ask_question.py

@action(reads=[], writes=["llm_answer"])
def ask_question(state: State, user_query: str) -> State:
    """Reply to the user's query using the webpage's content."""
    # Retrieve the most relevant chunks
    chunks_table = lancedb.connect("./webpages").open_table("chunks")
    search_results = (
        chunks_table
        .search(user_query)
        .select(["text", "url", "position"])
        .limit(3)
        .to_list()
    )
    relevant_content = "\n".join([r["text"] for r in search_results])

    # Prompt the LLM with the relevant content
    system_prompt = (
        "Answer the user's questions based on the provided webpage content. "
        f"WEBPAGE CONTENT:\n{relevant_content}"
    )

    client = openai.OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_query}
        ],
    )
    llm_answer = response.choices[0].message.content
    return state.update(llm_answer=llm_answer)