k

backend/alembic/env.py

@@ -1,5 +1,5 @@
 from sqlalchemy.engine.base import Connection
-from typing import Literal
+from typing import Any
 import asyncio
 from logging.config import fileConfig
 import logging
@@ -8,7 +8,6 @@ from alembic import context
 from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 from sqlalchemy.sql import text
-from sqlalchemy.sql.schema import SchemaItem
 
 from shared_configs.configs import MULTI_TENANT
 from danswer.db.engine import build_connection_string
@@ -36,18 +35,7 @@ logger = logging.getLogger(__name__)
 
 
 def include_object(
-    object: SchemaItem,
-    name: str | None,
-    type_: Literal[
-        "schema",
-        "table",
-        "column",
-        "index",
-        "unique_constraint",
-        "foreign_key_constraint",
-    ],
-    reflected: bool,
-    compare_to: SchemaItem | None,
+    object: Any, name: str, type_: str, reflected: bool, compare_to: Any
 ) -> bool:
     """
     Determines whether a database object should be included in migrations.

backend/alembic/versions/9f696734098f_combine_search_and_chat.py

@@ -1,36 +0,0 @@
-"""Combine Search and Chat
-
-Revision ID: 9f696734098f
-Revises: a8c2065484e6
-Create Date: 2024-11-27 15:32:19.694972
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "9f696734098f"
-down_revision = "a8c2065484e6"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.alter_column("chat_session", "description", nullable=True)
-    op.drop_column("chat_session", "one_shot")
-    op.drop_column("slack_channel_config", "response_type")
-
-
-def downgrade() -> None:
-    op.execute("UPDATE chat_session SET description = '' WHERE description IS NULL")
-    op.alter_column("chat_session", "description", nullable=False)
-    op.add_column(
-        "chat_session",
-        sa.Column("one_shot", sa.Boolean(), nullable=False, server_default=sa.false()),
-    )
-    op.add_column(
-        "slack_channel_config",
-        sa.Column(
-            "response_type", sa.String(), nullable=False, server_default="citations"
-        ),
-    )

backend/alembic_tenants/env.py

@@ -1,6 +1,5 @@
 import asyncio
 from logging.config import fileConfig
-from typing import Literal
 
 from sqlalchemy import pool
 from sqlalchemy.engine import Connection
@@ -38,15 +37,8 @@ EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
 
 def include_object(
     object: SchemaItem,
-    name: str | None,
-    type_: Literal[
-        "schema",
-        "table",
-        "column",
-        "index",
-        "unique_constraint",
-        "foreign_key_constraint",
-    ],
+    name: str,
+    type_: str,
     reflected: bool,
     compare_to: SchemaItem | None,
 ) -> bool:

backend/danswer/access/models.py

@@ -18,11 +18,6 @@ class ExternalAccess:
 
 @dataclass(frozen=True)
 class DocExternalAccess:
-    """
-    This is just a class to wrap the external access and the document ID
-    together. It's used for syncing document permissions to Redis.
-    """
-
     external_access: ExternalAccess
     # The document ID
     doc_id: str

backend/danswer/agent_search/answer_query/graph_builder.py

@@ -1,100 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.answer_query.nodes.answer_check import answer_check
-from danswer.agent_search.answer_query.nodes.answer_generation import answer_generation
-from danswer.agent_search.answer_query.nodes.format_answer import format_answer
-from danswer.agent_search.answer_query.states import AnswerQueryInput
-from danswer.agent_search.answer_query.states import AnswerQueryOutput
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-
-
-def answer_query_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=AnswerQueryState,
-        input=AnswerQueryInput,
-        output=AnswerQueryOutput,
-    )
-
-    ### Add nodes ###
-
-    expanded_retrieval = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="expanded_retrieval_for_initial_decomp",
-        action=expanded_retrieval,
-    )
-    graph.add_node(
-        node="answer_check",
-        action=answer_check,
-    )
-    graph.add_node(
-        node="answer_generation",
-        action=answer_generation,
-    )
-    graph.add_node(
-        node="format_answer",
-        action=format_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(
-        start_key=START,
-        end_key="expanded_retrieval_for_initial_decomp",
-    )
-    graph.add_edge(
-        start_key="expanded_retrieval_for_initial_decomp",
-        end_key="answer_generation",
-    )
-    graph.add_edge(
-        start_key="answer_generation",
-        end_key="answer_check",
-    )
-    graph.add_edge(
-        start_key="answer_check",
-        end_key="format_answer",
-    )
-    graph.add_edge(
-        start_key="format_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = answer_query_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="Who made Excel and what other products did they make?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = AnswerQueryInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-            query_to_answer="Who made Excel?",
-        )
-        output = compiled_graph.invoke(
-            input=inputs,
-            # debug=True,
-            # subgraphs=True,
-        )
-        print(output)
-        # for namespace, chunk in compiled_graph.stream(
-        #     input=inputs,
-        #     # debug=True,
-        #     subgraphs=True,
-        # ):
-        #     print(namespace)
-        #     print(chunk)

backend/danswer/agent_search/answer_query/nodes/answer_check.py

@@ -1,30 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import QACheckOutput
-from danswer.agent_search.shared_graph_utils.prompts import BASE_CHECK_PROMPT
-
-
-def answer_check(state: AnswerQueryState) -> QACheckOutput:
-    msg = [
-        HumanMessage(
-            content=BASE_CHECK_PROMPT.format(
-                question=state["search_request"].query,
-                base_answer=state["answer"],
-            )
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    response_str = merge_message_runs(response, chunk_separator="")[0].content
-
-    return QACheckOutput(
-        answer_quality=response_str,
-    )

backend/danswer/agent_search/answer_query/nodes/answer_generation.py

@@ -1,32 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import QAGenerationOutput
-from danswer.agent_search.shared_graph_utils.prompts import BASE_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def answer_generation(state: AnswerQueryState) -> QAGenerationOutput:
-    query = state["query_to_answer"]
-    docs = state["reordered_documents"]
-
-    print(f"Number of verified retrieval docs: {len(docs)}")
-
-    msg = [
-        HumanMessage(
-            content=BASE_RAG_PROMPT.format(question=query, context=format_docs(docs))
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    answer_str = merge_message_runs(response, chunk_separator="")[0].content
-    return QAGenerationOutput(
-        answer=answer_str,
-    )

backend/danswer/agent_search/answer_query/nodes/format_answer.py

@@ -1,16 +0,0 @@
-from danswer.agent_search.answer_query.states import AnswerQueryOutput
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import SearchAnswerResults
-
-
-def format_answer(state: AnswerQueryState) -> AnswerQueryOutput:
-    return AnswerQueryOutput(
-        decomp_answer_results=[
-            SearchAnswerResults(
-                query=state["query_to_answer"],
-                quality=state["answer_quality"],
-                answer=state["answer"],
-                documents=state["reordered_documents"],
-            )
-        ],
-    )

backend/danswer/agent_search/answer_query/states.py

@@ -1,45 +0,0 @@
-from typing import Annotated
-from typing import TypedDict
-
-from pydantic import BaseModel
-
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class SearchAnswerResults(BaseModel):
-    query: str
-    answer: str
-    quality: str
-    documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class QACheckOutput(TypedDict, total=False):
-    answer_quality: str
-
-
-class QAGenerationOutput(TypedDict, total=False):
-    answer: str
-
-
-class ExpandedRetrievalOutput(TypedDict):
-    reordered_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class AnswerQueryState(
-    PrimaryState,
-    QACheckOutput,
-    QAGenerationOutput,
-    ExpandedRetrievalOutput,
-    total=True,
-):
-    query_to_answer: str
-
-
-class AnswerQueryInput(PrimaryState, total=True):
-    query_to_answer: str
-
-
-class AnswerQueryOutput(TypedDict):
-    decomp_answer_results: list[SearchAnswerResults]

backend/danswer/agent_search/core_state.py

@@ -1,15 +0,0 @@
-from typing import TypedDict
-
-from sqlalchemy.orm import Session
-
-from danswer.context.search.models import SearchRequest
-from danswer.llm.interfaces import LLM
-
-
-class PrimaryState(TypedDict, total=False):
-    search_request: SearchRequest
-    primary_llm: LLM
-    fast_llm: LLM
-    # a single session for the entire agent search
-    # is fine if we are only reading
-    db_session: Session

backend/danswer/agent_search/deep_answer/nodes/answer_generation.py

@@ -1,114 +0,0 @@
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import COMBINED_CONTEXT
-from danswer.agent_search.shared_graph_utils.prompts import MODIFIED_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-from danswer.agent_search.shared_graph_utils.utils import normalize_whitespace
-
-
-# aggregate sub questions and answers
-def deep_answer_generation(state: MainState) -> dict[str, Any]:
-    """
-    Generate answer
-
-    Args:
-        state (messages): The current state
-
-    Returns:
-         dict: The updated state with re-phrased question
-    """
-    print("---DEEP GENERATE---")
-
-    question = state["original_question"]
-    docs = state["deduped_retrieval_docs"]
-
-    deep_answer_context = state["core_answer_dynamic_context"]
-
-    print(f"Number of verified retrieval docs - deep: {len(docs)}")
-
-    combined_context = normalize_whitespace(
-        COMBINED_CONTEXT.format(
-            deep_answer_context=deep_answer_context, formated_docs=format_docs(docs)
-        )
-    )
-
-    msg = [
-        HumanMessage(
-            content=MODIFIED_RAG_PROMPT.format(
-                question=question, combined_context=combined_context
-            )
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    return {
-        "deep_answer": response.content,
-    }
-
-
-def final_stuff(state: MainState) -> dict[str, Any]:
-    """
-    Invokes the agent model to generate a response based on the current state. Given
-    the question, it will decide to retrieve using the retriever tool, or simply end.
-
-    Args:
-        state (messages): The current state
-
-    Returns:
-        dict: The updated state with the agent response appended to messages
-    """
-    print("---FINAL---")
-
-    messages = state["log_messages"]
-    time_ordered_messages = [x.pretty_repr() for x in messages]
-    time_ordered_messages.sort()
-
-    print("Message Log:")
-    print("\n".join(time_ordered_messages))
-
-    initial_sub_qas = state["initial_sub_qas"]
-    initial_sub_qa_list = []
-    for initial_sub_qa in initial_sub_qas:
-        if initial_sub_qa["sub_answer_check"] == "yes":
-            initial_sub_qa_list.append(
-                f'  Question:\n  {initial_sub_qa["sub_question"]}\n  --\n  Answer:\n  {initial_sub_qa["sub_answer"]}\n  -----'
-            )
-
-    initial_sub_qa_context = "\n".join(initial_sub_qa_list)
-
-    base_answer = state["base_answer"]
-
-    print(f"Final Base Answer:\n{base_answer}")
-    print("--------------------------------")
-    print(f"Initial Answered Sub Questions:\n{initial_sub_qa_context}")
-    print("--------------------------------")
-
-    if not state.get("deep_answer"):
-        print("No Deep Answer was required")
-        return {}
-
-    deep_answer = state["deep_answer"]
-    sub_qas = state["sub_qas"]
-    sub_qa_list = []
-    for sub_qa in sub_qas:
-        if sub_qa["sub_answer_check"] == "yes":
-            sub_qa_list.append(
-                f'  Question:\n  {sub_qa["sub_question"]}\n  --\n  Answer:\n  {sub_qa["sub_answer"]}\n  -----'
-            )
-
-    sub_qa_context = "\n".join(sub_qa_list)
-
-    print(f"Final Base Answer:\n{base_answer}")
-    print("--------------------------------")
-    print(f"Final Deep Answer:\n{deep_answer}")
-    print("--------------------------------")
-    print("Sub Questions and Answers:")
-    print(sub_qa_context)
-
-    return {}

backend/danswer/agent_search/deep_answer/nodes/deep_decomp.py

@@ -1,78 +0,0 @@
-import json
-import re
-from datetime import datetime
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import DEEP_DECOMPOSE_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_entity_term_extraction
-from danswer.agent_search.shared_graph_utils.utils import generate_log_message
-
-
-def decompose(state: MainState) -> dict[str, Any]:
-    """ """
-
-    node_start_time = datetime.now()
-
-    question = state["original_question"]
-    base_answer = state["base_answer"]
-
-    # get the entity term extraction dict and properly format it
-    entity_term_extraction_dict = state["retrieved_entities_relationships"][
-        "retrieved_entities_relationships"
-    ]
-
-    entity_term_extraction_str = format_entity_term_extraction(
-        entity_term_extraction_dict
-    )
-
-    initial_question_answers = state["initial_sub_qas"]
-
-    addressed_question_list = [
-        x["sub_question"]
-        for x in initial_question_answers
-        if x["sub_answer_check"] == "yes"
-    ]
-    failed_question_list = [
-        x["sub_question"]
-        for x in initial_question_answers
-        if x["sub_answer_check"] == "no"
-    ]
-
-    msg = [
-        HumanMessage(
-            content=DEEP_DECOMPOSE_PROMPT.format(
-                question=question,
-                entity_term_extraction_str=entity_term_extraction_str,
-                base_answer=base_answer,
-                answered_sub_questions="\n - ".join(addressed_question_list),
-                failed_sub_questions="\n - ".join(failed_question_list),
-            ),
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    cleaned_response = re.sub(r"```json\n|\n```", "", response.pretty_repr())
-    parsed_response = json.loads(cleaned_response)
-
-    sub_questions_dict = {}
-    for sub_question_nr, sub_question_dict in enumerate(
-        parsed_response["sub_questions"]
-    ):
-        sub_question_dict["answered"] = False
-        sub_question_dict["verified"] = False
-        sub_questions_dict[sub_question_nr] = sub_question_dict
-
-    return {
-        "decomposed_sub_questions_dict": sub_questions_dict,
-        "log_messages": generate_log_message(
-            message="deep - decompose",
-            node_start_time=node_start_time,
-            graph_start_time=state["graph_start_time"],
-        ),
-    }

backend/danswer/agent_search/deep_answer/nodes/entity_term_extraction.py

@@ -1,40 +0,0 @@
-import json
-import re
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import ENTITY_TERM_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def entity_term_extraction(state: MainState) -> dict[str, Any]:
-    """Extract entities and terms from the question and context"""
-
-    question = state["original_question"]
-    docs = state["deduped_retrieval_docs"]
-
-    doc_context = format_docs(docs)
-
-    msg = [
-        HumanMessage(
-            content=ENTITY_TERM_PROMPT.format(question=question, context=doc_context),
-        )
-    ]
-    fast_llm = state["fast_llm"]
-    # Grader
-    llm_response_list = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
-
-    cleaned_response = re.sub(r"```json\n|\n```", "", llm_response)
-    parsed_response = json.loads(cleaned_response)
-
-    return {
-        "retrieved_entities_relationships": parsed_response,
-    }

backend/danswer/agent_search/deep_answer/nodes/sub_qa_level_aggregator.py

@@ -1,30 +0,0 @@
-from typing import Any
-
-from danswer.agent_search.main.states import MainState
-
-
-# aggregate sub questions and answers
-def sub_qa_level_aggregator(state: MainState) -> dict[str, Any]:
-    sub_qas = state["sub_qas"]
-
-    dynamic_context_list = [
-        "Below you will find useful information to answer the original question:"
-    ]
-    checked_sub_qas = []
-
-    for core_answer_sub_qa in sub_qas:
-        question = core_answer_sub_qa["sub_question"]
-        answer = core_answer_sub_qa["sub_answer"]
-        verified = core_answer_sub_qa["sub_answer_check"]
-
-        if verified == "yes":
-            dynamic_context_list.append(
-                f"Question:\n{question}\n\nAnswer:\n{answer}\n\n---\n\n"
-            )
-            checked_sub_qas.append({"sub_question": question, "sub_answer": answer})
-    dynamic_context = "\n".join(dynamic_context_list)
-
-    return {
-        "core_answer_dynamic_context": dynamic_context,
-        "checked_sub_qas": checked_sub_qas,
-    }

backend/danswer/agent_search/deep_answer/nodes/sub_qa_manager.py

@@ -1,19 +0,0 @@
-from typing import Any
-
-from danswer.agent_search.main.states import MainState
-
-
-def sub_qa_manager(state: MainState) -> dict[str, Any]:
-    """ """
-
-    sub_questions_dict = state["decomposed_sub_questions_dict"]
-
-    sub_questions = {}
-
-    for sub_question_nr, sub_question_dict in sub_questions_dict.items():
-        sub_questions[sub_question_nr] = sub_question_dict["sub_question"]
-
-    return {
-        "sub_questions": sub_questions,
-        "num_new_question_iterations": 0,
-    }

backend/danswer/agent_search/expanded_retrieval/edges.py

@@ -1,44 +0,0 @@
-from collections.abc import Hashable
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-from langgraph.types import Send
-
-from danswer.agent_search.expanded_retrieval.nodes.doc_retrieval import RetrieveInput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalInput
-from danswer.agent_search.shared_graph_utils.prompts import REWRITE_PROMPT_MULTI
-from danswer.llm.interfaces import LLM
-
-
-def parallel_retrieval_edge(state: ExpandedRetrievalInput) -> list[Send | Hashable]:
-    print(f"parallel_retrieval_edge state: {state.keys()}")
-
-    # This should be better...
-    question = state.get("query_to_answer") or state["search_request"].query
-    llm: LLM = state["fast_llm"]
-
-    msg = [
-        HumanMessage(
-            content=REWRITE_PROMPT_MULTI.format(question=question),
-        )
-    ]
-    llm_response_list = list(
-        llm.stream(
-            prompt=msg,
-        )
-    )
-    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
-
-    print(f"llm_response: {llm_response}")
-
-    rewritten_queries = llm_response.split("\n")
-
-    print(f"rewritten_queries: {rewritten_queries}")
-
-    return [
-        Send(
-            "doc_retrieval",
-            RetrieveInput(query_to_retrieve=query, **state),
-        )
-        for query in rewritten_queries
-    ]

backend/danswer/agent_search/expanded_retrieval/graph_builder.py

@@ -1,88 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.expanded_retrieval.edges import parallel_retrieval_edge
-from danswer.agent_search.expanded_retrieval.nodes.doc_reranking import doc_reranking
-from danswer.agent_search.expanded_retrieval.nodes.doc_retrieval import doc_retrieval
-from danswer.agent_search.expanded_retrieval.nodes.doc_verification import (
-    doc_verification,
-)
-from danswer.agent_search.expanded_retrieval.nodes.verification_kickoff import (
-    verification_kickoff,
-)
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalInput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-
-def expanded_retrieval_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=ExpandedRetrievalState,
-        input=ExpandedRetrievalInput,
-        output=ExpandedRetrievalOutput,
-    )
-
-    ### Add nodes ###
-
-    graph.add_node(
-        node="doc_retrieval",
-        action=doc_retrieval,
-    )
-    graph.add_node(
-        node="verification_kickoff",
-        action=verification_kickoff,
-    )
-    graph.add_node(
-        node="doc_verification",
-        action=doc_verification,
-    )
-    graph.add_node(
-        node="doc_reranking",
-        action=doc_reranking,
-    )
-
-    ### Add edges ###
-
-    graph.add_conditional_edges(
-        source=START,
-        path=parallel_retrieval_edge,
-        path_map=["doc_retrieval"],
-    )
-    graph.add_edge(
-        start_key="doc_retrieval",
-        end_key="verification_kickoff",
-    )
-    graph.add_edge(
-        start_key="doc_verification",
-        end_key="doc_reranking",
-    )
-    graph.add_edge(
-        start_key="doc_reranking",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = expanded_retrieval_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="Who made Excel and what other products did they make?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = ExpandedRetrievalInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-            query_to_answer="Who made Excel?",
-        )
-        for thing in compiled_graph.stream(inputs, debug=True):
-            print(thing)

backend/danswer/agent_search/expanded_retrieval/nodes/doc_reranking.py

@@ -1,11 +0,0 @@
-from danswer.agent_search.expanded_retrieval.states import DocRerankingOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-
-def doc_reranking(state: ExpandedRetrievalState) -> DocRerankingOutput:
-    print(f"doc_reranking state: {state.keys()}")
-
-    verified_documents = state["verified_documents"]
-    reranked_documents = verified_documents
-
-    return DocRerankingOutput(reranked_documents=reranked_documents)

backend/danswer/agent_search/expanded_retrieval/nodes/doc_retrieval.py

@@ -1,47 +0,0 @@
-from danswer.agent_search.expanded_retrieval.states import DocRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-from danswer.context.search.models import InferenceSection
-from danswer.context.search.models import SearchRequest
-from danswer.context.search.pipeline import SearchPipeline
-from danswer.db.engine import get_session_context_manager
-
-
-class RetrieveInput(ExpandedRetrievalState):
-    query_to_retrieve: str
-
-
-def doc_retrieval(state: RetrieveInput) -> DocRetrievalOutput:
-    # def doc_retrieval(state: RetrieveInput) -> Command[Literal["doc_verification"]]:
-    """
-    Retrieve documents
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        state (dict): New key added to state, documents, that contains retrieved documents
-    """
-    print(f"doc_retrieval state: {state.keys()}")
-
-    state["query_to_retrieve"]
-
-    documents: list[InferenceSection] = []
-    llm = state["primary_llm"]
-    fast_llm = state["fast_llm"]
-    # db_session = state["db_session"]
-    query_to_retrieve = state["search_request"].query
-    with get_session_context_manager() as db_session1:
-        documents = SearchPipeline(
-            search_request=SearchRequest(
-                query=query_to_retrieve,
-            ),
-            user=None,
-            llm=llm,
-            fast_llm=fast_llm,
-            db_session=db_session1,
-        ).reranked_sections
-
-    print(f"retrieved documents: {len(documents)}")
-    return DocRetrievalOutput(
-        retrieved_documents=documents,
-    )

backend/danswer/agent_search/expanded_retrieval/nodes/doc_verification.py

@@ -1,60 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.expanded_retrieval.states import DocVerificationOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-from danswer.agent_search.shared_graph_utils.models import BinaryDecision
-from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
-from danswer.context.search.models import InferenceSection
-
-
-class DocVerificationInput(ExpandedRetrievalState, total=True):
-    doc_to_verify: InferenceSection
-
-
-def doc_verification(state: DocVerificationInput) -> DocVerificationOutput:
-    """
-    Check whether the document is relevant for the original user question
-
-    Args:
-        state (VerifierState): The current state
-
-    Returns:
-        dict: ict: The updated state with the final decision
-    """
-
-    print(f"doc_verification state: {state.keys()}")
-
-    original_query = state["search_request"].query
-    doc_to_verify = state["doc_to_verify"]
-    document_content = doc_to_verify.combined_content
-
-    msg = [
-        HumanMessage(
-            content=VERIFIER_PROMPT.format(
-                question=original_query, document_content=document_content
-            )
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    response_string = merge_message_runs(response, chunk_separator="")[0].content
-    # Convert string response to proper dictionary format
-    decision_dict = {"decision": response_string.lower()}
-    formatted_response = BinaryDecision.model_validate(decision_dict)
-
-    print(f"Verdict: {formatted_response.decision}")
-
-    verified_documents = []
-    if formatted_response.decision == "yes":
-        verified_documents.append(doc_to_verify)
-
-    return DocVerificationOutput(
-        verified_documents=verified_documents,
-    )

