Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)

LangChain · Intermediate ·🔍 RAG & Vector Search ·2y ago

Key Takeaways

Open Source RAG with Nomic's New Embedding Model and ChromaDB and Ollama for long context window expansion using RoPE and self-extend methods

Original Description

The context window of OSS LLMs and embedding models has been relatively small vs proprietary models. But, methods to expand context window like RoPE and self-extend are quickly changing this. Nomic has launched a new open source, long context embedding model with 8k token context window (using RoPE), strong performance on several benchmarks (outperforms OpenAI's ada-002), and support for running locally (as well as via an API). Here, we show how to build a long context RAG app using OSS components from scratch: Nomic's new 8k context window embeddings and Mistral-instruct 32k context window (via Ollama). Cookbook - https://github.com/langchain-ai/langchain/blob/master/cookbook/nomic_embedding_rag.ipynb
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 ▶ Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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 60 LangGraph: Persistence
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This video demonstrates how to build a long context RAG app using open source components, including Nomic's new embedding model and ChromaDB and Ollama, with a focus on expanding the context window using RoPE and self-extend methods.

Key Takeaways
  1. Install required libraries and models
  2. Load Nomic's new embedding model and Mistral-instruct model
  3. Implement RAG search using ChromaDB and Ollama
  4. Expand context window using RoPE and self-extend methods
  5. Test and evaluate the RAG app
💡 The use of open source models and methods like RoPE and self-extend can significantly expand the context window of RAG search apps, improving their performance and capabilities.

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