Pigeon111111

Pigeon111111 / SeisLearner-RAG

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RAG Agent System - Spring AI + React + Multi-modal Document Parsing + Hybrid Vector Retrieval

14
0
89% credibility
Found Apr 05, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Java
AI Summary

SeisLearner is a user-friendly AI chat application that lets you upload seismic exploration documents and query them intelligently with cited sources.

How It Works

1
🔍 Discover SeisLearner

You hear about SeisLearner, a friendly chat helper that understands seismic exploration documents like PDFs and gives smart answers.

2
🚀 Open the app

Launch the simple web app on your computer to start building your personal knowledge assistant.

3
📚 Create a knowledge library

Make a new library to organize your seismic reports, papers, and notes.

4
📤 Upload your documents

Drag and drop your PDFs, slides, or images, and watch them magically turn into searchable knowledge the AI can use.

5
🤖 Build your chat assistant

Pick an AI personality, connect your library, and tweak how it thinks and responds.

6
💬 Start asking questions

Chat naturally about seismic topics, and get clear answers pulled straight from your documents with sources cited.

✅ Unlock expert insights

You now have a reliable helper that turns your documents into instant, accurate answers anytime you need them.

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AI-Generated Review

What is SeisLearner-RAG?

SeisLearner-RAG is a full-stack RAG agent system built in Java with Spring AI, letting you upload PDFs and docs for multi-modal parsing into searchable knowledge bases. It handles complex documents with tables, formulas, and OCR via an integrated API service, then powers agentic chats where the AI retrieves hybrid vectors (semantic + full-text), reasons with tools like email or DB queries, and streams responses over SSE. Users get a React frontend for creating agents, sessions, and evaluating RAG performance with a bundled metrics service—ideal for domain-specific Q&A like seismic exploration.

Why is it gaining traction?

This github rag example stands out as a rag agent system with out-of-box multi-modal document parsing, recursive hybrid retrieval, and configurable LLM agents (DeepSeek/GLM), skipping the usual LangChain/n8n boilerplate. Devs dig the local rag github docker setup, open source rag github code for rag agent openai alternatives, and RAGAs eval service for quick iteration—no Azure agent rag lock-in. At 14 stars, it's niche but hooks Java/Spring AI fans tired of Python-only rag github python projects.

Who should use this?

Geophysics engineers or domain experts building internal chatbots over technical PDFs. Backend devs prototyping rag agentic workflows with tools and hybrid search. Teams needing rag github local eval for rag agents llm without vendor deps.

Verdict

Grab it if you're in Spring AI and need a rag github project baseline—solid agent/document handling despite low maturity (14 stars, basic docs). Credibility score of 0.9% flags early risks, but fork and extend for custom rag agent ai.

(198 words)

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