Create 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 ToolNode, tools_condition
+from langchain_core.tools import tool
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+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, ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain.tools.retriever import create_retriever_tool
+from supabase.client import create_client
+
+load_dotenv()
+
+# --- System Prompt Loader ---
+def load_system_prompt(path="system_prompt.txt") -> SystemMessage:
+    try:
+        with open(path, encoding="utf-8") as f:
+            return SystemMessage(content=f.read())
+    except FileNotFoundError:
+        return SystemMessage(content="You are a helpful assistant.")
+
+sys_msg = load_system_prompt()
+
+# --- Math Tools Factory ---
+def math_tool(fn):
+    return tool(fn)
+
+@math_tool
+def add(a: int, b: int) -> int:        return a + b
+@math_tool
+def subtract(a: int, b: int) -> int:   return a - b
+@math_tool
+def multiply(a: int, b: int) -> int:   return a * b
+@math_tool
+def divide(a: int, b: int) -> float:
+    if b == 0: raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@math_tool
+def modulus(a: int, b: int) -> int:    return a % b
+
+# --- Document Formatting Helper ---
+def format_docs(docs, key: str, max_chars: int = None) -> dict:
+    content = "\n\n---\n\n".join(
+        f'<Document source="{d.metadata.get("source","")}" page="{d.metadata.get("page","")}" />\n'
+        f'{d.page_content[:max_chars] if max_chars else d.page_content}\n</Document>'
+        for d in docs
+    )
+    return {key: content}
+
+# --- Info Tools ---
+@tool
+def wiki_search(query: str) -> dict:
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    return format_docs(docs, "wiki_results")
+
+@tool
+def web_search(query: str) -> dict:
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    return format_docs(docs, "web_results")
+
+@tool
+def arvix_search(query: str) -> dict:
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    return format_docs(docs, "arvix_results", max_chars=1000)
+
+# --- Vector Retriever Setup ---
+def build_vector_retriever():
+    embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    supa = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_SERVICE_KEY"))
+    vs = SupabaseVectorStore(
+        client=supa,
+        embedding=embed_model,
+        table_name="documents",
+        query_name="match_documents_langchain"
+    )
+    return vs.as_retriever()
+
+# --- LLM Factory ---
+def get_llm(provider: str):
+    if provider == "google":
+        return ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    if provider == "groq":
+        return ChatGroq(model="qwen-qwq-32b", temperature=0)
+    if provider == "huggingface":
+        return ChatHuggingFace(llm=HuggingFaceEndpoint(
+            url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+            temperature=0))
+    raise ValueError(f"Unsupported provider: {provider}")
+
+# --- Build Graph ---
+def build_graph(provider: str = "google"):
+    # tools list
+    retriever = build_vector_retriever()
+    question_tool = create_retriever_tool(
+        retriever=retriever,
+        name="Question Search",
+        description="Retrieve similar Q&A from vector store"
+    )
+    tools = [
+        add, subtract, multiply, divide, modulus,
+        wiki_search, web_search, arvix_search,
+        question_tool
+    ]
+
+    # LLM w/ tools
+    llm = get_llm(provider).bind_tools(tools)
+
+    # Nodes
+    def assistant(state: MessagesState):
+        msgs = [sys_msg] + state["messages"]
+        resp = llm.invoke({"messages": msgs})
+        return {"messages": [resp]}
+
+    def retriever_node(state: MessagesState):
+        query = state["messages"][-1].content
+        doc = retriever.similarity_search(query, k=1)[0]
+        text = doc.page_content
+        answer = text.split("Final answer :")[-1].strip() if "Final answer :" in text else text
+        return {"messages": [AIMessage(content=answer)]}
+
+    # Graph assembly
+    graph = StateGraph(MessagesState)
+    graph.add_node("retriever", retriever_node)
+    graph.add_node("assistant", assistant)
+    graph.add_node("tools", ToolNode(tools))
+    graph.add_edge(START, "retriever")
+    graph.add_edge("retriever", "assistant")
+    graph.add_conditional_edges("assistant", tools_condition)
+    graph.add_edge("tools", "assistant")
+    graph.set_entry_point("retriever")
+    graph.set_finish_point("assistant")
+
+    return graph.compile()