Query_Agent.py

from pinecone import Pinecone
import openai
from openai import OpenAI


def extract_action(response, query=None):
    # TODO: Extract the action from the response
    # For example, retrieve document IDs and their similarity scores
    relevant_docs = [{"id": match["id"], "score": match["score"]} for match in response["matches"]]

    return relevant_docs


class Query_Agent:

    def __init__(self, pinecone_index, openai_client, embeddings) -> None:
        # TODO: Initialize the Query_Agent agent
        # Initialize the OpenAI client with the provided API key
        openai_api_key = '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'
        self.client = OpenAI(api_key=openai_api_key)

        # Specify the embeddings model to use for generating query embeddings
        self.embeddings_model = embeddings

        pc = Pinecone(api_key="52ef9136-6188-4e51-af13-9639bf95c163")

        # Initialize Pinecone client and connect to the specified index
        self.pinecone_index = pc.Index(pinecone_index)

    def query_vector_store(self, query, k=4):
        # TODO: Query the Pinecone vector store
        # Generate an embedding for the query

        query = query.replace("\n", " ")

        query_embedding = self.client.embeddings.create(
            input=[query],
            model=self.embeddings_model
        ).data[0].embedding

        # Query the Pinecone index using the generated embedding
        query_results = self.pinecone_index.query(
            vector=query_embedding,
            top_k=k,
            include_metadata=True)

        # Extract and return the most relevant documents along with their scores
        relevant_docs = [
            (result['id'], result['score'], result['metadata']['text'])
            for result in query_results['matches']
        ]

        return relevant_docs

    def set_prompt(self, prompt):
        # TODO: Set the prompt for the Query_Agent agent
        self.prompt = prompt