mirror of
https://github.com/lancedb/lancedb.git
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Brendan Clement
198812fd53
The existing docs have 254 findings from our shared Vale rules. Contributors would otherwise inherit these when editing the same lines. Clean up the prose across 80 pages: correct product names and a repeated word, simplify a few phrases, and replace dashes with punctuation that fits each sentence. The pinned Vale scan now reports zero findings, with no suppressions.
25 lines
993 B
Plaintext
25 lines
993 B
Plaintext
---
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title: "Contextual RAG"
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sidebarTitle: "Contextual RAG"
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description: "Prepend LLM-generated context to each chunk before embedding it."
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---
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Addresses the "lost in the middle" problem in retrieval. An LLM generates a
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succinct piece of context for each document chunk, which is prepended to the
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chunk before embedding, so a chunk carries enough surrounding meaning to be
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retrieved for the right queries.
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<Note>
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This example is a runnable notebook in the
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[vectordb-recipes](https://github.com/lancedb/vectordb-recipes) repository rather than a page here.
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</Note>
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<CardGroup cols={2}>
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<Card title="Open in Colab" icon="google" href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-RAG/Anthropic_Contextual_RAG.ipynb">
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Run it end to end in the browser.
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</Card>
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<Card title="View on GitHub" icon="github" href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Contextual-RAG">
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Read the source.
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</Card>
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</CardGroup>
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