backend/danswer/agent_search/expanded_retrieval/nodes/verification_kickoff.py

@@ -1,27 +0,0 @@
-from typing import Literal
-
-from langgraph.types import Command
-from langgraph.types import Send
-
-from danswer.agent_search.expanded_retrieval.nodes.doc_verification import (
-    DocVerificationInput,
-)
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-
-def verification_kickoff(
-    state: ExpandedRetrievalState,
-) -> Command[Literal["doc_verification"]]:
-    print(f"verification_kickoff state: {state.keys()}")
-
-    documents = state["retrieved_documents"]
-    return Command(
-        update={},
-        goto=[
-            Send(
-                node="doc_verification",
-                arg=DocVerificationInput(doc_to_verify=doc, **state),
-            )
-            for doc in documents
-        ],
-    )

backend/danswer/agent_search/expanded_retrieval/states.py

@@ -1,36 +0,0 @@
-from typing import Annotated
-from typing import TypedDict
-
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class DocRetrievalOutput(TypedDict, total=False):
-    retrieved_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class DocVerificationOutput(TypedDict, total=False):
-    verified_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class DocRerankingOutput(TypedDict, total=False):
-    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class ExpandedRetrievalState(
-    PrimaryState,
-    DocRetrievalOutput,
-    DocVerificationOutput,
-    DocRerankingOutput,
-    total=True,
-):
-    query_to_answer: str
-
-
-class ExpandedRetrievalInput(PrimaryState, total=True):
-    query_to_answer: str
-
-
-class ExpandedRetrievalOutput(TypedDict):
-    reordered_documents: Annotated[list[InferenceSection], dedup_inference_sections]

backend/danswer/agent_search/main/edges.py

@@ -1,61 +0,0 @@
-from collections.abc import Hashable
-
-from langgraph.types import Send
-
-from danswer.agent_search.answer_query.states import AnswerQueryInput
-from danswer.agent_search.main.states import MainState
-
-
-def parallelize_decompozed_answer_queries(state: MainState) -> list[Send | Hashable]:
-    return [
-        Send(
-            "answer_query",
-            AnswerQueryInput(
-                **state,
-                query_to_answer=query,
-            ),
-        )
-        for query in state["initial_decomp_queries"]
-    ]
-
-
-# def continue_to_answer_sub_questions(state: QAState) -> Union[Hashable, list[Hashable]]:
-#     # Routes re-written queries to the (parallel) retrieval steps
-#     # Notice the 'Send()' API that takes care of the parallelization
-#     return [
-#         Send(
-#             "sub_answers_graph",
-#             ResearchQAState(
-#                 sub_question=sub_question["sub_question_str"],
-#                 sub_question_nr=sub_question["sub_question_nr"],
-#                 graph_start_time=state["graph_start_time"],
-#                 primary_llm=state["primary_llm"],
-#                 fast_llm=state["fast_llm"],
-#             ),
-#         )
-#         for sub_question in state["sub_questions"]
-#     ]
-
-
-# def continue_to_deep_answer(state: QAState) -> Union[Hashable, list[Hashable]]:
-#     print("---GO TO DEEP ANSWER OR END---")
-
-#     base_answer = state["base_answer"]
-
-#     question = state["original_question"]
-
-#     BASE_CHECK_MESSAGE = [
-#         HumanMessage(
-#             content=BASE_CHECK_PROMPT.format(question=question, base_answer=base_answer)
-#         )
-#     ]
-
-#     model = state["fast_llm"]
-#     response = model.invoke(BASE_CHECK_MESSAGE)
-
-#     print(f"CAN WE CONTINUE W/O GENERATING A DEEP ANSWER? - {response.pretty_repr()}")
-
-#     if response.pretty_repr() == "no":
-#         return "decompose"
-#     else:
-#         return "end"

backend/danswer/agent_search/main/graph_builder.py

@@ -1,98 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.answer_query.graph_builder import answer_query_graph_builder
-from danswer.agent_search.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-from danswer.agent_search.main.edges import parallelize_decompozed_answer_queries
-from danswer.agent_search.main.nodes.base_decomp import main_decomp_base
-from danswer.agent_search.main.nodes.generate_initial_answer import (
-    generate_initial_answer,
-)
-from danswer.agent_search.main.states import MainInput
-from danswer.agent_search.main.states import MainState
-
-
-def main_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=MainState,
-        input=MainInput,
-    )
-
-    ### Add nodes ###
-
-    graph.add_node(
-        node="base_decomp",
-        action=main_decomp_base,
-    )
-    answer_query_subgraph = answer_query_graph_builder().compile()
-    graph.add_node(
-        node="answer_query",
-        action=answer_query_subgraph,
-    )
-    expanded_retrieval_subgraph = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="expanded_retrieval",
-        action=expanded_retrieval_subgraph,
-    )
-    graph.add_node(
-        node="generate_initial_answer",
-        action=generate_initial_answer,
-    )
-
-    ### Add edges ###
-    graph.add_edge(
-        start_key=START,
-        end_key="expanded_retrieval",
-    )
-
-    graph.add_edge(
-        start_key=START,
-        end_key="base_decomp",
-    )
-    graph.add_conditional_edges(
-        source="base_decomp",
-        path=parallelize_decompozed_answer_queries,
-        path_map=["answer_query"],
-    )
-    graph.add_edge(
-        start_key=["answer_query", "expanded_retrieval"],
-        end_key="generate_initial_answer",
-    )
-    graph.add_edge(
-        start_key="generate_initial_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = main_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="If i am familiar with the function that I need, how can I type it into a cell?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = MainInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-        )
-        for thing in compiled_graph.stream(
-            input=inputs,
-            # stream_mode="debug",
-            # debug=True,
-            subgraphs=True,
-        ):
-            # print(thing)
-            print()
-            print()

backend/danswer/agent_search/main/nodes/base_decomp.py

@@ -1,31 +0,0 @@
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import BaseDecompOutput
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import INITIAL_DECOMPOSITION_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import clean_and_parse_list_string
-
-
-def main_decomp_base(state: MainState) -> BaseDecompOutput:
-    question = state["search_request"].query
-
-    msg = [
-        HumanMessage(
-            content=INITIAL_DECOMPOSITION_PROMPT.format(question=question),
-        )
-    ]
-
-    # Get the rewritten queries in a defined format
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    content = response.pretty_repr()
-    list_of_subquestions = clean_and_parse_list_string(content)
-
-    decomp_list: list[str] = [
-        sub_question["sub_question"].strip() for sub_question in list_of_subquestions
-    ]
-
-    return BaseDecompOutput(
-        initial_decomp_queries=decomp_list,
-    )

backend/danswer/agent_search/main/nodes/generate_initial_answer.py

@@ -1,53 +0,0 @@
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import InitialAnswerOutput
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import INITIAL_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def generate_initial_answer(state: MainState) -> InitialAnswerOutput:
-    print("---GENERATE INITIAL---")
-
-    question = state["search_request"].query
-    docs = state["documents"]
-
-    decomp_answer_results = state["decomp_answer_results"]
-
-    good_qa_list: list[str] = []
-
-    _SUB_QUESTION_ANSWER_TEMPLATE = """
-    Sub-Question:\n  - {sub_question}\n  --\nAnswer:\n  - {sub_answer}\n\n
-    """
-    for decomp_answer_result in decomp_answer_results:
-        if (
-            decomp_answer_result.quality.lower() == "yes"
-            and len(decomp_answer_result.answer) > 0
-            and decomp_answer_result.answer != "I don't know"
-        ):
-            good_qa_list.append(
-                _SUB_QUESTION_ANSWER_TEMPLATE.format(
-                    sub_question=decomp_answer_result.query,
-                    sub_answer=decomp_answer_result.answer,
-                )
-            )
-
-    sub_question_answer_str = "\n\n------\n\n".join(good_qa_list)
-
-    msg = [
-        HumanMessage(
-            content=INITIAL_RAG_PROMPT.format(
-                question=question,
-                context=format_docs(docs),
-                answered_sub_questions=sub_question_answer_str,
-            )
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-    answer = response.pretty_repr()
-
-    print(answer)
-    return InitialAnswerOutput(initial_answer=answer)

backend/danswer/agent_search/main/states.py

@@ -1,37 +0,0 @@
-from operator import add
-from typing import Annotated
-from typing import TypedDict
-
-from danswer.agent_search.answer_query.states import SearchAnswerResults
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class BaseDecompOutput(TypedDict, total=False):
-    initial_decomp_queries: list[str]
-
-
-class InitialAnswerOutput(TypedDict, total=False):
-    initial_answer: str
-
-
-class MainState(
-    PrimaryState,
-    BaseDecompOutput,
-    InitialAnswerOutput,
-    total=True,
-):
-    documents: Annotated[list[InferenceSection], dedup_inference_sections]
-    decomp_answer_results: Annotated[list[SearchAnswerResults], add]
-
-
-class MainInput(PrimaryState, total=True):
-    pass
-
-
-class MainOutput(TypedDict):
-    """
-    This is not used because defining the output only matters for filtering the output of
-      a .invoke() call but we are streaming so we just yield the entire state.
-    """

backend/danswer/agent_search/run_graph.py

@@ -1,27 +0,0 @@
-from danswer.agent_search.primary_graph.graph_builder import build_core_graph
-from danswer.llm.answering.answer import AnswerStream
-from danswer.llm.interfaces import LLM
-from danswer.tools.tool import Tool
-
-
-def run_graph(
-    query: str,
-    llm: LLM,
-    tools: list[Tool],
-) -> AnswerStream:
-    graph = build_core_graph()
-
-    inputs = {
-        "original_query": query,
-        "messages": [],
-        "tools": tools,
-        "llm": llm,
-    }
-    compiled_graph = graph.compile()
-    output = compiled_graph.invoke(input=inputs)
-    yield from output
-
-
-if __name__ == "__main__":
-    pass
-    # run_graph("What is the capital of France?", llm, [])

backend/danswer/agent_search/shared_graph_utils/models.py

@@ -1,12 +0,0 @@
-from typing import Literal
-
-from pydantic import BaseModel
-
-
-# Pydantic models for structured outputs
-class RewrittenQueries(BaseModel):
-    rewritten_queries: list[str]
-
-
-class BinaryDecision(BaseModel):
-    decision: Literal["yes", "no"]

backend/danswer/agent_search/shared_graph_utils/operators.py

@@ -1,9 +0,0 @@
-from danswer.context.search.models import InferenceSection
-from danswer.llm.answering.prune_and_merge import _merge_sections
-
-
-def dedup_inference_sections(
-    list1: list[InferenceSection], list2: list[InferenceSection]
-) -> list[InferenceSection]:
-    deduped = _merge_sections(list1 + list2)
-    return deduped

backend/danswer/agent_search/shared_graph_utils/prompts.py

@@ -1,427 +0,0 @@
-REWRITE_PROMPT_MULTI_ORIGINAL = """ \n
-    Please convert an initial user question into a 2-3 more appropriate short and pointed search queries for retrievel from a
-    document store. Particularly, try to think about resolving ambiguities and make the search queries more specific,
-    enabling the system to search more broadly.
-    Also, try to make the search queries not redundant, i.e. not too similar! \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Formulate the queries separated by '--' (Do not say 'Query 1: ...', just write the querytext): """
-
-REWRITE_PROMPT_MULTI = """ \n
-    Please create a list of 2-3 sample documents that could answer an original question. Each document
-    should be about as long as the original question. \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Formulate the sample documents separated by '--' (Do not say 'Document 1: ...', just write the text): """
-
-BASE_RAG_PROMPT = """ \n
-    You are an assistant for question-answering tasks. Use the context provided below - and only the
-    provided context - to answer the question. If you don't know the answer or if the provided context is
-    empty, just say "I don't know". Do not use your internal knowledge!
-
-    Again, only use the provided context and do not use your internal knowledge! If you cannot answer the
-    question based on the context, say "I don't know". It is a matter of life and death that you do NOT
-    use your internal knowledge, just the provided information!
-
-    Use three sentences maximum and keep the answer concise.
-    answer concise.\nQuestion:\n {question} \nContext:\n {context} \n\n
-    \n\n
-    Answer:"""
-
-BASE_CHECK_PROMPT = """ \n
-    Please check whether 1) the suggested answer seems to fully address the original question AND 2)the
-    original question requests a simple, factual answer, and there are no ambiguities, judgements,
-    aggregations, or any other complications that may require extra context. (I.e., if the question is
-    somewhat addressed, but the answer would benefit from more context, then answer with 'no'.)
-
-    Please only answer with 'yes' or 'no' \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Here is the proposed answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-    Please answer with yes or no:"""
-
-VERIFIER_PROMPT = """ \n
-    Please check whether the document seems to be relevant for the answer of the question. Please
-    only answer with 'yes' or 'no' \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Here is the document text:
-    \n ------- \n
-    {document_content}
-    \n ------- \n
-    Please answer with yes or no:"""
-
-INITIAL_DECOMPOSITION_PROMPT_BASIC = """ \n
-    Please decompose an initial user question into not more than 4 appropriate sub-questions that help to
-    answer the original question. The purpose for this decomposition is to isolate individulal entities
-    (i.e., 'compare sales of company A and company B' -> 'what are sales for company A' + 'what are sales
-    for company B'), split ambiguous terms (i.e., 'what is our success with company A' -> 'what are our
-    sales with company A' + 'what is our market share with company A' + 'is company A a reference customer
-    for us'), etc. Each sub-question should be realistically be answerable by a good RAG system. \n
-
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Please formulate your answer as a list of subquestions:
-
-    Answer:
-    """
-
-REWRITE_PROMPT_SINGLE = """ \n
-    Please convert an initial user question into a more appropriate search query for retrievel from a
-    document store. \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Formulate the query: """
-
-MODIFIED_RAG_PROMPT = """You are an assistant for question-answering tasks. Use the context provided below
-    - and only this context - to answer the question. If you don't know the answer, just say "I don't know".
-    Use three sentences maximum and keep the answer concise.
-    Pay also particular attention to the sub-questions and their answers, at least it may enrich the answer.
-    Again, only use the provided context and do not use your internal knowledge! If you cannot answer the
-    question based on the context, say "I don't know". It is a matter of life and death that you do NOT
-    use your internal knowledge, just the provided information!
-
-    \nQuestion: {question}
-    \nContext: {combined_context} \n
-
-    Answer:"""
-
-ORIG_DEEP_DECOMPOSE_PROMPT = """ \n
-    An initial user question needs to be answered. An initial answer has been provided but it wasn't quite
-    good enough. Also, some sub-questions had been answered and this information has been used to provide
-    the initial answer. Some other subquestions may have been suggested based on little knowledge, but they
-    were not directly answerable. Also, some entities, relationships and terms are givenm to you so that
-    you have an idea of how the avaiolable data looks like.
-
-    Your role is to generate 3-5 new sub-questions that would help to answer the initial question,
-    considering:
-
-    1) The initial question
-    2) The initial answer that was found to be unsatisfactory
-    3) The sub-questions that were answered
-    4) The sub-questions that were suggested but not answered
-    5) The entities, relationships and terms that were extracted from the context
-
-    The individual questions should be answerable by a good RAG system.
-    So a good idea would be to use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question, but in a way that does
-    not duplicate questions that were already tried.
-
-    Additional Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    resolve ambiguities, or address shortcoming of the initial answer
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please provide a short explanation for why it is a good sub-question. So
-    generate a list of dictionaries with the following format:
-      [{{"sub_question": <sub-question>, "explanation": <explanation>, "search_term": <rewrite the
-      sub-question using as a search phrase for the document store>}}, ...]
-
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Here is the initial sub-optimal answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-
-    Here are the sub-questions that were answered:
-    \n ------- \n
-    {answered_sub_questions}
-    \n ------- \n
-
-    Here are the sub-questions that were suggested but not answered:
-    \n ------- \n
-    {failed_sub_questions}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Again, please find questions that are NOT overlapping too much with the already answered
-   sub-questions or those that already were suggested and failed.
-   In other words - what can we try in addition to what has been tried so far?
-
-   Please think through it step by step and then generate the list of json dictionaries with the following
-   format:
-
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "explanation": <explanation>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-DEEP_DECOMPOSE_PROMPT = """ \n
-    An initial user question needs to be answered. An initial answer has been provided but it wasn't quite
-    good enough. Also, some sub-questions had been answered and this information has been used to provide
-    the initial answer. Some other subquestions may have been suggested based on little knowledge, but they
-    were not directly answerable. Also, some entities, relationships and terms are givenm to you so that
-    you have an idea of how the avaiolable data looks like.
-
-    Your role is to generate 4-6 new sub-questions that would help to answer the initial question,
-    considering:
-
-    1) The initial question
-    2) The initial answer that was found to be unsatisfactory
-    3) The sub-questions that were answered
-    4) The sub-questions that were suggested but not answered
-    5) The entities, relationships and terms that were extracted from the context
-
-    The individual questions should be answerable by a good RAG system.
-    So a good idea would be to use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question, but in a way that does
-    not duplicate questions that were already tried.
-
-    Additional Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    resolve ambiguities, or address shortcoming of the initial answer
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please also provide a search term that can be used to retrieve relevant
-    documents from a document store.
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Here is the initial sub-optimal answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-
-    Here are the sub-questions that were answered:
-    \n ------- \n
-    {answered_sub_questions}
-    \n ------- \n
-
-    Here are the sub-questions that were suggested but not answered:
-    \n ------- \n
-    {failed_sub_questions}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Again, please find questions that are NOT overlapping too much with the already answered
-   sub-questions or those that already were suggested and failed.
-   In other words - what can we try in addition to what has been tried so far?
-
-   Generate the list of json dictionaries with the following format:
-
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-DECOMPOSE_PROMPT = """ \n
-    For an initial user question, please generate at 5-10 individual sub-questions whose answers would help
-    \n to answer the initial question. The individual questions should be answerable by a good RAG system.
-    So a good idea would be to \n use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question.
-
-    In order to arrive at meaningful sub-questions, please also consider the context retrieved from the
-    document store, expressed as entities, relationships and terms. You can also think about the types
-    mentioned in brackets
-
-    Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    and or resolve ambiguities
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please provide a short explanation for why it is a good sub-question. So
-    generate a list of dictionaries with the following format:
-      [{{"sub_question": <sub-question>, "explanation": <explanation>, "search_term": <rewrite the
-      sub-question using as a search phrase for the document store>}}, ...]
-
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Don't be too specific unless the original question is specific.
-   Please think through it step by step and then generate the list of json dictionaries with the following
-   format:
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "explanation": <explanation>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-#### Consolidations
-COMBINED_CONTEXT = """-------
-    Below you will find useful information to answer the original question. First, you see a number of
-    sub-questions with their answers. This information should be considered to be more focussed and
-    somewhat more specific to the original question as it tries to contextualized facts.
-    After that will see the documents that were considered to be relevant to answer the original question.
-
-    Here are the sub-questions and their answers:
-    \n\n {deep_answer_context} \n\n
-    \n\n Here are the documents that were considered to be relevant to answer the original question:
-    \n\n {formated_docs} \n\n
-    ----------------
-    """
-
-SUB_QUESTION_EXPLANATION_RANKER_PROMPT = """-------
-    Below you will find a question that we ultimately want to answer (the original question) and a list of
-    motivations in arbitrary order for generated sub-questions that are supposed to help us answering the
-    original question. The motivations are formatted as <motivation number>:  <motivation explanation>.
-    (Again, the numbering is arbitrary and does not necessarily mean that 1 is the most relevant
-    motivation and 2 is less relevant.)
-
-    Please rank the motivations in order of relevance for answering the original question. Also, try to
-    ensure that the top questions do not duplicate too much, i.e. that they are not too similar.
-    Ultimately, create a list with the motivation numbers where the number of the most relevant
-    motivations comes first.
-
-    Here is the original question:
-    \n\n {original_question} \n\n
-    \n\n Here is the list of sub-question motivations:
-    \n\n {sub_question_explanations} \n\n
-    ----------------
-
-    Please think step by step and then generate the ranked list of motivations.
-
-    Please format your answer as a json object in the following format:
-    {{"reasonning": <explain your reasoning for the ranking>,
-      "ranked_motivations": <ranked list of motivation numbers>}}
-    """
-
-
-INITIAL_DECOMPOSITION_PROMPT = """ \n
-    Please decompose an initial user question into 2 or 3 appropriate sub-questions that help to
-    answer the original question. The purpose for this decomposition is to isolate individulal entities
-    (i.e., 'compare sales of company A and company B' -> 'what are sales for company A' + 'what are sales
-    for company B'), split ambiguous terms (i.e., 'what is our success with company A' -> 'what are our
-    sales with company A' + 'what is our market share with company A' + 'is company A a reference customer
-    for us'), etc. Each sub-question should be realistically be answerable by a good RAG system. \n
-
-    For each sub-question, please also create one search term that can be used to retrieve relevant
-    documents from a document store.
-
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Please formulate your answer as a list of json objects with the following format:
-
-   [{{"sub_question": <sub-question>, "search_term": <search term>}}, ...]
-
-    Answer:
-    """
-
-INITIAL_RAG_PROMPT = """ \n
-    You are an assistant for question-answering tasks. Use the information provided below - and only the
-    provided information - to answer the provided question.
-
-    The information provided below consists of:
-     1) a number of answered sub-questions - these are very important(!) and definitely should be
-     considered to answer the question.
-     2) a number of documents that were also deemed relevant for the question.
-
-    If you don't know the answer or if the provided information is empty or insufficient, just say
-    "I don't know". Do not use your internal knowledge!
-
-    Again, only use the provided informationand do not use your internal knowledge! It is a matter of life
-    and death that you do NOT use your internal knowledge, just the provided information!
-
-    Try to keep your answer concise.
-
-    And here is the question and the provided information:
-    \n
-    \nQuestion:\n {question}
-
-    \nAnswered Sub-questions:\n {answered_sub_questions}
-
-    \nContext:\n {context} \n\n
-    \n\n
-
-    Answer:"""
-
-ENTITY_TERM_PROMPT = """ \n
-    Based on the original question and the context retieved from a dataset, please generate a list of
-    entities (e.g. companies, organizations, industries, products, locations, etc.), terms and concepts
-    (e.g. sales, revenue, etc.) that are relevant for the question, plus their relations to each other.
-
-    \n\n
-    Here is the original question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-   And here is the context retrieved:
-    \n ------- \n
-    {context}
-    \n ------- \n
-
-    Please format your answer as a json object in the following format:
-
-    {{"retrieved_entities_relationships": {{
-        "entities": [{{
-            "entity_name": <assign a name for the entity>,
-            "entity_type": <specify a short type name for the entity, such as 'company', 'location',...>
-        }}],
-        "relationships": [{{
-            "name": <assign a name for the relationship>,
-            "type": <specify a short type name for the relationship, such as 'sales_to', 'is_location_of',...>,
-            "entities": [<related entity name 1>, <related entity name 2>]
-        }}],
-        "terms": [{{
-            "term_name": <assign a name for the term>,
-            "term_type": <specify a short type name for the term, such as 'revenue', 'market_share',...>,
-            "similar_to": <list terms that are similar to this term>
-        }}]
-    }}
-    }}
-   """

