> ⚠️ **Reference Implementation Only**
> The rag-ai plugin and its modules are a reference implementation provided for demonstration and educational purposes.
> We provide minimal support for these components and do not actively maintain or update them.
---
# RAG AI Backend-embeddings OpenAI submodule
This is a submodule for the `@roadiehq/rag-ai-backend` module, which provides functionality to use OpenAI embeddings to generate a RAG AI Backend plugin for Backstage. It exposes configuration options to configure OpenAI API token and wanted embeddings model, as well as the parameters for the model.
## Initialization
```typescript
const vectorStore = await createRoadiePgVectorStore({ logger, database });
const augmentationIndexer = await initializeOpenAiEmbeddings({
logger,
catalogApi,
auth,
vectorStore,
discovery,
config,
});
```
>
## Configuration Options
The module expects an API Token, the name of the embeddings generative AI model and its configuration options to be configured via app-config.
You can generate an API Token in here: https://platform.openai.com/api-keys
```yaml
ai:
embeddings:
# OpenAI Embeddings configuration
openai:
# (Optional) The API key for accessing OpenAI services. Defaults to process.env.OPENAI_API_KEY
openAiApiKey: 'sk-123...'
# (Optional) Specify URL of self-hosted OpenAI compliant endpoint. Defaults to OpenAI's public API https://api.openai.com
openAiBaseUrl: ''
# (Optional) Name of the OpenAI model to use to create Embeddings. Defaults to text-embedding-3-large
modelName: 'text-embedding-3-large'
# The size of the batch to use when creating embeddings. Defaults to 512, max is 2048
batchSize: 512
# The number of dimensions to generate. Defaults to use the default value from the chosen model
embeddingsDimensions: 3072
```
Example minimal configuration
```yaml
ai:
embeddings:
openAI: {} # uses env variable OPENAI_API_KEY for API key, model 'text-embedding-3-large' for embeddings creation model
```
