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Show HN: I built a Privacy First local AI RAG GUI for your own documents

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Show HN: I built a Privacy First local AI RAG GUI for your own documents

#1
Byte-Vision is a privacy-first document intelligence platform that transforms static documents into an interactive, searchable knowledge base. Built on Elasticsearch with RAG (Retrieval-Augmented Generation) capabilities, it offers document parsing, OCR processing, and conversational AI interfaces.

Show HN: I built a Privacy First local AI RAG GUI for your own documents
github.com

Re: Show HN: I built a Privacy First local AI RAG GUI for your own documents

#2
How are you dealing with documents that exceed the context window? Chunking an db vectorization? One issue with some approaches is the Paragraph 40 that references Paragraph 6 in a document but the LLM doesn’t really have direct capability to “remember” that reference.

Re: Show HN: I built a Privacy First local AI RAG GUI for your own documents

#3

How are you dealing with documents that exceed the context window? Chunking an db vectorization? One issue with some approaches is the Paragraph 40 that references Paragraph 6 in a document but the LLM doesn’t really have direct capability to “remember” that reference.

I chunk the documents and use Elasticsearch to store the vectors. On a laptop with 8GB of GPU I can have a pretty large context window and not hallucinate.

Re: Show HN: I built a Privacy First local AI RAG GUI for your own documents

#4
post #3

How are you dealing with documents that exceed the context window? Chunking an db vectorization? One issue with some approaches is the Paragraph 40 that references Paragraph 6 in a document but the LLM doesn’t really have direct capability to “remember” that reference.

I chunk the documents and use Elasticsearch to store the vectors. On a laptop with 8GB of GPU I can have a pretty large context window and not hallucinate.

Nice! Will have to give it a go, looks interesting. Thanks for sharing.