google-genai[patch]: added parsing of function call / response (langc…

…hain-ai#17245)

libs/partners/google-genai/langchain_google_genai/chat_models.py

@@ -16,32 +16,28 @@
     Optional,
     Sequence,
     Tuple,
-    Type,
     Union,
     cast,
 )
 from urllib.parse import urlparse
 
+import google.ai.generativelanguage as glm
 import google.api_core
 
 # TODO: remove ignore once the google package is published with types
 import google.generativeai as genai  # type: ignore[import]
+import proto  # type: ignore[import]
 import requests
-from google.ai.generativelanguage_v1beta import FunctionCall
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import (
     AIMessage,
-    AIMessageChunk,
     BaseMessage,
-    ChatMessage,
-    ChatMessageChunk,
     FunctionMessage,
     HumanMessage,
-    HumanMessageChunk,
     SystemMessage,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
@@ -327,15 +323,30 @@ def _parse_chat_history(
             continue
         elif isinstance(message, AIMessage):
             role = "model"
-            # TODO: Handle AImessage with function call
-            parts = _convert_to_parts(message.content)
+            raw_function_call = message.additional_kwargs.get("function_call")
+            if raw_function_call:
+                function_call = glm.FunctionCall(
+                    {
+                        "name": raw_function_call["name"],
+                        "args": json.loads(raw_function_call["arguments"]),
+                    }
+                )
+                parts = [glm.Part(function_call=function_call)]
+            else:
+                parts = _convert_to_parts(message.content)
         elif isinstance(message, HumanMessage):
             role = "user"
             parts = _convert_to_parts(message.content)
         elif isinstance(message, FunctionMessage):
             role = "user"
-            # TODO: Handle FunctionMessage
-            parts = _convert_to_parts(message.content)
+            parts = [
+                glm.Part(
+                    function_response=glm.FunctionResponse(
+                        name=message.name,
+                        response=message.content,
+                    )
+                )
+            ]
         else:
             raise ValueError(
                 f"Unexpected message with type {type(message)} at the position {i}."
@@ -353,100 +364,44 @@ def _parse_chat_history(
     return messages
 
 
-def _retrieve_function_call_response(
-    parts: List[genai.types.PartType],
-) -> Optional[Dict]:
-    for idx, part in enumerate(parts):
-        if part.function_call and part.function_call.name:
-            fc: FunctionCall = part.function_call
-            return {
-                "function_call": {
-                    "name": fc.name,
-                    "arguments": json.dumps(
-                        dict(fc.args.items())
-                    ),  # dump to match other function calling llms for now
-                }
-            }
-    return None
-
-
-def _parts_to_content(
-    parts: List[genai.types.PartType],
-) -> Tuple[Union[str, List[Union[Dict[Any, Any], str]]], Optional[Dict]]:
-    """Converts a list of Gemini API Part objects into a list of LangChain messages."""
-    function_call_resp = _retrieve_function_call_response(parts)
-
-    if len(parts) == 1 and parts[0].text is not None and not parts[0].inline_data:
-        # Simple text response. The typical response
-        return parts[0].text, function_call_resp
-    elif not parts:
-        logger.warning("Gemini produced an empty response.")
-        return "", function_call_resp
-    messages: List[Union[Dict[Any, Any], str]] = []
-    for part in parts:
-        if part.text is not None:
-            messages.append(
-                {
-                    "type": "text",
-                    "text": part.text,
-                }
-            )
-        else:
-            # TODO: Handle inline_data if that's a thing?
-            raise ChatGoogleGenerativeAIError(f"Unexpected part type. {part}")
-    return messages, function_call_resp
+def _parse_response_candidate(response_candidate: glm.Candidate) -> AIMessage:
+    first_part = response_candidate.content.parts[0]
+    if first_part.function_call:
+        function_call = proto.Message.to_dict(first_part.function_call)
+        function_call["arguments"] = json.dumps(function_call.pop("args", {}))
+        return AIMessage(content="", additional_kwargs={"function_call": function_call})
+    else:
+        parts = response_candidate.content.parts
+
+    if len(parts) == 1 and parts[0].text:
+        content: Union[str, List[Union[str, Dict]]] = parts[0].text
+    else:
+        content = [proto.Message.to_dict(part) for part in parts]
+    return AIMessage(content=content, additional_kwargs={})
 
