ailey-tools-tag-n-rag

To index content into a local ChromaDB knowledge base, you retrieve files, Git repos, and URLs, convert them to text, scrub and chunk the data, and store the embeddings with rich metadata. Per-source collections enable scalable semantic search.

Yes, you can build a RAG knowledge base from audio, video, and image files. The pipeline supports optional transcription for audio and video, alongside OCR for images, to extract text before chunking and storing the embeddings in ChromaDB.

The best way to chunk text for retrieval-augmented generation in ChromaDB is using intelligent chunking with paragraph and sentence boundaries. This method structures the content logically before storing it as embeddings with metadata tagging.

You do not strictly need to tag metadata when indexing content sources, but applying metadata tagging and replacement rules tailors the indexing process. This enriches the stored embeddings in ChromaDB, enabling more precise and filtered semantic search results.

Yes, ChromaDB supports local RAG sets with per-source collections. This allows you to organize diverse content sources into distinct, searchable knowledge bases that scale efficiently while maintaining semantic search capabilities across the indexed data.