Restoring Little Ougway Ingestion and Writing a Reproducible Build Guide

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Provenance

Provider: ChatGPT
Original title: Ugoe Machine Ingestion Script
Conversation ID: 69ae2b1b-42d0-8330-af6e-1389ab929ba1
Created: 2026-03-08T19:06:39-07:00
Updated: 2026-03-08T20:53:09-07:00
Models: gpt-5-3, gpt-5-4-thinking
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Source transcript SHA-256: 237e9bef8bc60c0ced515b3cf3a730fbb763c72808cd6de8b437d2733ba5d610
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Record ID: chatgpt:69ae2b1b-42d0-8330-af6e-1389ab929ba1
Source member: conversations-001.json
Source record SHA-256: 812b051ab6f995131c1f300ab92ced2330b49ae54fae2e2483bb85fcc5f25e3b
Transcript verification status: canonical-verified; privacy-verified; source-order-preserved
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Publication/version history: public archive edition v1

Conversation

Darren — 2026-03-08T19:06:37-07:00

I found another Ugoe machine, and I’m getting ready to pick up where we left off with regards to the Olama installation here and incorporating the database into that functionality. The problem is it’s been long enough that I’m forgetting even what we were last doing other than ingestion, and I’m having a hard time figuring out even how to turn that on. I think that’d be step one is to find the scripts we were using, and I think I have the main one. I just need to get into the right vent.Apple ingestion file we were using. Take a look, let me know what you think.

ChatGPT — 2026-03-08T19:06:40-07:00 · gpt-5-4-thinking

This looks like the right ingestion script lineage, yes. It is a TokenSpace/Postgres ingestion script, not the Ollama server itself. It reads text files, chunks them, embeds them with a local SentenceTransformer model, and writes docs/chunks/embeddings into content.documents and content.chunks in Postgres. [Source-file reference retained for attachment review.]

What it is doing, in plain terms:

Uses env vars for the database, source folder, embedding model, chunk sizes, batch size, and logging. Defaults are already baked in. [Source-file reference retained for attachment review.]
Connects to Postgres at postgresql:///tokenspace?host=/var/run/postgresql&port=5433 unless you override DB_DSN. [Source-file reference retained for attachment review.]
Scans ROOT_DIR for .txt, .md, .markdown, and .log files, then ingests them in natural numeric order. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]
Loads a local embedding model from /mnt/storage/models/nomic-embed-768 by default. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]
Upserts into content.documents, upserts chunk text/token counts into content.chunks, and optionally updates the embedding vector column. [Source-file reference retained for attachment review.]
It is resumable: if chunks already exist for a document, it skips re-embedding unless FORCE_REEMBED=1. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

So my read is:

Yes, this is probably the ingestion file you were using.
Step one is not “turn on Ollama.” Step one is more likely:
– activate the correct Python venv
– make sure Postgres on port 5433 is running
– make sure the embedding model path exists
– run this script with the right ROOT_DIR

The biggest practical checkpoints are these:

sentence_transformers must exist in that venv or it will immediately die. [Source-file reference retained for attachment review.]
psycopg2 and numpy must also be installed. [Source-file reference retained for attachment review.]
Your database must already have the content.documents and content.chunks tables, with embedding VECTOR(768). [Source-file reference retained for attachment review.]
The script does not talk to Ollama at all. It uses SentenceTransformer(MODEL_DIR, trust_remote_code=True), so embeddings are local Hugging Face style model loading, not Ollama embeddings. [Source-file reference retained for attachment review.]

A clean way to re-enter this would be:

cd /path/to/where/this/script/is

source ~/ougway_env/venv/bin/activate
# or whatever your actual venv path was

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'
export MODEL_DIR='/mnt/storage/models/nomic-embed-768'

python3 ingest_pile_v2.py

If you want to test without doing the whole pile again, point ROOT_DIR at a tiny folder first.

Also, one very important detail: this script only ingests text-like files. If your later plan was “incorporate the database into Ollama functionality,” that is a separate step from this script. This script populates the retrieval database. Then a second script or app would:

take a user query
embed the query
search content.chunks by vector similarity
pass the retrieved chunks into Ollama as context

That means what you’re probably missing is not just this ingest file, but also the query / retrieval bridge script.

Based on this file alone, I’d say you were in the database population phase, not yet the final Ollama integration phase. [Source-file reference retained for attachment review.]

Next best move is to locate:
– your venv path
– the script that queried Postgres/pgvector
– any shell script or notes that exported these env vars before running ingestion

Upload the retrieval script too, and I can reconnect the whole chain from ingestion to Ollama.

Darren — 2026-03-08T19:09:47-07:00

the path to the ingest file is [HOME]/Documents/tokenspace and the venv seems to be [HOME]/Documents/ougway_venv

ChatGPT — 2026-03-08T19:09:51-07:00 · gpt-5-4-thinking

Good. That gives us the two key pieces.

Use this sequence from a terminal:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate
python --version

If that activates cleanly, check the script is there:

ls -l [HOME]/Documents/tokenspace/ingest_pile_v2.py

Then do a dry sanity check for the Python packages the script needs:

python -c "import psycopg2, numpy; print('base ok')"
python -c "from sentence_transformers import SentenceTransformer; print('st ok')"

If those pass, the next thing is to confirm your database and model path before you run ingestion:

echo $DB_DSN
echo $ROOT_DIR
echo $MODEL_DIR

If those are empty, set them explicitly for this session:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export ROOT_DIR='/path/to/the/text-files-you-were-ingesting'
export MODEL_DIR='/mnt/storage/models/nomic-embed-768'

Then launch the script:

python [HOME]/Documents/tokenspace/ingest_pile_v2.py

A few important notes from where you left off:

This script is for ingestion into Postgres/pgvector, not Ollama itself.
So if your thought was “turn Ollama back on,” that is a separate step.
Right now the immediate goal is to get back into the venv, confirm dependencies, confirm DB/model paths, and rerun ingestion.

To help you re-orient fast, here’s the likely order of operations:

activate ougway_venv
verify Python packages
verify Postgres is running
verify model path exists
verify ROOT_DIR points to your corpus
run ingestion
later, reconnect the retrieval/query script to Ollama

Use these checks too:

ls /mnt/storage/models/nomic-embed-768
pg_isready -h /var/run/postgresql -p 5433

And if you want a quick one-shot startup block:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate
export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export ROOT_DIR='/path/to/the/text-files-you-were-ingesting'
export MODEL_DIR='/mnt/storage/models/nomic-embed-768'
python ingest_pile_v2.py

The one missing piece is your actual corpus path for ROOT_DIR. Once you tell me that, I can reconstruct the exact command you should run.

