Rename agent.py to langgraph_agent.py

langgraph_agent.py

@@ -0,0 +1,115 @@
+import os
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition, ToolNode
+from langchain_openai import ChatOpenAI
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+
+#tools
+@tool
+def multiply(a: int, b: int) -> int:
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    return a + b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> float:
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    return a % b
+
+@tool
+def wiki_search(query: str) -> dict:
+    docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted = "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}"/>\n{d.page_content}'
+        for d in docs
+    )
+    return {"wiki_results": formatted}
+
+@tool
+def web_search(query: str) -> dict:
+    docs = TavilySearchResults(max_results=3).invoke(query=query)
+    formatted = "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}"/>\n{d.page_content}'
+        for d in docs
+    )
+    return {"web_results": formatted}
+
+@tool
+def arvix_search(query: str) -> dict:
+    docs = ArxivLoader(query=query, load_max_docs=3).load()
+    formatted = "\n\n---\n\n".join(
+        f'<Document source="{d.metadata["source"]}"/>\n{d.page_content[:1000]}'
+        for d in docs
+    )
+    return {"arvix_results": formatted}
+
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+HF_SPACE_TOKEN = os.getenv("HF_SPACE_TOKEN")
+
+# ───────────────────────────────────────────────────────────────────────────────
+# 4) Assemble tool list
+tools = [
+    multiply, add, subtract, divide, modulus,
+    wiki_search, web_search, arvix_search,
+]
+
+# ───────────────────────────────────────────────────────────────────────────────
+# 5) Load your system prompt
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+sys_msg = SystemMessage(content=system_prompt)
+
+# ───────────────────────────────────────────────────────────────────────────────
+def build_graph(provider: str = "openai"):
+    """Build the LangGraph agent with chosen LLM (default: OpenAI)."""
+    if provider == "openai":
+        llm = ChatOpenAI(
+            model_name="o4-mini-2025-04-16",
+            openai_api_key=OPENAI_API_KEY,
+            # no temperature override here
+        )
+    elif provider == "huggingface":
+        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'.")
+
+    llm_with_tools = llm.bind_tools(tools)
+
+    def assistant(state: MessagesState):
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges("assistant", tools_condition)
+    builder.add_edge("tools", "assistant")
+
+    return builder.compile()
+
+if __name__ == "__main__":
+    graph = build_graph()
+    msgs = graph.invoke({"messages":[ HumanMessage(content="What’s the capital of France?") ]})
+    for m in msgs["messages"]:
+        m.pretty_print()