Update agent.py

agent.py

@@ -1,8 +1,7 @@
 import os
 from dotenv import load_dotenv
 from langgraph.graph import START, END, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
+from langgraph.prebuilt import tools_condition, ToolNode
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
@@ -10,7 +9,7 @@ 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.messages import SystemMessage, HumanMessage, AIMessage
 from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
@@ -167,57 +166,47 @@ tools = [
 ]
 
 
+
+
 def build_graph(provider: str = "openai"):
     """Build the graph using OpenAI or Hugging Face"""
-    
-    if provider == "openai":
-        # OpenAI ChatGPT (e.g., GPT-4 or GPT-3.5)
-        from langchain.chat_models import ChatOpenAI
-        llm = ChatOpenAI(model="gpt-4", temperature=0)
-
-    elif provider == "huggingface":
-        # Hugging Face endpoint
-        from langchain.chat_models import ChatHuggingFace
-        from langchain.llms import HuggingFaceEndpoint
+    # Validate provider
+    if provider not in ["openai", "huggingface"]:
+        raise ValueError("Invalid provider. Choose 'openai' or 'huggingface'.")
 
+    # Initialize LLM based on provider
+    if provider == "openai":
+        from langchain_openai import ChatOpenAI
+        llm = ChatOpenAI(model="gpt-4o", temperature=0)
+    else:  # huggingface
+        from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
-                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                endpoint_url="https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct",
                 temperature=0,
             )
         )
 
-    else:
-        raise ValueError("Invalid provider. Choose 'openai' or 'huggingface'.")
-
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools)
 
-    # return llm_with_tools
-
-    # Node
+    # Define nodes
     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]}
-
-    from langchain_core.messages import AIMessage
-
     def retriever(state: MessagesState):
+        """Retriever node - provides context from vector store"""
         query = state["messages"][-1].content
-        similar_doc = vector_store.similarity_search(query, k=1)[0]
+        similar_docs = vector_store.similarity_search(query, k=1)
+        
         if not similar_docs:
-           return {"messages": [AIMessage(content="No relevant information found")]}
+            return {"messages": [AIMessage(content="No relevant information found")]}
+            
         similar_doc = similar_docs[0]
-
         content = similar_doc.page_content
+        
+        # Extract answer if formatted, otherwise use full content
         if "Final answer :" in content:
             answer = content.split("Final answer :")[-1].strip()
         else:
@@ -225,6 +214,74 @@ def build_graph(provider: str = "openai"):
 
         return {"messages": [AIMessage(content=answer)]}
 
+    # Build graph
+    builder = StateGraph(MessagesState)
+    
+    # Add nodes
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    
+    # Set up edges
+    builder.set_entry_point("retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+        {"continue": "tools", "end": END}
+    )
+    builder.add_edge("tools", "assistant")
+    
+    return builder.compile()
+# def build_graph(provider: str = "google"):
+#     """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)
+
+#     # 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]}
+
+#     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)
@@ -240,10 +297,89 @@ def build_graph(provider: str = "openai"):
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
 
-    
+    # Retriever ist Start und Endpunkt
     builder.set_entry_point("retriever")
     builder.set_finish_point("retriever")
 
     # Compile graph
     return builder.compile()
+# def build_graph(provider: str = "openai"):
+#     """Build the graph using OpenAI or Hugging Face"""
+    
+#     if provider == "openai":
+#         # OpenAI ChatGPT (e.g., GPT-4 or GPT-3.5)
+#         from langchain.chat_models import ChatOpenAI
+#         llm = ChatOpenAI(model="gpt-4", temperature=0)
+
+#     elif provider == "huggingface":
+#         # Hugging Face endpoint
+#         from langchain.chat_models import ChatHuggingFace
+#         from langchain.llms import HuggingFaceEndpoint
+
+#         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 'openai' or 'huggingface'.")
+
+#     # Bind tools to LLM
+#     llm_with_tools = llm.bind_tools(tools)
+
+#     # return llm_with_tools
+
+#     # 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]}
+
+#     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]
+#         if not similar_docs:
+#            return {"messages": [AIMessage(content="No relevant information found")]}
+#         similar_doc = similar_docs[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.set_entry_point("retriever")
+#     builder.set_finish_point("retriever")
+
+#     # Compile graph
+#     return builder.compile()