Skip to content

Latest commit

 

History

History
43 lines (32 loc) · 3.36 KB

setup.md

File metadata and controls

43 lines (32 loc) · 3.36 KB
Raw

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:

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