groqqer/README.md

# groqqer

`groqqer` is a lightweight, zero-dependency Go OpenAI-compatible API gateway and proxy for the Groq Streamlit space (`https://dromerosm-groq-chatbot.hf.space`).

It connects directly to Streamlit's binary WebSocket engine (`/_stcore/stream`) using a pure Go Protobuf wire-format encoder/decoder. It requires **no browser**, **no Chromium**, and **no Xvfb**, allowing it to run smoothly on minimal headless servers and low-resource containers.

## Key features

- **Direct WebSocket Protobuf protocol**: communicates directly with Streamlit's internal engine over RFC 6455 WebSockets and Protocol Buffers wire format.
- **Completely headless and zero dependencies**: written entirely in pure Go using only the standard library (`net/http`, `crypto/tls`, `encoding/binary`, `encoding/json`, etc.). No Chromium, Chrome, Xvfb, Puppeteer, or external Go modules required.
- **Sub-second latency**: bypasses browser rendering and DOM parsing entirely, delivering responses with minimal overhead.
- **OpenAI-compatible API**: exposes standard `/v1/models` and `/v1/chat/completions` endpoints. Drop-in replacement for OpenAI SDKs, LiteLLM, Open-WebUI, LibreChat, and LangChain.
- **System prompt support**: formats developer/system instructions cleanly for the target model.
- **Streaming and non-streaming**: supports Server-Sent Events (`stream: true`) with real-time token streaming and synchronous JSON responses.
- **Tool and function calling**: fully supports OpenAI `tools`, `tool_choice`, and multi-turn execution (`role: "tool"`). Automatically injects schemas and parses `<tool_call>` outputs into standard OpenAI `tool_calls` payloads with `finish_reason: "tool_calls"`.
- **Reasoning content**: extracts `<think>` and `</think>` tags from reasoning models (e.g., Qwen 3.6/3.8) and streams or populates `reasoning_content` following OpenAI O-series conventions.
- **Dynamic model discovery and switching**: discovers active models directly from the Streamlit space on startup and allows seamless model switching between requests (`qwen/qwen3.6-27b`, `openai/gpt-oss-120b`, `openai/gpt-oss-20b`, `groq/compound`, etc.).
- **SOCKS5 proxy support**: native pure Go SOCKS5 proxy client supporting authentication (`-socks` flag or `ALL_PROXY` / `SOCKS5_PROXY` environment variables).

## Architecture

```
+---------------------------+
| OpenAI Client / SDK / App |
+---------------------------+
              |  HTTP POST /v1/chat/completions
              v
+---------------------------+
|          groqqer          |
|  (Pure Go Standard Lib)   |
|   RFC 6455 WS + Protobuf  |
+---------------------------+
              |  WSS (TLS WebSocket)
              v
+---------------------------+
|   Groq Streamlit Space    |
| (dromerosm-groq-chatbot)  |
+---------------------------+
```

## Requirements

- Linux, macOS, or Windows
- Go 1.20+ (for building from source)
- No browser or graphics packages needed

## Installation and build

### Using `go install`

Install the binary directly to `$GOPATH/bin` (or `~/go/bin`):

```bash
go install code.luxferre.top/luxferre/groqqer@latest
```

Make sure `$GOPATH/bin` is in your `PATH`.

### Building from source

Clone or navigate to the repository, then compile the binary:

```bash
make
```

The optimized binary will be created at `bin/groqqer`. Alternatively, install into `$GOPATH/bin` from the local directory:

```bash
go install .
```

## Running the server

Start the gateway with default settings:

```bash
./bin/groqqer -port 8080
```

### CLI flags

| Flag | Default | Description |
|------|---------|-------------|
| `-port` | `8080` | HTTP server listening port |
| `-target` | `https://dromerosm-groq-chatbot.hf.space` | Target Groq Streamlit space URL |
| `-default-model` | `qwen/qwen3.6-27b` | Fallback model if request does not specify one |
| `-socks` | `""` | SOCKS5 proxy URL (e.g. `socks5://127.0.0.1:1080`) |
| `-user-agent` | *(Chrome UA)* | Custom User-Agent string |

