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import logging
import os
import yaml
from langchain_community.vectorstores import FAISS, Chroma
from langchain.schema.vectorstore import VectorStoreRetriever
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema.document import Document
from langchain_core.callbacks import AsyncCallbackManagerForRetrieverRun
from ragatouille import RAGPretrainedModel
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 *
from typing import List
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
) -> List[Document]:
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
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
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,
documents: list,
document_metadata: list,
):
if self.db_option in ["FAISS", "Chroma"]:
self.create_embedding_model()
# 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"],
),
)
elif self.db_option == "RAGatouille":
self.RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
index_path = self.RAG.index(
index_name="new_idx",
collection=documents,
document_ids=document_names,
document_metadatas=document_metadata,
)
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, documents, document_metadata = (
data_loader.get_chunks(files, urls)
)
self.logger.info("Completed loading data")
self.initialize_database(
document_chunks, document_names, documents, document_metadata
)
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
elif self.db_option == "RAGatouille":
# index is saved 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,
)
elif self.db_option == "RAGatouille":
self.vector_db = RAGPretrainedModel.from_index(
".ragatouille/colbert/indexes/new_idx"
)
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()
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