Darren — 2026-03-08T19:12:34-07:00

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate
export DB_DSN=’postgresql:///tokenspace?host=/var/run/postgresql&port=5433′
export ROOT_DIR=’/path/to/the/text-files-you-were-ingesting’
export MODEL_DIR=’/mnt/storage/models/nomic-embed-768′
python ingest_pile_v2.py
[2026-03-08 19:12:09] [init] MODEL_DIR=/mnt/storage/models/nomic-embed-768
[2026-03-08 19:12:09] [init] ROOT_DIR=/path/to/the/text-files-you-were-ingesting
[2026-03-08 19:12:09] [init] BATCH_SIZE=64 CHUNK_SIZE=1500 OVERLAP=200 FORCE_REEMBED=False
Traceback (most recent call last):
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 296, in
main()
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 192, in main
model = SentenceTransformer(MODEL_DIR, trust_remote_code=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “[HOME]/Documents/ougway_venv/lib/python3.12/site-packages/sentence_transformers/SentenceTransformer.py”, line 304, in init
raise FileNotFoundError(f”Path {model_name_or_path} not found”)
FileNotFoundError: Path /mnt/storage/models/nomic-embed-768 not found
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:12:36-07:00 · gpt-5-4-thinking

That failure is clean and useful.

Two things are wrong right now:

ROOT_DIR is still the placeholder path, not your real corpus path.
MODEL_DIR points to a model folder that does not exist on this machine.

The immediate blocker is the model path. The script died before it even got to ingestion.

Run these checks:

ls -ld /mnt/storage
ls -ld /mnt/storage/models
find /mnt -maxdepth 4 -type d | grep -i nomic
find [HOME] -maxdepth 5 -type d | grep -i nomic

That should tell us one of three things:

the drive is not mounted
the model folder is elsewhere
the model was never copied onto this Ugoe machine

Also check whether the storage drive is mounted at all:

df -h
lsblk
mount | grep /mnt

If /mnt/storage is missing, then the path is failing because the drive is not mounted.

If you want the fastest recovery path, do this in order.

First, find the actual model location:

find [HOME] /mnt /opt -maxdepth 5 -type d 2>/dev/null | grep 'nomic-embed'

If that finds something like:

[HOME]/models/nomic-embed-768

then set:

export MODEL_DIR='[HOME]/models/nomic-embed-768'

If it finds nothing, then this machine does not have that embedding model yet.

Next, fix the corpus path. Right now you literally told it:

export ROOT_DIR='/path/to/the/text-files-you-were-ingesting'

That was just a placeholder. You need the real folder, for example something like:

export ROOT_DIR='/mnt/storage/pile_uncopyrighted'

or whatever folder actually contains the .txt files.

You can hunt for likely corpus folders with:

find [HOME] /mnt -maxdepth 4 -type d 2>/dev/null | grep -Ei 'pile|text|corpus|dataset|data'

Then once you have both real paths:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate
export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export ROOT_DIR='/REAL/PATH/TO/YOUR/TEXTS'
export MODEL_DIR='/REAL/PATH/TO/nomic-embed-768'
python ingest_pile_v2.py

Before running again, verify both:

ls -ld "$MODEL_DIR"
ls -ld "$ROOT_DIR"
find "$ROOT_DIR" -maxdepth 2 -type f | head

That way you do not waste another launch.

Most likely diagnosis: this box either does not have the model copied over yet, or the storage mount point differs from the old machine. The script itself looks fine; it is just being given paths that do not exist.

Paste me the output of these three commands and I can tell you exactly what to set next:

df -h
find /mnt [HOME] -maxdepth 5 -type d 2>/dev/null | grep -i 'nomic|pile'
find /mnt [HOME] -maxdepth 3 -type d 2>/dev/null | sed -n '1,200p'

Darren — 2026-03-08T19:14:02-07:00

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ ls -ld /mnt/storage
ls -ld /mnt/storage/models
find /mnt -maxdepth 4 -type d | grep -i nomic
find [HOME] -maxdepth 5 -type d | grep -i nomic
drwx–x–x 8 darren darren 4096 Sep 26 23:55 /mnt/storage
drwxrwxr-x 2 darren darren 4096 Sep 24 18:56 /mnt/storage/models
find: ‘/mnt/storage/tokenspacedata’: Permission denied
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/blobs
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/snapshots
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/refs
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/.no_exist
/mnt/storage/hf-cache/models–nomic-ai–nomic-bert-2048
/mnt/storage/hf-cache/models–nomic-ai–nomic-bert-2048/blobs
/mnt/storage/hf-cache/models–nomic-ai–nomic-bert-2048/snapshots
/mnt/storage/hf-cache/models–nomic-ai–nomic-bert-2048/refs
/mnt/storage/hf-cache/.locks/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/.locks/models–nomic-ai–nomic-bert-2048
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-bert-2048
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ df -h
lsblk
mount | grep /mnt
Filesystem Size Used Avail Use% Mounted on
tmpfs 3.2G 2.3M 3.2G 1% /run
/dev/sda2 915G 165G 704G 19% /
tmpfs 16G 1.1M 16G 1% /dev/shm
tmpfs 5.0M 12K 5.0M 1% /run/lock
efivarfs 128K 52K 72K 42% /sys/firmware/efi/efivars
/dev/sda1 1.1G 6.2M 1.1G 1% /boot/efi
/dev/sdb 3.6T 972G 2.5T 28% /mnt/storage
tmpfs 3.2G 136K 3.2G 1% /run/user/1000
NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINTS
loop0 7:0 0 4K 1 loop /snap/bare/5
loop1 7:1 0 13M 1 loop /snap/canonical-livepatch/378
loop2 7:2 0 13.4M 1 loop /snap/canonical-livepatch/384
loop4 7:4 0 55.5M 1 loop /snap/core18/2979
loop5 7:5 0 74M 1 loop /snap/core22/2292
loop6 7:6 0 74M 1 loop /snap/core22/2339
loop7 7:7 0 66.8M 1 loop /snap/core24/1267
loop8 7:8 0 66.9M 1 loop /snap/core24/1349
loop9 7:9 0 4.9M 1 loop /snap/curl/2369
loop10 7:10 0 4.9M 1 loop /snap/curl/2384
loop11 7:11 0 254.6M 1 loop /snap/firefox/7869
loop12 7:12 0 55.5M 1 loop /snap/core18/2999
loop13 7:13 0 18.5M 1 loop /snap/firmware-updater/210
loop14 7:14 0 16.4M 1 loop /snap/firmware-updater/216
loop15 7:15 0 531.4M 1 loop /snap/gnome-42-2204/247
loop16 7:16 0 516.2M 1 loop /snap/gnome-42-2204/226
loop17 7:17 0 669.8M 1 loop /snap/gnome-46-2404/145
loop18 7:18 0 606.1M 1 loop /snap/gnome-46-2404/153
loop19 7:19 0 91.7M 1 loop /snap/gtk-common-themes/1535
loop20 7:20 0 1.2G 1 loop /snap/libreoffice/365
loop21 7:21 0 1.2G 1 loop /snap/libreoffice/366
loop22 7:22 0 395M 1 loop /snap/mesa-2404/1165
loop23 7:23 0 290.8M 1 loop /snap/mesa-2404/912
loop24 7:24 0 10.8M 1 loop /snap/snap-store/1270
loop25 7:25 0 17.5M 1 loop /snap/snap-store/1300
loop26 7:26 0 50.9M 1 loop /snap/snapd/25577
loop27 7:27 0 48.1M 1 loop /snap/snapd/25935
loop28 7:28 0 576K 1 loop /snap/snapd-desktop-integration/315
loop29 7:29 0 576K 1 loop /snap/snapd-desktop-integration/343
loop30 7:30 0 321.1M 1 loop /snap/vlc/3777
loop31 7:31 0 254.6M 1 loop /snap/firefox/7901
sda 8:0 0 931.5G 0 disk
├─sda1 8:1 0 1G 0 part /boot/efi
└─sda2 8:2 0 930.5G 0 part /
sdb 8:16 0 3.6T 0 disk /mnt/storage
/dev/sdb on /mnt/storage type ext4 (rw,relatime)
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:14:03-07:00 · gpt-5-4-thinking

Good. Storage is mounted, so the issue is just the wrong model path, not a missing drive.

