Persona enhancements

backend/danswer/danswerbot/slack/blocks.py

@@ -185,6 +185,7 @@ def build_qa_response_blocks(
     source_filters: list[DocumentSource] | None,
     time_cutoff: datetime | None,
     favor_recent: bool,
+    skip_quotes: bool = False,
 ) -> list[Block]:
     quotes_blocks: list[Block] = []
 
@@ -232,8 +233,9 @@ def build_qa_response_blocks(
     if filter_block is not None:
         response_blocks.append(filter_block)
 
-    response_blocks.extend(
-        [answer_block, feedback_block] + quotes_blocks + [DividerBlock()]
-    )
+    response_blocks.extend([answer_block, feedback_block])
+    if not skip_quotes:
+        response_blocks.extend(quotes_blocks)
+    response_blocks.append(DividerBlock())
 
     return response_blocks

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

@@ -104,6 +104,8 @@ def handle_message(
             document_set.name for document_set in persona.document_sets
         ]
 
+    should_respond_even_with_no_docs = persona.num_chunks == 0 if persona else False
+
     # List of user id to send message to, if None, send to everyone in channel
     send_to: list[str] | None = None
     respond_tag_only = False
@@ -257,7 +259,7 @@ def handle_message(
             logger.debug(answer.answer)
         return True
 
-    if not answer.top_documents:
+    if not answer.top_documents and not should_respond_even_with_no_docs:
         logger.error(f"Unable to answer question: '{msg}' - no documents found")
         # Optionally, respond in thread with the error message, Used primarily
         # for debugging purposes
@@ -288,6 +290,7 @@ def handle_message(
         source_filters=answer.source_type,
         time_cutoff=answer.time_cutoff,
         favor_recent=answer.favor_recent,
+        skip_quotes=persona is not None,  # currently Personas don't support quotes
     )
 
     # Get the chunks fed to the LLM only, then fill with other docs
@@ -298,9 +301,13 @@ def handle_message(
         doc for idx, doc in enumerate(top_docs) if idx not in llm_doc_inds
     ]
     priority_ordered_docs = llm_docs + remaining_docs
-    document_blocks = build_documents_blocks(
-        documents=priority_ordered_docs,
-        query_event_id=answer.query_event_id,
+    document_blocks = (
+        build_documents_blocks(
+            documents=priority_ordered_docs,
+            query_event_id=answer.query_event_id,
+        )
+        if priority_ordered_docs
+        else []
     )
 
     try:

backend/danswer/direct_qa/answer_question.py

@@ -54,6 +54,13 @@ def _get_qa_model(persona: Persona | None) -> QAModel:
     return get_default_qa_model()
 
 
+def _dummy_search_generator() -> Iterator[list[InferenceChunk] | list[bool]]:
+    """Mimics the interface of `full_chunk_search_generator` but returns empty lists
+    without actually running retrieval / re-ranking."""
+    yield cast(list[InferenceChunk], [])
+    yield cast(list[bool], [])
+
+
 @log_function_time()
 def answer_qa_query(
     new_message_request: NewMessageRequest,
@@ -91,6 +98,7 @@ def answer_qa_query(
         not persona.apply_llm_relevance_filter if persona else None
     )
     persona_num_chunks = persona.num_chunks if persona else None
+    persona_retrieval_disabled = persona.num_chunks == 0 if persona else False
     if persona:
         logger.info(f"Using persona: {persona.name}")
         logger.info(
@@ -113,14 +121,19 @@ def answer_qa_query(
     if disable_generative_answer:
         predicted_flow = QueryFlow.SEARCH
 
-    top_chunks, llm_chunk_selection = full_chunk_search(
-        query=retrieval_request,
-        document_index=get_default_document_index(),
-        retrieval_metrics_callback=retrieval_metrics_callback,
-        rerank_metrics_callback=rerank_metrics_callback,
-    )
+    if not persona_retrieval_disabled:
+        top_chunks, llm_chunk_selection = full_chunk_search(
+            query=retrieval_request,
+            document_index=get_default_document_index(),
+            retrieval_metrics_callback=retrieval_metrics_callback,
+            rerank_metrics_callback=rerank_metrics_callback,
+        )
 
-    top_docs = chunks_to_search_docs(top_chunks)
+        top_docs = chunks_to_search_docs(top_chunks)
+    else:
+        top_chunks = []
+        llm_chunk_selection = []
+        top_docs = []
 
     partial_response = partial(
         QAResponse,
@@ -133,7 +146,7 @@ def answer_qa_query(
         favor_recent=retrieval_request.favor_recent,
     )
 
-    if disable_generative_answer or not top_docs:
+    if disable_generative_answer or (not top_docs and not persona_retrieval_disabled):
         return partial_response(
             answer=None,
             quotes=None,
@@ -237,6 +250,7 @@ def answer_qa_query_stream(
         not persona.apply_llm_relevance_filter if persona else None
     )
     persona_num_chunks = persona.num_chunks if persona else None
+    persona_retrieval_disabled = persona.num_chunks == 0 if persona else False
     if persona:
         logger.info(f"Using persona: {persona.name}")
         logger.info(
@@ -245,6 +259,10 @@ def answer_qa_query_stream(
             f"num_chunks: {persona_num_chunks}"
         )
 
