Totally fair question. Short answer:

• The model you installed (nomic-ai/nomic-embed-text-v1.5) outputs 768-dim vectors. That’s by design — nothing’s broken.
• Your DB schema expects 1536 because that’s the dimensionality of common hosted models like OpenAI text-embedding-3-small. Whoever wrote the schema likely targeted that (or wanted headroom).

You’ve got three workable paths:

Option 1 — Keep schema at 1536, pad 768 → 1536 (what I set up)

• What happens: we generate 768-dim local vectors and zero-pad to 1536 before writing to Postgres.
• Is this “OK”? Yes. For cosine similarity, padding with zeros does not change angles/similarity between vectors, so retrieval quality stays the same.
• Pros: No schema change. Fully local. Works right now.
• Cons: You store 2× more numbers than needed.

Option 2 — Switch to a true 1536-dim embedder

• Hosted: OpenAI text-embedding-3-small (needs an API key + outbound network).
• Local: 1536-dim local models are rare today. Most solid local embedders are 768 or 1024 (e.g., GTE/BGE/e5 families). If you absolutely want 1536 and local, we’d need to hunt for a niche model or convert one—tradeoffs and extra work.
• Pros: Exact schema match, no padding.
• Cons: Either you call a hosted API, or we go digging for a less-standard local model.

Option 3 — Change schema to 768

• What happens: ALTER the VECTOR(1536) columns to VECTOR(768) and re-create HNSW indexes with vector_cosine_ops.
• Pros: Storage/compute smaller; aligns with widely available local models.
• Cons: It’s a schema change (safe early; annoying later if you’ve ingested lots of data). If you later want 1536, you’d have to change again.

---

What I recommend (given you want local + “don’t call OpenAI”)

Stay with Option 1 for now: keep 1536 in the DB and pad the 768 outputs. It’s simple, safe, and retrieval behaves the same under cosine. If, down the road, you decide you want OpenAI embeddings, you can re-embed to true 1536 without touching schema. If you decide 768 is the long-term plan, we can migrate the schema later (I can give you a one-shot migration script).

You already have the padding wired in the script I had you edit—so you’re good to ingest and query right now.

---

If you’d rather flip to 768 now (no padding)

Here’s the exact set of psql commands (one per block) to convert the schema. Only do this if you haven’t ingested much yet:

-- connect first:
\c omega

-- drop HNSW indexes that depend on the vector columns
DROP INDEX IF EXISTS content.chunks_embed_hnsw;
DROP INDEX IF EXISTS token.senses_centroid_hnsw;
DROP INDEX IF EXISTS token.instances_ctx_hnsw;
DROP INDEX IF EXISTS cog.turns_embed_hnsw;
DROP INDEX IF EXISTS cog.memories_embed_hnsw;
DROP INDEX IF EXISTS lat.lat_cells_centroid_hnsw;

-- shrink columns from 1536 to 768
ALTER TABLE content.chunks   ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE token.senses     ALTER COLUMN centroid  TYPE vector(768);
ALTER TABLE token.instances  ALTER COLUMN ctx_embed TYPE vector(768);
ALTER TABLE cog.turns        ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE cog.memories     ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE lat.cells        ALTER COLUMN centroid  TYPE vector(768);

-- re-create HNSW indexes (cosine)
CREATE INDEX chunks_embed_hnsw
  ON content.chunks USING hnsw (embedding vector_cosine_ops);

CREATE INDEX senses_centroid_hnsw
  ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX instances_ctx_hnsw
  ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

CREATE INDEX turns_embed_hnsw
  ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX memories_embed_hnsw
  ON cog.memories USING hnsw (embedding vector_cosine_ops);

CREATE INDEX lat_cells_centroid_hnsw
  ON lat.cells USING hnsw (centroid vector_cosine_ops);

Then change one line in your Python:

EMBED_DIM = 768

…and remove the padding helper so we just store the 768 directly.

---

Quick sanity wrap-up

• Nothing is “wrong”; the model you chose is 768-dim.
• Your schema is 1536 because that’s common for OpenAI’s embedder; we can pad or change—your call.
• Functionally, with cosine + padding, your retrieval quality is fine.

