GCP AlloyDB AI Developer
Overview
AlloyDB AI is a collection of features built into AlloyDB for PostgreSQL that enables AI-powered search and SQL-native inference - including pgvector integration for vector similarity search, hybrid search combining vector + full-text BM25 scoring, AI SQL functions that invoke hosted models directly from SQL queries, and model endpoint management for custom or Vertex AI models.
Core AlloyDB AI Capabilities
- Vector search with pgvector - store and query embeddings using
<=>, <->, <#> operators; HNSW and IVFFlat index types
- Hybrid search - combine pgvector cosine similarity with full-text search (tsvector/tsquery) for more relevant retrieval
- AI SQL functions -
google_ml.predict_row(), google_ml.embedding(), ai.generate_text(), ai.classify(), ai.score() - invoke AI models from SQL without leaving the database
- Model endpoint management - register Vertex AI model endpoints or Gemini models as AlloyDB model resources; control access via IAM
- AlloyDB Omni - run AlloyDB (including AlloyDB AI) on-premises or at the edge in a container
Quick Start (pgvector + hybrid search)
-- Enable extensions
CREATE EXTENSION vector;
CREATE EXTENSION google_ml_integration;
-- Create table with embedding column
CREATE TABLE documents (
id BIGSERIAL PRIMARY KEY,
content TEXT,
embedding vector(768)
);
-- Generate embeddings using AlloyDB AI function
UPDATE documents
SET embedding = google_ml.embedding('text-embedding-004', content);
-- Vector similarity search
SELECT id, content, embedding <=> $1 AS distance
FROM documents
ORDER BY distance LIMIT 10;
-- Hybrid search (vector + BM25 full-text)
SELECT id, content,
(1 - (embedding <=> $1)) * 0.7 + ts_rank(to_tsvector(content), query) * 0.3 AS score
FROM documents, to_tsquery($2) query
WHERE to_tsvector(content) @@ query
ORDER BY score DESC LIMIT 10;
Reference Directory
Load only when needed:
| Scenario |
Trigger Keywords |
Reference |
| Vector search setup |
pgvector, embedding, similarity, HNSW, IVFFlat |
references/vector-search.md |
| AI SQL functions |
ai_generate, ai_classify, google_ml, predict_row |
references/ai-functions.md |
| Hybrid search |
hybrid, BM25, full-text, combined search |
references/hybrid-search.md |
| Model endpoints |
Vertex AI model, custom model, endpoint, model registry |
references/model-endpoints.md |
| AlloyDB Omni |
on-premises, edge, container, Omni |
references/alloydb-omni.md |
| IAM & security |
auth, service account, IAM, private IP, PSC |
references/iam-security.md |
Key Rules
- Always use pgvector's HNSW index for production vector search - IVFFlat requires manual reindexing as data grows
- The
google_ml_integration extension must be enabled and the AlloyDB service account granted roles/aiplatform.user to call Vertex AI models from SQL
- Hybrid search weight tuning (e.g., 0.7 vector + 0.3 BM25) should be validated against your retrieval quality metrics - defaults are starting points
- AlloyDB AI functions execute synchronously within SQL transactions - avoid calling slow models in high-frequency OLTP paths
- AlloyDB Omni supports AlloyDB AI locally without Google Cloud connectivity - ideal for edge inference with pre-loaded models
- Separate the embedding pipeline (batch UPDATE) from the query path - do not regenerate embeddings on every SELECT
Official Docs
Security Notes
Read-only planning and advisory. Do not modify production AlloyDB schemas, model endpoint registrations, or IAM bindings without explicit approval.
