OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
RAG & Knowledge Systems

Vectoretl

Vectoretl, called VectorETL in its repository, is a Python framework for configuring data-ingestion and ETL pipelines that transform database, cloud-storage and local-file data into vector embeddings and load them into vector databases for generative AI applications.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vectoretl — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use VectorETL when you need a configurable pipeline between an existing database, cloud-storage location or local files and a vector-database destination.

Who it is for

  • Data and AI engineers preparing source data for vector-based applications.
  • Developers building semantic search, recommendation or other generative AI workflows that need data loaded into a vector database.

Documented uses

  • Move selected fields from a database into a vector database as embeddings.
  • Process cloud-storage files, such as CSV data in Amazon S3, for a vector-database destination.
  • Configure data batching and text chunking as part of an ingestion flow.
Before you choose

Requirements & limits

The repository is public, identifies the project as open source, and lists an MIT license. Installation instructions use pip or a Git repository URL.

  • A flow is configured by choosing a source, embedding model, target database and fields to embed.
  • Examples use provider-specific credentials, such as an OpenAI API key and a Pinecone API key.
  • The README describes batch processing and configurable text chunking with overlap.
  • The documented interfaces include a CLI and a Python package.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run a configured ETL flow from the command line

Load source data through an embedding provider into a configured vector database using a YAML file.

Before you start

  • Install VectorETL with pip.
  • Prepare a YAML configuration describing the source, embedding model, target and fields to embed.
  • Provide the credentials required by the selected source, embedding provider and destination.

Steps

  1. Install the package with the command below.

    Documented command
    pip install --upgrade vector-etl

  2. Create a YAML file with source, embedding, target and embed_columns settings.

  3. Run vector-etl -c /path/to/your/config.yaml.

Expected outcome

The configured ETL flow runs from the command line and sends the processed data to its configured vector-database target.

Guide limitations

  • The README's example configurations require credentials for the selected services.
  • Available source, embedding and target choices depend on the configuration and integrations used.
Documentation 1 ↗
Source-based practical guide

Load a YAML flow from a Python application

Configure and execute a VectorETL flow from Python using a YAML file.

Before you start

  • Install VectorETL with pip.
  • Prepare a YAML configuration describing the source, embedding model, target and fields to embed.

Steps

  1. Install the package with the command below.

    Documented command
    pip install --upgrade vector-etl

  2. Import create_flow from vector_etl and create a flow.

  3. Load the YAML file with flow.load_yaml('/path/to/your/config.yaml').

  4. Call flow.execute().

Expected outcome

The application executes the flow defined in the YAML configuration.

Guide limitations

  • The flow depends on valid configuration and any required credentials for the selected integrations.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
ContextData/VectorETL · Official homepage title · retained in its source language
How we review sourcesSuggest a correction