98 lines
4.3 KiB
Python
98 lines
4.3 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.deepseek import convert_deepseek_response_to_chatcompletion, map_messages_to_deepseek_format
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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.llm_config import LLMConfig
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from letta.schemas.message import Message as PydanticMessage
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from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
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from letta.settings import model_settings
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class DeepseekClient(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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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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) -> dict:
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# Override put_inner_thoughts_in_kwargs to False for DeepSeek
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llm_config.put_inner_thoughts_in_kwargs = False
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data = super().build_request_data(messages, llm_config, tools, force_tool_call)
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def add_functions_to_system_message(system_message: ChatMessage):
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system_message.content += f"<available functions> {''.join(json.dumps(f) for f in functions)} </available functions>"
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system_message.content += 'Select best function to call simply respond with a single json block with the fields "name" and "arguments". Use double quotes around the arguments.'
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if llm_config.model == "deepseek-reasoner": # R1 currently doesn't support function calling natively
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add_functions_to_system_message(
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data["messages"][0]
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) # Inject additional instructions to the system prompt with the available functions
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data["messages"] = map_messages_to_deepseek_format(data["messages"])
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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.deepseek_api_key or os.environ.get("DEEPSEEK_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.deepseek_api_key or os.environ.get("DEEPSEEK_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.deepseek_api_key or os.environ.get("DEEPSEEK_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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def convert_response_to_chat_completion(
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self,
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response_data: dict,
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input_messages: List[PydanticMessage], # Included for consistency, maybe used later
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llm_config: LLMConfig,
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) -> ChatCompletionResponse:
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"""
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Converts raw OpenAI response dict into the ChatCompletionResponse Pydantic model.
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Handles potential extraction of inner thoughts if they were added via kwargs.
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"""
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response = ChatCompletionResponse(**response_data)
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return convert_deepseek_response_to_chatcompletion(response)
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