Vector Store and RAG Integration

This is the technical reference for integrating custom agents with Agent Builder Pro’s Vector Store and RAG (Retrieval-Augmented Generation) pipeline. User-facing overview: AI Data Training. For free curated local knowledge, see Knowledge Wiki (OKF).

Where RAG sits in the stack

RAG is not the same as the agent’s system prompt or the free OKF wiki. Embeddings live in a hosted namespace; at query time matching passages are injected into context for the LLM.

Architecture Overview

Agent Builder’s RAG system has three layers:

1. Ingestion — content is chunked, embedded, and stored in a namespace-isolated vector index on Google Vertex AI
2. Retrieval — at query time, the user’s message is embedded and matched against stored vectors using semantic similarity search
3. Generation — retrieved passages are injected into the LLM context as grounding evidence, producing answers that cite your actual documents

All communication goes through the Agentic RAG API at https://rag.agentic-plugin.com. Authentication uses a per-site API secret stored in agentic_rag_api_secret.

Training Sources

There are two ways to feed content into the vector store:

1. Train on WordPress Content

From Agent Builder → Knowledge, the admin UI scans your published posts, pages, and custom post types. Select which content to train and click Train. Behind the scenes, the plugin calls:

POST /train/text
{
  "user_id": "site-unique-id",
  "source_id": "post-123",
  "title": "Post Title",
  "content": "The full post content as plain text...",
  "url": "https://yoursite.com/post-slug/",
  "metadata": {
    "post_type": "post",
    "author": "admin"
  }
}

Content is chunked server-side into semantically meaningful segments (typically 500–1,000 tokens) with overlap, then embedded using Google’s text-embedding model and indexed in your namespace.

2. Upload Files (PDF, TXT)

Upload documents directly from the Knowledge page. The plugin sends a multipart upload to:

POST /train (multipart/form-data)
Fields: user_id, file

Supported formats: PDF, plain text. Files up to 50 MB. The API extracts text, chunks, embeds, and indexes in the same pipeline as WordPress content.

RAG API Endpoints

All endpoints require the X-API-Key header with your site’s RAG secret.

Method	Endpoint	Purpose
POST	/train/text	Train on text content (posts, pages)
POST	/train	Upload and train on a file (multipart)
GET	/sources?user_id=X	List all trained sources
DELETE	/sources	Delete a specific source by ID
GET	/query?q=X&user_id=X	Semantic search across trained content

How Agents Use RAG Context

When a user sends a message to an agent on a site with trained data, the chat pipeline automatically:

1. Queries the vector store — the user’s message is sent to /query to find the most relevant passages from your trained content
2. Injects context — matching passages are prepended to the system prompt as grounding evidence, with source attribution
3. Generates response — the LLM receives both the user’s question and the retrieved passages, producing an answer grounded in your actual data
4. Cites sources — the response includes references to the source document and passage, so users can verify the answer

This happens transparently — your agent code does not need to call RAG tools explicitly. Any agent on a site with trained data automatically gets RAG-augmented context.

Programmatic Access

Developers can interact with the RAG system programmatically using the Agentic\RAG_Manager class:

use Agentic\RAG_Manager;

// Query the vector store
$results = RAG_Manager::api_request( '/query', 'GET', [
    'q'       => 'What is our refund policy?',
    'user_id' => get_option( 'agentic_site_id' ),
    'top_k'   => 5,
] );

// Train on custom content
$result = RAG_Manager::api_request( '/train/text', 'POST', [
    'user_id'   => get_option( 'agentic_site_id' ),
    'source_id' => 'custom-doc-1',
    'title'     => 'Refund Policy',
    'content'   => $my_document_text,
    'url'       => 'https://yoursite.com/refund-policy/',
] );

// List all trained sources
$sources = RAG_Manager::api_request( '/sources', 'GET', [
    'user_id' => get_option( 'agentic_site_id' ),
] );

// Delete a source
$deleted = RAG_Manager::api_request( '/sources', 'DELETE', [
    'user_id'   => get_option( 'agentic_site_id' ),
    'source_id' => 'custom-doc-1',
] );

AJAX Endpoints

The Knowledge admin page uses these WordPress AJAX actions (all require the agentic_train_data nonce):

AJAX Action	Purpose
agentic_td_get_overview	Get training stats and WordPress content scan
agentic_td_scan_content	Scan published posts/pages for training candidates
agentic_td_train_post	Train a single post by ID
agentic_td_upload_file	Upload and train on a document file
agentic_td_get_sources	List all trained sources from the vector store
agentic_td_delete_source	Remove a source from the vector store
agentic_td_get_credits	Check remaining credit balance
agentic_td_get_pricing	Fetch current credit pricing
agentic_td_get_transactions	Get credit transaction history

Data Isolation and Security

• Namespace isolation — every site gets its own vector namespace. Cross-tenant retrieval is impossible.
• Encryption — TLS 1.3 in transit, AES-256 at rest. Keys managed by Google Cloud KMS.
• No model training — your data is never used to train Google’s foundation models. Governed by Google’s Data Processing Addendum.
• Right to erasure — delete any source or your entire corpus at any time, effective immediately.

Credit Costs

Operation	Cost	Unit
Embed / Train	1 credit	per 1,000 tokens (~750 words)
Semantic Query	1 credit	per search query
Delete	Free	always

Credits are shared across all Agentic services (RAG, Image Generation, Text-to-Speech). Check your balance in Settings → Health → Credit Balance.

Requirements

• Active Personal or Agency license
• Prepaid credits (check balance in Settings → Health)
• PHP 8.1+ with cURL extension

Related

• AI Data Training — user-facing overview and enterprise use cases
• Building a Custom Agent — agent code structure
• Agent Tools — all available built-in tools
• GDPR and Data Protection — privacy compliance