Update retreiver.py

retreiver.py

@@ -0,0 +1,91 @@
+import datasets
+from langchain.docstore.document import Document
+
+# Load Dataset
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# Convert dataset entries to document
+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
+]
+# ---------------------------------------------------------------------------------------------
+from langchain_community.retreivers import BM25Retreiver
+from langchain.tools import Tool
+
+bm25_retriever = BM25Retreiver.from_documents(docs)
+
+def extract_text(query: str) -> str:
+    """ Retrieves detailed information on the guests attending the Gala based on the name and relation."""
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching information of tthe guests found"
+
+
+guest_info_tool = Tool(
+    name = "guest_info_retriever",
+    func = extract_text,
+    description = "Retrieves detailed information on thr guests attending the Gala based on the name and the relation"
+)
+# ---------------------------------------------------------------------------------------------
+
+from typing import TypeDict, 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
+
+llm = HuggingFaceEndpoint(
+    repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct",
+    huggingfacehub_api_token = 
+)
+
+chat = ChatHuggingFace(llm=llm, verbose=True)
+tools = [guest_info_tool]
+chat_with_tools = chat.bind_tools(tools)
+
+# Generate Agentstate & AgentGraph
+
+class AgentState(TypeDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state : AgentState):
+    retutn {
+        "messages" : [chat chat_with_tools.invoke(state["messages"])]
+    }
+
+builder = StateGraph(AgentState)
+
+#  Define the 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()
+
+messages = [HumanMessage(content="Tell me about our guest named 'Lady Ada Lovelace'.")]
+response = alfred.invoke({"messages": messages})
+
+print("🎩 Alfred's Response:")
+print(response['messages'][-1].content))