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is it possible to give user database to llm agent ...
# ask-ai
a
is it possible to give user database to llm agent in livekit so that it can answer to user according to database if possible then write function_tool function like this: "" @function_tool() async def get_weather( context: RunContext, # type: ignore city: str) -> str: """ Get the current weather for a given city. """ try: response = requests.get( f"https://wttr.in/{city}?format=3") if response.status_code == 200: logging.info(f"Weather for {city}: {response.text.strip()}") return response.text.strip() else: logging.error(f"Failed to get weather for {city}: {response.status_code}") return f"Could not retrieve weather for {city}." except Exception as e: logging.error(f"Error retrieving weather for {city}: {e}") return f"An error occurred while retrieving weather for {city}." "" so that when user ask something then first it will get info from database and then will give that context to llm and then will give naswer to user. currently database in in json form
t
Thanks for reaching out, Touseef. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Yes, it is possible to integrate a user database with an LLM agent in LiveKit to answer user queries based on the database. You can achieve this by: • Using function tools to query the database and retrieve relevant information. • Implementing Retrieval Augmented Generation (RAG) to fetch data dynamically from the database and provide it as context to the LLM. • Passing user-specific data via job metadata or participant attributes to personalize responses. • Optimizing load times by preloading static data and making network calls before connecting the context. Here is an example of a function tool that queries a database and provides context to the LLM:
Copy code
@function_tool()
async def get_user_data(context: RunContext, user_id: str) -> str:
    """
    Retrieve user data from the database and provide it as context.
    """
    try:
        # Example: Querying a JSON database
        with open('user_database.json', 'r') as db_file:
            database = json.load(db_file)
        user_data = database.get(user_id, {})
        if user_data:
            return json.dumps(user_data)
        else:
            return "User data not found."
    except Exception as e:
        logging.error(f"Error retrieving user data: {e}")
        return "An error occurred while retrieving user data."
This function can be used to fetch user data and pass it to the LLM for generating responses. Let me know if you need further assistance or specific implementation details. Sources: External data and RAG | LiveKit Docs | complex-agents/nutrition-assistant/README.md
a
will it get context 1 time then all conversation according to that context or it will need to get context on each query
t
LiveKit helps your LLM agent remember the context throughout your conversation by keeping it right within the session's chat context. So, you don’t have to pull context from the database every time you ask something. Instead, the conversation history stays handy and is passed along to the LLM using the 
chat_ctx
parameter. That said, if you ever need to bring in new or updated info during the chat, the agent can easily fetch it from the database whenever necessary. Just let me know if you'd like me to walk you through some examples or give you more details on how to set this up—I’m here to help! Sources: Workflows | LiveKit Docs | Workflows | LiveKit Docs | External data and RAG | LiveKit Docs
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