Update agentic_agent.py

agentic_agent.py

@@ -5,6 +5,8 @@ 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_openai import ChatOpenAI
+from langchain.agents import initialize_agent, Tool
 from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -105,15 +107,18 @@ def arvix_search(query: str) -> str:
     return {"arvix_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_URL"),
     os.environ.get("SUPABASE_SERVICE_KEY"))
 vector_store = SupabaseVectorStore(
     client=supabase,
@@ -127,14 +132,31 @@ create_retriever_tool = create_retriever_tool(
     description="A tool to retrieve similar questions from a vector store.",
 )
 
-tools = [add, subtract, multiply, divide, modulus, web_search, wiki_search, arvix_search]
 
-def build_graph(provider: str = "google"):
+
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+]
+
+def build_graph(provider: str = "groq"):
     """Build the graph"""
+    # Load environment variables from .env file
     if provider == "google":
+        # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) 
+        # Groq https://console.groq.com/docs/models
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+    elif provider == "openai":
+        # OpenAI
+        llm = ChatOpenAI(model="gpt-4", temperature=0)
     elif provider == "huggingface":
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
@@ -144,38 +166,33 @@ def build_graph(provider: str = "google"):
         )
     else:
         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
     # Node
     def assistant(state: MessagesState):
         """Assistant node"""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
-    from langchain_core.messages import AIMessage
-    def retriever(state: MessagesState):
-        query = state["messages"][-1].content
-        similar_doc = vector_store.similarity_search(query, k=1)[0]
 
-        content = similar_doc.page_content
-        if "Final answer :" in content:
-            answer = content.split("Final answer :")[-1].strip()
-        else:
-            answer = content.strip()
-        return {"messages": [AIMessage(content=answer)]}
+    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        example_msg = HumanMessage(
+            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+        )
+        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
-
-    # Retriever ist Start und Endpunkt
-    builder.set_entry_point("retriever")
-    builder.set_finish_point("retriever")
-
-    return builder.compile()
-
-
-
-
-
-
-
-
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    # Compile graph
+    return builder.compile()