Upload agent.py

agent.py

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+import os
+from dotenv import load_dotenv
+from langchain_community.tools import DuckDuckGoSearchResults
+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_openai import ChatOpenAI
+
+load_dotenv()
+
+@tool
+def add(x: int, y: int) -> int:
+    """Adds two numbers.
+    :arg x: The first number.
+    :arg y: The second number.
+    """
+    return x + y
+
+@tool
+def subtract(x: int, y: int) -> int:
+    """Subtracts two numbers.
+    :arg x: The first number.
+    :arg y: The second number.
+    """
+    return x - y
+@tool
+def multiply(x: int, y: int) -> int:
+    """Multiplies two numbers.
+    :arg x: The first number.
+    :arg y: The second number.
+    """
+    return x * y
+
+@tool
+def divide(x: int, y: int) -> float:
+    """Divides two numbers.
+    :arg x: The first number.
+    :arg y: The second number.
+    :raises ValueError: If y is zero.
+    """
+    if y == 0:
+        raise ValueError("Cannot divide by zero.")
+    return x / y
+
+@tool
+def modulus(x: int, y: int) -> int:
+    """Calculates the modulus of two numbers.
+    :arg x: The first number.
+    :arg y: The second number.
+    :raises ValueError: If y is zero.
+    """
+    return x % y
+@tool
+def wiki_search(query: str) -> str:
+    """Searches Wikipedia for the given query and returns the top results.
+    :arg query: The search query.
+    """
+    loader = WikipediaLoader(query=query, load_max_docs=2)
+    docs = loader.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 docs
+        ])
+    return {"wiki_results": formatted_search_docs}
+
+@tool
+def web_search(query: str) -> str:
+    """Searches the web for the given query using Tavily and returns the top results.
+    :arg query: The search query.
+    """
+    tavily_search = DuckDuckGoSearchResults(query=query, num_results=3)
+    print(f"Running web search for query(DuckDuckGo): {query}")
+    results = tavily_search.run()
+    formatted_results = "\n\n---\n\n".join(
+        [f'<Document source="{result["source"]}" page="{result.get("page", "")}"/>\n{result["content"]}\n</Document>'
+         for result in results])
+    return {"web_results": formatted_results}
+
+
+@tool
+def arvix_search(query: str) -> str:
+    """Searches Arxiv for the given query and returns the top results.
+    :arg query: The search query.
+    """
+    loader = ArxivLoader(query=query, load_max_docs=3)
+    docs = loader.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 docs
+        ])
+    return {"arxiv_results": formatted_search_docs}
+
+
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+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"))
+print("Supabase client created.")
+vector_store = SupabaseVectorStore(
+    client=supabase,
+    embedding= embeddings,
+    table_name="documents",
+    query_name="match_documents_langchain",
+)
+print("Vector store initialized with Supabase.")
+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.",
+)
+print("Retriever tool created.")
+tools = [
+    add,
+    subtract,
+    multiply,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+]
+
+def build_graph(provider: str = "huggingface") -> StateGraph:
+    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=="openai":
+        # OpenAI
+        llm = ChatOpenAI(model="gpt-4o", temperature=0)
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"
+            ),
+        )
+    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]}
+
+    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()
+
+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="openai")
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()