from pinecone import Pinecone
import os

from text_embedder_encoder import encoder_model_name


class Retriever:
    def __init__(self,
                 pinecone_api_key=os.environ["pinecone_api_key"],
                 question_index_name=f"hebrew-dentist-questions-{encoder_model_name.replace('/', '-')}".lower()):
        # Initialize Pinecone connection
        self.pc = Pinecone(api_key=pinecone_api_key)
        self.question_index_name = question_index_name

    def search_similar(self, query_vector, top_k=50):
        """
        Search for similar content using vector similarity in Pinecone
        """
        try:

            # Get Pinecone index
            index = self.pc.Index(self.question_index_name)

            # Execute search
            results = index.query(
                vector=query_vector,
                top_k=top_k,
                include_metadata=True,
            )

            answers_records_ids = []
            for match in results['matches']:
                answers_records_ids.append(
                    ':'.join(match['id'].split(':')[:-1]) + ":" + str(int(match['metadata']['answer_id'])))

            return answers_records_ids
        except Exception as e:
            print(f"Error performing retriever: {e}")
            return []