refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -16,7 +16,7 @@ log = logging.getLogger(__name__)
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def is_anthropic_url(url: str) -> bool:
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"""Check if the URL is an Anthropic API endpoint."""
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return "api.anthropic.com" in url
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return 'api.anthropic.com' in url
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async def get_anthropic_models(url: str, key: str, user: UserModel = None) -> dict:
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@@ -31,56 +31,56 @@ async def get_anthropic_models(url: str, key: str, user: UserModel = None) -> di
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try:
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async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
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headers = {
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"x-api-key": key,
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"anthropic-version": "2023-06-01",
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'x-api-key': key,
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'anthropic-version': '2023-06-01',
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}
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if ENABLE_FORWARD_USER_INFO_HEADERS and user:
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headers = include_user_info_headers(headers, user)
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while True:
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params = {"limit": 1000}
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params = {'limit': 1000}
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if after_id:
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params["after_id"] = after_id
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params['after_id'] = after_id
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async with session.get(
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f"{url}/models",
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f'{url}/models',
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headers=headers,
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params=params,
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ssl=AIOHTTP_CLIENT_SESSION_SSL,
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) as response:
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if response.status != 200:
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error_detail = f"HTTP Error: {response.status}"
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error_detail = f'HTTP Error: {response.status}'
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try:
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res = await response.json()
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if "error" in res:
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error_detail = f"External Error: {res['error']}"
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if 'error' in res:
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error_detail = f'External Error: {res["error"]}'
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except Exception:
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pass
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return {"object": "list", "data": [], "error": error_detail}
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return {'object': 'list', 'data': [], 'error': error_detail}
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data = await response.json()
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for model in data.get("data", []):
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for model in data.get('data', []):
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all_models.append(
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{
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"id": model.get("id"),
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"object": "model",
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"created": 0,
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"owned_by": "anthropic",
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"name": model.get("display_name", model.get("id")),
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'id': model.get('id'),
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'object': 'model',
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'created': 0,
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'owned_by': 'anthropic',
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'name': model.get('display_name', model.get('id')),
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}
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)
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if not data.get("has_more", False):
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if not data.get('has_more', False):
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break
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after_id = data.get("last_id")
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after_id = data.get('last_id')
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except Exception as e:
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log.error(f"Anthropic connection error: {e}")
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log.error(f'Anthropic connection error: {e}')
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return None
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return {"object": "list", "data": all_models}
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return {'object': 'list', 'data': all_models}
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##############################
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@@ -102,245 +102,241 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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openai_payload = {}
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# Model
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openai_payload["model"] = anthropic_payload.get("model", "")
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openai_payload['model'] = anthropic_payload.get('model', '')
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# Build messages list
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messages = []
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# System prompt (Anthropic has it as top-level, OpenAI as a system message)
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system = anthropic_payload.get("system")
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system = anthropic_payload.get('system')
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if system:
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if isinstance(system, str):
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messages.append({"role": "system", "content": system})
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messages.append({'role': 'system', 'content': system})
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elif isinstance(system, list):
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# Anthropic supports system as list of content blocks
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text_parts = []
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for block in system:
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if isinstance(block, dict) and block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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if isinstance(block, dict) and block.get('type') == 'text':
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text_parts.append(block.get('text', ''))
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elif isinstance(block, str):
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text_parts.append(block)
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messages.append({"role": "system", "content": "\n".join(text_parts)})
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messages.append({'role': 'system', 'content': '\n'.join(text_parts)})
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# Convert messages
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for msg in anthropic_payload.get("messages", []):
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role = msg.get("role", "user")
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content = msg.get("content")