backend/danswer/agent_search/shared_graph_utils/utils.py

@@ -1,101 +0,0 @@
-import ast
-import json
-import re
-from collections.abc import Sequence
-from datetime import datetime
-from datetime import timedelta
-from typing import Any
-
-from danswer.context.search.models import InferenceSection
-
-
-def normalize_whitespace(text: str) -> str:
-    """Normalize whitespace in text to single spaces and strip leading/trailing whitespace."""
-    import re
-
-    return re.sub(r"\s+", " ", text.strip())
-
-
-# Post-processing
-def format_docs(docs: Sequence[InferenceSection]) -> str:
-    return "\n\n".join(doc.combined_content for doc in docs)
-
-
-def clean_and_parse_list_string(json_string: str) -> list[dict]:
-    # Remove any prefixes/labels before the actual JSON content
-    json_string = re.sub(r"^.*?(?=\[)", "", json_string, flags=re.DOTALL)
-
-    # Remove markdown code block markers and any newline prefixes
-    cleaned_string = re.sub(r"```json\n|\n```", "", json_string)
-    cleaned_string = cleaned_string.replace("\\n", " ").replace("\n", " ")
-    cleaned_string = " ".join(cleaned_string.split())
-
-    # Try parsing with json.loads first, fall back to ast.literal_eval
-    try:
-        return json.loads(cleaned_string)
-    except json.JSONDecodeError:
-        try:
-            return ast.literal_eval(cleaned_string)
-        except (ValueError, SyntaxError) as e:
-            raise ValueError(f"Failed to parse JSON string: {cleaned_string}") from e
-
-
-def clean_and_parse_json_string(json_string: str) -> dict[str, Any]:
-    # Remove markdown code block markers and any newline prefixes
-    cleaned_string = re.sub(r"```json\n|\n```", "", json_string)
-    cleaned_string = cleaned_string.replace("\\n", " ").replace("\n", " ")
-    cleaned_string = " ".join(cleaned_string.split())
-    # Parse the cleaned string into a Python dictionary
-    return json.loads(cleaned_string)
-
-
-def format_entity_term_extraction(entity_term_extraction_dict: dict[str, Any]) -> str:
-    entities = entity_term_extraction_dict["entities"]
-    terms = entity_term_extraction_dict["terms"]
-    relationships = entity_term_extraction_dict["relationships"]
-
-    entity_strs = ["\nEntities:\n"]
-    for entity in entities:
-        entity_str = f"{entity['entity_name']} ({entity['entity_type']})"
-        entity_strs.append(entity_str)
-
-    entity_str = "\n - ".join(entity_strs)
-
-    relationship_strs = ["\n\nRelationships:\n"]
-    for relationship in relationships:
-        relationship_str = f"{relationship['name']} ({relationship['type']}): {relationship['entities']}"
-        relationship_strs.append(relationship_str)
-
-    relationship_str = "\n - ".join(relationship_strs)
-
-    term_strs = ["\n\nTerms:\n"]
-    for term in terms:
-        term_str = f"{term['term_name']} ({term['term_type']}): similar to {term['similar_to']}"
-        term_strs.append(term_str)
-
-    term_str = "\n - ".join(term_strs)
-
-    return "\n".join(entity_strs + relationship_strs + term_strs)
-
-
-def _format_time_delta(time: timedelta) -> str:
-    seconds_from_start = f"{((time).seconds):03d}"
-    microseconds_from_start = f"{((time).microseconds):06d}"
-    return f"{seconds_from_start}.{microseconds_from_start}"
-
-
-def generate_log_message(
-    message: str,
-    node_start_time: datetime,
-    graph_start_time: datetime | None = None,
-) -> str:
-    current_time = datetime.now()
-
-    if graph_start_time is not None:
-        graph_time_str = _format_time_delta(current_time - graph_start_time)
-    else:
-        graph_time_str = "N/A"
-
-    node_time_str = _format_time_delta(current_time - node_start_time)
-
-    return f"{graph_time_str} ({node_time_str} s): {message}"

backend/danswer/auth/users.py

@@ -87,7 +87,6 @@ from danswer.db.models import AccessToken
 from danswer.db.models import OAuthAccount
 from danswer.db.models import User
 from danswer.db.users import get_user_by_email
-from danswer.server.utils import BasicAuthenticationError
 from danswer.utils.logger import setup_logger
 from danswer.utils.telemetry import optional_telemetry
 from danswer.utils.telemetry import RecordType
@@ -100,6 +99,11 @@ from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 logger = setup_logger()
 
 
+class BasicAuthenticationError(HTTPException):
+    def __init__(self, detail: str):
+        super().__init__(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
+
+
 def is_user_admin(user: User | None) -> bool:
     if AUTH_TYPE == AuthType.DISABLED:
         return True

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

@@ -11,7 +11,6 @@ from celery.exceptions import WorkerShutdown
 from celery.states import READY_STATES
 from celery.utils.log import get_task_logger
 from celery.worker import strategy  # type: ignore
-from redis.lock import Lock as RedisLock
 from sentry_sdk.integrations.celery import CeleryIntegration
 from sqlalchemy import text
 from sqlalchemy.orm import Session
@@ -333,16 +332,16 @@ def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
         return
 
     logger.info("Releasing primary worker lock.")
-    lock: RedisLock = sender.primary_worker_lock
+    lock = sender.primary_worker_lock
     try:
         if lock.owned():
             try:
                 lock.release()
                 sender.primary_worker_lock = None
-            except Exception:
-                logger.exception("Failed to release primary worker lock")
-    except Exception:
-        logger.exception("Failed to check if primary worker lock is owned")
+            except Exception as e:
+                logger.error(f"Failed to release primary worker lock: {e}")
+    except Exception as e:
+        logger.error(f"Failed to check if primary worker lock is owned: {e}")
 
 
 def on_setup_logging(

backend/danswer/background/celery/apps/primary.py

@@ -11,7 +11,6 @@ from celery.signals import celeryd_init
 from celery.signals import worker_init
 from celery.signals import worker_ready
 from celery.signals import worker_shutdown
-from redis.lock import Lock as RedisLock
 
 import danswer.background.celery.apps.app_base as app_base
 from danswer.background.celery.apps.app_base import task_logger
@@ -39,6 +38,7 @@ from danswer.redis.redis_usergroup import RedisUserGroup
 from danswer.utils.logger import setup_logger
 from shared_configs.configs import MULTI_TENANT
 
+
 logger = setup_logger()
 
 celery_app = Celery(__name__)
@@ -116,13 +116,9 @@ def on_worker_init(sender: Any, **kwargs: Any) -> None:
     # it is planned to use this lock to enforce singleton behavior on the primary
     # worker, since the primary worker does redis cleanup on startup, but this isn't
     # implemented yet.
-
-    # set thread_local=False since we don't control what thread the periodic task might
-    # reacquire the lock with
-    lock: RedisLock = r.lock(
+    lock = r.lock(
         DanswerRedisLocks.PRIMARY_WORKER,
         timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
-        thread_local=False,
     )
 
     logger.info("Primary worker lock: Acquire starting.")
@@ -231,7 +227,7 @@ class HubPeriodicTask(bootsteps.StartStopStep):
             if not hasattr(worker, "primary_worker_lock"):
                 return
 
-            lock: RedisLock = worker.primary_worker_lock
+            lock = worker.primary_worker_lock
 
             r = get_redis_client(tenant_id=None)
 

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

@@ -2,55 +2,54 @@ from datetime import timedelta
 from typing import Any
 
 from danswer.configs.constants import DanswerCeleryPriority
-from danswer.configs.constants import DanswerCeleryTask
 
 
 tasks_to_schedule = [
     {
         "name": "check-for-vespa-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
+        "task": "check_for_vespa_sync_task",
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-connector-deletion",
-        "task": DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
+        "task": "check_for_connector_deletion_task",
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-indexing",
-        "task": DanswerCeleryTask.CHECK_FOR_INDEXING,
+        "task": "check_for_indexing",
         "schedule": timedelta(seconds=15),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-prune",
-        "task": DanswerCeleryTask.CHECK_FOR_PRUNING,
+        "task": "check_for_pruning",
         "schedule": timedelta(seconds=15),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "kombu-message-cleanup",
-        "task": DanswerCeleryTask.KOMBU_MESSAGE_CLEANUP_TASK,
+        "task": "kombu_message_cleanup_task",
         "schedule": timedelta(seconds=3600),
         "options": {"priority": DanswerCeleryPriority.LOWEST},
     },
     {
         "name": "monitor-vespa-sync",
-        "task": DanswerCeleryTask.MONITOR_VESPA_SYNC,
+        "task": "monitor_vespa_sync",
         "schedule": timedelta(seconds=5),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-doc-permissions-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_DOC_PERMISSIONS_SYNC,
+        "task": "check_for_doc_permissions_sync",
         "schedule": timedelta(seconds=30),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-external-group-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_EXTERNAL_GROUP_SYNC,
+        "task": "check_for_external_group_sync",
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },

backend/danswer/background/celery/tasks/connector_deletion/tasks.py

@@ -11,7 +11,6 @@ from sqlalchemy.orm import Session
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.connector_credential_pair import get_connector_credential_pairs
@@ -29,7 +28,7 @@ class TaskDependencyError(RuntimeError):
 
 
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
+    name="check_for_connector_deletion_task",
     soft_time_limit=JOB_TIMEOUT,
     trail=False,
     bind=True,

backend/danswer/background/celery/tasks/doc_permission_syncing/tasks.py

@@ -18,11 +18,9 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.configs.constants import DocumentSource
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
-from danswer.db.document import upsert_document_by_connector_credential_pair
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import AccessType
 from danswer.db.enums import ConnectorCredentialPairStatus
@@ -84,7 +82,7 @@ def _is_external_doc_permissions_sync_due(cc_pair: ConnectorCredentialPair) -> b
 
 
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_DOC_PERMISSIONS_SYNC,
+    name="check_for_doc_permissions_sync",
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -166,7 +164,7 @@ def try_creating_permissions_sync_task(
         custom_task_id = f"{redis_connector.permissions.generator_task_key}_{uuid4()}"
 
         result = app.send_task(
-            DanswerCeleryTask.CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK,
+            "connector_permission_sync_generator_task",
             kwargs=dict(
                 cc_pair_id=cc_pair_id,
                 tenant_id=tenant_id,
@@ -193,7 +191,7 @@ def try_creating_permissions_sync_task(
 
 
 @shared_task(
-    name=DanswerCeleryTask.CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK,
+    name="connector_permission_sync_generator_task",
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,
@@ -263,12 +261,7 @@ def connector_permission_sync_generator_task(
                 f"RedisConnector.permissions.generate_tasks starting. cc_pair={cc_pair_id}"
             )
             tasks_generated = redis_connector.permissions.generate_tasks(
-                celery_app=self.app,
-                lock=lock,
-                new_permissions=document_external_accesses,
-                source_string=source_type,
-                connector_id=cc_pair.connector.id,
-                credential_id=cc_pair.credential.id,
+                self.app, lock, document_external_accesses, source_type
             )
             if tasks_generated is None:
                 return None
@@ -293,7 +286,7 @@ def connector_permission_sync_generator_task(
 
 
 @shared_task(
-    name=DanswerCeleryTask.UPDATE_EXTERNAL_DOCUMENT_PERMISSIONS_TASK,
+    name="update_external_document_permissions_task",
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,
     max_retries=DOCUMENT_PERMISSIONS_UPDATE_MAX_RETRIES,
@@ -304,8 +297,6 @@ def update_external_document_permissions_task(
     tenant_id: str | None,
     serialized_doc_external_access: dict,
     source_string: str,
-    connector_id: int,
-    credential_id: int,
 ) -> bool:
     document_external_access = DocExternalAccess.from_dict(
         serialized_doc_external_access
@@ -314,28 +305,18 @@ def update_external_document_permissions_task(
     external_access = document_external_access.external_access
     try:
         with get_session_with_tenant(tenant_id) as db_session:
-            # Add the users to the DB if they don't exist
+            # Then we build the update requests to update vespa
             batch_add_ext_perm_user_if_not_exists(
                 db_session=db_session,
                 emails=list(external_access.external_user_emails),
             )
-            # Then we upsert the document's external permissions in postgres
-            created_new_doc = upsert_document_external_perms(
+            upsert_document_external_perms(
                 db_session=db_session,
                 doc_id=doc_id,
                 external_access=external_access,
                 source_type=DocumentSource(source_string),
             )
 
-            if created_new_doc:
-                # If a new document was created, we associate it with the cc_pair
-                upsert_document_by_connector_credential_pair(
-                    db_session=db_session,
-                    connector_id=connector_id,
-                    credential_id=credential_id,
-                    document_ids=[doc_id],
-                )
-
             logger.debug(
                 f"Successfully synced postgres document permissions for {doc_id}"
             )

backend/danswer/background/celery/tasks/external_group_syncing/tasks.py

@@ -17,7 +17,6 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector import mark_cc_pair_as_external_group_synced
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
@@ -32,14 +31,10 @@ from danswer.redis.redis_connector_ext_group_sync import (
 from danswer.redis.redis_pool import get_redis_client
 from danswer.utils.logger import setup_logger
 from ee.danswer.db.connector_credential_pair import get_all_auto_sync_cc_pairs
-from ee.danswer.db.connector_credential_pair import get_cc_pairs_by_source
 from ee.danswer.db.external_perm import ExternalUserGroup
 from ee.danswer.db.external_perm import replace_user__ext_group_for_cc_pair
 from ee.danswer.external_permissions.sync_params import EXTERNAL_GROUP_SYNC_PERIODS
 from ee.danswer.external_permissions.sync_params import GROUP_PERMISSIONS_FUNC_MAP
-from ee.danswer.external_permissions.sync_params import (
-    GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC,
-)
 
 logger = setup_logger()
 
@@ -90,7 +85,7 @@ def _is_external_group_sync_due(cc_pair: ConnectorCredentialPair) -> bool:
 
 
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_EXTERNAL_GROUP_SYNC,
+    name="check_for_external_group_sync",
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -111,22 +106,6 @@ def check_for_external_group_sync(self: Task, *, tenant_id: str | None) -> None:
         with get_session_with_tenant(tenant_id) as db_session:
             cc_pairs = get_all_auto_sync_cc_pairs(db_session)
 
-            # We only want to sync one cc_pair per source type in
-            # GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC
-            for source in GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC:
-                # These are ordered by cc_pair id so the first one is the one we want
-                cc_pairs_to_dedupe = get_cc_pairs_by_source(
-                    db_session, source, only_sync=True
-                )
-                # We only want to sync one cc_pair per source type
-                # in GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC so we dedupe here
-                for cc_pair_to_remove in cc_pairs_to_dedupe[1:]:
-                    cc_pairs = [
-                        cc_pair
-                        for cc_pair in cc_pairs
-                        if cc_pair.id != cc_pair_to_remove.id
-                    ]
-
             for cc_pair in cc_pairs:
                 if _is_external_group_sync_due(cc_pair):
                     cc_pair_ids_to_sync.append(cc_pair.id)
@@ -182,7 +161,7 @@ def try_creating_external_group_sync_task(
         custom_task_id = f"{redis_connector.external_group_sync.taskset_key}_{uuid4()}"
 
         result = app.send_task(
-            DanswerCeleryTask.CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK,
+            "connector_external_group_sync_generator_task",
             kwargs=dict(
                 cc_pair_id=cc_pair_id,
                 tenant_id=tenant_id,
@@ -212,7 +191,7 @@ def try_creating_external_group_sync_task(
 
 
 @shared_task(
-    name=DanswerCeleryTask.CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK,
+    name="connector_external_group_sync_generator_task",
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,

backend/danswer/background/celery/tasks/indexing/tasks.py

@@ -23,7 +23,6 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.configs.constants import DocumentSource
 from danswer.db.connector import mark_ccpair_with_indexing_trigger
@@ -157,7 +156,7 @@ def get_unfenced_index_attempt_ids(db_session: Session, r: redis.Redis) -> list[
 
 
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_INDEXING,
+    name="check_for_indexing",
     soft_time_limit=300,
     bind=True,
 )
@@ -487,7 +486,7 @@ def try_creating_indexing_task(
         # when the task is sent, we have yet to finish setting up the fence
         # therefore, the task must contain code that blocks until the fence is ready
         result = celery_app.send_task(
-            DanswerCeleryTask.CONNECTOR_INDEXING_PROXY_TASK,
+            "connector_indexing_proxy_task",
             kwargs=dict(
                 index_attempt_id=index_attempt_id,
                 cc_pair_id=cc_pair.id,
@@ -525,10 +524,7 @@ def try_creating_indexing_task(
 
 
 @shared_task(
-    name=DanswerCeleryTask.CONNECTOR_INDEXING_PROXY_TASK,
-    bind=True,
-    acks_late=False,
-    track_started=True,
+    name="connector_indexing_proxy_task", bind=True, acks_late=False, track_started=True
 )
 def connector_indexing_proxy_task(
     self: Task,
@@ -584,64 +580,39 @@ def connector_indexing_proxy_task(
 
         if self.request.id and redis_connector_index.terminating(self.request.id):
             task_logger.warning(
-                "Indexing watchdog - termination signal detected: "
+                "Indexing proxy - termination signal detected: "
                 f"attempt={index_attempt_id} "
                 f"tenant={tenant_id} "
                 f"cc_pair={cc_pair_id} "
                 f"search_settings={search_settings_id}"
             )
 
-            try:
-                with get_session_with_tenant(tenant_id) as db_session:
-                    mark_attempt_canceled(
-                        index_attempt_id,
-                        db_session,
-                        "Connector termination signal detected",
-                    )
-            finally:
-                # if the DB exceptions, we'll just get an unfriendly failure message
-                # in the UI instead of the cancellation message
-                logger.exception(
-                    "Indexing watchdog - transient exception marking index attempt as canceled: "
-                    f"attempt={index_attempt_id} "
-                    f"tenant={tenant_id} "
-                    f"cc_pair={cc_pair_id} "
-                    f"search_settings={search_settings_id}"
+            with get_session_with_tenant(tenant_id) as db_session:
+                mark_attempt_canceled(
+                    index_attempt_id,
+                    db_session,
+                    "Connector termination signal detected",
                 )
 
-                job.cancel()
-
+            job.cancel()
             break
 
+        # do nothing for ongoing jobs that haven't been stopped
         if not job.done():
-            # if the spawned task is still running, restart the check once again
-            # if the index attempt is not in a finished status
-            try:
-                with get_session_with_tenant(tenant_id) as db_session:
-                    index_attempt = get_index_attempt(
-                        db_session=db_session, index_attempt_id=index_attempt_id
-                    )
-
-                    if not index_attempt:
-                        continue
-
-                    if not index_attempt.is_finished():
-                        continue
-            except Exception:
-                # if the DB exceptioned, just restart the check.
-                # polling the index attempt status doesn't need to be strongly consistent
-                logger.exception(
-                    "Indexing watchdog - transient exception looking up index attempt: "
-                    f"attempt={index_attempt_id} "
-                    f"tenant={tenant_id} "
-                    f"cc_pair={cc_pair_id} "
-                    f"search_settings={search_settings_id}"
+            with get_session_with_tenant(tenant_id) as db_session:
+                index_attempt = get_index_attempt(
+                    db_session=db_session, index_attempt_id=index_attempt_id
                 )
-                continue
+
+                if not index_attempt:
+                    continue
+
+                if not index_attempt.is_finished():
+                    continue
 
         if job.status == "error":
             task_logger.error(
-                "Indexing watchdog - spawned task exceptioned: "
+                f"Indexing watchdog - spawned task exceptioned: "
                 f"attempt={index_attempt_id} "
                 f"tenant={tenant_id} "
                 f"cc_pair={cc_pair_id} "
@@ -789,12 +760,9 @@ def connector_indexing_task(
         )
         break
 
-    # set thread_local=False since we don't control what thread the indexing/pruning
-    # might run our callback with
     lock: RedisLock = r.lock(
         redis_connector_index.generator_lock_key,
         timeout=CELERY_INDEXING_LOCK_TIMEOUT,
-        thread_local=False,
     )
 
     acquired = lock.acquire(blocking=False)

backend/danswer/background/celery/tasks/periodic/tasks.py

@@ -13,13 +13,12 @@ from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.configs.app_configs import JOB_TIMEOUT
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import PostgresAdvisoryLocks
 from danswer.db.engine import get_session_with_tenant
 
 
 @shared_task(
-    name=DanswerCeleryTask.KOMBU_MESSAGE_CLEANUP_TASK,
+    name="kombu_message_cleanup_task",
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
     base=AbortableTask,

backend/danswer/background/celery/tasks/pruning/tasks.py

@@ -8,7 +8,6 @@ from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
 from redis import Redis
-from redis.lock import Lock as RedisLock
 from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
@@ -21,7 +20,6 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.connectors.factory import instantiate_connector
 from danswer.connectors.models import InputType
@@ -77,7 +75,7 @@ def _is_pruning_due(cc_pair: ConnectorCredentialPair) -> bool:
 
 
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_PRUNING,
+    name="check_for_pruning",
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -186,7 +184,7 @@ def try_creating_prune_generator_task(
         custom_task_id = f"{redis_connector.prune.generator_task_key}_{uuid4()}"
 
         celery_app.send_task(
-            DanswerCeleryTask.CONNECTOR_PRUNING_GENERATOR_TASK,
+            "connector_pruning_generator_task",
             kwargs=dict(
                 cc_pair_id=cc_pair.id,
                 connector_id=cc_pair.connector_id,
@@ -211,7 +209,7 @@ def try_creating_prune_generator_task(
 
 
 @shared_task(
-    name=DanswerCeleryTask.CONNECTOR_PRUNING_GENERATOR_TASK,
+    name="connector_pruning_generator_task",
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,
@@ -240,12 +238,9 @@ def connector_pruning_generator_task(
 
     r = get_redis_client(tenant_id=tenant_id)
 