*(Note: legacy flags `-browser`, `-xvfb`, and `-headless` are retained for backward compatibility but are ignored, as groqqer operates completely headless via direct WebSocket).*

## API usage examples

### 1. List available models

```bash
curl -s http://127.0.0.1:8080/v1/models
```

**Response:**
```json
{
  "object": "list",
  "data": [
    {"id": "qwen/qwen3.6-27b", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "qwen/qwen3.8-27b", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "openai/gpt-oss-120b", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "openai/gpt-oss-20b", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "groq/compound", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "groq/compound-mini", "object": "model", "created": 1788870683, "owned_by": "groq"},
    {"id": "allam-2-7b", "object": "model", "created": 1788870683, "owned_by": "groq"}
  ]
}
```

---

### 2. Chat completion (non-streaming)

```bash
curl -s -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "messages": [
      {"role": "system", "content": "You are a concise assistant. Reply in one sentence."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'
```

**Response:**
```json
{
  "id": "chatcmpl-893498c9-2808-4bec-8c6a-2a0352ed69e9",
  "object": "chat.completion",
  "created": 1788870700,
  "model": "openai/gpt-oss-20b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 0,
    "completion_tokens": 0,
    "total_tokens": 0
  }
}
```

---

### 3. Streaming chat completion (SSE)

```bash
curl -N -s -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.6-27b",
    "messages": [
      {"role": "user", "content": "Count from 1 to 4 separated by commas."}
    ],
    "stream": true
  }'
```

**Output:**
```
data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"role":"assistant"}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"reasoning_content":"Thinking process..."}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"content":"1,"}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"content":" 2,"}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"content":" 3,"}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{"content":" 4"}}]}

data: {"id":"chatcmpl-7a06d0fb","object":"chat.completion.chunk","created":1788870705,"model":"qwen/qwen3.6-27b","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]
```

---

### 4. Tool and function calling

```bash
curl -s -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.6-27b",
    "messages": [
      {"role": "user", "content": "What is the weather in Tokyo right now?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_current_weather",
          "description": "Get current weather for a city",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string", "description": "City and country"}
            },
            "required": ["location"]
          }
        }
      }
    ],
    "tool_choice": "required"
  }'
```

**Response:**
```json
{
  "id": "chatcmpl-29f63b3b-7ab6-444c-8579-49f906007d43",
  "object": "chat.completion",
  "created": 1788870711,
  "model": "qwen/qwen3.6-27b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null,
        "reasoning_content": "The user is asking for the current weather in Tokyo...",
        "tool_calls": [
          {
            "id": "call_163e8f97",
            "type": "function",
            "function": {
              "name": "get_current_weather",
              "arguments": "{\"location\":\"Tokyo\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}
```

---

### 5. Multi-turn tool response execution

Send back the tool execution results using `role: "tool"`:

```bash
curl -s -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.6-27b",
    "messages": [
      {"role": "user", "content": "What is the weather in Tokyo right now?"},
      {
        "role": "assistant",
        "tool_calls": [
          {
            "id": "call_163e8f97",
            "type": "function",
            "function": {
              "name": "get_current_weather",
              "arguments": "{\"location\":\"Tokyo\"}"
            }
          }
        ]
      },
      {
        "role": "tool",
        "tool_call_id": "call_163e8f97",
        "name": "get_current_weather",
        "content": "{\"temperature\": \"19°C\", \"weather\": \"Sunny with clear skies\"}"
      }
    ]
  }'
```

**Response:**
```json
{
  "id": "chatcmpl-23354bba-33c7-45be-8807-9c84243037b8",
  "object": "chat.completion",
  "created": 1788870718,
  "model": "qwen/qwen3.6-27b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "It's currently 19°C and sunny with clear skies in Tokyo"
      },
      "finish_reason": "stop"
    }
  ]
}
```

---

### 6. Python OpenAI SDK example

```python
from openai import OpenAI

client = OpenAI(
  base_url="http://127.0.0.1:8080/v1",
  api_key="not-needed"
)

response = client.chat.completions.create(
  model="qwen/qwen3.6-27b",
  messages=[
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to reverse a string."}
  ]
)

print(response.choices[0].message.content)
```

## License

Created by Luxferre in 2026, released into the public domain with no warranties.