You do not have:

/mnt/storage/models/nomic-embed-768

But you do have Hugging Face cache copies of the Nomic model under:

/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5

The actual usable model directory is usually the snapshot folder inside that cache. Find it with:

find /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots -maxdepth 1 -mindepth 1 -type d

That should return something like:

/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/xxxxxxxxxxxxxxxx

Use that full returned path as MODEL_DIR.

So the fix is:

export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/PUT_THE_HASH_HERE'

Then verify:

ls "$MODEL_DIR"

You should see model files like config/tokenizer/model files.

You also still need to fix ROOT_DIR, because right now it is still fake. Find likely corpus folders with:

find /mnt/storage [HOME] -maxdepth 4 -type d 2>/dev/null | grep -Ei 'pile|dataset|corpus|text|docs|chunks'

Then inspect the likely one:

find /that/path -maxdepth 2 -type f | head

You want a directory that actually contains .txt, .md, .log, or .markdown files.

So your next sequence should be:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/YOUR_HASH_HERE'
export ROOT_DIR='/REAL/CORPUS/PATH'

ls "$MODEL_DIR"
find "$ROOT_DIR" -maxdepth 2 -type f | head

python ingest_pile_v2.py

One more useful shortcut: if you want to avoid typing that giant snapshot path every time, make a symlink once:

mkdir -p /mnt/storage/models
ln -s /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/YOUR_HASH_HERE /mnt/storage/models/nomic-embed-768

Then you can go back to:

export MODEL_DIR='/mnt/storage/models/nomic-embed-768'

That is probably the cleanest fix.

Run these two commands next and paste the output:

find /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots -maxdepth 1 -mindepth 1 -type d
find /mnt/storage [HOME] -maxdepth 4 -type d 2>/dev/null | grep -Ei 'pile|dataset|corpus|text|docs|chunks'

Then I can give you the exact export lines.

Darren — 2026-03-08T19:15:16-07:00

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ find /mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots -maxdepth 1 -mindepth 1 -type d
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:15:17-07:00 · gpt-5-4-thinking

Perfect. Use this for the model path:

export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'

Then verify it:

ls "$MODEL_DIR"

If that looks good, the only thing still missing is the real ROOT_DIR.

So the next block to run is:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'

Now find the corpus folder:

find /mnt/storage [HOME] -maxdepth 4 -type d 2>/dev/null | grep -Ei 'pile|dataset|corpus|text|docs|chunks'

When you find the likely folder, test it:

find /that/folder -maxdepth 2 -type f | head

Once you confirm the folder with actual .txt or .md files, set:

export ROOT_DIR='/that/folder'

Then run:

python ingest_pile_v2.py

Optional cleanup so the old expected path works again:

mkdir -p /mnt/storage/models
ln -s /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398 /mnt/storage/models/nomic-embed-768

Then later you can use:

export MODEL_DIR='/mnt/storage/models/nomic-embed-768'

Paste the output of the corpus-folder search and I’ll point to the exact ROOT_DIR.

Darren — 2026-03-08T19:16:43-07:00

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate

export DB_DSN=’postgresql:///tokenspace?host=/var/run/postgresql&port=5433′
export MODEL_DIR=’/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398′
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ find /mnt/storage [HOME] -maxdepth 4 -type d 2>/dev/null | grep -Ei ‘pile|dataset|corpus|text|docs|chunks’
/mnt/storage/pile_uncopyrighted
/mnt/storage/pile_uncopyrighted/.cache
/mnt/storage/pile_uncopyrighted/.cache/huggingface
/mnt/storage/pile_uncopyrighted/.cache/huggingface/download
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/blobs
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/snapshots
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/refs
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/.no_exist
/mnt/storage/hf-cache/models–nomic-ai–nomic-embed-text-v1.5/.no_exist/e5cf08aadaa33385f5990def41f7a23405aec398
/mnt/storage/hf-cache/.locks/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/blobs
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/refs
/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/.no_exist
/mnt/storage/hf-cache/hub/.locks/models–nomic-ai–nomic-embed-text-v1.5
/mnt/storage/.Trash-1000/files/hf_datasets
/mnt/storage/.Trash-1000/files/hf_datasets/downloads
/mnt/storage/.Trash-1000/files/hf_datasets/bookcorpus
/mnt/storage/ougway_documents old/ougway_env/bookcorpus_clean
/mnt/storage/ougway_documents old/ougway_env/data/full_dataset
/mnt/storage/ougway_documents old/ougway_env/docs
/mnt/storage/ougway_documents old/ougway_env/docs/debian
[HOME]/stable-diffusion-webui/modules/textual_inversion
[HOME]/stable-diffusion-webui/textual_inversion_templates
[HOME]/Documents/Client/llama.cpp/docs
[HOME]/.local/share/org.gnome.TextEditor
[HOME]/.local/share/org.gnome.TextEditor/drafts
[HOME]/ai/stable-diffusion-webui/modules/textual_inversion
[HOME]/ai/stable-diffusion-webui/textual_inversion_templates
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:16:44-07:00 · gpt-5-4-thinking

There it is.

Your likely corpus path is:

/mnt/storage/pile_uncopyrighted

So set:

export ROOT_DIR='/mnt/storage/pile_uncopyrighted'

Before running ingestion, do one quick sanity check:

find "$ROOT_DIR" -maxdepth 2 -type f | head
ls "$MODEL_DIR" | head
pg_isready -h /var/run/postgresql -p 5433

If those look good, run:

python ingest_pile_v2.py

So the full working block is now:

cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'

find "$ROOT_DIR" -maxdepth 2 -type f | head
ls "$MODEL_DIR" | head
pg_isready -h /var/run/postgresql -p 5433

python ingest_pile_v2.py

If you want the old path convention back, make the symlink now:

mkdir -p /mnt/storage/models
ln -s /mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398 /mnt/storage/models/nomic-embed-768

Then later you can shorten it to:

export MODEL_DIR='/mnt/storage/models/nomic-embed-768'

One caution: I also see old possible corpus locations like:

/mnt/storage/ougway_documents old/ougway_env/data/full_dataset

So if /mnt/storage/pile_uncopyrighted does not show the expected text files, that older full_dataset path may be the other candidate.

Run the sanity block and paste the output if anything barks.

