Upload agent.py

agent.py

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+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_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
+from langchain_core.tools import tool
+from langchain.tools.retriever import create_retriever_tool
+from supabase.client import Client, create_client
+
+# Load environment variables
+load_dotenv()
+
+# --- Math Tools ---
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two integers."""
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """Add two integers."""
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    """Subtract b from a."""
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> float:
+    """Divide a by b, error on zero."""
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """Compute a mod b."""
+    return a % b
+
+# --- Browser Tools ---
+@tool
+def wiki_search(query: str) -> dict:
+    """Search Wikipedia and return up to 2 documents."""
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content}" for d in docs]
+    return {"wiki_results": "\n---\n".join(results)}
+
+@tool
+def web_search(query: str) -> dict:
+    """Search Tavily and return up to 3 results."""
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content}" for d in docs]
+    return {"web_results": "\n---\n".join(results)}
+
+@tool
+def arxiv_search(query: str) -> dict:
+    """Search Arxiv and return up to 3 docs."""
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content[:1000]}" for d in docs]
+    return {"arxiv_results": "\n---\n".join(results)}
+
+# --- Load system prompt ---
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+# --- System message ---
+sys_msg = SystemMessage(content=system_prompt)
+
+# --- Retriever Tool ---
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+supabase = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_SERVICE_KEY"))
+
+vector_store = SupabaseVectorStore(
+    client=supabase,
+    embedding=embeddings,                                  table_name="documents",
+    query_name="match_documents_langchain")
+
+retriever_tool = create_retriever_tool(
+    retriever=vector_store.as_retriever(
+
+        search_type="similarity",
+        search_kwargs={"k": 5}
+    ),
+    name="Question Search",
+    description="A tool to retrieve similar questions from the vector store."
+)
+
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arxiv_search,
+]
+
+# --- Graph Builder ---
+def build_graph(provider: str = "huggingface"):
+    llm = ChatHuggingFace(
+        llm=HuggingFaceEndpoint(
+            repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
+        ),
+    )
+
+    # Bind tools to LLM
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Define no    def assistant(state: MessagesState):
        """Assistant node"""
+      
+          return {"messages [ [llm_with_tools.invoke(state["messages"])]}se]}
+
+
+    # Retriever  returns AIMessage    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        print('Similar questions:')
+        print(similar_question)
+        if len(similar_question) > 0:
+            example_msg = HumanMessage(
+                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+      ntent}]}
+            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        return {"messages": [sys_msg] + state["m
+    # Add nodesessages"]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", 
+
+    # Add edgesToolNode(tools))
+    builder.add_edge(START, "retriever")
+    builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")ever")
+
+    # Compile graph
+    return builder.compile()