src/pinecone_store.py

import pinecone
import os
from dotenv import load_dotenv

load_dotenv() 

class PineconeStore:
    def __init__(self):
        api_key = os.getenv("PINECONE_API_KEY")
        environment = os.getenv("PINECONE_ENV")
        pinecone.init(api_key=api_key, environment=environment)

        self.index_name = "soccer-highlights"
        if self.index_name not in pinecone.list_indexes():
            pinecone.create_index(
                name=self.index_name,
                dimension=1024,  # Ensure this matches your embedding model's output dimension
                metric="cosine"  # Choose the appropriate metric (e.g., cosine, euclidean)
            )
        self.index = pinecone.Index(self.index_name)

    def upsert(self, id, vector, metadata):
        self.index.upsert([(id, vector.tolist(), metadata)])

    def query(self, vector, filter_key, top_k):
        return [
            m["metadata"] for m in self.index.query(
                vector.tolist(),
                top_k=top_k,
                include_metadata=True
            )["matches"] if filter_key in m["id"]
        ]