31 lines
2.1 KiB
Markdown
31 lines
2.1 KiB
Markdown
## How does Perplexica work?
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Curious about how Perplexica works? Don't worry, we'll cover it here. Before we begin, make sure you've read about the
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architecture of Perplexica to ensure you understand what it's made up of. Haven't read it? You can read
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it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
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We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?".
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We'll break down the process into steps to make it easier to understand. The steps are as follows:
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1. The message is sent via WS to the backend server where it invokes the chain. The chain will depend on your focus
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mode. For this example, let's assume we use the "webSearch" focus mode.
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2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat
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history and the question) whether there is a need for sources and searching the web. If there is, it will generate a
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query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will
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end there, and then the answer generator chain, also known as the response generator, will be started.
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3. The query returned by the first chain is passed to SearXNG to search the web for information.
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4. After the information is retrieved, it is based on keyword-based search. We then convert the information into
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embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the
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query.
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5. After all this is done, the sources are passed to the response generator. This chain takes all the chat history, the
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query, and the sources. It generates a response that is streamed to the UI.
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### How are the answers cited?
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The LLMs are prompted to do so. We've prompted them so well that they cite the answers themselves, and using some UI
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magic, we display it to the user.
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### Image and Video Search
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Image and video searches are conducted in a similar manner. A query is always generated first, then we search the web
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for images and videos that match the query. These results are then returned to the user.
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