app/data_indexing.py

import os
import uuid
from pathlib import Path
from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

current_dir = Path(__file__).resolve().parent


class DataIndexer:

    source_file =  os.path.join(current_dir, 'sources.txt')

    def __init__(self, index_name='langchain-repo') -> None:

        # TODO: choose your embedding model
        # self.embedding_client = InferenceClient(
        #     "dunzhang/stella_en_1.5B_v5",
        #      token=os.environ['HF_TOKEN'],
        # )
        self.embedding_client = OpenAIEmbeddings()
        self.index_name = index_name
        self.pinecone_client = Pinecone(api_key=os.environ.get('PINECONE_API_KEY'))

        if index_name not in self.pinecone_client.list_indexes().names():
            # TODO: create your index if it doesn't exist. Use the create_index function. 
            # Make sure to choose the dimension that corresponds to your embedding model
            pass

        self.index = self.pinecone_client.Index(self.index_name)
        # TODO: make sure to build the index.
        self.source_index = None

    def get_source_index(self):
        if not os.path.isfile(self.source_file):
            print('No source file')
            return None
        
        print('create source index')
        
        with open(self.source_file, 'r') as file:
            sources = file.readlines()
            
        sources = [s.rstrip('\n') for s in sources]
        vectorstore = Chroma.from_texts(
            sources, embedding=self.embedding_client
        )
        return vectorstore

    def index_data(self, docs, batch_size=32):

        with open(self.source_file, 'a') as file:
            for doc in docs:
                file.writelines(doc.metadata['source'] + '\n')

        for i in range(0, len(docs), batch_size):
            batch = docs[i: i + batch_size]

            # TODO: create a list of the vector representations of each text data in the batch
            # TODO: choose your embedding model
            # values = self.embedding_client.embed_documents([
            #     doc.page_content for doc in batch
            # ])

            # values = self.embedding_client.feature_extraction([
            #     doc.page_content for doc in batch
            # ])
            values = None

            # TODO: create a list of unique identifiers for each element in the batch with the uuid package.
            vector_ids = None

            # TODO: create a list of dictionaries representing the metadata. Capture the text data 
            # with the "text" key, and make sure to capture the rest of the doc.metadata.
            metadatas = None

            # create a list of dictionaries with keys "id" (the unique identifiers), "values"
            # (the vector representation), and "metadata" (the metadata).
            vectors = [{
                'id': vector_id,
                'values': value,
                'metadata': metadata
            } for vector_id, value, metadata in zip(vector_ids, values, metadatas)]

            try: 
                # TODO: Use the function upsert to upload the data to the database.
                upsert_response = None
                print(upsert_response)
            except Exception as e:
                print(e)

    def search(self, text_query, top_k=5, hybrid_search=False):

        filter = None
        if hybrid_search and self.source_index:
            # I implemented the filtering process to pull the 50 most relevant file names
            # to the question. Make sure to adjust this number as you see fit.
            source_docs = self.source_index.similarity_search(text_query, 50)
            filter = {"source": {"$in":[doc.page_content for doc in source_docs]}}

        # TODO: embed the text_query by using the embedding model
        # TODO: choose your embedding model
        # vector = self.embedding_client.feature_extraction(text_query)
        # vector = self.embedding_client.embed_query(text_query)
        vector = None

        # TODO: use the vector representation of the text_query to 
        # search the database by using the query function.
        result = None

        docs = []
        for res in result["matches"]:
            # TODO: From the result's metadata, extract the "text" element.
            pass

        return docs
    

if __name__ == '__main__':

    from langchain_community.document_loaders import GitLoader
    from langchain_text_splitters import (
        Language,
        RecursiveCharacterTextSplitter,
    )

    loader = GitLoader(
        clone_url="https://github.com/langchain-ai/langchain",
        repo_path="./code_data/langchain_repo/",
        branch="master",
    )

    python_splitter = RecursiveCharacterTextSplitter.from_language(
        language=Language.PYTHON, chunk_size=10000, chunk_overlap=100
    )

    docs = loader.load()
    docs = [doc for doc in docs if doc.metadata['file_type'] in ['.py', '.md']]
    docs = [doc for doc in docs if len(doc.page_content) < 50000]
    docs = python_splitter.split_documents(docs)
    for doc in docs:
        doc.page_content = '# {}\n\n'.format(doc.metadata['source']) + doc.page_content

    indexer = DataIndexer()
    with open('/app/sources.txt', 'a') as file:
        for doc in docs:
            file.writelines(doc.metadata['source'] + '\n')
    indexer.index_data(docs)