orionbelt-chat 1.0.1

OrionBelt Chat - Chainlit + Pydantic AI chat client for OrionBelt Analytics

OrionBelt Chat

AI-powered chat interface for OrionBelt Analytics & Semantic Layer

A production-ready chat application that connects to OrionBelt Analytics and OrionBelt Semantic Layer MCP servers, providing a conversational interface for database analysis, semantic modeling, and interactive data visualization. Built with Chainlit and Pydantic AI, supporting multiple LLM providers (cloud and local).

Better Together: Works seamlessly with OrionBelt Analytics and OrionBelt Semantic Layer. Connect to both MCP servers simultaneously for schema-aware ontology generation, semantic modeling, guaranteed-correct SQL compilation, and interactive chart rendering.

Startup — connected MCP servers, provider & model selection

Full pipeline — schema analysis, ontology generation, semantic enrichment & file download

Interactive charts — heatmap, line & grouped bar rendered natively via Plotly.js

Key Features

Multi-Provider LLM Support

• OpenRouter - Access 300+ models via single API (recommended for production)
• MLX - Local inference on Apple Silicon with mlx-openai-server
• Ollama - Cross-platform local inference with easy setup
• Anthropic - Direct API access (bypass OpenRouter)
• OpenAI - Direct API access (bypass OpenRouter)

MCP Integration

• Dual MCP server support - Connect to Analytics and Semantic Layer simultaneously
• Graceful degradation - One unreachable server won't block the app; agent starts with available servers
• Auto-reconnection - Detects MCP session loss and reconnects automatically
• Tool call resilience - Retries failed tool calls up to 3 times; preserves conversation context on errors
• Flexible transport - Stdio (local subprocess) or Streamable HTTP (remote) per server
• Tool visibility - Collapsible steps show tool calls with arguments and results
• Multi-turn context - Full conversation history management with Pydantic AI

Interactive Charts

• Native Plotly rendering - Charts render inline via Chainlit's bundled Plotly.js (no Python plotly package needed)
• FastMCP Apps integration - Fetches chart data from ui:// resource URIs returned by MCP tools
• Multiple chart types - Bar, line, scatter, heatmap with auto-detection
• Multiple extraction strategies - Handles Plotly figure dicts, Plotly.newPlot() in HTML, and bare trace arrays

Mermaid Diagrams

• Client-side rendering - Mermaid.js loaded from CDN renders erDiagram, flowchart, sequenceDiagram, and other diagram types inline
• Auto-detection - Mermaid syntax in MCP tool results is automatically surfaced as a rendered diagram
• Theme-aware - Diagrams re-render when switching between light and dark mode

File Downloads

• Auto-detection - Recognizes downloadable content in tool results and LLM response code blocks
• Supported formats - Turtle/RDF (.ttl), JSON, CSV, SQL, SPARQL, YAML, XML
• Smart extraction - Handles dict-shaped tool returns (e.g. {'success': True, 'content': '@prefix ...'})
• Inline attachments - Download buttons appear directly in the response message

Real-Time Streaming

• Token-by-token streaming - Smooth response rendering as the model generates
• Thinking indicator - Visual spinner while the model processes before responding
• Tool call tracking - Visual feedback for each MCP tool invocation with correct result matching
• Stop generation - Click the stop button or press Escape to cancel
• Error handling - Graceful failures with clear error messages

Chainlit UI

• Settings panel - Switch providers and models on the fly; header updates live
• Custom model input - Override default models with specific versions
• Customizable system prompt - Edit system_prompt.md or set SYSTEM_PROMPT_FILE env var
• Message recall - Press Arrow Up/Down in the input to navigate message history
• Responsive design - Works on desktop and mobile browsers

Quick Start

Prerequisites

• Python 3.11+ (3.13 recommended)
• uv package manager (install)
• OrionBelt Analytics and Semantic Layer repos cloned alongside this one

Installation

# Clone the repository
cd orionbelt-chat

# Install dependencies
uv sync

# Copy environment template
cp .env.example .env

# Edit .env and add your API keys

Configuration

Edit .env and configure your LLM provider:

Option 1: OpenRouter (recommended for cloud)

