Milvus

Operate Milvus vector database with pymilvus — collections, vector search, hybrid search, indexes, RBAC, partitions, and more via Python code.

Milvus Vector Database Skill

Operate Milvus vector databases directly through Python code using the pymilvus SDK. This skill covers the full lifecycle — connecting, schema design, collection management, vector CRUD, search, hybrid 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
• Manage indexes, partitions, databases
• Set up users, roles, and access control (RBAC)
• Build RAG pipelines, semantic search, or recommendation systems with Milvus

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, load, release
Vectors	Insert, upsert, search, hybrid search, query, get, delete
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

python

from pymilvus import MilvusClient

# Milvus Lite (embedded, file-based — great for dev/test)
client = MilvusClient(uri="./milvus_demo.db")

# Standalone / Cluster Milvus
client = MilvusClient(uri="http://localhost:19530", token="root:Milvus")

# Zilliz Cloud
client = MilvusClient(
    uri="https://in03-xxxx.api.gcp-us-west1.zillizcloud.com:19530",
    token="your_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

python

from pymilvus import AsyncMilvusClient

async with AsyncMilvusClient(uri="http://localhost:19530") as client:
    results = await client.search(...)

---

Collection Management

Quick Create (auto schema + auto index + auto load)

python

client.create_collection(
    collection_name="my_collection",
    dimension=768,
    metric_type="COSINE"  # Optional: "COSINE" (default), "L2", "IP"
)

This automatically creates:

• id field (INT64, primary key, auto_id)
• vector field (FLOAT_VECTOR, dim=dimension)
• AUTOINDEX on vector field
• Collection is auto-loaded

Custom Schema Create

python

from pymilvus import DataType

# Step 1: Define schema
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)

# Step 2: Define index
index_params = client.prepare_index_params()
index_params.add_index(
    field_name="embedding",
    index_type="AUTOINDEX",
    metric_type="COSINE"
)

# Step 3: Create collection
client.create_collection(
    collection_name="my_collection",
    schema=schema,
    index_params=index_params
)

Supported Data Types

Scalar types:

DataType	Notes
DataType.BOOL	Boolean
DataType.INT8 / INT16 / INT32 / INT64	Integers
DataType.FLOAT / DOUBLE	Floating point
DataType.VARCHAR	String (requires max_length)
DataType.JSON	JSON object
DataType.ARRAY	Array (requires element_type, max_capacity)

Vector types:

DataType	Notes
DataType.FLOAT_VECTOR	Float32 vector (requires dim)
DataType.FLOAT16_VECTOR	Float16 vector (requires dim)
DataType.BFLOAT16_VECTOR	BFloat16 vector (requires dim)
DataType.BINARY_VECTOR	Binary vector (requires dim)
DataType.SPARSE_FLOAT_VECTOR	Sparse vector (no dim needed)

add_field Parameters

python

schema.add_field(
    field_name="my_field",
    datatype=DataType.VARCHAR,
    is_primary=False,
    auto_id=False,
    max_length=256,          # Required for VARCHAR
    dim=768,                 # Required for vector types (except sparse)
    element_type=DataType.INT64,  # Required for ARRAY
    max_capacity=100,        # Required for ARRAY
    nullable=False,
    default_value=None,
    is_partition_key=False,
    description=""
)

Other Collection Operations

python

# List all collections
collections = client.list_collections()

# Describe a collection
info = client.describe_collection(collection_name="my_collection")

# Check if collection exists
exists = client.has_collection(collection_name="my_collection")

# Rename a collection
client.rename_collection(old_name="old_name", new_name="new_name")

# Drop a collection
client.drop_collection(collection_name="my_collection")

# Load collection into memory (required before search/query)
client.load_collection(collection_name="my_collection")

# Release collection from memory
client.release_collection(collection_name="my_collection")

# Get load state
state = client.get_load_state(collection_name="my_collection")

# Get collection statistics
stats = client.get_collection_stats(collection_name="my_collection")

Collection Guidance

• Quick create is best for prototyping; use custom schema for production.
• A collection must be loaded before search or query operations.
• Before dropping a collection, confirm with the user — this deletes all data.
• Use enable_dynamic_field=True to allow inserting fields not defined in the schema.

---

Vector Operations

Target collection must exist and be loaded.

Insert

python

data = [
    {"id": 1, "text": "AI advances", "embedding": [0.1, 0.2, ...]},
    {"id": 2, "text": "ML basics", "embedding": [0.3, 0.4, ...]},
]
res = client.insert(collection_name="my_collection", data=data)
# Returns: {"insert_count": 2, "ids": [1, 2]}

Upsert (insert or update if PK exists)

python

res = client.upsert(collection_name="my_collection", data=data)
# Returns: {"upsert_count": 2}

Search (vector similarity)

python

results = client.search(
    collection_name="my_collection",
    data=[[0.1, 0.2, ...]],           # List of query vectors
    anns_field="embedding",             # Vector field name
    limit=10,                           # Top-K
    output_fields=["text", "id"],       # Fields to return
    filter='age > 20 and status == "active"',  # Optional scalar filter
    search_params={
        "metric_type": "COSINE",
        "params": {"nprobe": 10}        # Index-specific params
    }
)
# Returns: List[List[dict]]
# Each hit: {"id": ..., "distance": ..., "entity": {"text": ...}}

