Perplexica/docs/architecture/README.md

Perplexica's Architecture

Perplexica's architecture consists of the following key components:

1. User Interface: A web-based interface that allows users to interact with Perplexica for searching images, videos, and much more.
2. Agent/Chains: These components predict Perplexica's next actions, understand user queries, and decide whether a web search is necessary.
3. SearXNG: A metadata search engine used by Perplexica to search the web for sources.
4. LLMs (Large Language Models): Utilized by agents and chains for tasks like understanding content, writing responses, and citing sources. Examples include Claude, GPTs, etc.
5. Embedding Models: To improve the accuracy of search results, embedding models re-rank the results using similarity search algorithms such as cosine similarity and dot product distance.
6. Web Content
   • In Agent mode, the application uses an agentic workflow to answer complex multi-part questions
     • The agent can use reasoning steps to provide comprehensive answers to complex questions
     • Agent mode is experimental and may consume lots of tokens and take a long time to produce responses
   • In Balanced mode, the application retrieves web content using Playwright and Mozilla Readability to extract relevant segments of web content
     • Because it only uses segments of web content, it can be less accurate than Agent mode
   • In Speed mode, the application only uses the preview content returned by SearXNG
     • This content is provided by the search engines and contains minimal context from the actual web page
     • This mode is the least accurate and is often prone to hallucination

For a more detailed explanation of how these components work together, see WORKING.md.