indexing

name	indexing
description	Use when user needs to create collections, indexes in Milvus. Triggers on: indexing, collection, create index, HNSW, IVF, schema, Milvus collection, vector storage.

Indexing - Index Management

Create Collections and indexes in Milvus.

Index Type Selection

Data Scale	Recommended Index	Features
<1M	HNSW	High precision, high memory
1M-10M	IVF_FLAT	Balanced
>10M	IVF_PQ	Compressed storage
Need exact	FLAT	Brute force search

Code Examples

Create Collection

from pymilvus import MilvusClient, DataType

# Connect (use local file or remote server)
client = MilvusClient(uri="./milvus.db")  # Local file
# client = MilvusClient(uri="http://localhost:19530")  # Docker/Server

# Create schema
schema = client.create_schema(auto_id=True, enable_dynamic_field=True)
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=1024)

# Prepare index params
index_params = client.prepare_index_params()
index_params.add_index(field_name="embedding", index_type="HNSW", metric_type="COSINE", params={"M": 16, "efConstruction": 256})

# Create collection with schema and index
client.create_collection(collection_name="my_collection", schema=schema, index_params=index_params)

Index Types

HNSW (Recommended, <1M data)

index_params = client.prepare_index_params()
index_params.add_index(
    field_name="embedding",
    index_type="HNSW",
    metric_type="COSINE",  # or L2, IP
    params={"M": 16, "efConstruction": 256}
)

IVF_FLAT (1M-10M)

index_params = client.prepare_index_params()
index_params.add_index(
    field_name="embedding",
    index_type="IVF_FLAT",
    metric_type="L2",
    params={"nlist": 1024}
)

IVF_PQ (>10M, memory efficient)

index_params = client.prepare_index_params()
index_params.add_index(
    field_name="embedding",
    index_type="IVF_PQ",
    metric_type="L2",
    params={"nlist": 1024, "m": 8, "nbits": 8}
)

Collection with Partitions

# Create partitions
client.create_partition(collection_name="my_collection", partition_name="2024_01")
client.create_partition(collection_name="my_collection", partition_name="2024_02")

# Insert with partition
client.insert(collection_name="my_collection", data=data, partition_name="2024_01")

# Search with partition
client.search(collection_name="my_collection", data=query_embedding, partition_names=["2024_01"], limit=10)

With Scalar Index

# Add scalar index to index_params (speeds up filtering)
index_params = client.prepare_index_params()
index_params.add_index(field_name="embedding", index_type="HNSW", metric_type="COSINE")
index_params.add_index(field_name="category", index_type="AUTOINDEX")  # Scalar index

Distance Metric Selection

Type	Use Case	Notes
COSINE	Text similarity	Normalized vectors
L2	Euclidean distance	General purpose
IP	Inner product	Recommendation systems

Next Steps

After index creation:

• Import data: Use core:data-ingestion
• Start searching: Use corresponding scenario skill