Integrating a vector database with traditional search backends like Elasticsearch involves combining vector-based similarity searches with keyword or structured queries. The goal is to leverage the strengths of both systems: vector databases excel at finding semantically similar data using embeddings, while Elasticsearch handles text-based searches, filtering, and aggregations. To connect them, you typically synchronize data between the systems and design a query workflow that merges results from both. For example, product descriptions stored in Elasticsearch can have corresponding vector embeddings stored in a vector database like Pinecone or Milvus, enabling hybrid searches that blend keyword matches with semantic similarity.
A common approach is to set up a pipeline that generates and syncs embeddings. When new data is added to Elasticsearch, you extract the text fields, compute their embeddings using a model like BERT or OpenAI’s API, and store those vectors in the vector database. This ensures both systems have the same data but optimized for their respective search methods. For queries, a user’s search term is sent to both systems: Elasticsearch returns keyword-matched results, while the vector database finds items with similar embeddings. The results are then combined, often using techniques like score fusion or re-ranking. For instance, a search for “waterproof hiking boots” might return exact matches from Elasticsearch and semantically related items like “rain-resistant trekking shoes” from the vector database, merged into a single ranked list.
Implementation details matter. To minimize latency, some teams precompute embeddings during data ingestion and use Elasticsearch plugins like the dense_vector field type to store vectors directly, avoiding a separate database. However, dedicated vector databases often scale better for high-dimensional data. Another consideration is maintaining consistency: use change-data-capture tools or Elasticsearch’s update hooks to keep embeddings in sync with source data. For hybrid queries, tools like Apache Kafka can stream updates between systems. Testing is critical—evaluate metrics like recall@k for vector search and precision for keyword results to ensure the combined system meets performance goals. This hybrid approach is particularly useful in applications like e-commerce (product recommendations) or content platforms (related articles), where both precise matches and contextual relevance matter.