Delete CustomAgent.py

CustomAgent.py

@@ -1,137 +0,0 @@
-import datasets
-from langchain.docstore.document import Document
-# Load the dataset
-# guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
-
-# Convert dataset entries into Document objects
-# docs = [
-#     Document(
-#         page_content="\n".join([
-#             f"Name: {guest['name']}",
-#             f"Relation: {guest['relation']}",
-#             f"Description: {guest['description']}",
-#             f"Email: {guest['email']}"
-#         ]),
-#         metadata={"name": guest["name"]}
-#     )
-#     for guest in guest_dataset
-# ]
-
-# from langchain_community.retrievers import BM25Retriever
-# from langchain.tools import Tool
-
-# bm25_retriever = BM25Retriever.from_documents(docs)
-
-# def extract_text(query: str) -> str:
-#     """Retrieves detailed information about gala guests based on their name or relation."""
-#     results = bm25_retriever.invoke(query)
-#     if results:
-#         return "\n\n".join([doc.page_content for doc in results[:3]])
-#     else:
-#         return "No matching guest information found."
-
-# guest_info_tool = Tool(
-#     name="guest_info_retriever",
-#     func=extract_text,
-#     description="Retrieves detailed information about gala guests based on their name or relation."
-# )
-#######################################################################################################################################################
-from typing import TypedDict, Annotated
-from langgraph.graph.message import add_messages
-from langchain_core.messages import AnyMessage, HumanMessage, AIMessage,SystemMessage
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import tools_condition
-from langchain_openai import ChatOpenAI
-#from WebSearch import weather_info_tool
-from other_tools import (
-    wiki_search, arvix_search, web_search, vector_search, 
-    multiply, add, subtract, divide, modulus, power, square_root
-)
-import os
-from dotenv import load_dotenv
-load_dotenv()
-# Generate the chat interface, including the tools
-llm = ChatOpenAI(temperature=0
-                   , model="gpt-4o-mini", openai_api_key=os.getenv("OPENAI_KEY"))
-
-tools = [
-    wiki_search, arvix_search, web_search, 
-    multiply, add, subtract, divide, modulus, power, square_root
-]
-chat_with_tools = llm.bind_tools(tools)
-
-#setting up prompt
-ai_message = SystemMessage(content="""You are a helpful assistant tasked with answering questions using a set of tools and reference materials.
-
-You may be provided with a reference set of questions and answers from a retriever. 
-If the current question is identical to or semantically equivalent to a reference question, or if a reference answer clearly applies, use that reference answer directly.
-
-Otherwise, reason through the question as needed to determine the correct answer.
-
-Your output must follow these formatting rules:
-- If the answer is a number, do not use commas or units (unless specifically requested).
-- If the answer is a string, do not use articles, abbreviations, or short forms. Write digits in full unless specified otherwise.
-- If the answer is a comma-separated list, apply the above rules to each item and include exactly one space after each comma.
-- If the question matches a reference question, return the reference answer exactly as it appears.
-
-Do not include any explanation, prefix, or extra text—output only the final answer.
-""")
-
-
-
-
-
-
-# Generate the AgentState and Agent graph
-from langgraph.graph import MessagesState #the same as AgentState
-# class AgentState(TypedDict):
-#     messages: Annotated[list[AnyMessage], add_messages]
-
-def assistant(state: MessagesState):
-    return {
-        "messages": [chat_with_tools.invoke(state["messages"])],
-    }
-
-def retriever(state: MessagesState):
-    """Retriever node"""
-    similar_question = vector_search(state["messages"][0].content)
-
-    if similar_question:
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question}",
-        )
-        print(f"Similar question found: {similar_question}")
-        return {"messages": [ai_message] + state["messages"] + [example_msg]}
-    else:
-        # Handle the case when no similar questions are found
-        print( "No similar question found.")
-        return {"messages": [ai_message] + state["messages"]}
-
-## The graph
-builder = StateGraph(MessagesState)
-
-
-# Define nodes: these do the work
-builder.add_node("assistant", assistant)
-builder.add_node("retriever", retriever)
-builder.add_node("tools", ToolNode(tools))
-
-# Define edges: these determine how the control flow moves
-builder.add_edge(START, "retriever")
-builder.add_edge("retriever", "assistant")
-builder.add_conditional_edges(
-    "assistant",
-    # If the latest message requires a tool, route to tools
-    # Otherwise, provide a direct response
-    tools_condition,
-)
-builder.add_edge("tools", "assistant")
-alfred = builder.compile()
-
-# messages = [HumanMessage(content="When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?")]
-# #messages = [HumanMessage(content="What the remainder of 30 divided by 7?")]
-# response = alfred.invoke({"messages": messages})
-
-
-# print(response['messages'][-1].content)