I was trying the application code in the link.
I am using the following Llang Chain version
langchain 0.0.327langchain-community 0.0.2langchain-core 0.1.0
Getting the following error:
Entering new AgentExecutor chain...Traceback (most recent call last): File "RAGWithAgent.py", line 54, in <module> result = agent_executor({"input": "hi, im bob"}) File "\lib\site-packages\langchain\chains\base.py", line 310, in __call__ raise e File "\lib\site-packages\langchain\chains\base.py", line 304, in __call__ self._call(inputs, run_manager=run_manager) File "\lib\site-packages\langchain\agents\agent.py", line 1146, in _call next_step_output = self._take_next_step( File "\lib\site-packages\langchain\agents\agent.py", line 933, in _take_next_step output = self.agent.plan( File "\lib\site-packages\langchain\agents\openai_functions_agent\base.py", line 104, in plan predicted_message = self.llm.predict_messages( File "\lib\site-packages\langchain\chat_models\base.py", line 650, in predict_messages return self(messages, stop=_stop, **kwargs) File "\lib\site-packages\langchain\chat_models\base.py", line 600, in __call__ generation = self.generate( File "\lib\site-packages\langchain\chat_models\base.py", line 349, in generate raise e File "\lib\site-packages\langchain\chat_models\base.py", line 339, in generate self._generate_with_cache( File "\lib\site-packages\langchain\chat_models\base.py", line 492, in _generate_with_cache return self._generate( File "\lib\site-packages\langchain\chat_models\openai.py", line 357, in _generate return _generate_from_stream(stream_iter) File "\lib\site-packages\langchain\chat_models\base.py", line 57, in _generate_from_stream for chunk in stream: File "\lib\site-packages\langchain\chat_models\openai.py", line 326, in _stream for chunk in self.completion_with_retry( File "\lib\site-packages\langchain\chat_models\openai.py", line 299, in completion_with_retry return _completion_with_retry(**kwargs) File "\lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f return self(f, *args, **kw) File "\lib\site-packages\tenacity\__init__.py", line 379, in __call__ do = self.iter(retry_state=retry_state) File "\lib\site-packages\tenacity\__init__.py", line 314, in iter return fut.result() File "D:\Program Files\Python38\lib\concurrent\futures\_base.py", line 432, in result return self.__get_result() File "D:\Program Files\Python38\lib\concurrent\futures\_base.py", line 388, in __get_result raise self._exception File "\lib\site-packages\tenacity\__init__.py", line 382, in __call__ result = fn(*args, **kwargs) File "\lib\site-packages\langchain\chat_models\openai.py", line 297, in _completion_with_retry return self.client.create(**kwargs) File "\lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) File "\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( File "\lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) File "\lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "\lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response(openai.error.InvalidRequestError: Unrecognized request argument supplied: functionsProcess finished with exit code 1I used Azure LLM instead openAI.FAISS was not working for me so used Chroma Vector Store.
Following is my code:
from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.agents.agent_toolkits import create_retriever_toolfrom langchain.agents.agent_toolkits import create_conversational_retrieval_agentfrom langchain.chat_models import AzureChatOpenAIfrom langchain.vectorstores import Chromafrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsimport osAZURE_OPENAI_API_KEY = ""os.environ["OPENAI_API_KEY"] = AZURE_OPENAI_API_KEYloader = TextLoader(r"Toward a Knowledge Graph of Cybersecurity Countermeasures.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)chunks = text_splitter.split_documents(documents)# create the open-source embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-mpnet-base-v2")current_directory = os.path.dirname("__file__")# load it into Chroma and save it to diskdb = Chroma.from_documents(chunks, embedding_function, collection_name="groups_collection", persist_directory=r"\rag_with_agent_chroma_db")retriever = db.as_retriever(search_kwargs={"k": 5})tool = create_retriever_tool( retriever,"search_state_of_union","Searches and returns documents regarding the state-of-the-union.",)tools = [tool]llm = AzureChatOpenAI( deployment_name='gtp35turbo', model_name='gpt-35-turbo', openai_api_key=AZURE_OPENAI_API_KEY, openai_api_version='2023-03-15-preview', openai_api_base='https://azureft.openai.azure.com/', openai_api_type='azure', streaming=True, verbose=True)agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True, remember_intermediate_steps=True, memory_key="chat_history")result = agent_executor({"input": "hi, im bob"})print(result["output"])