@radzor/rag-pipeline
End-to-end Retrieval-Augmented Generation pipeline. Ingests documents, chunks and embeds them into an in-memory vector index, then answers queries by retrieving relevant context and calling an LLM completion endpoint.
Install
View source on GitHub →Inputs
| Name | Type | Default | Description |
|---|---|---|---|
| embeddingApiKey* | string | — | API key for the embeddings endpoint (OpenAI-compatible).OPENAI_API_KEY |
| completionApiKey | string | — | API key for the completion endpoint (OpenAI-compatible). Defaults to embeddingApiKey if not set.OPENAI_API_KEY |
| chunkSize | number | 512 | Maximum number of characters per text chunk during ingestion. |
| overlapSize | number | 64 | Number of overlapping characters between consecutive chunks. |
| embeddingModel | string | text-embedding-3-small | Model identifier for embeddings. |