FAQ

Can I use this with my own vector database?

Yes! You can implement the Retriever interface and register it:

registry.register("retriever", "mydb", new MyRetriever());

Then use it via CLI or .ragrc.json.

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Is streaming LLM response supported?

Yes. LLM runners can yield async output tokens. This is handled internally via AsyncIterable in compatible models.

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How do I evaluate performance?

Use rag-pipeline evaluate dataset.json or open the dashboard at http://localhost:3000 to visualize BLEU/ROUGE and pass rates.

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What file types can I ingest?

Currently supported:

• .pdf (mocked)
• .md
• .html
• .csv
• Full directory ingestion

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Can I rerank results using a local model?

Yes, as long as your local LLM implements:

llm.generate(prompt, context): Promise<string>

Use it to power LLMReranker.

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How do I contribute?

Fork the repo → add your plugin or CLI feature → test → submit PR.

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Still stuck? Open an issue at GitHub.

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Next → Back to Introduction