more experimentation changes

* Related permission docs to cc_pair to prevent orphan docs

* added script

* group sync deduping

* logging

backend/alembic/versions/9f696734098f_combine_search_and_chat.py

@@ -0,0 +1,36 @@
+"""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/danswer/access/models.py

@@ -18,6 +18,11 @@ 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/core_qa_graph/edges.py

@@ -0,0 +1,42 @@
+from collections.abc import Hashable
+from typing import Union
+
+from langgraph.types import Send
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.primary_graph.states import RetrieverState
+from danswer.agent_search.primary_graph.states import VerifierState
+
+
+def sub_continue_to_verifier(state: BaseQAState) -> Union[Hashable, list[Hashable]]:
+    # Routes each de-douped retrieved doc to the verifier step - in parallel
+    # Notice the 'Send()' API that takes care of the parallelization
+
+    return [
+        Send(
+            "sub_verifier",
+            VerifierState(
+                document=doc,
+                #question=state["original_question"],
+                question=state["sub_question_str"],
+                graph_start_time=state["graph_start_time"],
+            ),
+        )
+        for doc in state["sub_question_deduped_retrieval_docs"]
+    ]
+
+
+def sub_continue_to_retrieval(state: BaseQAState) -> Union[Hashable, list[Hashable]]:
+    # Routes re-written queries to the (parallel) retrieval steps
+    # Notice the 'Send()' API that takes care of the parallelization
+    rewritten_queries = state["sub_question_search_queries"].rewritten_queries + [state["sub_question_str"]]
+    return [
+        Send(
+            "sub_custom_retrieve",
+            RetrieverState(
+                rewritten_query=query,
+                graph_start_time=state["graph_start_time"],
+            ),
+        )
+        for query in rewritten_queries
+    ]

backend/danswer/agent_search/core_qa_graph/graph_builder.py

@@ -0,0 +1,132 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from danswer.agent_search.core_qa_graph.edges import sub_continue_to_retrieval
+from danswer.agent_search.core_qa_graph.edges import sub_continue_to_verifier
+from danswer.agent_search.core_qa_graph.nodes.combine_retrieved_docs import (
+    sub_combine_retrieved_docs,
+)
+from danswer.agent_search.core_qa_graph.nodes.custom_retrieve import (
+    sub_custom_retrieve,
+)
+from danswer.agent_search.core_qa_graph.nodes.dummy import sub_dummy
+from danswer.agent_search.core_qa_graph.nodes.final_format import (
+    sub_final_format,
+)
+from danswer.agent_search.core_qa_graph.nodes.generate import sub_generate
+from danswer.agent_search.core_qa_graph.nodes.qa_check import sub_qa_check
+from danswer.agent_search.core_qa_graph.nodes.rewrite import sub_rewrite
+from danswer.agent_search.core_qa_graph.nodes.verifier import sub_verifier
+from danswer.agent_search.core_qa_graph.states import BaseQAOutputState
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.core_qa_graph.states import CoreQAInputState
+
+
+def build_core_qa_graph() -> StateGraph:
+    sub_answers_initial = StateGraph(
+        state_schema=BaseQAState,
+        output=BaseQAOutputState,
+    )
+
+    ### Add nodes ###
+    sub_answers_initial.add_node(node="sub_dummy", action=sub_dummy)
+    sub_answers_initial.add_node(node="sub_rewrite", action=sub_rewrite)
+    sub_answers_initial.add_node(
+        node="sub_custom_retrieve",
+        action=sub_custom_retrieve,
+    )
+    sub_answers_initial.add_node(
+        node="sub_combine_retrieved_docs",
+        action=sub_combine_retrieved_docs,
+    )
+    sub_answers_initial.add_node(
+        node="sub_verifier",
+        action=sub_verifier,
+    )
+    sub_answers_initial.add_node(
+        node="sub_generate",
+        action=sub_generate,
+    )
+    sub_answers_initial.add_node(
+        node="sub_qa_check",
+        action=sub_qa_check,
+    )
+    sub_answers_initial.add_node(
+        node="sub_final_format",
+        action=sub_final_format,
+    )
+
+    ### Add edges ###
+    sub_answers_initial.add_edge(START, "sub_dummy")
+    sub_answers_initial.add_edge("sub_dummy", "sub_rewrite")
+
+    sub_answers_initial.add_conditional_edges(
+        source="sub_rewrite",
+        path=sub_continue_to_retrieval,
+    )
+
+    sub_answers_initial.add_edge(
+        start_key="sub_custom_retrieve",
+        end_key="sub_combine_retrieved_docs",
+    )
+
+    sub_answers_initial.add_conditional_edges(
+        source="sub_combine_retrieved_docs",
+        path=sub_continue_to_verifier,
+        path_map=["sub_verifier"],
+    )
+
+    sub_answers_initial.add_edge(
+        start_key="sub_verifier",
+        end_key="sub_generate",
+    )
+
+    sub_answers_initial.add_edge(
+        start_key="sub_generate",
+        end_key="sub_qa_check",
+    )
+
+    sub_answers_initial.add_edge(
+        start_key="sub_qa_check",
+        end_key="sub_final_format",
+    )
+
+    sub_answers_initial.add_edge(
+        start_key="sub_final_format",
+        end_key=END,
+    )
+    # sub_answers_graph = sub_answers_initial.compile()
+    return sub_answers_initial
+
+
+if __name__ == "__main__":
+    # q = "Whose music is kind of hard to easily enjoy?"
+    # q = "What is voice leading?"
+    # q = "What are the types of motions in music?"
+    # q = "What are key elements of music theory?"
+    # q = "How can I best understand music theory using voice leading?"
+    q = "What makes good music?"
+    # q = "types of motions in music"
+    # q = "What is the relationship between music and physics?"
+    # q = "Can you compare various grunge styles?"
+    # q = "Why is quantum gravity so hard?"
+
+    inputs = CoreQAInputState(
+        original_question=q,
+        sub_question_str=q,
+    )
+    sub_answers_graph = build_core_qa_graph()
+    compiled_sub_answers = sub_answers_graph.compile()
+    output = compiled_sub_answers.invoke(inputs)
+    print("\nOUTPUT:")
+    print(output.keys())
+    for key, value in output.items():
+        if key in [
+            "sub_question_answer",
+            "sub_question_str",
+            "sub_qas",
+            "initial_sub_qas",
+            "sub_question_answer",
+        ]:
+            print(f"{key}: {value}")

backend/danswer/agent_search/core_qa_graph/nodes/combine_retrieved_docs.py

@@ -0,0 +1,36 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.context.search.models import InferenceSection
+
+
+def sub_combine_retrieved_docs(state: BaseQAState) -> dict[str, Any]:
+    """
+    Dedupe the retrieved docs.
+    """
+    node_start_time = datetime.now()
+
+    sub_question_base_retrieval_docs = state["sub_question_base_retrieval_docs"]
+
+    print(f"Number of docs from steps: {len(sub_question_base_retrieval_docs)}")
+    dedupe_docs: list[InferenceSection] = []
+    for base_retrieval_doc in sub_question_base_retrieval_docs:
+        if not any(
+            base_retrieval_doc.center_chunk.chunk_id == doc.center_chunk.chunk_id
+            for doc in dedupe_docs
+        ):
+            dedupe_docs.append(base_retrieval_doc)
+
+    print(f"Number of deduped docs: {len(dedupe_docs)}")
+    
+
+    return {
+        "sub_question_deduped_retrieval_docs": dedupe_docs,
+        "log_messages": generate_log_message(
+            message="sub - combine_retrieved_docs (dedupe)",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/custom_retrieve.py

@@ -0,0 +1,66 @@
+import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import RetrieverState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+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
+from danswer.llm.factory import get_default_llms
+
+
+def sub_custom_retrieve(state: RetrieverState) -> dict[str, Any]:
+    """
+    Retrieve documents
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): New key added to state, documents, that contains retrieved documents
+    """
+    print("---RETRIEVE SUB---")
+
+    node_start_time = datetime.datetime.now()
+
+    rewritten_query = state["rewritten_query"]
+
+    # Retrieval
+    # TODO: add the actual retrieval, probably from search_tool.run()
+    documents: list[InferenceSection] = []
+    llm, fast_llm = get_default_llms()
+    with get_session_context_manager() as db_session:
+        documents = SearchPipeline(
+            search_request=SearchRequest(
+                query=rewritten_query,
+            ),
+            user=None,
+            llm=llm,
+            fast_llm=fast_llm,
+            db_session=db_session,
+        )
+        
+        reranked_docs = documents.reranked_sections
+
+        # initial metric to measure fit TODO: implement metric properly
+
+        top_1_score = reranked_docs[0].center_chunk.score
+        top_5_score = sum([doc.center_chunk.score for doc in reranked_docs[:5]]) / 5
+        top_10_score = sum([doc.center_chunk.score for doc in reranked_docs[:10]]) / 10
+
+        fit_score = 1/3 * (top_1_score + top_5_score + top_10_score)
+
+        chunk_ids = {'query': rewritten_query, 
+                     'chunk_ids': [doc.center_chunk.chunk_id for doc in reranked_docs]}
+
+
+    return {
+        "sub_question_base_retrieval_docs": reranked_docs,
+        "sub_chunk_ids": [chunk_ids],
+        "log_messages": generate_log_message(
+            message=f"sub - custom_retrieve, fit_score: {fit_score}",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/dummy.py

@@ -0,0 +1,24 @@
+import datetime
+from typing import Any
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_dummy(state: BaseQAState) -> dict[str, Any]:
+    """
+    Dummy step
+    """
+
+    print("---Sub Dummy---")
+
+    node_start_time = datetime.datetime.now()
+
+    return {
+        "graph_start_time": node_start_time,
+        "log_messages": generate_log_message(
+            message="sub - dummy",
+            node_start_time=node_start_time,
+            graph_start_time=node_start_time,
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/final_format.py