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for msg in anthropic_payload.get('messages', []):
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role = msg.get('role', 'user')
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content = msg.get('content')
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if isinstance(content, str):
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messages.append({"role": role, "content": content})
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messages.append({'role': role, 'content': content})
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elif isinstance(content, list):
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# Convert Anthropic content blocks to OpenAI format
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openai_content = []
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tool_calls = []
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for block in content:
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block_type = block.get("type", "text")
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block_type = block.get('type', 'text')
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if block_type == "text":
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if block_type == 'text':
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openai_content.append(
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{
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"type": "text",
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"text": block.get("text", ""),
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'type': 'text',
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'text': block.get('text', ''),
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}
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)
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elif block_type == "image":
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source = block.get("source", {})
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if source.get("type") == "base64":
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media_type = source.get("media_type", "image/png")
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data = source.get("data", "")
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elif block_type == 'image':
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source = block.get('source', {})
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if source.get('type') == 'base64':
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media_type = source.get('media_type', 'image/png')
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data = source.get('data', '')
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openai_content.append(
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{media_type};base64,{data}",
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'type': 'image_url',
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'image_url': {
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'url': f'data:{media_type};base64,{data}',
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},
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}
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)
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elif source.get("type") == "url":
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elif source.get('type') == 'url':
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openai_content.append(
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{
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"type": "image_url",
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"image_url": {"url": source.get("url", "")},
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'type': 'image_url',
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'image_url': {'url': source.get('url', '')},
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}
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)
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elif block_type == "tool_use":
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elif block_type == 'tool_use':
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tool_calls.append(
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{
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"id": block.get("id", ""),
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"type": "function",
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"function": {
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"name": block.get("name", ""),
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"arguments": (
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json.dumps(block.get("input", {}))
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if isinstance(block.get("input"), dict)
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else str(block.get("input", "{}"))
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'id': block.get('id', ''),
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'type': 'function',
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'function': {
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'name': block.get('name', ''),
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'arguments': (
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json.dumps(block.get('input', {}))
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if isinstance(block.get('input'), dict)
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else str(block.get('input', '{}'))
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),
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},
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}
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)
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elif block_type == "tool_result":
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elif block_type == 'tool_result':
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# Tool results become separate tool messages in OpenAI format
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tool_content = block.get("content", "")
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tool_content = block.get('content', '')
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if isinstance(tool_content, list):
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tool_text_parts = []
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for tc in tool_content:
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if isinstance(tc, dict) and tc.get("type") == "text":
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tool_text_parts.append(tc.get("text", ""))
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tool_content = "\n".join(tool_text_parts)
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if isinstance(tc, dict) and tc.get('type') == 'text':
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tool_text_parts.append(tc.get('text', ''))
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tool_content = '\n'.join(tool_text_parts)
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# Propagate error status if present
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if block.get("is_error"):
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tool_content = f"Error: {tool_content}"
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if block.get('is_error'):
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tool_content = f'Error: {tool_content}'
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messages.append(
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{
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"role": "tool",
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"tool_call_id": block.get("tool_use_id", ""),
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"content": tool_content,
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'role': 'tool',
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'tool_call_id': block.get('tool_use_id', ''),
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'content': tool_content,
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}
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)
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# Build the message
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if tool_calls:
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# Assistant message with tool calls
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msg_dict = {"role": role}
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msg_dict = {'role': role}
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if openai_content:
