---

name: milvus description: Operate Milvus vector database with pymilvus Python SDK. Use when the user wants to connect to Milvus, create collections, insert vectors, perform similarity search, hybrid search, full-text search, manage indexes, partitions, databases, or RBAC via Python code. license: Apache-2.0 compatibility: Requires Python 3.8+ and pymilvus (pip install pymilvus). Runs on macOS and Linux. metadata: author: zilliztech version: "1.0.0" allowed-tools: Bash Read Write

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 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=True to allow inserting fields not defined in the schema.

---

Vector Operations

See 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.

---

Index Management

See 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)

client.list_indexes(collection_name="my_collection")
client.describe_index(collection_name="my_collection", index_name="my_index")
client.drop_index(collection_name="my_collection", index_name="my_index")

• AUTOINDEX is recommended for most use cases.
• An index is required before loading a collection.

---

Additional Features

| Feature | Reference | |---------|-----------| | Partition Management | references/partition.md | | Database Management | references/database.md | | User & Role Management (RBAC) | references/user-role.md | | Common Patterns (RAG, Semantic Search) | references/patterns.md |

---

General Guidance

• Always ask the user for connection details (URI, token/credentials) before writing connection code. Never assume or hardcode connection parameters.
• Never generate fake or placeholder vectors. Always use an embedding model to produce real vectors. Suggest pip install "pymilvus[model]" for built-in embedding functions.
• For quick prototyping, use Milvus Lite (uri="./file.db") — no server needed, but only if the user explicitly requests local/embedded mode.
• A collection must be loaded into memory before search/query.
• The vector dimension in search data must exactly match the collection schema.
• The query embedding model must be the same model used to generate the stored vectors.
• 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.
• Prefer AUTOINDEX unless the user has specific performance requirements.
• Use truncate_collection to clear all data without dropping the collection.
• For large datasets, use iterators (search_iterator, query_iterator) instead of increasing limit.