alembic heads, basic citations, search pipeline state

backend/alembic/versions/98a5008d8711_agent_tracking.py

@@ -1,7 +1,7 @@
 """agent_tracking
 
 Revision ID: 98a5008d8711
-Revises: 4d58345da04a
+Revises: 33ea50e88f24
 Create Date: 2025-01-29 17:00:00.000001
 
 """
@@ -12,7 +12,7 @@ from sqlalchemy.dialects.postgresql import UUID
 
 # revision identifiers, used by Alembic.
 revision = "98a5008d8711"
-down_revision = "4d58345da04a"
+down_revision = "33ea50e88f24"
 branch_labels = None
 depends_on = None
 

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/retrieve_documents.py

@@ -68,12 +68,16 @@ def retrieve_documents(
     query_info = None
     if search_tool is None:
         raise ValueError("search_tool must be provided for agentic search")
+
+    callback_container: list[list[InferenceSection]] = []
+
     # new db session to avoid concurrency issues
     with get_session_context_manager() as db_session:
         for tool_response in search_tool.run(
             query=query_to_retrieve,
             force_no_rerank=True,
             alternate_db_session=db_session,
+            retrieved_sections_callback=callback_container.append,
         ):
             # get retrieved docs to send to the rest of the graph
             if tool_response.id == SEARCH_RESPONSE_SUMMARY_ID:
@@ -87,13 +91,9 @@ def retrieve_documents(
                 break
 
     retrieved_docs = retrieved_docs[:AGENT_MAX_QUERY_RETRIEVAL_RESULTS]
-    pre_rerank_docs = retrieved_docs
-    if search_tool.search_pipeline is not None:
-        pre_rerank_docs = (
-            search_tool.search_pipeline._retrieved_sections or retrieved_docs
-        )
 
     if AGENT_RETRIEVAL_STATS:
+        pre_rerank_docs = callback_container[0]
         fit_scores = get_fit_scores(
             pre_rerank_docs,
             retrieved_docs,

backend/onyx/chat/process_message.py

@@ -982,9 +982,10 @@ def stream_chat_message_objects(
 
         # Saving Gen AI answer and responding with message info
 
+        basic_key = SubQuestionKey(level=BASIC_KEY[0], question_num=BASIC_KEY[1])
         info = (
-            info_by_subq[SubQuestionKey(level=BASIC_KEY[0], question_num=BASIC_KEY[1])]
+            info_by_subq[basic_key]
-            if BASIC_KEY in info_by_subq
+            if basic_key in info_by_subq
             else info_by_subq[
                 SubQuestionKey(
                     level=AGENT_SEARCH_INITIAL_KEY[0],
@@ -1018,7 +1019,7 @@ def stream_chat_message_objects(
             ),
         )
 
-        # TODO: add answers for levels >= 1, where each level has the previous as its parent. Use
+        # add answers for levels >= 1, where each level has the previous as its parent. Use
         # the answer_by_level method in answer.py to get the answers for each level
         next_level = 1
         prev_message = gen_ai_response_message

backend/onyx/context/search/pipeline.py

@@ -56,6 +56,8 @@ class SearchPipeline:
         retrieval_metrics_callback: (
             Callable[[RetrievalMetricsContainer], None] | None
         ) = None,
+        retrieved_sections_callback: Callable[[list[InferenceSection]], None]
+        | None = None,
         rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
         prompt_config: PromptConfig | None = None,
     ):
@@ -80,6 +82,8 @@ class SearchPipeline:
         self._retrieved_chunks: list[InferenceChunk] | None = None
         # Another call made to the document index to get surrounding sections
         self._retrieved_sections: list[InferenceSection] | None = None
+
+        self.retrieved_sections_callback = retrieved_sections_callback
         # Reranking and LLM section selection can be run together
         # If only LLM selection is on, the reranked chunks are yielded immediatly
         self._reranked_sections: list[InferenceSection] | None = None
@@ -328,9 +332,13 @@ class SearchPipeline:
         if self._reranked_sections is not None:
             return self._reranked_sections
 
+        retrieved_sections = self._get_sections()
+        if self.retrieved_sections_callback is not None:
+            self.retrieved_sections_callback(retrieved_sections)
+
         self._postprocessing_generator = search_postprocessing(
             search_query=self.search_query,
-            retrieved_sections=self._get_sections(),
+            retrieved_sections=retrieved_sections,
             llm=self.fast_llm,
             rerank_metrics_callback=self.rerank_metrics_callback,
         )

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

@@ -112,8 +112,6 @@ class SearchTool(Tool):
         self.fast_llm = fast_llm
         self.evaluation_type = evaluation_type
 
-        self.search_pipeline: SearchPipeline | None = None
-
         self.selected_sections = selected_sections
 
         self.full_doc = full_doc
@@ -282,6 +280,10 @@ class SearchTool(Tool):
         query = cast(str, kwargs["query"])
         force_no_rerank = cast(bool, kwargs.get("force_no_rerank", False))
         alternate_db_session = cast(Session, kwargs.get("alternate_db_session", None))
+        retrieved_sections_callback = cast(
+            Callable[[list[InferenceSection]], None],
+            kwargs.get("retrieved_sections_callback"),
+        )
 
         if self.selected_sections:
             yield from self._build_response_for_specified_sections(query)
@@ -326,8 +328,8 @@ class SearchTool(Tool):
             bypass_acl=self.bypass_acl,
             db_session=alternate_db_session or self.db_session,
             prompt_config=self.prompt_config,
+            retrieved_sections_callback=retrieved_sections_callback,
         )
-        self.search_pipeline = search_pipeline  # used for agent_search metrics
 
         search_query_info = SearchQueryInfo(
             predicted_search=search_pipeline.search_query.search_type,