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import httpx
from typing import Optional, List, Iterator, Dict, Any, Union, Tuple
from phi.llm.base import LLM
from phi.llm.message import Message
from phi.tools.function import FunctionCall
from phi.utils.log import logger
from phi.utils.timer import Timer
from phi.utils.functions import get_function_call
from phi.utils.tools import get_function_call_for_tool_call
try:
from openai import OpenAI as OpenAIClient, AsyncOpenAI as AsyncOpenAIClient
from openai.types.completion_usage import CompletionUsage
from openai.types.chat.chat_completion import ChatCompletion
from openai.types.chat.chat_completion_chunk import (
ChatCompletionChunk,
ChoiceDelta,
ChoiceDeltaFunctionCall,
ChoiceDeltaToolCall,
)
from openai.types.chat.chat_completion_message import (
ChatCompletionMessage,
FunctionCall as ChatCompletionFunctionCall,
)
from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall
except ImportError:
logger.error("`openai` not installed")
raise
class OpenAIChat(LLM):
name: str = "OpenAIChat"
model: str = "gpt-4-turbo"
# -*- Request parameters
frequency_penalty: Optional[float] = None
logit_bias: Optional[Any] = None
logprobs: Optional[bool] = None
max_tokens: Optional[int] = None
presence_penalty: Optional[float] = None
response_format: Optional[Dict[str, Any]] = None
seed: Optional[int] = None
stop: Optional[Union[str, List[str]]] = None
temperature: Optional[float] = None
top_logprobs: Optional[int] = None
user: Optional[str] = None
top_p: Optional[float] = None
extra_headers: Optional[Any] = None
extra_query: Optional[Any] = None
request_params: Optional[Dict[str, Any]] = None
# -*- Client parameters
api_key: Optional[str] = None
organization: Optional[str] = None
base_url: Optional[Union[str, httpx.URL]] = None
timeout: Optional[float] = None
max_retries: Optional[int] = None
default_headers: Optional[Any] = None
default_query: Optional[Any] = None
http_client: Optional[httpx.Client] = None
client_params: Optional[Dict[str, Any]] = None
# -*- Provide the OpenAI client manually
client: Optional[OpenAIClient] = None
async_client: Optional[AsyncOpenAIClient] = None
# Deprecated: will be removed in v3
openai_client: Optional[OpenAIClient] = None
def get_client(self) -> OpenAIClient:
if self.client:
return self.client
if self.openai_client:
return self.openai_client
_client_params: Dict[str, Any] = {}
if self.api_key:
_client_params["api_key"] = self.api_key
if self.organization:
_client_params["organization"] = self.organization
if self.base_url:
_client_params["base_url"] = self.base_url
if self.timeout:
_client_params["timeout"] = self.timeout
if self.max_retries:
_client_params["max_retries"] = self.max_retries
if self.default_headers:
_client_params["default_headers"] = self.default_headers
if self.default_query:
_client_params["default_query"] = self.default_query
if self.http_client:
_client_params["http_client"] = self.http_client
if self.client_params:
_client_params.update(self.client_params)
return OpenAIClient(**_client_params)
def get_async_client(self) -> AsyncOpenAIClient:
if self.async_client:
return self.async_client
_client_params: Dict[str, Any] = {}
if self.api_key:
_client_params["api_key"] = self.api_key
if self.organization:
_client_params["organization"] = self.organization
if self.base_url:
_client_params["base_url"] = self.base_url
if self.timeout:
_client_params["timeout"] = self.timeout
if self.max_retries:
_client_params["max_retries"] = self.max_retries
if self.default_headers:
_client_params["default_headers"] = self.default_headers
if self.default_query:
_client_params["default_query"] = self.default_query
if self.http_client:
_client_params["http_client"] = self.http_client
else:
_client_params["http_client"] = httpx.AsyncClient(
limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100)
)
if self.client_params:
_client_params.update(self.client_params)
return AsyncOpenAIClient(**_client_params)
@property
def api_kwargs(self) -> Dict[str, Any]:
_request_params: Dict[str, Any] = {}
if self.frequency_penalty:
_request_params["frequency_penalty"] = self.frequency_penalty
if self.logit_bias:
_request_params["logit_bias"] = self.logit_bias
if self.logprobs:
_request_params["logprobs"] = self.logprobs
