Chat¶
RAG (retrieval-augmented generation) chat lets you ask natural-language questions and get answers grounded in your document library. Requires Ollama — see Ollama Setup.
Asking a question¶
- Click Chat in the navigation bar
- Optionally select one or more documents to restrict the search scope
- Type your question and press Enter or click Send
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.
Citations¶
Each answer includes a citation panel showing the source pages used. Citations include:
- Document title
- Page number
- A short text excerpt from that page
If the answer says [p.42], you can cross-reference the citation panel to see exactly what text the model read.
Multi-document chat¶
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.
Context window¶
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.
Limitations¶
- The model answers using only the retrieved excerpts. If the relevant passage was not retrieved, the model will say it cannot find an answer.
- Chat history is kept in the browser session only. Refreshing the page clears the conversation.
- RAG chat is gated behind a local Ollama instance. Cloud LLM backends are not currently supported on the Free tier.
Feedback¶
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.