Uploaded November 2023 | Updated September 2026, 2 weeks ago
We'll walk you through the creation of a Retrieval-Augmented Generation (RAG) chatbot using open-source tools and AWS services like LangChain, Hugging Face, Amazon SageMaker, and Amazon OpenSearch Serverless.
Part 1: youtu.be/7kDaMz3Xnkw - LangChain, Hugging Face, FAISS, Amazon SageMaker, and Amazon TextTract.
⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos. Follow me on Medium at julsimon.medium.com or Substack at https://julsimon.substack.com. ⭐️⭐️⭐️
We start by deploying Mistral 7B, a cutting-edge open-source LLM, onto a SageMaker endpoint. Following this, we work with the Reuters dataset, a Hugging Face dataset comprising 20,000 news articles. We break down these articles into smaller sections and apply bge-small, a compact open-source embedding model, to them.
Next, we proceed to index these sections into an Amazon OpenSearch Serverless vector index, which we then query through LangChain.
Additionally, aside from the RAG demonstration, we delve into some vital yet often overlooked steps related to authentication and security for OpenSearch Serverless.
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/langchain/rag-demo-sagemaker-opensearch
- LangChain: langchain.com
- Amazon OpenSearch Serverless: docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless.html
- Embedding leaderboard: huggingface.co/spaces/mteb/leaderboard
- Embedding model: huggingface.co/BAAI/bge-small-en-v1.5
- LLM: huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
We'll walk you through the creation of a Retrieval-Augmented Generation (RAG) chatbot using open-source tools and AWS services like LangChain, Hugging Face, Amazon SageMaker, and Amazon OpenSearch Serverless.
Part 1: youtu.be/7kDaMz3Xnkw - LangChain, Hugging Face, FAISS, Amazon SageMaker, and Amazon TextTract.
⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos. Follow me on Medium at julsimon.medium.com or Substack at https://julsimon.substack.com. ⭐️⭐️⭐️
We start by deploying Mistral 7B, a cutting-edge open-source LLM, onto a SageMaker endpoint. Following this, we work with the Reuters dataset, a Hugging Face dataset comprising 20,000 news articles. We break down these articles into smaller sections and apply bge-small, a compact open-source embedding model, to them.
Next, we proceed to index these sections into an Amazon OpenSearch Serverless vector index, which we then query through LangChain.
Additionally, aside from the RAG demonstration, we delve into some vital yet often overlooked steps related to authentication and security for OpenSearch Serverless.
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/langchain/rag-demo-sagemaker-opensearch
- LangChain: langchain.com
- Amazon OpenSearch Serverless: docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless.html
- Embedding leaderboard: huggingface.co/spaces/mteb/leaderboard
- Embedding model: huggingface.co/BAAI/bge-small-en-v1.5
- LLM: huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