@@ -0,0 +1,22 @@
+from typing import Any
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+
+
+def sub_final_format(state: BaseQAState) -> dict[str, Any]:
+    """
+    Create the final output for the QA subgraph
+    """
+
+    print("---BASE FINAL FORMAT---")
+
+    return {
+        "sub_qas": [
+            {
+                "sub_question": state["sub_question_str"],
+                "sub_answer": state["sub_question_answer"],
+                "sub_answer_check": state["sub_question_answer_check"],
+            }
+        ],
+        "log_messages": state["log_messages"],
+    }

backend/danswer/agent_search/core_qa_graph/nodes/generate.py

@@ -0,0 +1,91 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.prompts import BASE_RAG_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import format_docs
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.factory import get_default_llms
+
+
+def sub_generate(state: BaseQAState) -> dict[str, Any]:
+    """
+    Generate answer
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+         dict: The updated state with re-phrased question
+    """
+    print("---GENERATE---")
+    
+    # Create sub-query results
+
+    verified_chunks = [chunk.center_chunk.chunk_id for chunk in state["sub_question_verified_retrieval_docs"]]
+    result_dict = {}
+
+    chunk_id_dicts = state["sub_chunk_ids"]
+    expanded_chunks = []
+    original_chunks = []
+    
+    for chunk_id_dict in chunk_id_dicts:
+        sub_question = chunk_id_dict['query']
+        verified_sq_chunks = [chunk_id for chunk_id in chunk_id_dict['chunk_ids'] if chunk_id in verified_chunks]
+
+        if sub_question != state["original_question"]:
+            expanded_chunks += verified_sq_chunks
+        else:
+            result_dict['ORIGINAL'] = len(verified_sq_chunks)
+            original_chunks += verified_sq_chunks
+        result_dict[sub_question[:30]] = len(verified_sq_chunks)
+    
+    expansion_chunks = set(expanded_chunks)
+    num_expansion_chunks = sum([1 for chunk_id in expansion_chunks if chunk_id in verified_chunks])
+    num_original_relevant_chunks = len(original_chunks)
+    num_missed_relevant_chunks = sum([1 for chunk_id in original_chunks if chunk_id not in expansion_chunks])
+    num_gained_relevant_chunks = sum([1 for chunk_id in expansion_chunks if chunk_id not in original_chunks])
+    result_dict['expansion_chunks'] = num_expansion_chunks
+
+
+        
+    print(result_dict)
+
+    node_start_time = datetime.now()
+
+    question = state["sub_question_str"]
+    docs = state["sub_question_verified_retrieval_docs"]
+
+    print(f"Number of verified retrieval docs: {len(docs)}")
+    
+    # Only take the top 10 docs. 
+    # TODO: Make this dynamic or use config param?
+    top_10_docs = docs[-10:]
+
+    msg = [
+        HumanMessage(
+            content=BASE_RAG_PROMPT.format(question=question, context=format_docs(top_10_docs))
+        )
+    ]
+
+    # Grader
+    _, fast_llm = get_default_llms()
+    response = list(
+        fast_llm.stream(
+            prompt=msg,
+            # structured_response_format=None,
+        )
+    )
+
+    answer_str = merge_message_runs(response, chunk_separator="")[0].content
+    return {
+        "sub_question_answer": answer_str,
+        "log_messages": generate_log_message(
+            message="base - generate",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/qa_check.py

@@ -0,0 +1,51 @@
+import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.prompts import BASE_CHECK_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.factory import get_default_llms
+
+
+def sub_qa_check(state: BaseQAState) -> dict[str, Any]:
+    """
+    Check if the sub-question answer is satisfactory.
+
+    Args:
+        state: The current SubQAState containing the sub-question and its answer
+
+    Returns:
+        dict containing the check result and log message
+    """
+    node_start_time = datetime.datetime.now()
+
+    msg = [
+        HumanMessage(
+            content=BASE_CHECK_PROMPT.format(
+                question=state["sub_question_str"],
+                base_answer=state["sub_question_answer"],
+            )
+        )
+    ]
+
+    _, fast_llm = get_default_llms()
+    response = list(
+        fast_llm.stream(
+            prompt=msg,
+            # structured_response_format=None,
+        )
+    )
+
+    response_str = merge_message_runs(response, chunk_separator="")[0].content
+
+    return {
+        "sub_question_answer_check": response_str,
+        "base_answer_messages": generate_log_message(
+            message="sub - qa_check",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/rewrite.py

@@ -0,0 +1,74 @@
+import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.models import RewrittenQueries
+from danswer.agent_search.shared_graph_utils.prompts import (
+    REWRITE_PROMPT_MULTI_ORIGINAL,
+)
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.factory import get_default_llms
+
+
+def sub_rewrite(state: BaseQAState) -> dict[str, Any]:
+    """
+    Transform the initial question into more suitable search queries.
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+        dict: The updated state with re-phrased question
+    """
+
+    print("---SUB TRANSFORM QUERY---")
+
+    node_start_time = datetime.datetime.now()
+
+    # messages = state["base_answer_messages"]
+    question = state["sub_question_str"]
+
+    msg = [
+        HumanMessage(
+            content=REWRITE_PROMPT_MULTI_ORIGINAL.format(question=question),
+        )
+    ]
+
+    """
+    msg = [
+        HumanMessage(
+            content=REWRITE_PROMPT_MULTI.format(question=question),
+        )
+    ]
+    """
+
+    _, fast_llm = get_default_llms()
+    llm_response_list = list(
+        fast_llm.stream(
+            prompt=msg,
+            # structured_response_format={"type": "json_object", "schema": RewrittenQueries.model_json_schema()},
+            # structured_response_format=RewrittenQueries.model_json_schema(),
+        )
+    )
+    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
+
+    print(f"llm_response: {llm_response}")
+
+    rewritten_queries = llm_response.split("--")
+    # rewritten_queries = [llm_response.split("\n")[0]]
+
+    print(f"rewritten_queries: {rewritten_queries}")
+
+    rewritten_queries = RewrittenQueries(rewritten_queries=rewritten_queries)
+
+    return {
+        "sub_question_search_queries": rewritten_queries,
+        "log_messages": generate_log_message(
+            message="sub - rewrite",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/nodes/verifier.py

@@ -0,0 +1,64 @@
+import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.primary_graph.states import VerifierState
+from danswer.agent_search.shared_graph_utils.models import BinaryDecision
+from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.factory import get_default_llms
+
+
+def sub_verifier(state: VerifierState) -> dict[str, Any]:
+    """
+    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("---VERIFY QUTPUT---")
+    node_start_time = datetime.datetime.now()
+
+    question = state["question"]
+    document_content = state["document"].combined_content
+
+    msg = [
+        HumanMessage(
+            content=VERIFIER_PROMPT.format(
+                question=question, document_content=document_content
+            )
+        )
+    ]
+
+    # Grader
+    llm, fast_llm = get_default_llms()
+    response = list(
+        llm.stream(
+            prompt=msg,
+            # structured_response_format=BinaryDecision.model_json_schema(),
+        )
+    )
+
+    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"Verification end time: {datetime.datetime.now()}")
+
+    return {
+        "sub_question_verified_retrieval_docs": [state["document"]]
+        if formatted_response.decision == "yes"
+        else [],
+        "log_messages": generate_log_message(
+            message=f"sub - verifier: {formatted_response.decision}",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/core_qa_graph/states.py

@@ -0,0 +1,90 @@
+import operator
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Annotated
+from typing import TypedDict
+
+from langchain_core.messages import BaseMessage
+from langgraph.graph.message import add_messages
+
+from danswer.agent_search.shared_graph_utils.models import RewrittenQueries
+from danswer.context.search.models import InferenceSection
+from danswer.llm.interfaces import LLM
+
+
+class SubQuestionRetrieverState(TypedDict):
+    # The state for the parallel Retrievers. They each need to see only one query
+    sub_question_rewritten_query: str
+
+
+class SubQuestionVerifierState(TypedDict):
+    # The state for the parallel verification step.  Each node execution need to see only one question/doc pair
+    sub_question_document: InferenceSection
+    sub_question: str
+
+
+class CoreQAInputState(TypedDict):
+    sub_question_str: str
+    original_question: str
+
+
+class BaseQAState(TypedDict):
+    # The 'core SubQuestion'  state.
+    original_question: str
+    graph_start_time: datetime
+    # start time for parallel initial sub-questionn thread
+    sub_query_start_time: datetime
+    sub_question_rewritten_queries: list[str]
+    sub_question_str: str
+    sub_question_search_queries: RewrittenQueries
+    sub_question_nr: int
+    sub_chunk_ids: Annotated[Sequence[dict], operator.add]
+    sub_question_base_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_deduped_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_verified_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_reranked_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    sub_question_answer: str
+    sub_question_answer_check: str
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    # Answers sent back to core
+    initial_sub_qas: Annotated[Sequence[dict], operator.add]
+    primary_llm: LLM
+    fast_llm: LLM
+
+
+class BaseQAOutputState(TypedDict):
+    # The 'SubQuestion'  output state. Removes all the intermediate states
+    sub_question_rewritten_queries: list[str]
+    sub_question_str: str
+    sub_question_search_queries: list[str]
+    sub_question_nr: int
+    # Answers sent back to core
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    # Answers sent back to core
+    initial_sub_qas: Annotated[Sequence[dict], operator.add]
+    sub_question_base_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_deduped_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_verified_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_reranked_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    sub_question_answer: str
+    sub_question_answer_check: str
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]

