[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/chat/completion.py

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+from fastapi import Body
+from sse_starlette.sse import EventSourceResponse
+from configs import LLM_MODELS, TEMPERATURE
+from server.utils import wrap_done, get_OpenAI
+from langchain.chains import LLMChain
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from typing import AsyncIterable, Optional
+import asyncio
+from langchain.prompts import PromptTemplate
+
+from server.utils import get_prompt_template
+
+
+async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
+                     stream: bool = Body(False, description="流式输出"),
+                     echo: bool = Body(False, description="除了输出之外，还回显输入"),
+                     model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
+                     temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
+                     max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"),
+                     # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
+                     prompt_name: str = Body("default",
+                                             description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
+                     ):
+
+    #todo 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理
+    async def completion_iterator(query: str,
+                                  model_name: str = LLM_MODELS[0],
+                                  prompt_name: str = prompt_name,
+                                  echo: bool = echo,
+                                  ) -> AsyncIterable[str]:
+        nonlocal max_tokens
+        callback = AsyncIteratorCallbackHandler()
+        if isinstance(max_tokens, int) and max_tokens <= 0:
+            max_tokens = None
+
+        model = get_OpenAI(
+            model_name=model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            callbacks=[callback],
+            echo=echo
+        )
+
+        prompt_template = get_prompt_template("completion", prompt_name)
+        prompt = PromptTemplate.from_template(prompt_template)
+        chain = LLMChain(prompt=prompt, llm=model)
+
+        # Begin a task that runs in the background.
+        task = asyncio.create_task(wrap_done(
+            chain.acall({"input": query}),
+            callback.done),
+        )
+
+        if stream:
+            async for token in callback.aiter():
+                # Use server-sent-events to stream the response
+                yield token
+        else:
+            answer = ""
+            async for token in callback.aiter():
+                answer += token
+            yield answer
+
+        await task
+
+    return EventSourceResponse(completion_iterator(query=query,
+                                                 model_name=model_name,
+                                                 prompt_name=prompt_name),
+                             )