Superpowered AI: API for Retrieval Augmented Generation
Summary
Our SuperStack suite of technologies directly targets common RAG failure modes, like hallucinations caused by out-of-context search results. We leverage proprietary RAG technology weve developed (we call it the SuperStack) to dramatically improve performance and reliability for a wide variety of use cases.Our solution is end-to-end, so you dont have to worry about stringing together different APIs for different parts of the retrieval and generation pipeline. Here are some key features: Support for uploading various types of text files, PDFs, website content, and audio files Use our knowledge retrieval pipeline on its own with our Query endpoint, or take advantage of our Chat endpoint for a fully end-to-end solution for building conversational applications connected to external knowledge sources. Usage-based pricing, so you only pay for what you use, and $50 in free credits to all new users!The SuperStack has three components that directly tackle the problems with standard RAG pipelines:AutoQuery Convert user inputs into well-formed search queries for better retrieval results.Relevant Segment Extraction (RSE) Dynamically group clusters of relevant results into longer sections of contiguous text to provide better context to the LLM. This is especially useful for more complex questions, where the answer isnt contained in a single sentence or paragraph.AutoContext Automatically inject descriptive context into text chunks and embeddings, to capture the full context of each chunk of text, reducing the likelihood of poor search results and hallucinations.Given that LLM applications often involve conversational interactions, we recently launched our Chat endpoint to make it easy to configure and deploy chat applications that utilize our knowledge retrieval pipeline.