databricks-ai-search
Databricks AI Search Client (formerly Vector Search)
Decision gist · record as of 2026-08-14
Yes, if you are already using Databricks and need vector search. The package is actively maintained, has low install friction, and integrates seamlessly with Databricks notebooks. However, the license is restrictive and proprietary—use is only permitted in connection with a Databricks Platform Services agreement. Do not install if you are not a Databricks customer or do not have a valid service agreement in place.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a Databricks workspace and valid authentication (auto-detected in notebooks, or explicit credentials via workspace_url and personal_access_token or service principal parameters).
- Low install friction; depends on four lightweight runtime packages (mlflow-skinny, requests, deprecation, httpx).
- Actively maintained with recent releases.
License · maintenance · safety
(unclear) — License is unclear and proprietary: use is restricted to connection with Databricks Platform Services under a separate agreement. You cannot use this package standalone or outside that contractual relationship.
last release 2026-08-03 (11 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,515,634 downloads/mo, #3,030 on PyPI
Alternatives
Verify before relying
pip install databricks-ai-search
from databricks.ai_search.client import AISearchClient
client = AISearchClient()
results = client.create_endpoint(name="my-endpoint", endpoint_type="STANDARD")- Whether the package works outside Databricks notebooks or requires a Databricks workspace
- Specific performance characteristics or rate limits for similarity search queries
- Whether backward-compat aliases (VectorSearchClient, etc.) are fully equivalent or have subtle differences
What it is and what it does
This is the official Python SDK for Databricks AI Search, the managed vector and keyword search service within the Databricks platform. It provides a client to create search endpoints, define delta-sync indexes backed by Databricks tables, and run similarity searches against stored embeddings. The package handles authentication automatically in Databricks notebooks and supports explicit credential methods (PAT, service principals, OAuth) for external use.
The SDK is a rename and continuation of the legacy `databricks-vectorsearch` package; the old class names are preserved as aliases for backward compatibility, but new projects should use the `databricks-ai-search` package directly. It depends on mlflow-skinny, requests, deprecation, and httpx, all lightweight and stable libraries.
Use it for
- Build semantic search over Databricks Delta tables by creating vector indexes and querying them with embedding vectors.
- Integrate vector search into Databricks notebooks for retrieval-augmented generation (RAG) workflows.
- Manage multiple search endpoints and indexes programmatically from Python without leaving the Databricks platform.
- Query both vector and keyword search from external applications using PAT or service principal authentication.
- Migrate from the legacy `databricks-vectorsearch` package to the new `databricks-ai-search` naming without code changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Databricks and need vector search.
The package is actively maintained, has low install friction, and integrates seamlessly with Databricks notebooks. However, the license is restrictive and proprietary—use is only permitted in connection with a Databricks Platform Services agreement. Do not install if you are not a Databricks customer or do not have a valid service agreement in place.
Install
databricks-ai-search on PyPI
Before you install
Low install friction; depends on four lightweight runtime packages (mlflow-skinny, requests, deprecation, httpx). Actively maintained with recent releases.
Requires a Databricks workspace and valid authentication (auto-detected in notebooks, or explicit credentials via workspace_url and personal_access_token or service principal parameters).
License in practice
License is unclear and proprietary: use is restricted to connection with Databricks Platform Services under a separate agreement. You cannot use this package standalone or outside that contractual relationship.
Quickstart
pip install databricks-ai-search
from databricks.ai_search.client import AISearchClient
client = AISearchClient()
results = client.create_endpoint(name="my-endpoint", endpoint_type="STANDARD")Verify before relying
- Whether the package works outside Databricks notebooks or requires a Databricks workspace
- Specific performance characteristics or rate limits for similarity search queries
- Whether backward-compat aliases (VectorSearchClient, etc.) are fully equivalent or have subtle differences
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesmlflow-skinnyrequestsdeprecationhttpx |
| Maintenance | Actively maintained 11 days since the last release |
| First released | |
| Downloads | 2,515,634 / month, #3,030 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: databricks_ai_search-0.78-py3-none-any.whl
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See also databricks-vectorsearch · databricks-agents · databricks-feature-engineering · databricks-ai-bridge · databricks-zerobus-ingest-sdk · databricksapi · databricks-openai · databricks-langchain · databricks-feature-store · azure-search-documents