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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 | |