Exception: AttributeError: 'int' object has no attribute 'name'

[baskaryan]

Discussed in https://github.com/langchain-ai/langchain/discussions/22882

^{Originally posted by **rvasa779** June 13, 2024} ### Checked other resources - [X] I added a very descriptive title to this question. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Commit to Help - [X] I commit to help with one of those options 👆 ### Example Code ```python import os from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from langchain.prompts import PromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import ConfigurableField from langchain_google_genai import (ChatGoogleGenerativeAI, HarmBlockThreshold, HarmCategory) from langchain_openai import AzureChatOpenAI # Setting up APIs # Azure OpenAI API OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] OPENAI_DEPLOYMENT_ENDPOINT = os.environ["AZURE_OPENAI_ENDPOINT"] OPENAI_DEPLOYMENT_NAME = os.environ["OPENAI_DEPLOYMENT_NAME"] OPENAI_DEPLOYMENT_VERSION = os.environ["OPENAI_API_VERSION"] # Models llm = AzureChatOpenAI(azure_endpoint=OPENAI_DEPLOYMENT_ENDPOINT, deployment_name=OPENAI_DEPLOYMENT_NAME, openai_api_version=OPENAI_DEPLOYMENT_VERSION, openai_api_key=OPENAI_API_KEY, verbose=True, request_timeout=60, temperature=0.8).configurable_alternatives( # This gives this field an id # When configuring the end runnable, we can then use this id to configure this field ConfigurableField(id="llm"), # This sets a default_key. # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used default_key="azure", # This adds a new option, with name `openai` that is equal to `ChatOpenAI()` gemini=ChatGoogleGenerativeAI( model="gemini-pro", google_api_key="", convert_system_message_to_human=True, max_tokens=16384, temperature=0.7, safety_settings={ HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE }, ), ) app = FastAPI(title="Joke Engine", version="1.0.0") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) class Joke(BaseModel): setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") ##Generic home end point @app.get("/") def home(): return {"msg": "App is running successfully"} @app.get("/joke") async def run_agent(topic: str): prompt = PromptTemplate( input_variables=["input",], template=""" Given a user input, tell a joke about the below topic: {input} """ ) chain = prompt | llm return chain.with_config(configurable={"llm": "gemini"}).invoke({'input':topic}) ``` ### Description I am using ChatGoogleGenerativeAI along with AzureOpenAI in configurable mode. I have written a simple fastapi to write a joke on user given topic. When I run the above code configuring my "llm" parameter as azure it works fine. However, when I run the same code with "llm" parameter as gemini the code fails with the below error: `[2024-06-14T05:30:47.913Z] System.Private.CoreLib: Exception while executing function: Functions.HttpTrigger1. System.Private.CoreLib: Result: Failure Exception: AttributeError: 'int' object has no attribute 'name' Stack: File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure_functions_worker\dispatcher.py", line 479, in _handle__invocation_request call_result = await self._loop.run_in_executor( File "C:\Users\Digital\AppData\Local\Programs\Python\Python39\lib\concurrent\futures\thread.py", line 52, in run result = self.fn(*self.args, **self.kwargs) File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure_functions_worker\dispatcher.py", line 752, in _run_sync_func return ExtensionManager.get_sync_invocation_wrapper(context, File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure_functions_worker\extension.py", line 215, in _raw_invocation_wrapper result = function(**args) File "\egg-gemini\HttpTrigger1\__init__.py", line 19, in main return func.AsgiMiddleware(app).handle(req, context) File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure\functions\_http_asgi.py", line 172, in handle return self._handle(req, context) File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure\functions\_http_asgi.py", line 177, in _handle asgi_response = asyncio.run( File "C:\Users\Digital\AppData\Local\Programs\Python\Python39\lib\asyncio\runners.py", line 44, in run return loop.run_until_complete(main) File "C:\Users\Digital\AppData\Local\Programs\Python\Python39\lib\asyncio\base_events.py", line 642, in run_until_complete return future.result() File "C:\Program Files\Microsoft\Azure Functions Core Tools\workers\python\3.9\WINDOWS\X64\azure\functions\_http_asgi.py", line 80, in from_app await app(scope, res._receive, res._send) File "\egg-gemini\.venv\lib\site-packages\fastapi\applications.py", line 1054, in __call__ await super().