39 lines
1.8 KiB
Markdown
39 lines
1.8 KiB
Markdown
# Chat
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RAG (retrieval-augmented generation) chat lets you ask natural-language questions and get answers grounded in your document library. Requires Ollama — see [Ollama Setup](../getting-started/ollama-setup.md).
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## Asking a question
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1. Click **Chat** in the navigation bar
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2. Optionally select one or more documents to restrict the search scope
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3. Type your question and press Enter or click Send
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Pagepiper retrieves the most relevant page excerpts using hybrid BM25 + vector search, then passes them to the local LLM with instructions to answer using only the provided text and cite every claim with a page number.
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## Citations
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Each answer includes a citation panel showing the source pages used. Citations include:
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- Document title
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- Page number
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- A short text excerpt from that page
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If the answer says `[p.42]`, you can cross-reference the citation panel to see exactly what text the model read.
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## Multi-document chat
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Leave the document selector empty to search across your entire library. When you have many books indexed, scoping to a specific document gives more precise results.
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## Context window
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Pagepiper fetches the top 10 matching pages plus one adjacent page on each side of every hit. This ensures mid-paragraph chunk boundaries don't cut off context that the model needs to understand a passage.
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## Limitations
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- The model answers using only the retrieved excerpts. If the relevant passage was not retrieved, the model will say it cannot find an answer.
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- Chat history is kept in the browser session only. Refreshing the page clears the conversation.
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- RAG chat is gated behind a local Ollama instance. Cloud LLM backends are not currently supported on the Free tier.
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## Feedback
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Use the thumbs up / thumbs down buttons after each answer to flag good and bad responses. Feedback is stored locally in `data/pagepiper.db` for future quality review.
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