Update veryfinal.py
Browse files- veryfinal.py +242 -242
veryfinal.py
CHANGED
@@ -1,242 +1,242 @@
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import os, json
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Imports
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from langchain_nvidia_ai_endpoints import
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langchain_community.document_loaders import WikipediaLoader
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from langchain_community.document_loaders import ArxivLoader
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from langchain_community.vectorstores import FAISS
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool
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from langchain.tools.retriever import create_retriever_tool
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import JSONLoader
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from langgraph.prebuilt import create_react_agent
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from langgraph.checkpoint.memory import MemorySaver
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# Define all tools
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@tool
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def multiply(a: int | float, b: int | float) -> int | float:
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"""Multiply two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a * b
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@tool
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def add(a: int | float, b: int | float) -> int | float:
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"""Add two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a + b
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@tool
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def subtract(a: int | float , b: int | float) -> int | float:
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"""Subtract two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a - b
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@tool
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def divide(a: int | float, b: int | float) -> int | float:
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"""Divide two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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if b == 0:
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raise ValueError("Cannot divide by zero.")
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return a / b
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@tool
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def modulus(a: int | float, b: int | float) -> int | float:
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"""Get the modulus of two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a % b
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@tool
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def wiki_search(query: str) -> str:
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"""Search the wikipedia for a query and return the first paragraph
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args:
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query: the query to search for
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"""
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loader = WikipediaLoader(query=query, load_max_docs=1)
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data = loader.load()
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formatted_search_docs = "\n\n---\n\n".join(
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[
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f'\n{doc.page_content}\n'
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for doc in data
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])
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return formatted_search_docs
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@tool
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def web_search(query: str) -> str:
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"""Search Tavily for a query and return maximum 3 results.
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Args:
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query: The search query.
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"""
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search_docs = TavilySearchResults(max_results=3).invoke(query=query)
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formatted_search_docs = "\n\n---\n\n".join(
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[
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f'\n{doc.get("content", "")}\n'
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for doc in search_docs
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])
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return formatted_search_docs
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@tool
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def arxiv_search(query: str) -> str:
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"""Search Arxiv for a query and return maximum 3 result.
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Args:
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query: The search query.
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"""
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search_docs = ArxivLoader(query=query, load_max_docs=3).load()
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formatted_search_docs = "\n\n---\n\n".join(
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[
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f'\n{doc.page_content[:1000]}\n'
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for doc in search_docs
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])
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return formatted_search_docs
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# Load and process your JSONL data
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jq_schema = """
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{
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page_content: .Question,
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metadata: {
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task_id: .task_id,
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Level: .Level,
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Final_answer: ."Final answer",
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file_name: .file_name,
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Steps: .["Annotator Metadata"].Steps,
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Number_of_steps: .["Annotator Metadata"]["Number of steps"],
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How_long: .["Annotator Metadata"]["How long did this take?"],
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Tools: .["Annotator Metadata"].Tools,
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Number_of_tools: .["Annotator Metadata"]["Number of tools"]
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}
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}
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"""
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# Load documents and create vector database
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json_loader = JSONLoader(file_path="metadata.jsonl", jq_schema=jq_schema, json_lines=True, text_content=False)
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json_docs = json_loader.load()
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# Split documents
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
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json_chunks = text_splitter.split_documents(json_docs)
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# Create vector database
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database = FAISS.from_documents(json_chunks, NVIDIAEmbeddings())
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# Initialize LLM
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llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
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# Create retriever and retriever tool
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retriever = database.as_retriever(search_type="similarity", search_kwargs={"k": 3})
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retriever_tool = create_retriever_tool(
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retriever=retriever,
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name="question_search",
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description="Search for similar questions and their solutions from the knowledge base."
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)
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# Combine all tools
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tools = [
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multiply,
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add,
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subtract,
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divide,
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modulus,
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wiki_search,
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web_search,
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arxiv_search,
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retriever_tool
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]
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# Create memory for conversation
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memory = MemorySaver()
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# Create the agent
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agent_executor = create_react_agent(
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model=llm,
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tools=tools,
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checkpointer=memory
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)
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# Function to run the agent
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def run_agent(query, thread_id="conversation_1"):
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"""Run the agent with a query"""
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config = {"configurable": {"thread_id": thread_id}}
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system_msg = SystemMessage(content='''You are a helpful assistant tasked with answering questions using a set of tools.
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Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
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FINAL ANSWER: [YOUR FINAL ANSWER].
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YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
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Your answer should only start with "FINAL ANSWER: ", then follows with the answer.''')
