rag-chatbot-3
/
ingest.py
23 строки · 879.0 Байт
1from langchain_community.embeddings import HuggingFaceEmbeddings
2from langchain_community.vectorstores import FAISS
3from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
4from langchain.text_splitter import RecursiveCharacterTextSplitter
5
6DATA_PATH = 'data/'
7DB_FAISS_PATH = 'vectorstore/db_faiss'
8
9# Create vector database
10def create_vector_db():
11loader = DirectoryLoader(DATA_PATH, glob='*.pdf', loader_cls=PyPDFLoader)
12
13documents = loader.load()
14text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
15texts = text_splitter.split_documents(documents)
16
17embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
18
19db = FAISS.from_documents(texts, embeddings)
20db.save_local(DB_FAISS_PATH)
21
22if __name__ == "__main__":
23create_vector_db()
24
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