Darren — 2026-03-08T19:18:08-07:00

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ cd [HOME]/Documents/tokenspace
source [HOME]/Documents/ougway_venv/bin/activate

find “$ROOT_DIR” -maxdepth 2 -type f | head
ls “$MODEL_DIR” | head
pg_isready -h /var/run/postgresql -p 5433

python ingest_pile_v2.py
/mnt/storage/pile_uncopyrighted/chunk_0000.txt
/mnt/storage/pile_uncopyrighted/chunk_0001.txt
/mnt/storage/pile_uncopyrighted/chunk_0002.txt
/mnt/storage/pile_uncopyrighted/chunk_0003.txt
/mnt/storage/pile_uncopyrighted/chunk_0004.txt
/mnt/storage/pile_uncopyrighted/chunk_0005.txt
/mnt/storage/pile_uncopyrighted/chunk_0006.txt
/mnt/storage/pile_uncopyrighted/chunk_0007.txt
/mnt/storage/pile_uncopyrighted/chunk_0008.txt
/mnt/storage/pile_uncopyrighted/chunk_0009.txt
1_Pooling
config.json
config_sentence_transformers.json
model.safetensors
modules.json
README.md
sentence_bert_config.json
special_tokens_map.json
tokenizer_config.json
tokenizer.json
/var/run/postgresql:5433 – no response
[2026-03-08 19:17:29] [init] MODEL_DIR=/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
[2026-03-08 19:17:29] [init] ROOT_DIR=/mnt/storage/pile_uncopyrighted
[2026-03-08 19:17:29] [init] BATCH_SIZE=64 CHUNK_SIZE=1500 OVERLAP=200 FORCE_REEMBED=False

[2026-03-08 19:17:36] [init] embedding model loaded
[2026-03-08 19:17:36] [scan] scanning /mnt/storage/pile_uncopyrighted
[2026-03-08 19:17:36] [scan] found 15325 file(s)
Traceback (most recent call last):
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 296, in
main()
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 204, in main
conn = psycopg2.connect(DB_DSN)
^^^^^^^^^^^^^^^^^^^^^^^^
File “[HOME]/Documents/ougway_venv/lib/python3.12/site-packages/psycopg2/init.py”, line 122, in connect
conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
psycopg2.OperationalError: connection to server on socket “/var/run/postgresql/.s.PGSQL.5433” failed: No such file or directory
Is the server running locally and accepting connections on that socket?

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:18:10-07:00 · gpt-5-4-thinking

You’re past the file-path mess. The script, model, and corpus are all good.

The only thing failing now is Postgres is not running on port 5433.

This line told us that before the script crashed:

/var/run/postgresql:5433 - no response

And then psycopg2 confirmed it.

So the next step is to find and start the PostgreSQL instance you were using.

Run these checks:

systemctl status postgresql --no-pager
pg_lsclusters
ps aux | grep postgres

Most likely outcomes:

Case 1: PostgreSQL is installed, but your cluster is down
You’ll probably see a cluster listed as down. Then start it with:

sudo pg_ctlcluster <version> main start

Example:

sudo pg_ctlcluster 16 main start

Then recheck:

pg_isready
pg_isready -h /var/run/postgresql
pg_isready -h /var/run/postgresql -p 5433

Case 2: PostgreSQL is running, but on the default port 5432
That is very likely. If so, either:
– change your DB_DSN to 5432, or
– reconfigure the cluster back to 5433

Fastest path is usually just use the live port.

If pg_lsclusters shows something like port 5432, then do:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
python ingest_pile_v2.py

Case 3: PostgreSQL is not started at all
Then do:

sudo systemctl start postgresql

and check again:

pg_lsclusters
pg_isready

Also, confirm the database itself exists:

psql -h /var/run/postgresql -p 5432 -lqt

or if it really uses 5433:

psql -h /var/run/postgresql -p 5433 -lqt

You want to see tokenspace in the list.

So right now, the shortest likely recovery path is:

pg_lsclusters

If it shows 5432, then:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
python ingest_pile_v2.py

If it shows down, start it first.

Paste the output of:

pg_lsclusters
systemctl status postgresql --no-pager

and I’ll tell you the exact next command.

Darren — 2026-03-08T19:21:53-07:00

Well, see, where this has all been set up prior, and I don’t see why I didn’t do this, but usually I’ll put hints to how it was started up and run, and so forth. Oh, Uboi startup command.txt. Well, I got something here. It does not appear correct, and I’m not sure why there’s a numeric code at the bottom of it. Here, I’ll paste it in.

source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile.py

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_HUB_DISABLE_TELEMETRY=1
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile.py
source: command not found
python3: can’t open file ‘[HOME]/Documents/tokenspace/ingest_pile.py’: [Errno 2] No such file or directory
(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

This is what I get when I run it.

ChatGPT — 2026-03-08T19:21:54-07:00 · gpt-5-4-thinking

Two separate issues are showing up there.

First, this:

source ~/Documents/ougway_venv/bin/activate

failed with:

source: command not found

because your text file almost certainly has a hidden BOM / junk character at the front. That weird invisible character before source turns it into a different command name. You can see it in your pasted line as the odd leading mark before source.

Second, this line:

python3 ~/Documents/tokenspace/ingest_pile.py

is pointing at the old filename, and that file is not there. The script you actually have is:

[HOME]/Documents/tokenspace/ingest_pile_v2.py

So the note is from an older state of the setup.

Use this cleaned version instead:

source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1
export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py

But before you run that again, the real blocker is still Postgres. Your earlier run already proved that the model and corpus are fine, and then died here:

connection to server on socket "/var/run/postgresql/.s.PGSQL.5433" failed

So do this next:

pg_lsclusters
systemctl status postgresql --no-pager

If pg_lsclusters shows your cluster on 5432 instead of 5433, change this line:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'

to:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'

If it shows the cluster is down, start it with the version shown there, for example:

sudo pg_ctlcluster 16 main start

or just:

sudo systemctl start postgresql

Then test:

pg_isready
pg_isready -h /var/run/postgresql -p 5432
pg_isready -h /var/run/postgresql -p 5433

One more cleanup item: fix that startup note so it stops biting you later. The easiest way is to rewrite it from scratch:

cat > ~/Documents/uboi_startup_command.txt <<'EOF'
source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1
export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5433'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py
EOF

Then run it safely with:

bash ~/Documents/uboi_startup_command.txt

Paste the output of pg_lsclusters and I’ll tell you whether to switch to 5432 or start the cluster.

Darren — 2026-03-08T19:23:03-07:00

158c3431-c479-41fc-8ba9-42b5d88e68ac

not seeing a leading mark though

ChatGPT — 2026-03-08T19:23:03-07:00 · gpt-5-3

You’re actually seeing two different things there, Darren. Neither is serious.

1️⃣ Why `source` failed earlier

The earlier line that failed:

source ~/Documents/ougway_venv/bin/activate

contained a hidden Unicode BOM character at the beginning. It is invisible in many editors, but the shell reads it as part of the command name. So instead of running source, Bash tries to run something like:

<U+FEFF>source

which obviously does not exist, hence:

source: command not found

Your pasted text here does not show it anymore, because the BOM often disappears when copying between programs.

The easiest fix is simply rewrite the file cleanly, which removes the BOM.

