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from typing import Any, Dict, List, Optional
import json
from pydantic import BaseModel
class LLMResponseError(Exception):
"""Represents an error in LLM response.
Attributes:
message: Error message
model: Model name
response: Original response object
"""
def __init__(self, message: str, model: str = "unknown", response: Any = None):
"""
Initialize LLM response error
Args:
message: Error message
model: Model name
response: Original response object
"""
self.message = message
self.model = model
self.response = response
super().__init__(f"LLM Error ({model}): {message}. Response: {response}")
class Function(BaseModel):
"""
Represents a function call made by a model
"""
name: str
arguments: str = None
class ToolCall(BaseModel):
"""
Represents a tool call made by a model
"""
id: str
type: str = "function"
function: Function = None
# name: str = None
# arguments: str = None
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'ToolCall':
"""
Create ToolCall from dictionary representation
Args:
data: Dictionary containing tool call data
Returns:
ToolCall object
"""
if not data:
return None
tool_id = data.get('id', f"call_{hash(str(data)) & 0xffffffff:08x}")
tool_type = data.get('type', 'function')
function_data = data.get('function', {})
name = function_data.get('name')
arguments = function_data.get('arguments')
# Ensure arguments is a string
if arguments is not None and not isinstance(arguments, str):
arguments = json.dumps(arguments, ensure_ascii=False)
function = Function(name=name, arguments=arguments)
return cls(
id=tool_id,
type=tool_type,
function=function,
# name=name,
# arguments=arguments,
)
def to_dict(self) -> Dict[str, Any]:
"""
Convert ToolCall to dictionary representation
Returns:
Dictionary representation
"""
return {
"id": self.id,
"type": self.type,
"function": {
"name": self.function.name,
"arguments": self.function.arguments
}
}
def __repr__(self):
return json.dumps(self.to_dict(), ensure_ascii=False)
def __iter__(self):
"""
Make ToolCall dict-like for JSON serialization
"""
yield from self.to_dict().items()
class ModelResponse:
"""
Unified model response class for encapsulating responses from different LLM providers
"""
def __init__(
self,
id: str,
model: str,
content: str = None,
tool_calls: List[ToolCall] = None,
usage: Dict[str, int] = None,
error: str = None,
raw_response: Any = None,
message: Dict[str, Any] = None
):
"""
Initialize ModelResponse object
Args:
id: Response ID
model: Model name used
content: Generated text content
tool_calls: List of tool calls
usage: Usage statistics (token counts, etc.)
error: Error message (if any)
raw_response: Original response object
message: Complete message object, can be used for subsequent API calls
"""
self.id = id
self.model = model
self.content = content
self.tool_calls = tool_calls
self.usage = usage or {
"completion_tokens": 0,
"prompt_tokens": 0,
"total_tokens": 0
}
self.error = error
self.raw_response = raw_response
# If message is not provided, construct one from other fields
if message is None:
self.message = {
"role": "assistant",
"content": content
}
if tool_calls:
self.message["tool_calls"] = [tool_call.to_dict() for tool_call in tool_calls]
else:
self.message = message
@classmethod
def from_openai_response(cls, response: Any) -> 'ModelResponse':
"""
Create ModelResponse from OpenAI response object
Args:
response: OpenAI response object
Returns:
ModelResponse object
Raises:
LLMResponseError: When LLM response error occurs
"""
# Handle error cases
if hasattr(response, 'error') or (isinstance(response, dict) and response.get('error')):
error_msg = response.error if hasattr(response, 'error') else response.get('error', 'Unknown error')
raise LLMResponseError(
error_msg,
response.model if hasattr(response, 'model') else response.get('model', 'unknown'),
response
)
# Normal case
message = None
if hasattr(response, 'choices') and response.choices:
message = response.choices[0].message
elif isinstance(response, dict) and response.get('choices'):
message = response['choices'][0].get('message', {})
if not message:
raise LLMResponseError(