backend/danswer/agent_search/deep_qa_graph/edges.py

@@ -0,0 +1,46 @@
+from collections.abc import Hashable
+from typing import Union
+
+from langgraph.types import Send
+
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.primary_graph.states import RetrieverState
+from danswer.agent_search.primary_graph.states import VerifierState
+
+
+def sub_continue_to_verifier(state: ResearchQAState) -> Union[Hashable, list[Hashable]]:
+    # Routes each de-douped retrieved doc to the verifier step - in parallel
+    # Notice the 'Send()' API that takes care of the parallelization
+
+    return [
+        Send(
+            "sub_verifier",
+            VerifierState(
+                document=doc,
+                question=state["sub_question"],
+                primary_llm=state["primary_llm"],
+                fast_llm=state["fast_llm"],
+                graph_start_time=state["graph_start_time"],
+            ),
+        )
+        for doc in state["sub_question_base_retrieval_docs"]
+    ]
+
+
+def sub_continue_to_retrieval(
+    state: ResearchQAState,
+) -> 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_custom_retrieve",
+            RetrieverState(
+                rewritten_query=query,
+                primary_llm=state["primary_llm"],
+                fast_llm=state["fast_llm"],
+                graph_start_time=state["graph_start_time"],
+            ),
+        )
+        for query in state["sub_question_rewritten_queries"]
+    ]

backend/danswer/agent_search/deep_qa_graph/graph_builder.py

@@ -0,0 +1,93 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from danswer.agent_search.deep_qa_graph.edges import sub_continue_to_retrieval
+from danswer.agent_search.deep_qa_graph.edges import sub_continue_to_verifier
+from danswer.agent_search.deep_qa_graph.nodes.combine_retrieved_docs import (
+    sub_combine_retrieved_docs,
+)
+from danswer.agent_search.deep_qa_graph.nodes.custom_retrieve import sub_custom_retrieve
+from danswer.agent_search.deep_qa_graph.nodes.dummy import sub_dummy
+from danswer.agent_search.deep_qa_graph.nodes.final_format import sub_final_format
+from danswer.agent_search.deep_qa_graph.nodes.generate import sub_generate
+from danswer.agent_search.deep_qa_graph.nodes.qa_check import sub_qa_check
+from danswer.agent_search.deep_qa_graph.nodes.verifier import sub_verifier
+from danswer.agent_search.deep_qa_graph.states import ResearchQAOutputState
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+
+
+def build_deep_qa_graph() -> StateGraph:
+    # Define the nodes we will cycle between
+    sub_answers = StateGraph(state_schema=ResearchQAState, output=ResearchQAOutputState)
+
+    ### Add Nodes ###
+
+    # Dummy node for initial processing
+    sub_answers.add_node(node="sub_dummy", action=sub_dummy)
+
+    # The retrieval step
+    sub_answers.add_node(node="sub_custom_retrieve", action=sub_custom_retrieve)
+
+    # The dedupe step
+    sub_answers.add_node(
+        node="sub_combine_retrieved_docs", action=sub_combine_retrieved_docs
+    )
+
+    # Verifying retrieved information
+    sub_answers.add_node(node="sub_verifier", action=sub_verifier)
+
+    # Generating the response
+    sub_answers.add_node(node="sub_generate", action=sub_generate)
+
+    # Checking the quality of the answer
+    sub_answers.add_node(node="sub_qa_check", action=sub_qa_check)
+
+    # Final formatting of the response
+    sub_answers.add_node(node="sub_final_format", action=sub_final_format)
+
+    ### Add Edges ###
+
+    # Generate multiple sub-questions
+    sub_answers.add_edge(start_key=START, end_key="sub_rewrite")
+
+    # For each sub-question, perform a retrieval in parallel
+    sub_answers.add_conditional_edges(
+        source="sub_rewrite",
+        path=sub_continue_to_retrieval,
+        path_map=["sub_custom_retrieve"],
+    )
+
+    # Combine the retrieved docs for each sub-question from the parallel retrievals
+    sub_answers.add_edge(
+        start_key="sub_custom_retrieve", end_key="sub_combine_retrieved_docs"
+    )
+
+    # Go over all of the combined retrieved docs and verify them against the original question
+    sub_answers.add_conditional_edges(
+        source="sub_combine_retrieved_docs",
+        path=sub_continue_to_verifier,
+        path_map=["sub_verifier"],
+    )
+
+    # Generate an answer for each verified retrieved doc
+    sub_answers.add_edge(start_key="sub_verifier", end_key="sub_generate")
+
+    # Check the quality of the answer
+    sub_answers.add_edge(start_key="sub_generate", end_key="sub_qa_check")
+
+    sub_answers.add_edge(start_key="sub_qa_check", end_key="sub_final_format")
+
+    sub_answers.add_edge(start_key="sub_final_format", end_key=END)
+
+    return sub_answers
+
+
+if __name__ == "__main__":
+    # TODO: add the actual question
+    inputs = {"sub_question": "Whose music is kind of hard to easily enjoy?"}
+    sub_answers_graph = build_deep_qa_graph()
+    compiled_sub_answers = sub_answers_graph.compile()
+    output = compiled_sub_answers.invoke(inputs)
+    print("\nOUTPUT:")
+    print(output)

backend/danswer/agent_search/deep_qa_graph/nodes/combine_retrieved_docs.py

@@ -0,0 +1,31 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_combine_retrieved_docs(state: ResearchQAState) -> dict[str, Any]:
+    """
+    Dedupe the retrieved docs.
+    """
+    node_start_time = datetime.now()
+
+    sub_question_base_retrieval_docs = state["sub_question_base_retrieval_docs"]
+
+    print(f"Number of docs from steps: {len(sub_question_base_retrieval_docs)}")
+    dedupe_docs = []
+    for base_retrieval_doc in sub_question_base_retrieval_docs:
+        if base_retrieval_doc not in dedupe_docs:
+            dedupe_docs.append(base_retrieval_doc)
+
+    print(f"Number of deduped docs: {len(dedupe_docs)}")
+
+    return {
+        "sub_question_deduped_retrieval_docs": dedupe_docs,
+        "log_messages": generate_log_message(
+            message="sub - combine_retrieved_docs (dedupe)",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/custom_retrieve.py

@@ -0,0 +1,33 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import RetrieverState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.context.search.models import InferenceSection
+
+
+def sub_custom_retrieve(state: RetrieverState) -> dict[str, Any]:
+    """
+    Retrieve documents
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): New key added to state, documents, that contains retrieved documents
+    """
+    print("---RETRIEVE SUB---")
+    node_start_time = datetime.now()
+
+    # Retrieval
+    # TODO: add the actual retrieval, probably from search_tool.run()
+    documents: list[InferenceSection] = []
+
+    return {
+        "sub_question_base_retrieval_docs": documents,
+        "log_messages": generate_log_message(
+            message="sub - custom_retrieve",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/dummy.py

@@ -0,0 +1,21 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_dummy(state: BaseQAState) -> dict[str, Any]:
+    """
+    Dummy step
+    """
+
+    print("---Sub Dummy---")
+
+    return {
+        "log_messages": generate_log_message(
+            message="sub - dummy",
+            node_start_time=datetime.now(),
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/final_format.py

@@ -0,0 +1,31 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_final_format(state: ResearchQAState) -> dict[str, Any]:
+    """
+    Create the final output for the QA subgraph
+    """
+
+    print("---SUB  FINAL FORMAT---")
+    node_start_time = datetime.now()
+
+    return {
+        # TODO: Type this
+        "sub_qas": [
+            {
+                "sub_question": state["sub_question"],
+                "sub_answer": state["sub_question_answer"],
+                "sub_question_nr": state["sub_question_nr"],
+                "sub_answer_check": state["sub_question_answer_check"],
+            }
+        ],
+        "log_messages": generate_log_message(
+            message="sub - final format",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/generate.py

@@ -0,0 +1,56 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.shared_graph_utils.prompts import BASE_RAG_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import format_docs
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_generate(state: ResearchQAState) -> dict[str, Any]:
+    """
+    Generate answer
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+         dict: The updated state with re-phrased question
+    """
+    print("---SUB GENERATE---")
+    node_start_time = datetime.now()
+
+    question = state["sub_question"]
+    docs = state["sub_question_verified_retrieval_docs"]
+
+    print(f"Number of verified retrieval docs for sub-question: {len(docs)}")
+
+    msg = [
+        HumanMessage(
+            content=BASE_RAG_PROMPT.format(question=question, context=format_docs(docs))
+        )
+    ]
+
+    # Grader
+    if len(docs) > 0:
+        model = state["fast_llm"]
+        response = list(
+            model.stream(
+                prompt=msg,
+            )
+        )
+        response_str = merge_message_runs(response, chunk_separator="")[0].content
+    else:
+        response_str = ""
+
+    return {
+        "sub_question_answer": response_str,
+        "log_messages": generate_log_message(
+            message="sub - generate",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/qa_check.py

@@ -0,0 +1,57 @@
+import json
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.deep_qa_graph.prompts import SUB_CHECK_PROMPT
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.shared_graph_utils.models import BinaryDecision
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_qa_check(state: ResearchQAState) -> dict[str, Any]:
+    """
+    Check whether the final output satisfies the original user question
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+        dict: The updated state with the final decision
+    """
+
+    print("---CHECK SUB QUTPUT---")
+    node_start_time = datetime.now()
+
+    sub_answer = state["sub_question_answer"]
+    sub_question = state["sub_question"]
+
+    msg = [
+        HumanMessage(
+            content=SUB_CHECK_PROMPT.format(
+                sub_question=sub_question, sub_answer=sub_answer
+            )
+        )
+    ]
+
+    # Grader
+    model = state["fast_llm"]
+    response = list(
+        model.stream(
+            prompt=msg,
+            structured_response_format=BinaryDecision.model_json_schema(),
+        )
+    )
+
+    raw_response = json.loads(response[0].pretty_repr())
+    formatted_response = BinaryDecision.model_validate(raw_response)
+
+    return {
+        "sub_question_answer_check": formatted_response.decision,
+        "log_messages": generate_log_message(
+            message=f"sub - qa check: {formatted_response.decision}",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/rewrite.py

