mzarnecki/php-rag

This application uses LLMs like DeepSeek, GPT-5, Claude, Gemini or Llama, Mixtral (locally) in order to generate text based on the user input. The user input is used to retrieve relevant information from the database and then the retrieved information is used to generate the text. This approach combines power of LLMs and access to source documen

39
/ 100
Emerging

This tool helps you get precise answers to your questions by combining the power of advanced AI models with your specific documents. You provide a question, and it gives you a detailed, context-aware answer based on its database of information. It's designed for anyone who needs accurate, contextually relevant information from a large text dataset, like a researcher or an information analyst.

Use this if you need to quickly retrieve accurate, context-specific answers from a large collection of documents, especially when dealing with nuanced topics or named entities that might be ambiguous.

Not ideal if you're looking for a simple chatbot for general knowledge or casual conversation, as its strength lies in leveraging a specific document database for factual retrieval.

information-retrieval knowledge-management semantic-search contextual-qa document-analysis
No Package No Dependents
Maintenance 6 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 9 / 25

How are scores calculated?

Stars

62

Forks

5

Language

PHP

License

MIT

Last pushed

Jan 04, 2026

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/rag/mzarnecki/php-rag"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.