2️⃣ The random UUID-looking number

This line:

158c3431-c479-41fc-8ba9-42b5d88e68ac

is almost certainly just garbage that got appended to the text file by whatever editor or clipboard operation was used earlier. It has no meaning for your system or script. It can be deleted.

3️⃣ The real issue still blocking ingestion

The ingestion failed earlier because PostgreSQL isn’t running on port 5433:

connection to server on socket "/var/run/postgresql/.s.PGSQL.5433" failed

So before running the script again, run this:

pg_lsclusters

That command will show something like:

Ver Cluster Port Status Owner Data directory
16  main    5432 online postgres /var/lib/postgresql/16/main

Two possible cases:

If you see `5432`

Change your DSN:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'

If you see `down`

Start it:

sudo pg_ctlcluster 16 main start

(or whatever version it shows)

4️⃣ Clean startup script (recommended)

Make a clean run script so you never have to remember this again:

nano ~/Documents/start_ougway_ingest.sh

Paste this:

#!/bin/bash

source ~/Documents/ougway_venv/bin/activate

export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'

CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py

Make it executable:

chmod +x ~/Documents/start_ougway_ingest.sh

Then you can start ingestion with one command:

~/Documents/start_ougway_ingest.sh

5️⃣ One thing I want to confirm before you run again

Run this and paste the output:

pg_lsclusters

That will tell me exactly what port your database is using, and then we can fire the ingestion back up properly.

Darren — 2026-03-08T19:29:46-07:00

So, it looks like I’ve got the right startup feature. The problem is that the database engine itself looks like it has not started. What would be the simplest method of starting something that’s already established?

ChatGPT — 2026-03-08T19:29:46-07:00 · gpt-5-3

You’re exactly right — the database simply isn’t running. The installation and cluster likely already exist; it just needs to be started.

The simplest method on Ubuntu is:

sudo systemctl start postgresql

Then confirm it came up:

pg_lsclusters

You should see something like:

Ver Cluster Port Status Owner    Data directory
16  main    5432 online postgres /var/lib/postgresql/16/main

The key word is online.

If it still shows down

Start the specific cluster directly:

sudo pg_ctlcluster 16 main start

(Replace 16 with whatever version pg_lsclusters shows.)

Quick health check

After starting it, run:

pg_isready

You should see:

/var/run/postgresql:5432 - accepting connections

One thing to adjust

Your script currently expects port 5433:

postgresql:///tokenspace?host=/var/run/postgresql&port=5433

But most Ubuntu installs run on 5432 unless you changed it earlier.

If pg_lsclusters shows 5432, change your env line to:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'

Then run your ingestion again.

Optional but recommended (so you never have to start it manually)

Enable PostgreSQL to start automatically at boot:

sudo systemctl enable postgresql

So your quick recovery sequence right now is:

sudo systemctl start postgresql
pg_lsclusters
pg_isready

Then rerun your ingest command.

If you want, I can also show you a one-line status check that verifies all 4 pieces of Ougway’s stack at once (GPU, model, DB, dataset) so you never have to hunt through this again.

Darren — 2026-03-08T19:31:35-07:00

[2026-03-08 19:31:11] [init] MODEL_DIR=/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
[2026-03-08 19:31:11] [init] ROOT_DIR=/mnt/storage/pile_uncopyrighted
[2026-03-08 19:31:11] [init] BATCH_SIZE=64 CHUNK_SIZE=1500 OVERLAP=200 FORCE_REEMBED=False

[2026-03-08 19:31:13] [init] embedding model loaded
[2026-03-08 19:31:13] [scan] scanning /mnt/storage/pile_uncopyrighted
[2026-03-08 19:31:13] [scan] found 15325 file(s)
Traceback (most recent call last):
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 296, in
main()
File “[HOME]/Documents/tokenspace/ingest_pile_v2.py”, line 204, in main
conn = psycopg2.connect(DB_DSN)
^^^^^^^^^^^^^^^^^^^^^^^^
File “[HOME]/Documents/ougway_venv/lib/python3.12/site-packages/psycopg2/init.py”, line 122, in connect
conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
psycopg2.OperationalError: connection to server on socket “/var/run/postgresql/.s.PGSQL.5433” failed: No such file or directory
Is the server running locally and accepting connections on that socket?

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$

ChatGPT — 2026-03-08T19:31:37-07:00 · gpt-5-4-thinking

Yep — this run shows the same thing, but with one extra clue:

You did not export DB_DSN in that launch, so the script fell back to its built-in default of 5433.

That means there are two steps:

1. Start PostgreSQL

Run:

sudo systemctl start postgresql

Then check what port it is actually using:

pg_lsclusters
pg_isready

Most likely, it will be 5432, not 5433.

2. Export the database DSN before launching ingestion

If pg_lsclusters shows port 5432, run:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'

Then launch again:

source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1
export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py

Why this happened

Your current launch command only set:

HF_HOME
offline Hugging Face flags
CUDA_VISIBLE_DEVICES

It did not set DB_DSN, so the script used its internal default and tried /var/run/postgresql on port 5433.

Fastest path now

Run these exact commands:

sudo systemctl start postgresql
pg_lsclusters
pg_isready

If pg_lsclusters shows 5432, then immediately do:

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py

Paste the output of pg_lsclusters if you want the exact final startup block.

Darren — 2026-03-08T20:46:31-07:00

this is as far as it gets, then nothing. though i can exit out easy enough. It’s like seeing it already did some and checking? gets f’d up in that process? IDK

(ougway_venv) [USER]@[HOST]:~/Documents/tokenspace$ source ~/Documents/ougway_venv/bin/activate
export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1
export DB_DSN=’postgresql:///tokenspace?host=/var/run/postgresql&port=5432′
CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py
[2026-03-08 20:45:01] [init] MODEL_DIR=/mnt/storage/hf-cache/hub/models–nomic-ai–nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
[2026-03-08 20:45:01] [init] ROOT_DIR=/mnt/storage/pile_uncopyrighted
[2026-03-08 20:45:01] [init] BATCH_SIZE=64 CHUNK_SIZE=1500 OVERLAP=200 FORCE_REEMBED=False

[2026-03-08 20:45:03] [init] embedding model loaded
[2026-03-08 20:45:03] [scan] scanning /mnt/storage/pile_uncopyrighted
[2026-03-08 20:45:03] [scan] found 15325 file(s)
[2026-03-08 20:45:16] [file 1/15325] START /mnt/storage/pile_uncopyrighted/chunk_0000.txt
[2026-03-08 20:45:16] [file 1] existing chunks for doc_id=2: 40974

ChatGPT — 2026-03-08T20:46:41-07:00 · gpt-5-4-thinking

It’s probably not hung. It’s almost certainly slogging through a very large metadata-upsert pass with no intermediate logging.

Why I think that:

The script logged existing chunks for doc_id=2: 40974. [Source-file reference retained for attachment review.]
After that, it reads the file, rechunks it, and then does a CHUNK_UPSERT_SQL once per chunk in a plain Python loop. There is no progress print inside that loop. [Source-file reference retained for attachment review.]
Only after finishing all those upserts does it log either “skipping re-embed” or the final [ok ...] line. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

So if that first document really maps to ~40,974 chunks, the script can appear frozen while it does tens of thousands of row updates one by one.

