import os import random import time from typing import List, Optional, Union import requests from letta.constants import CLI_WARNING_PREFIX from letta.llm_api.anthropic import anthropic_chat_completions_request from letta.llm_api.azure_openai import azure_openai_chat_completions_request from letta.llm_api.cohere import cohere_chat_completions_request from letta.llm_api.google_ai import ( convert_tools_to_google_ai_format, google_ai_chat_completions_request, ) from letta.llm_api.helpers import ( add_inner_thoughts_to_functions, derive_inner_thoughts_in_kwargs, unpack_all_inner_thoughts_from_kwargs, ) from letta.llm_api.openai import ( build_openai_chat_completions_request, openai_chat_completions_process_stream, openai_chat_completions_request, ) from letta.local_llm.chat_completion_proxy import get_chat_completion from letta.local_llm.constants import ( INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION, ) from letta.schemas.enums import OptionState from letta.schemas.llm_config import LLMConfig from letta.schemas.message import Message from letta.schemas.openai.chat_completion_request import ( ChatCompletionRequest, Tool, cast_message_to_subtype, ) from letta.schemas.openai.chat_completion_response import ChatCompletionResponse from letta.streaming_interface import ( AgentChunkStreamingInterface, AgentRefreshStreamingInterface, ) LLM_API_PROVIDER_OPTIONS = ["openai", "azure", "anthropic", "google_ai", "cohere", "local", "groq"] def retry_with_exponential_backoff( func, initial_delay: float = 1, exponential_base: float = 2, jitter: bool = True, max_retries: int = 20, # List of OpenAI error codes: https://github.com/openai/openai-python/blob/17ac6779958b2b74999c634c4ea4c7b74906027a/src/openai/_client.py#L227-L250 # 429 = rate limit error_codes: tuple = (429,), ): """Retry a function with exponential backoff.""" def wrapper(*args, **kwargs): pass # Initialize variables num_retries = 0 delay = initial_delay # Loop until a successful response or max_retries is hit or an exception is raised while True: try: return func(*args, **kwargs) except requests.exceptions.HTTPError as http_err: # Retry on specified errors if http_err.response.status_code in error_codes: # Increment retries num_retries += 1 # Check if max retries has been reached if num_retries > max_retries: raise Exception(f"Maximum number of retries ({max_retries}) exceeded.") # Increment the delay delay *= exponential_base * (1 + jitter * random.random()) # Sleep for the delay # printd(f"Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying...") print( f"{CLI_WARNING_PREFIX}Got a rate limit error ('{http_err}') on LLM backend request, waiting {int(delay)}s then retrying..." ) time.sleep(delay) else: # For other HTTP errors, re-raise the exception raise # Raise exceptions for any errors not specified except Exception as e: raise e return wrapper @retry_with_exponential_backoff def create( # agent_state: AgentState, llm_config: LLMConfig, messages: List[Message], user_id: Optional[str] = None, # option UUID to associate request with functions: Optional[list] = None, functions_python: Optional[list] = None, function_call: str = "auto", # hint first_message: bool = False, # use tool naming? # if false, will use deprecated 'functions' style use_tool_naming: bool = True, # streaming? stream: bool = False, stream_inferface: Optional[Union[AgentRefreshStreamingInterface, AgentChunkStreamingInterface]] = None, # TODO move to llm_config? # if unspecified (None), default to something we've tested inner_thoughts_in_kwargs_option: OptionState = OptionState.DEFAULT, max_tokens: Optional[int] = None, model_settings: Optional[dict] = None, # TODO: eventually pass from server ) -> ChatCompletionResponse: """Return response to chat completion with backoff""" from letta.utils import printd if not model_settings: from letta.settings import model_settings model_settings = model_settings printd(f"Using model {llm_config.model_endpoint_type}, endpoint: {llm_config.model_endpoint}") if function_call and not functions: printd("unsetting function_call because functions is None") function_call = None # openai if llm_config.model_endpoint_type == "openai": if model_settings.openai_api_key is None and llm_config.model_endpoint == "https://api.openai.com/v1": # only is a problem if we are *not* using an openai proxy raise ValueError(f"OpenAI key is missing from letta config file") inner_thoughts_in_kwargs = derive_inner_thoughts_in_kwargs(inner_thoughts_in_kwargs_option, model=llm_config.model) data = build_openai_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming, inner_thoughts_in_kwargs, max_tokens ) if stream: # Client requested token streaming data.stream = True assert isinstance(stream_inferface, AgentChunkStreamingInterface) or isinstance( stream_inferface, AgentRefreshStreamingInterface ), type(stream_inferface) response = openai_chat_completions_process_stream( url=llm_config.model_endpoint, # https://api.openai.com/v1 -> https://api.openai.com/v1/chat/completions api_key=model_settings.openai_api_key, chat_completion_request=data, stream_inferface=stream_inferface, ) else: # Client did not request token streaming (expect a blocking backend response) data.stream = False if isinstance(stream_inferface, AgentChunkStreamingInterface): stream_inferface.stream_start() try: response = openai_chat_completions_request( url=llm_config.model_endpoint, # https://api.openai.com/v1 -> https://api.openai.com/v1/chat/completions api_key=model_settings.openai_api_key, chat_completion_request=data, ) finally: if isinstance(stream_inferface, AgentChunkStreamingInterface): stream_inferface.stream_end() if inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response # azure elif llm_config.model_endpoint_type == "azure": if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if model_settings.azure_api_key is None: raise ValueError(f"Azure API key is missing. Did you set AZURE_API_KEY in your env?") if model_settings.azure_base_url is None: raise ValueError(f"Azure base url is missing. Did you set AZURE_BASE_URL in your