fecf6decfb
* base requirements * autofix * Configure ruff for Python linting and formatting - Set up minimal ruff configuration with basic checks (E, W, F, I) - Add temporary ignores for common issues during migration - Configure pre-commit hooks to use ruff with pass_filenames - This enables gradual migration from black to ruff * Delete sdj * autofixed only * migrate lint action * more autofixed * more fixes * change precommit * try changing the hook * try this stuff
55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
import os
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from typing import List
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from openai import AsyncOpenAI, OpenAI
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from openai.types.chat.chat_completion import ChatCompletion
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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.llm_config import LLMConfig
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from letta.settings import model_settings
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class TogetherClient(OpenAIClient):
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def requires_auto_tool_choice(self, llm_config: LLMConfig) -> bool:
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return True
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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, _, _ = self.get_byok_overrides(llm_config)
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if not api_key:
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api_key = model_settings.together_api_key or os.environ.get("TOGETHER_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, _, _ = await self.get_byok_overrides_async(llm_config)
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if not api_key:
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api_key = model_settings.together_api_key or os.environ.get("TOGETHER_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 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.together_api_key or os.environ.get("TOGETHER_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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