The important detail is this line in the code:

It skips re-embedding if chunks already exist.
It does not skip the chunk metadata upserts. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

That means the flow is effectively:

find doc
count existing chunks
read file
rechunk file
upsert every chunk row again, one by one
then maybe skip embedding
commit
finally log completion

So yes — “it already did some and now checking gets messed up” is close. It’s resuming, but the resume logic is only partial. It avoids recomputing embeddings, yet still reprocesses chunk metadata for every chunk.

What to do right now

First, confirm it is actively working rather than dead:

tail -f ~/Documents/tokenspace/ingest.log

In another terminal, watch Postgres activity:

ps aux | grep ingest_pile_v2
ps aux | grep postgres

And optionally check whether chunk count is moving:

psql -h /var/run/postgresql -p 5432 -d tokenspace -c "SELECT COUNT(*) FROM content.chunks;"

If that count is stable and the process is burning CPU a bit, it is probably still walking the big upsert loop.

The simplest practical move is to patch the script so that if a doc already has chunks and FORCE_REEMBED=0, it skips the whole file immediately instead of re-upserting all metadata again.

The change is this block, right after pre_existing_chunks is logged:

# check if chunks already exist for this doc
cur.execute("SELECT COUNT(*) FROM content.chunks WHERE doc_id = %s;", (doc_id,))
pre_existing_chunks = cur.fetchone()[0]
log(f"[file {idx}] existing chunks for doc_id={doc_id}: {pre_existing_chunks}")

if pre_existing_chunks > 0 and not FORCE_REEMBED:
    log(f"[file {idx}] skipping file entirely (chunks already exist and FORCE_REEMBED=0)")
    conn.commit()
    continue

That would make resume behavior actually fast.

If you want to try without editing the main script yet, a quick diagnostic is to test with a tiny subset folder, but since you’re resuming, the better answer is to fix the skip logic.

There’s one more possibility: chunk_0000.txt may itself be a very large file, and 40,974 chunks for one doc is plausible with this chunker. The script chunks text into ~1500-character windows with overlap, which can explode row count on a giant source file. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

So the diagnosis is:

database is now reachable
model is loading
corpus path is correct
the script is likely stalling on a huge per-chunk upsert loop during resume, not on Ollama or the DB startup anymore. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

Use this exact command to see whether that first file is enormous:

wc -c /mnt/storage/pile_uncopyrighted/chunk_0000.txt

And if you want, paste the result plus the current tail of ~/Documents/tokenspace/ingest.log, and I’ll give you the exact patched version of ingest_pile_v2.py.

Darren — 2026-03-08T20:48:57-07:00

Well, what I thought we had in place was a method of interpreting how much of the file had already been done on restart, and that there was a log somewhere that it could just skip to this point in that process. But that’s not the case. We’re going to have to work through how this actually gets processed a little more, I think. I’d like some changes, but not right now. I’m gonna let it run and see if it does at some point pick up where it left off.

ChatGPT — 2026-03-08T20:48:58-07:00 · gpt-5-4-thinking

That matches what the script is actually doing.

It has partial resume, not true checkpoint resume.

What it currently seems to do is:

rescan all files
for each file, look up the document record
count existing chunks
still walk the file/chunk logic again
avoid some embedding work if chunks already exist
but not cleanly skip to “next unfinished file”

So your memory of the intended behavior makes sense, but this script is not really doing that cleanly.

Letting it run is a good call for now. That will answer the immediate practical question: does it eventually move past chunk_0000.txt and continue, or is it effectively unusable in its current resume mode?

What I’d watch for is just two signs:

whether it eventually prints an [ok ...] or skipping re-embed style message for that first file
whether it starts logging file 2/15325

If it does, then it is slow but functional.
If it sits there forever, then the restart path needs surgery before it is worth using.

And yes, later the changes you probably want are:

a real progress ledger for completed files
immediate skip of files already fully ingested
optional --start-at or --resume-from-file
better progress output during large files
maybe batched SQL writes instead of per-chunk row handling

For now, you’re doing the right thing: observe first, patch second. When you’re ready, we can turn this into a proper resumable ingestion pipeline instead of this half-memory creature.

Darren — 2026-03-08T20:50:18-07:00

All right, I’m going to let it run for right now. And once we get it so that it can be, we can adjust to it on a regular basis when it’s not in use, I’d like to run ingestion, in other words, and not waste any more time. On top of that, once we have it nailed down to where everything is and where it’s pointing and what needs to get started first, etc., I want to have that right in the startup of the machine so that everything’s running every time it boots. I’m just going to have to see how that goes, where this is one machine and just how much it can run at once.

ChatGPT — 2026-03-08T20:50:19-07:00 · gpt-5-4-thinking

That is the right direction.

What you want is really two separate end states:

1. Reliable manual recovery
A single known-good startup order, so you can bring the whole stack up without guessing.

2. Controlled auto-start
The machine boots, the right services come up, and ingestion can run automatically only when conditions are right.

For now, letting ingestion run is the cleanest way to learn whether the current script is merely slow or actually broken on resume.

Once you’re ready to tighten it up, I’d structure it like this:

Boot order you’ll want nailed down

storage mount available
PostgreSQL starts
any vector/db dependencies confirmed
Python venv and env vars available
ingestion starts only if enabled
Ollama and any retrieval layer start separately

That separation matters, because ingestion is a batch workload, while Ollama is a service workload. You do not really want them tangled together at boot unless you deliberately choose that.

Best long-term setup

Have three little startup layers:

a system service for PostgreSQL
a system or user service for Ollama
a separate ingestion launcher that can be turned on or off

That way:
– the database always comes up
– Ollama always comes up if you want
– ingestion only runs when the machine is idle enough, or only when you enable it

What I would build next, once you’re ready

A small set of files:

check_ougway_stack.sh
Verifies mount, DB, model path, corpus path, GPU, and venv
start_ingest.sh
Exports the right vars and launches ingestion
start_ollama.sh
Starts Ollama cleanly
optionally a systemd service for ingestion, but only after the script is fixed

Why not auto-run ingestion immediately at boot

Because right now you still do not know:

whether resume is truly safe
how much RAM/VRAM/CPU it eats over time
whether it interferes with anything else you want that box doing

So the smart path is:

Phase 1: observe this current run
Phase 2: patch resume logic
Phase 3: create one-command startup scripts
Phase 4: move selected parts into boot-time services

What you’re really aiming for

A machine where, after boot, this is already true:

storage mounted
Postgres online
Ollama ready
model paths valid
ingestion can be started with one command, or auto-started if desired

That is very doable.

And yes, once the startup order is stable, we can wire it into boot so the machine becomes more like an appliance than a project bench.

For now, let this run and see if it advances past file 1. That result tells us whether the next job is optimization or repair.

Darren — 2026-03-08T20:51:19-07:00

Well, yeah, I mean, currently it’s been a manual process of make sure that the database is running, make sure that this is up and available, make sure that this can talk to that, this kind of stuff. I wanna get sorted out so that there’s at least a process on the desktop where I can go through files and follow a set of instructions in case, you know, I have another session of a long-term switch to another reality like we just did, and I’m just coming back to this now trying to remember what the hell I did. And I didn’t document it very well, so.

