docs: update graphile-llm README with standard header

graphile/graphile-llm/README.md

@@ -1,14 +1,34 @@
 # graphile-llm
 
-LLM integration plugin for PostGraphile v5. Provides server-side text-to-vector embedding for pgvector columns.
+<p align="center" width="100%">
+  <img height="250" src="https://raw.githubusercontent.com/constructive-io/constructive/refs/heads/main/assets/outline-logo.svg" />
+</p>
 
-## Features
+<p align="center" width="100%">
+  <a href="https://github.com/constructive-io/constructive/actions/workflows/run-tests.yaml">
+    <img height="20" src="https://github.com/constructive-io/constructive/actions/workflows/run-tests.yaml/badge.svg" />
+  </a>
+   <a href="https://github.com/constructive-io/constructive/blob/main/LICENSE"><img height="20" src="https://img.shields.io/badge/license-MIT-blue.svg"/></a>
+   <a href="https://www.npmjs.com/package/graphile-llm"><img height="20" src="https://img.shields.io/github/package-json/v/constructive-io/constructive?filename=graphile%2Fgraphile-llm%2Fpackage.json"/></a>
+</p>
+
+LLM integration plugin for PostGraphile v5 — server-side text-to-vector embedding, resolve-time vector injection, and RAG (Retrieval-Augmented Generation) for pgvector columns using `@agentic-kit/ollama`.
+
+## Table of contents
+
+- [Installation](#installation)
+- [Usage](#usage)
+- [Features](#features)
+- [Plugins](#plugins)
+- [Configuration](#configuration)
+- [RAG queries](#rag-queries)
+- [License](#license)
 
-- **Text-based vector search**: Adds `text: String` field to `VectorNearbyInput` — clients pass natural language instead of raw float vectors
-- **Text mutation fields**: Adds `{column}Text: String` companion fields on create/update inputs for vector columns
-- **Pluggable embedders**: Provider-based architecture (Ollama via `@agentic-kit/ollama`, with room for OpenAI, etc.)
-- **Per-database configuration**: Reads `llm_module` from `services_public.api_modules` for per-API embedder config
-- **Plugin-conditional**: Fields only appear in the schema when the plugin is loaded
+## Installation
+
+```bash
+npm install graphile-llm
+```
 
 ## Usage
 
@@ -27,3 +47,89 @@ const preset = {
   ],
 };
 ```
+
+The preset bundles all plugins listed below. You can also import each plugin individually (`createLlmModulePlugin`, `createLlmTextSearchPlugin`, `createLlmTextMutationPlugin`, `createLlmRagPlugin`) if you prefer a-la-carte.
+
+## Features
+
+- **Text-based vector search** — adds `text: String` field to `VectorNearbyInput`; clients pass natural language instead of raw float vectors
+- **Text mutation fields** — adds `{column}Text: String` companion fields on create/update inputs for vector columns
+- **RAG queries** — adds `ragQuery` and `embedText` root query fields; detects `@hasChunks` smart tags for chunk-aware retrieval
+- **Pluggable providers** — provider-based architecture for both embedding and chat completion (Ollama via `@agentic-kit/ollama`, extensible to OpenAI, etc.)
+- **Per-database configuration** — reads `llm_module` from `services_public.api_modules` for per-API provider config
+- **Toggleable** — each capability (`enableTextSearch`, `enableTextMutations`, `enableRag`) can be independently enabled or disabled
+- **Plugin-conditional** — fields only appear in the schema when the plugin is loaded
+
+## Plugins
+
+| Plugin | Description | Toggle |
+|--------|-------------|--------|
+| `LlmModulePlugin` | Resolves embedder and chat completer from config; stores on build context | Always included |
+| `LlmTextSearchPlugin` | Adds `text: String` to `VectorNearbyInput` with resolve-time embedding | `enableTextSearch` (default: `true`) |
+| `LlmTextMutationPlugin` | Adds `{column}Text` companion fields on mutation inputs | `enableTextMutations` (default: `true`) |
+| `LlmRagPlugin` | Adds `ragQuery` and `embedText` root query fields | `enableRag` (default: `false`) |
+
+## Configuration
+
+```typescript
+GraphileLlmPreset({
+  // Embedding provider (required for text fields and RAG)
+  defaultEmbedder: {
+    provider: 'ollama',
+    model: 'nomic-embed-text',
+    baseUrl: 'http://localhost:11434',
+  },
+
+  // Chat completion provider (required for RAG)
+  defaultChatCompleter: {
+    provider: 'ollama',
+    model: 'llama3',
+    baseUrl: 'http://localhost:11434',
+  },
+
+  // Toggle individual capabilities
+  enableTextSearch: true,      // text field on VectorNearbyInput
+  enableTextMutations: true,   // *Text companion fields on mutations
+  enableRag: false,            // ragQuery + embedText root fields
+
+  // RAG defaults (overridable per-query)
+  ragDefaults: {
+    contextLimit: 10,
+    maxTokens: 4000,
+    minSimilarity: 0.3,
+  },
+})
+```
+
+Providers can also be configured via environment variables (`EMBEDDER_PROVIDER`, `EMBEDDER_MODEL`, `EMBEDDER_BASE_URL`, `CHAT_PROVIDER`, `CHAT_MODEL`, `CHAT_BASE_URL`).
+
+## RAG queries
+
+When `enableRag: true` and tables have `@hasChunks` smart tags, the plugin adds:
+
+```graphql
+# Full RAG: embed prompt, search chunks, assemble context, call chat LLM
+query {
+  ragQuery(
+    prompt: "What is machine learning?"
+    contextLimit: 5
+    minSimilarity: 0.3
+  ) {
+    answer
+    sources { content similarity tableName parentId }
+    tokensUsed
+  }
+}
+
+# Standalone embedding
+query {
+  embedText(text: "machine learning concepts") {
+    vector
+    dimensions
+  }
+}
+```
+
+## License
+
+MIT