## Features

Super-Rag is performant Retrieval Augmented Generation pipeline specifcially created to be used by AI Agents. Super-Rag allows developers to ingest large amount of data and perform Q&A, Summarization and Computationl Q&A on that dataset. 

### Getting started

1. Connect your a embedding database in the [Superagent UI](https://beta.superagent.sh/integrations)
2. Create a workflow using SAML.

```yaml
workflows: 
  - superagent:
      name: Rag Agent
      llm: gpt-4-turbo-preview
      prompt: Use the earning reports to answer any questions
      superrag:
        - index: 
            name: earnings
            use_for: useful for answering questions about earning reports.
            urls:
              - https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q4-2023-Update.pdf
              - https://s2.q4cdn.com/299287126/files/doc_financials/2023/q4/AMZN-Q4-2023-Earnings-Release.pdf
```

3. Query the workflow using the API or SDKs

### Running Super-Rag as a stand-alone service

Super-Rag comes as a stand-alone REST API. You can read more on how to setup [here](https://github.com/superagent-ai/super-rag). Alternatively you can run the free Cloud API to ingest/retrieve data. 