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TencentDB for PostgreSQL

DocumentationTencentDB for PostgreSQLAI CapabilitiesOverview of AI Capabilities for TencentDB for PostgreSQL

Overview of AI Capabilities for TencentDB for PostgreSQL

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Last updated: 2026-06-08 10:32:12
This document provides an overview of TencentDB for PostgreSQL's AI capabilities.

Product Positioning

TencentDB for PostgreSQL provides a data foundation capability for the AI era. Centered around the two major directions of DB for AI (database empowering AI applications) and AI for DB (AI empowering database Ops), it offers a one-stop solution for enterprises to build AI-native applications.

Capability Landscape

DB for AI (Database Empowering AI)

Capability Module
Core Feature
Typical Scenario
Large Model Invocation
Directly invoke large language models in SQL via the tencentdb_ai plugin.
Text Classification, Sentiment Analysis, Intelligent Summarization
Vector Search
Based on pgvector (HNSW/IVFFlat) and pgvectorscale (DiskANN)
RAG search, semantic search, image search
Graph Database
Attribute graph storage and Cypher query based on Apache AGE
Knowledge Graph, GraphRAG, Relationship Reasoning
Agent Memory
Long-term memory storage for multiple agents, integrating vector + graph + relational data in a unified architecture
Long-term memory for AI agents, multi-agent collaboration
REST API
HTTP interface based on PostgREST
Frontend-backend separation, Serverless applications

AI for DB (AI Empowering Ops)

Capability Module
Core Feature
Typical Scenario
Intelligent Index Recommendation
tencentdb_index_advisor automatically analyzes workloads and recommends optimal indexes.
Performance tuning, new service launch assessment
AI Slow SQL Diagnosis
Automatically diagnoses slow queries and provides optimization suggestions by leveraging AI large models.
Routine Ops, performance troubleshooting

Quick Start

If you are using TencentDB for PostgreSQL's AI capabilities for the first time, we recommend following the path below to get started:
1. To call a large model in SQL, see tencentdb_ai plugin feature introduction.
2. To perform vector semantic search, see pgvector vector extension usage guide.
3. To build a knowledge graph, see Apache AGE graph database usage guide.
4. To enable an AI Agent to use a database, see Agent Long-term Memory Feature Overview.
5. To optimize database performance, see Intelligent Index Recommendation.

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