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user_msg = HumanMessage(content=query)
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print(f"User: {query}")
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print("\nAgent:")
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for step in agent_executor.stream(
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{"messages": [system_msg, user_msg]},
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config,
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stream_mode="values"
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):
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step["messages"][-1].pretty_print()
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# Function to run agent with error handling
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def robust_agent_run(query, thread_id="robust_conversation"):
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"""Run agent with error handling"""
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config = {"configurable": {"thread_id": thread_id}}
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try:
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system_msg = SystemMessage(content='''You are a helpful assistant tasked with answering questions using a set of tools.
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Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
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FINAL ANSWER: [YOUR FINAL ANSWER].
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YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
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Your answer should only start with "FINAL ANSWER: ", then follows with the answer.''')
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user_msg = HumanMessage(content=query)
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result = []
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for step in agent_executor.stream(
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{"messages": [system_msg, user_msg]},
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config,
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stream_mode="values"
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):
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result = step["messages"]
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return result[-1].content if result else "No response generated"
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except Exception as e:
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return f"Error occurred: {str(e)}"
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# Main function
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def main(query: str) -> str:
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"""Main function to run the agent"""
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return(robust_agent_run(query))
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# Or use the interactive version
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# run_agent("What is 25 * 4 + 10?")
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import os, json
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Imports
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from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langchain_community.document_loaders import WikipediaLoader
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from langchain_community.document_loaders import ArxivLoader
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from langchain_community.vectorstores import FAISS
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool
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from langchain.tools.retriever import create_retriever_tool
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import JSONLoader
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from langgraph.prebuilt import create_react_agent
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from langgraph.checkpoint.memory import MemorySaver
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# Define all tools
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@tool
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def multiply(a: int | float, b: int | float) -> int | float:
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"""Multiply two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a * b
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@tool
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def add(a: int | float, b: int | float) -> int | float:
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"""Add two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a + b
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@tool
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def subtract(a: int | float , b: int | float) -> int | float:
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"""Subtract two numbers.
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+
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Args:
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a: first int | float
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b: second int | float
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"""
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return a - b
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@tool
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def divide(a: int | float, b: int | float) -> int | float:
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"""Divide two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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if b == 0:
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raise ValueError("Cannot divide by zero.")
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return a / b
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@tool
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def modulus(a: int | float, b: int | float) -> int | float:
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"""Get the modulus of two numbers.
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Args:
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a: first int | float
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b: second int | float
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"""
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return a % b
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@tool
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76 |
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def wiki_search(query: str) -> str:
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77 |
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"""Search the wikipedia for a query and return the first paragraph
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78 |
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args:
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79 |
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query: the query to search for
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80 |
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"""
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81 |
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loader = WikipediaLoader(query=query, load_max_docs=1)
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data = loader.load()
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formatted_search_docs = "\n\n---\n\n".join(
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[
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f'\n{doc.page_content}\n'
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for doc in data
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])
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return formatted_search_docs
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+
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@tool
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91 |
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def web_search(query: str) -> str:
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92 |
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"""Search Tavily for a query and return maximum 3 results.
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93 |
+
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94 |
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Args:
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95 |
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query: The search query.
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96 |
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"""
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97 |
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search_docs = TavilySearchResults(max_results=3).invoke(query=query)
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98 |
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formatted_search_docs = "\n\n---\n\n".join(
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[
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f'\n{doc.get("content", "")}\n'
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101 |
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for doc in search_docs
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])
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return formatted_search_docs
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104 |
+
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105 |
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@tool
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106 |
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def arxiv_search(query: str) -> str:
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107 |
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"""Search Arxiv for a query and return maximum 3 result.
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108 |
+
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109 |
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Args:
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110 |
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query: The search query.
