v0.4.0

• Backends
  • GoogleGenAIAPI: Set httpx connection limits according to
    threadpool_size to allow higher throughput. Handled safety filter
    blocks more explicitly, raising a specific error. Removed redundant
    @caching.cache_method decorators from generate_text and chat
    methods to avoid double caching. Automatically retry on empty responses
    caused by safety filters. Refactored generate_text and chat to use
    generate_content as the base implementation. Added support for
    output_dimensionality and task_type in llm.embed.
  • Gemini API: Updated default embedding model name, as
    models/embedding-001 is deprecated.
• Core
  • Caching: Made SimpleCache.cache_value asynchronous for
    non-blocking I/O. Improved printing of cache summaries in colab_utils,
    including support for TwoLayerCaches and sorting keys. Made cache read
    resilient to empty file reads caused by race conditions. Added a generic
    TwoLayerCache implementation.
  • Execution & Parallelism: Optimized executing.parallel to reduce
    overhead for nested and fixed-size sequences using asyncio.gather
    directly in a fast path. Allowed changing the default value of
    max_parallel_executions globally. Propagated context variables in
    run_method_in_threadpool. Used ContextualExecutor instead of
    ThreadPoolExecutor to ensure context propagation for debugging tools
    like Sherlog. Propagated OneTwo tracers across async boundaries in
    batching.py. Improved exception handling and reporting within the
    tracing system.
• Agents
  • ReAct: Added option retry_on_parsing_error to provide more
    detailed error messages on action parsing failures, including the
    thought and failed action string to help the LLM self-correct.
• Standard library
  • Retrieval & QA: Added a comprehensive suite of modules under
    third_party/py/onetwo/stdlib/retrieval and
    third_party/py/onetwo/stdlib/qa to support building advanced
    Retrieval-Augmented Generation (RAG) and Question Answering systems. Key
    features include:
    • Core Interfaces: Introduced fundamental interfaces for RAG like
      Retriever, Index, Searcher, CorpusRewriter, Chunker and
      DocumentFormatter.
    • Data Structures: Added the Document dataclass for representing
      content to be indexed and retrieved.
    • Indexing: Introduced various index types: in particular
      EmbeddingBasedIndex, RewritingIndex.
    • CorpusRewriter: CorpusRewriter interface and implementations
      to enable flexible document processing pipelines before indexing.
      Indexes leverage the CorpusRewriter abstraction for more general
      transformations.
    • Chunking: Provided implementations like TextChunker,
      NoChunking, and ChunkByMaxTokens for splitting documents.
    • Formatter: Provided implementations for formatting documents.
    • Searcher: Added BruteForceSearcher for nearest neighbor search
      between embeddings.
    • Constrained Retrieval: Introduced RetrievalConstraint classes
      and the ConstrainedRetriever and ConstrainedSearcher protocols
      to enable efficient pre-filtering of documents based on metadata
      fields before vector search.
    • Index Serialization: Added EmbeddingBasedIndexState to store
      the state of EmbeddingBasedIndex and serializers for persisting
      and loading index data.
    • QA Strategies: Defined interfaces for question answering,
      including QAStrategy, ContextualQAStrategy, and
      RetrievalQAStrategy.
• Colabs
  • Introduced a Getting Started tutorial.
  • OneTwo tutorial: Demonstrated use of llm.embed() for multimodal
    contents.
  • Introduced a RAG Tutorial.
• Testing
  • Modified LLMForTest tokenizer and added detokenizer for better
    testing.
• Evaluation
  • Tracing: Improved tracing for metrics in metrics.py and
    evaluation.py.
  • ot.evaluate: Unified evaluation and agent_evaluation modules
    into onetwo.evaluation.evaluation. Made ot.evaluate more generic to
    support arbitrary example types and strategy signatures. Added
    dataset_name and dataset_description parameters to evaluate.