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# If there's only text, flatten it
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if len(openai_content) == 1 and openai_content[0]["type"] == "text":
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msg_dict["content"] = openai_content[0]["text"]
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if len(openai_content) == 1 and openai_content[0]['type'] == 'text':
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msg_dict['content'] = openai_content[0]['text']
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else:
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msg_dict["content"] = openai_content
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msg_dict['content'] = openai_content
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else:
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msg_dict["content"] = ""
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msg_dict["tool_calls"] = tool_calls
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msg_dict['content'] = ''
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msg_dict['tool_calls'] = tool_calls
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messages.append(msg_dict)
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elif openai_content:
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# If there's only a single text block, flatten it to a string
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if len(openai_content) == 1 and openai_content[0]["type"] == "text":
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messages.append(
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{"role": role, "content": openai_content[0]["text"]}
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)
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if len(openai_content) == 1 and openai_content[0]['type'] == 'text':
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messages.append({'role': role, 'content': openai_content[0]['text']})
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else:
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messages.append({"role": role, "content": openai_content})
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messages.append({'role': role, 'content': openai_content})
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else:
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messages.append({"role": role, "content": str(content) if content else ""})
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messages.append({'role': role, 'content': str(content) if content else ''})
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openai_payload["messages"] = messages
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openai_payload['messages'] = messages
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# max_tokens
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if "max_tokens" in anthropic_payload:
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openai_payload["max_tokens"] = anthropic_payload["max_tokens"]
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if 'max_tokens' in anthropic_payload:
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openai_payload['max_tokens'] = anthropic_payload['max_tokens']
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# Common parameters
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for param in ("temperature", "top_p", "stop_sequences", "stream"):
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for param in ('temperature', 'top_p', 'stop_sequences', 'stream'):
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if param in anthropic_payload:
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if param == "stop_sequences":
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openai_payload["stop"] = anthropic_payload[param]
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if param == 'stop_sequences':
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openai_payload['stop'] = anthropic_payload[param]
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else:
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openai_payload[param] = anthropic_payload[param]
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# Tools conversion: Anthropic → OpenAI
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if "tools" in anthropic_payload:
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if 'tools' in anthropic_payload:
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openai_tools = []
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for tool in anthropic_payload["tools"]:
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for tool in anthropic_payload['tools']:
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openai_tools.append(
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{
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"type": "function",
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"function": {
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"name": tool.get("name", ""),
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"description": tool.get("description", ""),
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"parameters": tool.get("input_schema", {}),
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'type': 'function',
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'function': {
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'name': tool.get('name', ''),
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'description': tool.get('description', ''),
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'parameters': tool.get('input_schema', {}),
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},
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}
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)
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openai_payload["tools"] = openai_tools
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openai_payload['tools'] = openai_tools
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# tool_choice
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if "tool_choice" in anthropic_payload:
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tc = anthropic_payload["tool_choice"]
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if 'tool_choice' in anthropic_payload:
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tc = anthropic_payload['tool_choice']
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if isinstance(tc, dict):
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tc_type = tc.get("type", "auto")
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if tc_type == "auto":
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openai_payload["tool_choice"] = "auto"
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elif tc_type == "any":
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openai_payload["tool_choice"] = "required"
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elif tc_type == "tool":
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openai_payload["tool_choice"] = {
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"type": "function",
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"function": {"name": tc.get("name", "")},
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tc_type = tc.get('type', 'auto')
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if tc_type == 'auto':
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openai_payload['tool_choice'] = 'auto'
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elif tc_type == 'any':
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openai_payload['tool_choice'] = 'required'
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elif tc_type == 'tool':
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openai_payload['tool_choice'] = {
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'type': 'function',
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'function': {'name': tc.get('name', '')},
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}
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return openai_payload
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def convert_openai_to_anthropic_response(
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openai_response: dict, model: str = ""
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) -> dict:
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def convert_openai_to_anthropic_response(openai_response: dict, model: str = '') -> dict:
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"""
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Convert a non-streaming OpenAI Chat Completions response to Anthropic Messages format.
