Text-Generator.io

Text Generator is a system for; * Balancing multiple models on the disk, RAM and GPU * Serving AI APIs via swapping in AI Networks. * Using data enrichment (OCR, crawling, image analysis to make prompt engineering easier) * Generating speech and text. * Understanding text and speech (speech to text with NVIDIA Parakeet).

Text generator can be used via API or self hosted.

Text generator balances multiple 7B models to generate text.

Text generator also enriches web links with text summaries.

If a prompt contains links to images they are converted to text using captioning and if necessary OCR.

Please support us!

You can support us by purchasing unlimited API use on text-generator io

Also checkout our other projects: Netwrck AI Social chat character network/art generator. Art Generator: AIArt-Generator.art AI Data Analyst: Helix.app.nz

Coming soon:

• Add support for other models and modalities like stable diffusion (done in https://github.com/netwrck/stable-diffusion-server)
• Train a classifier to first detect which model is best to use for a given piece of text.
• Add support/passthroughs to other models like ChatGPT and Palm.

API

Text Generator is API compatible with OpenAI (but not the ChatGPT API yet)

There's also more control of text generation via the Text-generator API, this includes;

• Early stopping based on probability (fast autocomplete the next likely parts of text)
• max_sentences (generate only a set number of sentences at most)

Text generator also has routes for speech to text and speech generation.

See https://text-generator.io/docs

Clone

cd
mkdir code
cd code
git clone 20-questions

Local dev setup

Env vars:

GOOGLE_APPLICATION_CREDENTIALS=$HOME/code/20-questions/secrets/google-credentials.json;
PYTHONPATH=$HOME/code/20-questions:$HOME/code/20-questions/OFA

machine setup without docker

install requirements

sudo apt install -y ffmpeg
sudo apt install -y tesseract-ocr
sudo apt install -y python3.9-distutils

pip install -r requirements.txt
pip install -r questions/inference_server/model-requirements.txt
pip install -r dev-requirements.txt
pip install -r requirements-test.txt

Using cuda is important to speed up inference.

python -m nltk.downloader punkt

Running offline integration tests

Offline integration tests exercise functionality that does not require internet access but may load heavy dependencies. After installing the punkt dataset you can run them with:

pytest -m "integration and not internet"

Set up some environment variables in this file (fake ones are okay for local dev)

mv sellerinfo_faked.py sellerinfo.py

Helper Makefile targets

Common development tasks are wrapped in the Makefile. Useful commands include:

make install          # install requirements using uv
make coverage         # run unit tests with coverage output
make ruff-fix         # run ruff with automatic fixes
make download-punkt   # download the punkt dataset for NLTK

Models

Download models from huggingface.

huggingface-cli download HuggingFaceTB/SmolLM3-3B --local-dir models/SmolLM3-3B
wget -P models https://huggingface.co/geneing/Kokoro/resolve/f610f07c62f8baa30d4ed731530e490230e4ee83/kokoro-v0_19.pth

there CAN be three models placed:

models/tg a general model accessible with model=multilingual models/tgz an instruct model accessible with model=instruct models/tgc a chat model accessible with model=chat

model=best is configured to figure out which model to use based on the prompt being scored based on perplexity of each model.

This needs tuning for the avg and std deviation of the perplexity as each model has different ideas about how confidenti it is. Overtrained models are more confident about all text being in the dataset (tend to generate text verbatim from the dataset).

This model based choosing is legacy now and superceeded by MoE models which are reccomended instead.

models can be pointed to using environment variables, e.g. using models from hugginface instead for testing

WEIGHTS_PATH_TGZ=bigscience/bloomz
WEIGHTS_PATH_TGC=decapoda-research/llama-7b-hf
WEIGHTS_PATH=bigscience/bloom

Other Models

The embedding model is a smaller model.

huggingface-cli download answerdotai/ModernBERT-base --local-dir models/ModernBERT-base

Generate embeddings from the command line with:

python scripts/embed_cli.py "Hello world"

Parakeet ASR models will be loaded on demand and placed in the huggingface cache.

Run

run the UI

uvicorn main:app --reload --workers=1
# or
uvicorn  -k uvicorn.workers.UvicornWorker -b :3004 main:app --timeout 60000 -w 1

Alternatively:

SERVER_SOFTWARE=Development/dev gunicorn -k uvicorn.workers.UvicornWorker -b :3004 main:app --timeout 60000 -w 1

local docker run

Docker building

Text Generator can be ran locally without docker.

install nvidia-docker2

sudo apt-get install nvidia-docker2

Text Generator is built with buildx

DOCKER_BUILDKIT=1 docker buildx build . -t questions

sudo docker run -v $(pwd)/models:/models -p 9000:8080 questions

app engine server run

The frontend API playground is available at https://text-generator.io and written for Google App Engine.

