Refactor code to deprecate text embedding

agents/CSA/__init__.py

@@ -0,0 +1,48 @@
+"""
+===软件架构师===
+用于分析项目的整体框架，抽取出清晰的项目结构和功能划分
+"""
+from langchain_core.messages import SystemMessage
+from langchain_core.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_community.callbacks.manager import get_openai_callback
+from agents.CSA.prompt import CSA_SYSTEM_PROMPT, CSA_HUMAN_PROMPT
+from logger import Logger
+
+
+class CSA:
+    def __init__(self, base_url, api_key, model, process_output_callback):
+        # LLM配置
+        self.llm = ChatOpenAI(base_url=base_url, api_key=api_key, model=model)
+
+        # 提示词配置
+        self.system_prompt = CSA_SYSTEM_PROMPT
+        self.human_prompt = CSA_HUMAN_PROMPT
+
+        # 日志器配置
+        self.log = Logger(name='CSA', callback=process_output_callback)
+
+
+    def analyse(self, project_structure):
+        self.log.info('CSA开始分析项目模块')
+
+        # 提示词模板
+        self.llm_tmpl = ChatPromptTemplate.from_messages([
+            SystemMessage(content=self.system_prompt),
+            HumanMessagePromptTemplate.from_template(template=self.human_prompt),
+        ])
+
+        # 调用链配置
+        self.llm_chain = self.llm_tmpl | self.llm
+
+        # 获取分析结果
+        with get_openai_callback() as cb:
+            result = self.llm_chain.invoke({'project_structure': project_structure})
+
+            # TODO: 接入token用量统计
+            # print(f"请求消耗的输入 token 数: {cb.prompt_tokens}")
+            # print(f"请求消耗的输出 token 数: {cb.completion_tokens}")
+            # print(f"请求总共消耗的 token 数: {cb.total_tokens}")
+
+        self.log.info('CSA完成分析项目模块')
+        return result.content