langchain_falcon_langsmith.py

from langchain import HuggingFaceHub
from langchain import PromptTemplate, LLMChain
import os

from dotenv import load_dotenv
import chainlit as cl

# Load environment variables from .env file
load_dotenv()

HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
LANGCHAIN_TRACING_V2 = os.getenv("LANGCHAIN_TRACING_V2")
LANGCHAIN_ENDPOINT = os.getenv("LANGCHAIN_ENDPOINT")
LANGCHAIN_API_KEY = os.getenv("LANGCHAIN_API_KEY")
LANGCHAIN_PROJECT = os.getenv("LANGCHAIN_PROJECT")

repo_id = "tiiuae/falcon-7b-instruct"
llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, 
                     repo_id=repo_id, 
                     model_kwargs={"temperature":0.7, "max_new_tokens":500})


template = """Question: {question}

Answer: Let's think step by step."""


@cl.on_chat_start
async def main():

    # Sending an image with the local file path
    elements = [
    cl.Image(name="image1", display="inline", path="falcon.jpeg")
    ]
    await cl.Message(content="Hello there, I am Falcon. How can I help you ?", elements=elements).send()

    # Instantiate the chain for that user session

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)

    # Store the chain in the user session
    cl.user_session.set("llm_chain", llm_chain)


@cl.on_message
async def main(message: str):
    # Retrieve the chain from the user session
    llm_chain = cl.user_session.get("llm_chain")  # type: LLMChain

    # Call the chain asynchronously
    res = await llm_chain.acall(message, callbacks=[cl.AsyncLangchainCallbackHandler()])

    # Do any post processing here

    # Send the response
    await cl.Message(content=res["text"]).send()