version 1

.gitignore

@@ -0,0 +1,4 @@
+.env
+__pycache__/
+*.pyc
+.DS_Store

app.py

@@ -1,7 +1,59 @@
-import gradio as gr
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Generate the chat interface, including the tools
+llm = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+chat = ChatHuggingFace(llm=llm, verbose=True)
+tools = [guest_info_tool]
+chat_with_tools = chat.bind_tools(tools)
+
+# Generate the 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"])],
+    }
+
+## The graph
+builder = StateGraph(AgentState)
+
+# Define nodes: these do the work
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# Define edges: these determine how the control flow moves
+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()
+
+def respond(message, history):
+    messages = [HumanMessage(content=message)]
+    response = alfred.invoke({"messages": messages})
+    return response["messages"][-1].content
+
+
+demo = gr.ChatInterface(
+    fn=respond,
+    title="Alfred - Your Gala Assistant",
+    description="Ask Alfred about your gala guests. Try: 'Tell me about Lady Ada Lovelace.'",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+langchain-community
+langchain-core
+langchain-huggingface
+langgraph
+datasets
+rank-bm25
+python-dotenv
+huggingface-hub

retriever.py

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+from langchain_community.retrievers import BM25Retriever
+from langchain_core.tools import Tool
+
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+def extract_text(query: str) -> str:
+    """Retrieves detailed information about gala guests based on their name or relation."""
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching guest information found."
+
+guest_info_tool = Tool(
+    name="guest_info_retriever",
+    func=extract_text,
+    description="Retrieves detailed information about gala guests based on their name or relation."
+)

tools.py

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+import datasets
+from langchain_core.documents import Document
+
+# Load the dataset
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# Convert dataset entries into Document objects
+docs = [
+    Document(
+        page_content="\n".join([
+            f"Name: {guest['name']}",
+            f"Relation: {guest['relation']}",
+            f"Description: {guest['description']}",
+            f"Email: {guest['email']}"
+        ]),
+        metadata={"name": guest["name"]}
+    )
+    for guest in guest_dataset
+]