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Sylwia Miksztal

sysia48

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upvoted a changelog about 1 hour ago
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replied to hassenhamdi's post about 9 hours ago
New release of HyDRA v0.2 is here! 🐍 HyDRA: Hybrid Dynamic RAG Agent. For addressing the limitations of simple, static RAG. HyDRA is the answer. It's an advanced, unified framework for agentic RAG, inspired by the latest research to create something truly powerful. 🧠 Moving beyond single-shot retrieval. HyDRA introduces a multi-turn, reflection-based system with coordinated agents: a Planner, Coordinator, and Executors (currently local & deep web search). πŸ”¬ At its core is an advanced 3-stage local retrieval pipeline that leaves basic RAG in the dust: πŸ₯‡ 1. Hybrid Search: Combines dense (semantic) and sparse (textual) embeddings in one go using the bge-m3 model. This alone is a massive upgrade. πŸ₯ˆ 2. RRF (Reciprocal Rank Fusion): Intelligently merges and reranks results from different search vectors for ultimate precision. πŸ₯‰ 3. Advanced Reranking: Uses the bge-m3-reranker model to score and surface the absolute most relevant documents for any query. ⚑️ This isn't just powerful, it's blazing fast. We're using SOTA ANN (HNSW) with vector and index quantization (down to 1-bit!) for near-instant retrieval with minimal quality loss. πŸ€– HyDRA is more than just retrieval. It incorporates memory from experience and reflection, creating a guiding policy for smarter future interactions and strategic planning. The result? A local retrieval system that significantly outperforms standard vector search RAG. 🌐 For deep web searches, HyDRA leverages the asynDDGS library and mcp (Model Context Protocol) for free, unrestricted web access. The entire reasoning engine is powered by the incredibly fast and efficient Google Gemini 2.5 Flash! πŸ‘¨β€πŸ’» Explore the project, dive into the code, and see it in action: πŸ”— GitHub: https://github.com/hassenhamdi/HyDRA (leave a star if you like the project) 🀝 Looking to implement cutting-edge AI solutions or collaborate? Let's connect! LinkedIn: linkedin.com/in/hassenhamdi Email: hassenhamdi12@gmail.com Discord: hassenhamdi
commented on a paper about 11 hours ago
HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding
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