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"""
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112 |
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search_docs = ArxivLoader(query=query, load_max_docs=3).load()
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113 |
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formatted_search_docs = "\n\n---\n\n".join(
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114 |
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[
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115 |
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f'\n{doc.page_content[:1000]}\n'
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116 |
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for doc in search_docs
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117 |
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])
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return formatted_search_docs
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119 |
+
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120 |
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# Load and process your JSONL data
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121 |
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jq_schema = """
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122 |
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{
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page_content: .Question,
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metadata: {
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task_id: .task_id,
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Level: .Level,
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127 |
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Final_answer: ."Final answer",
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128 |
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file_name: .file_name,
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129 |
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Steps: .["Annotator Metadata"].Steps,
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130 |
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Number_of_steps: .["Annotator Metadata"]["Number of steps"],
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131 |
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How_long: .["Annotator Metadata"]["How long did this take?"],
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Tools: .["Annotator Metadata"].Tools,
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Number_of_tools: .["Annotator Metadata"]["Number of tools"]
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}
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}
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"""
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# Load documents and create vector database
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json_loader = JSONLoader(file_path="metadata.jsonl", jq_schema=jq_schema, json_lines=True, text_content=False)
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json_docs = json_loader.load()
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# Split documents
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
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json_chunks = text_splitter.split_documents(json_docs)
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# Create vector database
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database = FAISS.from_documents(json_chunks, NVIDIAEmbeddings())
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# Initialize LLM
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llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
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# Create retriever and retriever tool
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retriever = database.as_retriever(search_type="similarity", search_kwargs={"k": 3})
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154 |
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retriever_tool = create_retriever_tool(
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retriever=retriever,
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name="question_search",
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158 |
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description="Search for similar questions and their solutions from the knowledge base."
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159 |
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)
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160 |
+
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161 |
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# Combine all tools
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162 |
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tools = [
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163 |
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multiply,
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164 |
+
add,
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165 |
+
subtract,
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166 |
+
divide,
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167 |
+
modulus,
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168 |
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wiki_search,
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169 |
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web_search,
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170 |
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arxiv_search,
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171 |
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retriever_tool
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172 |
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]
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173 |
+
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174 |
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# Create memory for conversation
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175 |
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memory = MemorySaver()
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176 |
+
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177 |
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# Create the agent
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178 |
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agent_executor = create_react_agent(
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179 |
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model=llm,
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180 |
+
tools=tools,
|
181 |
+
checkpointer=memory
|
182 |
+
)
|
183 |
+
|
184 |
+
# Function to run the agent
|
185 |
+
def run_agent(query, thread_id="conversation_1"):
|
186 |
+
"""Run the agent with a query"""
|
187 |
+
config = {"configurable": {"thread_id": thread_id}}
|
188 |
+
|
189 |
+
system_msg = SystemMessage(content='''You are a helpful assistant tasked with answering questions using a set of tools.
|
190 |
+
Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
|
191 |
+
FINAL ANSWER: [YOUR FINAL ANSWER].
|
192 |
+
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
|
193 |
+
Your answer should only start with "FINAL ANSWER: ", then follows with the answer.''')
|
194 |
+
|
195 |
+
user_msg = HumanMessage(content=query)
|
196 |
+
|
197 |
+
print(f"User: {query}")
|
198 |
+
print("\nAgent:")
|
199 |
+
|
200 |
+
for step in agent_executor.stream(
|
201 |
+
{"messages": [system_msg, user_msg]},
|
202 |
+
config,
|
203 |
+
stream_mode="values"
|
204 |
+
):
|
205 |
+
step["messages"][-1].pretty_print()
|
206 |
+
|
207 |
+
# Function to run agent with error handling
|
208 |
+
def robust_agent_run(query, thread_id="robust_conversation"):
|
209 |
+
"""Run agent with error handling"""
|
210 |
+
config = {"configurable": {"thread_id": thread_id}}
|
211 |
+
|
212 |
+
try:
|
213 |
+
system_msg = SystemMessage(content='''You are a helpful assistant tasked with answering questions using a set of tools.
|
214 |
+
Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:
|
215 |
+
FINAL ANSWER: [YOUR FINAL ANSWER].
|
216 |
+
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
|
217 |
+
Your answer should only start with "FINAL ANSWER: ", then follows with the answer.''')
|
218 |
+
|
219 |
+
user_msg = HumanMessage(content=query)
|
220 |
+
result = []
|
221 |
+
|
222 |
+
for step in agent_executor.stream(
|
223 |
+
{"messages": [system_msg, user_msg]},
|
224 |
+
config,
|
225 |
+
stream_mode="values"
|
226 |
+
):
|
227 |
+
result = step["messages"]
|
228 |
+
|
229 |
+
return result[-1].content if result else "No response generated"
|
230 |
+
|
231 |
+
except Exception as e:
|
232 |
+
return f"Error occurred: {str(e)}"
|
233 |
+
|
234 |
+
# Main function
|
235 |
+
def main(query: str) -> str:
|
236 |
+
"""Main function to run the agent"""
|
237 |
+
return(robust_agent_run(query))
|
238 |
+
|
239 |
+
|
240 |
+
|
241 |
+
# Or use the interactive version
|
242 |
+
# run_agent("What is 25 * 4 + 10?")
|