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"""
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import uuid as _uuid
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choice = {}
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if openai_response.get("choices"):
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choice = openai_response["choices"][0]
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if openai_response.get('choices'):
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choice = openai_response['choices'][0]
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message = choice.get("message", {})
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finish_reason = choice.get("finish_reason", "stop")
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message = choice.get('message', {})
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finish_reason = choice.get('finish_reason', 'stop')
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# Map finish_reason to stop_reason
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stop_reason_map = {
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"stop": "end_turn",
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"length": "max_tokens",
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"tool_calls": "tool_use",
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"content_filter": "end_turn",
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'stop': 'end_turn',
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'length': 'max_tokens',
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'tool_calls': 'tool_use',
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'content_filter': 'end_turn',
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}
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stop_reason = stop_reason_map.get(finish_reason, "end_turn")
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stop_reason = stop_reason_map.get(finish_reason, 'end_turn')
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# Build content blocks
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content = []
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msg_content = message.get("content")
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msg_content = message.get('content')
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if msg_content:
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content.append({"type": "text", "text": msg_content})
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content.append({'type': 'text', 'text': msg_content})
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# Tool calls → tool_use blocks
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tool_calls = message.get("tool_calls", [])
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tool_calls = message.get('tool_calls', [])
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for tc in tool_calls:
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func = tc.get("function", {})
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func = tc.get('function', {})
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try:
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tool_input = json.loads(func.get("arguments", "{}"))
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tool_input = json.loads(func.get('arguments', '{}'))
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except (json.JSONDecodeError, TypeError):
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tool_input = {}
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content.append(
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{
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"type": "tool_use",
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"id": tc.get("id", f"toolu_{_uuid.uuid4().hex[:24]}"),
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"name": func.get("name", ""),
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"input": tool_input,
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'type': 'tool_use',
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'id': tc.get('id', f'toolu_{_uuid.uuid4().hex[:24]}'),
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'name': func.get('name', ''),
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'input': tool_input,
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}
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)
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# Usage
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openai_usage = openai_response.get("usage", {})
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openai_usage = openai_response.get('usage', {})
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usage = {
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"input_tokens": openai_usage.get("prompt_tokens", 0),
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"output_tokens": openai_usage.get("completion_tokens", 0),
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'input_tokens': openai_usage.get('prompt_tokens', 0),
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'output_tokens': openai_usage.get('completion_tokens', 0),
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}
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return {
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"id": openai_response.get("id", f"msg_{_uuid.uuid4().hex[:24]}"),
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"type": "message",
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"role": "assistant",
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"content": content,
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"model": model or openai_response.get("model", ""),
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"stop_reason": stop_reason,
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"stop_sequence": None,
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"usage": usage,
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'id': openai_response.get('id', f'msg_{_uuid.uuid4().hex[:24]}'),
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'type': 'message',
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'role': 'assistant',
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'content': content,
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'model': model or openai_response.get('model', ''),
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'stop_reason': stop_reason,
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'stop_sequence': None,
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'usage': usage,
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}
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async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str = ""):
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async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str = ''):
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"""
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Convert an OpenAI SSE streaming response to Anthropic Messages SSE format.
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||||
@@ -352,10 +348,10 @@ async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str
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"""
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||||
import uuid as _uuid
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||||
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||||
msg_id = f"msg_{_uuid.uuid4().hex[:24]}"
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||||
msg_id = f'msg_{_uuid.uuid4().hex[:24]}'
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||||
input_tokens = 0
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||||
output_tokens = 0
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||||
stop_reason = "end_turn"
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||||
stop_reason = 'end_turn'
