Which integrations are available for this server?

Allows the MCP server to use Ollama for LLM-based answer generation and text embeddings.

How do I use RAG Chat Assistant MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RAG Chat Assistant MCP Server What are the key points from the uploaded document on RAG?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RAG Chat Assistant MCP Server

by AP46593

Overview Schema Related Servers Score Discussions

Python

Local

RAG Chat Assistant

A document Q&A Chat Assistant powered by Retrieval-Augmented Generation (RAG). Uses a hybrid retrieval system (semantic + keyword search) with an MCP (Model Context Protocol) server/client architecture, PII redaction, automated evaluation via RAGAS, and full observability tracing.

Architecture

Streamlit Chat UI (Client - .venv)
    ↕ MCP Protocol (Streamable HTTP on localhost:8000)
MCP Server (FastMCP - .mcpvenv)
    ├── Tools: filesystem, doc_loader, chunker, ingest, retriever
    ├── Agents: RAG Agent, Summarizer, PII Redactor, Evaluator
    ├── Storage: ChromaDB (vector) + BM25 (keyword) + Registry
    └── External: Ollama (LLM + Embeddings), Opik (Observability)

Related MCP server: Antigravity PDF MCP Server

Project Structure

L3June26_Assignment/
├── MCP_Stack/                  # MCP Server (runs in .mcpvenv)
│   ├── agents/
│   │   ├── rag_agent.py           # LangGraph RAG agent (retrieve → generate)
│   │   ├── summarizer_agent.py    # Iterative document summarization with caching
│   │   ├── pii_redactor.py        # Regex + optional LLM-based PII detection
│   │   └── evaluator_agent.py     # RAGAS evaluation + ground-truth generator
│   ├── tools/
│   │   ├── doc_loader.py          # Multi-format document loading (PDF/DOCX/TXT/CSV/XLSX/XML/images)
│   │   ├── chunker.py             # Semantic chunking with metadata
│   │   ├── ingest.py              # Ingestion pipeline + document registry
│   │   ├── retriever.py           # Hybrid search (ChromaDB + BM25 + reranking)
│   │   └── filesystem.py          # Sandboxed file browsing
│   ├── mcp_server.py              # FastMCP server entry point
│   ├── config.py                  # Server configuration
│   ├── .env.example               # Server secrets template
│   ├── requirements_mcp.txt       # Server dependencies
│   ├── knowledge_source/          # Drop documents here for ingestion
│   ├── knowledge_base/            # ChromaDB + BM25 index + registry.json (auto-generated)
│   ├── Server_Logs/               # Per-session JSONL logs
│   └── cache/                     # Summarizer cache (by content hash)
├── tests/                     # All tests
│   ├── test_property_*.py         # Property-based tests (Hypothesis)
│   ├── test_unit_*.py             # Unit tests
│   └── test_integration_*.py      # Integration tests
├── Client_Logs/               # Client JSONL logs
├── streamlit_app.py           # Streamlit chat UI (runs in .venv)
├── config.py                  # Client configuration
├── .env.example               # Client secrets template
├── requirements.txt           # Client dependencies
├── test_tools.py              # Manual test stub for tools/agents
└── README.md

Prerequisites

Dependency	Purpose
Python 3.12	Runtime (RAGAS has compatibility issues with 3.14)
uv	Package manager (replaces pip)
Ollama	Local/cloud LLM serving
Tesseract OCR (optional)	Primary OCR for images; if unavailable, falls back to `gemma4:31b-cloud` vision model

Setup

1. Pull Required Ollama Models

# Chat model (cloud-hosted, no local GPU needed)
ollama pull gpt-oss:120b-cloud 

# Embedding model
ollama pull nomic-embed-text

# Vision model (OCR fallback — cloud-hosted, no local GPU needed)
ollama pull gemma4:31b-cloud

2. Create Virtual Environments

MCP Server (.mcpvenv):

uv venv .mcpvenv --python 3.12

# Windows
.mcpvenv\Scripts\activate

# Linux/macOS
source .mcpvenv/bin/activate

uv pip install -r MCP_Stack/requirements_mcp.txt

Streamlit Client (.venv):

uv venv .venv --python 3.12

# Windows
.venv\Scripts\activate

# Linux/macOS
source .venv/bin/activate

uv pip install -r requirements.txt

3. Configure Environment Variables

# Copy templates
cp .env.example .env
cp MCP_Stack/.env.example MCP_Stack/.env

Edit each .env file with your actual values:

Client .env:

OLLAMA_BASE_URL=http://localhost:11434
ORCHESTRATOR_MODEL=gpt-oss:120b-cloud
MCP_SERVER_URL=http://localhost:8000/mcp
ENABLE_OPIK_TRACING=false
OPIK_API_KEY=<your-key>
OPIK_WORKSPACE=<your-workspace>
OPIK_PROJECT_NAME=rag-chat-assistant

Server MCP_Stack/.env:

OLLAMA_BASE_URL=http://localhost:11434
DEFAULT_MODEL=gpt-oss:120b-cloud
EMBEDDING_MODEL=nomic-embed-text
VISION_MODEL=gemma4:31b-cloud
CHUNK_SIZE=2000
CHUNK_OVERLAP=200
RETRIEVAL_TOP_K=5
SEMANTIC_WEIGHT=0.7
PII_USE_LLM=false
ENABLE_RAGAS_EVAL=false
MCP_SERVER_PORT=8000

4. Add Documents to Knowledge Source

Place your documents (PDF, DOCX, TXT, CSV, XLSX, XML, or images) into:

MCP_Stack/knowledge_source/

These will be automatically ingested when the MCP server starts.

