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73 lines
2.1 KiB
Markdown
73 lines
2.1 KiB
Markdown
# How Perplexica Works
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This is a high level overview of how Perplexica answers a question.
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If you want a component level overview, see [README.md](README.md).
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If you want implementation details, see [CONTRIBUTING.md](../../CONTRIBUTING.md).
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## What happens when you ask a question
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When you send a message in the UI, the app calls `POST /api/chat`.
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At a high level, we do three things:
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1. Classify the question and decide what to do next.
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2. Run research and widgets in parallel.
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3. Write the final answer and include citations.
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## Classification
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Before searching or answering, we run a classification step.
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This step decides things like:
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- Whether we should do research for this question
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- Whether we should show any widgets
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- How to rewrite the question into a clearer standalone form
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## Widgets
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Widgets are small, structured helpers that can run alongside research.
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Examples include weather, stocks, and simple calculations.
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If a widget is relevant, we show it in the UI while the answer is still being generated.
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Widgets are helpful context for the answer, but they are not part of what the model should cite.
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## Research
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If research is needed, we gather information in the background while widgets can run.
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Depending on configuration, research may include web lookup and searching user uploaded files.
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## Answer generation
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Once we have enough context, the chat model generates the final response.
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You can control the tradeoff between speed and quality using `optimizationMode`:
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- `speed`
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- `balanced`
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- `quality`
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## How citations work
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We prompt the model to cite the references it used. The UI then renders those citations alongside the supporting links.
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## Search API
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If you are integrating Perplexica into another product, you can call `POST /api/search`.
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It returns:
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- `message`: the generated answer
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- `sources`: supporting references used for the answer
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You can also enable streaming by setting `stream: true`.
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## Image and video search
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Image and video search use separate endpoints (`POST /api/images` and `POST /api/videos`). We generate a focused query using the chat model, then fetch matching results from a search backend.
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