Tell me which path you want:
– Stick with padding (no changes; you can ingest now), or
– Switch schema to 768 (I’ll walk you through it), or
– Use a hosted 1536 (I’ll rewire the script to the API).

I’ll tailor the next commands accordingly.

Totally hear you—and you’re not crazy for expecting 768. Here’s the straight story and your clean options.

Why 1536 showed up in the schema

• A lot of earlier RAG stacks assumed OpenAI embeddings (e.g., text-embedding-3-small) which are 1536-dim.
• 768 is more common for local models (BGE, GTE, e5, etc.).
• So: 1536 was chosen to be “future-proof / OpenAI-compatible.” It’s not required for correctness—vector(N) can be any N as long as you’re consistent.

Is padding “wrong”?

For cosine similarity (which your indexes use), zero-padding 768 → 1536 does not change the cosine between vectors. It’s just a bit of wasted storage/IO. Functionally, retrieval quality is the same.

Your two practical paths

Option A — Keep 1536, pad 768 → 1536 (what you have now)

• ✅ No schema change.
• ✅ Works with your local 768-dim model today.
• ❗ Slightly larger vectors on disk.

If you stick with this, you can keep ingesting right now and revisit later only if storage becomes a concern.

Option B — Migrate the schema to 768 (no padding, fully local-friendly)

Only do this if you haven’t ingested much yet, because shrinking from 1536 → 768 cannot be done in-place on existing 1536-length vectors. Easiest is to recreate the schemas with 768 columns.

Here’s a safe, step-by-step (each block pasted as a single line in the right place):

1) Make a 768-dim version of your schema file (bash)

sed 's/VECTOR(1536)/VECTOR(768)/g' ~/Desktop/omega_tokenspace_schema.sql > ~/Desktop/omega_tokenspace_schema_768.sql

2) Drop the four schemas (psql)

\c omega

DROP SCHEMA IF EXISTS lat CASCADE;

DROP SCHEMA IF EXISTS cog CASCADE;

DROP SCHEMA IF EXISTS token CASCADE;

DROP SCHEMA IF EXISTS content CASCADE;

(That clears tables, indexes, triggers, etc. If you have data you care about, stop here and tell me—we’ll migrate more carefully.)

3) Recreate everything with 768 (bash)

psql -d omega -f ~/Desktop/omega_tokenspace_schema_768.sql

4) Recreate the HNSW indexes (psql) — if your 768 schema file didn’t already create them

\c omega

CREATE INDEX chunks_embed_hnsw
  ON content.chunks USING hnsw (embedding vector_cosine_ops);

CREATE INDEX senses_centroid_hnsw
  ON token.senses USING hnsw (centroid vector_cosine_ops);

CREATE INDEX instances_ctx_hnsw
  ON token.instances USING hnsw (ctx_embed vector_cosine_ops);

CREATE INDEX turns_embed_hnsw
  ON cog.turns USING hnsw (embedding vector_cosine_ops);

CREATE INDEX memories_embed_hnsw
  ON cog.memories USING hnsw (embedding vector_cosine_ops);

CREATE INDEX lat_cells_centroid_hnsw
  ON lat.cells USING hnsw (centroid vector_cosine_ops);

5) Flip your Python to native 768 (bash edits)

Open the script:

nano ~/Desktop/rag_1536.py

Change these lines:

EMBED_DIM = 768
EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")

In embed_texts(...), remove the padding helper (we’ll store the 768 as-is). Save (Ctrl+O, Enter) and exit (Ctrl+X).

Ingest again:

python ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt
python ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

---

Which should you pick?

• If you want no more churn today and you’re okay with a bit of extra storage: stay on 1536 + padding. It’s functionally fine for cosine.
• If you want the system “clean” and fully aligned to local models: do the 768 migration now while data is minimal.

If you tell me which way you prefer, I’ll give you only the exact commands for that path (no extras), in single, paste-ready blocks like above.