if self.max_tokens:
_request_params["max_tokens"] = self.max_tokens
if self.presence_penalty:
_request_params["presence_penalty"] = self.presence_penalty
if self.response_format:
_request_params["response_format"] = self.response_format
if self.seed:
_request_params["seed"] = self.seed
if self.stop:
_request_params["stop"] = self.stop
if self.temperature:
_request_params["temperature"] = self.temperature
if self.top_logprobs:
_request_params["top_logprobs"] = self.top_logprobs
if self.user:
_request_params["user"] = self.user
if self.top_p:
_request_params["top_p"] = self.top_p
if self.extra_headers:
_request_params["extra_headers"] = self.extra_headers
if self.extra_query:
_request_params["extra_query"] = self.extra_query
if self.tools:
_request_params["tools"] = self.get_tools_for_api()
if self.tool_choice is None:
_request_params["tool_choice"] = "auto"
else:
_request_params["tool_choice"] = self.tool_choice
if self.request_params:
_request_params.update(self.request_params)
return _request_params
def to_dict(self) -> Dict[str, Any]:
_dict = super().to_dict()
if self.frequency_penalty:
_dict["frequency_penalty"] = self.frequency_penalty
if self.logit_bias:
_dict["logit_bias"] = self.logit_bias
if self.logprobs:
_dict["logprobs"] = self.logprobs
if self.max_tokens:
_dict["max_tokens"] = self.max_tokens
if self.presence_penalty:
_dict["presence_penalty"] = self.presence_penalty
if self.response_format:
_dict["response_format"] = self.response_format
if self.seed:
_dict["seed"] = self.seed
if self.stop:
_dict["stop"] = self.stop
if self.temperature:
_dict["temperature"] = self.temperature
if self.top_logprobs:
_dict["top_logprobs"] = self.top_logprobs
if self.user:
_dict["user"] = self.user
if self.top_p:
_dict["top_p"] = self.top_p
if self.extra_headers:
_dict["extra_headers"] = self.extra_headers
if self.extra_query:
_dict["extra_query"] = self.extra_query
if self.tools:
_dict["tools"] = self.get_tools_for_api()
if self.tool_choice is None:
_dict["tool_choice"] = "auto"
else:
_dict["tool_choice"] = self.tool_choice
return _dict
def invoke(self, messages: List[Message]) -> ChatCompletion:
return self.get_client().chat.completions.create(
model=self.model,
messages=[m.to_dict() for m in messages], # type: ignore
**self.api_kwargs,
)
async def ainvoke(self, messages: List[Message]) -> Any:
return await self.get_async_client().chat.completions.create(
model=self.model,
messages=[m.to_dict() for m in messages], # type: ignore
**self.api_kwargs,
)
def invoke_stream(self, messages: List[Message]) -> Iterator[ChatCompletionChunk]:
yield from self.get_client().chat.completions.create(
model=self.model,
messages=[m.to_dict() for m in messages], # type: ignore
stream=True,
**self.api_kwargs,
) # type: ignore
async def ainvoke_stream(self, messages: List[Message]) -> Any:
async_stream = await self.get_async_client().chat.completions.create(
model=self.model,
messages=[m.to_dict() for m in messages], # type: ignore
stream=True,
**self.api_kwargs,
)
async for chunk in async_stream: # type: ignore
yield chunk
def run_function(self, function_call: Dict[str, Any]) -> Tuple[Message, Optional[FunctionCall]]:
_function_name = function_call.get("name")
_function_arguments_str = function_call.get("arguments")
if _function_name is not None:
# Get function call
_function_call = get_function_call(
name=_function_name,
arguments=_function_arguments_str,
functions=self.functions,
)
if _function_call is None:
return Message(role="function", content="Could not find function to call."), None
if _function_call.error is not None:
return Message(role="function", content=_function_call.error), _function_call
if self.function_call_stack is None:
self.function_call_stack = []
# -*- Check function call limit
if len(self.function_call_stack) > self.function_call_limit:
self.tool_choice = "none"
return Message(
role="function",
content=f"Function call limit ({self.function_call_limit}) exceeded.",
), _function_call
# -*- Run function call
self.function_call_stack.append(_function_call)
_function_call_timer = Timer()
_function_call_timer.start()
_function_call.execute()
_function_call_timer.stop()
_function_call_message = Message(
role="function",
name=_function_call.function.name,
