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6b1678e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 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)) |