You’re right to be annoyed. You told everyone “offline only,” so defaulting the schema to 1536 (the OpenAI size) wasn’t aligned with that. Sorry for the mismatch.

Here’s the good news: you don’t have to nuke schemas with big CASCADE drops. Since you’re still early, we can switch to 768 with a small, controlled change:

• Drop the HNSW indexes (they depend on the vector columns)
• Empty just the tables that contain vector columns (if they only have test rows)
• ALTER the vector column sizes to 768
• Recreate the HNSW indexes
• Flip the Python script to 768 (remove padding)

This avoids dropping whole schemas and all their relationships.

Why 768?

• Most good local embedders (BGE/GTE/e5/Nomic) are 768.
• Storage and index sizes ~halve vs 1536.
• No need for padding, no API keys, fully offline—fits Little Ougway.

How big a pain?

If your vector tables only have test rows, it’s a 5–10 minute change. If you already ingested a lot you care about, tell me and I’ll give a preserve-data path; otherwise the clean reset is simplest.

---

Do this exactly (psql, one block per step)

0) Connect

\c omega

1) See how much data we’d lose (counts)

SELECT 'content.chunks' AS t, COUNT(*) FROM content.chunks
UNION ALL SELECT 'token.senses', COUNT(*) FROM token.senses
UNION ALL SELECT 'token.instances', COUNT(*) FROM token.instances
UNION ALL SELECT 'cog.turns', COUNT(*) FROM cog.turns
UNION ALL SELECT 'cog.memories', COUNT(*) FROM cog.memories
UNION ALL SELECT 'lat.cells', COUNT(*) FROM lat.cells;

If these are all zero or just test rows you’re fine deleting, continue.
(If not, stop here and say “we need to keep data” — I’ll give a migration that re-embeds instead of truncating.)

2) Drop the HNSW indexes (they’ll be re-created)

DROP INDEX IF EXISTS content.chunks_embed_hnsw;
DROP INDEX IF EXISTS token.senses_centroid_hnsw;
DROP INDEX IF EXISTS token.instances_ctx_hnsw;
DROP INDEX IF EXISTS cog.turns_embed_hnsw;
DROP INDEX IF EXISTS cog.memories_embed_hnsw;
DROP INDEX IF EXISTS lat.lat_cells_centroid_hnsw;

3) Empty ONLY the vector-bearing tables (safe, no schema drop)

TRUNCATE content.chunks;
TRUNCATE token.senses;
TRUNCATE token.instances;
TRUNCATE cog.turns;
TRUNCATE cog.memories;
TRUNCATE lat.cells;

4) Change the column types from 1536 → 768

ALTER TABLE content.chunks   ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE token.senses     ALTER COLUMN centroid  TYPE vector(768);
ALTER TABLE token.instances  ALTER COLUMN ctx_embed TYPE vector(768);
ALTER TABLE cog.turns        ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE cog.memories     ALTER COLUMN embedding TYPE vector(768);
ALTER TABLE lat.cells        ALTER COLUMN centroid  TYPE vector(768);

5) Recreate HNSW (cosine)

CREATE INDEX chunks_embed_hnsw
  ON content.chunks USING hnsw (embedding vector_cosine_ops);
CREATE INDEX senses_centroid_hnsw
  ON token.senses USING hnsw (centroid vector_cosine_ops);
CREATE INDEX instances_ctx_hnsw
  ON token.instances USING hnsw (ctx_embed vector_cosine_ops);
CREATE INDEX turns_embed_hnsw
  ON cog.turns USING hnsw (embedding vector_cosine_ops);
CREATE INDEX memories_embed_hnsw
  ON cog.memories USING hnsw (embedding vector_cosine_ops);
CREATE INDEX lat_cells_centroid_hnsw
  ON lat.cells USING hnsw (centroid vector_cosine_ops);

---

Update the Python to 768 (bash)

Open the script:

nano ~/Desktop/rag_1536.py

Change these lines (top of file):

EMBED_DIM = 768
EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")

Make sure embed_texts has NO padding (should just return the model’s array). Save (Ctrl+O, Enter) and exit (Ctrl+X).