content=_function_call.result,
metrics={"time": _function_call_timer.elapsed},
)
if "function_call_times" not in self.metrics:
self.metrics["function_call_times"] = {}
if _function_call.function.name not in self.metrics["function_call_times"]:
self.metrics["function_call_times"][_function_call.function.name] = []
self.metrics["function_call_times"][_function_call.function.name].append(_function_call_timer.elapsed)
return _function_call_message, _function_call
return Message(role="function", content="Function name is None."), None
def response(self, messages: List[Message]) -> str:
logger.debug("---------- OpenAI Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
response_timer = Timer()
response_timer.start()
response: ChatCompletion = self.invoke(messages=messages)
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
# -*- Parse response
response_message: ChatCompletionMessage = response.choices[0].message
response_role = response_message.role
response_content: Optional[str] = response_message.content
response_function_call: Optional[ChatCompletionFunctionCall] = response_message.function_call
response_tool_calls: Optional[List[ChatCompletionMessageToolCall]] = response_message.tool_calls
# -*- Create assistant message
assistant_message = Message(
role=response_role or "assistant",
content=response_content,
)
if response_function_call is not None:
assistant_message.function_call = response_function_call.model_dump()
if response_tool_calls is not None:
assistant_message.tool_calls = [t.model_dump() for t in response_tool_calls]
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
response_usage: Optional[CompletionUsage] = response.usage
prompt_tokens = response_usage.prompt_tokens if response_usage is not None else None
if prompt_tokens is not None:
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
completion_tokens = response_usage.completion_tokens if response_usage is not None else None
if completion_tokens is not None:
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = response_usage.total_tokens if response_usage is not None else None
if total_tokens is not None:
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
# -*- Parse and run function call
need_to_run_functions = assistant_message.function_call is not None or assistant_message.tool_calls is not None
if need_to_run_functions and self.run_tools:
if assistant_message.function_call is not None:
function_call_message, function_call = self.run_function(function_call=assistant_message.function_call)
messages.append(function_call_message)
# -*- Get new response using result of function call
final_response = ""
if self.show_tool_calls and function_call is not None:
final_response += f"\n - Running: {function_call.get_call_str()}\n\n"
final_response += self.response(messages=messages)
return final_response
elif assistant_message.tool_calls is not None:
final_response = ""
function_calls_to_run: List[FunctionCall] = []
for tool_call in assistant_message.tool_calls:
_tool_call_id = tool_call.get("id")
_function_call = get_function_call_for_tool_call(tool_call, self.functions)
if _function_call is None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content="Could not find function to call.",
)
)
continue
if _function_call.error is not None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content=_function_call.error,
)
)
continue
function_calls_to_run.append(_function_call)
if self.show_tool_calls:
if len(function_calls_to_run) == 1:
final_response += f"\n - Running: {function_calls_to_run[0].get_call_str()}\n\n"
elif len(function_calls_to_run) > 1:
final_response += "\nRunning:"
for _f in function_calls_to_run:
final_response += f"\n - {_f.get_call_str()}"
final_response += "\n\n"
function_call_results = self.run_function_calls(function_calls_to_run)
if len(function_call_results) > 0:
messages.extend(function_call_results)
# -*- Get new response using result of tool call
final_response += self.response(messages=messages)
return final_response
logger.debug("---------- OpenAI Response End ----------")
# -*- Return content if no function calls are present
if assistant_message.content is not None:
return assistant_message.get_content_string()
return "Something went wrong, please try again."
