df5c997da0
* Add subsequent flag * Finish integrating constrained/unconstrained toggling on v3 agent loop * Update tests to run on v3 * Run lint
88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
import os
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from typing import List, Optional
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from openai import AsyncOpenAI, AsyncStream, OpenAI
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from openai.types.chat.chat_completion import ChatCompletion
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from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
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from letta.llm_api.openai_client import OpenAIClient
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from letta.otel.tracing import trace_method
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.enums import AgentType
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import Message as PydanticMessage
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from letta.settings import model_settings
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class XAIClient(OpenAIClient):
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def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
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return False
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def supports_structured_output(self, llm_config: LLMConfig) -> bool:
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return False
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@trace_method
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def build_request_data(
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self,
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agent_type: AgentType,
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messages: List[PydanticMessage],
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llm_config: LLMConfig,
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tools: Optional[List[dict]] = None,
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force_tool_call: Optional[str] = None,
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requires_subsequent_tool_call: bool = False,
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) -> dict:
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data = super().build_request_data(agent_type, messages, llm_config, tools, force_tool_call, requires_subsequent_tool_call)
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# Specific bug for the mini models (as of Apr 14, 2025)
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# 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: presencePenalty'}
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# 400 - {'code': 'Client specified an invalid argument', 'error': 'Argument not supported on this model: frequencyPenalty'}
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if "grok-3-mini-" in llm_config.model:
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data.pop("presence_penalty", None)
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data.pop("frequency_penalty", None)
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return data
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@trace_method
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def request(self, request_data: dict, llm_config: LLMConfig) -> dict:
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"""
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Performs underlying synchronous request to OpenAI API and returns raw response dict.
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"""
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api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
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client = OpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
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response: ChatCompletion = client.chat.completions.create(**request_data)
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return response.model_dump()
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@trace_method
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async def request_async(self, request_data: dict, llm_config: LLMConfig) -> dict:
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"""
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Performs underlying asynchronous request to OpenAI API and returns raw response dict.
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"""
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api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
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client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
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response: ChatCompletion = await client.chat.completions.create(**request_data)
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return response.model_dump()
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@trace_method
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async def stream_async(self, request_data: dict, llm_config: LLMConfig) -> AsyncStream[ChatCompletionChunk]:
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"""
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Performs underlying asynchronous streaming request to OpenAI and returns the async stream iterator.
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"""
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api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
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client = AsyncOpenAI(api_key=api_key, base_url=llm_config.model_endpoint)
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response_stream: AsyncStream[ChatCompletionChunk] = await client.chat.completions.create(
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**request_data, stream=True, stream_options={"include_usage": True}
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)
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return response_stream
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@trace_method
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async def request_embeddings(self, inputs: List[str], embedding_config: EmbeddingConfig) -> List[List[float]]:
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"""Request embeddings given texts and embedding config"""
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api_key = model_settings.xai_api_key or os.environ.get("XAI_API_KEY")
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client = AsyncOpenAI(api_key=api_key, base_url=embedding_config.embedding_endpoint)
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response = await client.embeddings.create(model=embedding_config.embedding_model, input=inputs)
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# TODO: add total usage
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return [r.embedding for r in response.data]
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