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import torch | |
torch.device('cpu') | |
# Imports and Initializations for Chainlit | |
import chainlit as cl | |
from dotenv import load_dotenv | |
# Imports and Initializations for Haystack | |
from haystack.document_stores import InMemoryDocumentStore | |
from datasets import load_dataset | |
from haystack.nodes import BM25Retriever, PromptTemplate, AnswerParser, PromptNode | |
import os | |
from haystack.pipelines import Pipeline | |
# Load environment variables (if any) | |
load_dotenv(".env") | |
load_dotenv() | |
MY_API_KEY = os.getenv("OPENAI_API_KEY") | |
# Initialize Haystack's QA system | |
document_store = InMemoryDocumentStore(use_bm25=True) | |
dataset = load_dataset("bilgeyucel/seven-wonders", split="train") | |
document_store.write_documents(dataset) | |
rag_prompt = PromptTemplate( | |
prompt="""Synthesize a brief answer from the following text for the given question. | |
Provide a clear and concise response that summarizes the key points and information presented in the text. | |
Your answer should be in your own words and be no longer than 50 words. | |
\n\n Related text: {join(documents)} \n\n Question: {query} \n\n Answer:""", | |
output_parser=AnswerParser(), | |
) | |
# Set up nodes | |
retriever = BM25Retriever(document_store=document_store, top_k=2) | |
pn = PromptNode("gpt-3.5-turbo", | |
api_key=MY_API_KEY, | |
model_kwargs={"stream":False}, | |
default_prompt_template=rag_prompt) | |
# Set up pipeline | |
pipe = Pipeline() | |
pipe.add_node(component=retriever, name="retriever", inputs=["Query"]) | |
pipe.add_node(component=pn, name="prompt_node", inputs=["retriever"]) | |
async def main(message: str): | |
# Use the pipeline to get a response | |
output = pipe.run(query=message) | |
# Create a Chainlit message with the response | |
response = output['answers'][0].answer | |
msg = cl.Message(content=response) | |
# Send the message to the user | |
await msg.send() | |
if __name__ == "__main__": | |
# You can add any Chainlit app initialization or run code here | |
pass | |