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fix(file_search): address greptile review — dead code, follow-up context, cost tracking

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- Remove dead `should_use_emulated_file_search` (main.py uses its own inline guard)
- Remove dead `fallback_vector_store_ids` param from `_run_vector_searches`
- Include all first_response.output items in follow_up_input so text blocks/reasoning
  from providers like Anthropic aren't dropped from conversation context
- Accumulate first provider call's response_cost into synthesized _hidden_params so
  billing callbacks see the total cost of both emulated-flow LLM calls
- Remove broad tools=[] filter from transformation.py (backward-incompatible); the
  follow-up call already passes tools=None which is filtered by the v is not None guard

Made-with: Cursor
This commit is contained in:
Sameer Kankute
2026-03-18 10:10:29 +05:30
parent 8b7eac5dc9
commit dc7b7f852d
2 changed files with 23 additions and 47 deletions
Download Patch File Download Diff File Expand all files Collapse all files
litellm/responses/file_search/emulated_handler.py
+21 -43
View File
@@ -26,25 +26,6 @@ ToolParam = Any
FILE_SEARCH_FUNCTION_NAME = "litellm_file_search"
# ---------------------------------------------------------------------------
# Detection
# ---------------------------------------------------------------------------
def should_use_emulated_file_search(
tools: Optional[Iterable[ToolParam]],
provider_config: Any, # BaseResponsesAPIConfig
) -> bool:
"""Return True when there is a file_search tool and the provider can't handle it natively."""
if not tools:
return False
has_fs = any(
isinstance(t, dict) and t.get("type") == "file_search" for t in tools
)
if not has_fs:
return False
return provider_config is None or not provider_config.supports_native_file_search()
# ---------------------------------------------------------------------------
# Tool conversion
# ---------------------------------------------------------------------------
@@ -126,15 +107,13 @@ def _replace_file_search_tools(
async def _run_vector_searches(
queries: List[str],
vector_store_ids: List[str],
fallback_vector_store_ids: List[str],
) -> Tuple[List[str], List[VectorStoreSearchResult]]:
"""
Run `asearch` against all vector stores for all queries and collect results.
Args:
queries: List of search queries to execute (like OpenAI's multi-query approach)
vector_store_ids: Specific vector store IDs to search
fallback_vector_store_ids: Fallback IDs if vector_store_ids is empty
vector_store_ids: Vector store IDs to search
Returns:
(queries_list, combined_results)
@@ -142,7 +121,7 @@ async def _run_vector_searches(
import litellm.vector_stores.main as vs_main
all_results: List[VectorStoreSearchResult] = []
ids_to_search = vector_store_ids or fallback_vector_store_ids
ids_to_search = vector_store_ids
# Execute each query against all vector stores
for query in queries:
@@ -337,11 +316,16 @@ def _synthesize_responses_api_response(
original_response: ResponsesAPIResponse,
file_search_call_output: Dict[str, Any],
message_output: Dict[str, Any],
first_response: Optional[ResponsesAPIResponse] = None,
) -> ResponsesAPIResponse:
"""
Return a new ResponsesAPIResponse with:
output[0] = file_search_call item
output[1] = message item (with citations)
When first_response is provided, its response_cost is accumulated into the
synthesized _hidden_params so that billing callbacks see the total cost of
both provider calls that the emulated flow makes.
"""
synthesized = ResponsesAPIResponse(
id=getattr(original_response, "id", f"resp_{uuid.uuid4().hex}"),
@@ -354,7 +338,14 @@ def _synthesize_responses_api_response(
error=None,
)
if hasattr(original_response, "_hidden_params"):
synthesized._hidden_params = getattr(original_response, "_hidden_params")
hidden = dict(getattr(original_response, "_hidden_params") or {})
if first_response is not None and hasattr(first_response, "_hidden_params"):
first_hidden = getattr(first_response, "_hidden_params") or {}
first_cost = first_hidden.get("response_cost") if isinstance(first_hidden, dict) else getattr(first_hidden, "response_cost", None)
if first_cost is not None:
current_cost = hidden.get("response_cost") if isinstance(hidden, dict) else 0
hidden["response_cost"] = (current_cost or 0) + first_cost
synthesized._hidden_params = hidden
return synthesized
@@ -473,7 +464,6 @@ async def aresponses_with_emulated_file_search(
queries, results = await _run_vector_searches(
queries=queries_from_call,
vector_store_ids=vs_ids_for_call,
fallback_vector_store_ids=all_vs_ids,
)
all_queries.extend(queries)
all_results.extend(results)
@@ -486,28 +476,15 @@ async def aresponses_with_emulated_file_search(
}
)
# 5. Build follow-up input: original messages + all assistant tool calls + tool results
# 5. Build follow-up input: original messages + ALL first-response output items + tool results
# Including all output items (text blocks, reasoning, non-file-search calls) ensures providers
# like Anthropic that emit text before the tool call have complete conversation context.
original_input_items = list(input) if isinstance(input, (list, tuple)) else [{"role": "user", "content": str(input)}]
follow_up_function_calls: List[Dict[str, Any]] = []
for tc in file_search_calls:
if isinstance(tc, dict):
tc_call_id = tc.get("call_id") or tc.get("id") or file_search_call_id
tc_args = tc.get("arguments") or "{}"
else:
tc_call_id = getattr(tc, "call_id", None) or getattr(tc, "id", file_search_call_id)
tc_args = getattr(tc, "arguments", "{}") or "{}"
follow_up_function_calls.append(
{
"type": "function_call",
"name": FILE_SEARCH_FUNCTION_NAME,
"call_id": tc_call_id,
"arguments": tc_args,
}
)
first_response_output_items = list(first_response.output)
follow_up_input = (
original_input_items
+ follow_up_function_calls
+ first_response_output_items
+ tool_results
)
@@ -534,4 +511,5 @@ async def aresponses_with_emulated_file_search(
include_search_results=_include_search_results,
),
message_output=_build_message_output(response_text, all_results),
first_response=first_response,
)
litellm/responses/litellm_completion_transformation/transformation.py
+2 -4
View File
@@ -229,13 +229,11 @@ class LiteLLMCompletionResponsesConfig:
if litellm_logging_obj:
litellm_logging_obj.stream_options = stream_options
# only pass non-None / non-empty values
# Explicitly exclude an empty tools list — sending tools=[] to providers
# like Anthropic in a tool_result conversation makes them return empty content.
# only pass non-None values
litellm_completion_request = {
k: v
for k, v in litellm_completion_request.items()
if v is not None and not (k == "tools" and v == [])
if v is not None
}
return litellm_completion_request