Update agent.py

agent.py

@@ -1,3 +1,280 @@
+# """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 langchain_core.messages import SystemMessage, HumanMessage
+# from langchain_core.tools import tool
+# from langchain.tools.retriever import create_retriever_tool
+# from supabase.client import Client, create_client
+# from langchain_core.documents import Document
+# #load_dotenv()
+
+# load_dotenv(".env") 
+
+# @tool
+# def multiply(a: int, b: int) -> int:
+#     """Multiply two numbers.
+#     Args:
+#         a: first int
+#         b: second int
+#     """
+#     return a * b
+
+# @tool
+# def add(a: int, b: int) -> int:
+#     """Add two numbers.
+    
+#     Args:
+#         a: first int
+#         b: second int
+#     """
+#     return a + b
+
+# @tool
+# def subtract(a: int, b: int) -> int:
+#     """Subtract two numbers.
+    
+#     Args:
+#         a: first int
+#         b: second int
+#     """
+#     return a - b
+
+# @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
+
+# @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}
+
+# @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}
+
+# @tool
+# def arvix_search(query: str) -> str:
+#     """Search Arxiv for a query and return maximum 3 result.
+    
+#     Args:
+#         query: The search query."""
+#     search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+#     formatted_search_docs = "\n\n---\n\n".join(
+#         [
+#             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+#             for doc in search_docs
+#         ])
+#     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_SERVICE_KEY"))
+# supabase_url = os.getenv("SUPABASE_URL")
+# supabase_key = os.getenv("SUPABASE_KEY")
+
+# if not supabase_url or not supabase_key:
+#     raise ValueError("SUPABASE_URL and SUPABASE_KEY must be set in environment variables.")
+
+# supabase: Client = create_client(supabase_url, supabase_key)
+# docs = [Document(page_content="This is a test about AI.")]
+# vector_store = SupabaseVectorStore(
+#     client=supabase,  # should be your `supabase` client instance
+#     embedding=embeddings,
+#     table_name="documents",
+#     query_name="match_documents_langchain",
+# )
+
+# # Add documents
+# vector_store.add_documents(docs)
+
+# print("🔍 Testing similarity_search with: 'What is AI?'")
+# results = vector_store.similarity_search("What is AI?")
+# print(f"✅ Got {len(results)} results.")
+# if results:
+#     print("First result content:\n", results[0].page_content)
+# create_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,
+#     arvix_search,
+# ]
+
+# # Build graph function
+# 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":
+#         # Groq https://console.groq.com/docs/models
+#         llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+#     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,
+#             ),
+#         )
+#     else:
+#         raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+#     # Bind tools to LLM
+#     llm_with_tools = llm.bind_tools(tools)
+
+#     def assistant(state: MessagesState):
+#         """Assistant node"""
+#         print("\n🧠 Final prompt to model:")
+#         for m in state["messages"]:
+#             print(f"{m.type.upper()}: {m.content[:300]}...\n")  # truncate for readability
+        
+#         response = llm_with_tools.invoke(state["messages"])
+        
+#         print("💬 Model response:", response.content[:500], "\n")
+#         return {"messages": [response]}
+
+#     # Node
+#     # def assistant(state: MessagesState):
+#     #     """Assistant node"""
+#     #     return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+        
+    
+#     # 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]}
+
+#     def retriever(state: MessagesState):
+#         """Retriever node"""
+#         messages = state.get("messages", [])
+#         if not messages:
+#             print("⚠️ No messages received in retriever node.")
+#             return {"messages": []}
+        
+#         query = messages[0].content
+#         print(f"\n🔍 Query to vector store: {query}")
+        
+#         try:
+#             similar_question = vector_store.similarity_search(query)
+#         except Exception as e:
+#             print(f"❌ similarity_search failed: {e}")
+#             return {"messages": messages}
+        
+#         if not similar_question:
+#             print("⚠️ No similar questions found.")
+#             return {"messages": messages}
+        
+#         print(f"✅ Found {len(similar_question)} similar question(s).")
+#         print("📄 First retrieved doc:\n", similar_question[0].page_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] + messages + [example_msg]}
+
+
+
+
+#     builder = StateGraph(MessagesState)
+#     builder.add_node("retriever", retriever)
+#     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()
+
+# # test
+# if __name__ == "__main__":
+#     question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+#     # Build the graph
+#     graph = build_graph(provider="groq")
+#     # Run the graph
+#     messages = [HumanMessage(content=question)]
+#     messages = graph.invoke({"messages": messages})
+#     for m in messages["messages"]:
+#         m.pretty_print()
+
 """LangGraph Agent"""
 import os
 from dotenv import load_dotenv
@@ -15,10 +292,8 @@ from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
-from langchain_core.documents import Document
-#load_dotenv()
 