ChatGPT — 2026-03-08T20:51:20-07:00 · gpt-5-4-thinking

Yep. What you need is not more memory. You need a re-entry kit.

Make it so Future Darren can sit down cold and recover the whole box in a few minutes.

The clean setup would be:

On the desktop

A folder called something like:

~/Desktop/Ougway Startup

Inside it:

00_README_FIRST.txt
01_check_stack.sh
02_start_database.sh
03_start_ollama.sh
04_start_ingestion.sh
05_status_check.sh

That gives you both:
– a human-readable recovery path
– clickable or runnable helpers

What goes in `00_README_FIRST.txt`

Very short, very blunt:

storage must be mounted at /mnt/storage
Postgres must be online
venv is ~/Documents/ougway_venv
tokenspace scripts are in ~/Documents/tokenspace
corpus is /mnt/storage/pile_uncopyrighted
model is the Nomic snapshot path
ingestion script is ingest_pile_v2.py
DB port is whichever one you confirm, likely 5432

Then a dead simple startup order:

run 01_check_stack.sh
if DB is down, run 02_start_database.sh
if Ollama needed, run 03_start_ollama.sh
run 04_start_ingestion.sh
use 05_status_check.sh to verify everything

Why this helps

Because when your context is fragmented, you do not want to reconstruct architecture. You want a trail of breadcrumbs.

Best next build

Once this current ingestion test tells us what the script actually does on resume, the next thing to make is:

one plain-English checklist
one stack check script
one ingestion launcher
one status script

That gets you out of “what the hell did I do last time?” mode.

The status script should report

At minimum:

is /mnt/storage mounted
is Postgres running
which port is Postgres using
does tokenspace DB exist
is the venv present
does the model path exist
does the corpus path exist
is Ollama running
is ingestion already running

That way one command tells you the whole machine state.

Important design choice

Do not rely on memory for any path or port again.

Put every important path into either:

a single env file, or
the startup scripts themselves

For example:

VENV=~/Documents/ougway_venv
TOKENSCRIPT=~/Documents/tokenspace/ingest_pile_v2.py
ROOT_DIR=/mnt/storage/pile_uncopyrighted
MODEL_DIR=/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'

Then every helper script sources the same values.

The right order from here

Let the current run prove what it does.
After that, we build the re-entry kit.
Then, once that works, we decide what should auto-start at boot and what should remain manual.

That is the sane path.

When you’re ready, I’d make you the full set of scripts and the README in one shot.

Darren — 2026-03-08T20:52:12-07:00

Yes, and I’ve got actually an icon for Oogway startup command.txt on my desktop, and a few other pointers here and there that gave me the correct position, but even for those, I had to edit them a little bit because things had actually changed since I’d made those. So, I’m not much of a scientist in my old age, I guess. I’ll have to work on that.

ChatGPT — 2026-03-08T20:52:13-07:00 · gpt-5-4-thinking

That is normal, not failure.

You did leave breadcrumbs. They were just tied to a moving system, so the breadcrumbs aged.

What you need now is not “be more scientific.” What you need is a living startup pack that is easy to update when paths or filenames change.

Best form for that is:

one master config file
a few small launcher scripts
one plain-English desktop note

So instead of hardcoding everything in five places, you keep the moving parts in one file.

For example, later we should make something like:

~/Documents/ougway_config.env

with lines like:

VENV=[HOME]/Documents/ougway_venv
TOKENSPACE_DIR=[HOME]/Documents/tokenspace
INGEST_SCRIPT=[HOME]/Documents/tokenspace/ingest_pile_v2.py
HF_HOME=/mnt/storage/hf-cache
MODEL_DIR=/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398
ROOT_DIR=/mnt/storage/pile_uncopyrighted
DB_DSN=postgresql:///tokenspace?host=/var/run/postgresql&port=5432

Then every desktop helper script just loads that one file.

That way, when something changes, you update one place instead of chasing old notes.

Your desktop note can then stay simple:

start database
run stack check
start ingestion
start ollama if needed

And the scripts do the real work.

So no, this is not “you’re not much of a scientist.” This is just what happens when a hand-built system grows faster than its documentation.

Let this run for now. After that, the next useful step is to turn your current scattered hints into one single source of truth.

Darren — 2026-03-08T20:52:51-07:00

I guess what I mean by that is put some more time into documenting what I am doing in a way that’s organized.

ChatGPT — 2026-03-08T20:52:52-07:00 · gpt-5-3

That’s actually the right instinct, Darren — but the trick is to document in a way that future-you can use in 30 seconds, not in a way that becomes another pile of notes. The goal isn’t more documentation, it’s operational documentation.

Think of it like leaving a cockpit checklist for the machine.

Here’s a structure that works very well for systems like the one you’re building.

1. One “Single Source of Truth” File

Create one file that contains all critical paths and settings.

Example:

~/Documents/ougway_system_config.md

Inside:

OUGWAY SYSTEM CONFIG
====================

Virtual Environment
-------------------
[HOME]/Documents/ougway_venv

TokenSpace Scripts
------------------
[HOME]/Documents/tokenspace

Ingestion Script
----------------
ingest_pile_v2.py

Corpus Location
---------------
/mnt/storage/pile_uncopyrighted

Embedding Model
---------------
/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398

Database
--------
Name: tokenspace
Port: 5432
Socket: /var/run/postgresql

Startup Order
-------------
1. Start PostgreSQL
2. Activate venv
3. Set environment variables
4. Start ingestion
5. Start Ollama if needed

This is not a tutorial.
It’s just the facts.

2. A Simple “Cold Start” Checklist

This is the file you click when your brain is blank.

Example:

~/Desktop/Ougway Startup Checklist.txt

OUGWAY COLD START
=================

1. Start PostgreSQL
   sudo systemctl start postgresql

2. Check database
   pg_lsclusters

3. Activate environment
   source ~/Documents/ougway_venv/bin/activate

4. Run ingestion
   ~/Documents/start_ougway_ingest.sh

5. Verify progress
   tail -f ingest.log

This lets you recover the system even months later.

3. One Launcher Script

Instead of remembering commands, you run one script.

Example:

~/Documents/start_ougway_ingest.sh

#!/bin/bash

source ~/Documents/ougway_venv/bin/activate

export HF_HOME=/mnt/storage/hf-cache
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1
export HF_HUB_DISABLE_TELEMETRY=1

export DB_DSN='postgresql:///tokenspace?host=/var/run/postgresql&port=5432'
export ROOT_DIR='/mnt/storage/pile_uncopyrighted'
export MODEL_DIR='/mnt/storage/hf-cache/hub/models--nomic-ai--nomic-embed-text-v1.5/snapshots/e5cf08aadaa33385f5990def41f7a23405aec398'

CUDA_VISIBLE_DEVICES=0 python3 ~/Documents/tokenspace/ingest_pile_v2.py

Make it executable:

chmod +x ~/Documents/start_ougway_ingest.sh

Then future you runs:

~/Documents/start_ougway_ingest.sh

Done.

