Delete agent.py

agent.py

@@ -1,110 +0,0 @@
-import operator
-from typing import TypedDict, Annotated, Sequence
-from langchain_core.messages import BaseMessage, SystemMessage, HumanMessage
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_huggingface import ChatHuggingFace
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from tools import multiply, add, subtract, divide, modulus, wiki_search, web_search, arxiv_search, chess_suggest, youtube_transcript, analyze_image
-
-# 1. Define the state schema for the graph
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-
-# Load the system prompt from the file
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    SYSTEM_PROMPT = f.read()
-    
-# System message
-sys_msg = SystemMessage(content=SYSTEM_PROMPT)
-
-with open('metadata.json', 'r') as jsonl_file:
-    json_list = list(jsonl_file)
-
-tools = [
-    add,
-    subtract,
-    multiply,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arxiv_search,
-    youtube_transcript,
-    # load_excel,
-    analyze_image,
-    chess_suggest,
-]
-
-def build_graph(llm: ChatGoogleGenerativeAI | ChatGroq | ChatHuggingFace):
-    # bind the tools so that OpenAI function calls are dispatched automatically
-    llm_with_tools = llm.bind_tools(tools)
-
-
-    # planner node
-    def planner(state: AgentState):
-        ctx = state["messages"][-6:]        
-        if not isinstance(ctx[0], SystemMessage):
-            ctx = [sys_msg] + list(ctx)
-        return {"messages": [llm_with_tools.invoke(ctx)]}
-
-    def retriever(state):
-        """Retriever node that searches metadata.jsonl for similar content"""
-        user_question = state["messages"][-1].content
-        
-        # Simple keyword-based search through metadata.jsonl
-        import json
-        best_match = None
-        best_score = 0
-        
-        try:
-            for line in json_list:
-                if line.strip():  # Skip empty lines
-                    try:
-                        data = json.loads(line)
-                        # Assuming the JSON has 'question' and 'answer' fields
-                        # Adjust field names based on your actual JSON structure
-                        content = data.get('Question', '')
-                        # Simple keyword matching - count common words
-                        user_words = set(user_question.lower().split())
-                        content_words = set(content.lower().split())
-                        common_words = user_words.intersection(content_words)
-                        score = len(common_words)
-                        
-                        if score > best_score and score > 0:
-                            best_score = score
-                            best_match = data
-                            
-                    except json.JSONDecodeError:
-                        continue  # Skip malformed JSON lines
-            
-            if best_match:
-                # Create a reference message with the similar content
-                reference_content = best_match.get('Question', '') or best_match.get('Final answer', '') or str(best_match)
-                example_msg = HumanMessage(
-                    content=f"Here is relevant information from our knowledge base: \n\n{reference_content}. The correct answer is {best_match.get('Final answer', '') }",
-                )
-                return {"messages": state["messages"] + [example_msg]}
-            else:
-                return state
-                
-        except FileNotFoundError:
-            print("metadata.jsonl file not found")
-            return state
-        except Exception as e:
-            print(f"Error reading metadata.jsonl: {e}")
-            return state
-    graph = StateGraph(AgentState)
-    graph.add_node("planner", planner)
-    graph.add_node("retriever", retriever)
-    graph.add_node("tools", ToolNode(tools))
-
-    graph.set_entry_point("retriever")
-    graph.add_conditional_edges(
-        "planner",
-        tools_condition,
-    )
-    graph.add_edge("tools", "planner")
-    graph.add_edge("retriever", "planner")
-    return graph.compile()