Update agent.py

agent.py

@@ -1,187 +1,96 @@
-"""LangGraph Agent"""
-import os
-from dotenv import load_dotenv
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
+from typing import List
+from langgraph.graph import StateGraph, MessagesState, END
+from langgraph.prebuilt import ToolNode, tools_condition
+
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
-# from langchain_community.tools import create_retriever_tool
-from supabase.client import Client, create_client
 
-load_dotenv()
+from duckduckgo_search import DDGS
 
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-    Args:
-        a: first int
-        b: second int
-    """
-    return a * b
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
 
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a + b
+import os
 
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a - b
+# ---------------------------------------------------------
+# TOOLS
+# ---------------------------------------------------------
 
 @tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
+def web_search(query: str) -> str:
+    """Search the web using DuckDuckGo."""
+    with DDGS() as ddgs:
+        results = list(ddgs.text(query, max_results=3))
+    if not results:
+        return "No results found."
+    return "\n\n".join(r["body"] for r in results)
 
-@tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
 
 @tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"wiki_results": formatted_search_docs}
+def calculator(expression: str) -> str:
+    """Evaluate a math expression."""
+    try:
+        return str(eval(expression))
+    except Exception:
+        return "Error evaluating expression."
+
+
+TOOLS = [web_search, calculator]
+
+# ---------------------------------------------------------
+# BUILD GRAPH
+# ---------------------------------------------------------
 
-@tool
-def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"web_results": formatted_search_docs}
-
-
-# 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)
-
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-
-
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    retriever_tool,
-]
-
-# Build graph function
 def build_graph(provider: str = "google"):
-    """Build the graph"""
-    # Load environment variables from .env file
+    """
+    Build and return the LangGraph agent.
+    Supported providers: 'google', 'groq'
+    """
+
+    # -------------------------------
+    # LLM SELECTION
+    # -------------------------------
     if provider == "google":
-        # Google Gemini
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            temperature=0
+        )
     elif provider == "groq":
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
-    elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
-        llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
-                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-                temperature=0,
-            ),
+        llm = ChatGroq(
+            model="qwen-qwq-32b",
+            temperature=0
         )
     else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    # Bind tools to LLM
-    llm_with_tools = llm.bind_tools(tools)
+        raise ValueError("Invalid provider. Use 'google' or 'groq'.")
 
-    # Node
-    def assistant(state: MessagesState):
-        """Assistant node"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
-    from langchain_core.messages import AIMessage
+    llm_with_tools = llm.bind_tools(TOOLS)
 
-    def retriever(state: MessagesState):
-        query = state["messages"][-1].content
-        similar_doc = vector_store.similarity_search(query, k=1)[0]
+    # -------------------------------
+    # ASSISTANT NODE
+    # -------------------------------
+    def assistant(state: MessagesState):
+        return {
+            "messages": [
+                llm_with_tools.invoke(state["messages"])
+            ]
+        }
+
+    # -------------------------------
+    # GRAPH
+    # -------------------------------
+    builder = StateGraph(MessagesState)
 
-        content = similar_doc.page_content
-        if "Final answer :" in content:
-            answer = content.split("Final answer :")[-1].strip()
-        else:
-            answer = content.strip()
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(TOOLS))
 
-        return {"messages": [AIMessage(content=answer)]}
+    builder.set_entry_point("assistant")
 
-    builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition
+    )
 
-    # Retriever ist Start und Endpunkt
-    builder.set_entry_point("retriever")
-    builder.set_finish_point("retriever")
+    builder.add_edge("tools", "assistant")
+    builder.add_edge("assistant", END)
 
-    # Compile graph
     return builder.compile()