A Q not related to the new version do

At a glance

The post asks whether the QA and REFINE prompts, as well as the similarity top_k, work with LangChain agents using LlamaIndex as a tool or with GPTIndexChatMemory. The comments suggest that the prompts and top_k can be set in the query_kwargs of each tool. However, there are some issues with the code provided, where the similarity and prompt do not seem to work as expected. Community members discuss alternative solutions, including using GPTIndexChatMemory, but encounter an error when asking for index content. The community members are unable to figure out the cause of the error and discuss the potential differences in performance between the approaches, but there is no explicitly marked answer.

AAndreaSel93

A Q not related to the new version: do the QA and REFINE prompts (also the similarity top_k) work with langchain agents using llama index as tool? or with GPTIndexChatMemory?

8 comments

LLogan M

You should be able to set the prompts in the query_kwargs of each tool I think!

LLogan M

same with the similarity_top_k

AAndreaSel93

This code works well, but being directly from langchain I don't think similarity and prompt work here:

tools = [
    Tool(
        name = "GPT Index",
        func=lambda q: str(index.query(q)),
        description="useful for ...",
        return_direct=True,

    ),
]
# set Logging to DEBUG for more detailed outputs
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)
agent_chain.run(input="hi, i am bob")

AAndreaSel93

.

OTHER SOLUTION BELOW:

memory = GPTIndexChatMemory(
    index=index,
    memory_key="chat_history", 
    query_kwargs={"response_mode": "compact",
                  "similarity_top_k": 3},
    # return_source returns source nodes instead of querying index
    return_source=True,
    # return_messages returns context in message format
    return_messages=True
)
llm = OpenAIChat(temperature=0)
# llm=OpenAI(temperature=0)
agent_chain = initialize_agent([], llm, agent="conversational-react-description", memory=memory)
agent_chain.run(input="hi, i am bob")

This works fine with that query ("hi, i am bob"), but when I ask for index content I got this error:

Out[21]: 'Agent stopped due to max iterations.'

AAndreaSel93

I can't figure out why😩

LLogan M

Right, so where you do index.query() you can set the prompts and top_k 💪

LLogan M

Yea that one is a little weird 🤔

AAndreaSel93

🤦🏻‍♂️🤦🏻‍♂️🤦🏻‍♂️ don’t even seen!

Anyway, do you think are there any differences in performance? Which one do you suggest?

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