2025.08.05

This release introduces a major new feature: local, offline AI-powered text embeddings. By integrating the multilingual-e5-small model, DocWire can now generate high-quality vector representations for text in over 100 languages, enabling advanced NLP tasks like semantic search and RAG. This update also includes a significant dependency modernization, replacing OpenNMT-Tokenizer with Google's SentencePiece, and numerous build and stability fixes, particularly for MSVC and Valgrind.

A new dimension, in vectors bright,
Where words find place, and meanings take flight.
With models sharp and logic so keen,
A deeper understanding, clearly seen.
From text to numbers, a seamless art,
DocWire now reads the document's heart.
✨🧠🔢

• Features

  • Local AI Embeddings: Introduced a powerful new local_ai::embed chain element to generate high-quality, multilingual text embeddings using the multilingual-e5-small model. This enables advanced NLP tasks like semantic search, RAG, and text clustering to be performed entirely offline.
  • Cosine Similarity Function: Added a cosine_similarity utility function to calculate the similarity between two embedding vectors, making it easy to compare documents and queries.
  • Public Tokenizer API: The local_ai::tokenizer class, now powered by Google's SentencePiece, is exposed as a public API. It includes an encode() method to convert text into token IDs, supporting different tokenizer models like T5Tokenizer and XLMRobertaTokenizer.

• Improvements

  • Unified Model Runner: The local_ai::model_runner has been enhanced to dynamically load and manage both sequence-to-sequence (Translator) and encoder-only (Encoder) models, enabling it to handle both text generation and embedding tasks within a single class.
  • Advanced Pooling and Normalization: Implemented mean pooling over token outputs and L2 normalization for the embedding model to ensure high-quality, standardized vectors as required by models like E5.
  • Simplified AI Chain Element: Added a new convenience constructor to local_ai::model_chain_element that uses a default model, simplifying its usage in common scenarios.
  • CLI Enhancements: The command-line interface now supports embedding generation via the --local-ai-embed option.

• Refactor

  • Dependency Modernization: Replaced the OpenNMT-Tokenizer dependency with a direct integration of Google's SentencePiece library, reducing complexity and aligning with modern NLP tooling.

• Fixes

  • Build (MSVC): Resolved Address Sanitizer (ASan) linking errors on MSVC by adding _DISABLE_STRING_ANNOTATION and _DISABLE_VECTOR_ANNOTATION definitions.
  • Build (CI): Increased CI timeouts for Valgrind-based sanitizers (memcheck, helgrind, callgrind) to prevent premature job termination.
  • Build (CI): Disabled resource-intensive local AI tests when running under Callgrind to ensure CI stability.
  • Build (Valgrind): Added suppressions for known memory leaks in the Abseil library to clean up Valgrind reports.

• Documentation

  • New Embedding Example: Added a comprehensive example to README.md demonstrating how to generate embeddings for a document and multiple queries, and then calculate their cosine similarity.

• Tests

  • Added unit tests for the new local_ai::tokenizer to validate its behavior with both flan-t5 and multilingual-e5 models.
  • The new local AI embedding example from README.md is now compiled and executed as part of the automated test suite.