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jerry-ai-dev/MODULAR-RAG-MCP-SERVER

A modular RAG (Retrieval-Augmented Generation) system with MCP Server architecture. Using Skill to make AI follow each step of the spec and complete the code 100% by AI. observed · 2026-08-28

github.com/jerry-ai-dev/MODULAR-RAG-MCP-SERVER · Python observed · 2026-08-28

Health v2 · maintenance only

47/100

  • Activity 71
  • Release rhythm 35
  • Longevity 16

Flags: no_releases no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 225
  • days_rel: n/a
  • days_push: 176
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1109 stars · 253 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A modular, pluggable RAG (Retrieval-Augmented Generation) framework exposed as an MCP (Model Context Protocol) server, so AI assistants like Copilot and Claude Desktop can query a knowledge base directly. It includes a full ingestion pipeline (PDF to embeddings), hybrid search with reranking, multimodal image captioning, Ragas-based evaluation, a Streamlit dashboard, and doubles as a hands-on learning project for LLM engineering job seekers.

Use cases

  • expose a document knowledge base to Claude Desktop or Copilot via MCP
  • build a RAG pipeline over PDFs with hybrid BM25 and dense vector search
  • add reranking with cross-encoders or LLM rerankers to retrieval
  • make images searchable by captioning them with a vision LLM
  • evaluate RAG quality with Ragas and golden test sets
  • trace ingestion and query pipelines end to end
  • learn RAG engineering for LLM job interviews
  • swap LLM, embedding, reranker, or vector store backends via config

When to choose

  • you want a working MCP-accessible RAG server with pluggable components
  • you need hybrid search plus reranking out of the box
  • you want a complete, observable RAG reference implementation to study or extend
  • you are preparing for LLM/RAG engineering interviews and want a portfolio project

When to avoid

  • you need a production-hardened, licensed product (no license is provided)
  • you want a simple drop-in library rather than a full server with dashboard and pipelines
  • you need non-Python runtimes or managed cloud deployment
  • you require guaranteed long-term maintenance or enterprise support

Facets

service · maturity active

rag search-engine mcp llm-inference pdf data-visualization monitoring machine-learning large-language-models artificial-intelligence developer-tools tutorials python self-hosted mcp-server hybrid-search rerank bm25 vector-search image-captioning ragas-evaluation streamlit-dashboard skill-driven-development interview-preparation pluggable-architecture retrieval-augmented-generation natural-language-processing search docker web-server

1 source

Member repositories

RepositoryRoleHealth v2
jerry-ai-dev/MODULAR-RAG-MCP-SERVERmain47

For agents

markdown · JSON · MCP: product_card(name="jerry-ai-dev/MODULAR-RAG-MCP-SERVER")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem