vectorflow
High volume vector embedding pipeline with support for multiple vector databases
GraphCanon updated 3d · GitHub synced 3d
Decision brief
VectorFlow is a Python library that supports high volume transformation of raw data into vector embeddings and storage in multiple vector databases.
Good fit when
- - When your project requires handling large volumes of data that need to be transformed into vector embeddings efficiently.
- - In scenarios where compatibility with various vector database systems is crucial for seamless integration.
Avoid when
- - If your application only deals with small datasets and does not benefit from high-volume processing capabilities offered by VectorFlow.
- - When the specific requirements of your project mandate using a single, particular vector database system as opposed to leveraging multiple options(VectorFlow provides).
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (704) are secondary evidence.
- Docker Hub pulls (30d)
- 149·Docker Hub API·3d
Maintenance and security
Full trust report- Maintenance
- Dormant (828d since push)
- As of 3d
- Provenance
- Not a fork · Personal account
- As of 3d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install vectorflowSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
VectorFlow is a Python library designed to transform raw data into vector embeddings and store them in various vector databases.
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Aug 23, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Aug 23, 2026
- Languages
- python
Source: github.language · Aug 23, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
nt, make sure to pull version 1.9.1 since that is the version the qdrant client python package is supposed to work with.Source link
Tags
README
Docker-Compose
The best way to run VectorFlow is via docker compose. If you are running this on Mac, please grant Docker permissions to read from your Documents folder as instructed here. If this fails, remove the volume section from the docker-compose.yml.
2) Run Docker-Compose
Make sure you pull Rabbit MQ, Postgres, Min.io into your local docker repo. We also recommend running a vector DB in locally, so make sure to pull the image of the one you are using. Our docker-compose file will spin up qdrant by default and create two index/collections. If you plan to run Milvus or Weaviate, you will have to configure them on your own.
docker pull rabbitmq
docker pull postgres
docker pull qdrant/qdrant | docker pull semitechnologies/weaviate
docker pull minio/minio
Then run:
docker-compose build --no-cache
docker-compose up -d
Note that the init containers are running a script that sets up the database schema, vector DB and Min.io object store. These containers stop after the script completes. For qdrant, make sure to pull version 1.9.1 since that is the version the qdrant client python package is supposed to work with.
Building the Individual Images with Docker Commands
If you want to use docker build and docker run to build and run individual images instead of docker-compose follow these steps:
cd src/docker build --file api/Dockerfile -t vectorflow_api:latest .to build - don't forget the period at the enddocker run --network=vectorflow --name=vectorflow_api -d --env-file=../env_scripts/env_vars.env -p 8000:8000 vectorflow_api:latestto run the api. you don't need the port argument to run the worker
For agents
This page has a .md twin and JSON over the API.