-    # set thread_local=False since we don't control what thread the indexing/pruning
-    # might run our callback with
-    lock: RedisLock = r.lock(
+    lock = r.lock(
         DanswerRedisLocks.PRUNING_LOCK_PREFIX + f"_{redis_connector.id}",
         timeout=CELERY_PRUNING_LOCK_TIMEOUT,
-        thread_local=False,
     )
 
     acquired = lock.acquire(blocking=False)

backend/danswer/background/celery/tasks/shared/tasks.py

@@ -9,7 +9,6 @@ from tenacity import RetryError
 from danswer.access.access import get_access_for_document
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.background.celery.tasks.shared.RetryDocumentIndex import RetryDocumentIndex
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.document import delete_document_by_connector_credential_pair__no_commit
 from danswer.db.document import delete_documents_complete__no_commit
 from danswer.db.document import get_document
@@ -32,7 +31,7 @@ LIGHT_TIME_LIMIT = LIGHT_SOFT_TIME_LIMIT + 15
 
 
 @shared_task(
-    name=DanswerCeleryTask.DOCUMENT_BY_CC_PAIR_CLEANUP_TASK,
+    name="document_by_cc_pair_cleanup_task",
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,
     max_retries=DOCUMENT_BY_CC_PAIR_CLEANUP_MAX_RETRIES,

backend/danswer/background/celery/tasks/vespa/tasks.py

@@ -25,7 +25,6 @@ from danswer.background.celery.tasks.shared.tasks import LIGHT_TIME_LIMIT
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector import fetch_connector_by_id
 from danswer.db.connector import mark_cc_pair_as_permissions_synced
@@ -81,7 +80,7 @@ logger = setup_logger()
 # celery auto associates tasks created inside another task,
 # which bloats the result metadata considerably. trail=False prevents this.
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
+    name="check_for_vespa_sync_task",
     soft_time_limit=JOB_TIMEOUT,
     trail=False,
     bind=True,
@@ -655,28 +654,24 @@ def monitor_ccpair_indexing_taskset(
     # outer = result.state in READY state
     status_int = redis_connector_index.get_completion()
     if status_int is None:  # inner signal not set ... possible error
-        task_state = result.state
+        result_state = result.state
         if (
-            task_state in READY_STATES
+            result_state in READY_STATES
         ):  # outer signal in terminal state ... possible error
             # Now double check!
             if redis_connector_index.get_completion() is None:
                 # inner signal still not set (and cannot change when outer result_state is READY)
                 # Task is finished but generator complete isn't set.
                 # We have a problem! Worker may have crashed.
-                task_result = str(result.result)
-                task_traceback = str(result.traceback)
 
                 msg = (
                     f"Connector indexing aborted or exceptioned: "
                     f"attempt={payload.index_attempt_id} "
                     f"celery_task={payload.celery_task_id} "
+                    f"result_state={result_state} "
                     f"cc_pair={cc_pair_id} "
                     f"search_settings={search_settings_id} "
-                    f"elapsed_submitted={elapsed_submitted.total_seconds():.2f} "
-                    f"result.state={task_state} "
-                    f"result.result={task_result} "
-                    f"result.traceback={task_traceback}"
+                    f"elapsed_submitted={elapsed_submitted.total_seconds():.2f}"
                 )
                 task_logger.warning(msg)
 
@@ -708,7 +703,7 @@ def monitor_ccpair_indexing_taskset(
     redis_connector_index.reset()
 
 
-@shared_task(name=DanswerCeleryTask.MONITOR_VESPA_SYNC, soft_time_limit=300, bind=True)
+@shared_task(name="monitor_vespa_sync", soft_time_limit=300, bind=True)
 def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
     """This is a celery beat task that monitors and finalizes metadata sync tasksets.
     It scans for fence values and then gets the counts of any associated tasksets.
@@ -819,7 +814,7 @@ def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
 
 
 @shared_task(
-    name=DanswerCeleryTask.VESPA_METADATA_SYNC_TASK,
+    name="vespa_metadata_sync_task",
     bind=True,
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,

backend/danswer/chat/chat_utils.py

@@ -2,79 +2,20 @@ import re
 from typing import cast
 from uuid import UUID
 
-from fastapi import HTTPException
 from fastapi.datastructures import Headers
 from sqlalchemy.orm import Session
 
-from danswer.auth.users import is_user_admin
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import LlmDoc
-from danswer.chat.models import PersonaOverrideConfig
-from danswer.chat.models import ThreadMessage
-from danswer.configs.constants import DEFAULT_PERSONA_ID
-from danswer.configs.constants import MessageType
 from danswer.context.search.models import InferenceSection
-from danswer.context.search.models import RerankingDetails
-from danswer.context.search.models import RetrievalDetails
-from danswer.db.chat import create_chat_session
 from danswer.db.chat import get_chat_messages_by_session
-from danswer.db.llm import fetch_existing_doc_sets
-from danswer.db.llm import fetch_existing_tools
 from danswer.db.models import ChatMessage
-from danswer.db.models import Persona
-from danswer.db.models import Prompt
-from danswer.db.models import Tool
-from danswer.db.models import User
-from danswer.db.persona import get_prompts_by_ids
 from danswer.llm.answering.models import PreviousMessage
-from danswer.natural_language_processing.utils import BaseTokenizer
-from danswer.server.query_and_chat.models import CreateChatMessageRequest
-from danswer.tools.tool_implementations.custom.custom_tool import (
-    build_custom_tools_from_openapi_schema_and_headers,
-)
 from danswer.utils.logger import setup_logger
 
 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,
-    prompt: Prompt | None,
-    message_ts_to_respond_to: str | None,
-    retrieval_details: RetrievalDetails | None,
-    rerank_settings: RerankingDetails | None,
-    db_session: Session,
-) -> CreateChatMessageRequest:
-    # Typically used for one shot flows like SlackBot or non-chat API endpoint use cases
-    new_chat_session = 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,
-        danswerbot_flow=True,
-        slack_thread_id=message_ts_to_respond_to,
-    )
-
-    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
-        prompt_id=prompt.id if prompt else None,
-        # 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,
-    )
-
-
 def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDoc:
     return LlmDoc(
         document_id=inference_section.center_chunk.document_id,
@@ -90,49 +31,9 @@ def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDo
         if inference_section.center_chunk.source_links
         else None,
         source_links=inference_section.center_chunk.source_links,
-        match_highlights=inference_section.center_chunk.match_highlights,
     )
 
 
-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_chain(
     chat_session_id: UUID,
     db_session: Session,
@@ -295,71 +196,3 @@ def extract_headers(
             if lowercase_key in headers:
                 extracted_headers[lowercase_key] = headers[lowercase_key]
     return extracted_headers
-
-
-def create_temporary_persona(
-    persona_config: PersonaOverrideConfig, db_session: Session, user: User | None = None
-) -> Persona:
-    if not is_user_admin(user):
-        raise HTTPException(
-            status_code=403,
-            detail="User is not authorized to create a persona in one shot queries",
-        )
-
-    """Create a temporary Persona object from the provided configuration."""
-    persona = Persona(
-        name=persona_config.name,
-        description=persona_config.description,
-        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,
-        llm_model_provider_override=persona_config.llm_model_provider_override,
-        llm_model_version_override=persona_config.llm_model_version_override,
-    )
-
-    if persona_config.prompts:
-        persona.prompts = [
-            Prompt(
-                name=p.name,
-                description=p.description,
-                system_prompt=p.system_prompt,
-                task_prompt=p.task_prompt,
-                include_citations=p.include_citations,
-                datetime_aware=p.datetime_aware,
-            )
-            for p in persona_config.prompts
-        ]
-    elif persona_config.prompt_ids:
-        persona.prompts = get_prompts_by_ids(
-            db_session=db_session, prompt_ids=persona_config.prompt_ids
-        )
-
-    persona.tools = []
-    if persona_config.custom_tools_openapi:
-        for schema in persona_config.custom_tools_openapi:
-            tools = cast(
-                list[Tool],
-                build_custom_tools_from_openapi_schema_and_headers(schema),
-            )
-            persona.tools.extend(tools)
-
-    if persona_config.tools:
-        tool_ids = [tool.id for tool in persona_config.tools]
-        persona.tools.extend(
-            fetch_existing_tools(db_session=db_session, tool_ids=tool_ids)
-        )
-
-    if persona_config.tool_ids:
-        persona.tools.extend(
-            fetch_existing_tools(
-                db_session=db_session, tool_ids=persona_config.tool_ids
-            )
-        )
-
-    fetched_docs = fetch_existing_doc_sets(
-        db_session=db_session, doc_ids=persona_config.document_set_ids
-    )
-    persona.document_sets = fetched_docs
-
-    return persona

backend/danswer/chat/models.py

@@ -4,14 +4,12 @@ from enum import Enum
 from typing import Any
 
 from pydantic import BaseModel
-from pydantic import Field
 
 from danswer.configs.constants import DocumentSource
-from danswer.configs.constants import MessageType
 from danswer.context.search.enums import QueryFlow
-from danswer.context.search.enums import RecencyBiasSetting
 from danswer.context.search.enums import SearchType
 from danswer.context.search.models import RetrievalDocs
+from danswer.context.search.models import SearchResponse
 from danswer.tools.tool_implementations.custom.base_tool_types import ToolResultType
 
 
@@ -27,7 +25,6 @@ class LlmDoc(BaseModel):
     updated_at: datetime | None
     link: str | None
     source_links: dict[int, str] | None
-    match_highlights: list[str] | None
 
 
 # First chunk of info for streaming QA
@@ -120,6 +117,20 @@ class StreamingError(BaseModel):
     stack_trace: str | None = None
 
 
+class DanswerQuote(BaseModel):
+    # This is during inference so everything is a string by this point
+    quote: str
+    document_id: str
+    link: str | None
+    source_type: str
+    semantic_identifier: str
+    blurb: str
+
+
+class DanswerQuotes(BaseModel):
+    quotes: list[DanswerQuote]
+
+
 class DanswerContext(BaseModel):
     content: str
     document_id: str
@@ -135,20 +146,14 @@ class DanswerAnswer(BaseModel):
     answer: str | None
 
 
-class ThreadMessage(BaseModel):
-    message: str
-    sender: str | None = None
-    role: MessageType = MessageType.USER
-
-
-class ChatDanswerBotResponse(BaseModel):
-    answer: str | None = None
-    citations: list[CitationInfo] | None = None
-    docs: QADocsResponse | None = None
+class QAResponse(SearchResponse, DanswerAnswer):
+    quotes: list[DanswerQuote] | None
+    contexts: list[DanswerContexts] | None
+    predicted_flow: QueryFlow
+    predicted_search: SearchType
+    eval_res_valid: bool | None = None
     llm_selected_doc_indices: list[int] | None = None
     error_msg: str | None = None
-    chat_message_id: int | None = None
-    answer_valid: bool = True  # Reflexion result, default True if Reflexion not run
 
 
 class FileChatDisplay(BaseModel):
@@ -160,41 +165,9 @@ class CustomToolResponse(BaseModel):
     tool_name: str
 
 
-class ToolConfig(BaseModel):
-    id: int
-
-
-class PromptOverrideConfig(BaseModel):
-    name: str
-    description: str = ""
-    system_prompt: str
-    task_prompt: str = ""
-    include_citations: bool = True
-    datetime_aware: bool = True
-
-
-class PersonaOverrideConfig(BaseModel):
-    name: str
-    description: str
-    search_type: SearchType = SearchType.SEMANTIC
-    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
-
-    prompts: list[PromptOverrideConfig] = Field(default_factory=list)
-    prompt_ids: list[int] = Field(default_factory=list)
-
-    document_set_ids: list[int] = Field(default_factory=list)
-    tools: list[ToolConfig] = Field(default_factory=list)
-    tool_ids: list[int] = Field(default_factory=list)
-    custom_tools_openapi: list[dict[str, Any]] = Field(default_factory=list)
-
-
 AnswerQuestionPossibleReturn = (
     DanswerAnswerPiece
+    | DanswerQuotes
     | CitationInfo
     | DanswerContexts
     | FileChatDisplay

backend/danswer/chat/process_message.py

@@ -7,13 +7,10 @@ from typing import cast
 from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import create_chat_chain
-from danswer.chat.chat_utils import create_temporary_persona
 from danswer.chat.models import AllCitations
-from danswer.chat.models import ChatDanswerBotResponse
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import CustomToolResponse
 from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerContexts
 from danswer.chat.models import FileChatDisplay
 from danswer.chat.models import FinalUsedContextDocsResponse
 from danswer.chat.models import LLMRelevanceFilterResponse
@@ -105,7 +102,6 @@ from danswer.tools.tool_implementations.internet_search.internet_search_tool imp
 from danswer.tools.tool_implementations.search.search_tool import (
     FINAL_CONTEXT_DOCUMENTS_ID,
 )
-from danswer.tools.tool_implementations.search.search_tool import SEARCH_DOC_CONTENT_ID
 from danswer.tools.tool_implementations.search.search_tool import (
     SEARCH_RESPONSE_SUMMARY_ID,
 )
@@ -117,10 +113,8 @@ from danswer.tools.tool_implementations.search.search_tool import (
 from danswer.tools.tool_runner import ToolCallFinalResult
 from danswer.utils.logger import setup_logger
 from danswer.utils.long_term_log import LongTermLogger
-from danswer.utils.timing import log_function_time
 from danswer.utils.timing import log_generator_function_time
 
-
 logger = setup_logger()
 
 
@@ -262,7 +256,6 @@ def _get_force_search_settings(
 ChatPacket = (
     StreamingError
     | QADocsResponse
-    | DanswerContexts
     | LLMRelevanceFilterResponse
     | FinalUsedContextDocsResponse
     | ChatMessageDetail
@@ -293,8 +286,6 @@ def stream_chat_message_objects(
     custom_tool_additional_headers: dict[str, str] | None = None,
     is_connected: Callable[[], bool] | None = None,
     enforce_chat_session_id_for_search_docs: bool = True,
-    bypass_acl: bool = False,
-    include_contexts: bool = False,
 ) -> ChatPacketStream:
     """Streams in order:
     1. [conditional] Retrieved documents if a search needs to be run
@@ -331,31 +322,17 @@ def stream_chat_message_objects(
             metadata={"user_id": str(user_id), "chat_session_id": str(chat_session_id)}
         )
 
+        # use alternate persona if alternative assistant id is passed in
         if alternate_assistant_id is not None:
-            # Allows users to specify a temporary persona (assistant) in the chat session
-            # this takes highest priority since it's user specified
             persona = get_persona_by_id(
                 alternate_assistant_id,
                 user=user,
                 db_session=db_session,
                 is_for_edit=False,
             )
-        elif new_msg_req.persona_override_config:
-            # Certain endpoints allow users to specify arbitrary persona settings
-            # this should never conflict with the alternate_assistant_id
-            persona = persona = create_temporary_persona(
-                db_session=db_session,
-                persona_config=new_msg_req.persona_override_config,
-                user=user,
-            )
         else:
             persona = chat_session.persona
 
-        if not persona:
-            raise RuntimeError("No persona specified or found for chat session")
-
-        # If a prompt override is specified via the API, use that with highest priority
-        # but for saving it, we are just mapping it to an existing prompt
         prompt_id = new_msg_req.prompt_id
         if prompt_id is None and persona.prompts:
             prompt_id = sorted(persona.prompts, key=lambda x: x.id)[-1].id
@@ -578,34 +555,19 @@ def stream_chat_message_objects(
             reserved_message_id=reserved_message_id,
         )
 
-        prompt_override = new_msg_req.prompt_override or chat_session.prompt_override
-        if new_msg_req.persona_override_config:
-            prompt_config = PromptConfig(
-                system_prompt=new_msg_req.persona_override_config.prompts[
-                    0
-                ].system_prompt,
-                task_prompt=new_msg_req.persona_override_config.prompts[0].task_prompt,
-                datetime_aware=new_msg_req.persona_override_config.prompts[
-                    0
-                ].datetime_aware,
-                include_citations=new_msg_req.persona_override_config.prompts[
-                    0
-                ].include_citations,
-            )
-        elif prompt_override:
-            if not final_msg.prompt:
-                raise ValueError(
-                    "Prompt override cannot be applied, no base prompt found."
-                )
-            prompt_config = PromptConfig.from_model(
-                final_msg.prompt,
-                prompt_override=prompt_override,
-            )
-        elif final_msg.prompt:
-            prompt_config = PromptConfig.from_model(final_msg.prompt)
-        else:
-            prompt_config = PromptConfig.from_model(persona.prompts[0])
+        if not final_msg.prompt:
+            raise RuntimeError("No Prompt found")
 
+        prompt_config = (
+            PromptConfig.from_model(
+                final_msg.prompt,
+                prompt_override=(
+                    new_msg_req.prompt_override or chat_session.prompt_override
+                ),
+            )
+            if not persona
+            else PromptConfig.from_model(persona.prompts[0])
+        )
         answer_style_config = AnswerStyleConfig(
             citation_config=CitationConfig(
                 all_docs_useful=selected_db_search_docs is not None
@@ -625,13 +587,11 @@ def stream_chat_message_objects(
                 answer_style_config=answer_style_config,
                 document_pruning_config=document_pruning_config,
                 retrieval_options=retrieval_options or RetrievalDetails(),
-                rerank_settings=new_msg_req.rerank_settings,
                 selected_sections=selected_sections,
                 chunks_above=new_msg_req.chunks_above,
                 chunks_below=new_msg_req.chunks_below,
                 full_doc=new_msg_req.full_doc,
                 latest_query_files=latest_query_files,
-                bypass_acl=bypass_acl,
             ),
             internet_search_tool_config=InternetSearchToolConfig(
                 answer_style_config=answer_style_config,
@@ -777,8 +737,6 @@ def stream_chat_message_objects(
                             response=custom_tool_response.tool_result,
                             tool_name=custom_tool_response.tool_name,
                         )
-                elif packet.id == SEARCH_DOC_CONTENT_ID and include_contexts:
-                    yield cast(DanswerContexts, packet.response)
 
             elif isinstance(packet, StreamStopInfo):
                 pass
@@ -887,30 +845,3 @@ def stream_chat_message(
         )
         for obj in objects:
             yield get_json_line(obj.model_dump())
-
-
-@log_function_time()
-def gather_stream_for_slack(
-    packets: ChatPacketStream,
-) -> ChatDanswerBotResponse:
-    response = ChatDanswerBotResponse()
-
-    answer = ""
-    for packet in packets:
-        if isinstance(packet, DanswerAnswerPiece) and packet.answer_piece:
-            answer += packet.answer_piece
-        elif isinstance(packet, QADocsResponse):
-            response.docs = packet
-        elif isinstance(packet, StreamingError):
-            response.error_msg = packet.error
-        elif isinstance(packet, ChatMessageDetail):
-            response.chat_message_id = packet.message_id
-        elif isinstance(packet, LLMRelevanceFilterResponse):
-            response.llm_selected_doc_indices = packet.llm_selected_doc_indices
-        elif isinstance(packet, AllCitations):
-            response.citations = packet.citations
-
-    if answer:
-        response.answer = answer
-
-    return response

backend/danswer/configs/app_configs.py

@@ -308,22 +308,6 @@ CONFLUENCE_CONNECTOR_ATTACHMENT_CHAR_COUNT_THRESHOLD = int(
     os.environ.get("CONFLUENCE_CONNECTOR_ATTACHMENT_CHAR_COUNT_THRESHOLD", 200_000)
 )
 
-# Due to breakages in the confluence API, the timezone offset must be specified client side
-# to match the user's specified timezone.
-
-# The current state of affairs:
-# CQL queries are parsed in the user's timezone and cannot be specified in UTC
-# no API retrieves the user's timezone
-# All data is returned in UTC, so we can't derive the user's timezone from that
-
-# https://community.developer.atlassian.com/t/confluence-cloud-time-zone-get-via-rest-api/35954/16
-# https://jira.atlassian.com/browse/CONFCLOUD-69670
-
-# enter as a floating point offset from UTC in hours (-24 < val < 24)
-# this will be applied globally, so it probably makes sense to transition this to per
-# connector as some point.
-CONFLUENCE_TIMEZONE_OFFSET = float(os.environ.get("CONFLUENCE_TIMEZONE_OFFSET", 0.0))
-
 JIRA_CONNECTOR_LABELS_TO_SKIP = [
     ignored_tag
     for ignored_tag in os.environ.get("JIRA_CONNECTOR_LABELS_TO_SKIP", "").split(",")
@@ -522,6 +506,3 @@ API_KEY_HASH_ROUNDS = (
 
 POD_NAME = os.environ.get("POD_NAME")
 POD_NAMESPACE = os.environ.get("POD_NAMESPACE")
-
-
-DEV_MODE = os.environ.get("DEV_MODE", "").lower() == "true"

backend/danswer/configs/constants.py

@@ -31,8 +31,6 @@ DISABLED_GEN_AI_MSG = (
     "You can still use Danswer as a search engine."
 )
 
-DEFAULT_PERSONA_ID = 0
-
 # Postgres connection constants for application_name
 POSTGRES_WEB_APP_NAME = "web"
 POSTGRES_INDEXER_APP_NAME = "indexer"
@@ -261,32 +259,6 @@ class DanswerCeleryPriority(int, Enum):
     LOWEST = auto()
 
 
-class DanswerCeleryTask:
-    CHECK_FOR_CONNECTOR_DELETION = "check_for_connector_deletion_task"
-    CHECK_FOR_VESPA_SYNC_TASK = "check_for_vespa_sync_task"
-    CHECK_FOR_INDEXING = "check_for_indexing"
-    CHECK_FOR_PRUNING = "check_for_pruning"
-    CHECK_FOR_DOC_PERMISSIONS_SYNC = "check_for_doc_permissions_sync"
-    CHECK_FOR_EXTERNAL_GROUP_SYNC = "check_for_external_group_sync"
-    MONITOR_VESPA_SYNC = "monitor_vespa_sync"
-    KOMBU_MESSAGE_CLEANUP_TASK = "kombu_message_cleanup_task"
-    CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK = (
-        "connector_permission_sync_generator_task"
-    )
-    UPDATE_EXTERNAL_DOCUMENT_PERMISSIONS_TASK = (
-        "update_external_document_permissions_task"
-    )
-    CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK = (
-        "connector_external_group_sync_generator_task"
-    )
-    CONNECTOR_INDEXING_PROXY_TASK = "connector_indexing_proxy_task"
-    CONNECTOR_PRUNING_GENERATOR_TASK = "connector_pruning_generator_task"
-    DOCUMENT_BY_CC_PAIR_CLEANUP_TASK = "document_by_cc_pair_cleanup_task"
-    VESPA_METADATA_SYNC_TASK = "vespa_metadata_sync_task"
-    CHECK_TTL_MANAGEMENT_TASK = "check_ttl_management_task"
-    AUTOGENERATE_USAGE_REPORT_TASK = "autogenerate_usage_report_task"
-
-
 REDIS_SOCKET_KEEPALIVE_OPTIONS = {}
 REDIS_SOCKET_KEEPALIVE_OPTIONS[socket.TCP_KEEPINTVL] = 15
 REDIS_SOCKET_KEEPALIVE_OPTIONS[socket.TCP_KEEPCNT] = 3

backend/danswer/configs/danswerbot_configs.py

@@ -4,8 +4,11 @@ import os
 # Danswer Slack Bot Configs
 #####
 DANSWER_BOT_NUM_RETRIES = int(os.environ.get("DANSWER_BOT_NUM_RETRIES", "5"))
+DANSWER_BOT_ANSWER_GENERATION_TIMEOUT = int(
+    os.environ.get("DANSWER_BOT_ANSWER_GENERATION_TIMEOUT", "90")
+)
 # How much of the available input context can be used for thread context
-MAX_THREAD_CONTEXT_PERCENTAGE = 512 * 2 / 3072
+DANSWER_BOT_TARGET_CHUNK_PERCENTAGE = 512 * 2 / 3072
 # Number of docs to display in "Reference Documents"
 DANSWER_BOT_NUM_DOCS_TO_DISPLAY = int(
     os.environ.get("DANSWER_BOT_NUM_DOCS_TO_DISPLAY", "5")
@@ -44,6 +47,17 @@ DANSWER_BOT_DISPLAY_ERROR_MSGS = os.environ.get(
 DANSWER_BOT_RESPOND_EVERY_CHANNEL = (
     os.environ.get("DANSWER_BOT_RESPOND_EVERY_CHANNEL", "").lower() == "true"
 )
+# Add a second LLM call post Answer to verify if the Answer is valid
+# Throws out answers that don't directly or fully answer the user query
+# This is the default for all DanswerBot channels unless the channel is configured individually
+# Set/unset by "Hide Non Answers"
+ENABLE_DANSWERBOT_REFLEXION = (
+    os.environ.get("ENABLE_DANSWERBOT_REFLEXION", "").lower() == "true"
+)
+# Currently not support chain of thought, probably will add back later
+DANSWER_BOT_DISABLE_COT = True
+# if set, will default DanswerBot to use quotes and reference documents
+DANSWER_BOT_USE_QUOTES = os.environ.get("DANSWER_BOT_USE_QUOTES", "").lower() == "true"
 
