Milvus Overview

Milvus is an open-source vector database designed for managing, searching, and analyzing large amounts of vector data efficiently. It serves various applications, including AI, machine learning, and deep learning, where high-dimensional vector embeddings are common.

2.1 Vectors

Vectors are multi-dimensional arrays that represent entities in a continuous space, often used in AI and machine learning to capture features of data.

2.2 Vector Indexing

Vector indexing allows for efficient querying and searching of vectors using various algorithms (e.g., IVF, HNSW).

2.3 Similarity Search

Milvus supports various types of similarity searches, including approximate nearest neighbor (ANN) search, allowing users to find similar vectors quickly.

3.1 Requirements

• Docker installed on your machine
• At least 4 GB of RAM
• Linux or MacOS (Windows is supported with WSL)

3.2 Quick Installation

To install Milvus using Docker, run the following command:

docker run -d --name milvus \
                    -p 19530:19530 \
                    -p 19121:19121 \
                    milvusdb/milvus:latest

4.1 Inserting Vectors

To insert vectors into Milvus, you will need to define a collection and then insert your vectors:

from pymilvus import Collection, connections

# Connect to Milvus
connections.connect("default", host="localhost", port="19530")

# Define a collection
collection_name = "example_collection"
schema = {
    "fields": [
        {"name": "embedding", "type": "FLOAT_VECTOR", "params": {"dim": 128}}
    ],
    "segment_row_limit": 4096,
    "auto_id": True,
}
collection = Collection(name=collection_name, schema=schema)

# Insert vectors
collection.insert([[0.1]*128, [0.2]*128])  # Example 128-dimensional vectors

4.2 Querying Vectors

To query vectors, you can use the following syntax:

query_vectors = [[0.1]*128]
results = collection.query(expr="embedding in @query_vectors", limit=5)
print(results)

• Use appropriate vector dimensions to balance performance and accuracy.
• Choose the right indexing algorithm based on your query performance requirements.
• Regularly monitor database performance and optimize configurations as necessary.