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
79 lines
3.9 KiB
Python
79 lines
3.9 KiB
Python
import os
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from typing import List, Optional, Tuple
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from openai import AsyncAzureOpenAI, AzureOpenAI
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from openai.types.chat.chat_completion import ChatCompletion
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from letta.helpers.json_helpers import sanitize_unicode_surrogates
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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 ProviderCategory
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from letta.schemas.llm_config import LLMConfig
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from letta.settings import model_settings
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class AzureClient(OpenAIClient):
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def get_byok_overrides(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
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if llm_config.provider_category == ProviderCategory.byok:
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from letta.services.provider_manager import ProviderManager
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return ProviderManager().get_azure_credentials(llm_config.provider_name, actor=self.actor)
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return None, None, None
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async def get_byok_overrides_async(self, llm_config: LLMConfig) -> Tuple[Optional[str], Optional[str], Optional[str]]:
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if llm_config.provider_category == ProviderCategory.byok:
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from letta.services.provider_manager import ProviderManager
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return await ProviderManager().get_azure_credentials_async(llm_config.provider_name, actor=self.actor)
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return None, None, None
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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, base_url, api_version = self.get_byok_overrides(llm_config)
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if not api_key or not base_url or not api_version:
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api_key = model_settings.azure_api_key or os.environ.get("AZURE_API_KEY")
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base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
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api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
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client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
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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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request_data = sanitize_unicode_surrogates(request_data)
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api_key, base_url, api_version = await self.get_byok_overrides_async(llm_config)
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if not api_key or not base_url or not api_version:
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api_key = model_settings.azure_api_key or os.environ.get("AZURE_API_KEY")
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base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
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api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
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try:
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client = AsyncAzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
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response: ChatCompletion = await client.chat.completions.create(**request_data)
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except Exception as e:
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raise self.handle_llm_error(e)
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return response.model_dump()
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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.azure_api_key or os.environ.get("AZURE_API_KEY")
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base_url = model_settings.azure_base_url or os.environ.get("AZURE_BASE_URL")
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api_version = model_settings.azure_api_version or os.environ.get("AZURE_API_VERSION")
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client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=base_url)
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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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