 # Maximum Questions Per Minute, Default Uncapped
 DANSWER_BOT_MAX_QPM = int(os.environ.get("DANSWER_BOT_MAX_QPM") or 0) or None

backend/danswer/connectors/confluence/connector.py

@@ -1,11 +1,9 @@
 from datetime import datetime
-from datetime import timedelta
 from datetime import timezone
 from typing import Any
 from urllib.parse import quote
 
 from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_LABELS_TO_SKIP
-from danswer.configs.app_configs import CONFLUENCE_TIMEZONE_OFFSET
 from danswer.configs.app_configs import CONTINUE_ON_CONNECTOR_FAILURE
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import DocumentSource
@@ -71,7 +69,6 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         # skip it. This is generally used to avoid indexing extra sensitive
         # pages.
         labels_to_skip: list[str] = CONFLUENCE_CONNECTOR_LABELS_TO_SKIP,
-        timezone_offset: float = CONFLUENCE_TIMEZONE_OFFSET,
     ) -> None:
         self.batch_size = batch_size
         self.continue_on_failure = continue_on_failure
@@ -107,8 +104,6 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
             )
             self.cql_label_filter = f" and label not in ({comma_separated_labels})"
 
-        self.timezone: timezone = timezone(offset=timedelta(hours=timezone_offset))
-
     @property
     def confluence_client(self) -> OnyxConfluence:
         if self._confluence_client is None:
@@ -209,14 +204,12 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         confluence_page_ids: list[str] = []
 
         page_query = self.cql_page_query + self.cql_label_filter + self.cql_time_filter
-        logger.debug(f"page_query: {page_query}")
         # Fetch pages as Documents
         for page in self.confluence_client.paginated_cql_retrieval(
             cql=page_query,
             expand=",".join(_PAGE_EXPANSION_FIELDS),
             limit=self.batch_size,
         ):
-            logger.debug(f"_fetch_document_batches: {page['id']}")
             confluence_page_ids.append(page["id"])
             doc = self._convert_object_to_document(page)
             if doc is not None:
@@ -249,10 +242,10 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
 
     def poll_source(self, start: float, end: float) -> GenerateDocumentsOutput:
         # Add time filters
-        formatted_start_time = datetime.fromtimestamp(start, tz=self.timezone).strftime(
+        formatted_start_time = datetime.fromtimestamp(start, tz=timezone.utc).strftime(
             "%Y-%m-%d %H:%M"
         )
-        formatted_end_time = datetime.fromtimestamp(end, tz=self.timezone).strftime(
+        formatted_end_time = datetime.fromtimestamp(end, tz=timezone.utc).strftime(
             "%Y-%m-%d %H:%M"
         )
         self.cql_time_filter = f" and lastmodified >= '{formatted_start_time}'"

backend/danswer/connectors/confluence/onyx_confluence.py

@@ -134,32 +134,6 @@ class OnyxConfluence(Confluence):
         super(OnyxConfluence, self).__init__(url, *args, **kwargs)
         self._wrap_methods()
 
-    def get_current_user(self, expand: str | None = None) -> Any:
-        """
-        Implements a method that isn't in the third party client.
-
-        Get information about the current user
-        :param expand: OPTIONAL expand for get status of user.
-                Possible param is "status". Results are "Active, Deactivated"
-        :return: Returns the user details
-        """
-
-        from atlassian.errors import ApiPermissionError  # type:ignore
-
-        url = "rest/api/user/current"
-        params = {}
-        if expand:
-            params["expand"] = expand
-        try:
-            response = self.get(url, params=params)
-        except HTTPError as e:
-            if e.response.status_code == 403:
-                raise ApiPermissionError(
-                    "The calling user does not have permission", reason=e
-                )
-            raise
-        return response
-
     def _wrap_methods(self) -> None:
         """
         For each attribute that is callable (i.e., a method) and doesn't start with an underscore,
@@ -332,13 +306,6 @@ def _validate_connector_configuration(
     )
     spaces = confluence_client_with_minimal_retries.get_all_spaces(limit=1)
 
-    # uncomment the following for testing
-    # the following is an attempt to retrieve the user's timezone
-    # Unfornately, all data is returned in UTC regardless of the user's time zone
-    # even tho CQL parses incoming times based on the user's time zone
-    # space_key = spaces["results"][0]["key"]
-    # space_details = confluence_client_with_minimal_retries.cql(f"space.key={space_key}+AND+type=space")
-
     if not spaces:
         raise RuntimeError(
             f"No spaces found at {wiki_base}! "

backend/danswer/connectors/confluence/utils.py

@@ -32,11 +32,7 @@ def get_user_email_from_username__server(
             response = confluence_client.get_mobile_parameters(user_name)
             email = response.get("email")
         except Exception:
-            # For now, we'll just return a string that indicates failure
-            # We may want to revert to returning None in the future
-            # email = None
-            email = f"FAILED TO GET CONFLUENCE EMAIL FOR {user_name}"
-            logger.warning(f"failed to get confluence email for {user_name}")
+            email = None
         _USER_EMAIL_CACHE[user_name] = email
     return _USER_EMAIL_CACHE[user_name]
 

backend/danswer/danswerbot/slack/blocks.py

@@ -16,7 +16,7 @@ from slack_sdk.models.blocks import SectionBlock
 from slack_sdk.models.blocks.basic_components import MarkdownTextObject
 from slack_sdk.models.blocks.block_elements import ImageElement
 
-from danswer.chat.models import ChatDanswerBotResponse
+from danswer.chat.models import DanswerQuote
 from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
 from danswer.configs.app_configs import WEB_DOMAIN
 from danswer.configs.constants import DocumentSource
@@ -40,7 +40,10 @@ from danswer.danswerbot.slack.utils import translate_vespa_highlight_to_slack
 from danswer.db.chat import get_chat_session_by_message_id
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.models import ChannelConfig
+from danswer.db.models import Persona
+from danswer.one_shot_answer.models import OneShotQAResponse
 from danswer.utils.text_processing import decode_escapes
+from danswer.utils.text_processing import replace_whitespaces_w_space
 
 _MAX_BLURB_LEN = 45
 
@@ -324,7 +327,7 @@ def _build_sources_blocks(
 
 
 def _priority_ordered_documents_blocks(
-    answer: ChatDanswerBotResponse,
+    answer: OneShotQAResponse,
 ) -> list[Block]:
     docs_response = answer.docs if answer.docs else None
     top_docs = docs_response.top_documents if docs_response else []
@@ -347,7 +350,7 @@ def _priority_ordered_documents_blocks(
 
 
 def _build_citations_blocks(
-    answer: ChatDanswerBotResponse,
+    answer: OneShotQAResponse,
 ) -> list[Block]:
     docs_response = answer.docs if answer.docs else None
     top_docs = docs_response.top_documents if docs_response else []
@@ -366,8 +369,51 @@ def _build_citations_blocks(
     return citations_block
 
 
+def _build_quotes_block(
+    quotes: list[DanswerQuote],
+) -> list[Block]:
+    quote_lines: list[str] = []
+    doc_to_quotes: dict[str, list[str]] = {}
+    doc_to_link: dict[str, str] = {}
+    doc_to_sem_id: dict[str, str] = {}
+    for q in quotes:
+        quote = q.quote
+        doc_id = q.document_id
+        doc_link = q.link
+        doc_name = q.semantic_identifier
+        if doc_link and doc_name and doc_id and quote:
+            if doc_id not in doc_to_quotes:
+                doc_to_quotes[doc_id] = [quote]
+                doc_to_link[doc_id] = doc_link
+                doc_to_sem_id[doc_id] = (
+                    doc_name
+                    if q.source_type != DocumentSource.SLACK.value
+                    else "#" + doc_name
+                )
+            else:
+                doc_to_quotes[doc_id].append(quote)
+
+    for doc_id, quote_strs in doc_to_quotes.items():
+        quotes_str_clean = [
+            replace_whitespaces_w_space(q_str).strip() for q_str in quote_strs
+        ]
+        longest_quotes = sorted(quotes_str_clean, key=len, reverse=True)[:5]
+        single_quote_str = "\n".join([f"```{q_str}```" for q_str in longest_quotes])
+        link = doc_to_link[doc_id]
+        sem_id = doc_to_sem_id[doc_id]
+        quote_lines.append(
+            f"<{link}|{sem_id}>:\n{remove_slack_text_interactions(single_quote_str)}"
+        )
+
+    if not doc_to_quotes:
+        return []
+
+    return [SectionBlock(text="*Relevant Snippets*\n" + "\n".join(quote_lines))]
+
+
 def _build_qa_response_blocks(
-    answer: ChatDanswerBotResponse,
+    answer: OneShotQAResponse,
+    skip_quotes: bool = False,
     process_message_for_citations: bool = False,
 ) -> list[Block]:
     retrieval_info = answer.docs
@@ -376,10 +422,13 @@ def _build_qa_response_blocks(
         raise RuntimeError("Failed to retrieve docs, cannot answer question.")
 
     formatted_answer = format_slack_message(answer.answer) if answer.answer else None
+    quotes = answer.quotes.quotes if answer.quotes else None
 
     if DISABLE_GENERATIVE_AI:
         return []
 
+    quotes_blocks: list[Block] = []
+
     filter_block: Block | None = None
     if (
         retrieval_info.applied_time_cutoff
@@ -422,6 +471,16 @@ def _build_qa_response_blocks(
         answer_blocks = [
             SectionBlock(text=text) for text in _split_text(answer_processed)
         ]
+        if quotes:
+            quotes_blocks = _build_quotes_block(quotes)
+
+        # if no quotes OR `_build_quotes_block()` did not give back any blocks
+        if not quotes_blocks:
+            quotes_blocks = [
+                SectionBlock(
+                    text="*Warning*: no sources were quoted for this answer, so it may be unreliable 😔"
+                )
+            ]
 
     response_blocks: list[Block] = []
 
@@ -430,6 +489,9 @@ def _build_qa_response_blocks(
 
     response_blocks.extend(answer_blocks)
 
+    if not skip_quotes:
+        response_blocks.extend(quotes_blocks)
+
     return response_blocks
 
 
@@ -505,9 +567,10 @@ def build_follow_up_resolved_blocks(
 
 
 def build_slack_response_blocks(
-    answer: ChatDanswerBotResponse,
     tenant_id: str | None,
     message_info: SlackMessageInfo,
+    answer: OneShotQAResponse,
+    persona: Persona | None,
     channel_conf: ChannelConfig | None,
     use_citations: bool,
     feedback_reminder_id: str | None,
@@ -524,6 +587,7 @@ def build_slack_response_blocks(
 
     answer_blocks = _build_qa_response_blocks(
         answer=answer,
+        skip_quotes=persona is not None or use_citations,
         process_message_for_citations=use_citations,
     )
 
@@ -553,7 +617,8 @@ def build_slack_response_blocks(
 
     citations_blocks = []
     document_blocks = []
-    if use_citations and answer.citations:
+    if use_citations:
+        # if citations are enabled, only show cited documents
         citations_blocks = _build_citations_blocks(answer)
     else:
         document_blocks = _priority_ordered_documents_blocks(answer)
@@ -572,5 +637,4 @@ def build_slack_response_blocks(
         + web_follow_up_block
         + follow_up_block
     )
-
     return all_blocks

backend/danswer/danswerbot/slack/handlers/handle_regular_answer.py

@@ -1,6 +1,7 @@
 import functools
 from collections.abc import Callable
 from typing import Any
+from typing import cast
 from typing import Optional
 from typing import TypeVar
 
@@ -8,36 +9,46 @@ from retry import retry
 from slack_sdk import WebClient
 from slack_sdk.models.blocks import SectionBlock
 
-from danswer.chat.chat_utils import prepare_chat_message_request
-from danswer.chat.models import ChatDanswerBotResponse
-from danswer.chat.process_message import gather_stream_for_slack
-from danswer.chat.process_message import stream_chat_message_objects
 from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
-from danswer.configs.constants import DEFAULT_PERSONA_ID
+from danswer.configs.danswerbot_configs import DANSWER_BOT_ANSWER_GENERATION_TIMEOUT
+from danswer.configs.danswerbot_configs import DANSWER_BOT_DISABLE_COT
 from danswer.configs.danswerbot_configs import DANSWER_BOT_DISABLE_DOCS_ONLY_ANSWER
 from danswer.configs.danswerbot_configs import DANSWER_BOT_DISPLAY_ERROR_MSGS
 from danswer.configs.danswerbot_configs import DANSWER_BOT_NUM_RETRIES
+from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTAGE
+from danswer.configs.danswerbot_configs import DANSWER_BOT_USE_QUOTES
 from danswer.configs.danswerbot_configs import DANSWER_FOLLOWUP_EMOJI
 from danswer.configs.danswerbot_configs import DANSWER_REACT_EMOJI
-from danswer.configs.danswerbot_configs import MAX_THREAD_CONTEXT_PERCENTAGE
+from danswer.configs.danswerbot_configs import ENABLE_DANSWERBOT_REFLEXION
 from danswer.context.search.enums import OptionalSearchSetting
 from danswer.context.search.models import BaseFilters
+from danswer.context.search.models import RerankingDetails
 from danswer.context.search.models import RetrievalDetails
 from danswer.danswerbot.slack.blocks import build_slack_response_blocks
 from danswer.danswerbot.slack.handlers.utils import send_team_member_message
-from danswer.danswerbot.slack.handlers.utils import slackify_message_thread
 from danswer.danswerbot.slack.models import SlackMessageInfo
 from danswer.danswerbot.slack.utils import respond_in_thread
 from danswer.danswerbot.slack.utils import SlackRateLimiter
 from danswer.danswerbot.slack.utils import update_emote_react
 from danswer.db.engine import get_session_with_tenant
+from danswer.db.models import Persona
+from danswer.db.models import SlackBotResponseType
 from danswer.db.models import SlackChannelConfig
-from danswer.db.models import User
-from danswer.db.persona import get_persona_by_id
+from danswer.db.persona import fetch_persona_by_id
+from danswer.db.search_settings import get_current_search_settings
 from danswer.db.users import get_user_by_email
-from danswer.server.query_and_chat.models import CreateChatMessageRequest
+from danswer.llm.answering.prompts.citations_prompt import (
+    compute_max_document_tokens_for_persona,
+)
+from danswer.llm.factory import get_llms_for_persona
+from danswer.llm.utils import check_number_of_tokens
+from danswer.llm.utils import get_max_input_tokens
+from danswer.one_shot_answer.answer_question import get_search_answer
+from danswer.one_shot_answer.models import DirectQARequest
+from danswer.one_shot_answer.models import OneShotQAResponse
 from danswer.utils.logger import DanswerLoggingAdapter
 
+
 srl = SlackRateLimiter()
 
 RT = TypeVar("RT")  # return type
@@ -72,14 +83,16 @@ def handle_regular_answer(
     feedback_reminder_id: str | None,
     tenant_id: str | None,
     num_retries: int = DANSWER_BOT_NUM_RETRIES,
-    thread_context_percent: float = MAX_THREAD_CONTEXT_PERCENTAGE,
+    answer_generation_timeout: int = DANSWER_BOT_ANSWER_GENERATION_TIMEOUT,
+    thread_context_percent: float = DANSWER_BOT_TARGET_CHUNK_PERCENTAGE,
     should_respond_with_error_msgs: bool = DANSWER_BOT_DISPLAY_ERROR_MSGS,
     disable_docs_only_answer: bool = DANSWER_BOT_DISABLE_DOCS_ONLY_ANSWER,
+    disable_cot: bool = DANSWER_BOT_DISABLE_COT,
+    reflexion: bool = ENABLE_DANSWERBOT_REFLEXION,
 ) -> bool:
     channel_conf = slack_channel_config.channel_config if slack_channel_config else None
 
     messages = message_info.thread_messages
-
     message_ts_to_respond_to = message_info.msg_to_respond
     is_bot_msg = message_info.is_bot_msg
     user = None
@@ -89,18 +102,9 @@ def handle_regular_answer(
                 user = get_user_by_email(message_info.email, db_session)
 
     document_set_names: list[str] | None = None
-    prompt = None
-    # If no persona is specified, use the default search based persona
-    # This way slack flow always has a persona
     persona = slack_channel_config.persona if slack_channel_config else None
-    if not persona:
-        with get_session_with_tenant(tenant_id) as db_session:
-            persona = get_persona_by_id(DEFAULT_PERSONA_ID, user, db_session)
-            document_set_names = [
-                document_set.name for document_set in persona.document_sets
-            ]
-            prompt = persona.prompts[0] if persona.prompts else None
-    else:
+    prompt = None
+    if persona:
         document_set_names = [
             document_set.name for document_set in persona.document_sets
         ]
@@ -108,26 +112,6 @@ def handle_regular_answer(
 
     should_respond_even_with_no_docs = persona.num_chunks == 0 if persona else False
 
-    # TODO: Add in support for Slack to truncate messages based on max LLM context
-    # llm, _ = get_llms_for_persona(persona)
-
-    # llm_tokenizer = get_tokenizer(
-    #     model_name=llm.config.model_name,
-    #     provider_type=llm.config.model_provider,
-    # )
-
-    # # In cases of threads, split the available tokens between docs and thread context
-    # input_tokens = get_max_input_tokens(
-    #     model_name=llm.config.model_name,
-    #     model_provider=llm.config.model_provider,
-    # )
-    # max_history_tokens = int(input_tokens * thread_context_percent)
-    # combined_message = combine_message_thread(
-    #     messages, max_tokens=max_history_tokens, llm_tokenizer=llm_tokenizer
-    # )
-
-    combined_message = slackify_message_thread(messages)
-
     bypass_acl = False
     if (
         slack_channel_config
@@ -138,6 +122,13 @@ def handle_regular_answer(
         # with non-public document sets
         bypass_acl = True
 
+    # figure out if we want to use citations or quotes
+    use_citations = (
+        not DANSWER_BOT_USE_QUOTES
+        if slack_channel_config is None
+        else slack_channel_config.response_type == SlackBotResponseType.CITATIONS
+    )
+
     if not message_ts_to_respond_to and not is_bot_msg:
         # if the message is not "/danswer" command, then it should have a message ts to respond to
         raise RuntimeError(
@@ -150,23 +141,75 @@ def handle_regular_answer(
         backoff=2,
     )
     @rate_limits(client=client, channel=channel, thread_ts=message_ts_to_respond_to)
-    def _get_slack_answer(
-        new_message_request: CreateChatMessageRequest, danswer_user: User | None
-    ) -> ChatDanswerBotResponse:
+    def _get_answer(new_message_request: DirectQARequest) -> OneShotQAResponse | None:
+        max_document_tokens: int | None = None
+        max_history_tokens: int | None = None
+
         with get_session_with_tenant(tenant_id) as db_session:
-            packets = stream_chat_message_objects(
-                new_msg_req=new_message_request,
-                user=danswer_user,
+            if len(new_message_request.messages) > 1:
+                if new_message_request.persona_config:
+                    raise RuntimeError("Slack bot does not support persona config")
+                elif new_message_request.persona_id is not None:
+                    persona = cast(
+                        Persona,
+                        fetch_persona_by_id(
+                            db_session,
+                            new_message_request.persona_id,
+                            user=None,
+                            get_editable=False,
+                        ),
+                    )
+                else:
+                    raise RuntimeError(
+                        "No persona id provided, this should never happen."
+                    )
+
+                llm, _ = get_llms_for_persona(persona)
+
+                # In cases of threads, split the available tokens between docs and thread context
+                input_tokens = get_max_input_tokens(
+                    model_name=llm.config.model_name,
+                    model_provider=llm.config.model_provider,
+                )
+                max_history_tokens = int(input_tokens * thread_context_percent)
+
+                remaining_tokens = input_tokens - max_history_tokens
+
+                query_text = new_message_request.messages[0].message
+                if persona:
+                    max_document_tokens = compute_max_document_tokens_for_persona(
+                        persona=persona,
+                        actual_user_input=query_text,
+                        max_llm_token_override=remaining_tokens,
+                    )
+                else:
+                    max_document_tokens = (
+                        remaining_tokens
+                        - 512  # Needs to be more than any of the QA prompts
+                        - check_number_of_tokens(query_text)
+                    )
+
+            if DISABLE_GENERATIVE_AI:
+                return None
+
+            # This also handles creating the query event in postgres
+            answer = get_search_answer(
+                query_req=new_message_request,
+                user=user,
+                max_document_tokens=max_document_tokens,
+                max_history_tokens=max_history_tokens,
                 db_session=db_session,
+                answer_generation_timeout=answer_generation_timeout,
+                enable_reflexion=reflexion,
                 bypass_acl=bypass_acl,
+                use_citations=use_citations,
+                danswerbot_flow=True,
             )
 
-            answer = gather_stream_for_slack(packets)
-
-        if answer.error_msg:
-            raise RuntimeError(answer.error_msg)
-
-        return answer
+            if not answer.error_msg:
+                return answer
+            else:
+                raise RuntimeError(answer.error_msg)
 
     try:
         # By leaving time_cutoff and favor_recent as None, and setting enable_auto_detect_filters
@@ -196,24 +239,26 @@ def handle_regular_answer(
             enable_auto_detect_filters=auto_detect_filters,
         )
 
