What is LlamaIndex?
Data framework for building RAG pipelines and LLM applications over custom data. Connects LLMs to external data sources.
Searching Walmart with LlamaIndex
This integration lets your LlamaIndex agent search Walmart in real time via the Scavio API. The agent gets back structured JSON with product listings, prices, ratings, review counts -- ready for reasoning and decision-making.
Setup
Bash
pip install llama-index requestsCode Example
Here is a complete LlamaIndex agent that searches Walmart using Scavio:
Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import json
import os
import requests
# Product detail is a separate call: POST /api/v1/walmart/product with product_id (the id field below).
def search_walmart(query: str) -> str:
"""Query Walmart through the Scavio API (POST /api/v1/walmart/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/walmart/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
tool = FunctionTool.from_defaults(fn=search_walmart)
llm = OpenAI(model="gpt-5.5")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("standing desk")
print(response)Full Working Example
A production-ready example with error handling:
Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import json
import os
import requests
# Product detail is a separate call: POST /api/v1/walmart/product with product_id (the id field below).
def search_walmart(query: str) -> str:
"""Query Walmart through the Scavio API (POST /api/v1/walmart/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/walmart/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
tool = FunctionTool.from_defaults(fn=search_walmart)
llm = OpenAI(model="gpt-5.5")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("standing desk")
print(response)Pricing
Scavio offers a free tier with 50 credits on signup (1 credit per search). No credit card required. This is enough to build and test your LlamaIndex integration. Paid plans start at $30/month for higher volumes.