__call__(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\applications.py", line 123, in __call__ await self.middleware_stack(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\middleware\errors.py", line 186, in __call__ raise exc File "\egg-gemini\.venv\lib\site-packages\starlette\middleware\errors.py", line 164, in __call__ await self.app(scope, receive, _send) File "\egg-gemini\.venv\lib\site-packages\starlette\middleware\cors.py", line 85, in __call__ await self.app(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\middleware\exceptions.py", line 65, in __call__ await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\_exception_handler.py", line 64, in wrapped_app raise exc File "\egg-gemini\.venv\lib\site-packages\starlette\_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "\egg-gemini\.venv\lib\site-packages\starlette\routing.py", line 756, in __call__ await self.middleware_stack(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\routing.py", line 776, in app await route.handle(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\routing.py", line 297, in handle await self.app(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\routing.py", line 77, in app await wrap_app_handling_exceptions(app, request)(scope, receive, send) File "\egg-gemini\.venv\lib\site-packages\starlette\_exception_handler.py", line 64, in wrapped_app raise exc File "\egg-gemini\.venv\lib\site-packages\starlette\_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "\egg-gemini\.venv\lib\site-packages\starlette\routing.py", line 72, in app response = await func(request) File "\egg-gemini\.venv\lib\site-packages\fastapi\routing.py", line 278, in app raw_response = await run_endpoint_function( File "\egg-gemini\.venv\lib\site-packages\fastapi\routing.py", line 191, in run_endpoint_function return await dependant.call(**values) File "\egg-gemini\src\__init__.py", line 100, in run_agent return chain.with_config(configurable={"llm": "gemini"}).invoke({'input':topic}) File "\egg-gemini\.venv\lib\site-packages\langchain_core\runnables\base.py", line 4573, in invoke return self.bound.invoke( File "\egg-gemini\.venv\lib\site-packages\langchain_core\runnables\base.py", line 2504, in invoke input = step.invoke(input, config) File "\egg-gemini\.venv\lib\site-packages\langchain_core\runnables\configurable.py", line 117, in invoke return runnable.invoke(input, config, **kwargs) File "\egg-gemini\.venv\lib\site-packages\langchain_core\language_models\chat_models.py", line 170, in invoke self.generate_prompt( File "\egg-gemini\.venv\lib\site-packages\langchain_core\language_models\chat_models.py", line 599, in generate_prompt return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs) File "\egg-gemini\.venv\lib\site-packages\langchain_core\language_models\chat_models.py", line 456, in generate raise e File "\egg-gemini\.venv\lib\site-packages\langchain_core\language_models\chat_models.py", line 446, in generate self._generate_with_cache( File "\egg-gemini\.venv\lib\site-packages\langchain_core\language_models\chat_models.py", line 671, in _generate_with_cache result = self._generate( File "\egg-gemini\.venv\lib\site-packages\langchain_google_genai\chat_models.py", line 766, in _generate return _response_to_result(response) File "\egg-gemini\.venv\lib\site-packages\langchain_google_genai\chat_models.py", line 551, in _response_to_result generation_info["finish_reason"] = candidate.finish_reason.name` Attached is my requirement.txt file too [requirements.txt](https://github.com/user-attachments/files/15832125/requirements.txt) ### System Info System Information ------------------ > OS: Windows > OS Version: 10.0.19041 > Python Version: 3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:34:40) [MSC v.1927 64 bit (AMD64)] Package Information ------------------- > langsmith: 0.1.77 > langchain_google_genai: 1.0.6 > langchain_openai: 0.1.8 > langchain_text_splitters: 0.2.1 Packages not installed (Not Necessarily a Problem) -------------------------------------------------- The following packages were not found: > langgraph > langserve

[baskaryan]

cc @lkuligin

[lkuligin]

I can't reproduce it on my side, unfortunately. What topic's value makes the chain to fail?

[lkuligin]

it might be the dependency issue, smth similar has been reported: #57 and updating dependencies helped