+    # NOTE: it's not ideal that we're still doing `retrieval_preprocessing` even
+    # if `persona_retrieval_disabled == True`, but it's a bit tricky to separate this
+    # out. Since this flow is being re-worked shortly with the move to chat, leaving it
+    # like this for now.
     retrieval_request, predicted_search_type, predicted_flow = retrieval_preprocessing(
         new_message_request=new_message_request,
         user=user,
@@ -257,10 +275,13 @@ def answer_qa_query_stream(
     if persona:
         predicted_flow = QueryFlow.QUESTION_ANSWER
 
-    search_generator = full_chunk_search_generator(
-        query=retrieval_request,
-        document_index=get_default_document_index(),
-    )
+    if not persona_retrieval_disabled:
+        search_generator = full_chunk_search_generator(
+            query=retrieval_request,
+            document_index=get_default_document_index(),
+        )
+    else:
+        search_generator = _dummy_search_generator()
 
     # first fetch and return to the UI the top chunks so the user can
     # immediately see some results
@@ -280,7 +301,9 @@ def answer_qa_query_stream(
     ).dict()
     yield get_json_line(initial_response)
 
-    if not top_chunks:
+    # some personas intentionally don't retrieve any documents, so we should
+    # not return early here
+    if not top_chunks and not persona_retrieval_disabled:
         logger.debug("No Documents Found")
         return
 

backend/danswer/direct_qa/qa_block.py

@@ -26,6 +26,7 @@ from danswer.prompts.direct_qa_prompts import COT_PROMPT
 from danswer.prompts.direct_qa_prompts import JSON_PROMPT
 from danswer.prompts.direct_qa_prompts import LANGUAGE_HINT
 from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT
+from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTEXT
 from danswer.prompts.direct_qa_prompts import WEAK_LLM_PROMPT
 from danswer.utils.logger import setup_logger
 from danswer.utils.text_processing import clean_up_code_blocks
@@ -210,19 +211,31 @@ class PersonaBasedQAHandler(QAHandler):
     ) -> list[BaseMessage]:
         context_docs_str = build_context_str(context_chunks)
 
-        single_message = PARAMATERIZED_PROMPT.format(
-            context_docs_str=context_docs_str,
-            user_query=query,
-            system_prompt=self.system_prompt,
-            task_prompt=self.task_prompt,
-        ).strip()
+        if not context_chunks:
+            single_message = PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
+                user_query=query,
+                system_prompt=self.system_prompt,
+                task_prompt=self.task_prompt,
+            ).strip()
+        else:
+            single_message = PARAMATERIZED_PROMPT.format(
+                context_docs_str=context_docs_str,
+                user_query=query,
+                system_prompt=self.system_prompt,
+                task_prompt=self.task_prompt,
+            ).strip()
 
         prompt: list[BaseMessage] = [HumanMessage(content=single_message)]
         return prompt
 
-    def build_dummy_prompt(
-        self,
-    ) -> str:
+    def build_dummy_prompt(self, retrieval_disabled: bool) -> str:
+        if retrieval_disabled:
+            return PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
+                user_query="<USER_QUERY>",
+                system_prompt=self.system_prompt,
+                task_prompt=self.task_prompt,
+            ).strip()
+
         return PARAMATERIZED_PROMPT.format(
             context_docs_str="<CONTEXT_DOCS>",
             user_query="<USER_QUERY>",

backend/danswer/prompts/direct_qa_prompts.py

@@ -137,6 +137,15 @@ CONTEXT:
 RESPONSE:
 """.strip()
 
+PARAMATERIZED_PROMPT_WITHOUT_CONTEXT = f"""
+{{system_prompt}}
+
+{{task_prompt}}
+
+{QUESTION_PAT.upper()} {{user_query}}
+RESPONSE:
+""".strip()
+
 
 # User the following for easy viewing of prompts
 if __name__ == "__main__":

backend/danswer/server/features/persona/api.py

@@ -131,12 +131,13 @@ def get_persona(
 def build_final_template_prompt(
     system_prompt: str,
     task_prompt: str,
+    retrieval_disabled: bool = False,
     _: User | None = Depends(current_user),
 ) -> PromptTemplateResponse:
     return PromptTemplateResponse(
         final_prompt_template=PersonaBasedQAHandler(
             system_prompt=system_prompt, task_prompt=task_prompt
-        ).build_dummy_prompt()
+        ).build_dummy_prompt(retrieval_disabled=retrieval_disabled)
     )
 
 

backend/danswer/server/features/persona/models.py

@@ -21,6 +21,7 @@ class PersonaSnapshot(BaseModel):
     description: str
     system_prompt: str
     task_prompt: str
+    num_chunks: int | None
     document_sets: list[DocumentSet]
     llm_model_version_override: str | None
 
@@ -32,6 +33,7 @@ class PersonaSnapshot(BaseModel):
             description=persona.description or "",
             system_prompt=persona.system_text or "",
             task_prompt=persona.hint_text or "",
+            num_chunks=persona.num_chunks,
             document_sets=[
                 DocumentSet.from_model(document_set_model)
                 for document_set_model in persona.document_sets