Add ability to specify persona in API request

backend/alembic/versions/c99d76fcd298_add_nullable_to_persona_id_in_chat_.py

@@ -0,0 +1,31 @@
+"""add nullable to persona id in Chat Session
+
+Revision ID: c99d76fcd298
+Revises: 5c7fdadae813
+Create Date: 2024-07-09 19:27:01.579697
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "c99d76fcd298"
+down_revision = "5c7fdadae813"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.alter_column(
+        "chat_session", "persona_id", existing_type=sa.INTEGER(), nullable=True
+    )
+
+
+def downgrade() -> None:
+    op.alter_column(
+        "chat_session",
+        "persona_id",
+        existing_type=sa.INTEGER(),
+        nullable=False,
+    )

backend/danswer/chat/process_message.py

@@ -675,9 +675,11 @@ def stream_chat_message_objects(
                         db_session=db_session,
                         selected_search_docs=selected_db_search_docs,
                         # Deduping happens at the last step to avoid harming quality by dropping content early on
-                        dedupe_docs=retrieval_options.dedupe_docs
-                        if retrieval_options
-                        else False,
+                        dedupe_docs=(
+                            retrieval_options.dedupe_docs
+                            if retrieval_options
+                            else False
+                        ),
                     )
                     yield qa_docs_response
                 elif packet.id == SECTION_RELEVANCE_LIST_ID:
@@ -786,16 +788,18 @@ def stream_chat_message_objects(
             if message_specific_citations
             else None,
             error=None,
-            tool_calls=[
-                ToolCall(
-                    tool_id=tool_name_to_tool_id[tool_result.tool_name],
-                    tool_name=tool_result.tool_name,
-                    tool_arguments=tool_result.tool_args,
-                    tool_result=tool_result.tool_result,
-                )
-            ]
-            if tool_result
-            else [],
+            tool_calls=(
+                [
+                    ToolCall(
+                        tool_id=tool_name_to_tool_id[tool_result.tool_name],
+                        tool_name=tool_result.tool_name,
+                        tool_arguments=tool_result.tool_args,
+                        tool_result=tool_result.tool_result,
+                    )
+                ]
+                if tool_result
+                else []
+            ),
         )
 
         logger.debug("Committing messages")

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

@@ -5,6 +5,7 @@
 from typing import Optional
 from typing import TypeVar
 
+from fastapi import HTTPException
 from retry import retry
 from slack_sdk import WebClient
 from slack_sdk.models.blocks import DividerBlock
@@ -153,15 +154,23 @@ def _get_answer(new_message_request: DirectQARequest) -> OneShotQAResponse | Non
 
         with Session(get_sqlalchemy_engine()) as db_session:
             if len(new_message_request.messages) > 1:
-                persona = cast(
-                    Persona,
-                    fetch_persona_by_id(
-                        db_session,
-                        new_message_request.persona_id,
-                        user=None,
-                        get_editable=False,
-                    ),
-                )
+                if new_message_request.persona_config:
+                    raise HTTPException(
+                        status_code=403,
+                        detail="Slack bot does not support persona config",
+                    )
+
+                elif new_message_request.persona_id:
+                    persona = cast(
+                        Persona,
+                        fetch_persona_by_id(
+                            db_session,
+                            new_message_request.persona_id,
+                            user=None,
+                            get_editable=False,
+                        ),
+                    )
+
                 llm, _ = get_llms_for_persona(persona)
 
                 # In cases of threads, split the available tokens between docs and thread context

backend/danswer/db/chat.py

@@ -226,7 +226,7 @@ def create_chat_session(
     db_session: Session,
     description: str,
     user_id: UUID | None,
-    persona_id: int,
+    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,

backend/danswer/db/llm.py

@@ -4,9 +4,11 @@
 from sqlalchemy.orm import Session
 
 from danswer.db.models import CloudEmbeddingProvider as CloudEmbeddingProviderModel
+from danswer.db.models import DocumentSet
 from danswer.db.models import LLMProvider as LLMProviderModel
 from danswer.db.models import LLMProvider__UserGroup
 from danswer.db.models import SearchSettings
+from danswer.db.models import Tool as ToolModel
 from danswer.db.models import User
 from danswer.db.models import User__UserGroup
 from danswer.server.manage.embedding.models import CloudEmbeddingProvider
@@ -103,6 +105,20 @@ def fetch_existing_embedding_providers(
     return list(db_session.scalars(select(CloudEmbeddingProviderModel)).all())
 
 
+def fetch_existing_doc_sets(
+    db_session: Session, doc_ids: list[int]
+) -> list[DocumentSet]:
+    return list(
+        db_session.scalars(select(DocumentSet).where(DocumentSet.id.in_(doc_ids))).all()
+    )
+
+
+def fetch_existing_tools(db_session: Session, tool_ids: list[int]) -> list[ToolModel]:
+    return list(
+        db_session.scalars(select(ToolModel).where(ToolModel.id.in_(tool_ids))).all()
+    )
+
+
 def fetch_existing_llm_providers(
     db_session: Session,
     user: User | None = None,

backend/danswer/db/models.py

@@ -866,7 +866,9 @@ class ChatSession(Base):
 
