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import os
import logging
import faiss
from langchain.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def create_faiss_index():
documents = []
docs_dir = "docs"
if not os.path.exists(docs_dir):
logger.error(f"The directory '{docs_dir}' does not exist.")
return
for root, dirs, files in os.walk(docs_dir):
for file in files:
if file.endswith(".pdf"):
file_path = os.path.join(root, file)
logger.info(f"Loading document: {file_path}")
try:
loader = PDFMinerLoader(file_path)
loaded_docs = loader.load()
if loaded_docs:
logger.info(f"Loaded {len(loaded_docs)} documents from {file_path}")
documents.extend(loaded_docs)
else:
logger.warning(f"No documents loaded from {file_path}")
except Exception as e:
logger.error(f"Error loading {file_path}: {e}")
if not documents:
logger.error("No documents were loaded. Check the 'docs' directory and file paths.")
return
logger.info(f"Loaded {len(documents)} documents.")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
logger.info(f"Created {len(texts)} text chunks.")
if not texts:
logger.error("No text chunks created. Check the text splitting process.")
return
try:
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
logger.info("Embeddings initialized successfully.")
except Exception as e:
logger.error(f"Failed to initialize embeddings: {e}")
return
try:
faiss_index = FAISS.from_documents(texts, embeddings)
faiss_index.save_local("faiss_index")
logger.info(f"Created FAISS index with {len(texts)} vectors.")
except Exception as e:
logger.error(f"Failed to create FAISS index: {e}")
if __name__ == "__main__":
create_faiss_index() |