A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChain
In this tutorial, we lean hard on Together AI’s growing ecosystem to show how quickly we can turn unstructured text into a question-answering service that cites its sources. We’ll scrape a handful of live web pages, slice them into coherent chunks, and feed those chunks to the togethercomputer/m2-bert-80M-8k-retrieval embedding model. Those vectors land in a […] The post A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChain appeared first on MarkTechPost.
In this tutorial, we lean hard on Together AI’s growing ecosystem to show how quickly we can turn unstructured text into a question-answering service that cites its sources. We’ll scrape a handful of live web pages, slice them into coherent chunks, and feed those chunks to the togethercomputer/m2-bert-80M-8k-retrieval embedding model. Those vectors land in a FAISS index for millisecond similarity search, after which a lightweight ChatTogether model drafts answers that stay grounded in the retrieved passages. Because Together AI handles embeddings and chat behind a single API key, we avoid juggling multiple providers, quotas, or SDK dialects.
!pip -q install --upgrade langchain-core langchain-community langchain-together
faiss-cpu tiktoken beautifulsoup4 html2text
This quiet (-q) pip command upgrades and installs everything the Colab RAG needs. It pulls core LangChain libraries plus the Together AI integration, FAISS for vector search, token-handling with tiktoken, and lightweight HTML parsing via beautifulsoup4 and html2text, ensuring the notebook runs end-to-end without additional setup.
import os, getpass, warnings, textwrap, json
if "TOGETHER_API_KEY" not in os.environ:
os.environ["TOGETHER_API_KEY"] = getpass.getpass("
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