Hybrid Search (multi-vector with reranking)

python

from pymilvus import AnnSearchRequest, RRFRanker, WeightedRanker

req1 = AnnSearchRequest(
    data=[[0.1, 0.2, ...]],
    anns_field="dense_embedding",
    param={"metric_type": "COSINE", "params": {"nprobe": 10}},
    limit=10
)
req2 = AnnSearchRequest(
    data=[{1: 0.5, 100: 0.3}],          # Sparse vector
    anns_field="sparse_embedding",
    param={"metric_type": "IP"},
    limit=10
)

# RRF reranking
results = client.hybrid_search(
    collection_name="my_collection",
    reqs=[req1, req2],
    ranker=RRFRanker(k=60),
    limit=10,
    output_fields=["text"]
)

# Or weighted reranking
results = client.hybrid_search(
    collection_name="my_collection",
    reqs=[req1, req2],
    ranker=WeightedRanker(0.7, 0.3),
    limit=10
)

Query (filter-based retrieval)

python

results = client.query(
    collection_name="my_collection",
    filter='id in [1, 2, 3]',
    output_fields=["text", "embedding"],
    limit=100
)

Get (by primary key)

python

results = client.get(
    collection_name="my_collection",
    ids=[1, 2, 3],
    output_fields=["text"]
)

Delete

python

# By primary keys
client.delete(collection_name="my_collection", ids=[1, 2, 3])

# By filter expression
client.delete(collection_name="my_collection", filter='status == "obsolete"')

Filter Expression Syntax

Expression	Example
Comparison	age > 20
Equality	status == "active"
IN list	id in [1, 2, 3]
AND/OR	age > 20 and status == "active"
String match	text like "hello%"
Array contains	ARRAY_CONTAINS(tags, "ml")
JSON field	json_field["key"] > 100
Match all	id > 0

Vector Guidance

• The data parameter in search must match the collection's vector dimension exactly.
• For text-to-vector search, convert text to vectors using an embedding model first.
• For large inserts, batch data into chunks (e.g., 1000 rows per batch).
• Always specify output_fields to control which fields are returned.

---

Index Management

Create Index

python

index_params = client.prepare_index_params()

# Vector index
index_params.add_index(
    field_name="embedding",
    index_type="HNSW",               # See index types table below
    metric_type="COSINE",            # "COSINE", "L2", "IP"
    params={"M": 16, "efConstruction": 256}
)

# Optional: scalar index
index_params.add_index(
    field_name="text",
    index_type=""                    # Auto-select for scalars
)

client.create_index(
    collection_name="my_collection",
    index_params=index_params
)

Common Index Types

Index Type	For	Key Params	Notes
AUTOINDEX	Dense vectors	Auto-tuned	Recommended for most cases
FLAT	Dense vectors	None	Brute force, 100% recall
IVF_FLAT	Dense vectors	nlist	Good balance
IVF_SQ8	Dense vectors	nlist	Compressed, less memory
HNSW	Dense vectors	M, efConstruction	High recall, more memory
DISKANN	Dense vectors	None	Disk-based, large datasets
SPARSE_INVERTED_INDEX	Sparse vectors	drop_ratio_build	For sparse vectors
SPARSE_WAND	Sparse vectors	drop_ratio_build	Faster sparse search

Metric Types

Metric	Description	Use With
"COSINE"	Cosine similarity (larger = more similar)	Dense vectors
"L2"	Euclidean distance (smaller = more similar)	Dense vectors
"IP"	Inner product (larger = more similar)	Dense & Sparse vectors

Other Index Operations

python

# List indexes
indexes = client.list_indexes(collection_name="my_collection")

# Describe an index
info = client.describe_index(collection_name="my_collection", index_name="my_index")

# Drop an index
client.drop_index(collection_name="my_collection", index_name="my_index")

Index Guidance

• AUTOINDEX is recommended for most use cases.
• An index is required before loading a collection.
• After creating an index, load the collection before searching.
• Sparse vectors only support "IP" metric type.

---

Partition Management

python

# Create a partition
client.create_partition(collection_name="my_collection", partition_name="partition_A")

# List partitions
partitions = client.list_partitions(collection_name="my_collection")
# Returns: ["_default", "partition_A"]

# Check if partition exists
exists = client.has_partition(collection_name="my_collection", partition_name="partition_A")

# Load specific partitions
client.load_partitions(collection_name="my_collection", partition_names=["partition_A"])

# Release specific partitions
client.release_partitions(collection_name="my_collection", partition_names=["partition_A"])

# Drop a partition
client.drop_partition(collection_name="my_collection", partition_name="partition_A")

Partition Guidance

• Every collection has a _default partition.
• Use is_partition_key=True on a field to enable automatic partitioning by field value.
• A partition must be loaded before search.
• Before dropping a partition, confirm with the user — all data in it will be deleted.

