正确使用lpmm构建prompt

src/chat/knowledge/embedding_store.py

@@ -12,8 +12,6 @@ import pandas as pd
 # import tqdm
 import faiss
 
-# from .llm_client import LLMClient
-# from .lpmmconfig import global_config
 from .utils.hash import get_sha256
 from .global_logger import logger
 from rich.traceback import install

src/chat/knowledge/llm_client.py

@@ -1,45 +0,0 @@
-from openai import OpenAI
-
-
-class LLMMessage:
-    def __init__(self, role, content):
-        self.role = role
-        self.content = content
-
-    def to_dict(self):
-        return {"role": self.role, "content": self.content}
-
-
-class LLMClient:
-    """LLM客户端，对应一个API服务商"""
-
-    def __init__(self, url, api_key):
-        self.client = OpenAI(
-            base_url=url,
-            api_key=api_key,
-        )
-
-    def send_chat_request(self, model, messages):
-        """发送对话请求，等待返回结果"""
-        response = self.client.chat.completions.create(model=model, messages=messages, stream=False)
-        if hasattr(response.choices[0].message, "reasoning_content"):
-            # 有单独的推理内容块
-            reasoning_content = response.choices[0].message.reasoning_content
-            content = response.choices[0].message.content
-        else:
-            # 无单独的推理内容块
-            response = response.choices[0].message.content.split("<think>")[-1].split("</think>")
-            # 如果有推理内容，则分割推理内容和内容
-            if len(response) == 2:
-                reasoning_content = response[0]
-                content = response[1]
-            else:
-                reasoning_content = None
-                content = response[0]
-
-        return reasoning_content, content
-
-    def send_embedding_request(self, model, text):
-        """发送嵌入请求，等待返回结果"""
-        text = text.replace("\n", " ")
-        return self.client.embeddings.create(input=[text], model=model).data[0].embedding

src/chat/knowledge/qa_manager.py

@@ -2,11 +2,7 @@ import time
 from typing import Tuple, List, Dict, Optional
 
 from .global_logger import logger
-
-# from . import prompt_template
 from .embedding_store import EmbeddingManager
-
-# from .llm_client import LLMClient
 from .kg_manager import KGManager
 
 # from .lpmmconfig import global_config