Load Airtable data into LlamaIndex for RAG pipelines

Airtable Loader is a Python reader that extracts data from Airtable tables and converts it into LlamaIndex Document objects. It authenticates using your Airtable API token and retrieves records from specified tables and bases, making Airtable data available for indexing, search, and retrieval-augmented generation workflows.

Use Airtable Loader when you need to index Airtable data for semantic search, question-answering systems, or AI applications built with LlamaIndex. It's ideal when your team stores structured data, project information, or knowledge bases in Airtable that you want to make queryable through natural language. Use it when you need to keep your LlamaIndex application synchronized with Airtable as a source of truth.

You provide three inputs: an Airtable API token for authentication, a table_id identifying the specific table to read, and a base_id identifying the Airtable base containing that table. The loader returns an array of LlamaIndex Document objects, with each document representing data extracted from your Airtable table.

This loader integrates with LlamaIndex and connects to Airtable to retrieve table data.

Airtable Loader serves developers building LlamaIndex applications who need to incorporate Airtable data into their AI workflows.

Install the package:

pip install llama-index-readers-airtable

Load documents from an Airtable table:

import os

from llama_index.readers.airtable import AirtableReader

reader = AirtableReader("<Airtable_TOKEN>")
documents = reader.load_data(table_id="<TABLE_ID>", base_id="<BASE_ID>")