v1

.gitignore

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+.env

.python-version

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+3.12

agent.py

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+# import libraries for langgraph, huggingface
+import os
+from dotenv import load_dotenv
+from typing import TypedDict, List, Dict, Any, Optional, Annotated
+
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.graph.message import add_messages
+from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage, AIMessage
+from langchain_core.messages.ai import subtract_usage
+
+from langchain.tools import Tool
+from langchain_core.tools import tool
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_community.utilities import SerpAPIWrapper
+from langchain_community.utilities import ArxivAPIWrapper
+from langchain_community.retrievers import BM25Retriever
+
+from langgraph.prebuilt import ToolNode, tools_condition
+
+
+# load environment variables
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+
+# maths tool
+@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 multiply(a:int, b:int) -> int:
+    """multiply two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+
+@tool
+def divide(a:int, b:int) -> float:
+    """divide two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    try:
+        # Attempt the division
+        result = a / b
+        return result
+    except ZeroDivisionError:
+        # Handle the case where b is zero
+        raise ValueError("Cannot divide by zero.")
+
+
+@tool
+def modulus(a:int, b:int) -> int:
+    """modulus remainder of two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a % b
+
+
+# wikipedia search tool
+@tool
+def search_wiki(query: str) -> Dict[str, str]:
+    """search wikipedia with a query
+    args:
+        query: a search query
+    """
+    docs = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+    docs.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# internet search tool
+@tool
+def search_web(query: str) -> Dict[str, str]:
+    """search internet with a query
+    args:
+        query: a search query
+    """
+    docs = SerpAPIWrapper()
+    docs.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# ArXiv search tool
+@tool
+def search_arxiv(query: str) -> Dict[str, str]:
+    """search ArXiv for the paper with the given identifier
+    args:
+        query: a search identifier
+    """
+    arxiv = ArxivAPIWrapper()
+    docs = arxiv.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# build retriever
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+
+# load system prompt from file
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+
+# init system message
+sys_msg = SystemMessage(content=system_prompt)
+
+
+# generate the chat interface and tools
+llm = HuggingFaceEndpoint(
+    repo_id = "microsoft/Phi-4-reasoning-plus",
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
+
+
+chat = ChatHuggingFace(llm=llm, verbose=False)
+
+
+tools = [
+    add,
+    subtract,
+    multiply,
+    divide,
+    modulus,
+    search_wiki,
+    search_web,
+    search_arxiv
+]
+
+
+chat_with_tools = chat.bind_tools(tools)
+
+
+# generate AgentState and Agent graph
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    return {
+        "messages": [chat_with_tools.invoke(state["messages"])],
+    }
+
+
+# build graph
+builder = StateGraph(AgentState)
+
+
+# define nodes
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+
+# define edges
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # If the latest message requires a tool, route to tools
+    # Otherwise, provide a direct response
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+alfred = 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?"
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

pyproject.toml

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+[project]
+name = "hfagentscourse-finalassignment"
+version = "0.1.0"
+description = "Huggingface agents course -- Final assignment"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "dotenv>=0.9.9",
+    "gradio>=5.29.0",
+    "langchain-community>=0.3.23",
+    "langchain-huggingface>=0.2.0",
+    "langchain-openai>=0.3.16",
+    "langchain-tools>=0.1.34",
+    "langgraph>=0.4.3",
+    "pandas>=2.2.3",
+    "rank-bm25>=0.2.2",
+    "requests>=2.32.3",
+    "wikipedia>=1.4.0",
+]

requirements.txt

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+dotenv
 gradio
-requests
+langchain-community
+langchain-huggingface
+langchain-openai
+langchain-tools
+langgraph
+pandas
+rank-bm25
+requests
+wikipedia

system_prompt.txt

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+You are a helpful assistant.