NovaSearch-Team/RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
This tool helps improve how well your AI applications find and rank relevant information from large document collections. It takes existing search models (like embedding, ColBERT, or reranker models) and fine-tunes them to be more accurate for your specific content. The output is a more efficient and precise search component for applications like chatbots or document retrieval systems. This is ideal for AI product managers or data scientists building advanced search and question-answering systems.
1,103 stars. No commits in the last 6 months.
Use this if you need to significantly improve the accuracy and relevance of search results within your RAG (Retrieval Augmented Generation) applications by training custom retrieval models.
Not ideal if you are looking for a pre-built, ready-to-use search engine or a simple API to query documents without needing to fine-tune underlying models.
Stars
1,103
Forks
90
Language
Python
License
MIT
Category
Last pushed
Jul 05, 2025
Commits (30d)
0
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