 
 def _response_to_result(
-    response: genai.types.GenerateContentResponse,
-    ai_msg_t: Type[BaseMessage] = AIMessage,
-    human_msg_t: Type[BaseMessage] = HumanMessage,
-    chat_msg_t: Type[BaseMessage] = ChatMessage,
-    generation_t: Type[ChatGeneration] = ChatGeneration,
+    response: glm.GenerateContentResponse,
 ) -> ChatResult:
     """Converts a PaLM API response into a LangChain ChatResult."""
-    llm_output = {}
-    if response.prompt_feedback:
-        try:
-            prompt_feedback = type(response.prompt_feedback).to_dict(
-                response.prompt_feedback, use_integers_for_enums=False
-            )
-            llm_output["prompt_feedback"] = prompt_feedback
-        except Exception as e:
-            logger.debug(f"Unable to convert prompt_feedback to dict: {e}")
+    llm_output = {"prompt_feedback": proto.Message.to_dict(response.prompt_feedback)}
 
     generations: List[ChatGeneration] = []
 
-    role_map = {
-        "model": ai_msg_t,
-        "user": human_msg_t,
-    }
-
     for candidate in response.candidates:
-        content = candidate.content
-        parts_content, additional_kwargs = _parts_to_content(content.parts)
-        if content.role not in role_map:
-            logger.warning(
-                f"Unrecognized role: {content.role}. Treating as a ChatMessage."
-            )
-            msg = chat_msg_t(
-                content=parts_content,
-                role=content.role,
-                additional_kwargs=additional_kwargs or {},
-            )
-        else:
-            msg = role_map[content.role](
-                content=parts_content,
-                additional_kwargs=additional_kwargs or {},
-            )
         generation_info = {}
         if candidate.finish_reason:
             generation_info["finish_reason"] = candidate.finish_reason.name
-        if candidate.safety_ratings:
-            generation_info["safety_ratings"] = [
-                type(rating).to_dict(rating) for rating in candidate.safety_ratings
-            ]
-        generations.append(generation_t(message=msg, generation_info=generation_info))
+        generation_info["safety_ratings"] = [
+            proto.Message.to_dict(safety_rating, use_integers_for_enums=False)
+            for safety_rating in candidate.safety_ratings
+        ]
+        generations.append(
+            ChatGeneration(
+                message=_parse_response_candidate(candidate),
+                generation_info=generation_info,
+            )
+        )
     if not response.candidates:
         # Likely a "prompt feedback" violation (e.g., toxic input)
         # Raising an error would be different than how OpenAI handles it,
@@ -455,7 +410,9 @@ def _response_to_result(
             "Gemini produced an empty response. Continuing with empty message\n"
             f"Feedback: {response.prompt_feedback}"
         )
-        generations = [generation_t(message=ai_msg_t(content=""), generation_info={})]
+        generations = [
+            ChatGeneration(message=AIMessage(content=""), generation_info={})
+        ]
     return ChatResult(generations=generations, llm_output=llm_output)
 
 
@@ -616,13 +573,7 @@ def _stream(
             stream=True,
         )
         for chunk in response:
-            _chat_result = _response_to_result(
-                chunk,
-                ai_msg_t=AIMessageChunk,
-                human_msg_t=HumanMessageChunk,
-                chat_msg_t=ChatMessageChunk,
-                generation_t=ChatGenerationChunk,
-            )
+            _chat_result = _response_to_result(chunk)
             gen = cast(ChatGenerationChunk, _chat_result.generations[0])
             if run_manager:
                 run_manager.on_llm_new_token(gen.text)
@@ -646,13 +597,7 @@ async def _astream(
             generation_method=chat.send_message_async,
             stream=True,
         ):
-            _chat_result = _response_to_result(
-                chunk,
-                ai_msg_t=AIMessageChunk,
-                human_msg_t=HumanMessageChunk,
-                chat_msg_t=ChatMessageChunk,
-                generation_t=ChatGenerationChunk,
-            )
+            _chat_result = _response_to_result(chunk)
             gen = cast(ChatGenerationChunk, _chat_result.generations[0])
             if run_manager:
                 await run_manager.on_llm_new_token(gen.text)