# """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'\n{doc.page_content}\n' # 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'\n{doc.page_content}\n' # 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'\n{doc.page_content[:1000]}\n' # 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("šŸ” Testing similarity_search with: 'What is AI?'") # results = vector_store.similarity_search("What is AI?") # print(f"āœ… 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("\nšŸ§  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("šŸ’¬ 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("āš ļø No messages received in retriever node.") # return {"messages": []} # query = messages[0].content # print(f"\nšŸ” Query to vector store: {query}") # try: # similar_question = vector_store.similarity_search(query) # except Exception as e: # print(f"āŒ similarity_search failed: {e}") # return {"messages": messages} # if not similar_question: # print("āš ļø No similar questions found.") # return {"messages": messages} # print(f"āœ… Found {len(similar_question)} similar question(s).") # print("šŸ“„ 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 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 load_dotenv() @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'\n{doc.page_content}\n' 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'\n{doc.page_content}\n' 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'\n{doc.page_content[:1000]}\n' 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")) vector_store = SupabaseVectorStore( client=supabase, embedding= embeddings, table_name="documents", query_name="match_documents_langchain", ) 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 = "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) #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) # Retriever ist Start und Endpunkt builder.set_entry_point("retriever") builder.set_finish_point("retriever") # Compile graph return builder.compile()