async def aresponse(self, messages: List[Message]) -> str:
logger.debug("---------- OpenAI Async Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
response_timer = Timer()
response_timer.start()
response: ChatCompletion = await self.ainvoke(messages=messages)
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
# -*- Parse response
response_message: ChatCompletionMessage = response.choices[0].message
response_role = response_message.role
response_content: Optional[str] = response_message.content
response_function_call: Optional[ChatCompletionFunctionCall] = response_message.function_call
response_tool_calls: Optional[List[ChatCompletionMessageToolCall]] = response_message.tool_calls
# -*- Create assistant message
assistant_message = Message(
role=response_role or "assistant",
content=response_content,
)
if response_function_call is not None:
assistant_message.function_call = response_function_call.model_dump()
if response_tool_calls is not None:
assistant_message.tool_calls = [t.model_dump() for t in response_tool_calls]
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
response_usage: Optional[CompletionUsage] = response.usage
prompt_tokens = response_usage.prompt_tokens if response_usage is not None else None
if prompt_tokens is not None:
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
completion_tokens = response_usage.completion_tokens if response_usage is not None else None
if completion_tokens is not None:
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = response_usage.total_tokens if response_usage is not None else None
if total_tokens is not None:
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
# -*- Parse and run function call
need_to_run_functions = assistant_message.function_call is not None or assistant_message.tool_calls is not None
if need_to_run_functions and self.run_tools:
if assistant_message.function_call is not None:
function_call_message, function_call = self.run_function(function_call=assistant_message.function_call)
messages.append(function_call_message)
# -*- Get new response using result of function call
final_response = ""
if self.show_tool_calls and function_call is not None:
final_response += f"\n - Running: {function_call.get_call_str()}\n\n"
final_response += self.response(messages=messages)
return final_response
elif assistant_message.tool_calls is not None:
final_response = ""
function_calls_to_run: List[FunctionCall] = []
for tool_call in assistant_message.tool_calls:
_tool_call_id = tool_call.get("id")
_function_call = get_function_call_for_tool_call(tool_call, self.functions)
if _function_call is None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content="Could not find function to call.",
)
)
continue
if _function_call.error is not None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content=_function_call.error,
)
)
continue
function_calls_to_run.append(_function_call)
if self.show_tool_calls:
if len(function_calls_to_run) == 1:
final_response += f"\n - Running: {function_calls_to_run[0].get_call_str()}\n\n"
elif len(function_calls_to_run) > 1:
final_response += "\nRunning:"
for _f in function_calls_to_run:
final_response += f"\n - {_f.get_call_str()}"
final_response += "\n\n"
function_call_results = self.run_function_calls(function_calls_to_run)
if len(function_call_results) > 0:
messages.extend(function_call_results)
# -*- Get new response using result of tool call
final_response += await self.aresponse(messages=messages)
return final_response
logger.debug("---------- OpenAI Async Response End ----------")
# -*- Return content if no function calls are present
if assistant_message.content is not None:
return assistant_message.get_content_string()
return "Something went wrong, please try again."