"No message found in response",
response.model if hasattr(response, 'model') else response.get('model', 'unknown'),
response
)
# Extract usage information
usage = {}
if hasattr(response, 'usage'):
usage = {
"completion_tokens": response.usage.completion_tokens if hasattr(response.usage,
'completion_tokens') else 0,
"prompt_tokens": response.usage.prompt_tokens if hasattr(response.usage, 'prompt_tokens') else 0,
"total_tokens": response.usage.total_tokens if hasattr(response.usage, 'total_tokens') else 0
}
elif isinstance(response, dict) and response.get('usage'):
usage = response['usage']
# Build message object
message_dict = {}
if hasattr(message, '__dict__'):
# Convert object to dictionary
for key, value in message.__dict__.items():
if not key.startswith('_'):
message_dict[key] = value
elif isinstance(message, dict):
message_dict = message
else:
# Extract common properties
message_dict = {
"role": "assistant",
"content": message.content if hasattr(message, 'content') else "",
"tool_calls": message.tool_calls if hasattr(message, 'tool_calls') else None,
}
message_dict["content"] = '' if message_dict.get('content') is None else message_dict.get('content', '')
# Process tool calls
processed_tool_calls = []
raw_tool_calls = message.tool_calls if hasattr(message, 'tool_calls') else message_dict.get('tool_calls')
if raw_tool_calls:
for tool_call in raw_tool_calls:
if isinstance(tool_call, dict):
processed_tool_calls.append(ToolCall.from_dict(tool_call))
else:
# Handle OpenAI object
tool_call_dict = {
"id": tool_call.id if hasattr(tool_call,
'id') else f"call_{hash(str(tool_call)) & 0xffffffff:08x}",
"type": tool_call.type if hasattr(tool_call, 'type') else "function"
}
if hasattr(tool_call, 'function'):
function = tool_call.function
tool_call_dict["function"] = {
"name": function.name if hasattr(function, 'name') else None,
"arguments": function.arguments if hasattr(function, 'arguments') else None
}
processed_tool_calls.append(ToolCall.from_dict(tool_call_dict))
if message_dict and processed_tool_calls:
message_dict["tool_calls"] = [tool_call.to_dict() for tool_call in processed_tool_calls]
# Create and return ModelResponse
return cls(
id=response.id if hasattr(response, 'id') else response.get('id', 'unknown'),
model=response.model if hasattr(response, 'model') else response.get('model', 'unknown'),
content=message.content if hasattr(message, 'content') else message.get('content') or "",
tool_calls=processed_tool_calls or None,
usage=usage,
raw_response=response,
message=message_dict
)
@classmethod
def from_openai_stream_chunk(cls, chunk: Any) -> 'ModelResponse':
"""
Create ModelResponse from OpenAI stream response chunk
Args:
chunk: OpenAI stream chunk
Returns:
ModelResponse object
Raises:
LLMResponseError: When LLM response error occurs
"""
# Handle error cases
if hasattr(chunk, 'error') or (isinstance(chunk, dict) and chunk.get('error')):
error_msg = chunk.error if hasattr(chunk, 'error') else chunk.get('error', 'Unknown error')
raise LLMResponseError(
error_msg,
chunk.model if hasattr(chunk, 'model') else chunk.get('model', 'unknown'),
chunk
)
# Handle finish reason chunk (end of stream)
if hasattr(chunk, 'choices') and chunk.choices and chunk.choices[0].finish_reason:
return cls(
id=chunk.id if hasattr(chunk, 'id') else chunk.get('id', 'unknown'),
model=chunk.model if hasattr(chunk, 'model') else chunk.get('model', 'unknown'),
content=None,
raw_response=chunk,
message={"role": "assistant", "content": "", "finish_reason": chunk.choices[0].finish_reason}
)
# Normal chunk with delta content
content = None
processed_tool_calls = []
if hasattr(chunk, 'choices') and chunk.choices:
delta = chunk.choices[0].delta
if hasattr(delta, 'content') and delta.content:
content = delta.content
if hasattr(delta, 'tool_calls') and delta.tool_calls:
raw_tool_calls = delta.tool_calls
for tool_call in raw_tool_calls:
if isinstance(tool_call, dict):
processed_tool_calls.append(ToolCall.from_dict(tool_call))
else:
# Handle OpenAI object
tool_call_dict = {
"id": tool_call.id if hasattr(tool_call,
'id') else f"call_{hash(str(tool_call)) & 0xffffffff:08x}",
