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c5ef8e2 b80a29a c5ef8e2 8aec252 c5ef8e2 8aec252 d8ef173 8aec252 d8ef173 8aec252 d8ef173 8aec252 d8ef173 32a72a3 d8ef173 8abd413 0465af8 8aec252 b80a29a c5ef8e2 | 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 | from smolagents import Tool
from langchain_community.retrievers import BM25Retriever
from langchain.docstore.document import Document
from tools import CrashInfoRetrieverTool # Import the CrashInfoRetrieverTool
import datasets
import pandas as pd
import requests
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, docs):
self.is_initialized = False
self.retriever = BM25Retriever.from_documents(docs)
def forward(self, query: str):
results = self.retriever.get_relevant_documents(query)
if results:
return "\n\n".join([doc.page_content for doc in results[:3]])
else:
return "No matching guest information found."
def load_guest_dataset():
# 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
]
# Return the tool
return GuestInfoRetrieverTool(docs)
def load_crash_data():
# Public Philly crash fatality (csv)
url = "https://phl.carto.com/api/v2/sql?filename=fatal_crashes&format=csv&skipfields=cartodb_id,the_geom,the_geom_webmercator&q=SELECT%20*,%20ST_Y(the_geom)%20AS%20lat,%20ST_X(the_geom)%20AS%20lng%20FROM%20fatal_crashes"
response = requests.get(url)
with open("fatal_crashes.csv", "wb") as f:
f.write(response.content)
df = pd.read_csv("fatal_crashes.csv")
# Convert to LangChain documents
docs = []
for _, row in df.iterrows():
content = "\n".join([
f"Year: {row.get('year', 'N/A')}",
f"Street: {row.get('primary_st', 'N/A')}",
f"Age: {row.get('age', 'N/A')}",
f"Arrest: {row.get('arrest_yes', 'N/A')}",
f"Primary Vehicle: {row.get('veh1', 'N/A')}",
f"Crash Type: {row.get('veh2', 'N/A')}",
f"Outcome: {row.get('investigat', 'N/A')}",
f"RecordID: {row.get('objectid', 'N/A')}"
])
docs.append(Document(page_content=content, metadata={"id": row.get("id", "unknown")}))
# Return an instance of CrashInfoRetrieverTool
return CrashInfoRetrieverTool(docs)
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