[Task] Chunking and Ingestion to Vector DB

[karthik18495]

Use your preferred chunking method (e.g., RecursiveCharacterTextSplitter).

• For each chunk, embed and ingest into your vector DB (Pinecone/Chroma/etc) with all metadata.
• Ensure all chunks from each document are properly referenced to their meeting and document.
• Test on a subset before full ingestion.

[onestardao]

Heads up: your plan looks solid on paper — but we've seen quite a few RAG pipelines silently fail even when the chunks are clean and the metadata is embedded correctly.

Common trap?
You ingest with RecursiveCharacterSplitter, keep all metadata, and still the model answers wrong but confident because of something we call semantic boundary drift or proximity injection conflicts.

We’ve mapped out 16 real-world failure modes like these — and more than half of them aren’t caught by standard chunking + ingestion tests.

If you're curious, happy to share our diagnostic map (open source, MIT license, with endorsements from the Tesseract.js creator).
Could save you from debugging ghosts later on.

Just let me know.