GraphRAG MCP

🧠 GraphRAG MCP

Entity-centric Retrieval-Augmented Generation for Crypto Whitepapers _Local-first • Private • FastMCP-ready_

!GraphRAG MCP – Eeva AI Cyberpunk Header

<!-- Badges --> !Python !Ollama !ChromaDB !GraphDB !FastMCP !LangChain !Privacy

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1️⃣ ✨ Overview

GraphRAG MCP is a modular, local-first system that turns crypto whitepapers into an entity-centric Knowledge Graph and a vector-searchable corpus, then answers questions with RAG + optional KG enrichment + LLM synthesis — all via standardized FastMCP tools.

Why this project?

• 🛡️ Privacy by default: runs entirely on your machine (Ollama, Chroma, GraphDB).
• ⚡ Fast & focused: entity-filtered retrieval narrows context to the right tokens/protocols.
• 🧩 Composable: exposes rag. and kg. tools so an MCP Coordinator or Streamlit app can orchestrate multi-tool workflows.
• 🧠 Explainable answers: returns citations with doc/chunk/entity IDs for every response.

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🔁 Typical usage

1. Ingest and label whitepapers → build embeddings and insert entities.
2. Ask questions via rag.qa (semantic + entity-filtered retrieval), optionally enrich with KG labels/aliases.
3. Get concise LLM answers with inline citations to source chunks.

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2️⃣ Features

🧩 Knowledge Graph (KG)

• Entity-only architecture using RDF/OWL ontologies (mcp-core.ttl, mcp-crypto.ttl).
• Built on Ontotext GraphDB 11+ with SHACL validation and SPARQL/GraphQL endpoints.
• Stores canonical entities such as tokens, protocols, components, and organizations.
• Enables KG enrichment for RAG answers via aliases, labels, and relationships.

🔍 Vector Retrieval (RAG)

• ChromaDB acts as the persistent vector store for chunk embeddings.
• Embeddings generated using Ollama’s nomic-embed-text model.
• Supports semantic and entity-filtered retrieval modes for accurate context fetching.
• Each chunk contains structured metadata: doc_id, chunk_id, entity_ids, section_type, and page.

🧠 Local LLM Inference

• Uses Ollama for fully local inference — no external API keys required.
• Compatible with models like llama3.1:latest, qwen2.5:14b-instruct, or mistral.
• Performs labeling, summarization, and final QA synthesis.
• Includes deterministic mock mode for offline testing and CI.

⚙️ FastMCP Servers

• Two modular servers expose tools via FastMCP 2.x:
• rag → rag.search, rag.embed_and_index, rag.reindex, rag.delete, rag.health, rag.qa
• kg → sparql_query, sparql_update, push_labels, validate_labels, list_documents, kg.health
• Both run locally via stdio and are MCP-Coordinator compatible.

🔒 Privacy & Portability

• 100% offline operation — suitable for air-gapped or research environments.
• Reproducible local stack (GraphDB + Chroma + Ollama + FastMCP).
• Works seamlessly on Windows 11, macOS, or Linux.

🚀 Integration Ready

• Plug-and-play with MCP Coordinators or Streamlit apps for end-user Q&A.
• Can interoperate with other MCPs such as:
• Brave API MCP (web search)
• MongoDB MCP (strategy data)
• Telegram MCP (messaging)
• Gmail MCP (email retrieval)
• Returns clean JSON outputs for easy chaining into agentic workflows.

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3️⃣ 🏗️ Architecture

The GraphRAG MCP architecture combines Knowledge Graph reasoning, Vector-based retrieval, and Local LLM synthesis — all under the MCP interoperability standard. It’s designed for _clarity_, _privacy_, and _modular scalability_.

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🧭 High-Level Overview

| Layer | Technology | Purpose | Example Components | |:------|:------------|:---------|:--------------------| | 🗂 Ingestion Layer | Python + LangChain | Reads PDFs, splits into semantic chunks, labels with LLMs | pdf_reader.py, semantic_splitter.py, llm_chunk_tagger.py | | 🧩 Knowledge Graph Layer (KG) | GraphDB (Ontotext) + RDFLib | Stores canonical entities (tokens, protocols, organizations) | graphdb_sink.py, namespaces.py, SHACL shapes | | 💾 Vector Retrieval Layer (RAG) | ChromaDB + Ollama embeddings | Stores text chunks + metadata + embeddings for semantic retrieval | chroma_store.py, .chroma/ | | ⚙️ MCP Layer | FastMCP 2.x | Exposes standardized MCP tools (rag., kg.) | rag_server.py, kg_server.py | | 🧠 LLM Synthesis Layer | Ollama LLMs (llama3.1, qwen2.5) | Answers questions with retrieved context + KG enrichment | rag.qa, llm_chunk_tagger | | 💬 User Interface Layer | MCP Coordinator / Streamlit | Connects multiple MCPs for conversational Q&A | Coordinator UI or custom Streamlit dashboard |