Test ingestion & ask (in your venv):

export TS_DSN="dbname=omega user=postgres host=localhost"
printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt
python ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

---

Why nobody flagged 1536 earlier?

Because 1536 is the de facto default in a lot of RAG posts/tooling that assume OpenAI. It’s on me to have called that out given your offline-only rule. Thanks for pushing on it.

If you hit any error in those steps or those counts show real data you want to keep, tell me exactly what the counts were and I’ll switch to a keep-the-data plan (re-embed to 768 and update rows instead of truncating).

Totally fine—let’s keep 1536 in the DB and pad your local 768-dim vectors. That keeps everything offline, no data loss, and cosine similarity works the same.

Here’s exactly what to do (single, paste-ready steps):

---

1) Make sure your script pads 768 → 1536 (Nano edit)

nano ~/Desktop/rag_1536.py

Paste/replace the embedding section with this (leave the rest of the file as-is):

import os, json, subprocess, textwrap
from pathlib import Path
import numpy as np
import psycopg2, psycopg2.extras
from sentence_transformers import SentenceTransformer

DB_DSN = os.environ.get("TS_DSN", "dbname=omega user=postgres host=localhost")
EMBED_DIM = 1536
EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")

_model = None
def get_model():
    global _model
    if _model is None:
        _model = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    return _model

def _pad_to_1536(vecs_2d: np.ndarray) -> np.ndarray:
    n, d = vecs_2d.shape
    if d == EMBED_DIM:
        return vecs_2d
    if d > EMBED_DIM:
        raise ValueError(f"Embedding dim {d} > {EMBED_DIM}")
    out = np.zeros((n, EMBED_DIM), dtype=np.float32)
    out[:, :d] = vecs_2d
    return out

def embed_texts(texts):
    m = get_model()
    v = m.encode(texts, normalize_embeddings=True)
    v = np.asarray(v, dtype=np.float32)
    v = _pad_to_1536(v)
    return v

Save & exit Nano: Ctrl+O, Enter, Ctrl+X

---

2) Ensure you’re in the venv and env var set

source ~/ougway_env/venv/bin/activate

export TS_DSN="dbname=omega user=postgres host=localhost"

---

3) Quick ingest test (pads automatically to 1536)

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt
python ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt

---

4) Retrieval test (builds a prompt from DB)

python ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

(Optional: have your local model answer)

python ~/Desktop/rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

---

Why padding is OK (in one line)

Your HNSW indexes use cosine; adding trailing zeros doesn’t change vector angles → same retrieval behavior, only a bit more disk.

If anything throws an error (dimension mismatch, etc.), paste the last ~10 lines and I’ll fix on the spot.

You’re right to be pissed. You said “offline only,” so a schema that quietly assumes OpenAI’s 1536 was a bad fit. Here’s the straight answer and what we can do today without tearing anything down.

Why you’re seeing 768 (and not 1536)

• The model you installed—nomic-ai/nomic-embed-text-v1.5—outputs 768-dim vectors by design. That’s normal. citeturn0search8
• Most strong local embedders are 768 or 1024 (e.g., BGE large 1024, GTE large 1024, E5-large-v2 1024, Snowflake Arctic 384/1024, Jina v2 768 / v3 1024). 1536 is mainly the size used by OpenAI text-embedding-3-small (hosted). citeturn0search1turn0search17turn0search2turn0search10turn0search3turn0search11turn0search4turn0search20turn0search13turn0search6

In other words: it’s not that you installed the “wrong” thing—local 1536 models just aren’t a common standard.

“Do we need 1536 in the schema?”

No. Postgres vector(N) just needs a consistent N. Retrieval quality comes from the model, not the number itself. If you use cosine similarity (you are), zero-padding 768 → 1536 preserves cosine—so results don’t change, you only spend extra disk/IO.

