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| from langchain.document_loaders import ReadTheDocsLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.vectorstores import Qdrant | |
| # from qdrant_client import QdrantClient | |
| from config import get_db_config | |
| CHUNK_SIZE = 500 | |
| def get_documents(path: str): | |
| loader = ReadTheDocsLoader(path, encoding="utf-8") | |
| docs = loader.load() | |
| return docs | |
| def get_text_chunk(docs): | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=0) | |
| texts = text_splitter.split_documents(docs) | |
| return texts | |
| def store(texts): | |
| embeddings = OpenAIEmbeddings() | |
| db_url, db_api_key, db_collection_name = get_db_config() | |
| # client = QdrantClient(url=db_url, api_key=db_api_key, prefer_grpc=True) | |
| _ = Qdrant.from_documents( | |
| texts, | |
| embeddings, | |
| url=db_url, | |
| api_key=db_api_key, | |
| collection_name=db_collection_name | |
| ) | |
| def main(path: str): | |
| docs = get_documents(path) | |
| texts = get_text_chunk(docs) | |
| store(texts) | |
| if __name__ == "__main__": | |
| """ | |
| $ python store.py "data/rtdocs/nvdajp-book.readthedocs.io/ja/latest" | |
| """ | |
| import sys | |
| args = sys.argv | |
| if len(args) != 2: | |
| print("No args, you need two args for html_path") | |
| else: | |
| path = args[1] | |
| # dir_name = args[2] | |
| main(path) | |