allowed empty Search Tool for non-agentic search

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/doc_reranking.py

@@ -33,6 +33,8 @@ def doc_reranking(
 
     agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
     question = state.question if state.question else agent_a_config.search_request.query
+    if agent_a_config.search_tool is None:
+        raise ValueError("search_tool must be provided for agentic search")
     with get_session_context_manager() as db_session:
         _search_query = retrieval_preprocessing(
             search_request=SearchRequest(query=question),

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/doc_retrieval.py

@@ -54,6 +54,8 @@ def doc_retrieval(state: RetrievalInput, config: RunnableConfig) -> DocRetrieval
         )
 
     query_info = None
+    if search_tool is None:
+        raise ValueError("search_tool must be provided for agentic search")
     # new db session to avoid concurrency issues
     with get_session_context_manager() as db_session:
         for tool_response in search_tool.run(

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/format_results.py

@@ -45,6 +45,8 @@ def format_results(
             # the top 3 for that one. We may want to revisit this.
             stream_documents = state.expanded_retrieval_results[-1].search_results[:3]
 
+        if agent_a_config.search_tool is None:
+            raise ValueError("search_tool must be provided for agentic search")
         for tool_response in yield_search_responses(
             query=state.question,
             reranked_sections=state.retrieved_documents,  # TODO: rename params. (sections pre-merging here.)

backend/onyx/agents/agent_search/deep_search_a/main/edges.py

@@ -30,6 +30,7 @@ def route_initial_tool_choice(
     if state.tool_choice is not None:
         if (
             agent_config.use_agentic_search
+            and agent_config.search_tool is not None
             and state.tool_choice.tool.name == agent_config.search_tool.name
         ):
             return "agent_search_start"

backend/onyx/agents/agent_search/deep_search_a/main/nodes/agent_logging.py

@@ -61,6 +61,7 @@ def agent_logging(state: MainState, config: RunnableConfig) -> MainOutput:
         persona_id = agent_a_config.search_request.persona.id
 
     user_id = None
+    if agent_a_config.search_tool is not None:
         user = agent_a_config.search_tool.user
         if user:
             user_id = user.id

backend/onyx/agents/agent_search/deep_search_a/main/nodes/agent_search_start.py

@@ -32,6 +32,8 @@ def agent_search_start(
     # Initial search to inform decomposition. Just get top 3 fits
 
     search_tool = agent_a_config.search_tool
+    if search_tool is None:
+        raise ValueError("search_tool must be provided for agentic search")
     retrieved_docs: list[InferenceSection] = retrieve_search_docs(search_tool, question)
 
     exploratory_search_results = retrieved_docs[:AGENT_EXPLORATORY_SEARCH_RESULTS]

backend/onyx/agents/agent_search/deep_search_a/main/nodes/generate_initial_answer.py

@@ -99,7 +99,8 @@ def generate_initial_answer(
     else:
         # Use the query info from the base document retrieval
         query_info = get_query_info(state.original_question_retrieval_results)
-
+        if agent_a_config.search_tool is None:
+            raise ValueError("search_tool must be provided for agentic search")
         for tool_response in yield_search_responses(
             query=question,
             reranked_sections=relevant_docs,

backend/onyx/agents/agent_search/deep_search_a/main/nodes/generate_refined_answer.py

@@ -71,6 +71,8 @@ def generate_refined_answer(
     combined_documents = dedup_inference_sections(initial_documents, revised_documents)
 
     query_info = get_query_info(state.original_question_retrieval_results)
+    if agent_a_config.search_tool is None:
+        raise ValueError("search_tool must be provided for agentic search")
     # stream refined answer docs
     for tool_response in yield_search_responses(
         query=question,

backend/onyx/agents/agent_search/models.py

@@ -25,7 +25,6 @@ class AgentSearchConfig:
 
     primary_llm: LLM
     fast_llm: LLM
-    search_tool: SearchTool
 
     # Whether to force use of a tool, or to
     # force tool args IF the tool is used
@@ -37,6 +36,8 @@ class AgentSearchConfig:
     # single_message_history: str | None = None
     prompt_builder: AnswerPromptBuilder
 
+    search_tool: SearchTool | None = None
+
     use_agentic_search: bool = False
 
     # For persisting agent search data
@@ -79,6 +80,12 @@ class AgentSearchConfig:
             )
         return self
 
+    @model_validator(mode="after")
+    def validate_search_tool(self) -> "AgentSearchConfig":
+        if self.use_agentic_search and self.search_tool is None:
+            raise ValueError("search_tool must be provided for agentic search")
+        return self
+
 
 class AgentDocumentCitations(BaseModel):
     document_id: str

backend/onyx/chat/process_message.py

@@ -743,12 +743,19 @@ def stream_chat_message_objects(
         # we should construct this unconditionally inside Answer instead
         # Leaving it here for the time being to avoid breaking changes
         search_tools = [tool for tool in tools if isinstance(tool, SearchTool)]
-        if len(search_tools) == 0:
+        search_tool: SearchTool | None = None
-            raise ValueError("No search tool found")
+
-        elif len(search_tools) > 1:
+        if len(search_tools) > 1:
             # TODO: handle multiple search tools
-            raise ValueError("Multiple search tools found")
+            logger.warning("Multiple search tools found, using first one")
             search_tool = search_tools[0]
+        elif len(search_tools) == 1:
+            search_tool = search_tools[0]
+        else:
+            logger.warning("No search tool found")
+            if new_msg_req.use_agentic_search:
+                raise ValueError("No search tool found, cannot use agentic search")
+
         using_tool_calling_llm = explicit_tool_calling_supported(
             llm.config.model_provider, llm.config.model_name
         )