84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
|
"""DeepSeek / OpenAI-compatible LLM adapter — implements LLMProvider port."""
|
||
|
|
|
||
|
|
from collections.abc import AsyncIterator
|
||
|
|
|
||
|
|
from langchain_openai import ChatOpenAI
|
||
|
|
|
||
|
|
|
||
|
|
class DeepSeekLLMProvider:
|
||
|
|
"""LangChain-based LLM adapter for DeepSeek and OpenAI-compatible APIs."""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
api_key: str,
|
||
|
|
base_url: str = "https://api.deepseek.com",
|
||
|
|
model: str = "deepseek-chat",
|
||
|
|
temperature: float = 0.7,
|
||
|
|
):
|
||
|
|
self._default_model = model
|
||
|
|
self._default_temperature = temperature
|
||
|
|
kwargs: dict = {
|
||
|
|
"temperature": temperature,
|
||
|
|
"model": model,
|
||
|
|
"api_key": api_key,
|
||
|
|
}
|
||
|
|
if base_url:
|
||
|
|
cleaned = base_url.rstrip("/")
|
||
|
|
for suffix in ("/v1/chat/completions", "/v1"):
|
||
|
|
if cleaned.endswith(suffix):
|
||
|
|
cleaned = cleaned[: -len(suffix)]
|
||
|
|
kwargs["base_url"] = cleaned
|
||
|
|
|
||
|
|
self._llm = ChatOpenAI(**kwargs)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def langchain_llm(self) -> ChatOpenAI:
|
||
|
|
"""Expose underlying ChatOpenAI for LangChain agent interop (Phase 2 will remove)."""
|
||
|
|
return self._llm
|
||
|
|
|
||
|
|
async def complete(
|
||
|
|
self,
|
||
|
|
messages: list[dict],
|
||
|
|
*,
|
||
|
|
temperature: float | None = None,
|
||
|
|
model: str | None = None,
|
||
|
|
) -> str:
|
||
|
|
llm = self._get_llm(temperature, model)
|
||
|
|
lc_messages = _to_langchain_messages(messages)
|
||
|
|
result = await llm.ainvoke(lc_messages)
|
||
|
|
return str(result.content)
|
||
|
|
|
||
|
|
async def stream(
|
||
|
|
self,
|
||
|
|
messages: list[dict],
|
||
|
|
*,
|
||
|
|
temperature: float | None = None,
|
||
|
|
model: str | None = None,
|
||
|
|
) -> AsyncIterator[str]:
|
||
|
|
llm = self._get_llm(temperature, model)
|
||
|
|
lc_messages = _to_langchain_messages(messages)
|
||
|
|
async for chunk in llm.astream(lc_messages):
|
||
|
|
if chunk.content:
|
||
|
|
yield str(chunk.content)
|
||
|
|
|
||
|
|
def _get_llm(self, temperature: float | None, model: str | None):
|
||
|
|
if temperature is None and model is None:
|
||
|
|
return self._llm
|
||
|
|
kwargs: dict = {}
|
||
|
|
if temperature is not None:
|
||
|
|
kwargs["temperature"] = temperature
|
||
|
|
if model is not None:
|
||
|
|
kwargs["model"] = model
|
||
|
|
return self._llm.bind(**kwargs) if kwargs else self._llm
|
||
|
|
|
||
|
|
|
||
|
|
def _to_langchain_messages(messages: list[dict]) -> list:
|
||
|
|
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||
|
|
|
||
|
|
mapping = {"system": SystemMessage, "human": HumanMessage, "user": HumanMessage, "ai": AIMessage, "assistant": AIMessage}
|
||
|
|
result = []
|
||
|
|
for msg in messages:
|
||
|
|
cls = mapping.get(msg.get("role", ""), HumanMessage)
|
||
|
|
result.append(cls(content=msg.get("content", "")))
|
||
|
|
return result
|