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🔹 Data Flow Diagram

            ┌────────────────────────────────────────┐
            │              Whitepapers               │
            │ (PDFs, research papers, documentation) │
            └───────────────────┬────────────────────┘
                                │
                                ▼
            ┌─────────────────────────────────────────────┐
            │      📄 Ingestion & Labeling                │
            │  pdf_reader → semantic_splitter →           │
            │  llm_chunk_tagger → postprocess             │
            └───────────────────┬─────────────────────────┘
                                 │
                        ┌────────┴────────┐
                        │                 │
                        ▼                 ▼
            ┌────────────────┐    ┌──────────────────────┐
            │ 🧠 GraphDB KG  │    │ 💾 Chroma RAG      │
            │ Entities & IRIs │   │ Chunks + Embeddings │
            └────────┬────────┘   └────────┬────────────┘
                     │                     │
                     ▼                     ▼
               ┌───────────────┐      ┌───────────────┐
               │ ⚙️ kg_server  │     │ ⚙️ rag_server │
               │ (FastMCP)     │      │ (FastMCP)     │
               └────────┬──────┘      └──────┬────────┘
                        │                    │
                        └────────┬───────────┘
                                 ▼
                  ┌────────────────────────────────┐
                  │ 💬 MCP Coordinator / Streamlit │
                  │  User-facing Q&A Interface     │
                  └────────────────────────────────┘

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🧠 How It Works (Step-by-Step)

| Step | Description | Input | Output | |:----:|:-------------|:------|:--------| | 1️⃣ | PDF Parsing | Whitepaper PDF | Raw text pages | | 2️⃣ | Semantic Splitting | Raw text | Meaningful chunks (by section/topic) | | 3️⃣ | LLM Labeling | Chunk text | Entities, relations, and section labels | | 4️⃣ | Postprocessing | Labeled chunks | Cleaned JSONL with canonical entity IRIs | | 5️⃣ | Indexing | JSONL labels | Chroma embeddings + KG triples | | 6️⃣ | Retrieval (rag.search) | Query text / entities | Relevant chunks | | 7️⃣ | Enrichment (optional) | Retrieved entities | KG aliases, definitions | | 8️⃣ | Answer Synthesis (rag.qa) | Question + context | Concise answer with citations |

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🌐 Data Modalities

| Data Type | Storage | Example | |:-----------|:--------|:--------| | 🧱 Entity | GraphDB | <https://kg.mcp.ai/id/token/bitcoin> → rdf:type crypto:Token | | 📜 Chunk | Chroma | “Bitcoin is a peer-to-peer electronic cash system…” | | 🧩 Embedding | Chroma / Ollama | 768-dim nomic-embed-text vector | | 🧮 Provenance | Metadata | doc_id, chunk_id, page, entity_ids[] | | 💬 Answer | MCP JSON | { "answer": "...", "citations": [...] } |

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🧱 Core MCP Tools

| Server | Tool | Description | |:--------|:------|:-------------| | 🧩 RAG | rag.search | Semantic search over chunks | | | rag.embed_and_index | Add new labeled chunks to index | | | rag.reindex | Rebuild from outputs directory | | | rag.delete | Delete by IDs or filters | | | rag.qa | Question answering with LLM synthesis | | | rag.health | Diagnostics and store info | | 🧠 KG | sparql_query / sparql_update | Execute SPARQL against GraphDB | | | push_labels / validate_labels | Add or validate KG entries | | | list_documents, get_chunk | Retrieve document metadata | | | kg.health | Check GraphDB repository status |

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4️⃣ ⚙️ Installation & Setup

Set up your local GraphRAG MCP environment in just a few steps! This stack runs fully offline and integrates seamlessly with Ollama, GraphDB, and Chroma.

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🧾 Prerequisites

| Requirement | Description | Example | |:-------------|:-------------|:----------| | 🐍 Python | Version 3.11+ recommended | python --version → Python 3.11.8 | | 🧠 Ollama | Local LLM runtime (for inference + embeddings) | ollama pull llama3.1:latest | | 🧩 GraphDB Desktop 11+ | Local Knowledge Graph database | runs at http://localhost:7200 | | 💾 ChromaDB | Vector store for embeddings | auto-initialized under .chroma/ | | 🧰 FastMCP | Multi-Component Platform runtime (2.x) | installed via pip |

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🧱 Folder Layout (simplified)

| Folder | Purpose | Example Contents | |:--------|:----------|:----------------| | src/ | Core codebase | pipeline.py, mcp/, kg/, rag/ | | outputs/run_simple/ | Generated outputs | labeled chunks, reports, embeddings | | .chroma/ | Chroma persistent vector store | chroma.sqlite3, index/ | | .env | Environment configuration | Ollama, GraphDB, Chroma settings | | tests/ | Offline unit tests | test_rag_qa.py, test_kg_server.py |

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🧰 Step-by-Step Setup

🪄 1️⃣ Clone & Create Virtual Environment

git clone https://github.com/Swissbit92/GraphDB_Desktop.git

⚡ 2️⃣ Activate Environment

| OS | Command | |:---|:---------| | 🪟 Windows (PowerShell) | .venv\Scripts\activate | | 🐧 Linux / macOS | source .venv/bin/activate |

📦 3️⃣ Install Dependencies

pip install -r requirements.txt

⚙️ 4️⃣ Verify Installation

python -m src.mcp.rag_server --list-tools
python -m src.mcp.kg_server --list-tools

✅ You should see tools like rag.qa, rag.search, and kg.health.

