feat: add function tools to openai responses

llama_stack/apis/agents/openai_responses.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field
 from typing_extensions import Annotated
@@ -133,8 +133,30 @@ class OpenAIResponseInputToolWebSearch(BaseModel):
     # TODO: add user_location
 
 
+@json_schema_type
+class OpenAIResponseInputToolFunction(BaseModel):
+    type: Literal["function"] = "function"
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]]
+    strict: Optional[bool]
+
+
+class FileSearchRankingOptions(BaseModel):
+    ranker: Optional[str] = None
+    score_threshold: Optional[float] = Field(default=0.0, ge=0.0, le=1.0)
+
+
+@json_schema_type
+class OpenAIResponseInputToolFileSearch(BaseModel):
+    type: Literal["file_search"] = "file_search"
+    vector_store_id: List[str]
+    ranking_options: Optional[FileSearchRankingOptions] = None
+    # TODO: add filters
+
+
 OpenAIResponseInputTool = Annotated[
-    Union[OpenAIResponseInputToolWebSearch,],
+    Union[OpenAIResponseInputToolWebSearch, OpenAIResponseInputToolFileSearch, OpenAIResponseInputToolFunction],
     Field(discriminator="type"),
 ]
 register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")

llama_stack/distribution/server/server.py

@@ -17,6 +17,7 @@ from importlib.metadata import version as parse_version
 from pathlib import Path
 from typing import Any, List, Optional, Union
 
+import rich.pretty
 import yaml
 from fastapi import Body, FastAPI, HTTPException, Request
 from fastapi import Path as FastapiPath
@@ -187,11 +188,30 @@ async def sse_generator(event_gen_coroutine):
         )
 
 
+async def log_request_pre_validation(request: Request):
+    if request.method in ("POST", "PUT", "PATCH"):
+        try:
+            body_bytes = await request.body()
+            if body_bytes:
+                try:
+                    parsed_body = json.loads(body_bytes.decode())
+                    log_output = rich.pretty.pretty_repr(parsed_body)
+                except (json.JSONDecodeError, UnicodeDecodeError):
+                    log_output = repr(body_bytes)
+                logger.debug(f"Incoming raw request body for {request.method} {request.url.path}:\n{log_output}")
+            else:
+                logger.debug(f"Incoming {request.method} {request.url.path} request with empty body.")
+        except Exception as e:
+            logger.warning(f"Could not read or log request body for {request.method} {request.url.path}: {e}")
+
+
 def create_dynamic_typed_route(func: Any, method: str, route: str):
     async def endpoint(request: Request, **kwargs):
         # Get auth attributes from the request scope
         user_attributes = request.scope.get("user_attributes", {})
 
+        await log_request_pre_validation(request)
+
         # Use context manager with both provider data and auth attributes
         with request_provider_data_context(request.headers, user_attributes):
             is_streaming = is_streaming_request(func.__name__, request, **kwargs)

llama_stack/providers/inline/agents/meta_reference/openai_responses.py

@@ -192,6 +192,7 @@ class OpenAIResponsesImpl:
             status="completed",
             output=output_messages,
         )
+        logger.debug(f"OpenAI Responses response: {response}")
 
         if store:
             # Store in kvstore
@@ -218,7 +219,9 @@ class OpenAIResponsesImpl:
         chat_tools: List[ChatCompletionToolParam] = []
         for input_tool in tools:
             # TODO: Handle other tool types
-            if input_tool.type == "web_search":
+            if input_tool.type == "function":
+                chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump()))
+            elif input_tool.type == "web_search":
                 tool_name = "web_search"
                 tool = await self.tool_groups_api.get_tool(tool_name)
                 tool_def = ToolDefinition(