1---2name: techtide-gcp-alloydb-ai-developer3description: Design and build AI-powered applications on AlloyDB for PostgreSQL using AlloyDB AI - covering vector search, hybrid search (vector + full-text), AI SQL functions (ai_generate, ai_classify, ai_score, ai_embed), model endpoint management, and the AlloyDB Omni edge runtime. Prefer techtide-gcp-alloydb-cloudsql-dba for cluster operations, backup, HA, and DBA tasks; use this skill when the request is primarily about AlloyDB AI search, SQL AI functions, or embedding pipelines.4---56# GCP AlloyDB AI Developer78## Overview910AlloyDB AI is a collection of features built into AlloyDB for PostgreSQL that enables AI-powered search and SQL-native inference - including pgvector integration for vector similarity search, hybrid search combining vector + full-text BM25 scoring, AI SQL functions that invoke hosted models directly from SQL queries, and model endpoint management for custom or Vertex AI models.1112## Core AlloyDB AI Capabilities13141. **Vector search with pgvector** - store and query embeddings using `<=>`, `<->`, `<#>` operators; HNSW and IVFFlat index types152. **Hybrid search** - combine pgvector cosine similarity with full-text search (tsvector/tsquery) for more relevant retrieval163. **AI SQL functions** - `google_ml.predict_row()`, `google_ml.embedding()`, `ai.generate_text()`, `ai.classify()`, `ai.score()` - invoke AI models from SQL without leaving the database174. **Model endpoint management** - register Vertex AI model endpoints or Gemini models as AlloyDB model resources; control access via IAM185. **AlloyDB Omni** - run AlloyDB (including AlloyDB AI) on-premises or at the edge in a container1920## Quick Start (pgvector + hybrid search)2122```sql23-- Enable extensions24CREATE EXTENSION vector;25CREATE EXTENSION google_ml_integration;2627-- Create table with embedding column28CREATE TABLE documents (29 id BIGSERIAL PRIMARY KEY,30 content TEXT,31 embedding vector(768)32);3334-- Generate embeddings using AlloyDB AI function35UPDATE documents36SET embedding = google_ml.embedding('text-embedding-004', content);3738-- Vector similarity search39SELECT id, content, embedding <=> $1 AS distance40FROM documents41ORDER BY distance LIMIT 10;4243-- Hybrid search (vector + BM25 full-text)44SELECT id, content,45 (1 - (embedding <=> $1)) * 0.7 + ts_rank(to_tsvector(content), query) * 0.3 AS score46FROM documents, to_tsquery($2) query47WHERE to_tsvector(content) @@ query48ORDER BY score DESC LIMIT 10;49```5051## Reference Directory5253Load only when needed:5455| Scenario | Trigger Keywords | Reference |56|---|---|---|57| Vector search setup | pgvector, embedding, similarity, HNSW, IVFFlat | references/vector-search.md |58| AI SQL functions | ai_generate, ai_classify, google_ml, predict_row | references/ai-functions.md |59| Hybrid search | hybrid, BM25, full-text, combined search | references/hybrid-search.md |60| Model endpoints | Vertex AI model, custom model, endpoint, model registry | references/model-endpoints.md |61| AlloyDB Omni | on-premises, edge, container, Omni | references/alloydb-omni.md |62| IAM & security | auth, service account, IAM, private IP, PSC | references/iam-security.md |6364## Key Rules6566- Always use pgvector's HNSW index for production vector search - IVFFlat requires manual reindexing as data grows67- The `google_ml_integration` extension must be enabled and the AlloyDB service account granted `roles/aiplatform.user` to call Vertex AI models from SQL68- Hybrid search weight tuning (e.g., 0.7 vector + 0.3 BM25) should be validated against your retrieval quality metrics - defaults are starting points69- AlloyDB AI functions execute synchronously within SQL transactions - avoid calling slow models in high-frequency OLTP paths70- AlloyDB Omni supports AlloyDB AI locally without Google Cloud connectivity - ideal for edge inference with pre-loaded models71- Separate the embedding pipeline (batch UPDATE) from the query path - do not regenerate embeddings on every SELECT7273## Official Docs7475- https://cloud.google.com/alloydb/docs/ai/overview76- https://cloud.google.com/alloydb/docs/ai/vector-embeddings77- https://cloud.google.com/alloydb/docs/omni/overview7879## Security Notes8081Read-only planning and advisory. Do not modify production AlloyDB schemas, model endpoint registrations, or IAM bindings without explicit approval.
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