sri-mathi/Modular-RAG-System
Advanced Modular RAG Pipeline featuring GraphRAG, LLM-based Re-ranking, and Multi-Query Expansion. Optimized with Groq & Ollama. Includes automated RAGAS evaluation and interactive Knowledge Graph visualizations. Engineered for high precision and production-level performance.
SUMMARY AI summary by gpt-5-mini
Modular RAG System is an expert-grade pipeline to convert static documents into a searchable, structurally-aware knowledge base. Intended for ML engineers, RAG developers, and teams building document-QA or retrieval-augmented apps, it combines semantic vector search with graph-based relationship mapping so agents can retrieve facts and reason about entity connections. Key features: - Hybrid retrieval (GraphRAG): ChromaDB embeddings + NetworkX graph traversal. - Provider abstraction (BaseEmbedder/BaseLLM) for pluggable models (Ollama embeddings, Groq LLM). - LLM re-ranking (Groq cross-encoder) to trim candidates. - Multi-query expansion for technical queries. - Visualization (PCA embedding clusters, interactive knowledge graphs). - Automated evaluation (RAGAS) measuring faithfulness and relevance. - Tools to ingest PDFs, build graphs, query, rerank, and visualize.
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Total AI relevance score: 75 / 100
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We track 19,182 Python repos, of which 14,957 score 40 or above on AI relevance. 3,374 have more stars than this one (top 17.6%), and 897 score higher (top 4.7%).
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Dates
| Created on GitHub | 2026-05-14 |
| Last push | 2026-05-15 |
| First seen here | 2026-05-14 |
| Last fetched | 2026-09-28 16:27 |
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