@@ -0,0 +1,64 @@
+import json
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.shared_graph_utils.models import RewrittenQueries
+from danswer.agent_search.shared_graph_utils.prompts import REWRITE_PROMPT_MULTI
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.interfaces import LLM
+
+
+def sub_rewrite(state: ResearchQAState) -> dict[str, Any]:
+    """
+    Transform the initial question into more suitable search queries.
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+        dict: The updated state with re-phrased question
+    """
+
+    print("---SUB TRANSFORM QUERY---")
+    node_start_time = datetime.now()
+
+    question = state["sub_question"]
+
+    msg = [
+        HumanMessage(
+            content=REWRITE_PROMPT_MULTI.format(question=question),
+        )
+    ]
+    fast_llm: LLM = state["fast_llm"]
+    llm_response = list(
+        fast_llm.stream(
+            prompt=msg,
+            structured_response_format=RewrittenQueries.model_json_schema(),
+        )
+    )
+
+    # Get the rewritten queries in a defined format
+    rewritten_queries: RewrittenQueries = json.loads(llm_response[0].pretty_repr())
+
+    print(f"rewritten_queries: {rewritten_queries}")
+
+    rewritten_queries = RewrittenQueries(
+        rewritten_queries=[
+            "music hard to listen to",
+            "Music that is not fun or pleasant",
+        ]
+    )
+
+    print(f"hardcoded rewritten_queries: {rewritten_queries}")
+
+    return {
+        "sub_question_rewritten_queries": rewritten_queries,
+        "log_messages": generate_log_message(
+            message="sub - rewrite",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/nodes/verifier.py

@@ -0,0 +1,59 @@
+import json
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import VerifierState
+from danswer.agent_search.shared_graph_utils.models import BinaryDecision
+from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_verifier(state: VerifierState) -> dict[str, Any]:
+    """
+    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("---SUB VERIFY QUTPUT---")
+    node_start_time = datetime.now()
+
+    question = state["question"]
+    document_content = state["document"].combined_content
+
+    msg = [
+        HumanMessage(
+            content=VERIFIER_PROMPT.format(
+                question=question, document_content=document_content
+            )
+        )
+    ]
+
+    # Grader
+    model = state["fast_llm"]
+    response = list(
+        model.stream(
+            prompt=msg,
+            structured_response_format=BinaryDecision.model_json_schema(),
+        )
+    )
+
+    raw_response = json.loads(response[0].pretty_repr())
+    formatted_response = BinaryDecision.model_validate(raw_response)
+
+    return {
+        "deduped_retrieval_docs": [state["document"]]
+        if formatted_response.decision == "yes"
+        else [],
+        "log_messages": generate_log_message(
+            message=f"core - verifier: {formatted_response.decision}",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/deep_qa_graph/prompts.py

@@ -0,0 +1,13 @@
+SUB_CHECK_PROMPT = """ \n
+    Please check whether the suggested answer seems to address the original question.
+
+    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:"""

backend/danswer/agent_search/deep_qa_graph/states.py

@@ -0,0 +1,64 @@
+import operator
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Annotated
+from typing import TypedDict
+
+from langchain_core.messages import BaseMessage
+from langgraph.graph.message import add_messages
+
+from danswer.context.search.models import InferenceSection
+from danswer.llm.interfaces import LLM
+
+
+class ResearchQAState(TypedDict):
+    # The 'core SubQuestion'  state.
+    original_question: str
+    graph_start_time: datetime
+    sub_question_rewritten_queries: list[str]
+    sub_question: str
+    sub_question_nr: int
+    sub_question_base_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_deduped_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_verified_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_reranked_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    sub_question_answer: str
+    sub_question_answer_check: str
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    primary_llm: LLM
+    fast_llm: LLM
+
+
+class ResearchQAOutputState(TypedDict):
+    # The 'SubQuestion'  output state. Removes all the intermediate states
+    sub_question_rewritten_queries: list[str]
+    sub_question: str
+    sub_question_nr: int
+    # Answers sent back to core
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    sub_question_base_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_deduped_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_verified_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_reranked_retrieval_docs: Annotated[
+        Sequence[InferenceSection], operator.add
+    ]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    sub_question_answer: str
+    sub_question_answer_check: str
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]

backend/danswer/agent_search/primary_graph/edges.py

@@ -0,0 +1,75 @@
+from collections.abc import Hashable
+from typing import Union
+
+from langchain_core.messages import HumanMessage
+from langgraph.types import Send
+
+from danswer.agent_search.core_qa_graph.states import BaseQAState
+from danswer.agent_search.deep_qa_graph.states import ResearchQAState
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.prompts import BASE_CHECK_PROMPT
+
+
+def continue_to_initial_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_initial",
+            BaseQAState(
+                sub_question_str=initial_sub_question["sub_question_str"],
+                sub_question_search_queries=initial_sub_question[
+                    "sub_question_search_queries"
+                ],
+                sub_question_nr=initial_sub_question["sub_question_nr"],
+                primary_llm=state["primary_llm"],
+                fast_llm=state["fast_llm"],
+                graph_start_time=state["graph_start_time"],
+            ),
+        )
+        for initial_sub_question in state["initial_sub_questions"]
+    ]
+
+
+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/primary_graph/graph_builder.py

@@ -0,0 +1,171 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from danswer.agent_search.core_qa_graph.graph_builder import build_core_qa_graph
+from danswer.agent_search.deep_qa_graph.graph_builder import build_deep_qa_graph
+from danswer.agent_search.primary_graph.edges import continue_to_answer_sub_questions
+from danswer.agent_search.primary_graph.edges import continue_to_deep_answer
+from danswer.agent_search.primary_graph.edges import continue_to_initial_sub_questions
+from danswer.agent_search.primary_graph.nodes.base_wait import base_wait
+from danswer.agent_search.primary_graph.nodes.combine_retrieved_docs import (
+    combine_retrieved_docs,
+)
+from danswer.agent_search.primary_graph.nodes.custom_retrieve import custom_retrieve
+from danswer.agent_search.primary_graph.nodes.decompose import decompose
+from danswer.agent_search.primary_graph.nodes.deep_answer_generation import (
+    deep_answer_generation,
+)
+from danswer.agent_search.primary_graph.nodes.dummy_start import dummy_start
+from danswer.agent_search.primary_graph.nodes.entity_term_extraction import (
+    entity_term_extraction,
+)
+from danswer.agent_search.primary_graph.nodes.final_stuff import final_stuff
+from danswer.agent_search.primary_graph.nodes.generate_initial import generate_initial
+from danswer.agent_search.primary_graph.nodes.main_decomp_base import main_decomp_base
+from danswer.agent_search.primary_graph.nodes.rewrite import rewrite
+from danswer.agent_search.primary_graph.nodes.sub_qa_level_aggregator import (
+    sub_qa_level_aggregator,
+)
+from danswer.agent_search.primary_graph.nodes.sub_qa_manager import sub_qa_manager
+from danswer.agent_search.primary_graph.nodes.verifier import verifier
+from danswer.agent_search.primary_graph.states import QAState
+
+
+def build_core_graph() -> StateGraph:
+    # Define the nodes we will cycle between
+    core_answer_graph = StateGraph(state_schema=QAState)
+
+    ### Add Nodes ###
+    core_answer_graph.add_node(node="dummy_start", 
+                               action=dummy_start)
+
+    # Re-writing the question
+    core_answer_graph.add_node(node="rewrite", 
+                               action=rewrite)
+
+    # The retrieval step
+    core_answer_graph.add_node(node="custom_retrieve", 
+                               action=custom_retrieve)
+
+    # Combine and dedupe retrieved docs.
+    core_answer_graph.add_node(
+        node="combine_retrieved_docs", 
+        action=combine_retrieved_docs
+    )
+
+    # Extract entities, terms and relationships
+    core_answer_graph.add_node(
+        node="entity_term_extraction", 
+        action=entity_term_extraction
+    )
+
+    # Verifying that a retrieved doc is relevant
+    core_answer_graph.add_node(node="verifier", 
+                               action=verifier)
+
+    # Initial question decomposition
+    core_answer_graph.add_node(node="main_decomp_base", 
+                               action=main_decomp_base)
+
+    # Build the base QA sub-graph and compile it
+    compiled_core_qa_graph = build_core_qa_graph().compile()
+    # Add the compiled base QA sub-graph as a node to the core graph
+    core_answer_graph.add_node(
+        node="sub_answers_graph_initial", 
+        action=compiled_core_qa_graph
+    )
+
+    # Checking whether the initial answer is in the ballpark
+    core_answer_graph.add_node(node="base_wait", 
+                               action=base_wait)
+
+    # Decompose the question into sub-questions
+    core_answer_graph.add_node(node="decompose", 
+                               action=decompose)
+
+    # Manage the sub-questions
+    core_answer_graph.add_node(node="sub_qa_manager", 
+                               action=sub_qa_manager)
+
+    # Build the research QA sub-graph and compile it
+    compiled_deep_qa_graph = build_deep_qa_graph().compile()
+    # Add the compiled research QA sub-graph as a node to the core graph
+    core_answer_graph.add_node(node="sub_answers_graph", 
+                               action=compiled_deep_qa_graph)
+
+    # Aggregate the sub-questions
+    core_answer_graph.add_node(
+        node="sub_qa_level_aggregator", 
+        action=sub_qa_level_aggregator
+    )
+
+    # aggregate sub questions and answers
+    core_answer_graph.add_node(
+        node="deep_answer_generation", 
+        action=deep_answer_generation
+    )
+
+    # A final clean-up step
+    core_answer_graph.add_node(node="final_stuff", 
+                               action=final_stuff)
+
+    # Generating a response after we know the documents are relevant
+    core_answer_graph.add_node(node="generate_initial", 
+                               action=generate_initial)
+
+    ### Add Edges ###
+
+    # start the initial sub-question decomposition
+    core_answer_graph.add_edge(start_key=START, 
+                               end_key="main_decomp_base")
+
+    core_answer_graph.add_conditional_edges(
+        source="main_decomp_base",
+        path=continue_to_initial_sub_questions,
+    )
+
+    # use the retrieved information to generate the answer
+    core_answer_graph.add_edge(
+        start_key=["verifier", "sub_answers_graph_initial"], 
+        end_key="generate_initial"
+    )
+    core_answer_graph.add_edge(start_key="generate_initial", 
+                               end_key="base_wait")
+
+    core_answer_graph.add_conditional_edges(
+        source="base_wait",
+        path=continue_to_deep_answer,
+        path_map={"decompose": "entity_term_extraction", "end": "final_stuff"},
+    )
+
+    core_answer_graph.add_edge(start_key="entity_term_extraction", end_key="decompose")
+
+    core_answer_graph.add_edge(start_key="decompose", 
+                               end_key="sub_qa_manager")
+    core_answer_graph.add_conditional_edges(
+        source="sub_qa_manager",
+        path=continue_to_answer_sub_questions,
+    )
+
+    core_answer_graph.add_edge(
+        start_key="sub_answers_graph",
+          end_key="sub_qa_level_aggregator"
+    )
+
+    core_answer_graph.add_edge(
+        start_key="sub_qa_level_aggregator", 
+        end_key="deep_answer_generation"
+    )
+
+    core_answer_graph.add_edge(
+        start_key="deep_answer_generation", 
+        end_key="final_stuff"
+    )
+
+    core_answer_graph.add_edge(start_key="final_stuff", 
+                               end_key=END)
+    
+    core_answer_graph.compile()
+
+    return core_answer_graph

