Milvus

Milvus is the open-source, cloud-native vector database that scales to billions of vectors. It's the open-source version of Zilliz. It supports:

Various indexing algorithms and distance metrics
Scalar filtering and time travel searches
Rollback and snapshots
Multi-language SDKs
Storage and compute separation
Cloud scalability
A developer-first community with multi-language support

Visit the Github to learn more.

Deploying the Database

You can deploy and manage Milvus using Docker Compose, Helm, K8's Operator, or Ansible. Follow the instructions here to get started.

Environment Variables:

Name	Required	Description
`DATASTORE`	Yes	Datastore name, set to `milvus`
`BEARER_TOKEN`	Yes	Your bearer token
`OPENAI_API_KEY`	Yes	Your OpenAI API key
`MILVUS_COLLECTION`	Optional	Milvus collection name, defaults to a random UUID
`MILVUS_HOST`	Optional	Milvus host IP, defaults to `localhost`
`MILVUS_PORT`	Optional	Milvus port, defaults to `19530`
`MILVUS_USER`	Optional	Milvus username if RBAC is enabled, defaults to `None`
`MILVUS_PASSWORD`	Optional	Milvus password if required, defaults to `None`
`MILVUS_INDEX_PARAMS`	Optional	Custom index options for the collection, defaults to `{"metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}}`
`MILVUS_SEARCH_PARAMS`	Optional	Custom search options for the collection, defaults to `{"metric_type": "IP", "params": {"ef": 10}}`
`MILVUS_CONSISTENCY_LEVEL`	Optional	Data consistency level for the collection, defaults to `Bounded`

Running Milvus Integration Tests

A suite of integration tests is available to verify the Milvus integration. To run the tests, run the milvus docker compose found in the examples folder.

Then, launch the test suite with this command:

bash

pytest ./tests/datastore/providers/milvus/test_milvus_datastore.py