Run locally:

gunicorn -k uvicorn.workers.UvicornWorker -b :3030 main:app

inference server run

 PYTHONPATH=$(pwd):$PYTHONPATH:$(pwd)/OFA gunicorn -k uvicorn.workers.UvicornWorker -b :3030 questions.inference_server.inference_server:app

inference server run no docker with web server

PYTHONPATH=$(pwd):$(pwd)/OFA GOOGLE_APPLICATION_CREDENTIALS=secrets/google-credentials.json gunicorn -k uvicorn.workers.UvicornWorker -b :9080 questions.inference_server.inference_server:app --timeout 180000 --workers 1
PYTHONPATH=$HOME/code/20-questions:$HOME/code/20-questions/OFA:$HOME/code/20-questions/OFA/fairseq GOOGLE_APPLICATION_CREDENTIALS=secrets/google-credentials.json gunicorn -k uvicorn.workers.UvicornWorker -b :9080 questions.inference_server.inference_server:app --timeout 180000 --workers 1

Then go to localhost:9080/docs to use the API

Speech To Text Endpoint

The API supports transcription of audio via the /api/v1/audio-extraction and /api/v1/audio-file-extraction routes. Example usage with curl:

curl -X POST "http://localhost:9080/api/v1/audio-extraction" \
     -H "Content-Type: application/json" \
     -d '{"audio_url": "AUDIO_URL", "translate_to_english": false}'

run audio server only

Just the Parakeet speech to text part. This isn't required as the inference server automatically balances these requests

PYTHONPATH=$(pwd):$(pwd)/OFA GOOGLE_APPLICATION_CREDENTIALS=secrets/google-credentials.json gunicorn -k uvicorn.workers.UvicornWorker -b :9080 audio_server.audio_server:app --timeout 180000 --workers 1

Testing

GOOGLE_APPLICATION_CREDENTIALS=secrets/google-credentials.json;PYTHONPATH=$HOME/code/20-questions:$HOME/code/20-questions/OFA pytest

Docker

Download Self Hosted Server

Direct Docker Image Download

Docker Container .tar Download

Container download script

curl https://static.text-generator.io/static/resources/download_container.sh | bash

After downloading the container with either method, proceed to follow the self host instructions available for Kubernetes, Docker

Kubernetes Deployment

See https://text-generator.io/self-hosting

Ensure tested docker locally/built one

You can setup kubernetes locally with kind if doing local kubernetes development.

k delete -f kuber/prod/deployment-gpu.yaml
k apply -f kuber/prod/deployment-gpu.yaml
k get pods

Debugging docker

Run a shell in docker container

docker run -i -t -u root -v $(pwd)/models:/models --entrypoint=/bin/bash questions -c /bin/bash;

running prodish: webapp: DEV=False gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8083 --timeout 300 AI server:

new models

clone from huggingface

cd models
git clone https://huggingface.co/answerdotai/ModernBERT-base

maintenence

dev you can just use one .venv and install all in there to save space although there are multiple services (main.py and the inference server) uv pip install -r questions/inference_server/requirements.in --no-build-isolation

run a discord bot

PYTHONPATH=$(pwd):$(pwd)/OFA python questions/disbot/disbot.py

compile dependencies

use uv pip to compile the dependencies

uv pip compile questions/inference_server/model-requirements.in    --universal -o questions/inference_server/model-requirements.txt

uv pip sync questions/inference_server/model-requirements.txt

remember to stretch!

stretch your body every 30 mins with the say command...

watch -n 1800 'echo "stretch your body" | espeak -s 120'

PostgreSQL Database

The application uses PostgreSQL for user authentication and document storage. Here are useful commands for database management and inspection:

Database Setup

Start PostgreSQL with Docker:

docker-compose -f docker-compose-postgres.yml up -d

Run database migrations:

alembic upgrade head

Useful SQL Commands

Connect to the database:

psql -h localhost -U postgres -d textgen

View all users:

SELECT id, email, created_at, is_active, stripe_id FROM users ORDER BY created_at DESC LIMIT 10;

Count total users:

SELECT COUNT(*) FROM users;

View recent user registrations:

SELECT email, created_at FROM users WHERE created_at > NOW() - INTERVAL '7 days' ORDER BY created_at DESC;

View users with Stripe subscriptions:

SELECT email, stripe_id, created_at FROM users WHERE stripe_id IS NOT NULL ORDER BY created_at DESC;

View all documents:

SELECT id, title, user_id, created_at FROM documents ORDER BY created_at DESC LIMIT 10;

Count documents by user:

SELECT u.email, COUNT(d.id) as document_count
FROM users u
LEFT JOIN documents d ON u.id = d.user_id
GROUP BY u.id, u.email
ORDER BY document_count DESC;

View recent document activity:

SELECT d.title, u.email, d.created_at, d.updated_at
FROM documents d
JOIN users u ON d.user_id = u.id
WHERE d.created_at > NOW() - INTERVAL '24 hours'
ORDER BY d.created_at DESC;

Check database schema:

\dt  -- List all tables
\d users  -- Describe users table
\d documents  -- Describe documents table

Database Migration Commands

Create a new migration:

alembic revision --autogenerate -m "Description of changes"

Apply migrations:

alembic upgrade head

View migration history:

alembic history --verbose

Rollback to previous migration:

alembic downgrade -1

Data Migration Scripts

Migrate users from NDB to PostgreSQL:

python migrate_users_to_postgres.py

Migrate documents from NDB to PostgreSQL:

python migrate_to_postgres.py

Sync users from Google Cloud Datastore:

python sync_users_datastore.py

Test Database Sharing

Run the test script to verify all servers can access the same PostgreSQL database:

python test_database_sharing.py