backend/danswer/agent_search/primary_graph/nodes/base_wait.py

@@ -0,0 +1,27 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def base_wait(state: QAState) -> dict[str, Any]:
+    """
+    Ensures that all required steps are completed before proceeding to the next step
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+        dict: {} (no operation, just logging)
+    """
+
+    print("---Base Wait ---")
+    node_start_time = datetime.now()
+    return {
+        "log_messages": generate_log_message(
+            message="core - base_wait",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/combine_retrieved_docs.py

@@ -0,0 +1,36 @@
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.context.search.models import InferenceSection
+
+
+def combine_retrieved_docs(state: QAState) -> dict[str, Any]:
+    """
+    Dedupe the retrieved docs.
+    """
+    node_start_time = datetime.now()
+
+    base_retrieval_docs: Sequence[InferenceSection] = state["base_retrieval_docs"]
+
+    print(f"Number of docs from steps: {len(base_retrieval_docs)}")
+    dedupe_docs: list[InferenceSection] = []
+    for base_retrieval_doc in base_retrieval_docs:
+        if not any(
+            base_retrieval_doc.center_chunk.document_id == doc.center_chunk.document_id
+            for doc in dedupe_docs
+        ):
+            dedupe_docs.append(base_retrieval_doc)
+
+    print(f"Number of deduped docs: {len(dedupe_docs)}")
+
+    return {
+        "deduped_retrieval_docs": dedupe_docs,
+        "log_messages": generate_log_message(
+            message="core - combine_retrieved_docs (dedupe)",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/custom_retrieve.py

@@ -0,0 +1,52 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import RetrieverState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+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
+from danswer.llm.factory import get_default_llms
+
+
+def custom_retrieve(state: RetrieverState) -> dict[str, Any]:
+    """
+    Retrieve documents
+
+    Args:
+        retriever_state (dict): The current graph state
+
+    Returns:
+        state (dict): New key added to state, documents, that contains retrieved documents
+    """
+    print("---RETRIEVE---")
+
+    node_start_time = datetime.now()
+
+    query = state["rewritten_query"]
+
+    # Retrieval
+    # TODO: add the actual retrieval, probably from search_tool.run()
+    llm, fast_llm = get_default_llms()
+    with get_session_context_manager() as db_session:
+        top_sections = SearchPipeline(
+            search_request=SearchRequest(
+                query=query,
+            ),
+            user=None,
+            llm=llm,
+            fast_llm=fast_llm,
+            db_session=db_session,
+        ).reranked_sections
+        print(len(top_sections))
+    documents: list[InferenceSection] = []
+
+    return {
+        "base_retrieval_docs": documents,
+        "log_messages": generate_log_message(
+            message="core - custom_retrieve",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/decompose.py

@@ -0,0 +1,78 @@
+import json
+import re
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import QAState
+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: QAState) -> 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/primary_graph/nodes/deep_answer_generation.py

@@ -0,0 +1,61 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import QAState
+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 generate_log_message
+from danswer.agent_search.shared_graph_utils.utils import normalize_whitespace
+
+
+# aggregate sub questions and answers
+def deep_answer_generation(state: QAState) -> dict[str, Any]:
+    """
+    Generate answer
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+         dict: The updated state with re-phrased question
+    """
+    print("---DEEP GENERATE---")
+
+    node_start_time = datetime.now()
+
+    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,
+        "log_messages": generate_log_message(
+            message="deep - deep answer generation",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/dummy_start.py

@@ -0,0 +1,11 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+
+
+def dummy_start(state: QAState) -> dict[str, Any]:
+    """
+    Dummy node to set the start time
+    """
+    return {"start_time": datetime.now()}

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

@@ -0,0 +1,51 @@
+import json
+import re
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+
+from danswer.agent_search.primary_graph.prompts import ENTITY_TERM_PROMPT
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import format_docs
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+from danswer.llm.factory import get_default_llms
+
+
+def entity_term_extraction(state: QAState) -> dict[str, Any]:
+    """Extract entities and terms from the question and context"""
+    node_start_time = datetime.now()
+
+    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 = get_default_llms()
+    # Grader
+    llm_response_list = list(
+        fast_llm.stream(
+            prompt=msg,
+            # structured_response_format={"type": "json_object", "schema": RewrittenQueries.model_json_schema()},
+            # structured_response_format=RewrittenQueries.model_json_schema(),
+        )
+    )
+    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,
+        "log_messages": generate_log_message(
+            message="deep - entity term extraction",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/final_stuff.py

@@ -0,0 +1,85 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def final_stuff(state: QAState) -> 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---")
+    node_start_time = datetime.now()
+
+    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)
+
+    log_message = generate_log_message(
+        message="all - final_stuff",
+        node_start_time=node_start_time,
+        graph_start_time=state["graph_start_time"],
+    )
+
+    print(log_message)
+    print("--------------------------------")
+
+    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 {
+            "log_messages": log_message,
+        }
+
+    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 {
+        "log_messages": generate_log_message(
+            message="all - final_stuff",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/generate.py

@@ -0,0 +1,52 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.prompts import BASE_RAG_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import format_docs
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def generate(state: QAState) -> dict[str, Any]:
+    """
+    Generate answer
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+         dict: The updated state with re-phrased question
+    """
+    print("---GENERATE---")
+    node_start_time = datetime.now()
+
+    question = state["original_question"]
+    docs = state["deduped_retrieval_docs"]
+
+    print(f"Number of verified retrieval docs: {len(docs)}")
+
+    msg = [
+        HumanMessage(
+            content=BASE_RAG_PROMPT.format(question=question, context=format_docs(docs))
+        )
+    ]
+
+    # Grader
+    llm = state["fast_llm"]
+    response = list(
+        llm.stream(
+            prompt=msg,
+            structured_response_format=None,
+        )
+    )
+
+    return {
+        "base_answer": response[0].pretty_repr(),
+        "log_messages": generate_log_message(
+            message="core - generate",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/generate_initial.py

@@ -0,0 +1,72 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.prompts import INITIAL_RAG_PROMPT
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import format_docs
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def generate_initial(state: QAState) -> dict[str, Any]:
+    """
+    Generate answer
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+         dict: The updated state with re-phrased question
+    """
+    print("---GENERATE INITIAL---")
+    node_start_time = datetime.now()
+
+    question = state["original_question"]
+    docs = state["deduped_retrieval_docs"]
+    print(f"Number of verified retrieval docs - base: {len(docs)}")
+
+    sub_question_answers = state["initial_sub_qas"]
+
+    sub_question_answers_list = []
+
+    _SUB_QUESTION_ANSWER_TEMPLATE = """
+    Sub-Question:\n  - {sub_question}\n  --\nAnswer:\n  - {sub_answer}\n\n
+    """
+    for sub_question_answer_dict in sub_question_answers:
+        if (
+            sub_question_answer_dict["sub_answer_check"] == "yes"
+            and len(sub_question_answer_dict["sub_answer"]) > 0
+            and sub_question_answer_dict["sub_answer"] != "I don't know"
+        ):
+            sub_question_answers_list.append(
+                _SUB_QUESTION_ANSWER_TEMPLATE.format(
+                    sub_question=sub_question_answer_dict["sub_question"],
+                    sub_answer=sub_question_answer_dict["sub_answer"],
+                )
+            )
+
+    sub_question_answer_str = "\n\n------\n\n".join(sub_question_answers_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)
+
+    return {
+        "base_answer": response.pretty_repr(),
+        "log_messages": generate_log_message(
+            message="core - generate initial",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/main_decomp_base.py

