b8e9a80d93
* wait I forgot to comit locally * cp the entire core directory and then rm the .git subdir
85 lines
3.7 KiB
Python
85 lines
3.7 KiB
Python
import os
|
|
from typing import List, Optional
|
|
|
|
from openai import AsyncOpenAI, AsyncStream, OpenAI
|
|
from openai.types.chat.chat_completion import ChatCompletion
|
|
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
|
|
|
|
from letta.llm_api.openai_client import OpenAIClient
|
|
from letta.otel.tracing import trace_method
|
|
from letta.schemas.embedding_config import EmbeddingConfig
|
|
from letta.schemas.llm_config import LLMConfig
|
|
from letta.schemas.message import Message as PydanticMessage
|
|
from letta.settings import model_settings
|
|
|
|
|
|
class XAIClient(OpenAIClient):
|
|
def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
|
|
return False
|
|
|
|
def supports_structured_output(self, llm_config: LLMConfig) -> bool:
|
|
return False
|
|
|
|
@trace_method
|
|
def build_request_data(
|
|
self,
|
|
messages: List[PydanticMessage],
|
|
llm_config: LLMConfig,
|
|
tools: Optional[List[dict]] = None,
|
|
force_tool_call: Optional[str] = None,
|
|
) -> dict:
|
|
data = super().build_request_data(messages, llm_config, tools, force_tool_call)
|
|
|
|
# Specific bug for the mini models (as of Apr 14, 2025)
|
|
# 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: presencePenalty'}
|
|
# 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: frequencyPenalty'}
|
|
if "grok-3-mini-" in llm_config.model:
|
|
data.pop("presence_penalty", None)
|
|
data.pop("frequency_penalty", None)
|
|
|
|
return data
|
|
|
|
@trace_method
|
|
def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
|
|
"""
|
|
Performs underlying synchronous request to OpenAI API and returns raw response dict.
|
|
"""
|
|
api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
|
|
client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
|
|
|
|
response: ChatCompletion = client.chat.completions.create(**request_data)
|
|
return response.model_dump()
|
|
|
|
@trace_method
|
|
async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
|
|
"""
|
|
Performs underlying asynchronous request to OpenAI API and returns raw response dict.
|
|
"""
|
|
api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
|
|
client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
|
|
|
|
response: ChatCompletion = await client.chat.completions.create(**request_data)
|
|
return response.model_dump()
|
|
|
|
@trace_method
|
|
async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
|
|
"""
|
|
Performs underlying asynchronous streaming request to OpenAI and returns the async stream iterator.
|
|
"""
|
|
api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
|
|
client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
|
|
response_stream: AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
|
|
**request_data, stream=True, stream_options={"include_usage": True}
|
|
)
|
|
return response_stream
|
|
|
|
@trace_method
|
|
async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
|
|
"""Request embeddings given texts and embedding config"""
|
|
api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
|
|
client = AsyncOpenAI(api_key=api_key, base_url=embedding_config.embedding_endpoint)
|
|
response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
|
|
|
|
# TODO: add total usage
|
|
return [r.embedding for r in response.data]
|