Changelog

All notable changes to ragit are documented here.

Version 0.7.4 (Current)

Performance Optimizations

  • Added HTTP connection pooling via requests.Session() for faster sequential requests

  • Added async parallel embedding via embed_batch_async() using trio + httpx (5-10x faster for large batches)

  • Added LRU cache for embeddings (2048 entries) to avoid redundant API calls

  • Added use_cache parameter to OllamaProvider (default: True)

  • Added clear_embedding_cache() and embedding_cache_info() static methods

  • Added close() method for explicit resource cleanup

New Dependencies

  • Added trio>=0.24.0 for async concurrency

  • Added httpx>=0.27.0 for async HTTP client

Code Quality

  • 166 tests with 90%+ coverage

Version 0.7.1

Performance Optimizations

  • Pre-normalized embedding matrices for O(1) cosine similarity

  • Batch embedding API calls instead of individual calls

  • Efficient top-k selection using numpy.argpartition

  • Immutable EmbeddingResponse with tuple embeddings

Code Quality

  • Full mypy –strict compliance

  • 94% test coverage with 150 tests

  • Thread-safety documentation for non-thread-safe classes

Version 0.7.0

New Features

  • High-level RAGAssistant class for document Q&A

  • Document loading utilities (load_text, load_directory)

  • Multiple chunking strategies (overlap, separator, RST sections)

  • Code generation with generate_code()

API Changes

  • EmbeddingResponse.embedding is now a tuple (immutable)

  • Added embed_batch() for efficient batch embeddings

Version 0.4.0

New Features

  • RagitExperiment for hyperparameter optimization

  • Grid search over chunk sizes, overlaps, and retrieval parameters

  • Evaluation metrics: answer correctness, context relevance, faithfulness

Improvements

  • Better error handling in provider classes

  • Progress bars for long-running operations

Version 0.3.0

New Features

  • Ollama provider for LLM and embedding operations

  • Environment variable configuration

  • Support for cloud Ollama providers

Version 0.2.0

Initial Release

  • Core experiment framework

  • Document and chunk data structures

  • Basic vector store implementation

  • Evaluation result classes

Versioning

ragit follows Semantic Versioning:

  • MAJOR: Incompatible API changes

  • MINOR: New functionality, backwards compatible

  • PATCH: Bug fixes, backwards compatible

Deprecation Policy

  • Deprecated features are marked in documentation

  • Deprecated features remain for at least one minor version

  • Removal is announced in changelog

Upgrade Guide

0.6.x to 0.7.x

Breaking Changes

  1. EmbeddingResponse.embedding is now a tuple:

# Before (0.6.x)
embedding = response.embedding  # list[float]

# After (0.7.x)
embedding = response.embedding  # tuple[float, ...]

# If you need a list
embedding_list = list(response.embedding)

  1. embed_batch() is now required for custom providers:

class MyProvider(BaseEmbeddingProvider):
    def embed_batch(self, texts: list[str], model: str) -> list[EmbeddingResponse]:
        # Required implementation
        pass

Migration Steps

  1. Update any code that modifies embeddings (they’re now immutable)

  2. Implement embed_batch() in custom providers

  3. Run tests to verify compatibility