Modify ingest script — Part 1

Great question.

Short version: the hash method is fast because it does almost no work, and the outcome is not the same.

• Hash embeddings (what we used when USE_ST=0)
  We generate a deterministic random vector from each file’s SHA-256. That’s basically O(text length) string processing + one RNG call—no neural net math, no GPU. ⚡️
  But there’s no semantic meaning: “quantum gravity” and “chocolate cake” won’t land near each other unless by accident. Nearest-neighbor search is essentially random. Hash mode is great for smoke tests and pipeline debugging, not for real RAG quality.

• SentenceTransformer (or any real embedding model)
  Runs a neural model over the text to produce vectors where semantic neighbors are close. That’s far slower (matrix multiplies), but it’s what makes retrieval actually pull relevant chunks.

So: speed vs. quality. If you want the QA to “feel right,” you need real embeddings.

How to switch to real embeddings (and keep it reasonable)

• Pick a 768-dim model so it matches your DB schema and the script won’t fall back:
• Good options: nomic-ai/nomic-embed-text-v1.5, intfloat/e5-base-v2 (both 768-d).
• Run with ST on and try a small slice:
  bash
USE_ST=1 EMBED_MODEL="intfloat/e5-base-v2" \
python ~/Desktop/ingestpile.py --start-name chunk_0315.txt --limit 20
• If it’s slow, you can:
• Increase batch size in the script later,
• Run on a GPU (if your ST install uses CUDA),
• Or re-embed overnight.

Practical plan

• Use hash to validate ingest + resume + schema (you’ve done that ✅).
• Then re-embed the same docs with a real model for quality. I can give you a tiny reembed_doc.py that walks an existing doc_id, recomputes embeddings with your chosen model, and updates content.chunks.embedding in place—no re-ingest required. Want me to drop that script next?

Yep — if you want real vectors, run with SentenceTransformers on.

Recommended start command

Use either the default (nomic v1.5) or a solid 768-dim model like e5-base-v2:

# keep default nomic model
USE_ST=1 python ~/Desktop/ingestpile.py --start-name chunk_0315.txt

# or explicitly pick e5-base-v2 (also 768-dim)
USE_ST=1 EMBED_MODEL="intfloat/e5-base-v2" \
python ~/Desktop/ingestpile.py --start-name chunk_0315.txt

(You can swap --start-name for --start-index 315—whichever is easier. Drop --limit to let it run.)

Heads up on quality

Anything you ingested earlier with USE_ST=0 used hash embeddings (fast but non-semantic). Mixing hash + real embeddings in the same corpus will hurt retrieval quality. Once you’re happy with the run, we should re-embed the earlier chunks so everything is consistent. I can give you a tiny reembed_doc.py to update the existing rows in place when you’re ready.

Optional knobs

• If you want stronger durability during long runs:
  bash
COMMIT_EVERY=500 USE_ST=1 python ~/Desktop/ingestpile.py --start-name chunk_0315.txt
• If GPU/CPU gets tight, you can test a few first with --limit 10, then remove it for the full pass.