d48932bdb6
Multiple OpenAI-compatible LLM clients (Azure, Deepseek, Groq, Together, XAI, ZAI) and Anthropic-compatible clients (Anthropic, MiniMax, Google Vertex) were overriding request_async/stream_async without calling sanitize_unicode_surrogates, causing UnicodeEncodeError when message content contained lone UTF-16 surrogates. Root cause: Child classes override parent methods but omit the sanitization step that the base OpenAIClient includes. This allows corrupted Unicode (unpaired surrogates from malformed emoji) to reach the httpx layer, which rejects it during UTF-8 encoding. Fix: Import and call sanitize_unicode_surrogates in all overridden request methods. Also removed duplicate sanitize_unicode_surrogates definition from openai_client.py that shadowed the canonical implementation in letta.helpers.json_helpers. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> Issue-ID: 10c0f2e4-f87b-11f0-b91c-da7ad0900000
94 lines
4.1 KiB
Python
94 lines
4.1 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.helpers.json_helpers import sanitize_unicode_surrogates
|
|
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.enums import AgentType
|
|
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,
|
|
agent_type: AgentType,
|
|
messages: List[PydanticMessage],
|
|
llm_config: LLMConfig,
|
|
tools: Optional[List[dict]] = None,
|
|
force_tool_call: Optional[str] = None,
|
|
requires_subsequent_tool_call: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> dict:
|
|
data = super().build_request_data(agent_type, messages, llm_config, tools, force_tool_call, requires_subsequent_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.
|
|
"""
|
|
request_data = sanitize_unicode_surrogates(request_data)
|
|
|
|
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.
|
|
"""
|
|
request_data = sanitize_unicode_surrogates(request_data)
|
|
|
|
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]
|