Juniper Technical Documentation RAG Engine

A fast, lightweight, and self-contained Retrieval-Augmented Generation (RAG) and search engine for Juniper technical documentation (KBs) and networking books (PDFs).

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🚀 Features

• Semantic Section-based Chunking: Automatically parses Juniper KB metadata and chunks sections (Description, Symptoms, Cause, Solution/Workaround, and Fixed/Upgrade) separately.
• Accurate PDF Extraction: Uses pdfplumber with word boundary reconstruction (x_tolerance=1.5) to parse text from networking book PDFs without run-together words.
• Ultra-Fast Vector Search: Uses optimized NumPy matrix cosine similarity operations, delivering query retrieval times under 5 milliseconds.
• Resumable Indexing: Remembers indexed files using a SQLite tracking table to ensure indexing is safe to pause and resume.
• RAG Generation Endpoint: Generates detailed, structured network engineering responses using Ollama LLMs by passing retrieved context chunks.
• FastAPI REST API Service: Exposes clean endpoints for /health, /search, and /generate (RAG synthesis) with autogenerated OpenAPI Swagger documentation.
• Dockerized Deployment: Fully containerized environment packaged with a pre-computed 170MB vector database for instant deployment.

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📁 Repository Structure

├── Dockerfile              # Docker deployment configuration
├── README.md               # Project documentation
├── app.py                  # FastAPI REST API web server
├── embed_docs.py           # Document ingestion, parsing, and embedding script
├── search_kb.py            # CLI query retrieval engine (numpy powered)
├── mcp_server.py           # Model Context Protocol (MCP) stdio server
├── rag_system_prompt.md    # System prompt guide for RAG Copilot integration
├── requirements.txt        # Python library dependencies
├── knowledge_base.db       # SQLite pre-computed vector database (~170MB)
└── .gitignore              # Configured to exclude raw datasets, cache and log files

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⚙️ Configuration Variables

The RAG Engine can be configured using environment variables:

Variable	Description	Default
OLLAMA_URL	URL to Ollama embedding API	http://localhost:11434/api/embed
OLLAMA_GENERATE_URL	URL to Ollama text generation API	http://localhost:11434/api/generate
EMBEDDING_MODEL	Model used to generate embeddings	qwen3-embedding:0.6b
DB_PATH	Path to the SQLite database	./knowledge_base.db
KB_JSON_PATH	Path to Juniper KB clean dataset	./juniper-kb/juniper_kb_data_clean.json
BOOKS_DIR	Path to directory containing PDF books	./network-books

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💻 Local Setup & Usage

Prerequisites

1. Ollama: Ensure Ollama is installed and running.
2. Embedding Model: Pull the embedding model:

   ollama pull qwen3-embedding:0.6b
   

3. LLM Generation Model (Optional for RAG):

   ollama pull qwen2.5  # or llama3
   

1. Ingesting & Indexing Documents

If you need to re-index the raw KB files and PDFs, configure Ollama, populate ./juniper-kb/ and ./network-books/, then run:

pip install -r requirements.txt
python3 embed_docs.py

2. Querying via Command Line

Search the index directly from the CLI:

python3 search_kb.py "BGP down QFX" --top-k 3

To view the full document text or page content of the matched results, append --full:

python3 search_kb.py "EVPN routing instances" --top-k 1 --full

3. Running the REST API Server

Start the FastAPI web server locally:

uvicorn app:app --host 0.0.0.0 --port 8000

Open your browser and navigate to http://localhost:8000/docs to interact with the OpenAPI Swagger interface.

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🐳 Docker Deployment

The project is packaged with the pre-computed database knowledge_base.db so you can run it immediately without indexing.

Build the Image

docker build -t rag-engine:latest .

Run the Container

For Linux hosts (accessing host Ollama):

docker run -d \
  -p 8010:8000 \
  --name rag-engine \
  --add-host=host.docker.internal:host-gateway \
  rag-engine:latest

For macOS/Windows hosts:

docker run -d \
  -p 8010:8000 \
  --name rag-engine \
  rag-engine:latest

Exposed API Endpoints

1. System Health & Stats (GET /health)

curl http://localhost:8010/health

2. Vector Search (POST /search)

curl -X POST http://localhost:8010/search \
  -H "Content-Type: application/json" \
  -d '{"query": "BGP down SRX", "top_k": 3}'

3. RAG Answer Synthesis (POST /generate)

curl -X POST http://localhost:8010/generate \
  -H "Content-Type: application/json" \
  -d '{"query": "Tại sao BGP down trên QFX lại ảnh hưởng SRX?", "top_k": 3, "llm_model": "qwen2.5:0.5b"}'

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🔌 Model Context Protocol (MCP) Server

You can expose the Juniper RAG Engine as tools to your AI Agent (like Claude Desktop) using the Model Context Protocol (MCP). The python script mcp_server.py implements the standard JSON-RPC stdio protocol.

How it Works

The MCP server is a lightweight stdio subprocess. When the AI Agent starts, it spawns mcp_server.py which communicates with the running RAG Engine Docker container on http://localhost:8010.

Configure your AI Agent Client

Add the following config block to your MCP client config file (e.g. claude_desktop_config.json):

Linux / macOS / WSL Path: ~/.config/Claude/claude_desktop_config.json Windows Path: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "juniper-rag": {
      "command": "python3",
      "args": ["/root/work/knowledge-base/mcp_server.py"],
      "env": {
        "RAG_API_URL": "http://localhost:8010"
      }
    }
  }
}

Exposed Tools

Once connected, the AI Agent will have access to the following tools:

1. search_juniper_kb: Search technical documents and books for relevant snippets.
   • Parameters: query (string, required), top_k (integer), source ("kb" | "book").
2. ask_juniper_rag: Ask technical questions and get synthesized answers with citations.
   • Parameters: query (string, required), top_k (integer), source ("kb" | "book"), llm_model (string, e.g. "qwen2.5:0.5b").