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Python Quick Start

This guide walks you from installation to your first deterministic workflows in Python. If you're targeting SQL-first scenarios, head to the Postgres Quick Start.

1. Prerequisites

  • Python >= 3.10 (3.11+ recommended)
  • RAM: 4 GB minimum (8 GB+ for larger models)
  • Disk: 5–15 GB for cached models
  • OS: Linux, macOS, or Windows

2. Install the SDK

curl -LsSf https://astral.sh/uv/install.sh | sh
uv add steadytext

pip install steadytext

poetry add steadytext

First model download

On first use SteadyText downloads roughly 5 GB of models to ~/.cache/steadytext/models/ (or %LOCALAPPDATA%\\steadytext\\steadytext\\models\\ on Windows). Downloads are cached for future runs.

3. Optional: Start the Daemon

The background daemon keeps models warm and shared between processes. You can skip this step, but your first request will be slower.

st daemon start
st daemon status  # confirm it's running

Shut it down with st daemon stop when you're done.

4. First Deterministic Generation

import steadytext

code = steadytext.generate("Implement binary search in Python")
assert "def binary_search" in code

variant = steadytext.generate(
    "Implement binary search in Python",
    seed=1234,  # choose any integer seed
)
  • Same prompt + seed ⇒ same output on any machine.
  • Different seeds ⇒ controlled, reproducible variations for tests and reviews.

Streaming uses the same deterministic guarantees:

for token in steadytext.generate_iter("Explain dynamic programming", seed=42):
    print(token, end="", flush=True)

5. Deterministic Embeddings

vector = steadytext.embed("Hello world")
print(vector.shape)  # (1024,)

pair = steadytext.embed(["coffee", "espresso"])

Embeddings also respect seeds:

vec_a = steadytext.embed("release checklist", seed=100)
vec_b = steadytext.embed("release checklist", seed=100)
assert (vec_a == vec_b).all()

Use CLI helpers for quick experiments:

echo "Explain retrievers" | st --seed 7
echo "incident report" | st embed --seed 99

6. Structured Outputs & Validation

from pydantic import BaseModel
from steadytext import generate

class Ticket(BaseModel):
    title: str
    severity: str

result = generate(
    "Create a sev2 incident summary about retries failing",
    schema=Ticket,
)
print(result)  # deterministic JSON payload

Key follow-ups:

7. Reranking & Retrieval Workflows

from steadytext import rerank

docs = ["Reset password steps", "SSL handshake failure", "CORS issue"]
ranked = rerank("TLS handshake failed", docs)

Combine embeddings, indexes, and reranking to build full retrieval pipelines. See:

8. Keep Going

Let us know what you build—anchor your questions with AIDEV-ANCHOR: references for faster feedback. variations = []

for i, style_seed in enumerate([300, 400, 500], 1): variation = steadytext.generate(base_prompt, seed=style_seed) variations.append(f"Version {i}: {variation}")

for variation in variations: print(variation[:80] + "...\n") 

## PostgreSQL Integration

SteadyText now includes a PostgreSQL extension:

```bash
# Install the PostgreSQL extension
git clone https://github.com/julep-ai/steadytext.git
cd steadytext/pg_steadytext
make && sudo make install

# Enable in PostgreSQL
psql -c "CREATE EXTENSION pg_steadytext CASCADE;"


-- Use in SQL queries
SELECT steadytext_generate('Write a product description', max_tokens := 200, seed := 123);

-- Generate embeddings
SELECT steadytext_embed('machine learning', seed := 456);

-- Semantic search with pgvector
SELECT title, content <-> steadytext_embed('AI technology') AS distance
FROM documents
ORDER BY distance
LIMIT 5;

Next Steps

Common Issues and Solutions

Model Loading Errors

If you see "Failed to load model": 

# Use fallback models
export STEADYTEXT_USE_FALLBACK_MODEL=true

# Or clear model cache and re-download
rm -rf ~/.cache/steadytext/models/

llama-cpp-python Build Issues

If installation fails with llama-cpp-python errors: 

# Set required build flags
export FORCE_CMAKE=1
export CMAKE_ARGS="-DLLAVA_BUILD=OFF -DGGML_ACCELERATE=OFF -DGGML_BLAS=OFF -DGGML_CUDA=OFF"

# Then reinstall
pip install --force-reinstall steadytext

Daemon Connection Issues

# Check if daemon is running
st daemon status

# Start daemon if not running
st daemon start

# Or disable daemon and use direct loading
export STEADYTEXT_DISABLE_DAEMON=1

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