Milvus Vector Database Skill
Operate Milvus vector databases directly through Python code using the pymilvus SDK. Covers the full lifecycle — connecting, schema design, collection management, vector CRUD, search, hybrid search, full-text search, indexing, partitions, databases, and RBAC.
When to Use
Use this skill when the user wants to:
- Connect to a Milvus instance (local, standalone, cluster, or Milvus Lite)
- Create collections with custom schemas
- Insert, upsert, search, query, get, or delete vectors
- Perform hybrid search with reranking
- Perform full-text search (BM25)
- Manage indexes, partitions, databases
- Set up users, roles, and access control (RBAC)
- Build RAG pipelines, semantic search, or recommendation systems with Milvus
- Iterate over large result sets with search/query iterators
Requirements
- Python 3.8+
pymilvus(pip install pymilvus)- A running Milvus instance, or use Milvus Lite (embedded, file-based) for development
Capabilities Overview
| Area | What You Can Do | |------|----------------| | Connection | Connect to Milvus Lite, Standalone, Cluster, or Zilliz Cloud | | Collections | Create (quick or custom schema), list, describe, drop, rename, truncate, load, release | | Vectors | Insert, upsert, search, hybrid search, query, get, delete | | Full-Text Search | BM25-based keyword search with sparse vectors | | Iterators | Paginated search and query over large datasets | | Indexes | Create (AUTOINDEX, HNSW, IVF_FLAT, etc.), list, describe, drop | | Partitions | Create, list, load, release, drop | | Databases | Create, list, switch, drop | | RBAC | Users, roles, privileges management |
Connection
IMPORTANT: Before writing any connection code, you MUST ask the user for their connection details. Ask:
1. Deployment type — Milvus Lite (local file), Standalone/Cluster (self-hosted), or Zilliz Cloud (managed)?
2. URI — For self-hosted: host and port (e.g., http://localhost:19530). For Zilliz Cloud: the endpoint URL.
3. Authentication — Token, API key, or username/password if required.
4. Database name — If not using the default database.
Never assume or hardcode connection parameters. Use Milvus Lite (uri="./milvus.db") only if the user explicitly wants local/embedded mode for development.
from pymilvus import MilvusClient
# Milvus Lite (embedded, file-based — great for dev/test)
client = MilvusClient(uri="./milvus.db")
# Standalone / Cluster Milvus (ask user for actual host:port and credentials)
client = MilvusClient(uri="<USER_URI>", token="<USER_TOKEN>")
# Zilliz Cloud (ask user for endpoint and API key)
client = MilvusClient(uri="<USER_ZILLIZ_ENDPOINT>", token="<USER_API_KEY>")Parameters:
| Parameter | Type | Description | |-----------|------|-------------| | uri | str | "./file.db" for Milvus Lite, "http://host:19530" for server | | token | str | API key or "username:password" | | user | str | Username (alternative to token) | | password | str | Password (alternative to token) | | db_name | str | Target database (default: "default") | | timeout | float | Operation timeout in seconds |
Async Client
from pymilvus import AsyncMilvusClient
async with AsyncMilvusClient(uri="<USER_URI>") as client:
results = await client.search(collection_name="my_collection", data=[query_vector], limit=10)Collection Management
Quick Create (auto schema + auto index + auto load)
client.create_collection(
collection_name="my_collection",
dimension=768,
metric_type="COSINE" # Optional: "COSINE" (default), "L2", "IP"
)This automatically creates an id field (INT64, primary key, auto_id), a vector field (FLOAT_VECTOR), AUTOINDEX, and auto-loads the collection.
Custom Schema Create
from pymilvus import DataType
schema = client.create_schema(auto_id=False, enable_dynamic_field=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("text", DataType.VARCHAR, max_length=512)
schema.add_field("embedding", DataType.FLOAT_VECTOR, dim=768)
index_params = client.prepare_index_params()
index_params.add_index(field_name="embedding", index_type="AUTOINDEX", metric_type="COSINE")
client.create_collection(collection_name="my_collection", schema=schema, index_params=index_params)See [references/collection.md](references/collection.md) for data types, add_field parameters, and all collection operations.
Other Collection Operations
client.list_collections()
client.describe_collection(collection_name="my_collection")
client.has_collection(collection_name="my_collection")
client.rename_collection(old_name="old", new_name="new")
client.drop_collection(collection_name="my_collection")
client.truncate_collection(collection_name="my_collection")
client.load_collection(collection_name="my_collection")
client.release_collection(collection_name="my_collection")
client.get_load_state(collection_name="my_collection")
client.get_collection_stats(collection_name="my_collection")- Quick create is best for prototyping; use custom schema for production.
- A collection must be loaded before search or query.
- Use
enable_dynamic_field=Trueto allow inserting fields not defined in the schema.
Vector Operations
See [references/vector.md](references/vector.md) for hybrid search, full-text search, iterators, filter syntax, and detailed examples.
Insert / Upsert
# Vectors must come from an embedding model — never use fake/placeholder vectors
from pymilvus import model
embedding_fn = model.dense.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
docs = ["AI advances in 2024", "ML basics for beginners"]
vectors = embedding_fn.encode_documents(docs)
data = [
{"id": 1, "text": docs[0], "embedding": vectors[0]},
{"id": 2, "text": docs[1], "embedding": vectors[1]},
]
client.insert(collection_name="my_collection", data=data)
client.upsert(collection_name="my_collection", data=data)Search (vector similarity)
# Use the same embedding model to encode the query
query_vectors = embedding_fn.encode_queries(["What is artificial intelligence?"])
results = client.search(
collection_name="my_collection",
data=query_vectors,
anns_field="embedding",
limit=10,
output_fields=["text", "id"],
filter='age > 20 and status == "active"',
search_params={"metric_type": "COSINE", "params": {"nprobe": 10}}
)Query / Get / Delete
# Query by filter
client.query(collection_name="my_collection", filter='id in [1, 2, 3]', output_fields=["text"], limit=100)
# Get by primary key
client.get(collection_name="my_collection", ids=[1, 2, 3], output_fields=["text"])
# Delete
client.delete(collection_name="my_collection", ids=[1, 2, 3])
client.delete(collection_name="my_collection", filter='status == "obsolete"')- Never use fake or placeholder vectors (e.g.,
[0.1, 0.2, ...]). Always generate vectors from an embedding model. - Use
pip install "pymilvus[model]"for built-in embedding functions, or use any embedding model (OpenAI, Cohere, etc.). - Vector dimension in search must match the collection schema exactly.
- The query embedding model must be the same model used to generate the stored vectors.
- For large inserts, batch data into chunks (e.g., 1000 rows per batch).
- For large result sets, use iterators — see [references/vector.md](references/vector.md).
Index Management
See [references/index.md](references/index.md) for index types, metric types, and parameters.
index_params = client.prepare_index_params()
index_params.add_index(
field_name="embedding",
index_type="HNSW",
metric_type="COSINE",
params={"M": 16, "efConstruction": 256}
)
client.create_index(collection_name="my_collection", index_params=index_params)