+        # Always apply reranking settings if it exists, this is the non-streaming flow
         with get_session_with_tenant(tenant_id) as db_session:
-            answer_request = prepare_chat_message_request(
-                message_text=combined_message,
-                user=user,
-                persona_id=persona.id,
-                # This is not used in the Slack flow, only in the answer API
-                persona_override_config=None,
-                prompt=prompt,
-                message_ts_to_respond_to=message_ts_to_respond_to,
-                retrieval_details=retrieval_details,
-                rerank_settings=None,  # Rerank customization supported in Slack flow
-                db_session=db_session,
+            saved_search_settings = get_current_search_settings(db_session)
+
+        # This includes throwing out answer via reflexion
+        answer = _get_answer(
+            DirectQARequest(
+                messages=messages,
+                multilingual_query_expansion=saved_search_settings.multilingual_expansion
+                if saved_search_settings
+                else None,
+                prompt_id=prompt.id if prompt else None,
+                persona_id=persona.id if persona is not None else 0,
+                retrieval_options=retrieval_details,
+                chain_of_thought=not disable_cot,
+                rerank_settings=RerankingDetails.from_db_model(saved_search_settings)
+                if saved_search_settings
+                else None,
             )
-
-        answer = _get_slack_answer(
-            new_message_request=answer_request, danswer_user=user
         )
-
     except Exception as e:
         logger.exception(
             f"Unable to process message - did not successfully answer "
@@ -314,7 +359,7 @@ def handle_regular_answer(
     top_docs = retrieval_info.top_documents
     if not top_docs and not should_respond_even_with_no_docs:
         logger.error(
-            f"Unable to answer question: '{combined_message}' - no documents found"
+            f"Unable to answer question: '{answer.rephrase}' - no documents found"
         )
         # Optionally, respond in thread with the error message
         # Used primarily for debugging purposes
@@ -335,18 +380,18 @@ def handle_regular_answer(
         )
         return True
 
-    only_respond_if_citations = (
+    only_respond_with_citations_or_quotes = (
         channel_conf
         and "well_answered_postfilter" in channel_conf.get("answer_filters", [])
     )
-
+    has_citations_or_quotes = bool(answer.citations or answer.quotes)
     if (
-        only_respond_if_citations
-        and not answer.citations
+        only_respond_with_citations_or_quotes
+        and not has_citations_or_quotes
         and not message_info.bypass_filters
     ):
         logger.error(
-            f"Unable to find citations to answer: '{answer.answer}' - not answering!"
+            f"Unable to find citations or quotes to answer: '{answer.rephrase}' - not answering!"
         )
         # Optionally, respond in thread with the error message
         # Used primarily for debugging purposes
@@ -364,8 +409,9 @@ def handle_regular_answer(
         tenant_id=tenant_id,
         message_info=message_info,
         answer=answer,
+        persona=persona,
         channel_conf=channel_conf,
-        use_citations=True,  # No longer supporting quotes
+        use_citations=use_citations,
         feedback_reminder_id=feedback_reminder_id,
     )
 

backend/danswer/danswerbot/slack/handlers/utils.py

@@ -1,33 +1,8 @@
 from slack_sdk import WebClient
 
-from danswer.chat.models import ThreadMessage
-from danswer.configs.constants import MessageType
 from danswer.danswerbot.slack.utils import respond_in_thread
 
 
-def slackify_message_thread(messages: list[ThreadMessage]) -> str:
-    # Note: this does not handle extremely long threads, every message will be included
-    # with weaker LLMs, this could cause issues with exceeeding the token limit
-    if not messages:
-        return ""
-
-    message_strs: list[str] = []
-    for message in messages:
-        if message.role == MessageType.USER:
-            message_text = (
-                f"{message.sender or 'Unknown User'} said in Slack:\n{message.message}"
-            )
-        elif message.role == MessageType.ASSISTANT:
-            message_text = f"AI said in Slack:\n{message.message}"
-        else:
-            message_text = (
-                f"{message.role.value.upper()} said in Slack:\n{message.message}"
-            )
-        message_strs.append(message_text)
-
-    return "\n\n".join(message_strs)
-
-
 def send_team_member_message(
     client: WebClient,
     channel: str,

backend/danswer/danswerbot/slack/listener.py

@@ -19,8 +19,6 @@ from slack_sdk.socket_mode.request import SocketModeRequest
 from slack_sdk.socket_mode.response import SocketModeResponse
 from sqlalchemy.orm import Session
 
-from danswer.chat.models import ThreadMessage
-from danswer.configs.app_configs import DEV_MODE
 from danswer.configs.app_configs import POD_NAME
 from danswer.configs.app_configs import POD_NAMESPACE
 from danswer.configs.constants import DanswerRedisLocks
@@ -76,6 +74,7 @@ from danswer.db.slack_bot import fetch_slack_bots
 from danswer.key_value_store.interface import KvKeyNotFoundError
 from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
 from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
+from danswer.one_shot_answer.models import ThreadMessage
 from danswer.redis.redis_pool import get_redis_client
 from danswer.server.manage.models import SlackBotTokens
 from danswer.utils.logger import setup_logger
@@ -251,7 +250,7 @@ class SlackbotHandler:
                 nx=True,
                 ex=TENANT_LOCK_EXPIRATION,
             )
-            if not acquired and not DEV_MODE:
+            if not acquired:
                 logger.debug(f"Another pod holds the lock for tenant {tenant_id}")
                 continue
 

backend/danswer/danswerbot/slack/models.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel
 
-from danswer.chat.models import ThreadMessage
+from danswer.one_shot_answer.models import ThreadMessage
 
 
 class SlackMessageInfo(BaseModel):

backend/danswer/danswerbot/slack/utils.py

@@ -30,13 +30,13 @@ from danswer.configs.danswerbot_configs import (
 from danswer.connectors.slack.utils import make_slack_api_rate_limited
 from danswer.connectors.slack.utils import SlackTextCleaner
 from danswer.danswerbot.slack.constants import FeedbackVisibility
-from danswer.danswerbot.slack.models import ThreadMessage
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.users import get_user_by_email
 from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.factory import get_default_llms
 from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
 from danswer.llm.utils import message_to_string
+from danswer.one_shot_answer.models import ThreadMessage
 from danswer.prompts.miscellaneous_prompts import SLACK_LANGUAGE_REPHRASE_PROMPT
 from danswer.utils.logger import setup_logger
 from danswer.utils.telemetry import optional_telemetry

backend/danswer/db/chat.py

@@ -145,10 +145,16 @@ def get_chat_sessions_by_user(
     user_id: UUID | None,
     deleted: bool | None,
     db_session: Session,
+    only_one_shot: bool = False,
     limit: int = 50,
 ) -> list[ChatSession]:
     stmt = select(ChatSession).where(ChatSession.user_id == user_id)
 
+    if only_one_shot:
+        stmt = stmt.where(ChatSession.one_shot.is_(True))
+    else:
+        stmt = stmt.where(ChatSession.one_shot.is_(False))
+
     stmt = stmt.order_by(desc(ChatSession.time_created))
 
     if deleted is not None:
@@ -220,11 +226,12 @@ def delete_messages_and_files_from_chat_session(
 
 def create_chat_session(
     db_session: Session,
-    description: str | None,
+    description: str,
     user_id: UUID | None,
     persona_id: int | None,  # Can be none if temporary persona is used
     llm_override: LLMOverride | None = None,
     prompt_override: PromptOverride | None = None,
+    one_shot: bool = False,
     danswerbot_flow: bool = False,
     slack_thread_id: str | None = None,
 ) -> ChatSession:
@@ -234,6 +241,7 @@ def create_chat_session(
         description=description,
         llm_override=llm_override,
         prompt_override=prompt_override,
+        one_shot=one_shot,
         danswerbot_flow=danswerbot_flow,
         slack_thread_id=slack_thread_id,
     )
@@ -279,6 +287,8 @@ def duplicate_chat_session_for_user_from_slack(
         description="",
         llm_override=chat_session.llm_override,
         prompt_override=chat_session.prompt_override,
+        # Chat sessions from Slack should put people in the chat UI, not the search
+        one_shot=False,
         # Chat is in UI now so this is false
         danswerbot_flow=False,
         # Maybe we want this in the future to track if it was created from Slack

backend/danswer/db/engine.py

@@ -37,7 +37,6 @@ from danswer.configs.app_configs import POSTGRES_PORT
 from danswer.configs.app_configs import POSTGRES_USER
 from danswer.configs.app_configs import USER_AUTH_SECRET
 from danswer.configs.constants import POSTGRES_UNKNOWN_APP_NAME
-from danswer.server.utils import BasicAuthenticationError
 from danswer.utils.logger import setup_logger
 from shared_configs.configs import MULTI_TENANT
 from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
@@ -427,9 +426,7 @@ def get_session() -> Generator[Session, None, None]:
     """Generate a database session with the appropriate tenant schema set."""
     tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
     if tenant_id == POSTGRES_DEFAULT_SCHEMA and MULTI_TENANT:
-        raise BasicAuthenticationError(
-            detail="User must authenticate",
-        )
+        raise HTTPException(status_code=401, detail="User must authenticate")
 
     engine = get_sqlalchemy_engine()
 

backend/danswer/db/models.py

@@ -1,5 +1,6 @@
 import datetime
 import json
+from enum import Enum as PyEnum
 from typing import Any
 from typing import Literal
 from typing import NotRequired
@@ -963,8 +964,9 @@ class ChatSession(Base):
     persona_id: Mapped[int | None] = mapped_column(
         ForeignKey("persona.id"), nullable=True
     )
-    description: Mapped[str | None] = mapped_column(Text, nullable=True)
-    # This chat created by DanswerBot
+    description: Mapped[str] = mapped_column(Text)
+    # One-shot direct answering, currently the two types of chats are not mixed
+    one_shot: Mapped[bool] = mapped_column(Boolean, default=False)
     danswerbot_flow: Mapped[bool] = mapped_column(Boolean, default=False)
     # Only ever set to True if system is set to not hard-delete chats
     deleted: Mapped[bool] = mapped_column(Boolean, default=False)
@@ -1486,6 +1488,11 @@ class ChannelConfig(TypedDict):
     show_continue_in_web_ui: NotRequired[bool]  # defaults to False
 
 
+class SlackBotResponseType(str, PyEnum):
+    QUOTES = "quotes"
+    CITATIONS = "citations"
+
+
 class SlackChannelConfig(Base):
     __tablename__ = "slack_channel_config"
 
@@ -1498,6 +1505,9 @@ class SlackChannelConfig(Base):
     channel_config: Mapped[ChannelConfig] = mapped_column(
         postgresql.JSONB(), nullable=False
     )
+    response_type: Mapped[SlackBotResponseType] = mapped_column(
+        Enum(SlackBotResponseType, native_enum=False), nullable=False
+    )
 
     enable_auto_filters: Mapped[bool] = mapped_column(
         Boolean, nullable=False, default=False

backend/danswer/db/persona.py

@@ -415,6 +415,9 @@ def upsert_prompt(
     return prompt
 
 
+# NOTE: This operation cannot update persona configuration options that
+# are core to the persona, such as its display priority and
+# whether or not the assistant is a built-in / default assistant
 def upsert_persona(
     user: User | None,
     name: str,
@@ -446,12 +449,6 @@ def upsert_persona(
     chunks_above: int = CONTEXT_CHUNKS_ABOVE,
     chunks_below: int = CONTEXT_CHUNKS_BELOW,
 ) -> Persona:
-    """
-    NOTE: This operation cannot update persona configuration options that
-    are core to the persona, such as its display priority and
-    whether or not the assistant is a built-in / default assistant
-    """
-
     if persona_id is not None:
         persona = db_session.query(Persona).filter_by(id=persona_id).first()
     else:
@@ -489,8 +486,6 @@ def upsert_persona(
         validate_persona_tools(tools)
 
     if persona:
-        # Built-in personas can only be updated through YAML configuration.
-        # This ensures that core system personas are not modified unintentionally.
         if persona.builtin_persona and not builtin_persona:
             raise ValueError("Cannot update builtin persona with non-builtin.")
 
@@ -499,9 +494,6 @@ def upsert_persona(
             db_session=db_session, persona_id=persona.id, user=user, get_editable=True
         )
 
-        # The following update excludes `default`, `built-in`, and display priority.
-        # Display priority is handled separately in the `display-priority` endpoint.
-        # `default` and `built-in` properties can only be set when creating a persona.
         persona.name = name
         persona.description = description
         persona.num_chunks = num_chunks

backend/danswer/db/slack_channel_config.py

@@ -10,6 +10,7 @@ from danswer.db.constants import SLACK_BOT_PERSONA_PREFIX
 from danswer.db.models import ChannelConfig
 from danswer.db.models import Persona
 from danswer.db.models import Persona__DocumentSet
+from danswer.db.models import SlackBotResponseType
 from danswer.db.models import SlackChannelConfig
 from danswer.db.models import User
 from danswer.db.persona import get_default_prompt
@@ -82,6 +83,7 @@ def insert_slack_channel_config(
     slack_bot_id: int,
     persona_id: int | None,
     channel_config: ChannelConfig,
+    response_type: SlackBotResponseType,
     standard_answer_category_ids: list[int],
     enable_auto_filters: bool,
 ) -> SlackChannelConfig:
@@ -113,6 +115,7 @@ def insert_slack_channel_config(
         slack_bot_id=slack_bot_id,
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=response_type,
         standard_answer_categories=existing_standard_answer_categories,
         enable_auto_filters=enable_auto_filters,
     )
@@ -127,6 +130,7 @@ def update_slack_channel_config(
     slack_channel_config_id: int,
     persona_id: int | None,
     channel_config: ChannelConfig,
+    response_type: SlackBotResponseType,
     standard_answer_category_ids: list[int],
     enable_auto_filters: bool,
 ) -> SlackChannelConfig:
@@ -166,6 +170,7 @@ def update_slack_channel_config(
     # will encounter `violates foreign key constraint` errors
     slack_channel_config.persona_id = persona_id
     slack_channel_config.channel_config = channel_config
+    slack_channel_config.response_type = response_type
     slack_channel_config.standard_answer_categories = list(
         existing_standard_answer_categories
     )

backend/danswer/llm/answering/answer.py

@@ -18,12 +18,18 @@ from danswer.llm.answering.models import PromptConfig
 from danswer.llm.answering.prompts.build import AnswerPromptBuilder
 from danswer.llm.answering.prompts.build import default_build_system_message
 from danswer.llm.answering.prompts.build import default_build_user_message
+from danswer.llm.answering.stream_processing.answer_response_handler import (
+    AnswerResponseHandler,
+)
 from danswer.llm.answering.stream_processing.answer_response_handler import (
     CitationResponseHandler,
 )
 from danswer.llm.answering.stream_processing.answer_response_handler import (
     DummyAnswerResponseHandler,
 )
+from danswer.llm.answering.stream_processing.answer_response_handler import (
+    QuotesResponseHandler,
+)
 from danswer.llm.answering.stream_processing.utils import map_document_id_order
 from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
 from danswer.llm.interfaces import LLM
@@ -208,23 +214,18 @@ class Answer:
 
         search_result = SearchTool.get_search_result(current_llm_call) or []
 
-        # Quotes are no longer supported
-        # answer_handler: AnswerResponseHandler
-        # if self.answer_style_config.citation_config:
-        #     answer_handler = CitationResponseHandler(
-        #         context_docs=search_result,
-        #         doc_id_to_rank_map=map_document_id_order(search_result),
-        #     )
-        # elif self.answer_style_config.quotes_config:
-        #     answer_handler = QuotesResponseHandler(
-        #         context_docs=search_result,
-        #     )
-        # else:
-        #     raise ValueError("No answer style config provided")
-        answer_handler = CitationResponseHandler(
-            context_docs=search_result,
-            doc_id_to_rank_map=map_document_id_order(search_result),
-        )
+        answer_handler: AnswerResponseHandler
+        if self.answer_style_config.citation_config:
+            answer_handler = CitationResponseHandler(
+                context_docs=search_result,
+                doc_id_to_rank_map=map_document_id_order(search_result),
+            )
+        elif self.answer_style_config.quotes_config:
+            answer_handler = QuotesResponseHandler(
+                context_docs=search_result,
+            )
+        else:
+            raise ValueError("No answer style config provided")
 
         response_handler_manager = LLMResponseHandlerManager(
             tool_call_handler, answer_handler, self.is_cancelled

backend/danswer/llm/answering/llm_response_handler.py

@@ -8,6 +8,7 @@ from pydantic.v1 import BaseModel as BaseModel__v1
 
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import DanswerQuotes
 from danswer.chat.models import StreamStopInfo
 from danswer.chat.models import StreamStopReason
 from danswer.file_store.models import InMemoryChatFile
@@ -29,6 +30,7 @@ if TYPE_CHECKING:
 ResponsePart = (
     DanswerAnswerPiece
     | CitationInfo
+    | DanswerQuotes
     | ToolCallKickoff
     | ToolResponse
     | ToolCallFinalResult

backend/danswer/llm/answering/stream_processing/answer_response_handler.py

@@ -9,6 +9,9 @@ from danswer.llm.answering.llm_response_handler import ResponsePart
 from danswer.llm.answering.stream_processing.citation_processing import (
     CitationProcessor,
 )
+from danswer.llm.answering.stream_processing.quotes_processing import (
+    QuotesProcessor,
+)
 from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
 from danswer.utils.logger import setup_logger
 
@@ -67,29 +70,28 @@ class CitationResponseHandler(AnswerResponseHandler):
         yield from self.citation_processor.process_token(content)
 
 
-# No longer in use, remove later
-# class QuotesResponseHandler(AnswerResponseHandler):
-#     def __init__(
-#         self,
-#         context_docs: list[LlmDoc],
-#         is_json_prompt: bool = True,
-#     ):
-#         self.quotes_processor = QuotesProcessor(
-#             context_docs=context_docs,
-#             is_json_prompt=is_json_prompt,
-#         )
+class QuotesResponseHandler(AnswerResponseHandler):
+    def __init__(
+        self,
+        context_docs: list[LlmDoc],
+        is_json_prompt: bool = True,
+    ):
+        self.quotes_processor = QuotesProcessor(
+            context_docs=context_docs,
+            is_json_prompt=is_json_prompt,
+        )
 
-#     def handle_response_part(
-#         self,
-#         response_item: BaseMessage | None,
-#         previous_response_items: list[BaseMessage],
-#     ) -> Generator[ResponsePart, None, None]:
-#         if response_item is None:
-#             yield from self.quotes_processor.process_token(None)
-#             return
+    def handle_response_part(
+        self,
+        response_item: BaseMessage | None,
+        previous_response_items: list[BaseMessage],
+    ) -> Generator[ResponsePart, None, None]:
+        if response_item is None:
+            yield from self.quotes_processor.process_token(None)
+            return
 
-#         content = (
-#             response_item.content if isinstance(response_item.content, str) else ""
-#         )
+        content = (
+            response_item.content if isinstance(response_item.content, str) else ""
+        )
 
-#         yield from self.quotes_processor.process_token(content)
+        yield from self.quotes_processor.process_token(content)

backend/danswer/llm/answering/stream_processing/quotes_processing.py

@@ -1,4 +1,3 @@
-# THIS IS NO LONGER IN USE
 import math
 import re
 from collections.abc import Generator
@@ -6,10 +5,11 @@ from json import JSONDecodeError
 from typing import Optional
 
 import regex
-from pydantic import BaseModel
 
 from danswer.chat.models import DanswerAnswer
 from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import DanswerQuote
+from danswer.chat.models import DanswerQuotes
 from danswer.chat.models import LlmDoc
 from danswer.configs.chat_configs import QUOTE_ALLOWED_ERROR_PERCENT
 from danswer.context.search.models import InferenceChunk
@@ -26,20 +26,6 @@ logger = setup_logger()
 answer_pattern = re.compile(r'{\s*"answer"\s*:\s*"', re.IGNORECASE)
 
 
-class DanswerQuote(BaseModel):
-    # This is during inference so everything is a string by this point
-    quote: str
-    document_id: str
-    link: str | None
-    source_type: str
-    semantic_identifier: str
-    blurb: str
-
-
-class DanswerQuotes(BaseModel):
-    quotes: list[DanswerQuote]
-
-
 def _extract_answer_quotes_freeform(
     answer_raw: str,
 ) -> tuple[Optional[str], Optional[list[str]]]:

backend/danswer/llm/factory.py

@@ -1,6 +1,5 @@
 from typing import Any
 
-from danswer.chat.models import PersonaOverrideConfig
 from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
 from danswer.configs.chat_configs import QA_TIMEOUT
 from danswer.configs.model_configs import GEN_AI_MODEL_FALLBACK_MAX_TOKENS
@@ -14,11 +13,8 @@ from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.interfaces import LLM
 from danswer.llm.override_models import LLMOverride
 from danswer.utils.headers import build_llm_extra_headers
-from danswer.utils.logger import setup_logger
 from danswer.utils.long_term_log import LongTermLogger
 
