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Perplexica/docs/architecture/README.md
Willie Zutz d66300e78e feat(agent): Refactor search agents and implement SpeedSearchAgent 
- Updated FileSearchAgent to improve code readability and formatting.
- Refactored SynthesizerAgent for better prompt handling and document processing.
- Enhanced TaskManagerAgent with clearer file context handling.
- Modified AgentSearch to maintain consistent parameter formatting.
- Introduced SpeedSearchAgent for optimized search functionality.
- Updated metaSearchAgent to support new SpeedSearchAgent.
- Improved file processing utilities for better document handling.
- Added test attachments for sporting events queries.
2025-06-28 17:59:12 -06:00

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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 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 prioritizes quick responses over accuracy

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