code/modules/vector_db.py

import logging
import os
import yaml
from langchain.vectorstores import FAISS, Chroma
from langchain.schema.vectorstore import VectorStoreRetriever
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema.document import Document

try:
    from modules.embedding_model_loader import EmbeddingModelLoader
    from modules.data_loader import DataLoader
    from modules.constants import *
    from modules.helpers import *
except:
    from embedding_model_loader import EmbeddingModelLoader
    from data_loader import DataLoader
    from constants import *
    from helpers import *


class VectorDBScore(VectorStoreRetriever):
    # See https://github.com/langchain-ai/langchain/blob/61dd92f8215daef3d9cf1734b0d1f8c70c1571c3/libs/langchain/langchain/vectorstores/base.py#L500
    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ):
        docs_and_similarities = (
            self.vectorstore.similarity_search_with_relevance_scores(
                query, **self.search_kwargs
            )
        )
        # Make the score part of the document metadata
        for doc, similarity in docs_and_similarities:
            doc.metadata["score"] = similarity

        docs = [doc for doc, _ in docs_and_similarities]
        return docs


class VectorDB:
    def __init__(self, config, logger=None):
        self.config = config
        self.db_option = config["embedding_options"]["db_option"]
        self.document_names = None
        self.webpage_crawler = WebpageCrawler()

        # Set up logging to both console and a file
        if logger is None:
            self.logger = logging.getLogger(__name__)
            self.logger.setLevel(logging.INFO)

            # Console Handler
            console_handler = logging.StreamHandler()
            console_handler.setLevel(logging.INFO)
            formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
            console_handler.setFormatter(formatter)
            self.logger.addHandler(console_handler)

            # File Handler
            log_file_path = "vector_db.log"  # Change this to your desired log file path
            file_handler = logging.FileHandler(log_file_path, mode="w")
            file_handler.setLevel(logging.INFO)
            file_handler.setFormatter(formatter)
            self.logger.addHandler(file_handler)
        else:
            self.logger = logger

        self.logger.info("VectorDB instance instantiated")

    def load_files(self):
        files = os.listdir(self.config["embedding_options"]["data_path"])
        files = [
            os.path.join(self.config["embedding_options"]["data_path"], file)
            for file in files
        ]
        urls = get_urls_from_file(self.config["embedding_options"]["url_file_path"])
        if self.config["embedding_options"]["expand_urls"]:
            all_urls = []
            for url in urls:
                base_url = get_base_url(url)
                all_urls.extend(self.webpage_crawler.get_all_pages(url, base_url))
            urls = all_urls
        return files, urls

    def clean_url_list(self, urls):
        # get lecture pdf links
        lecture_pdfs = [link for link in urls if link.endswith(".pdf")]
        lecture_pdfs = [link for link in lecture_pdfs if "lecture" in link.lower()]
        urls = [
            link for link in urls if link.endswith("/")
        ]  # only keep links that end with a '/'. Extract Files Seperately

        return urls, lecture_pdfs

    def create_embedding_model(self):
        self.logger.info("Creating embedding function")
        self.embedding_model_loader = EmbeddingModelLoader(self.config)
        self.embedding_model = self.embedding_model_loader.load_embedding_model()

    def initialize_database(self, document_chunks: list, document_names: list):
        # Track token usage
        self.logger.info("Initializing vector_db")
        self.logger.info("\tUsing {} as db_option".format(self.db_option))
        if self.db_option == "FAISS":
            self.vector_db = FAISS.from_documents(
                documents=document_chunks, embedding=self.embedding_model
            )
        elif self.db_option == "Chroma":
            self.vector_db = Chroma.from_documents(
                documents=document_chunks,
                embedding=self.embedding_model,
                persist_directory=os.path.join(
                    self.config["embedding_options"]["db_path"],
                    "db_"
                    + self.config["embedding_options"]["db_option"]
                    + "_"
                    + self.config["embedding_options"]["model"],
                ),
            )
        self.logger.info("Completed initializing vector_db")

    def create_database(self):
        data_loader = DataLoader(self.config)
        self.logger.info("Loading data")
        files, urls = self.load_files()
        urls, lecture_pdfs = self.clean_url_list(urls)
        files += lecture_pdfs
        if "storage/data/urls.txt" in files:
            files.remove("storage/data/urls.txt")
        document_chunks, document_names = data_loader.get_chunks(files, urls)
        self.logger.info("Completed loading data")

        self.create_embedding_model()
        self.initialize_database(document_chunks, document_names)

    def save_database(self):
        if self.db_option == "FAISS":
            self.vector_db.save_local(
                os.path.join(
                    self.config["embedding_options"]["db_path"],
                    "db_"
                    + self.config["embedding_options"]["db_option"]
                    + "_"
                    + self.config["embedding_options"]["model"],
                )
            )
        elif self.db_option == "Chroma":
            # db is saved in the persist directory during initialization
            pass
        self.logger.info("Saved database")

    def load_database(self):
        self.create_embedding_model()
        if self.db_option == "FAISS":
            self.vector_db = FAISS.load_local(
                os.path.join(
                    self.config["embedding_options"]["db_path"],
                    "db_"
                    + self.config["embedding_options"]["db_option"]
                    + "_"
                    + self.config["embedding_options"]["model"],
                ),
                self.embedding_model,
                allow_dangerous_deserialization=True,
            )
        elif self.db_option == "Chroma":
            self.vector_db = Chroma(
                persist_directory=os.path.join(
                    self.config["embedding_options"]["db_path"],
                    "db_"
                    + self.config["embedding_options"]["db_option"]
                    + "_"
                    + self.config["embedding_options"]["model"],
                ),
                embedding_function=self.embedding_model,
            )
        self.logger.info("Loaded database")
        return self.vector_db


if __name__ == "__main__":
    with open("code/config.yml", "r") as f:
        config = yaml.safe_load(f)
    print(config)
    vector_db = VectorDB(config)
    vector_db.create_database()
    vector_db.save_database()