-logger = setup_logger()
-
 
 def _build_extra_model_kwargs(provider: str) -> dict[str, Any]:
     """Ollama requires us to specify the max context window.
@@ -36,15 +32,11 @@ def get_main_llm_from_tuple(
 
 
 def get_llms_for_persona(
-    persona: Persona | PersonaOverrideConfig | None,
+    persona: Persona,
     llm_override: LLMOverride | None = None,
     additional_headers: dict[str, str] | None = None,
     long_term_logger: LongTermLogger | None = None,
 ) -> tuple[LLM, LLM]:
-    if persona is None:
-        logger.warning("No persona provided, using default LLMs")
-        return get_default_llms()
-
     model_provider_override = llm_override.model_provider if llm_override else None
     model_version_override = llm_override.model_version if llm_override else None
     temperature_override = llm_override.temperature if llm_override else None
@@ -79,7 +71,6 @@ def get_llms_for_persona(
             api_base=llm_provider.api_base,
             api_version=llm_provider.api_version,
             custom_config=llm_provider.custom_config,
-            temperature=temperature_override,
             additional_headers=additional_headers,
             long_term_logger=long_term_logger,
         )
@@ -137,13 +128,11 @@ def get_llm(
     api_base: str | None = None,
     api_version: str | None = None,
     custom_config: dict[str, str] | None = None,
-    temperature: float | None = None,
+    temperature: float = GEN_AI_TEMPERATURE,
     timeout: int = QA_TIMEOUT,
     additional_headers: dict[str, str] | None = None,
     long_term_logger: LongTermLogger | None = None,
 ) -> LLM:
-    if temperature is None:
-        temperature = GEN_AI_TEMPERATURE
     return DefaultMultiLLM(
         model_provider=provider,
         model_name=model,

backend/danswer/main.py

@@ -25,6 +25,7 @@ from danswer.auth.schemas import UserCreate
 from danswer.auth.schemas import UserRead
 from danswer.auth.schemas import UserUpdate
 from danswer.auth.users import auth_backend
+from danswer.auth.users import BasicAuthenticationError
 from danswer.auth.users import create_danswer_oauth_router
 from danswer.auth.users import fastapi_users
 from danswer.configs.app_configs import APP_API_PREFIX
@@ -91,7 +92,6 @@ from danswer.server.settings.api import basic_router as settings_router
 from danswer.server.token_rate_limits.api import (
     router as token_rate_limit_settings_router,
 )
-from danswer.server.utils import BasicAuthenticationError
 from danswer.setup import setup_danswer
 from danswer.setup import setup_multitenant_danswer
 from danswer.utils.logger import setup_logger
@@ -206,7 +206,7 @@ def log_http_error(_: Request, exc: Exception) -> JSONResponse:
 
     if isinstance(exc, BasicAuthenticationError):
         # For BasicAuthenticationError, just log a brief message without stack trace (almost always spam)
-        logger.warning(f"Authentication failed: {str(exc)}")
+        logger.error(f"Authentication failed: {str(exc)}")
 
     elif status_code >= 400:
         error_msg = f"{str(exc)}\n"

backend/danswer/one_shot_answer/answer_question.py

@@ -0,0 +1,456 @@
+from collections.abc import Callable
+from collections.abc import Iterator
+from typing import cast
+
+from sqlalchemy.orm import Session
+
+from danswer.chat.chat_utils import reorganize_citations
+from danswer.chat.models import CitationInfo
+from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import DanswerContexts
+from danswer.chat.models import DanswerQuotes
+from danswer.chat.models import DocumentRelevance
+from danswer.chat.models import LLMRelevanceFilterResponse
+from danswer.chat.models import QADocsResponse
+from danswer.chat.models import RelevanceAnalysis
+from danswer.chat.models import StreamingError
+from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
+from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
+from danswer.configs.chat_configs import QA_TIMEOUT
+from danswer.configs.constants import MessageType
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.models import RerankMetricsContainer
+from danswer.context.search.models import RetrievalMetricsContainer
+from danswer.context.search.utils import chunks_or_sections_to_search_docs
+from danswer.context.search.utils import dedupe_documents
+from danswer.db.chat import create_chat_session
+from danswer.db.chat import create_db_search_doc
+from danswer.db.chat import create_new_chat_message
+from danswer.db.chat import get_or_create_root_message
+from danswer.db.chat import translate_db_message_to_chat_message_detail
+from danswer.db.chat import translate_db_search_doc_to_server_search_doc
+from danswer.db.chat import update_search_docs_table_with_relevance
+from danswer.db.engine import get_session_context_manager
+from danswer.db.models import Persona
+from danswer.db.models import User
+from danswer.db.persona import get_prompt_by_id
+from danswer.llm.answering.answer import Answer
+from danswer.llm.answering.models import AnswerStyleConfig
+from danswer.llm.answering.models import CitationConfig
+from danswer.llm.answering.models import DocumentPruningConfig
+from danswer.llm.answering.models import PromptConfig
+from danswer.llm.answering.models import QuotesConfig
+from danswer.llm.factory import get_llms_for_persona
+from danswer.llm.factory import get_main_llm_from_tuple
+from danswer.natural_language_processing.utils import get_tokenizer
+from danswer.one_shot_answer.models import DirectQARequest
+from danswer.one_shot_answer.models import OneShotQAResponse
+from danswer.one_shot_answer.models import QueryRephrase
+from danswer.one_shot_answer.qa_utils import combine_message_thread
+from danswer.one_shot_answer.qa_utils import slackify_message_thread
+from danswer.secondary_llm_flows.answer_validation import get_answer_validity
+from danswer.secondary_llm_flows.query_expansion import thread_based_query_rephrase
+from danswer.server.query_and_chat.models import ChatMessageDetail
+from danswer.server.utils import get_json_line
+from danswer.tools.force import ForceUseTool
+from danswer.tools.models import ToolResponse
+from danswer.tools.tool_implementations.search.search_tool import SEARCH_DOC_CONTENT_ID
+from danswer.tools.tool_implementations.search.search_tool import (
+    SEARCH_RESPONSE_SUMMARY_ID,
+)
+from danswer.tools.tool_implementations.search.search_tool import SearchResponseSummary
+from danswer.tools.tool_implementations.search.search_tool import SearchTool
+from danswer.tools.tool_implementations.search.search_tool import (
+    SECTION_RELEVANCE_LIST_ID,
+)
+from danswer.tools.tool_runner import ToolCallKickoff
+from danswer.utils.logger import setup_logger
+from danswer.utils.long_term_log import LongTermLogger
+from danswer.utils.timing import log_generator_function_time
+from danswer.utils.variable_functionality import fetch_ee_implementation_or_noop
+
+logger = setup_logger()
+
+AnswerObjectIterator = Iterator[
+    QueryRephrase
+    | QADocsResponse
+    | LLMRelevanceFilterResponse
+    | DanswerAnswerPiece
+    | DanswerQuotes
+    | DanswerContexts
+    | StreamingError
+    | ChatMessageDetail
+    | CitationInfo
+    | ToolCallKickoff
+    | DocumentRelevance
+]
+
+
+def stream_answer_objects(
+    query_req: DirectQARequest,
+    user: User | None,
+    # These need to be passed in because in Web UI one shot flow,
+    # we can have much more document as there is no history.
+    # For Slack flow, we need to save more tokens for the thread context
+    max_document_tokens: int | None,
+    max_history_tokens: int | None,
+    db_session: Session,
+    # Needed to translate persona num_chunks to tokens to the LLM
+    default_num_chunks: float = MAX_CHUNKS_FED_TO_CHAT,
+    timeout: int = QA_TIMEOUT,
+    bypass_acl: bool = False,
+    use_citations: bool = False,
+    danswerbot_flow: bool = False,
+    retrieval_metrics_callback: (
+        Callable[[RetrievalMetricsContainer], None] | None
+    ) = None,
+    rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
+) -> AnswerObjectIterator:
+    """Streams in order:
+    1. [always] Retrieved documents, stops flow if nothing is found
+    2. [conditional] LLM selected chunk indices if LLM chunk filtering is turned on
+    3. [always] A set of streamed DanswerAnswerPiece and DanswerQuotes at the end
+                or an error anywhere along the line if something fails
+    4. [always] Details on the final AI response message that is created
+    """
+    user_id = user.id if user is not None else None
+    query_msg = query_req.messages[-1]
+    history = query_req.messages[:-1]
+
+    chat_session = create_chat_session(
+        db_session=db_session,
+        description="",  # One shot queries don't need naming as it's never displayed
+        user_id=user_id,
+        persona_id=query_req.persona_id,
+        one_shot=True,
+        danswerbot_flow=danswerbot_flow,
+    )
+
+    # permanent "log" store, used primarily for debugging
+    long_term_logger = LongTermLogger(
+        metadata={"user_id": str(user_id), "chat_session_id": str(chat_session.id)}
+    )
+
+    temporary_persona: Persona | None = None
+
+    if query_req.persona_config is not None:
+        temporary_persona = fetch_ee_implementation_or_noop(
+            "danswer.server.query_and_chat.utils", "create_temporary_persona", None
+        )(db_session=db_session, persona_config=query_req.persona_config, user=user)
+
+    persona = temporary_persona if temporary_persona else chat_session.persona
+
+    try:
+        llm, fast_llm = get_llms_for_persona(
+            persona=persona, long_term_logger=long_term_logger
+        )
+    except ValueError as e:
+        logger.error(
+            f"Failed to initialize LLMs for persona '{persona.name}': {str(e)}"
+        )
+        if "No LLM provider" in str(e):
+            raise ValueError(
+                "Please configure a Generative AI model to use this feature."
+            ) from e
+        raise ValueError(
+            "Failed to initialize the AI model. Please check your configuration and try again."
+        ) from e
+
+    llm_tokenizer = get_tokenizer(
+        model_name=llm.config.model_name,
+        provider_type=llm.config.model_provider,
+    )
+
+    # Create a chat session which will just store the root message, the query, and the AI response
+    root_message = get_or_create_root_message(
+        chat_session_id=chat_session.id, db_session=db_session
+    )
+
+    history_str = combine_message_thread(
+        messages=history,
+        max_tokens=max_history_tokens,
+        llm_tokenizer=llm_tokenizer,
+    )
+
+    rephrased_query = query_req.query_override or thread_based_query_rephrase(
+        user_query=query_msg.message,
+        history_str=history_str,
+    )
+
+    # Given back ahead of the documents for latency reasons
+    # In chat flow it's given back along with the documents
+    yield QueryRephrase(rephrased_query=rephrased_query)
+
+    prompt = None
+    if query_req.prompt_id is not None:
+        # NOTE: let the user access any prompt as long as the Persona is shared
+        # with them
+        prompt = get_prompt_by_id(
+            prompt_id=query_req.prompt_id, user=None, db_session=db_session
+        )
+    if prompt is None:
+        if not persona.prompts:
+            raise RuntimeError(
+                "Persona does not have any prompts - this should never happen"
+            )
+        prompt = persona.prompts[0]
+
+    user_message_str = query_msg.message
+    # For this endpoint, we only save one user message to the chat session
+    # However, for slackbot, we want to include the history of the entire thread
+    if danswerbot_flow:
+        # Right now, we only support bringing over citations and search docs
+        # from the last message in the thread, not the entire thread
+        # in the future, we may want to retrieve the entire thread
+        user_message_str = slackify_message_thread(query_req.messages)
+
+    # Create the first User query message
+    new_user_message = create_new_chat_message(
+        chat_session_id=chat_session.id,
+        parent_message=root_message,
+        prompt_id=query_req.prompt_id,
+        message=user_message_str,
+        token_count=len(llm_tokenizer.encode(user_message_str)),
+        message_type=MessageType.USER,
+        db_session=db_session,
+        commit=True,
+    )
+
+    prompt_config = PromptConfig.from_model(prompt)
+    document_pruning_config = DocumentPruningConfig(
+        max_chunks=int(
+            persona.num_chunks if persona.num_chunks is not None else default_num_chunks
+        ),
+        max_tokens=max_document_tokens,
+    )
+
+    answer_config = AnswerStyleConfig(
+        citation_config=CitationConfig() if use_citations else None,
+        quotes_config=QuotesConfig() if not use_citations else None,
+        document_pruning_config=document_pruning_config,
+    )
+
+    search_tool = SearchTool(
+        db_session=db_session,
+        user=user,
+        evaluation_type=(
+            LLMEvaluationType.SKIP
+            if DISABLE_LLM_DOC_RELEVANCE
+            else query_req.evaluation_type
+        ),
+        persona=persona,
+        retrieval_options=query_req.retrieval_options,
+        prompt_config=prompt_config,
+        llm=llm,
+        fast_llm=fast_llm,
+        pruning_config=document_pruning_config,
+        answer_style_config=answer_config,
+        bypass_acl=bypass_acl,
+        chunks_above=query_req.chunks_above,
+        chunks_below=query_req.chunks_below,
+        full_doc=query_req.full_doc,
+    )
+
+    answer = Answer(
+        question=query_msg.message,
+        answer_style_config=answer_config,
+        prompt_config=PromptConfig.from_model(prompt),
+        llm=get_main_llm_from_tuple(
+            get_llms_for_persona(persona=persona, long_term_logger=long_term_logger)
+        ),
+        single_message_history=history_str,
+        tools=[search_tool] if search_tool else [],
+        force_use_tool=(
+            ForceUseTool(
+                tool_name=search_tool.name,
+                args={"query": rephrased_query},
+                force_use=True,
+            )
+        ),
+        # for now, don't use tool calling for this flow, as we haven't
+        # tested quotes with tool calling too much yet
+        skip_explicit_tool_calling=True,
+        return_contexts=query_req.return_contexts,
+        skip_gen_ai_answer_generation=query_req.skip_gen_ai_answer_generation,
+    )
+    # won't be any FileChatDisplay responses since that tool is never passed in
+    for packet in cast(AnswerObjectIterator, answer.processed_streamed_output):
+        # for one-shot flow, don't currently do anything with these
+        if isinstance(packet, ToolResponse):
+            # (likely fine that it comes after the initial creation of the search docs)
+            if packet.id == SEARCH_RESPONSE_SUMMARY_ID:
+                search_response_summary = cast(SearchResponseSummary, packet.response)
+
+                top_docs = chunks_or_sections_to_search_docs(
+                    search_response_summary.top_sections
+                )
+
+                # Deduping happens at the last step to avoid harming quality by dropping content early on
+                deduped_docs = top_docs
+                if query_req.retrieval_options.dedupe_docs:
+                    deduped_docs, dropped_inds = dedupe_documents(top_docs)
+
+                reference_db_search_docs = [
+                    create_db_search_doc(server_search_doc=doc, db_session=db_session)
+                    for doc in deduped_docs
+                ]
+
+                response_docs = [
+                    translate_db_search_doc_to_server_search_doc(db_search_doc)
+                    for db_search_doc in reference_db_search_docs
+                ]
+
+                initial_response = QADocsResponse(
+                    rephrased_query=rephrased_query,
+                    top_documents=response_docs,
+                    predicted_flow=search_response_summary.predicted_flow,
+                    predicted_search=search_response_summary.predicted_search,
+                    applied_source_filters=search_response_summary.final_filters.source_type,
+                    applied_time_cutoff=search_response_summary.final_filters.time_cutoff,
+                    recency_bias_multiplier=search_response_summary.recency_bias_multiplier,
+                )
+
+                yield initial_response
+
+            elif packet.id == SEARCH_DOC_CONTENT_ID:
+                yield packet.response
+
+            elif packet.id == SECTION_RELEVANCE_LIST_ID:
+                document_based_response = {}
+
+                if packet.response is not None:
+                    for evaluation in packet.response:
+                        document_based_response[
+                            evaluation.document_id
+                        ] = RelevanceAnalysis(
+                            relevant=evaluation.relevant, content=evaluation.content
+                        )
+
+                evaluation_response = DocumentRelevance(
+                    relevance_summaries=document_based_response
+                )
+                if reference_db_search_docs is not None:
+                    update_search_docs_table_with_relevance(
+                        db_session=db_session,
+                        reference_db_search_docs=reference_db_search_docs,
+                        relevance_summary=evaluation_response,
+                    )
+                yield evaluation_response
+
+        else:
+            yield packet
+
+    # Saving Gen AI answer and responding with message info
+    gen_ai_response_message = create_new_chat_message(
+        chat_session_id=chat_session.id,
+        parent_message=new_user_message,
+        prompt_id=query_req.prompt_id,
+        message=answer.llm_answer,
+        token_count=len(llm_tokenizer.encode(answer.llm_answer)),
+        message_type=MessageType.ASSISTANT,
+        error=None,
+        reference_docs=reference_db_search_docs,
+        db_session=db_session,
+        commit=True,
+    )
+
+    msg_detail_response = translate_db_message_to_chat_message_detail(
+        gen_ai_response_message
+    )
+    yield msg_detail_response
+
+
+@log_generator_function_time()
+def stream_search_answer(
+    query_req: DirectQARequest,
+    user: User | None,
+    max_document_tokens: int | None,
+    max_history_tokens: int | None,
+) -> Iterator[str]:
+    with get_session_context_manager() as session:
+        objects = stream_answer_objects(
+            query_req=query_req,
+            user=user,
+            max_document_tokens=max_document_tokens,
+            max_history_tokens=max_history_tokens,
+            db_session=session,
+        )
+        for obj in objects:
+            yield get_json_line(obj.model_dump())
+
+
+def get_search_answer(
+    query_req: DirectQARequest,
+    user: User | None,
+    max_document_tokens: int | None,
+    max_history_tokens: int | None,
+    db_session: Session,
+    answer_generation_timeout: int = QA_TIMEOUT,
+    enable_reflexion: bool = False,
+    bypass_acl: bool = False,
+    use_citations: bool = False,
+    danswerbot_flow: bool = False,
+    retrieval_metrics_callback: (
+        Callable[[RetrievalMetricsContainer], None] | None
+    ) = None,
+    rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
+) -> OneShotQAResponse:
+    """Collects the streamed one shot answer responses into a single object"""
+    qa_response = OneShotQAResponse()
+
+    results = stream_answer_objects(
+        query_req=query_req,
+        user=user,
+        max_document_tokens=max_document_tokens,
+        max_history_tokens=max_history_tokens,
+        db_session=db_session,
+        bypass_acl=bypass_acl,
+        use_citations=use_citations,
+        danswerbot_flow=danswerbot_flow,
+        timeout=answer_generation_timeout,
+        retrieval_metrics_callback=retrieval_metrics_callback,
+        rerank_metrics_callback=rerank_metrics_callback,
+    )
+
+    answer = ""
+    for packet in results:
+        if isinstance(packet, QueryRephrase):
+            qa_response.rephrase = packet.rephrased_query
+        if isinstance(packet, DanswerAnswerPiece) and packet.answer_piece:
+            answer += packet.answer_piece
+        elif isinstance(packet, QADocsResponse):
+            qa_response.docs = packet
+        elif isinstance(packet, LLMRelevanceFilterResponse):
+            qa_response.llm_selected_doc_indices = packet.llm_selected_doc_indices
+        elif isinstance(packet, DanswerQuotes):
+            qa_response.quotes = packet
+        elif isinstance(packet, CitationInfo):
+            if qa_response.citations:
+                qa_response.citations.append(packet)
+            else:
+                qa_response.citations = [packet]
+        elif isinstance(packet, DanswerContexts):
+            qa_response.contexts = packet
+        elif isinstance(packet, StreamingError):
+            qa_response.error_msg = packet.error
+        elif isinstance(packet, ChatMessageDetail):
+            qa_response.chat_message_id = packet.message_id
+
+    if answer:
+        qa_response.answer = answer
+
+    if enable_reflexion:
+        # Because follow up messages are explicitly tagged, we don't need to verify the answer
+        if len(query_req.messages) == 1:
+            first_query = query_req.messages[0].message
+            qa_response.answer_valid = get_answer_validity(first_query, answer)
+        else:
+            qa_response.answer_valid = True
+
+    if use_citations and qa_response.answer and qa_response.citations:
+        # Reorganize citation nums to be in the same order as the answer
+        qa_response.answer, qa_response.citations = reorganize_citations(
+            qa_response.answer, qa_response.citations
+        )
+
+    return qa_response

backend/danswer/one_shot_answer/models.py

@@ -0,0 +1,114 @@
+from typing import Any
+
+from pydantic import BaseModel
+from pydantic import Field
+from pydantic import model_validator
+
+from danswer.chat.models import CitationInfo
+from danswer.chat.models import DanswerContexts
+from danswer.chat.models import DanswerQuotes
+from danswer.chat.models import QADocsResponse
+from danswer.configs.constants import MessageType
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.enums import RecencyBiasSetting
+from danswer.context.search.enums import SearchType
+from danswer.context.search.models import ChunkContext
+from danswer.context.search.models import RerankingDetails
+from danswer.context.search.models import RetrievalDetails
+
+
+class QueryRephrase(BaseModel):
+    rephrased_query: str
+
+
+class ThreadMessage(BaseModel):
+    message: str
+    sender: str | None = None
+    role: MessageType = MessageType.USER
+
+
+class PromptConfig(BaseModel):
+    name: str
+    description: str = ""
+    system_prompt: str
+    task_prompt: str = ""
+    include_citations: bool = True
+    datetime_aware: bool = True
+
+
+class ToolConfig(BaseModel):
+    id: int
+
+
+class PersonaConfig(BaseModel):
+    name: str
+    description: str
+    search_type: SearchType = SearchType.SEMANTIC
+    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
+
+    prompts: list[PromptConfig] = Field(default_factory=list)
+    prompt_ids: list[int] = Field(default_factory=list)
+
+    document_set_ids: list[int] = Field(default_factory=list)
+    tools: list[ToolConfig] = Field(default_factory=list)
+    tool_ids: list[int] = Field(default_factory=list)
+    custom_tools_openapi: list[dict[str, Any]] = Field(default_factory=list)
+
+
+class DirectQARequest(ChunkContext):
+    persona_config: PersonaConfig | None = None
+    persona_id: int | None = None
+
+    messages: list[ThreadMessage]
+    prompt_id: int | None = None
+    multilingual_query_expansion: list[str] | None = None
+    retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
+    rerank_settings: RerankingDetails | None = None
+    evaluation_type: LLMEvaluationType = LLMEvaluationType.UNSPECIFIED
+
+    chain_of_thought: bool = False
+    return_contexts: bool = False
+
+    # allows the caller to specify the exact search query they want to use
+    # can be used if the message sent to the LLM / query should not be the same
+    # will also disable Thread-based Rewording if specified
+    query_override: str | None = None
+
+    # If True, skips generative an AI response to the search query
+    skip_gen_ai_answer_generation: bool = False
+
+    @model_validator(mode="after")
+    def check_persona_fields(self) -> "DirectQARequest":
+        if (self.persona_config is None) == (self.persona_id is None):
+            raise ValueError("Exactly one of persona_config or persona_id must be set")
+        return self
+
+    @model_validator(mode="after")
+    def check_chain_of_thought_and_prompt_id(self) -> "DirectQARequest":
+        if self.chain_of_thought and self.prompt_id is not None:
+            raise ValueError(
+                "If chain_of_thought is True, prompt_id must be None"
+                "The chain of thought prompt is only for question "
+                "answering and does not accept customizing."
+            )
+
+        return self
+
+
+class OneShotQAResponse(BaseModel):
+    # This is built piece by piece, any of these can be None as the flow could break
+    answer: str | None = None
+    rephrase: str | None = None
+    quotes: DanswerQuotes | None = None
+    citations: list[CitationInfo] | None = None
+    docs: QADocsResponse | None = None
+    llm_selected_doc_indices: list[int] | None = None
+    error_msg: str | None = None
+    answer_valid: bool = True  # Reflexion result, default True if Reflexion not run
+    chat_message_id: int | None = None
+    contexts: DanswerContexts | None = None

backend/danswer/one_shot_answer/qa_utils.py

@@ -0,0 +1,81 @@
+from collections.abc import Generator
+
+from danswer.configs.constants import MessageType
+from danswer.natural_language_processing.utils import BaseTokenizer
+from danswer.one_shot_answer.models import ThreadMessage
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def simulate_streaming_response(model_out: str) -> Generator[str, None, None]:
+    """Mock streaming by generating the passed in model output, character by character"""
+    for token in model_out:
+        yield token
+
+
+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 slackify_message(message: ThreadMessage) -> str:
+    if message.role != MessageType.USER:
+        return message.message
+
+    return f"{message.sender or 'Unknown User'} said in Slack:\n{message.message}"
+
+
+def slackify_message_thread(messages: list[ThreadMessage]) -> str:
+    if not messages:
+        return ""
+
+    message_strs: list[str] = []
+    for message in messages:
+        if message.role == MessageType.USER:
+            message_text = (
+                f"{message.sender or 'Unknown User'} said in Slack:\n{message.message}"
+            )
+        elif message.role == MessageType.ASSISTANT:
+            message_text = f"DanswerBot said in Slack:\n{message.message}"
+        else:
+            message_text = (
+                f"{message.role.value.upper()} said in Slack:\n{message.message}"
+            )
+        message_strs.append(message_text)
+
+    return "\n\n".join(message_strs)

backend/danswer/redis/redis_connector_credential_pair.py

@@ -10,7 +10,6 @@ from sqlalchemy.orm import Session
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.document import (
     construct_document_select_for_connector_credential_pair_by_needs_sync,
@@ -106,7 +105,7 @@ class RedisConnectorCredentialPair(RedisObjectHelper):
 