Running the Application

Step 1: Start the MCP Server

Open a terminal and activate the server environment:

# Windows
.mcpvenv\Scripts\activate

# Linux/macOS
source .mcpvenv/bin/activate

# Start the server
python -m MCP_Stack.mcp_server

On startup, the server will:

Inject SSL certificates (truststore)
Load existing knowledge base from disk
Scan knowledge_source/ and ingest any new or modified documents
Skip unchanged documents (based on content hash)
Register all tools and agents
Serve MCP protocol on http://localhost:8000/mcp

Note: Documents added to knowledge_source/ while the server is running will NOT be auto-detected. Restart the server to ingest new files.

Step 2: Start the Streamlit Client

Open a separate terminal and activate the client environment:

# Windows
.venv\Scripts\activate

# Linux/macOS
source .venv/bin/activate

# Start the UI
streamlit run streamlit_app.py

The chat UI will open in your browser (typically at http://localhost:8501).

Usage

Asking Questions

Type your question in the chat input. The RAG agent will:

Search the knowledge base using hybrid retrieval (semantic + keyword)
Generate an answer with citations to source documents
Display RAGAS evaluation scores (if enabled)

Document Management

Through the chat interface you can:

Browse files — list and inspect documents in knowledge_source/
Ingest manually — force re-ingest of a specific file or all files
List documents — see all ingested documents with metadata
Delete documents — remove a document from the knowledge base
Summarize — get a concise summary of a long document

Ground-Truth Test Data Generation

Generate evaluation test data from your documents:

Provide a document name from knowledge_source/
The system generates question-answer pairs with context passages
Output is saved as JSON for use with RAGAS evaluation (faithfulness, answer relevancy, context precision, context recall)

Configuration Reference

Server Configuration (`MCP_Stack/config.py`)

Parameter	Default	Description
`OLLAMA_BASE_URL`	`http://localhost:11434`	Ollama API endpoint
`DEFAULT_MODEL`	`gpt-oss:120b-cloud`	Chat model for answer generation
`EMBEDDING_MODEL`	`nomic-embed-text`	Embedding model for vector search
`VISION_MODEL`	`gemma4:31b-cloud`	Cloud vision model (OCR fallback)
`MAX_TOKENS`	`2048`	Max tokens for generated responses
`TEMPERATURE`	`0.7`	LLM temperature
`CHUNK_SIZE`	`2000`	Characters per chunk (~500 tokens)
`CHUNK_OVERLAP`	`200`	Overlap between consecutive chunks
`RETRIEVAL_TOP_K`	`5`	Number of chunks to retrieve
`SEMANTIC_WEIGHT`	`0.7`	Semantic vs keyword balance (0.7 = 70% semantic)
`PII_USE_LLM`	`false`	Enable LLM-based PII detection (slower, catches more)
`ENABLE_RAGAS_EVAL`	`false`	Auto-evaluate responses with RAGAS
`MCP_SERVER_PORT`	`8000`	Server port

Client Configuration (`config.py`)

Parameter	Default	Description
`OLLAMA_BASE_URL`	`http://localhost:11434`	Ollama API endpoint
`ORCHESTRATOR_MODEL`	`gpt-oss:120b-cloud`	Model for client-side orchestration
`MCP_SERVER_URL`	`http://localhost:8000/mcp`	MCP server endpoint
`ENABLE_OPIK_TRACING`	`false`	Enable Opik observability tracing

Running Tests

# Activate the server environment (has all dependencies)
# Windows
.mcpvenv\Scripts\activate

# Linux/macOS
source .mcpvenv/bin/activate

# Run all tests
python -m pytest tests/ -v

# Run only property-based tests
python -m pytest tests/test_property_*.py -v

# Run only unit tests
python -m pytest tests/test_unit_*.py -v

# Run a specific test file
python -m pytest tests/test_unit_chunker.py -v

Key Design Decisions

Decision	Choice	Rationale
Protocol	MCP over Streamable HTTP	Standardized tool/agent interface; single endpoint
Agent Framework	LangGraph	Stateful graph workflows with conditional routing
Vector Store	ChromaDB (persistent)	Local file-based; no external service needed
Keyword Search	rank-bm25 (BM25Okapi)	Lightweight in-process; complements semantic search
Embedding	nomic-embed-text via Ollama	Dedicated embedding model; local inference
OCR	Tesseract → gemma4:31b-cloud fallback	Tesseract is fast; cloud vision is available everywhere
Observability	Opik (by Comet)	Native LangChain callback integration
Evaluation	RAGAS	Standard RAG evaluation framework
SSL	truststore	Corporate proxy support via Windows cert store

Troubleshooting

Issue	Solution
SSL errors behind corporate proxy	Ensure `truststore` is installed and imported first in entry points
Ollama connection refused	Verify Ollama is running: `ollama list`
Empty OCR results	Install Tesseract, or ensure `gemma4:31b-cloud` is available via `ollama pull gemma4:31b-cloud`
MCP connection timeout	Check that the server is running on the configured port (default 8000)
Documents not appearing after adding	Restart the MCP server — ingestion only happens at startup
RAGAS scores not showing	Set `ENABLE_RAGAS_EVAL=true` in `MCP_Stack/.env`

Supported Document Formats

Format	Extensions	Method
PDF	`.pdf`	pypdf + pdfplumber fallback
Word	`.docx`	python-docx
Plain Text	`.txt`	Direct read with encoding detection
CSV	`.csv`	pandas
Excel	`.xlsx`	openpyxl via pandas
XML	`.xml`	xml.etree + lxml fallback
Images	`.png`, `.jpg`, `.jpeg`, `.tiff`	Tesseract OCR → gemma4:31b-cloud fallback

This server cannot be installed

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How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

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GitHub Repository

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