Embeddings integrate with vector databases like Milvus by serving as the primary data format for storing and querying high-dimensional representations of unstructured data. When you generate embeddings—numerical vectors that capture semantic features of text, images, or other data—they are stored in Milvus as indexed entries. The database organizes these vectors to enable fast similarity searches, which is its core function. For example, a text embedding model like BERT might convert a sentence into a 768-dimensional vector, and Milvus stores this vector in a way that allows it to quickly find other sentences with similar meanings. This integration bridges machine learning models (which create embeddings) and scalable query systems (which use them).
The process involves three key steps: embedding generation, indexing, and querying. First, data is passed through a model (e.g., ResNet for images or OpenAI’s text-embeddings) to produce embeddings. These vectors are then inserted into Milvus, where they’re indexed using algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index). These algorithms group similar vectors or create graph structures to reduce search time. During queries, the input (e.g., a search phrase or image) is converted into an embedding using the same model, and Milvus scans its indexes to find the nearest neighbors. For instance, a search for “red sports car” might return vectors representing images of Ferraris or text about vehicles, ranked by cosine similarity or Euclidean distance.
Developers use this integration to build applications like recommendation systems, semantic search, or image retrieval. Suppose you’re building a music recommendation engine: user listening history could be embedded into vectors, and Milvus would find songs with similar embeddings. Milvus handles scalability challenges—like distributing data across clusters or optimizing memory usage—so developers focus on application logic. Tools like Milvus’ SDKs simplify inserting embeddings and querying them with a few API calls. For example, after training a model to embed product descriptions, a developer could index millions of vectors in Milvus and enable real-time “find similar products” features. This workflow avoids brute-force comparisons, making it feasible to handle large datasets efficiently.