     id: Mapped[int] = mapped_column(primary_key=True)
     user_id: Mapped[UUID | None] = mapped_column(ForeignKey("user.id"), nullable=True)
-    persona_id: Mapped[int] = mapped_column(ForeignKey("persona.id"))
+    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)
@@ -900,7 +902,6 @@ class ChatSession(Base):
     prompt_override: Mapped[PromptOverride | None] = mapped_column(
         PydanticType(PromptOverride), nullable=True
     )
-
     time_updated: Mapped[datetime.datetime] = mapped_column(
         DateTime(timezone=True),
         server_default=func.now(),
@@ -909,7 +910,6 @@ class ChatSession(Base):
     time_created: Mapped[datetime.datetime] = mapped_column(
         DateTime(timezone=True), server_default=func.now()
     )
-
     user: Mapped[User] = relationship("User", back_populates="chat_sessions")
     folder: Mapped["ChatFolder"] = relationship(
         "ChatFolder", back_populates="chat_sessions"

backend/danswer/db/persona.py

@@ -563,13 +563,15 @@ def validate_persona_tools(tools: list[Tool]) -> None:
             )
 
 
-def get_prompts_by_ids(prompt_ids: list[int], db_session: Session) -> Sequence[Prompt]:
+def get_prompts_by_ids(prompt_ids: list[int], db_session: Session) -> list[Prompt]:
     """Unsafe, can fetch prompts from all users"""
     if not prompt_ids:
         return []
-    prompts = db_session.scalars(select(Prompt).where(Prompt.id.in_(prompt_ids))).all()
+    prompts = db_session.scalars(
+        select(Prompt).where(Prompt.id.in_(prompt_ids)).where(Prompt.deleted.is_(False))
+    ).all()
 
-    return prompts
+    return list(prompts)
 
 
 def get_prompt_by_id(

backend/danswer/one_shot_answer/answer_question.py

@@ -26,6 +26,7 @@
 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
@@ -60,7 +61,7 @@
 from danswer.tools.tool_runner import ToolCallKickoff
 from danswer.utils.logger import setup_logger
 from danswer.utils.timing import log_generator_function_time
-
+from ee.danswer.server.query_and_chat.utils import create_temporary_persona
 
 logger = setup_logger()
 
@@ -118,7 +119,17 @@ def stream_answer_objects(
         one_shot=True,
         danswerbot_flow=danswerbot_flow,
     )
-    llm, fast_llm = get_llms_for_persona(persona=chat_session.persona)
+
+    temporary_persona: Persona | None = None
+    if query_req.persona_config is not None:
+        new_persona = create_temporary_persona(
+            db_session=db_session, persona_config=query_req.persona_config, user=user
+        )
+        temporary_persona = new_persona
+
+    persona = temporary_persona if temporary_persona else chat_session.persona
+
+    llm, fast_llm = get_llms_for_persona(persona=persona)
 
     llm_tokenizer = get_tokenizer(
         model_name=llm.config.model_name,
@@ -153,11 +164,11 @@ def stream_answer_objects(
             prompt_id=query_req.prompt_id, user=None, db_session=db_session
         )
     if prompt is None:
-        if not chat_session.persona.prompts:
+        if not persona.prompts:
             raise RuntimeError(
                 "Persona does not have any prompts - this should never happen"
             )
-        prompt = chat_session.persona.prompts[0]
+        prompt = persona.prompts[0]
 
     # Create the first User query message
     new_user_message = create_new_chat_message(
@@ -174,9 +185,7 @@ def stream_answer_objects(
     prompt_config = PromptConfig.from_model(prompt)
     document_pruning_config = DocumentPruningConfig(
         max_chunks=int(
-            chat_session.persona.num_chunks
-            if chat_session.persona.num_chunks is not None
-            else default_num_chunks
+            persona.num_chunks if persona.num_chunks is not None else default_num_chunks
         ),
         max_tokens=max_document_tokens,
     )
@@ -187,16 +196,16 @@ def stream_answer_objects(
         evaluation_type=LLMEvaluationType.SKIP
         if DISABLE_LLM_DOC_RELEVANCE
         else query_req.evaluation_type,
-        persona=chat_session.persona,
+        persona=persona,
         retrieval_options=query_req.retrieval_options,
         prompt_config=prompt_config,
         llm=llm,
         fast_llm=fast_llm,
         pruning_config=document_pruning_config,
+        bypass_acl=bypass_acl,
         chunks_above=query_req.chunks_above,
         chunks_below=query_req.chunks_below,
         full_doc=query_req.full_doc,
-        bypass_acl=bypass_acl,
     )
 
     answer_config = AnswerStyleConfig(
@@ -209,23 +218,23 @@ def stream_answer_objects(
         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=chat_session.persona)),
+        llm=get_main_llm_from_tuple(get_llms_for_persona(persona=persona)),
         single_message_history=history_str,
-        tools=[search_tool],
-        force_use_tool=ForceUseTool(
-            force_use=True,
-            tool_name=search_tool.name,
-            args={"query": rephrased_query},
+        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 ImageGenerationDisplay 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):
@@ -261,6 +270,7 @@ def stream_answer_objects(
                     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:
@@ -287,6 +297,7 @@ def stream_answer_objects(
                         relevance_summary=evaluation_response,
                     )
                 yield evaluation_response
+
         else:
             yield packet