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Update agentic_agent.py

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  1. agentic_agent.py +181 -0
agentic_agent.py CHANGED
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+ """LangGraph Agent"""
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+ import os
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+ from dotenv import load_dotenv
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+ from langgraph.graph import START, StateGraph, MessagesState
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+ from langgraph.prebuilt import tools_condition
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+ from langgraph.prebuilt import ToolNode
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+ from langchain_google_genai import ChatGoogleGenerativeAI
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+ from langchain_groq import ChatGroq
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+ from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
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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 SupabaseVectorStore
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+ from langchain_core.messages import SystemMessage, HumanMessage
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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 supabase.client import Client, create_client
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+
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+ load_dotenv()
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+
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+ @tool
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+ def multiply(a: int, b: int) -> int:
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+ """Multiply two numbers.
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+ Args:
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+ a: first int
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+ b: second int
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+ """
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+ return a * b
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+
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+ @tool
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+ def add(a: int, b: int) -> int:
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+ """Add two numbers.
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+ Args:
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+ a: first int
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+ b: second int
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+ """
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+ return a + b
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+
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+ @tool
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+ def subtract(a: int, b: int) -> int:
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+ """Subtract two numbers.
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+ Args:
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+ a: first int
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+ b: second int
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+ """
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+ return a - b
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+
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+ @tool
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+ def divide(a: int, b: int) -> int:
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+ """Divide two numbers.
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+ Args:
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+ a: first int
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+ b: second int
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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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+
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+ @tool
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+ def modulus(a: int, b: int) -> int:
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+ """Get the modulus of two numbers.
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+ Args:
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+ a: first int
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+ b: second int
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+ """
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+ return a % b
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+
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+ @tool
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+ def wiki_search(query: str) -> str:
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+ """Search Wikipedia for a query and return maximum 2 results.
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+ Args:
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+ query: The search query."""
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+ search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
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+ formatted_search_docs = "\n\n---\n\n".join(
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+ [
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+ f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
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+ for doc in search_docs
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+ ])
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+ return {"wiki_results": formatted_search_docs}
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+
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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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+ 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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
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+ for doc in search_docs
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+ ])
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+ return {"web_results": formatted_search_docs}
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+
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+ @tool
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+ def arvix_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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+ 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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
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+ for doc in search_docs
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+ ])
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+ return {"arvix_results": formatted_search_docs}
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+
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+
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+ with open("system_prompt.txt", "r", encoding="utf-8") as f:
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+ system_prompt = f.read()
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+
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+ sys_msg = SystemMessage(content=system_prompt)
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+
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+ # build a retriever
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+ embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768
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+ supabase: Client = create_client(
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+ os.environ.get("SUPABASE_URL"),
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+ os.environ.get("SUPABASE_SERVICE_KEY"))
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+ vector_store = SupabaseVectorStore(
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+ client=supabase,
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+ embedding= embeddings,
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+ table_name="documents",
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+ query_name="match_documents_langchain",
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+ )
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+ create_retriever_tool = create_retriever_tool(
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+ retriever=vector_store.as_retriever(),
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+ name="Question Search",
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+ description="A tool to retrieve similar questions from a vector store.",
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+ )
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+
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+ tools = [add, subtract, multiply, divide, modulus, web_search, wiki_search, arvix_search]
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+
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+ def build_graph(provider: str = "google"):
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+ """Build the graph"""
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+ if provider == "google":
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+ llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
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+ elif provider == "groq":
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+ llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
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+ elif provider == "huggingface":
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+ llm = ChatHuggingFace(
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+ llm=HuggingFaceEndpoint(
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+ url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
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+ temperature=0,
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+ ),
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+ )
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+ else:
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+ raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
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+ llm_with_tools = llm.bind_tools(tools)
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+
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+ # Node
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+ def assistant(state: MessagesState):
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+ """Assistant node"""
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+ return {"messages": [llm_with_tools.invoke(state["messages"])]}
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+
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+ from langchain_core.messages import AIMessage
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+ def retriever(state: MessagesState):
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+ query = state["messages"][-1].content
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+ similar_doc = vector_store.similarity_search(query, k=1)[0]
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+
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+ content = similar_doc.page_content
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+ if "Final answer :" in content:
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+ answer = content.split("Final answer :")[-1].strip()
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+ else:
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+ answer = content.strip()
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+ return {"messages": [AIMessage(content=answer)]}
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+
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+ builder = StateGraph(MessagesState)
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+ builder.add_node("retriever", retriever)
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+
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+ # Retriever ist Start und Endpunkt
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+ builder.set_entry_point("retriever")
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+ builder.set_finish_point("retriever")
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+
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+ return builder.compile()
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+
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+
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+
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+
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+
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+
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+
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+