fix: remove deprecated files

conversation_memory.py

@@ -1,66 +0,0 @@
-import json
-import os
-class ConversationMemory:
-    def __init__(self):
-        self.tools_used = []
-        self.all_queries = []
-        self.query_results = []
-        self.request = []
-
-    def update_from_parsed(self, parsed_steps, request):
-        tools = []
-        self.request.append(request)
-        for step in parsed_steps:
-            if step['type'] == 'ai_function_call':
-                tools.append(step['tool'])
-                if 'query' in step['args']:
-                    args_dict = json.loads(step['args'])
-                    query = args_dict['query']
-                    self.all_queries.append(query)
-            elif step['type'] == 'ai_final_answer':
-                if step['ai_said']:
-                    if 'query' in step['ai_said'][0]:
-                        self.all_queries.append(step['ai_said'][1])
-            elif step['type'] == 'tool_response':
-                if step['tool'] == 'execute_query':
-                    self.query_results.append(step['response'])
-
-        self.tools_used.extend(tools)
-
-    def get_last_n_queries(self):
-        return list(set(self.all_queries))
-
-    def get_last_n_results(self):
-        return list(set(self.query_results))
-
-    def get_all_tools_used(self):
-        return list(set(self.tools_used))
-
-    def get_all_user_messages(self):
-        return list(set(self.request))
-
-    def reset(self, path = "memory.json"):
-        os.remove(path)
-        self.__init__()  # Re-initialize the object
-
-    def summary(self):
-        return {
-            "total_requests": len(self.request),
-            "tools_used": self.get_all_tools_used(),
-            "last_request": self.request[-1] if self.request else None,
-            "last_query": self.all_queries[-1] if self.all_queries else None,
-            "last_result": self.query_results[-1] if self.query_results else None,
-        }
-
-    def save_memory(self, path="memory.json"):
-        with open(path, "w") as f:
-            json.dump(self.__dict__, f)
-
-    def load_memory(self, path="memory.json"):
-        try:
-            with open(path, "r") as f:
-                data = json.load(f)
-            self.__dict__.update(data)
-            return self
-        except FileNotFoundError:
-            return ConversationMemory()

utils.py

@@ -1,104 +0,0 @@
-import re
-import os
-from conversation_memory import ConversationMemory
-import logging
-
-logger = logging.getLogger(__name__)
-def parse_mcp_output(output_dict):
-    result = []
-    messages = output_dict.get("messages", [])
-    query_store = []
-    last_tool_answer = ""
-    last_answer = ""
-    for msg in messages:
-        role_name = msg.__class__.__name__  # Example: HumanMessage, AIMessage, ToolMessage
-        content = getattr(msg, "content", "")
-
-        # AIMessage with tool call
-        if role_name == "AIMessage":
-            function_call = getattr(msg, "additional_kwargs", {}).get("function_call")
-            if function_call:
-                tool_name = function_call.get("name")
-                arguments = function_call.get("arguments")
-
-                # Check if arguments is a JSON string or a dict
-                if isinstance(arguments, str):
-                    import json
-                    try:
-                        arguments_dict = json.loads(arguments)
-                    except json.JSONDecodeError:
-                        arguments_dict = {}
-                else:
-                    arguments_dict = arguments or {}
-
-                # Check for presence of "query" key
-                if "query" in arguments_dict:
-                    #print("query detected!!!")
-
-                    if content:
-                        print(f"=============== AI Reasoning Step ===============")
-                        print(content[0])
-                        print()
-                    print("=============== AI used the following tools ===============")
-                    print(tool_name)
-                    print()
-                    print("=============== AI generated the following query ===============")
-                    print(arguments_dict['query'])
-
-                    logger.info(f"ai said:{content[0]}")
-                    logger.info(f"ai used:{tool_name}")
-                    logger.info(f"generated query:{arguments_dict['query']}")
-                    #print(arguments_dict["query"])
-                    query_store.append(arguments_dict["query"])
-
-                    result.append({
-                        "type": "ai_function_call",
-                        "ai_said": content,
-                        "tool": tool_name,
-                        "args": arguments
-                    })
-                else:
-                    #print(f"ai said:{content}")
-                    logger.info(f"ai said:{content}")
-                    logger.info(f"ai used:{tool_name}")
-                    print(f"=============== AI Reasoning Step ===============")
-                    print(content)
-                    print()
-                    print("=============== AI used the following tools ===============")
-                    print(tool_name)
-                    print()
-
-                    result.append({
-                        "type": "ai_function_call",
-                        "ai_said": content,
-                        "tool": tool_name,
-                        "args": arguments
-                    })
-
-            else:
-                final_answer = content
-                #print(f"ai final answer:{content}")
-                logger.info(f"ai final answer:{content}")
-                print("=============== AI's final answer ===============")
-                print(content)
-                result.append({
-                    "type": "ai_final_answer",
-                    "ai_said": content
-                })
-
-        # ToolMessage
-        elif role_name == "ToolMessage":
-            tool_name = getattr(msg, "name", None)
-            print("=============== The tool returned the following response ===============")
-            print(content)
-            last_tool_answer = content
-            logger.info(f"tool response:{content}")
-            result.append({
-                "type": "tool_response",
-                "tool": tool_name,
-                "response": content
-            })
-
-    return result, final_answer, last_tool_answer, query_store
-
-