Version History¶

This document outlines the major versions of SteadyText and the key features introduced in each.

Latest Version: 2.3.0 - Structured Generation

Version	Key Features	Default Generation Model	Default Embedding Model	Python Versions
2.3.x	- Structured Generation: Added support for JSON, Regex, and Choice-constrained generation via `outlines`. - New API parameters: `schema`, `regex`, `choices` added to `generate()`. - New convenience functions: `generate_json()`, `generate_regex()`, `generate_choice()`.	`ggml-org/gemma-3n-E2B-it-GGUF` (gemma-3n-E2B-it-Q8_0.gguf)	`Qwen/Qwen3-Embedding-0.6B-GGUF` (Qwen3-Embedding-0.6B-Q8_0.gguf)	`>=3.10, <3.14`
2.1.x	- Custom Seeds: Added seed parameter to all generation and embedding functions. - PostgreSQL Extension: Released pg_steadytext extension. - Enhanced Reproducibility: Full control over deterministic generation.	`ggml-org/gemma-3n-E2B-it-GGUF` (gemma-3n-E2B-it-Q8_0.gguf)	`Qwen/Qwen3-Embedding-0.6B-GGUF` (Qwen3-Embedding-0.6B-Q8_0.gguf)	`>=3.10, <3.14`
2.0.x	- Daemon Mode: Persistent model serving with ZeroMQ. - Gemma-3n Models: Switched to `gemma-3n` for generation. - Thinking Mode Deprecated: Removed thinking mode.	`ggml-org/gemma-3n-E2B-it-GGUF` (gemma-3n-E2B-it-Q8_0.gguf)	`Qwen/Qwen3-Embedding-0.6B-GGUF` (Qwen3-Embedding-0.6B-Q8_0.gguf)	`>=3.10, <3.14`
1.x	- Model Switching: Added support for switching models via environment variables and a model registry. - Qwen3 Models: Switched to `qwen3-1.7b` for generation. - Indexing: Added support for FAISS indexing.	`Qwen/Qwen3-1.7B-GGUF` (Qwen3-1.7B-Q8_0.gguf)	`Qwen/Qwen3-Embedding-0.6B-GGUF` (Qwen3-Embedding-0.6B-Q8_0.gguf)	`>=3.10, <3.14`
0.x	- Initial Release: Deterministic text generation and embedding.	`Qwen/Qwen1.5-0.5B-Chat-GGUF` (qwen1_5-0_5b-chat-q4_k_m.gguf)	`Qwen/Qwen1.5-0.5B-Chat-GGUF` (qwen1_5-0_5b-chat-q8_0.gguf)	`>=3.10`

Detailed Release Notes¶

Version 2.3.0 - Structured Generation¶

Release Date: July 2025

✨ Structured Generation¶

Major Feature: Introduced structured generation capabilities powered by the Outlines library.

JSON Generation: Generate JSON that conforms to a JSON schema or a Pydantic model.
steadytext.generate(prompt, schema=MyPydanticModel)
steadytext.generate_json(prompt, schema={"type": "object", ...})
Regex-Constrained Generation: Force output to match a regular expression.
steadytext.generate(prompt, regex=r"\d{3}-\d{3}-\d{4}")
Multiple Choice: Force model to choose from a list of options.
steadytext.generate(prompt, choices=["A", "B", "C"])

Use Cases: - Reliable data extraction - Building robust function-calling systems - Creating predictable application logic - Generating structured data for databases

Version 2.1.0+ - Custom Seeds & PostgreSQL Extension¶

Release Date: June 2025

🎯 Custom Seed Support¶

Major Enhancement: Added comprehensive custom seed support across all SteadyText APIs.

Python API: All functions now accept optional seed: int = DEFAULT_SEED parameter
steadytext.generate(prompt, seed=123)
steadytext.generate_iter(prompt, seed=456)
steadytext.embed(text, seed=789)
CLI Support: Added --seed flag to all commands
st generate "prompt" --seed 123
st embed "text" --seed 456
st vector similarity "text1" "text2" --seed 789
Daemon Integration: Seeds are properly passed through daemon protocol
Fallback Behavior: Deterministic fallbacks now respect custom seeds
Cache Keys: Seeds are included in cache keys to prevent collisions

Use Cases: - Reproducible Research: Document and reproduce exact results - A/B Testing: Generate controlled variations of content - Experimental Design: Systematic exploration of model behavior - Content Variations: Create different versions while maintaining quality

🐘 PostgreSQL Extension (pg_steadytext)¶

New Release: Complete PostgreSQL extension for SteadyText integration.

Core Features: - SQL Functions: Native PostgreSQL functions for text generation and embeddings - steadytext_generate(prompt, max_tokens, use_cache, seed) - steadytext_embed(text, use_cache, seed) - steadytext_daemon_start(), steadytext_daemon_status(), steadytext_daemon_stop()

Vector Integration: Full compatibility with pgvector extension
Built-in Caching: PostgreSQL-based frecency cache with eviction
Daemon Support: Integrates with SteadyText's ZeroMQ daemon for performance
Configuration Management: SQL-based configuration with steadytext_config table

Installation:

# Install Python dependencies
pip3 install steadytext>=2.1.0

# Build and install 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;"

Docker Support:

# Standard build
docker build -t pg_steadytext .

# With fallback model for compatibility
docker build --build-arg STEADYTEXT_USE_FALLBACK_MODEL=true -t pg_steadytext .

🔧 Technical Improvements¶

Validation: Added validate_seed() function for input validation
Environment Setup: Enhanced set_deterministic_environment() with custom seeds
Error Handling: Improved error messages and fallback behavior
Documentation: Comprehensive documentation and examples

📖 Documentation Updates¶

API Documentation: Updated all function signatures with seed parameters
CLI Reference: Added --seed flag documentation for all commands
Examples: New comprehensive examples for custom seed usage
PostgreSQL Guide: Complete integration guide for pg_steadytext
Migration Guide: Instructions for upgrading from previous versions

🔄 Breaking Changes¶

None - Version 2.1.0+ is fully backward compatible with 2.0.x. All existing code continues to work unchanged, with new seed parameters being optional.

🐛 Bug Fixes¶

Fixed cache key generation to include seed for proper isolation
Improved daemon protocol to handle seed parameters correctly
Enhanced fallback behavior to be deterministic with custom seeds
Resolved edge cases in streaming generation with custom seeds

📋 Requirements¶

Python: 3.10+ (unchanged)
PostgreSQL: 14+ (for pg_steadytext extension)
Dependencies: All existing dependencies remain compatible