refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -62,13 +62,15 @@ async def get_anthropic_models(url: str, key: str, user: UserModel = None) -> di
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data = await response.json()
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for model in data.get("data", []):
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all_models.append({
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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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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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}
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)
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if not data.get("has_more", False):
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break
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@@ -136,37 +138,47 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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block_type = block.get("type", "text")
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if block_type == "text":
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openai_content.append({
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"type": "text",
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"text": block.get("text", ""),
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})
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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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)
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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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"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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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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},
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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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"type": "image_url",
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"image_url": {"url": source.get("url", "")},
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})
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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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)
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elif block_type == "tool_use":
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tool_calls.append({
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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": 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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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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),
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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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# Tool results become separate tool messages in OpenAI format
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tool_content = block.get("content", "")
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@@ -181,11 +193,13 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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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"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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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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}
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)
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# Build the message
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if tool_calls:
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@@ -204,7 +218,9 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> 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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messages.append(
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{"role": role, "content": openai_content[0]["text"]}
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)
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else:
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messages.append({"role": role, "content": openai_content})
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else:
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@@ -228,14 +244,16 @@ def convert_anthropic_to_openai_payload(anthropic_payload: dict) -> dict:
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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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openai_tools.append({
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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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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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},
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}
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)
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openai_payload["tools"] = openai_tools
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# tool_choice
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@@ -294,12 +312,14 @@ def convert_openai_to_anthropic_response(
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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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"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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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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}
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)
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# Usage
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openai_usage = openai_response.get("usage", {})
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@@ -320,9 +340,7 @@ def convert_openai_to_anthropic_response(
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}
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async def openai_stream_to_anthropic_stream(
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openai_stream_generator, model: str = ""
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):
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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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@@ -391,9 +409,7 @@ async def openai_stream_to_anthropic_stream(
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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(
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"prompt_tokens", input_tokens
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)
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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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@@ -404,9 +420,7 @@ async def openai_stream_to_anthropic_stream(
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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(
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"prompt_tokens", input_tokens
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)
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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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@@ -454,9 +468,7 @@ async def openai_stream_to_anthropic_stream(
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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(
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"id", f"toolu_{_uuid.uuid4().hex[:24]}"
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)
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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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@@ -473,9 +485,7 @@ async def openai_stream_to_anthropic_stream(
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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(
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"arguments", ""
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)
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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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@@ -522,4 +532,3 @@ async def openai_stream_to_anthropic_stream(
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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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