Spaces:
Sleeping
Sleeping
File size: 1,612 Bytes
ad2b3b2 38812af ad2b3b2 38812af ad2b3b2 7fd0db3 ad2b3b2 38812af ad2b3b2 38812af ad2b3b2 38812af 8507438 38812af ad2b3b2 38812af ad2b3b2 | 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 | from uuid import uuid4
import datasets
from smolagents import Tool
class GuestInfoRetrieverTool(Tool):
name = "guest_info_retriever"
description = "Retrieves detailed information about gala guests based on their name or relation."
inputs = {
"query": {
"type": "string",
"description": "The name or relation of the guest you want information about."
}
}
output_type = "string"
def __init__(self, vector_store):
self.is_initialized = False
self.vector_store = vector_store
def forward(self, query: str):
result = self.vector_store.query(
query_texts=[query],
n_results=3
)
distances = [distance for distance in result['distances'][0] if distance < 1.3]
docs = result['documents'][0]
return "\n\n".join([docs[idx] for idx in range(0, len(distances))])
def load_guest_dataset(vector_store):
# Load the dataset
guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
# Convert dataset entries into Document objects
for guest in guest_dataset:
vector_store.add(
documents=[
"\n".join([
f"Name: {guest['name']}",
f"Relation: {guest['relation']}",
f"Description: {guest['description']}",
f"Email: {guest['email']}"
])
],
metadatas=[{"name": guest["name"]}],
ids=[str(uuid4())])
# Return the tool
return GuestInfoRetrieverTool(vector_store)
|