---

Database Management

python

# Create a database
client.create_database(db_name="my_database")

# List all databases
databases = client.list_databases()
# Returns: ["default", "my_database"]

# Switch to a database
client.using_database(db_name="my_database")

# Drop a database (must drop all collections first)
client.drop_database(db_name="my_database")

# Or connect to a specific database at init
client = MilvusClient(uri="http://localhost:19530", db_name="my_database")

Database Guidance

• Every Milvus instance has a "default" database.
• Before dropping a database, all collections in it must be dropped first.

---

User & Role Management (RBAC)

User Operations

python

# Create a user
client.create_user(user_name="analyst", password="SecureP@ss123")

# List users
users = client.list_users()

# Describe a user (shows assigned roles)
info = client.describe_user(user_name="analyst")

# Update password
client.update_password(user_name="analyst", old_password="SecureP@ss123", new_password="NewP@ss456")

# Grant role to user
client.grant_role(user_name="analyst", role_name="read_only")

# Revoke role from user
client.revoke_role(user_name="analyst", role_name="read_only")

# Drop a user
client.drop_user(user_name="analyst")

Role Operations

python

# Create a role
client.create_role(role_name="read_only")

# List roles
roles = client.list_roles()

# Grant privilege (v2 API — recommended)
client.grant_privilege_v2(
    role_name="read_only",
    privilege="Search",                 # e.g., "Search", "Insert", "Query", "Delete"
    collection_name="my_collection",    # Use "*" for all collections
    db_name="default"                   # Use "*" for all databases
)

# Built-in privilege groups
client.grant_privilege_v2(
    role_name="admin_role",
    privilege="ClusterAdmin",           # See privilege groups below
    collection_name="*",
    db_name="*"
)

# Revoke privilege
client.revoke_privilege_v2(
    role_name="read_only",
    privilege="Search",
    collection_name="my_collection",
    db_name="default"
)

# Describe role (see granted privileges)
info = client.describe_role(role_name="read_only")

# Drop a role
client.drop_role(role_name="read_only")

Built-in Privilege Groups

Group	Scope
ClusterAdmin	Full cluster access
ClusterReadOnly	Read-only cluster access
ClusterReadWrite	Read-write cluster access
DatabaseAdmin	Full database access
DatabaseReadOnly	Read-only database access
DatabaseReadWrite	Read-write database access
CollectionAdmin	Full collection access
CollectionReadOnly	Read-only collection access
CollectionReadWrite	Read-write collection access

Common Individual Privileges

Search, Query, Insert, Delete, Upsert, CreateIndex, DropIndex, CreateCollection, DropCollection, Load, Release, CreatePartition, DropPartition

RBAC Guidance

• Recommended workflow: create role → grant privileges → create user → assign role.
• Use "*" for collection_name/db_name to grant on all resources.
• Before dropping a user or role, confirm with the user.

---

Common Patterns

RAG Pipeline Pattern

python

from pymilvus import MilvusClient, DataType

# 1. Connect
client = MilvusClient(uri="http://localhost:19530")

# 2. Create collection
schema = client.create_schema(auto_id=True, enable_dynamic_field=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("text", DataType.VARCHAR, max_length=2048)
schema.add_field("embedding", DataType.FLOAT_VECTOR, dim=768)
schema.add_field("source", DataType.VARCHAR, max_length=256)

index_params = client.prepare_index_params()
index_params.add_index(field_name="embedding", index_type="AUTOINDEX", metric_type="COSINE")

client.create_collection(collection_name="knowledge_base", schema=schema, index_params=index_params)

# 3. Insert documents (after embedding with your model)
client.insert("knowledge_base", data=[
    {"text": "chunk text...", "embedding": [...], "source": "doc1.pdf"},
])

# 4. Retrieve relevant context
results = client.search(
    collection_name="knowledge_base",
    data=[query_embedding],
    limit=5,
    output_fields=["text", "source"],
    search_params={"metric_type": "COSINE"}
)

Quick Semantic Search Pattern

python

# Simplest possible setup
client = MilvusClient(uri="./search.db")
client.create_collection(collection_name="docs", dimension=768)
client.insert("docs", data=[{"id": i, "vector": emb, "text": txt} for i, (emb, txt) in enumerate(zip(embeddings, texts))])
results = client.search("docs", data=[query_vector], limit=10, output_fields=["text"])

---

General Guidance

• Always check if pymilvus is installed: pip install pymilvus.
• For quick prototyping, use Milvus Lite (uri="./file.db") — no server needed.
• A collection must be loaded into memory before search/query.
• The vector dimension in search data must exactly match the collection schema.
• For text queries, users need an embedding model to convert text to vectors first. Suggest pymilvus[model] for built-in embedding support.
• Before any destructive operation (drop collection, drop database, delete vectors), always confirm with the user.
• Use enable_dynamic_field=True when the schema may evolve.
• For large-scale inserts, batch data into chunks of ~1000 rows.
• Prefer AUTOINDEX unless the user has specific performance requirements.