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Damien Laureaux f3e918c3e3
chore(docs): fix Markdown lint issues in the docs 		2024-11-15 07:04:45 +01:00
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README.md chore(docs): fix Markdown lint issues in the docs 2024-11-15 07:04:45 +01:00
WORKING.md chore(docs): fix Markdown lint issues in the docs 2024-11-15 07:04:45 +01:00

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.

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