OPENROUTER_API_KEY=sk-or-v1-...
OPENROUTER_DEFAULT_MODEL=anthropic/claude-sonnet-4-5
DEFAULT_PROVIDER=openrouter

Option 2: Anthropic direct

ANTHROPIC_API_KEY=sk-ant-...
ANTHROPIC_DEFAULT_MODEL=claude-sonnet-4-6   # or claude-opus-4-6
DEFAULT_PROVIDER=anthropic

Option 3: OpenAI direct

OPENAI_API_KEY=sk-...
OPENAI_DEFAULT_MODEL=gpt-4o
DEFAULT_PROVIDER=openai

Option 4: MLX local (Apple Silicon)

# Start mlx-openai-server first:
mlx-openai-server launch \
  --model-path mlx-community/Qwen2.5-14B-Instruct-4bit \
  --model-type lm \
  --enable-auto-tool-choice \
  --port 8000

MLX_DEFAULT_MODEL=mlx-community/Qwen2.5-14B-Instruct-4bit
DEFAULT_PROVIDER=mlx

Option 5: Ollama local (cross-platform)

# Start Ollama first: ollama serve
OLLAMA_DEFAULT_MODEL=qwen2.5:14b
DEFAULT_PROVIDER=ollama

MCP Server Paths:

# Each can be a local directory (stdio) or HTTP(S) URL (Streamable HTTP):
ANALYTICS_SERVER_DIR=../orionbelt-analytics
SEMANTIC_LAYER_SERVER_DIR=../orionbelt-semantic-layer-mcp
# Remote example: ANALYTICS_SERVER_DIR=https://analytics.example.com/mcp

System Prompt (optional):

# Override the default system prompt file (defaults to system_prompt.md)
# SYSTEM_PROMPT_FILE=~/my_custom_prompt.md

Run

uv run chainlit run app.py --watch

Open http://localhost:8080 in your browser.

Usage Examples

Connect to database:

Connect to my PostgreSQL database at localhost

Schema analysis:

Analyze the schema and show me all tables with their relationships

Query with charts:

Show me revenue by product category as a bar chart

Download an ontology:

Generate an ontology for the schema and download it as Turtle

Explore semantic models:

What OBML models are available in the semantic layer?

Generate OBML model:

Create an OBML model for customer analytics with metrics for revenue, order count, and average order value

Architecture

┌──────────────────────────────────────────────────────────────┐
│          OrionBelt Chat (Chainlit + Pydantic AI)             │
│                                                              │
│  ┌──────────┐         ┌──────────────────────────────────┐   │
│  │  Chat UI │         │  Pydantic AI Agent + MCP Client  │   │
│  │          │────────>│  - Multi-turn context            │   │
│  │ Chainlit │         │  - Streaming events              │   │
│  │  2.10+   │         │  - Tool orchestration            │   │
│  └──────────┘         └──────────────────────────────────┘   │
└──────────────────────────────────────────────────────────────┘
         │                      │
         │                      ├──> orionbelt-analytics (MCP stdio or HTTP)
         │                      │    - Schema analysis
         │                      │    - Ontology generation & download
         │                      │    - SQL execution
         │                      │    - Interactive Plotly charts
         │                      │
         │                      └──> orionbelt-semantic-layer (MCP stdio or HTTP)
         │                           - OBML model management
         │                           - Semantic query compilation
         │                           - Guaranteed-correct SQL
         │
         └──> LLM Provider (OpenRouter/MLX/Ollama/Anthropic/OpenAI)

Key Components:

• Chainlit 2.10+ - Chat UI framework with streaming, steps, and settings
• Pydantic AI 1.77+ - Agent framework with node-by-node iteration (agent.iter())
• MCP Transport - Stdio (local subprocess) or Streamable HTTP (remote) per server
• Chart Renderer - Native Plotly rendering from FastMCP Apps ui:// resources
• Mermaid Renderer - Client-side diagram rendering via Mermaid.js CDN
• File Downloads - Auto-detect downloadable content (TTL, JSON, CSV, SQL) in tool results