@@ -0,0 +1,64 @@
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.prompts import INITIAL_DECOMPOSITION_PROMPT
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import clean_and_parse_list_string
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def main_decomp_base(state: QAState) -> dict[str, Any]:
+    """
+    Perform an initial question decomposition, incl. one search term
+
+    Args:
+        state (messages): The current state
+
+    Returns:
+        dict: The updated state with initial decomposition
+    """
+
+    print("---INITIAL DECOMP---")
+    node_start_time = datetime.now()
+
+    question = state["original_question"]
+
+    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 = []
+
+    for sub_question_nr, sub_question in enumerate(list_of_subquestions):
+        sub_question_str = sub_question["sub_question"].strip()
+        # temporarily
+        sub_question_search_queries = [sub_question["search_term"]]
+
+        decomp_list.append(
+            {
+                "sub_question_str": sub_question_str,
+                "sub_question_search_queries": sub_question_search_queries,
+                "sub_question_nr": sub_question_nr,
+            }
+        )
+
+    return {
+        "initial_sub_questions": decomp_list,
+        "sub_query_start_time": node_start_time,
+        "log_messages": generate_log_message(
+            message="core - initial decomp",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/rewrite.py

@@ -0,0 +1,55 @@
+import json
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.models import RewrittenQueries
+from danswer.agent_search.shared_graph_utils.prompts import REWRITE_PROMPT_MULTI
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def rewrite(state: QAState) -> dict[str, Any]:
+    """
+    Transform the initial question into more suitable search queries.
+
+    Args:
+        qa_state (messages): The current state
+
+    Returns:
+        dict: The updated state with re-phrased question
+    """
+    print("---STARTING GRAPH---")
+    graph_start_time = datetime.now()
+
+    print("---TRANSFORM QUERY---")
+    node_start_time = datetime.now()
+
+    question = state["original_question"]
+
+    msg = [
+        HumanMessage(
+            content=REWRITE_PROMPT_MULTI.format(question=question),
+        )
+    ]
+
+    # Get the rewritten queries in a defined format
+    fast_llm = state["fast_llm"]
+    llm_response = list(
+        fast_llm.stream(
+            prompt=msg,
+            structured_response_format=RewrittenQueries.model_json_schema(),
+        )
+    )
+
+    formatted_response: RewrittenQueries = json.loads(llm_response[0].pretty_repr())
+
+    return {
+        "rewritten_queries": formatted_response.rewritten_queries,
+        "log_messages": generate_log_message(
+            message="core - rewrite",
+            node_start_time=node_start_time,
+            graph_start_time=graph_start_time,
+        ),
+    }

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

@@ -0,0 +1,39 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+# aggregate sub questions and answers
+def sub_qa_level_aggregator(state: QAState) -> dict[str, Any]:
+    sub_qas = state["sub_qas"]
+
+    node_start_time = datetime.now()
+
+    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,
+        "log_messages": generate_log_message(
+            message="deep - sub qa level aggregator",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

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

@@ -0,0 +1,28 @@
+from datetime import datetime
+from typing import Any
+
+from danswer.agent_search.primary_graph.states import QAState
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def sub_qa_manager(state: QAState) -> dict[str, Any]:
+    """ """
+
+    node_start_time = datetime.now()
+
+    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,
+        "log_messages": generate_log_message(
+            message="deep - sub qa manager",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/nodes/verifier.py

@@ -0,0 +1,59 @@
+import json
+from datetime import datetime
+from typing import Any
+
+from langchain_core.messages import HumanMessage
+
+from danswer.agent_search.primary_graph.states import VerifierState
+from danswer.agent_search.shared_graph_utils.models import BinaryDecision
+from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
+from danswer.agent_search.shared_graph_utils.utils import generate_log_message
+
+
+def verifier(state: VerifierState) -> dict[str, Any]:
+    """
+    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("---VERIFY QUTPUT---")
+    node_start_time = datetime.now()
+
+    question = state["question"]
+    document_content = state["document"].combined_content
+
+    msg = [
+        HumanMessage(
+            content=VERIFIER_PROMPT.format(
+                question=question, document_content=document_content
+            )
+        )
+    ]
+
+    # Grader
+    llm = state["fast_llm"]
+    response = list(
+        llm.stream(
+            prompt=msg,
+            structured_response_format=BinaryDecision.model_json_schema(),
+        )
+    )
+
+    raw_response = json.loads(response[0].pretty_repr())
+    formatted_response = BinaryDecision.model_validate(raw_response)
+
+    return {
+        "deduped_retrieval_docs": [state["document"]]
+        if formatted_response.decision == "yes"
+        else [],
+        "log_messages": generate_log_message(
+            message=f"core - verifier: {formatted_response.decision}",
+            node_start_time=node_start_time,
+            graph_start_time=state["graph_start_time"],
+        ),
+    }

backend/danswer/agent_search/primary_graph/prompts.py

@@ -0,0 +1,86 @@
+INITIAL_DECOMPOSITION_PROMPT = """ \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
+
+    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/primary_graph/states.py

@@ -0,0 +1,73 @@
+import operator
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Annotated
+from typing import TypedDict
+
+from langchain_core.messages import BaseMessage
+from langgraph.graph.message import add_messages
+
+from danswer.agent_search.shared_graph_utils.models import RewrittenQueries
+from danswer.context.search.models import InferenceSection
+
+
+class QAState(TypedDict):
+    # The 'main' state of the answer graph
+    original_question: str
+    graph_start_time: datetime
+    # start time for parallel initial sub-questionn thread
+    sub_query_start_time: datetime
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]
+    rewritten_queries: RewrittenQueries
+    sub_questions: list[dict]
+    initial_sub_questions: list[dict]
+    ranked_subquestion_ids: list[int]
+    decomposed_sub_questions_dict: dict
+    rejected_sub_questions: Annotated[list[str], operator.add]
+    rejected_sub_questions_handled: bool
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    initial_sub_qas: Annotated[Sequence[dict], operator.add]
+    checked_sub_qas: Annotated[Sequence[dict], operator.add]
+    base_retrieval_docs: Annotated[Sequence[InferenceSection], operator.add]
+    deduped_retrieval_docs: Annotated[Sequence[InferenceSection], operator.add]
+    reranked_retrieval_docs: Annotated[Sequence[InferenceSection], operator.add]
+    retrieved_entities_relationships: dict
+    questions_context: list[dict]
+    qa_level: int
+    top_chunks: list[InferenceSection]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    num_new_question_iterations: int
+    core_answer_dynamic_context: str
+    dynamic_context: str
+    initial_base_answer: str
+    base_answer: str
+    deep_answer: str
+
+
+class QAOuputState(TypedDict):
+    # The 'main' output state of the answer graph. Removes all the intermediate states
+    original_question: str
+    log_messages: Annotated[Sequence[BaseMessage], add_messages]
+    sub_questions: list[dict]
+    sub_qas: Annotated[Sequence[dict], operator.add]
+    initial_sub_qas: Annotated[Sequence[dict], operator.add]
+    checked_sub_qas: Annotated[Sequence[dict], operator.add]
+    reranked_retrieval_docs: Annotated[Sequence[InferenceSection], operator.add]
+    retrieved_entities_relationships: dict
+    top_chunks: list[InferenceSection]
+    sub_question_top_chunks: Annotated[Sequence[dict], operator.add]
+    base_answer: str
+    deep_answer: str
+
+
+class RetrieverState(TypedDict):
+    # The state for the parallel Retrievers. They each need to see only one query
+    rewritten_query: str
+    graph_start_time: datetime
+
+
+class VerifierState(TypedDict):
+    # The state for the parallel verification step.  Each node execution need to see only one question/doc pair
+    document: InferenceSection
+    question: str
+    graph_start_time: datetime

backend/danswer/agent_search/run_graph.py

@@ -0,0 +1,22 @@
+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_question": query,
+        "messages": [],
+        "tools": tools,
+        "llm": llm,
+    }
+    compiled_graph = graph.compile()
+    output = compiled_graph.invoke(input=inputs)
+    yield from output

backend/danswer/agent_search/shared_graph_utils/models.py

@@ -0,0 +1,16 @@
+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"]
+
+
+class SubQuestions(BaseModel):
+    sub_questions: list[str]

backend/danswer/agent_search/shared_graph_utils/prompts.py

@@ -0,0 +1,342 @@
+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 original 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>}}
+    """

backend/danswer/agent_search/shared_graph_utils/utils.py

@@ -0,0 +1,91 @@
+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 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 ast.literal_eval(cleaned_string)
+
+
+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

@@ -87,6 +87,7 @@ 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
@@ -99,11 +100,6 @@ 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/primary.py

@@ -39,7 +39,6 @@ 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__)
@@ -117,9 +116,13 @@ 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(
         DanswerRedisLocks.PRIMARY_WORKER,
         timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
+        thread_local=False,
     )
 
     logger.info("Primary worker lock: Acquire starting.")