"type": tool_call.type if hasattr(tool_call, 'type') else "function"
}
if hasattr(tool_call, 'function'):
function = tool_call.function
tool_call_dict["function"] = {
"name": function.name if hasattr(function, 'name') else None,
"arguments": function.arguments if hasattr(function, 'arguments') else None
}
processed_tool_calls.append(ToolCall.from_dict(tool_call_dict))
elif isinstance(chunk, dict) and chunk.get('choices'):
delta = chunk['choices'][0].get('delta', {})
if not delta:
delta = chunk['choices'][0].get('message', {})
content = delta.get('content')
raw_tool_calls = delta.get('tool_calls')
if raw_tool_calls:
for tool_call in raw_tool_calls:
processed_tool_calls.append(ToolCall.from_dict(tool_call))
# Extract usage information
usage = {}
if hasattr(chunk, 'usage'):
usage = {
"completion_tokens": chunk.usage.completion_tokens if hasattr(chunk.usage, 'completion_tokens') else 0,
"prompt_tokens": chunk.usage.prompt_tokens if hasattr(chunk.usage, 'prompt_tokens') else 0,
"total_tokens": chunk.usage.total_tokens if hasattr(chunk.usage, 'total_tokens') else 0
}
elif isinstance(chunk, dict) and chunk.get('usage'):
usage = chunk['usage']
# Create message object
message = {
"role": "assistant",
"content": content or "",
"tool_calls": [tool_call.to_dict() for tool_call in processed_tool_calls] if processed_tool_calls else None,
"is_chunk": True
}
# Create and return ModelResponse
return cls(
id=chunk.id if hasattr(chunk, 'id') else chunk.get('id', 'unknown'),
model=chunk.model if hasattr(chunk, 'model') else chunk.get('model', 'unknown'),
content=content,
tool_calls=processed_tool_calls or None,
usage=usage,
raw_response=chunk,
message=message
)
@classmethod
def from_anthropic_stream_chunk(cls, chunk: Any) -> 'ModelResponse':
"""
Create ModelResponse from Anthropic stream response chunk
Args:
chunk: Anthropic stream chunk
Returns:
ModelResponse object
Raises:
LLMResponseError: When LLM response error occurs
"""
try:
# Handle error cases
if not chunk or (isinstance(chunk, dict) and chunk.get('error')):
error_msg = chunk.get('error', 'Unknown error') if isinstance(chunk, dict) else 'Empty response'
raise LLMResponseError(
error_msg,
chunk.model if hasattr(chunk, 'model') else chunk.get('model', 'unknown'),
chunk)
# Handle stop reason (end of stream)
if hasattr(chunk, 'stop_reason') and chunk.stop_reason:
return cls(
id=chunk.id if hasattr(chunk, 'id') else 'unknown',
model=chunk.model if hasattr(chunk, 'model') else 'claude',
content=None,
raw_response=chunk,
message={"role": "assistant", "content": "", "stop_reason": chunk.stop_reason}
)
# Handle delta content
content = None
processed_tool_calls = []
if hasattr(chunk, 'delta') and chunk.delta:
delta = chunk.delta
if hasattr(delta, 'text') and delta.text:
content = delta.text
elif hasattr(delta, 'tool_use') and delta.tool_use:
tool_call_dict = {
"id": f"call_{delta.tool_use.id}",
"type": "function",
"function": {
"name": delta.tool_use.name,
"arguments": delta.tool_use.input if isinstance(delta.tool_use.input, str) else json.dumps(
delta.tool_use.input, ensure_ascii=False)
}
}
processed_tool_calls.append(ToolCall.from_dict(tool_call_dict))
# Create message object
message = {
"role": "assistant",
"content": content or "",
"tool_calls": [tool_call.to_dict() for tool_call in
processed_tool_calls] if processed_tool_calls else None,
"is_chunk": True
}
# Create and return ModelResponse
return cls(
id=chunk.id if hasattr(chunk, 'id') else 'unknown',
model=chunk.model if hasattr(chunk, 'model') else 'claude',
content=content,
tool_calls=processed_tool_calls or None,
raw_response=chunk,
message=message
)
except Exception as e:
if isinstance(e, LLMResponseError):
raise e
raise LLMResponseError(
f"Error processing Anthropic stream chunk: {str(e)}",
chunk.model if hasattr(chunk, 'model') else chunk.get('model', 'unknown'),
chunk)
@classmethod
def from_anthropic_response(cls, response: Any) -> 'ModelResponse':
"""
Create ModelResponse from Anthropic original response object
Args:
response: Anthropic response object
Returns:
ModelResponse object
Raises:
LLMResponseError: When LLM response error occurs
"""
try:
