Initial commit

Author: Daniele Briggi

.github/workflows/test.yaml

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+name: Test
+
+on:
+  push:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Clone the SQLite Cloud docs repository
+        run: git clone https://github.com/sqlitecloud/docs
+        shell: bash
+      - uses: ./
+        with:
+          connection_string: ${{ secrets.CONNECTION_STRING }}
+          base_url: https://docs.sqlitecloud.io/docs/
+          database: aisearch-action-test.sqlite
+          source_files: docs

action.yaml

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+name: "SQLite AI - AI search for documents"
+description: "Parses document files to create a database on your SQLite Cloud project."
+author: "SQLite AI Team"
+
+inputs:
+  connection_string:
+    description: The SQLite Cloud project connection string.
+    required: true
+  base_url:
+    description: Your website's documentation base url.
+    required: true
+  database:
+    description: The name of the database to use on SQLite Cloud, just remeber to create first a database on your project!
+    required: true
+  source_files:
+    description: The path of the files, by default it will parse every file recursively starting from the working directory.
+    required: false
+    default: $(pwd)
+  hf_model_id: 
+    description: The Hugging Face model ID to use for generating embeddings.
+    required: false
+    default: "Qwen/Qwen3-Embedding-0.6B-GGUF"
+  hf_gguf_file:
+    description: The GGUF file name for the Hugging Face model.
+    required: false
+    default: "Qwen3-Embedding-0.6B-f16.gguf"
+  hf_gguf_update_date:
+    description: The date to force update the GGUF model cache, format YYYYMMDD.
+    required: false
+    default: "202507014"
+  hf_model_local_path:
+    description: The local path to store the downloaded Hugging Face model.
+    required: false
+    default: "./models"
+  
+
+branding:
+  icon: "search"
+  color: "blue"
+
+runs:
+  using: "composite"
+  steps:
+
+    - name: Set GitHub Path
+      run: echo "${{ github.action_path }}/src" >> $GITHUB_PATH
+      shell: bash
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+
+    - name: Install SQLite RAG
+      run: |
+        python -m pip install --upgrade pip
+        pip install -i https://test.pypi.org/simple/ sqlite-rag
+      shell: bash
+
+      # Cache the downloaded model between workflows
+    - name: Restore GGUF model cache
+      uses: actions/cache@v4
+      id: cache-model
+      with:
+        path: ${{ inputs.hf_model_local_path }}
+        # Change the HF_GGUF_UPDATE_DATE variable to force update the cache
+        key: gguf-${{ inputs.hf_model_id }}-${{ inputs.hf_gguf_file }}-${{ inputs.hf_gguf_update_date }}
+        restore-keys: |
+          gguf-${{ inputs.hf_model_id }}-${{ inputs.hf_gguf_file }}-${{ inputs.hf_gguf_update_date }}-
+
+    - name: Parse documents and create the database
+      run: |
+        sqlite-rag download-model "${{ inputs.hf_model_id }}" "${{ inputs.hf_gguf_file }}" --local-dir="${{ inputs.hf_model_local_path }}"
+        sqlite-rag add \
+          --recursive {{ inputs.source_files }} \
+          --metadata '{"base_url": "{{ inputs.base_url }}"}'
+      shell: bash
+
+    - name: Upload the database to SQLite Cloud
+      run: bash $GITHUB_ACTION_PATH/upload.sh
+      env:
+        GITHUB_ACTION_PATH: ${{ github.action_path }}
+        CONNECTION_STRING: ${{ inputs.connection_string }}
+        DATABASE_PATH: ${{ inputs.database_path }}
+        DATABASE: ${{ inputs.database }}
+      shell: bash

chinook.sqlite

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+#!/bin/bash
+
+
+# === Inputs ===
+# CONNECTION_STRING: The SQLite Cloud project connection string.
+# DATABASE: The name of the database to use on SQLite Cloud.
+# DATABASE_PATH: The path to the SQLite database file to upload.
+
+echo "Database: $DATABASE"
+
+
+
+if [[ ! "${CONNECTION_STRING}" =~ ^sqlitecloud:// ]]; then
+    echo "${CONNECTION_STRING} incorrect project connection string"
+    exit 1
+fi
+
+if [[ -z "${DATABASE}" ]]; then
+    echo "database input is empty"
+    exit 1
+fi
+
+API_BASE="https:$(echo ${CONNECTION_STRING} | awk -F ':' '{print $2}')"
+# Extract the apikey from the project string
+API_KEY=$(echo "${CONNECTION_STRING}" | awk -F 'apikey=' '{print $2}' | awk -F '&' '{print $1}')
+
+if [[ -z "${API_KEY}" ]]; then
+    echo "API key not found in project connection string"
+    exit 1
+fi
+
+# --- 1. Get upload URL from v2/storage/databases/singlepart ---
+STORAGE_URL="${API_BASE}/v2/storage/databases/singlepart"
+STORAGE_RES=$(curl "${STORAGE_URL}" \
+    -H "Authorization: Bearer ${API_KEY}" \
+    -H 'accept: application/json')
+
+# --- 2. Extract data.url from JSON response ---
+if command -v jq >/dev/null 2>&1; then
+    UPLOAD_URL=$(echo "${STORAGE_RES}" | jq -r '.data.url')
+fi
+
+if [[ -z "${UPLOAD_URL}" || "${UPLOAD_URL}" == "null" ]]; then
+    echo "Failed to get upload URL: ${STORAGE_RES}"
+    exit 1
+fi
+
+# --- 3. Upload the database file ---
+PUT_RES=$(curl -s -X PUT --data-binary @"${DATABASE_PATH}" \
+    -H 'Content-Type: application/octet-stream' \
+    "${UPLOAD_URL}")
+if [[ $? -ne 0 ]]; then
+    echo "Failed to upload database file"
+    exit 1
+fi
+
+# --- 4. Add or replace the database via Weblite ---
+PATCH_URL="${API_BASE}/v2/weblite/${DATABASE}"
+PATCH_BODY="{\"location\":\"${UPLOAD_URL}\"}"
+PATCH_RES=$(curl --compressed -s -X PATCH "${PATCH_URL}" \
+    -H 'Content-Type: application/json' \
+    -H "Authorization: Bearer ${API_KEY}" \
+    -H 'accept: application/json' \
+    -d "${PATCH_BODY}")
+
+echo "${PATCH_RES}"
+if [[ "${PATCH_RES}" =~ error ]]; then
+    echo "Error updating SQLite Cloud database: ${PATCH_RES}"
+    exit 1
+fi
+
+echo "Database uploaded successfully."