If you want a pure 1536 local model

Based on a fresh sweep, there isn’t a widely-used, reputable open-source 1536-dim text embedder that runs locally out-of-the-box today; the strong locals cluster at 768 or 1024. (If that changes, we can swap later.) citeturn0search1turn0search2turn0search3turn0search4turn0search10turn0search20turn0search13turn0search6

You could hack it by:
– concatenating two 768-dim models (double compute, meh gains), or
– learning a 768→1536 projection (adds parameters but no new information),
but neither gives you a true 1536 signal like OpenAI’s.

Your choices (no surprises this time)

1) Keep the 1536 schema and pad 768 → 1536 (what you’re doing now)
– Pros: no schema churn, fully offline, same cosine behavior.
– Cons: ~2× vector storage vs 768.

2) Standardize on a local 768 (or 1024) model and later migrate
– When/if you want to reclaim space, we switch the columns to 768 and re-embed. That’s a controlled migration we can script.

What I recommend right now

Stay with padding so you don’t drop anything or touch schemas. You can keep ingesting immediately, fully offline. If later you decide space matters or you standardize on a 768 model long-term, we’ll do a no-drama migration.

If you want, I’ll also:
– pin your current embedder (Nomic v1.5) to avoid surprise updates,
– add a simple EMBED_PAD_TO=1536 flag in your script so you can flip between padded vs native with one env var,
– and write a one-click check that confirms DB vector lengths match what we’re inserting.

If you do want me to hunt for any new 1536-dim locals again later, say the word and I’ll scan and vet candidates (with links and dims) before we touch anything.

Got it. We’ll do a clean, nuclear reset to a fresh database using 768 everywhere. I’ll keep commands short, one-per-block, and label bash vs psql so nothing gets mixed up.

If you might want a backup, do this first; if not, skip it.

---

(Optional) Backup before wipe

bash

pg_dump -U postgres -F c -f ~/Desktop/omega_backup.dump omega

---

A) Make a 768-dim schema file (from your current .sql)

bash

sed 's/VECTOR(1536)/VECTOR(768)/g' ~/Desktop/omega_tokenspace_schema.sql > ~/Desktop/omega_tokenspace_schema_768.sql

---

B) Stop any running psql

bash

jobs

bash

fg

psql

\q

(If “no such job”, you’re already out—carry on.)

---

C) Drop and recreate the database (empty, clean)

psql (as postgres superuser)

sudo -u postgres psql

psql

DROP DATABASE IF EXISTS omega;

psql
(use darren if that’s the owner you want; otherwise keep postgres)

CREATE DATABASE omega OWNER darren;

psql

\q

---

D) Load the 768 schema into the empty DB

bash

psql -d omega -f ~/Desktop/omega_tokenspace_schema_768.sql

(If your schema file didn’t include the extension creation, add:)

psql

psql -d omega -c "CREATE EXTENSION IF NOT EXISTS vector;"

---

E) Recreate HNSW indexes (cosine) if your file didn’t already

psql

psql -d omega -c "CREATE INDEX chunks_embed_hnsw ON content.chunks USING hnsw (embedding vector_cosine_ops);"

psql

psql -d omega -c "CREATE INDEX senses_centroid_hnsw ON token.senses USING hnsw (centroid vector_cosine_ops);"

psql

psql -d omega -c "CREATE INDEX instances_ctx_hnsw ON token.instances USING hnsw (ctx_embed vector_cosine_ops);"

psql

psql -d omega -c "CREATE INDEX turns_embed_hnsw ON cog.turns USING hnsw (embedding vector_cosine_ops);"

psql

psql -d omega -c "CREATE INDEX memories_embed_hnsw ON cog.memories USING hnsw (embedding vector_cosine_ops);"

psql

psql -d omega -c "CREATE INDEX lat_cells_centroid_hnsw ON lat.cells USING hnsw (centroid vector_cosine_ops);"

---

F) Update your Python script to native 768 (no padding)

bash

nano ~/Desktop/rag_1536.py

Change these lines near the top:

EMBED_DIM = 768
EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")

Ensure embed_texts returns the model vectors directly (no padding):

def embed_texts(texts):
    m = get_model()
    v = m.encode(texts, normalize_embeddings=True)
    v = np.asarray(v, dtype=np.float32)
    if v.shape[1] != EMBED_DIM:
        raise ValueError(f"Embedding dim mismatch: got {v.shape[1]}, expected {EMBED_DIM}")
    return v

Save & exit (Ctrl+O, Enter, Ctrl+X).

