Hybrid RAG System Developed with FAISS, BM25 and Agentic AI
agents rag vector-db
| Source: Mastodon | Original article
An AI engineer built a hybrid Retrieval‑Augmented Generation system that merges FAISS vector search with BM25 keyword search to retrieve knowledge‑base information.
A recent AI‑engineering project demonstrates a hands‑on approach to Retrieval‑Augmented Generation by stitching together dense vector search, classic keyword matching and an agentic orchestration layer. The author built a hybrid RAG pipeline that runs FAISS for embedding‑based retrieval alongside BM25 for term‑frequency search, then merges the two result lists with Reciprocal Rank Fusion. LangGraph (formerly LangChain) coordinates the workflow, while lightweight SmolAgents handle knowledge grounding and score normalization. The design also incorporates a cross‑encoder reranker, allowing the combined list to be refined before feeding the top passages to a language model for generation.
The effort matters because hybrid search addresses a known limitation of pure vector retrieval: it can miss exact terminology, identifiers or rare technical phrases that BM25 captures reliably. By fusing sparse and dense signals, the system delivers more comprehensive relevance, which is crucial for applications that require precise grounding—such as technical support bots, research assistants or domain‑specific question answering. Moreover, embedding the retrieval steps within an agentic loop showcases how autonomous components can manage retrieval, reranking and prompt construction without manual intervention, echoing the broader push toward modular AI agents.
Looking ahead, the community will be watching for benchmarks that compare this hybrid‑agentic stack against single‑mode baselines, as well as for open‑source extensions that automate the fusion and reranking stages. Integration with larger foundation models and deployment on scalable cloud services could turn the prototype into a production‑ready service. Continued experimentation with SmolAgents and LangGraph is likely to spawn more plug‑and‑play RAG kits, accelerating the adoption of agentic retrieval pipelines across Nordic enterprises and research labs.
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