┌──────────────────────────────────────────────────┐
│                   Dify Platform                   │
│  ┌──────────┐  ┌───────────┐  ┌──────────────┐    │
│  │ RAG Engine │  │ Agent     │  │ Workflow Engine   │    │
│  └─────┬────┘  └─────┬─────┘  └──────┬───────┘    │
│        └─────────────┼───────────────┘             │
└──────────────────────┼─────────────────────────────┘
                       │
         ┌─────────────┼─────────────┐
         ▼             ▼             ▼
┌──────────────┐ ┌──────────┐ ┌──────────────┐
│ PostgreSQL   │ │ LLM   │ │ External Tools     │
│ + pgvector   │ │ (Reasoning)   │ │ (API)        │
│ (Vector Storage)   │ │          │ │              │
└──────────────┘ └──────────┘ └──────────────┘

# Clone Dify
git clone https://github.com/langgenius/dify.git
cd dify/docker

# Configure Environment Variables
cp .env.example .env

# Select pgvector as the Vector Database Type
VECTOR_STORE=pgvector

# PostgreSQL Connection Information
PGVECTOR_HOST=your-pg-instance.tencentcdb.com
PGVECTOR_PORT=5432
PGVECTOR_USER=dify_user
PGVECTOR_PASSWORD=your-password
PGVECTOR_DATABASE=dify_vectors

-- Create a Dify-specific database
CREATE DATABASE dify_vectors;

-- Execute after connecting to the dify_vectors database:
CREATE EXTENSION IF NOT EXISTS vector;

-- Create a Dify user
CREATE USER dify_user WITH PASSWORD 'secure-password';
GRANT ALL PRIVILEGES ON DATABASE dify_vectors TO dify_user;
GRANT ALL ON SCHEMA public TO dify_user;

-- Vector tables automatically created by Dify (illustrative)
CREATE TABLE embedding_documents (
    id UUID PRIMARY KEY,
    dataset_id UUID,
    document_id UUID,
    content TEXT,
    embedding vector(1024),
    metadata JSONB,
    created_at TIMESTAMPTZ
);

-- HNSW index
CREATE INDEX ON embedding_documents
USING hnsw (embedding vector_cosine_ops);

# Custom Tool Configuration
name: pg_query
description: Query the PostgreSQL database.
parameters:
  - name: sql
    type: string
    description: The SQL query to be executed.

POST https://your-pg-api/rpc/ai_answer
{"question": "{{user_input}}"}

User Input → Intent Recognition → Conditional Branching
              ├── Knowledge Q&A → RAG Search → LLM Generation
              ├── Data Query → SQL Generation → Execution → Formatting
              └── Operation Command → Tool Invocation → Result Return