TechPrufer
Get started
Pinecone
Pinecone logoPinecone · Feature trail

Test Data Generation for Vector Databases

1 post · Sep 2024 – Jun 2026

What this trail covers

Pinecone has introduced a comprehensive workflow for generating and managing test data for vector databases. This includes utilities for creating realistic datasets from real text, generating embeddings locally or with hosted models, preparing metadata in a format compatible with Pinecone's bulk import (Parquet files with specific schema requirements), and partitioning data for distributed processing. The workflow supports iterative development and testing of vector search capabilities at various scales. This post adds a new method for bulk data ingestion directly from object storage, supporting Parquet files and offering significant cost and efficiency improvements over traditional upsert methods for initial data loading and large-scale migrations.

All Pinecone trails
  1. Generating Test Data for PineconeThis post details the first part of a workflow for generating test data for vector databases, focusing on creating realistic datasets from real text and preparing them for embedding and import into Pinecone. It describes a utility for generating Parquet files from the CC News dataset, including chunking article text, handling metadata size limits (40KB per vector), filtering null metadata values, and writing Parquet files with explicit schemas using PyArrow. The post also discusses the trade-offs of local vs. Pinecone-hosted embedding generation and the importance of realistic embeddings for recall and accuracy testing. It introduces a two-stage workflow to separate data generation from embedding generation for better scalability, with the first stage producing Parquet files with placeholder 'values' columns.
  2. The Import Tax Is GoneThis post details a significant cost reduction and performance improvement for Pinecone's bulk data import feature. The import cost has been reduced to $0.25/GB after a free tier of 1 TB, a 75% decrease from the previous $1/GB. The post explains how bulk import works by reading directly from object storage into the index builder, bypassing the standard write path for efficiency. It provides a concrete example of importing a bird species dataset, including code snippets for generating embeddings using Pinecone's inference API, writing Parquet files to S3 partitioned by namespace, and starting the import operation. The post also clarifies that bulk import supports up to 1 TB of data or 100 million records across up to 100 namespaces and notes that it is not compatible with indexes having schema definitions (e.g., full-text search, integrated embedding indexes).
  3. Introducing import from object storage for more efficient data transfer to Pinecone serverlessIntroduced a new bulk import capability for Pinecone serverless, allowing direct data ingestion from object storage (initially S3, with GCS and Azure Blob Storage planned). This feature supports Parquet files and is designed as an asynchronous, long-running operation. It aims to reduce initial ingest costs by up to 6x compared to upsert-based processes and simplifies large-scale data onboarding (up to 100M records per import in preview). The feature requires integrating object storage credentials and writes data to a new serverless namespace.