# LLM Proxy Gateway

A unified, high-performance LLM proxy gateway built in Go. It provides a single OpenAI-compatible API to access multiple providers (OpenAI, Gemini, DeepSeek, Grok, Ollama) with built-in token tracking, real-time cost calculation, multi-user authentication, and a management dashboard.

## Features

- **Unified API:** OpenAI-compatible `/v1/chat/completions` and `/v1/models` endpoints.
- **Multi-Provider Support:**
  - **OpenAI:** GPT-4o, GPT-4o Mini, o1, o3 reasoning models.
  - **Google Gemini:** Gemini 2.0 Flash, Pro, and vision models (with native CoT support).
  - **DeepSeek:** DeepSeek Chat and Reasoner (R1) models.
  - **xAI Grok:** Grok-2 models.
  - **Ollama:** Local LLMs running on your network.
- **Observability & Tracking:**
  - **Asynchronous Logging:** Non-blocking request logging to SQLite using background workers.
  - **Token Counting:** Precise estimation and tracking of prompt, completion, and reasoning tokens.
  - **Database Persistence:** Every request logged to SQLite for historical analysis and dashboard analytics.
  - **Streaming Support:** Full SSE (Server-Sent Events) support for all providers.
- **Multimodal (Vision):** Image processing (Base64 and remote URLs) across compatible providers.
- **Multi-User Access Control:**
  - **Admin Role:** Full access to all dashboard features, user management, and system configuration.
  - **Viewer Role:** Read-only access to usage analytics, costs, and monitoring.
  - **Client API Keys:** Create and manage multiple client tokens for external integrations.
- **Reliability:**
  - **Circuit Breaking:** Automatically protects when providers are down (coming soon).
  - **Rate Limiting:** Per-client and global rate limits (coming soon).

## Security

LLM Proxy is designed with security in mind:

- **Signed Session Tokens:** Management dashboard sessions are secured using HMAC-SHA256 signed tokens.
- **Encrypted Storage:** Support for encrypted provider API keys in the database.
- **Auth Middleware:** Secure client authentication via database-backed API keys.

**Note:** You must define an `LLM_PROXY__ENCRYPTION_KEY` in your `.env` file for secure session signing and encryption.

## Tech Stack

- **Runtime:** Go 1.22+
- **Web Framework:** Gin Gonic
- **Database:** sqlx with SQLite (CGO-free via `modernc.org/sqlite`)
- **Frontend:** Vanilla JS/CSS with Chart.js for visualizations

## Getting Started

### Prerequisites

- Go (1.22+)
- SQLite3 (optional, driver is built-in)
- Docker (optional, for containerized deployment)

### Quick Start

1. Clone and build:
   ```bash
   git clone <repository-url>
   cd llm-proxy
   go build -o llm-proxy ./cmd/llm-proxy
   ```

2. Configure environment:
   ```bash
   cp .env.example .env
   # Edit .env and add your configuration:
   # LLM_PROXY__ENCRYPTION_KEY=... (32-byte hex or base64 string)
   # OPENAI_API_KEY=sk-...
   # GEMINI_API_KEY=AIza...
   ```

3. Run the proxy:
   ```bash
   ./llm-proxy
   ```

The server starts on `http://0.0.0.0:8080` by default.

### Deployment (Docker)

```bash
# Build the container
docker build -t llm-proxy .

# Run the container
docker run -p 8080:8080 \
  -e LLM_PROXY__ENCRYPTION_KEY=your-secure-key \
  -v ./data:/app/data \
  llm-proxy
```

## Management Dashboard

Access the dashboard at `http://localhost:8080`.

- **Auth:** Login, session management, and status tracking.
- **Usage:** Summary stats, time-series analytics, and provider breakdown.
- **Clients:** API key management and per-client usage tracking.
- **Providers:** Provider configuration and status monitoring.
- **Users:** Admin-only user management for dashboard access.
- **Monitoring:** Live request stream via WebSocket.

### Default Credentials

- **Username:** `admin`
- **Password:** `admin` (You will be prompted to change this or should change it manually in the dashboard)

## API Usage

The proxy is a drop-in replacement for OpenAI. Configure your client:

### Python
```python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="YOUR_CLIENT_API_KEY"  # Create in dashboard
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

## License

MIT