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||||
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||||
# Track content blocks with a running index.
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||||
# Each text block or tool_use block gets its own index.
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@@ -369,35 +365,35 @@ async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str
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# Emit message_start
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message_start = {
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"type": "message_start",
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"message": {
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"id": msg_id,
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"type": "message",
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"role": "assistant",
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"content": [],
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"model": model,
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"stop_reason": None,
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"stop_sequence": None,
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"usage": {"input_tokens": 0, "output_tokens": 0},
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'type': 'message_start',
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'message': {
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'id': msg_id,
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'type': 'message',
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'role': 'assistant',
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'content': [],
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'model': model,
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'stop_reason': None,
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'stop_sequence': None,
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'usage': {'input_tokens': 0, 'output_tokens': 0},
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},
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}
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yield f"event: message_start\ndata: {json.dumps(message_start)}\n\n".encode()
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yield f'event: message_start\ndata: {json.dumps(message_start)}\n\n'.encode()
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try:
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async for chunk in openai_stream_generator:
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if isinstance(chunk, bytes):
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chunk = chunk.decode("utf-8", errors="ignore")
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chunk = chunk.decode('utf-8', errors='ignore')
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for line in chunk.strip().split("\n"):
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for line in chunk.strip().split('\n'):
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line = line.strip()
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if not line or not line.startswith("data:"):
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if not line or not line.startswith('data:'):
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continue
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data_str = line[5:].strip()
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if data_str == "[DONE]":
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if data_str == '[DONE]':
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continue
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if data_str == "{}":
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if data_str == '{}':
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continue
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try:
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@@ -405,62 +401,58 @@ async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str
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except (json.JSONDecodeError, TypeError):
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continue
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choices = data.get("choices", [])
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choices = data.get('choices', [])
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if not choices:
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# Check for usage in the final chunk
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if data.get("usage"):
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input_tokens = data["usage"].get("prompt_tokens", input_tokens)
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output_tokens = data["usage"].get(
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"completion_tokens", output_tokens
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)
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if data.get('usage'):
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input_tokens = data['usage'].get('prompt_tokens', input_tokens)
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output_tokens = data['usage'].get('completion_tokens', output_tokens)
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continue
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delta = choices[0].get("delta", {})
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finish_reason = choices[0].get("finish_reason")
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delta = choices[0].get('delta', {})
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finish_reason = choices[0].get('finish_reason')
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# Update usage if present
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if data.get("usage"):
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input_tokens = data["usage"].get("prompt_tokens", input_tokens)
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output_tokens = data["usage"].get(
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"completion_tokens", output_tokens
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)
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if data.get('usage'):
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input_tokens = data['usage'].get('prompt_tokens', input_tokens)
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output_tokens = data['usage'].get('completion_tokens', output_tokens)
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# --- Handle text content ---
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content = delta.get("content")
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content = delta.get('content')
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if content is not None:
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if not text_block_open:
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# Start a new text content block
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block_start = {
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"type": "content_block_start",
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"index": current_block_index,
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"content_block": {"type": "text", "text": ""},
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'type': 'content_block_start',
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'index': current_block_index,
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'content_block': {'type': 'text', 'text': ''},
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}
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yield f"event: content_block_start\ndata: {json.dumps(block_start)}\n\n".encode()
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yield f'event: content_block_start\ndata: {json.dumps(block_start)}\n\n'.encode()
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text_block_open = True
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# Send text delta
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block_delta = {
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"type": "content_block_delta",
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"index": current_block_index,
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"delta": {"type": "text_delta", "text": content},
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'type': 'content_block_delta',
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'index': current_block_index,
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'delta': {'type': 'text_delta', 'text': content},
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}
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yield f"event: content_block_delta\ndata: {json.dumps(block_delta)}\n\n".encode()
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yield f'event: content_block_delta\ndata: {json.dumps(block_delta)}\n\n'.encode()
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# --- Handle tool calls ---
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tool_calls = delta.get("tool_calls")
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tool_calls = delta.get('tool_calls')
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if tool_calls:
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# Close text block if one is open (text comes before tools)
