| """ |
| ollama_provider.py |
| ------------------ |
| Adds native Ollama support to openclaude. |
| Lets Claude Code route requests to any locally-running Ollama model |
| (llama3, mistral, codellama, phi3, qwen2, deepseek-coder, etc.) |
| without needing an API key. |
| |
| Usage (.env): |
| PREFERRED_PROVIDER=ollama |
| OLLAMA_BASE_URL=http://localhost:11434 |
| BIG_MODEL=codellama:34b |
| SMALL_MODEL=llama3:8b |
| """ |
|
|
| import httpx |
| import logging |
| import os |
| from typing import AsyncIterator |
|
|
| logger = logging.getLogger(__name__) |
| OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434") |
|
|
|
|
| async def check_ollama_running() -> bool: |
| try: |
| async with httpx.AsyncClient(timeout=3.0) as client: |
| resp = await client.get(f"{OLLAMA_BASE_URL}/api/tags") |
| return resp.status_code == 200 |
| except Exception: |
| return False |
|
|
|
|
| async def list_ollama_models() -> list[str]: |
| try: |
| async with httpx.AsyncClient(timeout=5.0) as client: |
| resp = await client.get(f"{OLLAMA_BASE_URL}/api/tags") |
| resp.raise_for_status() |
| data = resp.json() |
| return [m["name"] for m in data.get("models", [])] |
| except Exception as e: |
| logger.warning(f"Could not list Ollama models: {e}") |
| return [] |
|
|
|
|
| def normalize_ollama_model(model_name: str) -> str: |
| if model_name.startswith("ollama/"): |
| return model_name[len("ollama/"):] |
| return model_name |
|
|
|
|
| def _extract_ollama_image_data(block: dict) -> str | None: |
| source = block.get("source") |
| if not isinstance(source, dict): |
| return None |
| if source.get("type") != "base64": |
| return None |
| data = source.get("data") |
| if isinstance(data, str) and data: |
| return data |
| return None |
|
|
|
|
| def anthropic_to_ollama_messages(messages: list[dict]) -> list[dict]: |
| ollama_messages = [] |
| for msg in messages: |
| role = msg.get("role", "user") |
| content = msg.get("content", "") |
| if isinstance(content, str): |
| ollama_messages.append({"role": role, "content": content}) |
| elif isinstance(content, list): |
| text_parts = [] |
| image_parts = [] |
| for block in content: |
| if isinstance(block, dict): |
| if block.get("type") == "text": |
| text_parts.append(block.get("text", "")) |
| elif block.get("type") == "image": |
| image_data = _extract_ollama_image_data(block) |
| if image_data: |
| image_parts.append(image_data) |
| else: |
| text_parts.append("[image]") |
| elif isinstance(block, str): |
| text_parts.append(block) |
| ollama_message = {"role": role, "content": "\n".join(text_parts)} |
| if image_parts: |
| ollama_message["images"] = image_parts |
| ollama_messages.append(ollama_message) |
| return ollama_messages |
|
|
|
|
| async def ollama_chat( |
| model: str, |
| messages: list[dict], |
| system: str | None = None, |
| max_tokens: int = 4096, |
| temperature: float = 1.0, |
| ) -> dict: |
| model = normalize_ollama_model(model) |
| ollama_messages = anthropic_to_ollama_messages(messages) |
| if system: |
| ollama_messages.insert(0, {"role": "system", "content": system}) |
| payload = { |
| "model": model, |
| "messages": ollama_messages, |
| "stream": False, |
| "options": {"num_predict": max_tokens, "temperature": temperature}, |
| } |
| async with httpx.AsyncClient(timeout=120.0) as client: |
| resp = await client.post(f"{OLLAMA_BASE_URL}/api/chat", json=payload) |
| resp.raise_for_status() |
| data = resp.json() |
| assistant_text = data.get("message", {}).get("content", "") |
| return { |
| "id": f"msg_ollama_{data.get('created_at', 'unknown')}", |
| "type": "message", |
| "role": "assistant", |
| "content": [{"type": "text", "text": assistant_text}], |
| "model": model, |
| "stop_reason": "end_turn", |
| "stop_sequence": None, |
| "usage": { |
| "input_tokens": data.get("prompt_eval_count", 0), |
| "output_tokens": data.get("eval_count", 0), |
| }, |
| } |
|
|
|
|
| async def ollama_chat_stream( |
| model: str, |
| messages: list[dict], |
| system: str | None = None, |
| max_tokens: int = 4096, |
| temperature: float = 1.0, |
| ) -> AsyncIterator[str]: |
| import json |
| model = normalize_ollama_model(model) |
| ollama_messages = anthropic_to_ollama_messages(messages) |
| if system: |
| ollama_messages.insert(0, {"role": "system", "content": system}) |
| payload = { |
| "model": model, |
| "messages": ollama_messages, |
| "stream": True, |
| "options": {"num_predict": max_tokens, "temperature": temperature}, |
| } |
| yield "event: message_start\n" |
| yield f'data: {json.dumps({"type": "message_start", "message": {"id": "msg_ollama_stream", "type": "message", "role": "assistant", "content": [], "model": model, "stop_reason": None, "usage": {"input_tokens": 0, "output_tokens": 0}}})}\n\n' |
| yield "event: content_block_start\n" |
| yield f'data: {json.dumps({"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}})}\n\n' |
| async with httpx.AsyncClient(timeout=120.0) as client: |
| async with client.stream("POST", f"{OLLAMA_BASE_URL}/api/chat", json=payload) as resp: |
| resp.raise_for_status() |
| async for line in resp.aiter_lines(): |
| if not line: |
| continue |
| try: |
| chunk = json.loads(line) |
| delta_text = chunk.get("message", {}).get("content", "") |
| if delta_text: |
| yield "event: content_block_delta\n" |
| yield f'data: {json.dumps({"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": delta_text}})}\n\n' |
| if chunk.get("done"): |
| yield "event: content_block_stop\n" |
| yield f'data: {json.dumps({"type": "content_block_stop", "index": 0})}\n\n' |
| yield "event: message_delta\n" |
| yield f'data: {json.dumps({"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence": None}, "usage": {"output_tokens": chunk.get("eval_count", 0)}})}\n\n' |
| yield "event: message_stop\n" |
| yield f'data: {json.dumps({"type": "message_stop"})}\n\n' |
| break |
| except json.JSONDecodeError: |
| continue |
|
|