env?") if model_settings.azure_api_version is None: raise ValueError(f"Azure API version is missing. Did you set AZURE_API_VERSION in your env?") # Set the llm config model_endpoint from model_settings # For Azure, this model_endpoint is required to be configured via env variable, so users don't need to provide it in the LLM config llm_config.model_endpoint = model_settings.azure_base_url inner_thoughts_in_kwargs = derive_inner_thoughts_in_kwargs(inner_thoughts_in_kwargs_option, llm_config.model) chat_completion_request = build_openai_chat_completions_request( llm_config, messages, user_id, functions, function_call, use_tool_naming, inner_thoughts_in_kwargs, max_tokens ) response = azure_openai_chat_completions_request( model_settings=model_settings, llm_config=llm_config, api_key=model_settings.azure_api_key, chat_completion_request=chat_completion_request, ) if inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response elif llm_config.model_endpoint_type == "google_ai": if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if not use_tool_naming: raise NotImplementedError("Only tool calling supported on Google AI API requests") # NOTE: until Google AI supports CoT / text alongside function calls, # we need to put it in a kwarg (unless we want to split the message into two) google_ai_inner_thoughts_in_kwarg = True if functions is not None: tools = [{"type": "function", "function": f} for f in functions] tools = [Tool(**t) for t in tools] tools = convert_tools_to_google_ai_format(tools, inner_thoughts_in_kwargs=google_ai_inner_thoughts_in_kwarg) else: tools = None return google_ai_chat_completions_request( inner_thoughts_in_kwargs=google_ai_inner_thoughts_in_kwarg, base_url=llm_config.model_endpoint, model=llm_config.model, api_key=model_settings.gemini_api_key, # see structure of payload here: https://ai.google.dev/docs/function_calling data=dict( contents=[m.to_google_ai_dict() for m in messages], tools=tools, ), ) elif llm_config.model_endpoint_type == "anthropic": if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if not use_tool_naming: raise NotImplementedError("Only tool calling supported on Anthropic API requests") if functions is not None: tools = [{"type": "function", "function": f} for f in functions] tools = [Tool(**t) for t in tools] else: tools = None return anthropic_chat_completions_request( url=llm_config.model_endpoint, api_key=model_settings.anthropic_api_key, data=ChatCompletionRequest( model=llm_config.model, messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages], tools=[{"type": "function", "function": f} for f in functions] if functions else None, # tool_choice=function_call, # user=str(user_id), # NOTE: max_tokens is required for Anthropic API max_tokens=1024, # TODO make dynamic ), ) elif llm_config.model_endpoint_type == "cohere": if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") if not use_tool_naming: raise NotImplementedError("Only tool calling supported on Cohere API requests") if functions is not None: tools = [{"type": "function", "function": f} for f in functions] tools = [Tool(**t) for t in tools] else: tools = None return cohere_chat_completions_request( # url=llm_config.model_endpoint, url="https://api.cohere.ai/v1", # TODO api_key=os.getenv("COHERE_API_KEY"), # TODO remove chat_completion_request=ChatCompletionRequest( model="command-r-plus", # TODO messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages], tools=tools, tool_choice=function_call, # user=str(user_id), # NOTE: max_tokens is required for Anthropic API # max_tokens=1024, # TODO make dynamic ), ) elif llm_config.model_endpoint_type == "groq": if stream: raise NotImplementedError(f"Streaming not yet implemented for Groq.") if model_settings.groq_api_key is None and llm_config.model_endpoint == "https://api.groq.com/openai/v1/chat/completions": raise ValueError(f"Groq key is missing from letta config file") # force to true for groq, since they don't support 'content' is non-null inner_thoughts_in_kwargs = True if inner_thoughts_in_kwargs: functions = add_inner_thoughts_to_functions( functions=functions, inner_thoughts_key=INNER_THOUGHTS_KWARG, inner_thoughts_description=INNER_THOUGHTS_KWARG_DESCRIPTION, ) tools = [{"type": "function", "function": f} for f in functions] if functions is not None else None data = ChatCompletionRequest( model=llm_config.model, messages=[m.to_openai_dict(put_inner_thoughts_in_kwargs=inner_thoughts_in_kwargs) for m in messages], tools=tools, tool_choice=function_call, user=str(user_id), ) # https://console.groq.com/docs/openai # "The following fields are currently not supported and will result in a 400 error (yikes) if they are supplied:" assert data.top_logprobs is None assert data.logit_bias is None assert data.logprobs == False assert data.n == 1 # They mention that none of the messages can have names, but it seems to not error out (for now) data.stream = False if isinstance(stream_inferface, AgentChunkStreamingInterface): stream_inferface.stream_start() try: # groq uses the openai chat completions API, so this component should be reusable assert model_settings.groq_api_key is not None, "Groq key is missing" response = openai_chat_completions_request( url=llm_config.model_endpoint, api_key=model_settings.groq_api_key, chat_completion_request=data, ) finally: if isinstance(stream_inferface, AgentChunkStreamingInterface): stream_inferface.stream_end() if inner_thoughts_in_kwargs: response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG) return response # local model else: if stream: raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}") return get_chat_completion( model=llm_config.model, messages=messages, functions=functions, functions_python=functions_python, function_call=function_call, context_window=llm_config.context_window, endpoint=llm_config.model_endpoint, endpoint_type=llm_config.model_endpoint_type, wrapper=llm_config.model_wrapper, user=str(user_id), # hint first_message=first_message, # auth-related auth_type=model_settings.openllm_auth_type, auth_key=model_settings.openllm_api_key, )