4. One Status Script (Optional but Powerful)

~/Documents/check_ougway_stack.sh

This can report:

storage mounted
postgres running
model exists
corpus exists
ingestion running

One command tells you the whole machine state.

5. The Philosophy That Keeps Systems Maintainable

Never rely on remembering:

paths
ports
filenames
startup order

Always assume future-you remembers nothing.

Your documentation should be written for the version of you who just woke up from a two-month gap and says:

“What the hell was I doing?”

If that version of you can restart the system in five minutes, the documentation is perfect.

When your ingestion finishes (or we see what it does), we can also tighten the system by:

fixing the resume logic
adding progress checkpoints
optionally letting ingestion auto-run when the machine boots

But for now, what you’re talking about—organized documentation for operations—is exactly the right next step.

Alternate preserved messages

These messages are preserved as alternate/regenerated branches and are not silently merged into the active path.

ChatGPT — 2026-03-08T20:46:39-07:00 · gpt-5-3

It’s probably not hung. It’s likely chewing through a very large first file with almost no intermediate logging.

What your script does is:

look up the doc
count existing chunks
read and re-chunk the whole file anyway
upsert every chunk row anyway
only then decide whether to skip embedding
commit
print the next [ok ...] line

That behavior is right in the script: it logs the existing chunk count, then reads/chunks the file, then runs CHUNK_UPSERT_SQL for every chunk, and only after that does it decide whether to skip re-embedding. [Source-file reference retained for attachment review.] [Source-file reference retained for attachment review.]

So this line:

[file 1] existing chunks for doc_id=2: 40974

means the first document already has 40,974 chunk rows associated with it. Since the script upserts chunk metadata one row at a time before it prints the next progress line, a long silent pause is expected. [Source-file reference retained for attachment review.]

Also, the script is explicitly designed as an “idempotent resume,” but in its current form that only skips embedding, not the metadata upsert work. [Source-file reference retained for attachment review.]

So your instinct is close: it is probably “seeing it already did some,” but then it still spends a long time reprocessing that file’s chunk rows.

Fast checks

In another terminal, watch whether Postgres is alive and the log is moving:

tail -f ~/Documents/tokenspace/ingest.log

and:

ps -fp $(pgrep -f ingest_pile_v2.py)

and optionally:

top

If Python is still burning CPU, it’s working, just slowly.

The real bottleneck

This loop is the expensive part:

for every chunk in the file, run an INSERT ... ON CONFLICT ... DO UPDATE RETURNING chunk_id one row at a time. [Source-file reference retained for attachment review.]

With tens of thousands of chunks on file 1, that can look frozen.

Best fix

You probably want the script changed so that when:

pre_existing_chunks > 0
and FORCE_REEMBED=0

it skips the entire file immediately, instead of re-reading, re-chunking, and re-upserting everything.

In other words, add a fast path right after this line:

pre_existing_chunks = cur.fetchone()[0]

like this:

if pre_existing_chunks > 0 and not FORCE_REEMBED:
    log(f"[file {idx}] skipping file entirely (already ingested)")
    continue

That would make resume behavior actually fast.

What I’d do right now

Stop the run and patch the script, because otherwise it may spend ages re-upserting already-ingested content.

Use this edit in ingest_pile_v2.py immediately after:

pre_existing_chunks = cur.fetchone()[0]
log(f"[file {idx}] existing chunks for doc_id={doc_id}: {pre_existing_chunks}")

Add:

if pre_existing_chunks > 0 and not FORCE_REEMBED:
    log(f"[file {idx}] skipping file entirely (already ingested)")
    continue

That change is consistent with how the script already intends to resume safely, but avoids the expensive metadata rewrite. [Source-file reference retained for attachment review.]

One caution: this assumes the source files have not changed. If they did change and you need metadata/text refreshed, then skipping whole files would miss that.

If you want, I can give you the exact sed command to patch the file without opening an editor.

Restoring Little Ougway Ingestion and Writing a Reproducible Build Guide

Restoring Little Ougway Ingestion and Writing a Reproducible Build Guide

Provenance

Conversation

Darren — 2026-03-08T19:06:37-07:00

ChatGPT — 2026-03-08T19:06:40-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:09:47-07:00

ChatGPT — 2026-03-08T19:09:51-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:12:34-07:00

ChatGPT — 2026-03-08T19:12:36-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:14:02-07:00

ChatGPT — 2026-03-08T19:14:03-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:15:16-07:00

ChatGPT — 2026-03-08T19:15:17-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:16:43-07:00

ChatGPT — 2026-03-08T19:16:44-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:18:08-07:00

ChatGPT — 2026-03-08T19:18:10-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:21:53-07:00

ChatGPT — 2026-03-08T19:21:54-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T19:23:03-07:00

ChatGPT — 2026-03-08T19:23:03-07:00 · gpt-5-3

1️⃣ Why source failed earlier

2️⃣ The random UUID-looking number

3️⃣ The real issue still blocking ingestion

If you see 5432

If you see down

4️⃣ Clean startup script (recommended)

5️⃣ One thing I want to confirm before you run again

Darren — 2026-03-08T19:29:46-07:00

ChatGPT — 2026-03-08T19:29:46-07:00 · gpt-5-3

If it still shows down

Quick health check

One thing to adjust

Optional but recommended (so you never have to start it manually)

So your quick recovery sequence right now is:

Darren — 2026-03-08T19:31:35-07:00

ChatGPT — 2026-03-08T19:31:37-07:00 · gpt-5-4-thinking

1. Start PostgreSQL

2. Export the database DSN before launching ingestion

Why this happened

Fastest path now

Darren — 2026-03-08T20:46:31-07:00

ChatGPT — 2026-03-08T20:46:41-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T20:48:57-07:00

ChatGPT — 2026-03-08T20:48:58-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T20:50:18-07:00

ChatGPT — 2026-03-08T20:50:19-07:00 · gpt-5-4-thinking

Boot order you’ll want nailed down

Best long-term setup

What I would build next, once you’re ready

Why not auto-run ingestion immediately at boot

What you’re really aiming for

Darren — 2026-03-08T20:51:19-07:00

ChatGPT — 2026-03-08T20:51:20-07:00 · gpt-5-4-thinking

On the desktop

What goes in 00_README_FIRST.txt

Why this helps

Best next build

The status script should report

Important design choice

The right order from here

Darren — 2026-03-08T20:52:12-07:00

ChatGPT — 2026-03-08T20:52:13-07:00 · gpt-5-4-thinking

Darren — 2026-03-08T20:52:51-07:00

ChatGPT — 2026-03-08T20:52:52-07:00 · gpt-5-3

1. One “Single Source of Truth” File

2. A Simple “Cold Start” Checklist

3. One Launcher Script

4. One Status Script (Optional but Powerful)

5. The Philosophy That Keeps Systems Maintainable

Alternate preserved messages

ChatGPT — 2026-03-08T20:46:39-07:00 · gpt-5-3

Fast checks

The real bottleneck

Best fix

What I’d do right now

1️⃣ Why `source` failed earlier

If you see `5432`

If you see `down`

What goes in `00_README_FIRST.txt`