-load_dotenv(".env") 
+load_dotenv()
 
 @tool
 def multiply(a: int, b: int) -> int:
@@ -124,32 +399,15 @@ 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"))
-supabase_url = os.getenv("SUPABASE_URL")
-supabase_key = os.getenv("SUPABASE_KEY")
-
-if not supabase_url or not supabase_key:
-    raise ValueError("SUPABASE_URL and SUPABASE_KEY must be set in environment variables.")
-
-supabase: Client = create_client(supabase_url, supabase_key)
-docs = [Document(page_content="This is a test about AI.")]
+supabase: Client = create_client(
+    os.environ.get("SUPABASE_URL"), 
+    os.environ.get("SUPABASE_SERVICE_KEY"))
 vector_store = SupabaseVectorStore(
-    client=supabase,  # should be your `supabase` client instance
-    embedding=embeddings,
+    client=supabase,
+    embedding= embeddings,
     table_name="documents",
     query_name="match_documents_langchain",
 )
-
-# Add documents
-vector_store.add_documents(docs)
-
-print("🔍 Testing similarity_search with: 'What is AI?'")
-results = vector_store.similarity_search("What is AI?")
-print(f"✅ Got {len(results)} results.")
-if results:
-    print("First result content:\n", results[0].page_content)
 create_retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),
     name="Question Search",
@@ -170,7 +428,7 @@ tools = [
 ]
 
 # Build graph function
-def build_graph(provider: str = "groq"):
+def build_graph(provider: str = "google"):
     """Build the graph"""
     # Load environment variables from .env file
     if provider == "google":
@@ -192,86 +450,51 @@ def build_graph(provider: str = "groq"):
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
+    # Node
     def assistant(state: MessagesState):
         """Assistant node"""
-        print("\n🧠 Final prompt to model:")
-        for m in state["messages"]:
-            print(f"{m.type.upper()}: {m.content[:300]}...\n")  # truncate for readability
-        
-        response = llm_with_tools.invoke(state["messages"])
-        
-        print("💬 Model response:", response.content[:500], "\n")
-        return {"messages": [response]}
-
-    # Node
-    # def assistant(state: MessagesState):
-    #     """Assistant node"""
-    #     return {"messages": [llm_with_tools.invoke(state["messages"])]}
-
-        
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
     
     # 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]}
-
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        messages = state.get("messages", [])
-        if not messages:
-            print("⚠️ No messages received in retriever node.")
-            return {"messages": []}
-        
-        query = messages[0].content
-        print(f"\n🔍 Query to vector store: {query}")
-        
-        try:
-            similar_question = vector_store.similarity_search(query)
-        except Exception as e:
-            print(f"❌ similarity_search failed: {e}")
-            return {"messages": messages}
-        
-        if not similar_question:
-            print("⚠️ No similar questions found.")
-            return {"messages": messages}
-        
-        print(f"✅ Found {len(similar_question)} similar question(s).")
-        print("📄 First retrieved doc:\n", similar_question[0].page_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] + messages + [example_msg]}
-
+      #  """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]}
 
+    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)]}
+
+   # builder = StateGraph(MessagesState)
+    #builder.add_node("retriever", retriever)
+    #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")
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
-    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")
+
+    # Retriever ist Start und Endpunkt
+    builder.set_entry_point("retriever")
+    builder.set_finish_point("retriever")
 
     # Compile graph
     return builder.compile()
-
-# test
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # Build the graph
-    graph = build_graph(provider="groq")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
-