Uploaded October 2023 | Updated September 2026, 2 weeks ago
In this video, I'll guide you through the process of creating a Retrieval-Augmented Generation (RAG) chatbot using open-source tools and AWS services, such as LangChain, Hugging Face, FAISS, Amazon SageMaker, and Amazon TextTract.
Part 2: youtu.be/x5SYNpfK4H0 - scaling indexing and search with Amazon OpenSearch Serverless!
⭐️⭐️⭐️ 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 begin by working with PDF files in the Energy domain. Our first step involves leveraging Amazon TextTract to extract valuable information from these PDFs. Following the extraction, we break down the text into smaller, more manageable chunks. These chunks are then enriched using a Hugging Face feature extraction model before being organized and stored within a FAISS index for efficient retrieval.
To ensure a seamless workflow, we employ LangChain to orchestrate the entire process. With LangChain as our backbone, we query a Mistral Large Language Model (LLM) deployed on Amazon SageMaker. These queries include semantically relevant context retrieved from our FAISS index, enabling our chatbot to provide accurate and context-aware responses.
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/langchain/rag-demo-sagemaker-textract
- LangChain: langchain.com
- FAISS: github.com/facebookresearch/faiss
- 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
In this video, I'll guide you through the process of creating a Retrieval-Augmented Generation (RAG) chatbot using open-source tools and AWS services, such as LangChain, Hugging Face, FAISS, Amazon SageMaker, and Amazon TextTract.
Part 2: youtu.be/x5SYNpfK4H0 - scaling indexing and search with Amazon OpenSearch Serverless!
⭐️⭐️⭐️ 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 begin by working with PDF files in the Energy domain. Our first step involves leveraging Amazon TextTract to extract valuable information from these PDFs. Following the extraction, we break down the text into smaller, more manageable chunks. These chunks are then enriched using a Hugging Face feature extraction model before being organized and stored within a FAISS index for efficient retrieval.
To ensure a seamless workflow, we employ LangChain to orchestrate the entire process. With LangChain as our backbone, we query a Mistral Large Language Model (LLM) deployed on Amazon SageMaker. These queries include semantically relevant context retrieved from our FAISS index, enabling our chatbot to provide accurate and context-aware responses.
- Notebook: gitlab.com/juliensimon/huggingface-demos/-/tree/main/langchain/rag-demo-sagemaker-textract
- LangChain: langchain.com
- FAISS: github.com/facebookresearch/faiss
- 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