# Handle error cases
if not response or (isinstance(response, dict) and response.get('error')):
error_msg = response.get('error', 'Unknown error') if isinstance(response, dict) else 'Empty response'
raise LLMResponseError(
error_msg,
response.model if hasattr(response, 'model') else response.get('model', 'unknown'),
response)
# Build message content
message = {
"content": "",
"role": "assistant",
"tool_calls": None,
}
processed_tool_calls = []
if hasattr(response, 'content') and response.content:
for content_block in response.content:
if content_block.type == "text":
message["content"] = content_block.text
elif content_block.type == "tool_use":
tool_call_dict = {
"id": f"call_{content_block.id}",
"type": "function",
"function": {
"name": content_block.name,
"arguments": content_block.input if isinstance(content_block.input,
str) else json.dumps(content_block.input)
}
}
processed_tool_calls.append(ToolCall.from_dict(tool_call_dict))
else:
message["content"] = ""
if processed_tool_calls:
message["tool_calls"] = [tool_call.to_dict() for tool_call in processed_tool_calls]
# Extract usage information
usage = {
"completion_tokens": 0,
"prompt_tokens": 0,
"total_tokens": 0
}
if hasattr(response, 'usage'):
if hasattr(response.usage, 'output_tokens'):
usage["completion_tokens"] = response.usage.output_tokens
if hasattr(response.usage, 'input_tokens'):
usage["prompt_tokens"] = response.usage.input_tokens
if hasattr(response.usage, 'input_tokens') and hasattr(response.usage, 'output_tokens'):
usage["total_tokens"] = response.usage.input_tokens + response.usage.output_tokens
# Create ModelResponse
return cls(
id=response.id if hasattr(response,
'id') else f"chatcmpl-anthropic-{hash(str(response)) & 0xffffffff:08x}",
model=response.model if hasattr(response, 'model') else "claude",
content=message["content"],
tool_calls=processed_tool_calls or None,
usage=usage,
raw_response=response,
message=message
)
except Exception as e:
if isinstance(e, LLMResponseError):
raise e
raise LLMResponseError(
f"Error processing Anthropic response: {str(e)}",
response.model if hasattr(response, 'model') else response.get('model', 'unknown'),
response)
@classmethod
def from_error(cls, error_msg: str, model: str = "unknown") -> 'ModelResponse':
"""
Create ModelResponse from error message
Args:
error_msg: Error message
model: Model name
Returns:
ModelResponse object
"""
return cls(
id="error",
model=model,
error=error_msg,
message={"role": "assistant", "content": f"Error: {error_msg}"}
)
def to_dict(self) -> Dict[str, Any]:
"""
Convert ModelResponse to dictionary representation
Returns:
Dictionary representation
"""
tool_calls_dict = None
if self.tool_calls:
tool_calls_dict = [tool_call.to_dict() for tool_call in self.tool_calls]
return {
"id": self.id,
"model": self.model,
"content": self.content,
"tool_calls": tool_calls_dict,
"usage": self.usage,
"error": self.error,
"message": self.message
}
def get_message(self) -> Dict[str, Any]:
"""
Return message object that can be directly used for subsequent API calls
Returns:
Message object dictionary
"""
return self.message
def serialize_tool_calls(self) -> List[Dict[str, Any]]:
"""
Convert tool call objects to JSON format, handling OpenAI object types
Returns:
List[Dict[str, Any]]: Tool calls list in JSON format
"""
if not self.tool_calls:
return []
result = []
for tool_call in self.tool_calls:
if hasattr(tool_call, 'to_dict'):
result.append(tool_call.to_dict())
elif isinstance(tool_call, dict):
result.append(tool_call)
else:
result.append(str(tool_call))
return result
def __repr__(self):
return json.dumps(self.to_dict(), ensure_ascii=False, indent=None,
default=lambda obj: obj.to_dict() if hasattr(obj, 'to_dict') else str(obj))
def _serialize_message(self) -> Dict[str, Any]:
"""
Serialize message object
Returns:
Dict[str, Any]: Serialized message dictionary
"""
if not self.message:
return {}
result = {}
# Copy basic fields
for key, value in self.message.items():
if key == 'tool_calls':
# Handle tool_calls
result[key] = self.serialize_tool_calls()
else:
result[key] = value
return result
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