• Integrations

Semantic Search with Milvus and VoyageAI

Open In Colab

This guide showcases how VoyageAI's Embedding API can be used with Milvus vector database to conduct semantic search on text.

Getting started

Before you start, make sure you have the Voyage API key ready, or you get one from the VoyageAI website.

The data used in this example are book titles. You can download the dataset here and put it in the same directory where you run the following code.

First, install the package for Milvus and Voyage AI:

$ pip install --upgrade voyageai pymilvus

If you are using Google Colab, to enable dependencies just installed, you may need to restart the runtime.

With this, we're ready to generate embeddings and use vector database to conduct semantic search.

Searching book titles with VoyageAI & Milvus

In the following example, we load book title data from the downloaded CSV file, use Voyage AI embedding model to generate vector representations, and store them in Milvus vector database for semantic search.

import voyageai
from pymilvus import MilvusClient

MODEL_NAME = "voyage-law-2"  # Which model to use, please check for available models
DIMENSION = 1024  # Dimension of vector embedding

# Connect to VoyageAI with API Key.
voyage_client = voyageai.Client(api_key="<YOUR_VOYAGEAI_API_KEY>")

docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",

vectors = voyage_client.embed(texts=docs, model=MODEL_NAME, truncation=False).embeddings

# Prepare data to be stored in Milvus vector database.
# We can store the id, vector representation, raw text and labels such as "subject" in this case in Milvus.
data = [
    {"id": i, "vector": vectors[i], "text": docs[i], "subject": "history"}
    for i in range(len(docs))

# Connect to Milvus, all data is stored in a local file named "milvus_voyage_demo.db"
# in current directory. You can also connect to a remote Milvus server following this
# instruction:
milvus_client = MilvusClient("milvus_voyage_demo.db")
COLLECTION_NAME = "demo_collection"  # Milvus collection name
# Create a collection to store the vectors and text.
milvus_client.create_collection(collection_name=COLLECTION_NAME, dimension=DIMENSION)

# Insert all data into Milvus vector database.
res = milvus_client.insert(collection_name="demo_collection", data=data)


With all data in Milvus vector database, we can now perform semantic search by generating vector embedding for the query and conduct vector search.

queries = ["When was artificial intelligence founded?"]

query_vectors = voyage_client.embed(
    texts=queries, model=MODEL_NAME, truncation=False

res =
    collection_name=COLLECTION_NAME,  # target collection
    data=query_vectors,  # query vectors
    limit=2,  # number of returned entities
    output_fields=["text", "subject"],  # specifies fields to be returned

for q in queries:
    print("Query:", q)
    for result in res:
Query: When was artificial intelligence founded?
[{'id': 0, 'distance': 0.7196218371391296, 'entity': {'text': 'Artificial intelligence was founded as an academic discipline in 1956.', 'subject': 'history'}}, {'id': 1, 'distance': 0.6297335028648376, 'entity': {'text': 'Alan Turing was the first person to conduct substantial research in AI.', 'subject': 'history'}}]