Integrating Schema Messages with RetrievalQA in My Application.

[himesh2795]

In my application, I've employed schema messages such as HumanMessage, SystemMessage, and AIMessage. I'm now looking to incorporate RetrievalQA into my system. Since I've already implemented these messages in multiple places, I need guidance on how to integrate them with RetrievalQA effectively. Could you provide some insights on how to achieve this?

Is there a more efficient way to manage messages within RetrievalQA, or should I convert them to strings throughout my codebase?

To provide more context, here's an example of how I've structured my messages and initiated RetrievalQA:

from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI()

messages = [
    SystemMessage(content=system_message),
    HumanMessage(content=user_message.format(message=user_reply)),
    AIMessage(content=ai_generated_reply),
    HumanMessage(content=message)
]

# getting response without using RetrievalQA
# response = chat(messages) 

ai_bot = RetrievalQA.from_chain_type(
    llm=chat,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

response = ai_bot.run(messages)  # TypeError: expected string or buffer

[dosubot[bot]]

🤖

Hello,

Based on the context you've provided, it seems like you're trying to run the RetrievalQA instance with a list of SystemMessage, HumanMessage, and AIMessage instances. However, the run method of RetrievalQA expects a string or a buffer as its argument, not a list of message instances. This is likely the cause of the TypeError: expected string or buffer you're encountering.

To integrate schema messages with RetrievalQA, you need to ensure that the messages are properly formatted as strings before passing them to the run method. Here's an example of how you can do this:

# Convert the messages to strings
messages_str = [str(message) for message in messages]

# Join the messages into a single string
messages_combined = "\n".join(messages_str)

# Run the bot with the combined messages
response = ai_bot.run(messages_combined)

In this example, each message in the messages list is converted to a string using the str function. The messages are then joined into a single string using the join method, with each message separated by a newline character (\n). This combined string is then passed to the run method of the RetrievalQA instance.

Regarding the integration of SystemMessage with RetrievalQA, there's a similar issue in the LangChain repository that suggests using an agent to include a system message with each call to qa.run(prompt). The agent instructs the system about its basic purpose. The best type of agent in this case will be CHAT_CONVERSATIONAL_REACT_DESCRIPTION. You can find the detailed solution in this issue.

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

Sources

• Question: How can I include SystemMessage with RetrievalQA
• libs/langchain/langchain/schema/messages.py
• libs/langchain/langchain/prompts/chat.py
• libs/langchain/langchain/chains/retrieval_qa/base.py

---

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[himesh2795]

Is there a more efficient way to manage messages within RetrievalQA, or should I convert them to strings throughout my codebase?

[dosubot[bot]]

🤖

Hello,

The TypeError: expected string or buffer error you're encountering is likely due to the fact that the run method of the RetrievalQA class expects a dictionary as input, not a list of message instances. The dictionary should contain a key that matches the input_key of the class, which is set to "query" by default. The value associated with this key is treated as the question for which the model retrieves relevant documents and generates an answer.

Here's an example of how you might modify your code to avoid this error:

from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI()

messages = [
    SystemMessage(content=system_message),
    HumanMessage(content=user_message.format(message=user_reply)),
    AIMessage(content=ai_generated_reply),
    HumanMessage(content=message)
]

ai_bot = RetrievalQA.from_chain_type(
    llm=chat,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

for message in messages:
    # Extract question string from message instance
    question = message.content  # Replace with actual method to extract question string

    # Run RetrievalQA instance with question string
    response = ai_bot.run({'query': question})

In this example, replace message.content with the actual method or attribute to extract the question string from a message instance. The exact method or attribute will depend on the structure of your message instances.

I hope this helps! Let me know if you have any other questions.

Sources

• libs/langchain/langchain/chains/retrieval_qa/base.py
• libs/langchain/langchain/memory/chat_message_histories/streamlit.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.