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

@@ -22,6 +22,7 @@ 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
@@ -262,7 +263,12 @@ def connector_permission_sync_generator_task(
                 f"RedisConnector.permissions.generate_tasks starting. cc_pair={cc_pair_id}"
             )
             tasks_generated = redis_connector.permissions.generate_tasks(
-                self.app, lock, document_external_accesses, source_type
+                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,
             )
             if tasks_generated is None:
                 return None
@@ -298,6 +304,8 @@ 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
@@ -306,18 +314,28 @@ def update_external_document_permissions_task(
     external_access = document_external_access.external_access
     try:
         with get_session_with_tenant(tenant_id) as db_session:
-            # Then we build the update requests to update vespa
+            # Add the users to the DB if they don't exist
             batch_add_ext_perm_user_if_not_exists(
                 db_session=db_session,
                 emails=list(external_access.external_user_emails),
             )
-            upsert_document_external_perms(
+            # Then we upsert the document's external permissions in postgres
+            created_new_doc = 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

@@ -32,10 +32,14 @@ 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()
 
@@ -107,6 +111,22 @@ 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)

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

@@ -789,9 +789,12 @@ 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/pruning/tasks.py

@@ -8,6 +8,7 @@ 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
@@ -239,9 +240,12 @@ def connector_pruning_generator_task(
 
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock = r.lock(
+    # set thread_local=False since we don't control what thread the indexing/pruning
+    # might run our callback with
+    lock: RedisLock = 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/chat/chat_utils.py

@@ -2,20 +2,79 @@ 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,
@@ -35,6 +94,45 @@ def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDo
     )
 
 
+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,
@@ -197,3 +295,71 @@ 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

@@ -4,12 +4,14 @@ 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
 
 
@@ -118,20 +120,6 @@ 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
@@ -147,14 +135,20 @@ class DanswerAnswer(BaseModel):
     answer: str | 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
+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
     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):
@@ -166,9 +160,41 @@ 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

@@ -7,10 +7,13 @@ 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
@@ -102,6 +105,7 @@ 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,
 )
@@ -113,8 +117,10 @@ 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()
 
 
@@ -256,6 +262,7 @@ def _get_force_search_settings(
 ChatPacket = (
     StreamingError
     | QADocsResponse
+    | DanswerContexts
     | LLMRelevanceFilterResponse
     | FinalUsedContextDocsResponse
     | ChatMessageDetail
@@ -286,6 +293,8 @@ 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
@@ -322,17 +331,31 @@ 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
@@ -555,19 +578,34 @@ def stream_chat_message_objects(
             reserved_message_id=reserved_message_id,
         )
 
-        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
-                ),
+        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,
             )
-            if not persona
-            else PromptConfig.from_model(persona.prompts[0])
-        )
+        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])
+
         answer_style_config = AnswerStyleConfig(
             citation_config=CitationConfig(
                 all_docs_useful=selected_db_search_docs is not None
@@ -587,11 +625,13 @@ 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,
@@ -737,6 +777,8 @@ 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
@@ -845,3 +887,30 @@ 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

@@ -522,3 +522,6 @@ 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

@@ -31,6 +31,8 @@ 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"

backend/danswer/configs/danswerbot_configs.py

@@ -4,11 +4,8 @@ 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
-DANSWER_BOT_TARGET_CHUNK_PERCENTAGE = 512 * 2 / 3072
+MAX_THREAD_CONTEXT_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")
@@ -47,17 +44,6 @@ 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/utils.py

@@ -32,7 +32,11 @@ def get_user_email_from_username__server(
             response = confluence_client.get_mobile_parameters(user_name)
             email = response.get("email")
         except Exception:
-            email = None
+            # 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}")
         _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 DanswerQuote
+from danswer.chat.models import ChatDanswerBotResponse
 from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
 from danswer.configs.app_configs import WEB_DOMAIN
 from danswer.configs.constants import DocumentSource
@@ -40,10 +40,7 @@ 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
 
@@ -327,7 +324,7 @@ def _build_sources_blocks(
 
 
 def _priority_ordered_documents_blocks(
-    answer: OneShotQAResponse,
+    answer: ChatDanswerBotResponse,
 ) -> list[Block]:
     docs_response = answer.docs if answer.docs else None
     top_docs = docs_response.top_documents if docs_response else []
@@ -350,7 +347,7 @@ def _priority_ordered_documents_blocks(
 
 
 def _build_citations_blocks(
-    answer: OneShotQAResponse,
+    answer: ChatDanswerBotResponse,
 ) -> list[Block]:
     docs_response = answer.docs if answer.docs else None
     top_docs = docs_response.top_documents if docs_response else []
@@ -369,51 +366,8 @@ 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: OneShotQAResponse,
-    skip_quotes: bool = False,
+    answer: ChatDanswerBotResponse,
     process_message_for_citations: bool = False,
 ) -> list[Block]:
     retrieval_info = answer.docs
@@ -422,13 +376,10 @@ 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
@@ -471,16 +422,6 @@ 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] = []
 
@@ -489,9 +430,6 @@ def _build_qa_response_blocks(
 
     response_blocks.extend(answer_blocks)
 
-    if not skip_quotes:
-        response_blocks.extend(quotes_blocks)
-
     return response_blocks
 
 
@@ -567,10 +505,9 @@ 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,
@@ -587,7 +524,6 @@ 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,
     )
 
@@ -617,8 +553,7 @@ def build_slack_response_blocks(
 
     citations_blocks = []
     document_blocks = []
-    if use_citations:
-        # if citations are enabled, only show cited documents
+    if use_citations and answer.citations:
         citations_blocks = _build_citations_blocks(answer)
     else:
         document_blocks = _priority_ordered_documents_blocks(answer)
@@ -637,4 +572,5 @@ def build_slack_response_blocks(
         + web_follow_up_block
         + follow_up_block
     )
+
     return all_blocks

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

@@ -1,7 +1,6 @@
 import functools
 from collections.abc import Callable
 from typing import Any
-from typing import cast
 from typing import Optional
 from typing import TypeVar
 
@@ -9,46 +8,36 @@ 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.danswerbot_configs import DANSWER_BOT_ANSWER_GENERATION_TIMEOUT
-from danswer.configs.danswerbot_configs import DANSWER_BOT_DISABLE_COT
+from danswer.configs.constants import DEFAULT_PERSONA_ID
 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 ENABLE_DANSWERBOT_REFLEXION
+from danswer.configs.danswerbot_configs import MAX_THREAD_CONTEXT_PERCENTAGE
 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.persona import fetch_persona_by_id
-from danswer.db.search_settings import get_current_search_settings
+from danswer.db.models import User
+from danswer.db.persona import get_persona_by_id
 from danswer.db.users import get_user_by_email
-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.server.query_and_chat.models import CreateChatMessageRequest
 from danswer.utils.logger import DanswerLoggingAdapter
 
-
 srl = SlackRateLimiter()
 
 RT = TypeVar("RT")  # return type
@@ -83,16 +72,14 @@ def handle_regular_answer(
     feedback_reminder_id: str | None,
     tenant_id: str | None,
     num_retries: int = DANSWER_BOT_NUM_RETRIES,
-    answer_generation_timeout: int = DANSWER_BOT_ANSWER_GENERATION_TIMEOUT,
-    thread_context_percent: float = DANSWER_BOT_TARGET_CHUNK_PERCENTAGE,
+    thread_context_percent: float = MAX_THREAD_CONTEXT_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
@@ -102,9 +89,18 @@ def handle_regular_answer(
                 user = get_user_by_email(message_info.email, db_session)
 
     document_set_names: list[str] | None = None
-    persona = slack_channel_config.persona if slack_channel_config else None
     prompt = None
-    if persona:
+    # 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:
         document_set_names = [
             document_set.name for document_set in persona.document_sets
         ]
@@ -112,6 +108,26 @@ 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
@@ -122,13 +138,6 @@ 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(
@@ -141,75 +150,23 @@ def handle_regular_answer(
         backoff=2,
     )
     @rate_limits(client=client, channel=channel, thread_ts=message_ts_to_respond_to)
-    def _get_answer(new_message_request: DirectQARequest) -> OneShotQAResponse | None:
-        max_document_tokens: int | None = None
-        max_history_tokens: int | None = None
-
+    def _get_slack_answer(
+        new_message_request: CreateChatMessageRequest, danswer_user: User | None
+    ) -> ChatDanswerBotResponse:
         with get_session_with_tenant(tenant_id) as db_session:
-            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,
+            packets = stream_chat_message_objects(
+                new_msg_req=new_message_request,
+                user=danswer_user,
                 db_session=db_session,
-                answer_generation_timeout=answer_generation_timeout,
-                enable_reflexion=reflexion,
                 bypass_acl=bypass_acl,
-                use_citations=use_citations,
-                danswerbot_flow=True,
             )
 
-            if not answer.error_msg:
-                return answer
-            else:
-                raise RuntimeError(answer.error_msg)
+            answer = gather_stream_for_slack(packets)
+
+        if answer.error_msg:
+            raise RuntimeError(answer.error_msg)
+
+        return answer
 
     try:
         # By leaving time_cutoff and favor_recent as None, and setting enable_auto_detect_filters
@@ -239,26 +196,24 @@ 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:
-            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_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,
             )
+
+        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 "
@@ -359,7 +314,7 @@ 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: '{answer.rephrase}' - no documents found"
+            f"Unable to answer question: '{combined_message}' - no documents found"
         )
         # Optionally, respond in thread with the error message
         # Used primarily for debugging purposes
@@ -380,18 +335,18 @@ def handle_regular_answer(
         )
         return True
 
-    only_respond_with_citations_or_quotes = (
+    only_respond_if_citations = (
         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_with_citations_or_quotes
-        and not has_citations_or_quotes
+        only_respond_if_citations
+        and not answer.citations
         and not message_info.bypass_filters
     ):
         logger.error(
-            f"Unable to find citations or quotes to answer: '{answer.rephrase}' - not answering!"
+            f"Unable to find citations to answer: '{answer.answer}' - not answering!"
         )
         # Optionally, respond in thread with the error message
         # Used primarily for debugging purposes
@@ -409,9 +364,8 @@ def handle_regular_answer(
         tenant_id=tenant_id,
         message_info=message_info,
         answer=answer,
-        persona=persona,
         channel_conf=channel_conf,
-        use_citations=use_citations,
+        use_citations=True,  # No longer supporting quotes
         feedback_reminder_id=feedback_reminder_id,
     )
 

