feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/chat_robot/script/agents/agent_executor.py

@@ -3,27 +3,20 @@ import json
 import logging
 from typing import Any, Dict, List, Optional, Union, Tuple
 
-from langchain.agents import AgentExecutor
-from langchain.callbacks.manager import Callbacks, CallbackManager
-from langchain.load.dump import dumpd
-from langchain.schema import RUN_KEY, RunInfo
+from langchain_classic.agents import AgentExecutor
+from langchain_classic.schema import RUN_KEY
 from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.callbacks import Callbacks, CallbackManager
+from langchain_core.load import dumpd
+from langchain_core.outputs import RunInfo
 
 
 class CustomAgentExecutor(AgentExecutor):
-    def __call__(
-            self,
-            inputs: Union[Dict[str, Any], Any],
-            return_only_outputs: bool = False,
-            callbacks: Callbacks = None,
-            session_key: str = "",
-            *,
-            tags: Optional[List[str]] = None,
-            include_run_info: bool = False,
-    ) -> Dict[str, Any]:
+    def __call__(self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, session_key: str = "", *, tags: Optional[List[str]] = None, include_run_info: bool = False, **kwargs) -> Dict[str, Any]:
         """Run the logic of this chain and add to output if desired.
 
         Args:
+            **kwargs:
             inputs: Dictionary of inputs, or single input if chain expects
                 only one param.
             return_only_outputs: boolean for whether to return only outputs in the
@@ -72,7 +65,7 @@ class CustomAgentExecutor(AgentExecutor):
         """Validate and prep outputs."""
         self._validate_outputs(outputs)
         if self.memory is not None and outputs['need_record']:
-            self.memory.save_context(inputs, outputs, session_key)
+            self.memory.save_context(inputs, outputs)
         if return_only_outputs:
             return outputs
         else:
@@ -95,7 +88,7 @@ class CustomAgentExecutor(AgentExecutor):
                 )
             inputs = {list(_input_keys)[0]: inputs}
         if self.memory is not None:
-            external_context = self.memory.load_memory_variables(inputs, session_key)
+            external_context = self.memory.load_memory_variables(inputs)
             inputs = dict(inputs, **external_context)
         self._validate_inputs(inputs)
         return inputs
@@ -119,7 +112,8 @@ class CustomAgentExecutor(AgentExecutor):
                     {return_value_key: observation},
                     "",
                 )
-        except:
+        except Exception as e:
+            print(e)
             pass
 
         # Invalid tools won't be in the map, so we return False.

app/service/chat_robot/script/agents/conversational_functions_agent.py

@@ -1,26 +1,15 @@
 import json
-import re
+from dataclasses import dataclass
 from json import JSONDecodeError
 from typing import List, Tuple, Any, Union
-from dataclasses import dataclass
 
-from langchain.callbacks.manager import Callbacks
-from langchain.agents import (
-    OpenAIFunctionsAgent,
-)
-from langchain.schema import (
-    AgentAction,
-    AgentFinish,
-    BaseMessage,
-    OutputParserException
-)
-from langchain.schema.messages import (
-    AIMessage,
-    FunctionMessage
-)
-from langchain.tools import BaseTool, StructuredTool
-# from langchain.tools.convert_to_openai import FunctionDescription
-from langchain.utils.openai_functions import FunctionDescription
+from langchain_classic.agents import OpenAIFunctionsAgent
+from langchain_community.utils.ernie_functions import FunctionDescription
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.callbacks import Callbacks
+from langchain_core.exceptions import OutputParserException
+from langchain_core.messages import BaseMessage, AIMessage, FunctionMessage
+from langchain_core.tools import BaseTool
 
 
 @dataclass
@@ -76,7 +65,6 @@ def _create_function_message(
         content = observation
     return FunctionMessage(
         name=agent_action.tool,
-        content=content,
     )
 
 
@@ -177,6 +165,7 @@ class ConversationalFunctionsAgent(OpenAIFunctionsAgent):
         into it.
 
         Args:
+            callbacks:
             intermediate_steps: Steps the LLM has taken to date, along with observations
             **kwargs: User inputs.
             **kwargs: Including user's input string