---

G) Smoke test (fully offline, 768 end-to-end)

bash (ensure venv active)

source ~/ougway_env/venv/bin/activate

bash

export TS_DSN="dbname=omega user=postgres host=localhost"

bash

printf "The Flower Lattice activation rule: align nodes on 3-6-9 and apply phase-locked rotation.\n" > ~/Desktop/test_lattice.txt

bash

python ~/Desktop/rag_1536.py --engest ~/Desktop/test_lattice.txt

(typo fix—use --ingest)

bash

python ~/Desktop/rag_1536.py --ingest ~/Desktop/test_lattice.txt

bash

python ~/Desktop/rag_1536.py --ask "What is the activation rule of the Flower Lattice?"

(Optional generation with your local Qwen):
bash

python ~/Desktop/rag_1536.py --ask "Summarize the activation rule." \
  --model ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf

---

That’s it

You’ll now have a clean 768-dim database and a local-only RAG pipeline—no padding, no OpenAI, no leftovers. If any single command barks, paste the exact last lines and I’ll correct course immediately.

Totally get it. You’ve been clear from day one: offline-only, in-house, no external services. We’ll fix the schema so it matches the tools you actually have.

Here’s how we’ll handle it next time—clean, boring, and bulletproof.

Plan for the next working session

1) Lock the decisions (so we don’t drift)

• Embedding model: nomic-ai/nomic-embed-text-v1.5 (local) → 768 dims
• Similarity: cosine (keeps retrieval stable and standard)
• Database: Postgres + pgvector
• Zero external calls: no OpenAI / no hosted APIs

2) Rewrite the schema to 768 (what we’ll change)

We’ll convert every VECTOR(1536) column to VECTOR(768) and keep everything else intact. Concretely:
– content.chunks.embedding → VECTOR(768)
– token.senses.centroid → VECTOR(768)
– token.instances.ctx_embed → VECTOR(768)
– cog.turns.embedding → VECTOR(768)
– cog.memories.embedding → VECTOR(768)
– lat.cells.centroid → VECTOR(768)
– Recreate HNSW indexes with vector_cosine_ops (same as now)

3) Two safe ways to apply it (we’ll pick one when we meet)

• A. Fresh DB (simplest): drop + recreate omega and load a *_768.sql schema file. Fastest if you don’t care about current test rows.
• B. In-place migration (no dropping schemas):
  1) Create parallel 768 columns (e.g., embedding_768)
  2) Re-embed current text with the local model (all offline) and write to the new columns
  3) Swap columns & indexes, then drop the old 1536 ones
  This keeps data, avoids CASCADEs, and never leaves you without a working set.

4) What I’ll bring to the session

• A clean 768-dim schema file (your current schema, but with dimensions changed and indexes set to cosine).
• A tiny migration script for the “in-place” route that:
• adds new 768 columns,
• runs an offline re-embed job (Python, uses your venv + the Nomic model),
• flips indexes and drops old columns.
• A sanity checklist (psql one-liners) to verify: extensions present, tables exist, HNSW ready, permissions ok.

5) Prep you can do now (no changes, just prep)

• Make sure your venv still works:
  source ~/ougway_env/venv/bin/activate
• Confirm the local embedder loads:
  python - <<'PY'
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
print("ok:", m.get_sentence_embedding_dimension())
PY
  (It should print ok: 768.)

That’s it—no more action tonight.

---

When we sit down next, we’ll generate the 768 schema, apply it using whichever path you prefer (fresh or in-place), and wire the ingestion so everything stays local and dimension-correct. I’ll keep every command one per block and clearly labeled bash vs psql so it’s impossible to mix up.