RAG chain response often includes "\n AI:" in front of actual response

[mhyeonsoo]

System Info

google-cloud-aiplatform==1.35.0, langchain-0.0.354

Who can help?

No response

Information

• [ ] The official example notebooks/scripts
• [X] 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
• [X] Chains
• [ ] Callbacks/Tracing
• [ ] Async

Reproduction

template = """
        **Role:**
        You are a helpful assistant.

        **Context:**
        You have to only use a reference stored document to generate a response.
        CONTEXT: {context}

        **Task:**
        1. task 1
            - some important requirements for task 1
        2. task 2
            - some important requirements for task 2  
        3. task 3 
            - some important requirements for task 3

        Question: {question}
        Helpful Answer:

        **Resource:** [reference source name]
    """

    qa_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", template),
        MessagesPlaceholder(variable_name="chat_history"),
        ("user", "{question}"),
    ]
    )

    rag_chain = (
        RunnablePassthrough.assign(
            context=contextualized_question | retriever | format_docs
        )
        | qa_prompt
        | llm
    )

    response = rag_chain.invoke({"question": message, "chat_history": memory.get_history()})

    print(response)

Expected behavior

I expect to get the response which is composed of only a proper response sentence. Sometimes, it does well as I expected. But frequently, it returns "\n AI:" in front of the actual response which is not suppose to do so.

For example,

"\n AI: The reference document says blah blah.", 

[dosubot[bot]]

🤖

Hello @mhyeonsoo! I'm Dosu, a friendly bot here to help you with your LangChain issues, answer questions, and guide you through your contribution journey. While the human maintainers are away, consider me your personal assistant on this repository. Let's crack this code together! 😄

Based on the code snippet you provided, it seems like the "\n AI:" prefix is not being added by the LangChain framework itself, but rather by the language model (llm) that you're using in your RAG chain.

In LangChain, the output from the language model is typically passed through an output parser, which formats the output into a string. However, the output parser does not add any prefixes to the output. It simply formats the output as it is.

Here is the relevant part of the LangChain code that handles the formatting of the output response from the RAG chain:

# RAG chain
chain = (
    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
    | prompt
    | model
    | StrOutputParser()
)

In this chain, the StrOutputParser() is the last step, which means it takes the output from the previous steps (context retrieval, prompt creation, and model prediction) and formats it into a string. You can find this in the templates/rag-gpt-crawler/rag_gpt_crawler/chain.py file.

If you're seeing "\n AI:" in your output, it's likely that this is being added by the language model itself. Some language models are trained to generate responses in a conversational format, which may include prefixes like "AI:" or "User:".

To remove this prefix, you could add a post-processing step to your RAG chain that removes any unwanted prefixes from the output. Here's an example of how you could do this:

def remove_prefix(output):
    return output.replace("\n AI:", "")

rag_chain = (
    RunnablePassthrough.assign(
        context=contextualized_question | retriever | format_docs
    )
    | qa_prompt
    | llm
    | remove_prefix
)

In this modified chain, the remove_prefix function is the last step, which means it takes the output from the previous steps and removes the "\n AI:" prefix before returning the final output.

I hope this helps! If you have any other questions or need further clarification, please let me know.

Sources

• libs/core/langchain_core/output_parsers/format_instructions.py
• templates/rag-gpt-crawler/rag_gpt_crawler/chain.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[mhyeonsoo]

Thanks @dosu-bot,

It seems reasonable to have prefix_removal function as a last step of rag_chain, but I am bit concerning about the cases that is not returning fixed prefix like "\n AI:" as you mentioned.

I think it would be great if I can have more flexible way to handle these exceptions. (I hope there would be another way such as adding more functions in chain or something)

Thanks.

[dosubot[bot]]

🤖

Based on your code snippet and the context provided, you can indeed add more functions to the LangChain RAG chain to handle the issue you're facing.

To add a post-processing step to your RAG chain, you can define a function that removes the unwanted prefix from the language model's response. This function can be added to the chain using the pipe operator (|). Here's an example of how you can do this:

def remove_prefix(response):
    # Define your logic to remove the unwanted prefix
    return modified_response

rag_chain = (
    RunnablePassthrough.assign(
        context=contextualized_question | retriever | format_docs
    )
    | qa_prompt
    | llm
    | remove_prefix  # Add your function to the chain
)

response = rag_chain.invoke({"question": message, "chat_history": memory.get_history()})

In this example, remove_prefix is a function that takes the response from the language model and removes the unwanted prefix. You can replace the comment with your own logic to remove the prefix.

As for handling exceptions, you can use a try/except block around the invocation of the chain. This will allow you to catch and handle any exceptions that might be raised during the execution of the chain. Here's an example:

try:
    response = rag_chain.invoke({"question": message, "chat_history": memory.get_history()})
except Exception as e:
    # Handle the exception here
    print(f"An error occurred: {e}")

In this example, any exceptions that are raised during the invocation of the chain will be caught and printed. You can replace the print statement with your own error handling logic.

Please note that the remove_prefix function and the try/except block are just examples. You'll need to implement the remove_prefix function and the error handling logic according to your specific requirements.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• templates/rag-aws-kendra/rag_aws_kendra/chain.py
• templates/rag-gpt-crawler/rag_gpt_crawler/chain.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[gianfrancodemarco]

I also had this problem when adding history. History is usually formatted as something like

Human: AI:

so the LLM thinks it has to use the AI: prefix. If that's your case, you could rely on the replace method or play with the prompt to let the LLM understand better-