RAG & Knowledge
Local_Pdf_Chat_RAG
Transparent Python RAG reference with FAISS + BM25 hybrid retrieval.
★ 957 starsMIT
View guide →Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
ragent is an open-source rag & knowledge project by nageoffer (nageoffer/ragent on GitHub). It has 4,208 GitHub stars and 848 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 7 October 2026.
Read the overview of the seven Maven modules and the infrastructure you will need (database, Redis, vector store).
Get the backend and console source from GitHub.
Build and run the platform using the step-by-step guide on the docs site, configuring your LLM providers.
Use the admin console to ingest documents into a knowledge base and run your first cited query.
Vector, keyword, knowledge-graph, and web search run together, then dedup, RRF fusion, and reranking produce the final context.
Query mapping, rewriting, and splitting, plus a tree-shaped intent recognizer that routes across multiple knowledge bases.
Multiple model tiers with first-token probing and circuit-breaker fallback when a provider degrades.
AgentScope-based discovery and calling of MCP tools as part of answering.
Redis fair queueing and distributed concurrency limits keep shared deployments stable under load.
A React admin console, answer sourcing/citations, feedback capture, and Langfuse tracing.
Clone the project and enter its directory.
git clone https://github.com/nageoffer/ragent.git
cd ragentEnvironment setup, configuration, and run commands are maintained on the project's documentation site.
# see the quick-start guide at nageoffer.com/ragent for the exact commandsStart the backend and console per the guide, then sign in to ingest documents and test queries.
# see the quick-start guide for service URLs and default credentialsDeveloper nageoffer maintains Ragent and its documentation site.
Yes, Apache-2.0 licensed; you run it on your own infrastructure and supply LLM provider keys.
It needs LLM (and typically embedding) provider access configured per the quick-start guide; costs depend on the providers you choose.
Those are broad low-code platforms; Ragent is a code-first Java/Spring platform focused on retrieval quality, model resilience, and production traffic controls that Java teams can extend directly.
Clone the repo and follow the quick-start guide at nageoffer.com/ragent to configure providers, start the services, and ingest your first knowledge base.
Transparent Python RAG reference with FAISS + BM25 hybrid retrieval.
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