rag2f-openai-embedder 0.0.1

OpenAI Embedder Plugin for RAG2F

RAG2F OpenAI Embedder Plugin

Plugin for integrating OpenAI embeddings into RAG2F.

Plugin Structure

rag2f_openai_embedder/
├── __init__.py                    # Plugin entry point
├── plugin.json                    # Plugin metadata
├── settings.json                  # Settings (empty)
├── pyproject.toml                 # Python package configuration
├── config.json.example            # Configuration example
├── CONFIG.md                      # Configuration documentation
├── src/
│   ├── __init__.py               # src package
│   ├── plugin_context.py         # Thread-safe plugin_id management
│   ├── embedder.py               # OpenAIEmbedder implementation
│   └── bootstrap_hook.py         # Bootstrap hook
└── test/
  ├── conftest.py               # pytest configuration
  ├── test_embedder_unit.py     # Embedder unit test
  └── test_bootstrap_hook.py    # Bootstrap hook test

Configuration Parameters

Required

• api_key: OpenAI API key (e.g. "sk-...")
• model: Embedding model name
  • "text-embedding-3-small" (1536 dim, economical)
  • "text-embedding-3-large" (3072 dim, high quality)
  • "text-embedding-ada-002" (1536 dim, legacy)
• size: Vector size (1536 or 3072)

Optional

• timeout: Request timeout in seconds (default: 30.0)
• max_retries: Maximum number of retries (default: 2)

Differences from Azure OpenAI

The standard OpenAI plugin differs from Azure OpenAI in:

1. Does NOT require azure_endpoint (uses OpenAI public endpoint)
2. Does NOT require api_version (automatically uses the latest version)
3. Does NOT require deployment (uses model directly)
4. Uses the OpenAI class instead of AzureOpenAI

Configuration

Using JSON (config.json)

{
  "plugins": {
    "openai_embedder": {
      "api_key": "sk-your-api-key",
      "model": "text-embedding-3-small",
      "size": 1536,
      "timeout": 30.0,
      "max_retries": 2
    }
  }
}

Using Environment Variables

export RAG2F__PLUGINS__RAG2F_OPENAI_EMBEDDER__API_KEY="sk-your-api-key"
export RAG2F__PLUGINS__RAG2F_OPENAI_EMBEDDER__MODEL="text-embedding-3-small"
export RAG2F__PLUGINS__RAG2F_OPENAI_EMBEDDER__SIZE="1536"
export RAG2F__PLUGINS__RAG2F_OPENAI_EMBEDDER__TIMEOUT="30.0"
export RAG2F__PLUGINS__RAG2F_OPENAI_EMBEDDER__MAX_RETRIES="2"

Installation

cd plugins/rag2f_openai_embedder
pip install -e .

Testing

cd plugins/rag2f_openai_embedder
pytest test/

Usage in Code

The plugin registers itself automatically via the bootstrap hook. Once configured, the embedder is available in RAG2F under the openai_embedder ID.

# Plugin loads automatically
rag2f = await RAG2F.create(
  plugins_folder="plugins/",
  config=config
)

# The embedder is available via OptimusPrime
embedder = rag2f.optimus_prime.get("rag2f_openai_embedder")
vector = embedder.getEmbedding("Hello, world!")

Validation

The plugin includes comprehensive validation:

• Ensures required parameters are present
• Type checking for size, timeout, and max_retries
• Detailed logging
• Appropriate error handling

Test Coverage

• ✅ Configuration validation
• ✅ Client initialization
• ✅ Correct API calls
• ✅ Edge cases (empty strings, Unicode)
• ✅ Error handling
• ✅ Various OpenAI models
• ✅ Bootstrap hook

Release Management

This project uses automated releases with semantic versioning and setuptools-scm.

Version Schema (PEP 440)

• Development builds (branch dev): X.Y.Z.devN (e.g., 0.1.0.dev123)

  • Published automatically to TestPyPI on every commit
  • N = GitHub Actions run number (monotonically increasing)
  • Base version (X.Y.Z) read from NEXT_VERSION file

• Release Candidates (tags vX.Y.ZrcN): X.Y.ZrcN (e.g., 1.0.0rc1)

  • Published to PyPI as pre-release
  • GitHub Release marked as pre-release

• Stable Releases (tags vX.Y.Z): X.Y.Z (e.g., 1.0.0)

  • Published to PyPI as stable
  • GitHub Release (normal)

Installing Versions

# Install latest stable from PyPI
pip install rag2f-openai-embedder

# Install specific stable version
pip install rag2f-openai-embedder==1.0.0

# Install specific release candidate
pip install rag2f-openai-embedder==1.0.0rc1

# Install specific dev build from TestPyPI
pip install --index-url https://test.pypi.org/simple/ \
            --extra-index-url https://pypi.org/simple/ \
            rag2f-openai-embedder==0.1.0.dev123

Version Information at Runtime

Every published package includes commit information:

from rag2f_openai_embedder._version import __version__, __commit__, __distance__

print(f"Version: {__version__}")    # e.g., "1.0.0" or "0.1.0.dev123"
print(f"Commit: {__commit__}")      # Git commit hash
print(f"Distance: {__distance__}")  # Commits since last tag

For Maintainers

Publishing Dev Builds

• Push to dev branch → automatic publish to TestPyPI
• Version: <NEXT_VERSION>.dev<run_number>

Creating Releases

Release Candidate:

git tag v1.0.0rc1
git push origin v1.0.0rc1

Stable Release:

git tag v1.0.0
git push origin v1.0.0

Updating Next Version

Edit the NEXT_VERSION file and commit to dev:

echo "1.1.0" > NEXT_VERSION
git add NEXT_VERSION
git commit -m "Bump next version to 1.1.0"
git push origin dev

CI/CD Workflows

• .github/workflows/ci-dev-testpypi.yml: Validates structure, builds, and publishes dev versions to TestPyPI
• .github/workflows/release-tags.yml: Builds from tags, publishes to PyPI, creates GitHub Releases