agents/live_api/app/vector_store.py (30 lines of code) (raw):

# Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from langchain.schema import Document from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import WebBaseLoader from langchain_community.vectorstores import SKLearnVectorStore from langchain_core.embeddings import Embeddings PERSIST_PATH = ".persist_vector_store" def load_and_split_documents(urls: list[str]) -> list[Document]: """Load and split documents from a list of URLs.""" docs = [WebBaseLoader(url).load() for url in urls] docs_list = [item for sublist in docs for item in sublist] logging.info(f"# of documents loaded (pre-chunking) = {len(docs_list)}") text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=50) doc_splits = text_splitter.split_documents(docs_list) logging.info(f"# of documents after split = {len(doc_splits)}") return doc_splits def get_vector_store( embedding: Embeddings, urls: list[str], persist_path: str = PERSIST_PATH ) -> SKLearnVectorStore: """Get or create a vector store.""" if os.path.exists(persist_path): vector_store = SKLearnVectorStore( embedding=embedding, persist_path=persist_path ) else: doc_splits = load_and_split_documents(urls=urls) vector_store = SKLearnVectorStore.from_documents( documents=doc_splits, embedding=embedding, persist_path=persist_path ) vector_store.persist() return vector_store