Development

# Install dev dependencies
uv sync --group dev

# Run tests (when available)
uv run pytest

# Format code
uv run ruff format

# Lint
uv run ruff check --fix

Provider Details

OpenRouter

• Access: 300+ models via single API
• Reliability: Best tool-calling support across vendors
• Recommended models:
  • anthropic/claude-sonnet-4-5 - Best balance of speed and reasoning
  • anthropic/claude-opus-4-5 - Maximum intelligence
  • google/gemini-2.5-pro - Fast and cost-effective
• Setup: Get API key at openrouter.ai

Anthropic (direct)

• Access: Direct API, no intermediary
• Available models:
  • claude-sonnet-4-6 - Fast, excellent tool use (default)
  • claude-opus-4-6 - Maximum intelligence
  • claude-haiku-4-5-20251001 - Fastest, lowest cost
• Setup: Get API key at console.anthropic.com

OpenAI (direct)

• Access: Direct API, no intermediary
• Available models:
  • gpt-4o - Best balance (default)
  • gpt-4o-mini - Fast and cost-effective
  • o3-mini - Reasoning model
• Setup: Get API key at platform.openai.com

MLX (Apple Silicon)

• Platform: Mac with Apple Silicon (M1/M2/M3/M4)
• Requirements: mlx-openai-server
• Recommended models:
  • mlx-community/Qwen2.5-14B-Instruct-4bit - Excellent tool use
  • mlx-community/Qwen2.5-32B-Instruct-4bit - Better reasoning (requires 32GB+ RAM)
• Setup: Install with pip install mlx-openai-server
• Notes: Must use --enable-auto-tool-choice flag for tool calling

Ollama

• Platform: Cross-platform (Mac/Linux/Windows)
• Ease of use: Simplest local setup
• Recommended models:
  • qwen2.5:14b - Good balance of speed and accuracy
  • qwen2.5:32b - Better reasoning (requires 32GB+ RAM)
• Setup: Download from ollama.com
• Notes: Built-in tool calling support with instruct models

Troubleshooting

MCP servers not connecting

Symptom: Status message shows "Failed to connect" for one or more servers

The app starts even when some servers are unreachable — it will show which connected and which failed. If a session drops mid-conversation, the app automatically reconnects.

Solutions:

• Ensure ANALYTICS_SERVER_DIR and SEMANTIC_LAYER_SERVER_DIR point to correct paths
• For local (stdio): check that repos have dependencies installed (uv sync)
• For remote (HTTP): verify the URL is reachable and the server is running
• Verify MCP servers can start independently (uv run server.py)

Charts not rendering

Symptom: Charts don't appear after generate_chart tool call

Solutions:

• Verify orionbelt-analytics has MCP Apps support (v1.2.0+)
• Check server logs for Chart URI detected and Plotly JSON extracted messages
• Ensure the analytics server returns a ui:// resource URI in the tool result
• Verify the resource content contains parseable Plotly figure data

MLX model not calling tools

Symptom: Model ignores tools and tries to answer directly

Solutions:

• Ensure --enable-auto-tool-choice flag is set when starting mlx-openai-server
• Use an instruct-tuned model (with -Instruct suffix)
• Try a different model (Qwen2.5 series has best tool support)
• Check mlx-openai-server logs for errors

Streaming stops or hangs

Symptom: Response stops mid-generation or "Thinking" indicator stays visible

Solutions:

• Press Escape or click the stop button to cancel, then retry
• Check MCP server logs for errors
• Verify tool calls are completing successfully (expand steps in the UI)
• Increase timeout settings if using slow local models
• Check the server console for detailed logs (each node transition is logged)

License

Licensed under the Business Source License 1.1 (BSL 1.1).

• Production use allowed for internal/personal use
• Commercial embedding/SaaS restrictions - contact [e-mail]
• Change Date: 2030-04-05
• Change License: Apache 2.0

See LICENSE for full terms.

Links

OrionBelt Platform

• OrionBelt Analytics - MCP server for database analysis and ontology generation
• OrionBelt Semantic Layer - MCP server for OBML models and semantic SQL compilation
• OrionBelt Ontology Builder - Visual ontology editor (Streamlit app)

Frameworks

• Chainlit - Chat UI framework
• Pydantic AI - Agent framework with MCP support
• Model Context Protocol - Tool integration standard

LLM Providers

• OpenRouter - Unified API for 300+ models
• MLX - Apple Silicon inference
• Ollama - Local LLM runtime

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