             # Priority on sync's triggered by new indexing should be medium
             result = celery_app.send_task(
-                DanswerCeleryTask.VESPA_METADATA_SYNC_TASK,
+                "vespa_metadata_sync_task",
                 kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
                 queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
                 task_id=custom_task_id,

backend/danswer/redis/redis_connector_delete.py

@@ -12,7 +12,6 @@ from sqlalchemy.orm import Session
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.document import construct_document_select_for_connector_credential_pair
 from danswer.db.models import Document as DbDocument
@@ -115,7 +114,7 @@ class RedisConnectorDelete:
 
             # Priority on sync's triggered by new indexing should be medium
             result = celery_app.send_task(
-                DanswerCeleryTask.DOCUMENT_BY_CC_PAIR_CLEANUP_TASK,
+                "document_by_cc_pair_cleanup_task",
                 kwargs=dict(
                     document_id=doc.id,
                     connector_id=cc_pair.connector_id,

backend/danswer/redis/redis_connector_doc_perm_sync.py

@@ -12,7 +12,6 @@ from danswer.access.models import DocExternalAccess
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 
 
 class RedisConnectorPermissionSyncPayload(BaseModel):
@@ -133,8 +132,6 @@ class RedisConnectorPermissionSync:
         lock: RedisLock | None,
         new_permissions: list[DocExternalAccess],
         source_string: str,
-        connector_id: int,
-        credential_id: int,
     ) -> int | None:
         last_lock_time = time.monotonic()
         async_results = []
@@ -152,13 +149,11 @@ class RedisConnectorPermissionSync:
             self.redis.sadd(self.taskset_key, custom_task_id)
 
             result = celery_app.send_task(
-                DanswerCeleryTask.UPDATE_EXTERNAL_DOCUMENT_PERMISSIONS_TASK,
+                "update_external_document_permissions_task",
                 kwargs=dict(
                     tenant_id=self.tenant_id,
                     serialized_doc_external_access=doc_perm.to_dict(),
                     source_string=source_string,
-                    connector_id=connector_id,
-                    credential_id=credential_id,
                 ),
                 queue=DanswerCeleryQueues.DOC_PERMISSIONS_UPSERT,
                 task_id=custom_task_id,

backend/danswer/redis/redis_connector_prune.py

@@ -10,7 +10,6 @@ from sqlalchemy.orm import Session
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 
 
@@ -135,7 +134,7 @@ class RedisConnectorPrune:
 
             # Priority on sync's triggered by new indexing should be medium
             result = celery_app.send_task(
-                DanswerCeleryTask.DOCUMENT_BY_CC_PAIR_CLEANUP_TASK,
+                "document_by_cc_pair_cleanup_task",
                 kwargs=dict(
                     document_id=doc_id,
                     connector_id=cc_pair.connector_id,

backend/danswer/redis/redis_document_set.py

@@ -11,7 +11,6 @@ from sqlalchemy.orm import Session
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.document_set import construct_document_select_by_docset
 from danswer.redis.redis_object_helper import RedisObjectHelper
 
@@ -77,7 +76,7 @@ class RedisDocumentSet(RedisObjectHelper):
             redis_client.sadd(self.taskset_key, custom_task_id)
 
             result = celery_app.send_task(
-                DanswerCeleryTask.VESPA_METADATA_SYNC_TASK,
+                "vespa_metadata_sync_task",
                 kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
                 queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
                 task_id=custom_task_id,

backend/danswer/redis/redis_usergroup.py

@@ -11,7 +11,6 @@ from sqlalchemy.orm import Session
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.redis.redis_object_helper import RedisObjectHelper
 from danswer.utils.variable_functionality import fetch_versioned_implementation
 from danswer.utils.variable_functionality import global_version
@@ -90,7 +89,7 @@ class RedisUserGroup(RedisObjectHelper):
             redis_client.sadd(self.taskset_key, custom_task_id)
 
             result = celery_app.send_task(
-                DanswerCeleryTask.VESPA_METADATA_SYNC_TASK,
+                "vespa_metadata_sync_task",
                 kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
                 queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
                 task_id=custom_task_id,

backend/danswer/server/documents/connector.py

@@ -20,7 +20,6 @@ from danswer.background.celery.celery_utils import get_deletion_attempt_snapshot
 from danswer.background.celery.versioned_apps.primary import app as primary_app
 from danswer.configs.app_configs import ENABLED_CONNECTOR_TYPES
 from danswer.configs.constants import DanswerCeleryPriority
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import FileOrigin
 from danswer.connectors.google_utils.google_auth import (
@@ -868,7 +867,7 @@ def connector_run_once(
 
     # run the beat task to pick up the triggers immediately
     primary_app.send_task(
-        DanswerCeleryTask.CHECK_FOR_INDEXING,
+        "check_for_indexing",
         priority=DanswerCeleryPriority.HIGH,
         kwargs={"tenant_id": tenant_id},
     )

backend/danswer/server/manage/administrative.py

@@ -13,7 +13,6 @@ from danswer.auth.users import current_curator_or_admin_user
 from danswer.background.celery.versioned_apps.primary import app as primary_app
 from danswer.configs.app_configs import GENERATIVE_MODEL_ACCESS_CHECK_FREQ
 from danswer.configs.constants import DanswerCeleryPriority
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import KV_GEN_AI_KEY_CHECK_TIME
 from danswer.db.connector_credential_pair import get_connector_credential_pair
@@ -200,7 +199,7 @@ def create_deletion_attempt_for_connector_id(
 
     # run the beat task to pick up this deletion from the db immediately
     primary_app.send_task(
-        DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
+        "check_for_connector_deletion_task",
         priority=DanswerCeleryPriority.HIGH,
         kwargs={"tenant_id": tenant_id},
     )

backend/danswer/server/manage/models.py

@@ -1,5 +1,4 @@
 from datetime import datetime
-from enum import Enum
 from typing import TYPE_CHECKING
 
 from pydantic import BaseModel
@@ -16,6 +15,7 @@ from danswer.danswerbot.slack.config import VALID_SLACK_FILTERS
 from danswer.db.models import AllowedAnswerFilters
 from danswer.db.models import ChannelConfig
 from danswer.db.models import SlackBot as SlackAppModel
+from danswer.db.models import SlackBotResponseType
 from danswer.db.models import SlackChannelConfig as SlackChannelConfigModel
 from danswer.db.models import User
 from danswer.server.features.persona.models import PersonaSnapshot
@@ -148,12 +148,6 @@ class SlackBotTokens(BaseModel):
     model_config = ConfigDict(frozen=True)
 
 
-# TODO No longer in use, remove later
-class SlackBotResponseType(str, Enum):
-    QUOTES = "quotes"
-    CITATIONS = "citations"
-
-
 class SlackChannelConfigCreationRequest(BaseModel):
     slack_bot_id: int
     # currently, a persona is created for each Slack channel config
@@ -203,6 +197,7 @@ class SlackChannelConfig(BaseModel):
     id: int
     persona: PersonaSnapshot | None
     channel_config: ChannelConfig
+    response_type: SlackBotResponseType
     # XXX this is going away soon
     standard_answer_categories: list[StandardAnswerCategory]
     enable_auto_filters: bool
@@ -222,6 +217,7 @@ class SlackChannelConfig(BaseModel):
                 else None
             ),
             channel_config=slack_channel_config_model.channel_config,
+            response_type=slack_channel_config_model.response_type,
             # XXX this is going away soon
             standard_answer_categories=[
                 StandardAnswerCategory.from_model(standard_answer_category_model)

backend/danswer/server/manage/slack_bot.py

@@ -118,6 +118,7 @@ def create_slack_channel_config(
         slack_bot_id=slack_channel_config_creation_request.slack_bot_id,
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=slack_channel_config_creation_request.response_type,
         standard_answer_category_ids=slack_channel_config_creation_request.standard_answer_categories,
         db_session=db_session,
         enable_auto_filters=slack_channel_config_creation_request.enable_auto_filters,
@@ -181,6 +182,7 @@ def patch_slack_channel_config(
         slack_channel_config_id=slack_channel_config_id,
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=slack_channel_config_creation_request.response_type,
         standard_answer_category_ids=slack_channel_config_creation_request.standard_answer_categories,
         enable_auto_filters=slack_channel_config_creation_request.enable_auto_filters,
     )

backend/danswer/server/manage/users.py

@@ -26,6 +26,7 @@ from danswer.auth.noauth_user import fetch_no_auth_user
 from danswer.auth.noauth_user import set_no_auth_user_preferences
 from danswer.auth.schemas import UserRole
 from danswer.auth.schemas import UserStatus
+from danswer.auth.users import BasicAuthenticationError
 from danswer.auth.users import current_admin_user
 from danswer.auth.users import current_curator_or_admin_user
 from danswer.auth.users import current_user
@@ -59,7 +60,6 @@ from danswer.server.manage.models import UserRoleUpdateRequest
 from danswer.server.models import FullUserSnapshot
 from danswer.server.models import InvitedUserSnapshot
 from danswer.server.models import MinimalUserSnapshot
-from danswer.server.utils import BasicAuthenticationError
 from danswer.server.utils import send_user_email_invite
 from danswer.utils.logger import setup_logger
 from danswer.utils.variable_functionality import fetch_ee_implementation_or_noop

backend/danswer/server/openai_assistants_api/runs_api.py

@@ -109,7 +109,6 @@ def process_run_in_background(
         prompt_id=chat_session.persona.prompts[0].id,
         search_doc_ids=None,
         retrieval_options=search_tool_retrieval_details,  # Adjust as needed
-        rerank_settings=None,
         query_override=None,
         regenerate=None,
         llm_override=None,

backend/danswer/server/query_and_chat/models.py

@@ -5,14 +5,12 @@ from uuid import UUID
 from pydantic import BaseModel
 from pydantic import model_validator
 
-from danswer.chat.models import PersonaOverrideConfig
 from danswer.chat.models import RetrievalDocs
 from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import MessageType
 from danswer.configs.constants import SearchFeedbackType
 from danswer.context.search.models import BaseFilters
 from danswer.context.search.models import ChunkContext
-from danswer.context.search.models import RerankingDetails
 from danswer.context.search.models import RetrievalDetails
 from danswer.context.search.models import SearchDoc
 from danswer.context.search.models import Tag
@@ -89,8 +87,6 @@ class CreateChatMessageRequest(ChunkContext):
     # If search_doc_ids provided, then retrieval options are unused
     search_doc_ids: list[int] | None
     retrieval_options: RetrievalDetails | None
-    # Useable via the APIs but not recommended for most flows
-    rerank_settings: RerankingDetails | 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
@@ -106,10 +102,6 @@ class CreateChatMessageRequest(ChunkContext):
     # allow user to specify an alternate assistnat
     alternate_assistant_id: int | None = None
 
-    # This takes the priority over the prompt_override
-    # This won't be a type that's passed in directly from the API
-    persona_override_config: PersonaOverrideConfig | None = None
-
     # used for seeded chats to kick off the generation of an AI answer
     use_existing_user_message: bool = False
 
@@ -153,7 +145,7 @@ class RenameChatSessionResponse(BaseModel):
 
 class ChatSessionDetails(BaseModel):
     id: UUID
-    name: str | None
+    name: str
     persona_id: int | None = None
     time_created: str
     shared_status: ChatSessionSharedStatus
@@ -206,14 +198,14 @@ class ChatMessageDetail(BaseModel):
 
 class SearchSessionDetailResponse(BaseModel):
     search_session_id: UUID
-    description: str | None
+    description: str
     documents: list[SearchDoc]
     messages: list[ChatMessageDetail]
 
 
 class ChatSessionDetailResponse(BaseModel):
     chat_session_id: UUID
-    description: str | None
+    description: str
     persona_id: int | None = None
     persona_name: str | None
     messages: list[ChatMessageDetail]

backend/danswer/server/query_and_chat/query_backend.py

@@ -1,11 +1,15 @@
+import json
+from collections.abc import Generator
 from uuid import UUID
 
 from fastapi import APIRouter
 from fastapi import Depends
 from fastapi import HTTPException
+from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
 from danswer.auth.users import current_curator_or_admin_user
+from danswer.auth.users import current_limited_user
 from danswer.auth.users import current_user
 from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import MessageType
@@ -28,6 +32,8 @@ from danswer.db.search_settings import get_current_search_settings
 from danswer.db.tag import find_tags
 from danswer.document_index.factory import get_default_document_index
 from danswer.document_index.vespa.index import VespaIndex
+from danswer.one_shot_answer.answer_question import stream_search_answer
+from danswer.one_shot_answer.models import DirectQARequest
 from danswer.server.query_and_chat.models import AdminSearchRequest
 from danswer.server.query_and_chat.models import AdminSearchResponse
 from danswer.server.query_and_chat.models import ChatSessionDetails
@@ -35,6 +41,7 @@ from danswer.server.query_and_chat.models import ChatSessionsResponse
 from danswer.server.query_and_chat.models import SearchSessionDetailResponse
 from danswer.server.query_and_chat.models import SourceTag
 from danswer.server.query_and_chat.models import TagResponse
+from danswer.server.query_and_chat.token_limit import check_token_rate_limits
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -133,7 +140,7 @@ def get_user_search_sessions(
 
     try:
         search_sessions = get_chat_sessions_by_user(
-            user_id=user_id, deleted=False, db_session=db_session
+            user_id=user_id, deleted=False, db_session=db_session, only_one_shot=True
         )
     except ValueError:
         raise HTTPException(
@@ -222,3 +229,29 @@ def get_search_session(
         ],
     )
     return response
+
+
+@basic_router.post("/stream-answer-with-quote")
+def get_answer_with_quote(
+    query_request: DirectQARequest,
+    user: User = Depends(current_limited_user),
+    _: None = Depends(check_token_rate_limits),
+) -> StreamingResponse:
+    query = query_request.messages[0].message
+
+    logger.notice(f"Received query for one shot answer with quotes: {query}")
+
+    def stream_generator() -> Generator[str, None, None]:
+        try:
+            for packet in stream_search_answer(
+                query_req=query_request,
+                user=user,
+                max_document_tokens=None,
+                max_history_tokens=0,
+            ):
+                yield json.dumps(packet) if isinstance(packet, dict) else packet
+        except Exception as e:
+            logger.exception("Error in search answer streaming")
+            yield json.dumps({"error": str(e)})
+
+    return StreamingResponse(stream_generator(), media_type="application/json")

backend/danswer/server/utils.py

@@ -6,9 +6,6 @@ from email.mime.text import MIMEText
 from textwrap import dedent
 from typing import Any
 
-from fastapi import HTTPException
-from fastapi import status
-
 from danswer.configs.app_configs import SMTP_PASS
 from danswer.configs.app_configs import SMTP_PORT
 from danswer.configs.app_configs import SMTP_SERVER
@@ -17,11 +14,6 @@ from danswer.configs.app_configs import WEB_DOMAIN
 from danswer.db.models import User
 
 
-class BasicAuthenticationError(HTTPException):
-    def __init__(self, detail: str):
-        super().__init__(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
-
-
 class DateTimeEncoder(json.JSONEncoder):
     """Custom JSON encoder that converts datetime objects to ISO format strings."""
 

backend/danswer/tools/message.py

@@ -25,9 +25,6 @@ class ToolCallSummary(BaseModel__v1):
     tool_call_request: AIMessage
     tool_call_result: ToolMessage
 
-    class Config:
-        arbitrary_types_allowed = True
-
 
 def tool_call_tokens(
     tool_call_summary: ToolCallSummary, llm_tokenizer: BaseTokenizer

backend/danswer/tools/tool_constructor.py

@@ -13,7 +13,6 @@ from danswer.configs.chat_configs import BING_API_KEY
 from danswer.configs.model_configs import GEN_AI_TEMPERATURE
 from danswer.context.search.enums import LLMEvaluationType
 from danswer.context.search.models import InferenceSection
-from danswer.context.search.models import RerankingDetails
 from danswer.context.search.models import RetrievalDetails
 from danswer.db.llm import fetch_existing_llm_providers
 from danswer.db.models import Persona
@@ -103,14 +102,11 @@ class SearchToolConfig(BaseModel):
         default_factory=DocumentPruningConfig
     )
     retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
-    rerank_settings: RerankingDetails | None = None
     selected_sections: list[InferenceSection] | None = None
     chunks_above: int = 0
     chunks_below: int = 0
     full_doc: bool = False
     latest_query_files: list[InMemoryChatFile] | None = None
-    # Use with care, should only be used for DanswerBot in channels with multiple users
-    bypass_acl: bool = False
 
 
 class InternetSearchToolConfig(BaseModel):
@@ -174,8 +170,6 @@ def construct_tools(
                         if persona.llm_relevance_filter
                         else LLMEvaluationType.SKIP
                     ),
-                    rerank_settings=search_tool_config.rerank_settings,
-                    bypass_acl=search_tool_config.bypass_acl,
                 )
                 tool_dict[db_tool_model.id] = [search_tool]
 

backend/danswer/tools/tool_implementations/internet_search/internet_search_tool.py

@@ -77,7 +77,6 @@ def llm_doc_from_internet_search_result(result: InternetSearchResult) -> LlmDoc:
         updated_at=datetime.now(),
         link=result.link,
         source_links={0: result.link},
-        match_highlights=[],
     )
 
 

backend/danswer/tools/tool_implementations/search/search_tool.py

@@ -19,7 +19,6 @@ from danswer.context.search.enums import QueryFlow
 from danswer.context.search.enums import SearchType
 from danswer.context.search.models import IndexFilters
 from danswer.context.search.models import InferenceSection
-from danswer.context.search.models import RerankingDetails
 from danswer.context.search.models import RetrievalDetails
 from danswer.context.search.models import SearchRequest
 from danswer.context.search.pipeline import SearchPipeline
@@ -104,7 +103,6 @@ class SearchTool(Tool):
         chunks_below: int | None = None,
         full_doc: bool = False,
         bypass_acl: bool = False,
-        rerank_settings: RerankingDetails | None = None,
     ) -> None:
         self.user = user
         self.persona = persona
@@ -120,9 +118,6 @@ class SearchTool(Tool):
         self.bypass_acl = bypass_acl
         self.db_session = db_session
 
-        # Only used via API
-        self.rerank_settings = rerank_settings
-
         self.chunks_above = (
             chunks_above
             if chunks_above is not None
@@ -297,7 +292,6 @@ class SearchTool(Tool):
                     self.retrieval_options.offset if self.retrieval_options else None
                 ),
                 limit=self.retrieval_options.limit if self.retrieval_options else None,
-                rerank_settings=self.rerank_settings,
                 chunks_above=self.chunks_above,
                 chunks_below=self.chunks_below,
                 full_doc=self.full_doc,

backend/danswer/utils/timing.py

@@ -33,12 +33,12 @@ def log_function_time(
             elapsed_time_str = f"{elapsed_time:.3f}"
             log_name = func_name or func.__name__
             args_str = f" args={args} kwargs={kwargs}" if include_args else ""
-            f"{log_name}{args_str} took {elapsed_time_str} seconds"
-            # if debug_only:
-            #     logger.debug(final_log)
-            # else:
-            #     # These are generally more important logs so the level is a bit higher
-            #     logger.notice(final_log)
+            final_log = f"{log_name}{args_str} took {elapsed_time_str} seconds"
+            if debug_only:
+                logger.debug(final_log)
+            else:
+                # These are generally more important logs so the level is a bit higher
+                logger.notice(final_log)
 
             if not print_only:
                 optional_telemetry(

backend/ee/danswer/background/celery/tasks/beat_schedule.py

@@ -4,17 +4,16 @@ from typing import Any
 from danswer.background.celery.tasks.beat_schedule import (
     tasks_to_schedule as base_tasks_to_schedule,
 )
-from danswer.configs.constants import DanswerCeleryTask
 
 ee_tasks_to_schedule = [
     {
         "name": "autogenerate_usage_report",
-        "task": DanswerCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
+        "task": "autogenerate_usage_report_task",
         "schedule": timedelta(days=30),  # TODO: change this to config flag
     },
     {
         "name": "check-ttl-management",
-        "task": DanswerCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
+        "task": "check_ttl_management_task",
         "schedule": timedelta(hours=1),
     },
 ]

backend/ee/danswer/chat/process_message.py

@@ -1,41 +0,0 @@
-from danswer.chat.models import AllCitations
-from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerContexts
-from danswer.chat.models import LLMRelevanceFilterResponse
-from danswer.chat.models import QADocsResponse
-from danswer.chat.models import StreamingError
-from danswer.chat.process_message import ChatPacketStream
-from danswer.server.query_and_chat.models import ChatMessageDetail
-from danswer.utils.timing import log_function_time
-from ee.danswer.server.query_and_chat.models import OneShotQAResponse
-
-
-@log_function_time()
-def gather_stream_for_answer_api(
-    packets: ChatPacketStream,
-) -> OneShotQAResponse:
-    response = OneShotQAResponse()
-
-    answer = ""
-    for packet in packets:
-        if isinstance(packet, DanswerAnswerPiece) and packet.answer_piece:
-            answer += packet.answer_piece
-        elif isinstance(packet, QADocsResponse):
-            response.docs = packet
-            # Extraneous, provided for backwards compatibility
-            response.rephrase = packet.rephrased_query
-        elif isinstance(packet, StreamingError):
-            response.error_msg = packet.error
-        elif isinstance(packet, ChatMessageDetail):
-            response.chat_message_id = packet.message_id
-        elif isinstance(packet, LLMRelevanceFilterResponse):
-            response.llm_selected_doc_indices = packet.llm_selected_doc_indices
-        elif isinstance(packet, AllCitations):
-            response.citations = packet.citations
-        elif isinstance(packet, DanswerContexts):
-            response.contexts = packet
-
-    if answer:
-        response.answer = answer
-
-    return response

backend/ee/danswer/danswerbot/slack/handlers/handle_standard_answers.py

@@ -155,6 +155,7 @@ def _handle_standard_answers(
             else 0,
             danswerbot_flow=True,
             slack_thread_id=slack_thread_id,
+            one_shot=True,
         )
 
         root_message = get_or_create_root_message(