use key config

lang_agent/pipeline.py

@@ -9,29 +9,28 @@ import os
 
 from langchain.chat_models import init_chat_model
 from langchain_core.messages import SystemMessage, HumanMessage
-# from langgraph.prebuilt import create_react_agent
+
 from langchain.agents import create_agent
 from langgraph.checkpoint.memory import MemorySaver
         
-from lang_agent.config import InstantiateConfig
+from lang_agent.config import InstantiateConfig, KeyConfig
 from lang_agent.tool_manager import ToolManager, ToolManagerConfig
 
+
+
 @tyro.conf.configure(tyro.conf.SuppressFixed)
 @dataclass
-class PipelineConfig(InstantiateConfig):
+class PipelineConfig(KeyConfig):
     _target: Type = field(default_factory=lambda: Pipeline)
 
     config_f: str = None
     """path to config file"""
 
-    llm_name: str = "qwen-plus"
+    llm_name: str = None
-    """name of llm"""
+    """name of llm; use default for qwen-plus"""
 
-    llm_provider:str = "openai"
+    llm_provider:str = None
-    """provider of the llm"""
+    """provider of the llm; use default for openai"""
-
-    api_key:str = None
-    """api key for llm"""
 
     base_url:str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
     """base url; could be used to overwrite the baseurl in llm provider"""
@@ -45,14 +44,6 @@ class PipelineConfig(InstantiateConfig):
     # NOTE: For reference
     tool_manager_config: ToolManagerConfig = field(default_factory=ToolManagerConfig)
 
-    def __post_init__(self):
-        if self.api_key == "wrong-key" or self.api_key is None:
-            # logger.info("wrong embedding key, using simple retrieval method")
-            self.api_key = os.environ.get("ALI_API_KEY")
-            if self.api_key is None:
-                logger.error(f"no ALI_API_KEY provided for embedding")
-            else:
-                logger.info("ALI_API_KEY loaded from environ")
 
 
 
@@ -63,6 +54,11 @@ class Pipeline:
         self.populate_module()
     
     def populate_module(self):
+        if self.config.llm_name is None:
+            logger.info(f"setting llm_provider to default")
+            self.config.llm_name = "qwen-turbo"
+            self.config.llm_provider = "openai"
+
         self.llm = init_chat_model(model=self.config.llm_name,
                                    model_provider=self.config.llm_provider,
                                    api_key=self.config.api_key,
@@ -149,30 +145,6 @@ class Pipeline:
         return out['messages'][-1].content
 
 
-# if __name__ == "__main__":
-#     pipeline:Pipeline = PipelineConfig().setup()
-
-#     # u = pipeline.chat("查查光与尘这杯茶的特点", as_stream=True)
-#     pipeline.chat("我想和红茶有什么推荐的吗", as_stream=True)
-
-#     # pipeline.chat("我叫什么名字", as_stream=True)
-    
-
-# def main():
-#     pipeline_config = PipelineConfig()
-#     pipeline: Pipeline = pipeline_config.setup()
-
-#     # 进行循环对话
-#     while True:
-#         try:
-#             user_input = input("请讲：")
-#             if user_input.lower() == "exit":
-#                 break
-#             response = pipeline.chat(user_input, as_stream=True)
-#             print(f"回答: {response}")
-#         except Exception as e:
-#             logger.error(f"对话过程中出现错误: {e}")
-
 import signal
 import sys
 def signal_handler(sig, frame):
@@ -189,18 +161,12 @@ def main():
 
     # 进行循环对话
     while True:
-        try:
+        
-            user_input = input("请讲：")
+        user_input = input("请讲：")
-            if user_input.lower() == "exit":
+        if user_input.lower() == "exit":
-                break
-            response = pipeline.chat(user_input, as_stream=True)
-            print(f"回答: {response}")
-        except KeyboardInterrupt:
-            # Handle Ctrl+C during input
-            print("\n程序正在退出...")
             break
-        except Exception as e:
+        response = pipeline.chat(user_input, as_stream=True)
-            logger.error(f"对话过程中出现错误: {e}")
+        print(f"回答: {response}")
 
 
 if __name__ == "__main__":

lang_agent/rag/simple.py

@@ -9,35 +9,21 @@ from langchain_community.vectorstores import FAISS
 from langchain_core.documents.base import Document
 
 from lang_agent.rag.emb import QwenEmbeddings
-from lang_agent.config import ToolConfig
+from lang_agent.config import ToolConfig, KeyConfig
 from lang_agent.base import LangToolBase
 
 
 @tyro.conf.configure(tyro.conf.SuppressFixed)
 @dataclass
-class SimpleRagConfig(ToolConfig):
+class SimpleRagConfig(ToolConfig, KeyConfig):
     _target: Type = field(default_factory=lambda: SimpleRag)
 
     model_name:str = "text-embedding-v4"
     """embedding model name"""
 
-    api_key:str = "wrong-key"
-    """api_key for model; for generic text splitting; give a wrong key <-- wrong, MUST have api key"""
-
     folder_path:str = "/home/smith/projects/work/langchain-agent/assets/xiaozhan_emb"
     """path to local database"""
 
-    def __post_init__(self):
-        if self.api_key == "wrong-key":
-            # logger.info("wrong embedding key, using simple retrieval method")
-            self.api_key = os.environ.get("ALI_API_KEY")
-            if self.api_key is None:
-                logger.error(f"no ALI_API_KEY provided for embedding")
-            else:
-                logger.info("ALI_API_KEY loaded from environ")
-        
-        logger.info(f"using {self.folder_path} as database")
-
 
 
 class SimpleRag(LangToolBase):