mirror of
https://github.com/open-webui/open-webui.git
synced 2026-07-09 20:09:02 +02:00
refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -89,6 +89,26 @@ async def get_anthropic_models(url: str, key: str, user: UserModel = None) -> di
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##############################
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def _copy_cache_control(source: dict, target: dict) -> dict:
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if isinstance(source, dict) and 'cache_control' in source:
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target['cache_control'] = source['cache_control']
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return target
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def _has_cache_control(blocks: list) -> bool:
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return any(isinstance(block, dict) and 'cache_control' in block for block in blocks)
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def _finalize_openai_content(blocks: list) -> str | list:
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if not blocks:
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return ''
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if len(blocks) == 1 and blocks[0].get('type') == 'text' and not _has_cache_control(blocks):
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return blocks[0].get('text', '')
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return blocks
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def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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"""
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Convert an Anthropic Messages API request to OpenAI Chat Completions format.
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@@ -112,14 +132,21 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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if isinstance(system, str):
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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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openai_content = []
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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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openai_content.append(
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_copy_cache_control(
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block,
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{
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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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)
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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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openai_content.append({'type': 'text', 'text': block})
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messages.append({'role': 'system', 'content': _finalize_openai_content(openai_content)})
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# Convert messages
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for msg in anthropic_payload.get('messages', []):
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@@ -138,10 +165,13 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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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}
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_copy_cache_control(
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block,
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{
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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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)
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elif block_type == 'image':
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source = block.get('source', {})
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@@ -149,19 +179,25 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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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_copy_cache_control(
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block,
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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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},
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},
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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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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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}
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_copy_cache_control(
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block,
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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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},
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)
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)
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elif block_type == 'tool_use':
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tool_calls.append(
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@@ -196,10 +232,13 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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if content_type == 'text':
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converted_parts.append(
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{
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'type': 'text',
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'text': content_block.get('text', ''),
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}
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_copy_cache_control(
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content_block,
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{
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'type': 'text',
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'text': content_block.get('text', ''),
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},
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)
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)
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elif content_type == 'image':
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source = content_block.get('source', {})
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@@ -207,21 +246,27 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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media_type = source.get('media_type', 'image/png')
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data = source.get('data', '')
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converted_parts.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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_copy_cache_control(
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content_block,
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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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},
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},
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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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converted_parts.append(
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{
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'type': 'image_url',
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'image_url': {
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'url': source.get('url', ''),
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_copy_cache_control(
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content_block,
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{
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'type': 'image_url',
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'image_url': {
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'url': source.get('url', ''),
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},
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},
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}
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)
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)
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elif content_type == 'document':
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# Documents have no direct OpenAI equivalent;
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@@ -254,7 +299,9 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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converted_parts.append({'type': 'text', 'text': search_text})
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# Flatten to string when only text parts are present
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if all(part.get('type') == 'text' for part in converted_parts):
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if all(part.get('type') == 'text' for part in converted_parts) and not _has_cache_control(
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converted_parts
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):
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tool_content = '\n'.join(part.get('text', '') for part in converted_parts)
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elif converted_parts:
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tool_content = converted_parts
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@@ -287,21 +334,13 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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# Assistant message with tool calls
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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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else:
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msg_dict['content'] = openai_content
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msg_dict['content'] = _finalize_openai_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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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({'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': _finalize_openai_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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@@ -312,7 +351,7 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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', 'top_k', 'stop_sequences', 'stream', 'metadata', 'service_tier'):
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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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@@ -324,14 +363,17 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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openai_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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_copy_cache_control(
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tool,
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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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},
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},
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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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@@ -382,7 +424,7 @@ def convert_openai_to_anthropic_response(openai_response: dict, model: str = '')
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content.append({'type': 'text', 'text': message_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') or []
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for tool_call in tool_calls:
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function = tool_call.get('function', {})
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try:
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@@ -404,6 +446,10 @@ def convert_openai_to_anthropic_response(openai_response: dict, model: str = '')
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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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if 'cache_creation_input_tokens' in openai_usage:
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usage['cache_creation_input_tokens'] = openai_usage['cache_creation_input_tokens']
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if 'cache_read_input_tokens' in openai_usage:
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usage['cache_read_input_tokens'] = openai_usage['cache_read_input_tokens']
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return {
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'id': openai_response.get('id', f'msg_{_uuid.uuid4().hex[:24]}'),
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@@ -703,4 +749,3 @@ async def openai_stream_to_anthropic_stream(openai_stream_generator, model: str
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# Emit message_stop
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yield f'event: message_stop\ndata: {json.dumps({"type": "message_stop"})}\n\n'.encode()
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