def generate(self, messages: List[Message]) -> Dict:
logger.debug("---------- OpenAI Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
response_timer = Timer()
response_timer.start()
response: ChatCompletion = self.invoke(messages=messages)
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
# -*- Parse response
response_message: ChatCompletionMessage = response.choices[0].message
response_role = response_message.role
response_content: Optional[str] = response_message.content
response_function_call: Optional[ChatCompletionFunctionCall] = response_message.function_call
response_tool_calls: Optional[List[ChatCompletionMessageToolCall]] = response_message.tool_calls
# -*- Create assistant message
assistant_message = Message(
role=response_role or "assistant",
content=response_content,
)
if response_function_call is not None:
assistant_message.function_call = response_function_call.model_dump()
if response_tool_calls is not None:
assistant_message.tool_calls = [t.model_dump() for t in response_tool_calls]
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
response_usage: Optional[CompletionUsage] = response.usage
prompt_tokens = response_usage.prompt_tokens if response_usage is not None else None
if prompt_tokens is not None:
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
completion_tokens = response_usage.completion_tokens if response_usage is not None else None
if completion_tokens is not None:
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = response_usage.total_tokens if response_usage is not None else None
if total_tokens is not None:
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
# -*- Return response
response_message_dict = response_message.model_dump()
logger.debug("---------- OpenAI Response End ----------")
return response_message_dict
def response_stream(self, messages: List[Message]) -> Iterator[str]:
logger.debug("---------- OpenAI Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
assistant_message_content = ""
assistant_message_function_name = ""
assistant_message_function_arguments_str = ""
assistant_message_tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
completion_tokens = 0
response_timer = Timer()
response_timer.start()
for response in self.invoke_stream(messages=messages):
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
response_content: Optional[str] = None
response_function_call: Optional[ChoiceDeltaFunctionCall] = None
response_tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
if len(response.choices) > 0:
# -*- Parse response
response_delta: ChoiceDelta = response.choices[0].delta
response_content = response_delta.content
response_function_call = response_delta.function_call
response_tool_calls = response_delta.tool_calls
# -*- Return content if present, otherwise get function call
if response_content is not None:
assistant_message_content += response_content
completion_tokens += 1
yield response_content
# -*- Parse function call
if response_function_call is not None:
_function_name_stream = response_function_call.name
if _function_name_stream is not None:
assistant_message_function_name += _function_name_stream
_function_args_stream = response_function_call.arguments
if _function_args_stream is not None:
assistant_message_function_arguments_str += _function_args_stream
# -*- Parse tool calls
if response_tool_calls is not None:
if assistant_message_tool_calls is None:
assistant_message_tool_calls = []
assistant_message_tool_calls.extend(response_tool_calls)
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# -*- Create assistant message
assistant_message = Message(role="assistant")
# -*- Add content to assistant message
if assistant_message_content != "":
assistant_message.content = assistant_message_content
# -*- Add function call to assistant message
if assistant_message_function_name != "":
assistant_message.function_call = {
"name": assistant_message_function_name,
"arguments": assistant_message_function_arguments_str,
}
# -*- Add tool calls to assistant message
if assistant_message_tool_calls is not None:
# Build tool calls
tool_calls: List[Dict[str, Any]] = []
for _tool_call in assistant_message_tool_calls:
_index = _tool_call.index
_tool_call_id = _tool_call.id
_tool_call_type = _tool_call.type
_tool_call_function_name = _tool_call.function.name if _tool_call.function is not None else None
_tool_call_function_arguments_str = (
_tool_call.function.arguments if _tool_call.function is not None else None
)
tool_call_at_index = tool_calls[_index] if len(tool_calls) > _index else None
if tool_call_at_index is None:
tool_call_at_index_function_dict = {}
if _tool_call_function_name is not None:
tool_call_at_index_function_dict["name"] = _tool_call_function_name
if _tool_call_function_arguments_str is not None:
tool_call_at_index_function_dict["arguments"] = _tool_call_function_arguments_str
tool_call_at_index_dict = {
"id": _tool_call.id,
"type": _tool_call_type,
"function": tool_call_at_index_function_dict,
}
tool_calls.insert(_index, tool_call_at_index_dict)
else:
if _tool_call_function_name is not None:
if "name" not in tool_call_at_index["function"]:
tool_call_at_index["function"]["name"] = _tool_call_function_name
else:
tool_call_at_index["function"]["name"] += _tool_call_function_name
if _tool_call_function_arguments_str is not None:
if "arguments" not in tool_call_at_index["function"]:
tool_call_at_index["function"]["arguments"] = _tool_call_function_arguments_str
else:
tool_call_at_index["function"]["arguments"] += _tool_call_function_arguments_str
if _tool_call_id is not None:
tool_call_at_index["id"] = _tool_call_id
if _tool_call_type is not None:
tool_call_at_index["type"] = _tool_call_type
assistant_message.tool_calls = tool_calls
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
# TODO: compute prompt tokens
prompt_tokens = 0
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
logger.debug(f"Estimated completion tokens: {completion_tokens}")
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = prompt_tokens + completion_tokens
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
# -*- Parse and run function call
need_to_run_functions = assistant_message.function_call is not None or assistant_message.tool_calls is not None
if need_to_run_functions and self.run_tools:
if assistant_message.function_call is not None:
function_call_message, function_call = self.run_function(function_call=assistant_message.function_call)
messages.append(function_call_message)
if self.show_tool_calls and function_call is not None:
yield f"\n - Running: {function_call.get_call_str()}\n\n"
# -*- Yield new response using result of function call
yield from self.response_stream(messages=messages)
elif assistant_message.tool_calls is not None:
function_calls_to_run: List[FunctionCall] = []
for tool_call in assistant_message.tool_calls:
_tool_call_id = tool_call.get("id")
_function_call = get_function_call_for_tool_call(tool_call, self.functions)
if _function_call is None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content="Could not find function to call.",
)
)
continue
if _function_call.error is not None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content=_function_call.error,
)
)
continue
function_calls_to_run.append(_function_call)
if self.show_tool_calls:
if len(function_calls_to_run) == 1:
yield f"\n - Running: {function_calls_to_run[0].get_call_str()}\n\n"
elif len(function_calls_to_run) > 1:
yield "\nRunning:"
for _f in function_calls_to_run:
yield f"\n - {_f.get_call_str()}"
yield "\n\n"
function_call_results = self.run_function_calls(function_calls_to_run)
if len(function_call_results) > 0:
messages.extend(function_call_results)
# Code to show function call results
# for f in function_call_results:
# yield "\n"
# yield f.get_content_string()
# yield "\n"
# -*- Yield new response using results of tool calls
yield from self.response_stream(messages=messages)
logger.debug("---------- OpenAI Response End ----------")
async def aresponse_stream(self, messages: List[Message]) -> Any:
logger.debug("---------- OpenAI Async Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
assistant_message_content = ""
assistant_message_function_name = ""
assistant_message_function_arguments_str = ""
assistant_message_tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
completion_tokens = 0
response_timer = Timer()
response_timer.start()
async_stream = self.ainvoke_stream(messages=messages)
async for response in async_stream:
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
response_content: Optional[str] = None
response_function_call: Optional[ChoiceDeltaFunctionCall] = None
response_tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
if len(response.choices) > 0:
# -*- Parse response
response_delta: ChoiceDelta = response.choices[0].delta
response_content = response_delta.content
response_function_call = response_delta.function_call
response_tool_calls = response_delta.tool_calls
# -*- Return content if present, otherwise get function call
if response_content is not None:
assistant_message_content += response_content
completion_tokens += 1
yield response_content
# -*- Parse function call
if response_function_call is not None:
_function_name_stream = response_function_call.name
if _function_name_stream is not None:
assistant_message_function_name += _function_name_stream
_function_args_stream = response_function_call.arguments
if _function_args_stream is not None:
assistant_message_function_arguments_str += _function_args_stream
# -*- Parse tool calls
if response_tool_calls is not None:
if assistant_message_tool_calls is None:
assistant_message_tool_calls = []
assistant_message_tool_calls.extend(response_tool_calls)
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# -*- Create assistant message
assistant_message = Message(role="assistant")
# -*- Add content to assistant message
if assistant_message_content != "":
assistant_message.content = assistant_message_content
# -*- Add function call to assistant message
if assistant_message_function_name != "":
assistant_message.function_call = {
"name": assistant_message_function_name,
"arguments": assistant_message_function_arguments_str,
}
# -*- Add tool calls to assistant message
if assistant_message_tool_calls is not None:
# Build tool calls
tool_calls: List[Dict[str, Any]] = []
for _tool_call in assistant_message_tool_calls:
_index = _tool_call.index
_tool_call_id = _tool_call.id
_tool_call_type = _tool_call.type
_tool_call_function_name = _tool_call.function.name if _tool_call.function is not None else None
_tool_call_function_arguments_str = (
_tool_call.function.arguments if _tool_call.function is not None else None
)
tool_call_at_index = tool_calls[_index] if len(tool_calls) > _index else None
if tool_call_at_index is None:
tool_call_at_index_function_dict = {}
if _tool_call_function_name is not None:
tool_call_at_index_function_dict["name"] = _tool_call_function_name
if _tool_call_function_arguments_str is not None:
tool_call_at_index_function_dict["arguments"] = _tool_call_function_arguments_str
tool_call_at_index_dict = {
"id": _tool_call.id,
"type": _tool_call_type,
"function": tool_call_at_index_function_dict,
}
tool_calls.insert(_index, tool_call_at_index_dict)
else:
if _tool_call_function_name is not None:
if "name" not in tool_call_at_index["function"]:
tool_call_at_index["function"]["name"] = _tool_call_function_name
else:
tool_call_at_index["function"]["name"] += _tool_call_function_name
if _tool_call_function_arguments_str is not None:
if "arguments" not in tool_call_at_index["function"]:
tool_call_at_index["function"]["arguments"] = _tool_call_function_arguments_str
else:
tool_call_at_index["function"]["arguments"] += _tool_call_function_arguments_str
if _tool_call_id is not None:
tool_call_at_index["id"] = _tool_call_id
if _tool_call_type is not None:
tool_call_at_index["type"] = _tool_call_type
assistant_message.tool_calls = tool_calls
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
# TODO: compute prompt tokens
prompt_tokens = 0
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
logger.debug(f"Estimated completion tokens: {completion_tokens}")
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = prompt_tokens + completion_tokens
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
# -*- Parse and run function call
need_to_run_functions = assistant_message.function_call is not None or assistant_message.tool_calls is not None
if need_to_run_functions and self.run_tools:
if assistant_message.function_call is not None:
function_call_message, function_call = self.run_function(function_call=assistant_message.function_call)
messages.append(function_call_message)
if self.show_tool_calls and function_call is not None:
yield f"\n - Running: {function_call.get_call_str()}\n\n"
# -*- Yield new response using result of function call
fc_stream = self.aresponse_stream(messages=messages)
async for fc in fc_stream:
yield fc
# yield from self.response_stream(messages=messages)
elif assistant_message.tool_calls is not None:
function_calls_to_run: List[FunctionCall] = []
for tool_call in assistant_message.tool_calls:
_tool_call_id = tool_call.get("id")
_function_call = get_function_call_for_tool_call(tool_call, self.functions)
if _function_call is None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content="Could not find function to call.",
)
)
continue
if _function_call.error is not None:
messages.append(
Message(
role="tool",
tool_call_id=_tool_call_id,
content=_function_call.error,
)
)
continue
function_calls_to_run.append(_function_call)
if self.show_tool_calls:
if len(function_calls_to_run) == 1:
yield f"\n - Running: {function_calls_to_run[0].get_call_str()}\n\n"
elif len(function_calls_to_run) > 1:
yield "\nRunning:"
for _f in function_calls_to_run:
yield f"\n - {_f.get_call_str()}"
yield "\n\n"
function_call_results = self.run_function_calls(function_calls_to_run)
if len(function_call_results) > 0:
messages.extend(function_call_results)
# Code to show function call results
# for f in function_call_results:
# yield "\n"
# yield f.get_content_string()
# yield "\n"
# -*- Yield new response using results of tool calls
fc_stream = self.aresponse_stream(messages=messages)
async for fc in fc_stream:
yield fc
# yield from self.response_stream(messages=messages)
logger.debug("---------- OpenAI Async Response End ----------")
def generate_stream(self, messages: List[Message]) -> Iterator[Dict]:
