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RagHost is a service that simplifies Retrieval-Augmented Generation (RAG) by providing an API to embed documents or text and ask questions. It allows users to build internal tools for searching documents or customer-facing apps for answering questions without managing databases, document parsing, chunking, or vector embeddings.
To use RagHost, upload your documents using a curl command to the /embed endpoint. Once uploaded, you can submit queries via a curl command to the /ask endpoint by providing your question as a parameter.
Retrieval-Augmented Generation (RAG) is a technique where you add documents to a database, retrieve the most relevant text chunks for each query, and provide them to your model alongside your question. This process supplies the LLM with the necessary context.
RagHost simplifies RAG by providing an API that allows you to upload and query your documents. It manages document parsing, chunking, and vector embeddings, allowing you to focus on building your application.
Yes, you can specify your preferred chunk size and chunk overlap when embedding a PDF or text file using the RagHost /embed API endpoint.
Yes, RagHost supports non-OpenAI models. While the current default is OpenAI's GPT-3.5 Turbo, additional models, such as Anthropic's Claude 2, will be added soon.
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