ValidationError: 1 validation error for ConversationalRetrievalChain chain_type_kwargs extra fields not permitted (type=value_error.extra)

[levalencia]

System Info

langchain 0.0.206 python 3.11.3

Who can help?

No response

Information

• [ ] The official example notebooks/scripts
• [ ] My own modified scripts

Related Components

• [ ] LLMs/Chat Models
• [ ] Embedding Models
• [X] Prompts / Prompt Templates / Prompt Selectors
• [ ] Output Parsers
• [ ] Document Loaders
• [ ] Vector Stores / Retrievers
• [ ] Memory
• [ ] Agents / Agent Executors
• [ ] Tools / Toolkits
• [ ] Chains
• [ ] Callbacks/Tracing
• [ ] Async

Reproduction

Code

tfretriever = TFIDFRetriever.from_texts(
            ["My name is Luis Valencia",
            "I am 70 years old",
            "I like gardening, baking and hockey"])
        
        template = """
            Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
            ------
            <ctx>
            {context}
            </ctx>
            ------
            <hs>
            {chat_history}
            </hs>
            ------
            {question}
            Answer:
            """
        
        prompt = PromptTemplate(
            input_variables=["chat_history", "context", "question"],
            template=template,
            )
        
        st.session_state['chain'] = chain = ConversationalRetrievalChain.from_llm(llm, 
                                                                                vectordb.as_retriever(),
                                                                                memory=memory,
                                                                                  chain_type_kwargs={
                                                                                    "verbose": True,
                                                                                    "prompt": prompt,
                                                                                    "memory": ConversationBufferMemory(
                                                                                        memory_key="chat_history",
                                                                                        input_key="question"),
                                                                                })

Error:

ValidationError: 1 validation error for ConversationalRetrievalChain chain_type_kwargs extra fields not permitted (type=value_error.extra)

Expected behavior

I should be able to provide custom context to my conversational retrieval chain, without custom prompt it works and gets good answers from vector db, but I cant use custom prompts

[devstein]

Answer generated by a 🤖

Answer

The error you're encountering is due to the fact that you're passing the chain_type_kwargs dictionary directly to the ConversationalRetrievalChain.from_llm() method. Instead, you should pass the prompt and memory arguments directly to the method.

python
tfretriever = TFIDFRetriever.from_texts(
    ["My name is Luis Valencia",
     "I am 70 years old",
     "I like gardening, baking and hockey"])

template = """
    Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
    ------
    <ctx>
    {context}
    </ctx>
    ------
    <hs>
    {chat_history}
    </hs>
    ------
    {question}
    Answer:
    """

prompt = PromptTemplate(
    input_variables=["chat_history", "context", "question"],
    template=template,
)

st.session_state['chain'] = chain = ConversationalRetrievalChain.from_llm(
    llm,
    vectordb.as_retriever(),
    memory=memory,
    verbose=True,
    prompt=prompt,
    memory=ConversationBufferMemory(
        memory_key="chat_history",
        input_key="question"
    )
)

By passing the prompt and memory arguments directly, you should be able to use custom prompts without encountering the ValidationError

Sources

• langchain/chains/question_answering/refine_prompts.py
• langchain/chains/conversation/base.py
• docs/extras/modules/memory/how_to/conversational_customization.ipynb

---

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[levalencia]

that doesnt work but the error changed.

1 validation error for ConversationalRetrievalChain prompt extra fields not permitted (type=value_error.extra)

[gennaro-dibrino-cardoai]

updating to langchain-0.0.228 solved the issue in my case

[Koushik15042000]

@gennaro-dibrino-cardoai , I updated langchain to 0.0.228, but still it didn't work. Can you show the sample code snippet explaining your implementation of ConversationalRetrievalChainwith chain_type_kwargs (to be more specific custom prompt engineering with ConversationalRetrievalChain)

[Koushik15042000]

@levalencia Did updating to langchain-0.0.228 work for you?

[shivanipatel7]

@Koushik15042000 Were you able to figure this out?

[Koushik15042000]

qa = ConversationalRetrievalChain.from_llm( ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"), retriever=vectorstore.as_retriever(search_kwargs={"k": 3}), return_source_documents=True, verbose=True, chain_type="stuff", get_chat_history=lambda h : h, combine_docs_chain_kwargs={'prompt': base_template}, memory = memory ) @shivanipatel7, You can try this way. It works.

[shivanipatel7]

@Koushik15042000 This worked for me!! Thank you so much

[imrankh46]

qa = ConversationalRetrievalChain.from_llm( ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"), retriever=vectorstore.as_retriever(search_kwargs={"k": 3}), return_source_documents=True, verbose=True, chain_type="stuff", get_chat_history=lambda h : h, combine_docs_chain_kwargs={'prompt': base_template}, memory = memory ) @shivanipatel7, You can try this way. It works.

This is not working, is there any documentation related this problem ?

[Koushik15042000]

qa = ConversationalRetrievalChain.from_llm( llm, retriever=vectorstore.as_retriever(search_kwargs={"k": 3}), return_source_documents=True, verbose=True, condense_question_llm = llm, chain_type="stuff", get_chat_history=lambda h : h, # combine_docs_chain_kwargs={'prompt': base_template}, # memory = memory ) qa.combine_docs_chain.llm_chain.prompt.messages[0] = SystemMessagePromptTemplate.from_template(sys_prompt)

    Define your own sys_prompt, set the context and try. Hope it works.

[realSAH]

Having same problem with latest version. The fixes proposed relate condense template, while the issue is with prompt template.