logger.debug("---------- OpenAI Response Start ----------")
# -*- Log messages for debugging
for m in messages:
m.log()
assistant_message_content = ""
assistant_message_function_name = ""
assistant_message_function_arguments_str = ""
assistant_message_tool_calls: Optional[List[ChoiceDeltaToolCall]] = None
completion_tokens = 0
response_timer = Timer()
response_timer.start()
for response in self.invoke_stream(messages=messages):
# logger.debug(f"OpenAI response type: {type(response)}")
# logger.debug(f"OpenAI response: {response}")
completion_tokens += 1
# -*- Parse response
response_delta: ChoiceDelta = response.choices[0].delta
# -*- Read content
response_content: Optional[str] = response_delta.content
if response_content is not None:
assistant_message_content += response_content
# -*- Parse function call
response_function_call: Optional[ChoiceDeltaFunctionCall] = response_delta.function_call
if response_function_call is not None:
_function_name_stream = response_function_call.name
if _function_name_stream is not None:
assistant_message_function_name += _function_name_stream
_function_args_stream = response_function_call.arguments
if _function_args_stream is not None:
assistant_message_function_arguments_str += _function_args_stream
# -*- Parse tool calls
response_tool_calls: Optional[List[ChoiceDeltaToolCall]] = response_delta.tool_calls
if response_tool_calls is not None:
if assistant_message_tool_calls is None:
assistant_message_tool_calls = []
assistant_message_tool_calls.extend(response_tool_calls)
yield response_delta.model_dump()
response_timer.stop()
logger.debug(f"Time to generate response: {response_timer.elapsed:.4f}s")
# -*- Create assistant message
assistant_message = Message(role="assistant")
# -*- Add content to assistant message
if assistant_message_content != "":
assistant_message.content = assistant_message_content
# -*- Add function call to assistant message
if assistant_message_function_name != "":
assistant_message.function_call = {
"name": assistant_message_function_name,
"arguments": assistant_message_function_arguments_str,
}
# -*- Add tool calls to assistant message
if assistant_message_tool_calls is not None:
# Build tool calls
tool_calls: List[Dict[str, Any]] = []
for tool_call in assistant_message_tool_calls:
_index = tool_call.index
_tool_call_id = tool_call.id
_tool_call_type = tool_call.type
_tool_call_function_name = tool_call.function.name if tool_call.function is not None else None
_tool_call_function_arguments_str = (
tool_call.function.arguments if tool_call.function is not None else None
)
tool_call_at_index = tool_calls[_index] if len(tool_calls) > _index else None
if tool_call_at_index is None:
tool_call_at_index_function_dict = (
{
"name": _tool_call_function_name,
"arguments": _tool_call_function_arguments_str,
}
if _tool_call_function_name is not None or _tool_call_function_arguments_str is not None
else None
)
tool_call_at_index_dict = {
"id": tool_call.id,
"type": _tool_call_type,
"function": tool_call_at_index_function_dict,
}
tool_calls.insert(_index, tool_call_at_index_dict)
else:
if _tool_call_function_name is not None:
tool_call_at_index["function"]["name"] += _tool_call_function_name
if _tool_call_function_arguments_str is not None:
tool_call_at_index["function"]["arguments"] += _tool_call_function_arguments_str
if _tool_call_id is not None:
tool_call_at_index["id"] = _tool_call_id
if _tool_call_type is not None:
tool_call_at_index["type"] = _tool_call_type
assistant_message.tool_calls = tool_calls
# -*- Update usage metrics
# Add response time to metrics
assistant_message.metrics["time"] = response_timer.elapsed
if "response_times" not in self.metrics:
self.metrics["response_times"] = []
self.metrics["response_times"].append(response_timer.elapsed)
# Add token usage to metrics
# TODO: compute prompt tokens
prompt_tokens = 0
assistant_message.metrics["prompt_tokens"] = prompt_tokens
if "prompt_tokens" not in self.metrics:
self.metrics["prompt_tokens"] = prompt_tokens
else:
self.metrics["prompt_tokens"] += prompt_tokens
logger.debug(f"Estimated completion tokens: {completion_tokens}")
assistant_message.metrics["completion_tokens"] = completion_tokens
if "completion_tokens" not in self.metrics:
self.metrics["completion_tokens"] = completion_tokens
else:
self.metrics["completion_tokens"] += completion_tokens
total_tokens = prompt_tokens + completion_tokens
assistant_message.metrics["total_tokens"] = total_tokens
if "total_tokens" not in self.metrics:
self.metrics["total_tokens"] = total_tokens
else:
self.metrics["total_tokens"] += total_tokens
# -*- Add assistant message to messages
messages.append(assistant_message)
assistant_message.log()
logger.debug("---------- OpenAI Response End ----------")
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