refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -181,17 +181,133 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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 results become separate tool messages in OpenAI format
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tool_content = block.get('content', '')
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tool_result_content = block.get('content', '')
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if isinstance(tool_content, list):
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tool_content: str | list = ''
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tool_text_parts = []
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for tc in tool_content:
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if isinstance(tool_result_content, str):
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if isinstance(tc, dict) and tc.get('type') == 'text':
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tool_content = tool_result_content
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tool_text_parts.append(tc.get('text', ''))
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elif isinstance(tool_result_content, list):
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tool_content = '\n'.join(tool_text_parts)
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# Build a multimodal content array to preserve
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# images and other non-text content types.
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converted_parts = []
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for content_block in tool_result_content:
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if not isinstance(content_block, dict):
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continue
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content_type = content_block.get('type', 'text')
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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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)
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elif content_type == 'image':
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source = content_block.get('source', {})
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if source.get('type') == 'base64':
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media_type = source.get(
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'media_type', 'image/png'
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)
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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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},
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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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},
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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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# convert to a text representation.
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document_source = content_block.get(
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'source', {}
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)
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document_title = content_block.get(
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'title', 'Document'
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)
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document_context = content_block.get(
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'context', ''
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)
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document_text = (
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f'[Document: {document_title}]'
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)
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if document_context:
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document_text += f'\n{document_context}'
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if (
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document_source.get('type') == 'text'
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and document_source.get('data')
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):
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document_text += (
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f'\n{document_source["data"]}'
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)
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converted_parts.append(
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{'type': 'text', 'text': document_text}
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)
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elif content_type == 'search_result':
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# Convert search results to a text
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# representation with source attribution.
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search_title = content_block.get('title', '')
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search_url = content_block.get('source', '')
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search_content_blocks = content_block.get(
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'content', []
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)
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search_texts = []
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for search_block in search_content_blocks:
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if (
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isinstance(search_block, dict)
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and search_block.get('type') == 'text'
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):
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search_texts.append(
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search_block.get('text', '')
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)
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search_body = '\n'.join(search_texts)
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search_text = (
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f'[Search Result: {search_title}]'
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)
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if search_url:
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search_text += f'\nSource: {search_url}'
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if search_body:
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search_text += f'\n{search_body}'
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converted_parts.append(
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{'type': 'text', 'text': search_text}
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)
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# Flatten to string when only text parts are present
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if all(
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part.get('type') == 'text'
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for part in converted_parts
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):
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tool_content = '\n'.join(
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part.get('text', '')
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for part in converted_parts
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)
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elif converted_parts:
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tool_content = converted_parts
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else:
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tool_content = ''
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# Propagate error status if present
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# Propagate error status if present
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if block.get('is_error'):
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if block.get('is_error'):
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tool_content = f'Error: {tool_content}'
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if isinstance(tool_content, str):
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tool_content = f'Error: {tool_content}'
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elif isinstance(tool_content, list):
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tool_content.insert(
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0,
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{
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'type': 'text',
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'text': 'Error: ',
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},
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)
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messages.append(
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messages.append(
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{
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{
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