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

@@ -1,8 +1,33 @@
 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

@@ -19,6 +19,8 @@ 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
@@ -74,7 +76,6 @@ 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
@@ -250,7 +251,7 @@ class SlackbotHandler:
                 nx=True,
                 ex=TENANT_LOCK_EXPIRATION,
             )
-            if not acquired:
+            if not acquired and not DEV_MODE:
                 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.one_shot_answer.models import ThreadMessage
+from danswer.chat.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

@@ -145,16 +145,10 @@ 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:
@@ -226,12 +220,11 @@ def delete_messages_and_files_from_chat_session(
 
 def create_chat_session(
     db_session: Session,
-    description: str,
+    description: str | None,
     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:
@@ -241,7 +234,6 @@ 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,
     )
@@ -287,8 +279,6 @@ 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

@@ -37,6 +37,7 @@ 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
@@ -426,7 +427,9 @@ 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 HTTPException(status_code=401, detail="User must authenticate")
+        raise BasicAuthenticationError(
+            detail="User must authenticate",
+        )
 
     engine = get_sqlalchemy_engine()
 

backend/danswer/db/models.py

@@ -1,6 +1,5 @@
 import datetime
 import json
-from enum import Enum as PyEnum
 from typing import Any
 from typing import Literal
 from typing import NotRequired
@@ -964,9 +963,8 @@ class ChatSession(Base):
     persona_id: Mapped[int | None] = mapped_column(
         ForeignKey("persona.id"), nullable=True
     )
-    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)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # This chat created by DanswerBot
     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)
@@ -1488,11 +1486,6 @@ 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"
 
@@ -1505,9 +1498,6 @@ 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

@@ -415,9 +415,6 @@ 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,
@@ -449,6 +446,12 @@ 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:
@@ -486,6 +489,8 @@ 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.")
 
@@ -494,6 +499,9 @@ 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

@@ -10,7 +10,6 @@ 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
@@ -83,7 +82,6 @@ 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:
@@ -115,7 +113,6 @@ 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,
     )
@@ -130,7 +127,6 @@ 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:
@@ -170,7 +166,6 @@ 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

@@ -18,18 +18,12 @@ 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
@@ -214,18 +208,23 @@ class Answer:
 
         search_result = SearchTool.get_search_result(current_llm_call) or []
 
-        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")
+        # 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),
+        )
 
         response_handler_manager = LLMResponseHandlerManager(
             tool_call_handler, answer_handler, self.is_cancelled

backend/danswer/llm/answering/llm_response_handler.py

@@ -8,7 +8,6 @@ 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
@@ -30,7 +29,6 @@ if TYPE_CHECKING:
 ResponsePart = (
     DanswerAnswerPiece
     | CitationInfo
-    | DanswerQuotes
     | ToolCallKickoff
     | ToolResponse
     | ToolCallFinalResult

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

@@ -9,9 +9,6 @@ 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
 
@@ -70,28 +67,29 @@ class CitationResponseHandler(AnswerResponseHandler):
         yield from self.citation_processor.process_token(content)
 
 
-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,
-        )
+# 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,
+#         )
 
-    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

@@ -1,3 +1,4 @@
+# THIS IS NO LONGER IN USE
 import math
 import re
 from collections.abc import Generator
@@ -5,11 +6,10 @@ 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
@@ -26,6 +26,20 @@ 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

@@ -1,5 +1,6 @@
 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
@@ -13,8 +14,11 @@ 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.
@@ -32,11 +36,15 @@ def get_main_llm_from_tuple(
 
 
 def get_llms_for_persona(
-    persona: Persona,
+    persona: Persona | PersonaOverrideConfig | None,
     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
@@ -71,6 +79,7 @@ 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,
         )
@@ -128,11 +137,13 @@ def get_llm(
     api_base: str | None = None,
     api_version: str | None = None,
     custom_config: dict[str, str] | None = None,
-    temperature: float = GEN_AI_TEMPERATURE,
+    temperature: float | None = None,
     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

@@ -25,7 +25,6 @@ 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
@@ -92,6 +91,7 @@ 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.error(f"Authentication failed: {str(exc)}")
+        logger.warning(f"Authentication failed: {str(exc)}")
 
     elif status_code >= 400:
         error_msg = f"{str(exc)}\n"

backend/danswer/one_shot_answer/answer_question.py

@@ -1,456 +0,0 @@
-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

@@ -1,114 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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_doc_perm_sync.py

@@ -133,6 +133,8 @@ 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 = []
@@ -155,6 +157,8 @@ class RedisConnectorPermissionSync:
                     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/server/manage/models.py

@@ -1,4 +1,5 @@
 from datetime import datetime
+from enum import Enum
 from typing import TYPE_CHECKING
 
 from pydantic import BaseModel
@@ -15,7 +16,6 @@ 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
@@ -148,6 +148,12 @@ 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
@@ -197,7 +203,6 @@ 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
@@ -217,7 +222,6 @@ 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

@@ -118,7 +118,6 @@ 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,
@@ -182,7 +181,6 @@ 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

@@ -26,7 +26,6 @@ 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
@@ -60,6 +59,7 @@ 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

@@ -109,6 +109,7 @@ 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

@@ -5,12 +5,14 @@ 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
@@ -87,6 +89,8 @@ 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
@@ -102,6 +106,10 @@ 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
 
@@ -145,7 +153,7 @@ class RenameChatSessionResponse(BaseModel):
 
 class ChatSessionDetails(BaseModel):
     id: UUID
-    name: str
+    name: str | None
     persona_id: int | None = None
     time_created: str
     shared_status: ChatSessionSharedStatus
@@ -198,14 +206,14 @@ class ChatMessageDetail(BaseModel):
 
 class SearchSessionDetailResponse(BaseModel):
     search_session_id: UUID
-    description: str
+    description: str | None
     documents: list[SearchDoc]
     messages: list[ChatMessageDetail]
 
 
 class ChatSessionDetailResponse(BaseModel):
     chat_session_id: UUID
-    description: str
+    description: str | None
     persona_id: int | None = None
     persona_name: str | None
     messages: list[ChatMessageDetail]

backend/danswer/server/query_and_chat/query_backend.py

@@ -1,15 +1,11 @@
-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
@@ -32,8 +28,6 @@ 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
@@ -41,7 +35,6 @@ 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()
@@ -140,7 +133,7 @@ def get_user_search_sessions(
 
     try:
         search_sessions = get_chat_sessions_by_user(
-            user_id=user_id, deleted=False, db_session=db_session, only_one_shot=True
+            user_id=user_id, deleted=False, db_session=db_session
         )
     except ValueError:
         raise HTTPException(
@@ -229,29 +222,3 @@ 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

@@ -6,6 +6,9 @@ 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
@@ -14,6 +17,11 @@ 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

@@ -25,6 +25,9 @@ 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

@@ -13,6 +13,7 @@ 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
@@ -102,11 +103,14 @@ 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):
@@ -170,6 +174,8 @@ 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/search/search_tool.py

@@ -19,6 +19,7 @@ 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
@@ -103,6 +104,7 @@ 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
@@ -118,6 +120,9 @@ 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
@@ -292,6 +297,7 @@ 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/ee/danswer/chat/process_message.py

@@ -0,0 +1,41 @@
+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

@@ -155,7 +155,6 @@ 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(

backend/ee/danswer/db/connector_credential_pair.py

@@ -37,10 +37,15 @@ def get_cc_pairs_by_source(
     source_type: DocumentSource,
     only_sync: bool,
 ) -> list[ConnectorCredentialPair]:
+    """
+    Get all cc_pairs for a given source type (and optionally only sync)
+    result is sorted by cc_pair id
+    """
     query = (
         db_session.query(ConnectorCredentialPair)
         .join(ConnectorCredentialPair.connector)
         .filter(Connector.source == source_type)
+        .order_by(ConnectorCredentialPair.id)
     )
 
     if only_sync:

backend/ee/danswer/db/document.py

@@ -55,9 +55,10 @@ def upsert_document_external_perms(
     doc_id: str,
     external_access: ExternalAccess,
     source_type: DocumentSource,
-) -> None:
+) -> bool:
     """
-    This sets the permissions for a document in postgres.
+    This sets the permissions for a document in postgres. Returns True if the
+    a new document was created, False otherwise.
     NOTE: this will replace any existing external access, it will not do a union
     """
     document = db_session.scalars(
@@ -85,7 +86,7 @@ def upsert_document_external_perms(
         )
         db_session.add(document)
         db_session.commit()
-        return
+        return True
 
     # If the document exists, we need to check if the external access has changed
     if (
@@ -98,3 +99,5 @@ def upsert_document_external_perms(
         document.is_public = external_access.is_public
         document.last_modified = datetime.now(timezone.utc)
         db_session.commit()
+
+    return False

backend/ee/danswer/db/usage_export.py

@@ -33,12 +33,7 @@ def get_empty_chat_messages_entries__paginated(
 
     message_skeletons: list[ChatMessageSkeleton] = []
     for chat_session in chat_sessions:
-        if chat_session.one_shot:
-            flow_type = FlowType.SEARCH
-        elif chat_session.danswerbot_flow:
-            flow_type = FlowType.SLACK
-        else:
-            flow_type = FlowType.CHAT
+        flow_type = FlowType.SLACK if chat_session.danswerbot_flow else FlowType.CHAT
 
         for message in chat_session.messages:
             # Only count user messages