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🧠 Optional: Preload Ollama Models

| Model | Purpose | Pull Command | |:-------|:----------|:--------------| | 🦙 llama3.1:latest | Default reasoning + summarization model | ollama pull llama3.1:latest | | 🧩 nomic-embed-text | Embedding model for RAG vectorization | ollama pull nomic-embed-text | | 🤖 qwen2.5:14b-instruct | Larger model for complex QA tasks | ollama pull qwen2.5:14b-instruct |

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🔍 Quick Sanity Check

Run a quick health diagnostic to ensure everything is configured correctly:

pytest -q
python -m src.mcp.rag_server --run-tool rag.health
python -m src.mcp.kg_server --run-tool kg.health

If both return ✅ OK, you’re ready to run the pipeline and start querying your Knowledge Graph + RAG system!

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5️⃣ 🧪 How to Use & Test

📥 Ingest Whitepapers & Build the Index

# Place your PDFs under .\whitepapers\ then run:
python -m src.pipeline --input ".\whitepapers\*.pdf"

✅ Outputs:

• Labeled JSONL → outputs\run_simple\labels\
• Chroma index → .chroma\
• (If enabled) Entities pushed to GraphDB repository mcp_kg

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🖧 Start the MCP Servers (RAG + KG)

# Terminal A
python -m src.mcp.rag_server

# Terminal B
python -m src.mcp.kg_server

💡 Tip: In another PowerShell window, confirm the tools are available:

python -m src.mcp.rag_server --list-tools
python -m src.mcp.kg_server --list-tools

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🔎 Quick Retrieval Check (RAG)

# Example: semantic search for "peer-to-peer electronic cash"
python -m src.mcp.rag_server --run-tool rag.search --input '{ "text": "peer-to-peer electronic cash", "k": 3 }'

You should see matching chunks with doc_id, chunk_id, and distances.

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❓ Ask Questions with Citations (rag.qa)

# Fully offline (deterministic mock answer)
python -m src.mcp.rag_server --run-tool rag.qa --input '{ "question": "What problem does Bitcoin aim to solve?", "k": 5, "kg_enrich": true, "use_mock_llm": true }'

➡️ Returns:

• answer: concise response (mock or LLM)
• citations: [ {doc_id, chunk_id, entity_ids, text} ]
• took_ms, model_used

Switch to real LLM synthesis by omitting use_mock_llm (requires Ollama running).

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🧠 Optional: Entity-Filtered QA

python -m src.mcp.rag_server --run-tool rag.qa --input '{ "question": "How does proof-of-work secure the network?", "entity_ids": ["https://kg.mcp.ai/id/token/bitcoin"], "k": 5, "kg_enrich": true, "use_mock_llm": true }'

This restricts retrieval to chunks tagged with the specified KG entity(ies).

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🧪 Run the Test Suite

pytest -q

Key tests (all offline):

• tests\test_rag_qa.py: verifies retrieval normalization and mock LLM mode
• tests\test_kg_server.py: checks KG connectivity (skips if GraphDB not running)

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🩺 Health Checks

python -m src.mcp.rag_server --run-tool rag.health
python -m src.mcp.kg_server --run-tool kg.health

Expect collection info, document counts, and OK status.

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🧩 MCP Coordinator / UI Hookup (Optional)

Ensure your mcp.json references the running servers:

{
  "mcpServers": {
    "rag": { "command": "python", "args": ["-m", "src.mcp.rag_server"] },
    "kg":  { "command": "python", "args": ["-m", "src.mcp.kg_server"] }
  }
}

Then connect via your MCP Coordinator or Streamlit app to interactively call rag.qa and kg.* tools.

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🙏 Closing Words

GraphRAG MCP is part of the broader Eeva AI ecosystem — an open, modular framework for intelligent crypto research and strategy generation. This project wouldn’t exist without the incredible open-source community that continues to push the boundaries of local AI and knowledge engineering.

If you find this useful:

• ⭐ Star the repository to support ongoing development
• 🧩 Contribute improvements or new MCP modules
• 🧠 Explore integrations with other MCPs (Brave API, MongoDB, Telegram, etc.)
• 💬 Share feedback — every suggestion helps make the system smarter, faster, and more reliable

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_“Knowledge is only powerful when it’s connected.”_ — __Eeva AI Research__

Thank you for being part of the open-source journey. 🚀

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