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@arizeai/phoenix-mcp

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by Arize-ai
rag.py1.51 kB
import bs4 from langchain_community.document_loaders import WebBaseLoader from langchain_core.vectorstores import InMemoryVectorStore from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter from loguru import logger vector_store: InMemoryVectorStore = None def load_web_content_to_vector_store(web_post_url): bs4_strainer = bs4.SoupStrainer(class_=("post-title", "post-header", "post-content")) loader = WebBaseLoader( web_paths=(web_post_url,), bs_kwargs={"parse_only": bs4_strainer}, ) docs = loader.load() assert len(docs) == 1 text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200, add_start_index=True ) all_splits = text_splitter.split_documents(docs) logger.info(f"Split blog post into {len(all_splits)} sub-documents.") document_ids = vector_store.add_documents(documents=all_splits) logger.info(f"Loaded {len(document_ids)} to vector store.") def initialize_vector_store(web_post_url): # "https://lilianweng.github.io/posts/2023-06-23-agent/" logger.info("Loading Vector Store.....") embeddings = OpenAIEmbeddings(model="text-embedding-3-large") global vector_store vector_store = InMemoryVectorStore(embeddings) logger.info("Initialized InMemoryVectorStore") load_web_content_to_vector_store(web_post_url) logger.info("Loaded Web Content to the vector store...") def get_vector_store(): return vector_store

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