Overview
Milvus is an open-source vector database built for scalable similarity search and AI applications. It supports embedded (Lite), local, and cloud deployments with advanced indexing options and consistency levels. Provider Class:MilvusProviderConfig Class:
MilvusConfig
Dependencies
Examples
Parameters
| Parameter | Type | Description | Default | Source |
|---|---|---|---|---|
collection_name | str | Name of the collection | "default_collection" | Base |
vector_size | int | Dimension of vectors | Required | Base |
distance_metric | DistanceMetric | Similarity metric (COSINE, EUCLIDEAN, DOT_PRODUCT) | COSINE | Base |
recreate_if_exists | bool | Recreate collection if it exists | False | Base |
default_top_k | int | Default number of results | 10 | Base |
default_similarity_threshold | Optional[float] | Minimum similarity score | None | Base |
connection | ConnectionConfig | Connection configuration | Required | Specific |
index | IndexConfig | Index type configuration | HNSWIndexConfig() | Specific |
consistency_level | Literal['Strong', 'Bounded', 'Session', 'Eventually'] | Consistency level | 'Bounded' | Specific |
index_params | Optional[Dict[str, Any]] | Additional index parameters | None | Specific |
use_sparse_vectors | bool | Enable sparse vector support | False | Specific |
dense_vector_field | str | Dense vector field name | "dense_vector" | Specific |
sparse_vector_field | str | Sparse vector field name | "sparse_vector" | Specific |
search_params | Optional[Dict[str, Any]] | Search parameters | None | Specific |
rrf_k | int | RRF ranker k parameter | 60 | Specific |
batch_size | int | Batch size for upsert operations | 100 | Specific |

