Implement provider-agnostic tool calling for Anthropic streaming responses.
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@@ -96,6 +96,17 @@ class AnthropicProvider(BaseLLMProvider):
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response = self.client.messages.create(**params)
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logger.info("Received response from Anthropic")
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# Log the response (with sensitive information redacted)
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logger.info(f"Anthropic response type: {type(response).__name__}")
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# Log as much information as possible
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if hasattr(response, "json"):
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logger.info(f"Anthropic response json: {json.dumps(response.json)}")
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# Log response attributes
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response_attrs = [attr for attr in dir(response) if not attr.startswith("_") and not callable(getattr(response, attr))]
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logger.info(f"Anthropic response attributes: {response_attrs}")
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return response
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except Exception as e:
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error_msg = str(e)
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@@ -139,15 +150,17 @@ class AnthropicProvider(BaseLLMProvider):
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Check if the response contains tool calls.
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Args:
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response: Anthropic response object
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response: Anthropic response object or generator with tool_call attribute
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Returns:
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True if the response contains tool calls, False otherwise
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"""
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# Check if any content block is a tool_use block
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if not hasattr(response, "content"):
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return False
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# Check if response is a generator with a tool_call attribute
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if hasattr(response, "tool_call") and response.tool_call is not None:
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return True
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# Check if any content block is a tool_use block (for non-streaming responses)
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if hasattr(response, "content"):
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for block in response.content:
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if isinstance(block, dict) and block.get("type") == "tool_use":
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return True
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@@ -159,7 +172,7 @@ class AnthropicProvider(BaseLLMProvider):
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Process tool calls from the response.
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Args:
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response: Anthropic response object
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response: Anthropic response object or generator with tool_call attribute
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cookie: Airflow cookie for authentication
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Returns:
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@@ -170,13 +183,29 @@ class AnthropicProvider(BaseLLMProvider):
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if not self.has_tool_calls(response):
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return results
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# Extract tool_use blocks
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tool_use_blocks = [block for block in response.content if isinstance(block, dict) and block.get("type") == "tool_use"]
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tool_calls = []
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for block in tool_use_blocks:
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tool_id = block.get("id")
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tool_name = block.get("name")
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tool_input = block.get("input", {})
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# Check if response is a generator with a tool_call attribute
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if hasattr(response, "tool_call") and response.tool_call is not None:
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logger.info(f"Processing tool call from generator: {response.tool_call}")
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tool_calls.append(response.tool_call)
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# Otherwise, extract tool calls from response content (for non-streaming responses)
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elif hasattr(response, "content"):
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logger.info("Processing tool calls from response content")
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tool_calls = [block for block in response.content if isinstance(block, dict) and block.get("type") == "tool_use"]
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for tool_call in tool_calls:
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# Extract tool details - handle both formats (generator's tool_call and content block)
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if isinstance(tool_call, dict) and "id" in tool_call:
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# This is from the generator's tool_call attribute
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tool_id = tool_call.get("id")
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tool_name = tool_call.get("name")
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tool_input = tool_call.get("input", {})
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else:
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# This is from the content blocks
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tool_id = tool_call.get("id")
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tool_name = tool_call.get("name")
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tool_input = tool_call.get("input", {})
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try:
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# Execute the Airflow tool with the provided arguments and cookie
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@@ -270,15 +299,63 @@ class AnthropicProvider(BaseLLMProvider):
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response: Anthropic streaming response object
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Returns:
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Generator yielding content chunks
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Generator yielding content chunks with tool_call attribute if detected
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"""
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logger.info("Starting Anthropic streaming response processing")
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# Track only the first tool call detected during streaming
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tool_call = None
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tool_use_detected = False
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def generate():
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nonlocal tool_call, tool_use_detected
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for chunk in response:
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logger.debug(f"Chunk type: {type(chunk)}")
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logger.debug(f"Chunk content: {json.dumps(chunk.json) if hasattr(chunk, 'json') else str(chunk)}")
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# Handle different types of chunks from Anthropic API
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# Check for content_block_start events with type "tool_use"
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if not tool_use_detected and hasattr(chunk, "type") and chunk.type == "content_block_start":
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if hasattr(chunk, "content_block") and hasattr(chunk.content_block, "type"):
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if chunk.content_block.type == "tool_use":
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logger.info(f"Tool use detected in streaming response: {json.dumps(chunk.json) if hasattr(chunk, 'json') else str(chunk)}")
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tool_use_detected = True
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tool_call = {"id": getattr(chunk.content_block, "id", ""), "name": getattr(chunk.content_block, "name", ""), "input": getattr(chunk.content_block, "input", {})}
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# We don't signal to the frontend during streaming
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# The tool will only be executed after streaming ends
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continue
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# Handle content_block_delta events for tool_use (input updates)
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if tool_use_detected and hasattr(chunk, "type") and chunk.type == "content_block_delta":
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if hasattr(chunk, "delta") and hasattr(chunk.delta, "type") and chunk.delta.type == "input_json_delta":
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if hasattr(chunk.delta, "partial_json"):
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logger.info(f"Tool use input update: {chunk.delta.partial_json}")
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# Update the current tool call input
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if tool_call:
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try:
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# Try to parse the partial JSON and update the input
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partial_input = json.loads(chunk.delta.partial_json)
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tool_call["input"].update(partial_input)
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except json.JSONDecodeError:
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logger.warning(f"Failed to parse partial JSON: {chunk.delta.partial_json}")
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continue
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# Handle content_block_stop events for tool_use
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if tool_use_detected and hasattr(chunk, "type") and chunk.type == "content_block_stop":
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logger.info("Tool use block completed")
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# Log the complete tool call for debugging
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if tool_call:
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logger.info(f"Completed tool call: {json.dumps(tool_call)}")
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continue
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# Handle message_delta events with stop_reason "tool_use"
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if hasattr(chunk, "type") and chunk.type == "message_delta":
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if hasattr(chunk, "delta") and hasattr(chunk.delta, "stop_reason"):
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if chunk.delta.stop_reason == "tool_use":
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logger.info("Message stopped due to tool use")
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continue
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# Handle regular content chunks
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content = None
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if hasattr(chunk, "type") and chunk.type == "content_block_delta":
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if hasattr(chunk, "delta") and hasattr(chunk.delta, "text"):
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@@ -294,4 +371,12 @@ class AnthropicProvider(BaseLLMProvider):
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# Don't do any newline replacement here
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yield content
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return generate()
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# Create the generator
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gen = generate()
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# Attach the single tool_call to the generator object for later reference
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# This will be used after streaming is complete
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gen.tool_call = tool_call
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# Return the enhanced generator
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return gen
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