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Jack Ye
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The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
18 lines
3.7 KiB
Plaintext
18 lines
3.7 KiB
Plaintext
---
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title: "RAG and Agents"
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sidebarTitle: "Notebooks"
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description: "Explore a variety of RAG (Retrieval-Augmented Generation) and agent applications built with LanceDB."
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---
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The table below lists example notebooks we've prepared for a variety of RAG (Retrieval-Augmented Generation)
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and agent applications built with LanceDB.
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| Project | Description |
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|:----------|:------------|
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| **Contextual RAG** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-RAG/Anthropic_Contextual_RAG.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Contextual-RAG) | Improves retrieval by combatting the "lost in the middle" problem. This technique uses an LLM to generate succinct context for each document chunk, then prepends that context to the chunk before embedding, leading to more accurate retrieval. |
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| **NVIDIA RAG Blueprint with LanceDB** <br /> [Read the tutorial](/tutorials/agents/nvidia-rag-blueprint/)<br />[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/nvidia-rag-blueprint-lancedb) | Shows how to use LanceDB as the retrieval layer for NVIDIA RAG Blueprint with a Docker-first, retrieval-only integration path that includes hybrid search and pluggable rerankers. |
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| **Matryoshka Embeddings** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/main.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex) | Demonstrates a RAG pipeline using Matryoshka Embeddings with LanceDB and LlamaIndex. This method allows for efficient storage and retrieval of nested, variable-sized embeddings. |
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| **HyDE (Hypothetical Document Embeddings)** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Advance-RAG-with-HyDE) | An advanced RAG technique that uses an LLM to generate a "hypothetical" document in response to a query. This hypothetical document is then used to retrieve actual, similar documents, improving relevance. |
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| **Late Chunking** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advanced_RAG_Late_Chunking/Late_Chunking_(Chunked_Pooling).ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Advanced_RAG_Late_Chunking) | An advanced RAG method where documents are retrieved first, and then chunking is performed on the retrieved documents just before synthesis. This helps maintain context that might be lost with pre-chunking. |
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| **Agentic RAG** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>[View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Agentic_RAG) | This tutorial demonstrates how to build a RAG system where multiple AI agents collaborate to retrieve information and generate answers, leading to more robust and intelligent applications. |
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