += INTERNAL_SEARCH_GUIDANCE
 
         if has_web_search or include_all_guidance:
-            system_prompt += WEB_SEARCH_GUIDANCE
+            site_disabled_guidance = ""
+            if has_web_search:
+                web_search_tool = next(
+                    (t for t in tools if isinstance(t, WebSearchTool)), None
+                )
+                if web_search_tool and not web_search_tool.supports_site_filter:
+                    site_disabled_guidance = WEB_SEARCH_SITE_DISABLED_GUIDANCE
+            system_prompt += WEB_SEARCH_GUIDANCE.format(
+                site_colon_disabled=site_disabled_guidance
+            )
 
         if has_open_urls or include_all_guidance:
             system_prompt += OPEN_URLS_GUIDANCE
@@ -206,4 +226,7 @@ def build_system_prompt(
         if has_generate_image or include_all_guidance:
             system_prompt += GENERATE_IMAGE_GUIDANCE
 
+        if has_memory or include_all_guidance:
+            system_prompt += MEMORY_GUIDANCE
+
     return system_prompt