ibm-self-serve-assets/Blended-RAG
📦 Open Source Projectibm-self-serve-assets
A hybrid RAG framework combining semantic search and query-based retrieval to boost LLM accuracy.
Blended-RAG addresses the inherent challenges of standard RAG architectures, which often struggle with nuanced queries or domain-specific terminology. This project implements a 'blended' approach that leverages the strengths of vector-based semantic search alongside traditional keyword-based or hybrid query retrieval methods. By combining these methodologies, the system ensures a more comprehensive retrieval process, capturing both the intent and the specific lexical requirements of a user's prompt.
The repository includes modular Jupyter Notebooks that walk users through the implementation of hybrid retrieval pipelines. Key features include techniques for reranking retrieved documents, optimizing query expansion, and balancing semantic similarity scores with keyword density. This approach is particularly effective for enterprise environments where precision is critical and data is often unstructured or technical in nature. Developers can adapt these notebooks to integrate with various vector databases and LLM providers, making it a versatile toolkit for building robust, production-ready RAG applications.
💡Highlights
- ├─Hybrid semantic & keyword retrieval
- ├─Optimized RAG accuracy pipelines
- └─Modular Jupyter Notebook implementation
🎯For
- ├─AI Engineers
- ├─Data Scientists
- └─RAG Developers