SQLite AI Search Action

Index your documentation files with SQLite AI-powered embeddings for intelligent search capabilities.

Overview

The SQLite AI Search Action automatically processes your documentation files and creates an AI-powered searchable database on SQLite Cloud. This action uses both embeddings to understand document content semantically and traditional full-text search, enabling more intelligent and contextual search results.

This action uses sqlite-rag.

How it works

1. Downloads AI Model: Retrieves the specified Hugging Face model to generate embeddings for semantic search
2. Processes Documents: Recursively scans and parses all documentation files in your repository
3. Creates Dual Index: Generates both AI embeddings and traditional full-text search indices for comprehensive search capabilities
4. Builds Database: Creates a SQLite database containing documents, embeddings, and search indices
5. Uploads to SQLite Cloud: Transfers the complete database to your SQLite Cloud project
6. Integrates with Edge Function: Works out-of-the-box with the predefined SQLite Cloud Edge Function template (aisearch-docs.js) for instant search functionality

Note: the SQLite Cloud Search Edge Function supports only the default model. Do not change the model settings if you want to use our search edge function.

Usage

Setup your workflow

1. Get your Connection String: Ensure you have a project on SQLite Cloud dashboard. If not, sign up to SQLite AI to create one for free.

2. Set GitHub Secret: Add your connection string as SQLITECLOUD_CONNECTION_STRING in your repository secrets.

3. Add to Workflow: Create or update your GitHub workflow:

name: AI Search Index

on:
  push:
    branches: [main]
  workflow_dispatch:

jobs:
  build-search:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Build AI Search Database
        uses: sqliteai/sqlite-aisearch-action@v1
        with:
          connection_string: ${{ secrets.SQLITECLOUD_CONNECTION_STRING }}
          base_url: https://docs.yoursite.com
          database_name: aidocs_search.db
          source_files: ./path/to/documents

Create the Search Edge Function

To enable search functionality on your indexed database, you need to create an Edge Function using the provided template:

1. Access your dashboard on https://dashboard.sqlitecloud.io
2. Enter the same project where the created database has been uploaded
3. Go to the Edge Functions section
4. Create a new Javascript Function and copy and paste the code from aisearch-docs.js in the editor
5. Deploy and test

How to perform a search

1. Go in the Detail in the Edge Function panel and copy the Function URL

2. Execute a GET request and send a URL-econded query as query parameter.

   Example: GET https://myproject.cloud/v2/functions/aisearch-docs?query=what%27s+Offsync%3F

Response example:

{
  "data": {
    "search": [
      {
        "id": "c41a6c2e-34e9-4e8e-92b9-41b8065047c7",
        "uri": "docs/sqlite-cloud/sdks/php/methods.mdx",
        "metadata": "{\"base_url\": \"https://docs.sqlitecloud.io/docs/\"}",
        "snippet": "---\ntitle: OffSync\ndescription: Enable local-first applications with automatic data synchronization between edge devices and SQLite Cloud...",
        "vec_rank": 1,
        "fts_rank": null,
        "combined_rank": 0.0163934426229508,
        "vec_distance": 0.581515073776245,
        "fts_score": null
      },
      ...
    ]
  }
}

Support

• 📖 SQLite Cloud Documentation
• 💬 Community Support