Databricks Vector Search: Semantic Search on Lakehouse
Traditional keyword-based search often fails to capture meaning, context, or similarity between data points. For example, searching for "revenue report" may not return related entries like "Q4 sales summary" if the exact words aren’t present.
Databricks Vector Search solves this problem by using embeddings and AI-powered semantic search, allowing users to find relevant data based on meaning rather than exact keywords. This transforms the Lakehouse into a smart, searchable knowledge hub.
Why Vector Search Matters
Consider a data analyst searching for similar customer complaints across a large dataset:
- Keyword search may miss semantically similar feedback.
- Manually comparing records is impractical at scale.
- Retrieving insights quickly becomes difficult.
With Databricks Vector Search:
- Embeddings capture semantic meaning of text, images, or other data.
- AI-powered similarity search identifies relevant records even without exact keyword matches.
- Integrates directly with the Lakehouse, enabling fast, scalable retrieval.
How Databricks Vector Search Works
- Create Embeddings: Convert your data into vector representations using AI models.
- Store Vectors in Lakehouse: Index these vectors for efficient similarity search.
- Query with Semantic Search: Provide a text query, and the system retrieves the most similar vectors/data points.
- Return Results Ranked by Similarity: Results are sorted based on semantic relevance rather than keywords.
Example: Finding Similar Customer Feedback
Suppose we have a customer_feedback table:
| feedback_id | feedback |
|---|---|
| 1 | "Product arrived damaged" |
| 2 | "Item broke after one week" |
| 3 | "Great product, very satisfied" |
Step 1: Generate embeddings using Databricks AI
from databricks.feature_store import FeatureStoreClient
from databricks_ai import EmbeddingModel
embedding_model = EmbeddingModel(model_name="text-embedding-ada-002")
feedback_embeddings = [embedding_model.embed(f) for f in customer_feedback_list]
Step 2: Perform semantic search
query = "Item damaged on delivery"
query_vector = embedding_model.embed(query)
# Retrieve top 2 most similar feedbacks
similar_feedbacks = vector_search(query_vector, top_k=2)
Example Output:
| feedback_id | feedback | similarity_score |
|---|---|---|
| 1 | "Product arrived damaged" | 0.92 |
| 2 | "Item broke after one week" | 0.88 |
Even though the words "damaged" and "delivery" do not exactly match all entries, the semantic meaning is captured, returning the most relevant results.
Key Benefits of Databricks Vector Search
| Feature | Benefit |
|---|---|
| Semantic Understanding | Finds relevant results beyond exact keywords |
| Scalable & Fast | Handles large datasets efficiently |
| Lakehouse Integration | Works seamlessly with structured/unstructured data |
| AI-Powered Embeddings | Captures contextual meaning for search and recommendations |
| Enhanced Insights | Improves analysis, recommendation, and retrieval accuracy |
Summary
Databricks Vector Search transforms your Lakehouse into a semantic knowledge engine, enabling AI-powered similarity search across structured and unstructured data. By leveraging embeddings and semantic search, analysts and engineers can discover insights faster, improve recommendations, and work smarter with their data.
The next topic is “Databricks Feature Store — Central Feature Management”.