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if text_block_open:
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block_stop = {
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"type": "content_block_stop",
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"index": current_block_index,
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'type': 'content_block_stop',
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'index': current_block_index,
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}
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yield f"event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n".encode()
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yield f'event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n'.encode()
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text_block_open = False
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current_block_index += 1
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for tc in tool_calls:
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tc_index = tc.get("index", 0)
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tc_index = tc.get('index', 0)
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if tc_index not in tool_call_started:
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# First time seeing this tool call — emit content_block_start
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@@ -468,67 +460,67 @@ async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str
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tool_call_started[tc_index] = True
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# Extract tool call ID and name from the first chunk
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tc_id = tc.get("id", f"toolu_{_uuid.uuid4().hex[:24]}")
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tc_name = tc.get("function", {}).get("name", "")
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tc_id = tc.get('id', f'toolu_{_uuid.uuid4().hex[:24]}')
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tc_name = tc.get('function', {}).get('name', '')
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block_start = {
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"type": "content_block_start",
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"index": current_block_index,
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"content_block": {
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"type": "tool_use",
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"id": tc_id,
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"name": tc_name,
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"input": {},
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'type': 'content_block_start',
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'index': current_block_index,
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'content_block': {
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'type': 'tool_use',
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'id': tc_id,
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'name': tc_name,
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'input': {},
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},
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}
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yield f"event: content_block_start\ndata: {json.dumps(block_start)}\n\n".encode()
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yield f'event: content_block_start\ndata: {json.dumps(block_start)}\n\n'.encode()
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current_block_index += 1
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# Emit argument chunks as input_json_delta
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args_chunk = tc.get("function", {}).get("arguments", "")
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args_chunk = tc.get('function', {}).get('arguments', '')
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if args_chunk:
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block_delta = {
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"type": "content_block_delta",
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"index": tool_call_blocks[tc_index],
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"delta": {
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"type": "input_json_delta",
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"partial_json": args_chunk,
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'type': 'content_block_delta',
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'index': tool_call_blocks[tc_index],
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'delta': {
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'type': 'input_json_delta',
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'partial_json': args_chunk,
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},
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}
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yield f"event: content_block_delta\ndata: {json.dumps(block_delta)}\n\n".encode()
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yield f'event: content_block_delta\ndata: {json.dumps(block_delta)}\n\n'.encode()
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# --- Handle finish reason ---
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||||
if finish_reason is not None:
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||||
stop_reason_map = {
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||||
"stop": "end_turn",
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||||
"length": "max_tokens",
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||||
"tool_calls": "tool_use",
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||||
'stop': 'end_turn',
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||||
'length': 'max_tokens',
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||||
'tool_calls': 'tool_use',
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||||
}
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stop_reason = stop_reason_map.get(finish_reason, "end_turn")
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stop_reason = stop_reason_map.get(finish_reason, 'end_turn')
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except Exception as e:
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||||
log.error(f"Error in Anthropic stream conversion: {e}")
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log.error(f'Error in Anthropic stream conversion: {e}')
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||||
# Close any open text block
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||||
if text_block_open:
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||||
block_stop = {"type": "content_block_stop", "index": current_block_index}
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||||
yield f"event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n".encode()
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||||
block_stop = {'type': 'content_block_stop', 'index': current_block_index}
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yield f'event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n'.encode()
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||||
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||||
# Close any open tool call blocks
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||||
for tc_index, block_index in tool_call_blocks.items():
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block_stop = {"type": "content_block_stop", "index": block_index}
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||||
yield f"event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n".encode()
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||||
block_stop = {'type': 'content_block_stop', 'index': block_index}
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||||
yield f'event: content_block_stop\ndata: {json.dumps(block_stop)}\n\n'.encode()
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||||
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||||
# Emit message_delta with stop reason
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||||
message_delta = {
|
||||
"type": "message_delta",
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||||
"delta": {
|
||||
"stop_reason": stop_reason,
|
||||
"stop_sequence": None,
|
||||
'type': 'message_delta',
|
||||
'delta': {
|
||||
'stop_reason': stop_reason,
|
||||
'stop_sequence': None,
|
||||
},
|
||||
"usage": {"output_tokens": output_tokens},
|
||||
'usage': {'output_tokens': output_tokens},
|
||||
}
|
||||
yield f"event: message_delta\ndata: {json.dumps(message_delta)}\n\n".encode()
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||||
yield f'event: message_delta\ndata: {json.dumps(message_delta)}\n\n'.encode()
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||||
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||||
# Emit message_stop
|
||||
yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n".encode()
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||||
yield f'event: message_stop\ndata: {json.dumps({"type": "message_stop"})}\n\n'.encode()
|
||||
|
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Reference in New Issue
Block a user