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CHANGELOG.md

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CHANGELOG

Unreleased

  • Add new fusion strategies for the hybrid vector store: RRF and DBSF (#413)
  • move sources from ragbits-document-search to ragbits-core (#496)
  • adding connection check to Azure get_blob_service (#502)

0.13.0 (2025-04-02)

  • Make the score in VectorStoreResult consistent (always bigger is better)
  • Add router option to LiteLLMEmbedder (#440)
  • Make LLM / Embedder APIs consistent (#463)
  • New methods in Prompt class for appending conversation history (#480)
  • Fix: make unflatten_dict symmetric to flatten_dict (#461)
  • Cost and capabilities config for custom litellm models (#481)

0.12.0 (2025-03-25)

  • Allow Prompt class to accept the asynchronous response_parser. Change the signature of parse_response method.
  • Fix from_config for LiteLLM class (#441)
  • Fix Qdrant vector store serialization (#419)

0.11.0 (2025-03-25)

  • Add HybridSearchVectorStore which can aggregate results from multiple VectorStores (#412)

0.10.2 (2025-03-21)

0.10.1 (2025-03-19)

  • Better handling of cases when text and image embeddings are mixed in VectorStore

0.10.0 (2025-03-17)

  • BREAKING CHANGE: Vector Stores are now responsible for creating embeddings (#288)
  • Qdrant vector store can now be serialized during Ray processing (#394)
  • Improve cli trace handler
  • Add traceable to some method
  • Add support for images in few shot prompts (#155)
  • Add instruction on how to use local servers for LLMs (#395).
  • Introduce intermediate image elements (#139)
  • Correct typos in doc strings (#398)
  • Enable GPU support and (un)pickling for fastembed embedders (#409).

0.9.0 (2025-02-25)

  • Add support to fastembed dense & sparse embeddings.
  • Rename "default configuration" to "preferred configuration" (#361).
  • Allow to pass str or dict to LLM.generate() (#286)
  • Fix: changed variable type from Filter to WhereQuery in the Qdrant vector store in list method.
  • Rename all embedders to have Embedder in their name (instead of Embeddings).

0.8.0 (2025-01-29)

Added

  • Add support for pgvector as VectorStore (#267).

0.7.0 (2025-01-21)

Added

  • Add nice-looking CLI logging for audit module (#273).
  • Add support for returning metadata from LLMs (#274).

Changed

  • Fix: limiting in qdrant vector store (#282).
  • Refactor: remove LLM client abstraction and lift it up to LLM (#270).

0.6.0 (2024-12-27)

Added

  • Add option to pass LiteLLM router to LLM instances (#262).
  • Add commands to browse vector stores (#244).

Changed

  • Implement generic Options class (#248).
  • Fix LiteLLM crash in python 3.13 (#245).

0.5.1 (2024-12-09)

Changed

  • Refactor: added standardized way to create ragbits objects from config (#233).

0.5.0 (2024-12-05)

Added

  • Default LLM factory when configuration is not provided (#209).
  • Add remove operation to VectorStore (#210).
  • Install litellm package by default (#236).

0.4.0 (2024-11-27)

Added

  • Add support for Qdrant VectorStore (#163).
  • Add streaming interface to LLMs (#188).
  • Better images support in Prompt abstractions (#201).

0.3.0 (2024-11-06)

Added

  • Observability toolset, with initial support to export traces to OpenTelemetry (#168)
  • CLI commands to render / exec prompts (#146)
  • Support of images in Prompt abstractions (#149)
  • Support for different MetadataStores in VectorStore (#144)
  • Now LLMs can be configured separately for vision, text and structured. (#153)

Changed

  • refactor: Add dynamic loading for modules that depend on optional dependencies (#148).
  • refactor: Refactor vector store public API. (#151)

0.2.0 (2024-10-23)

Added

  • Project README.md (#103).
  • Listing entries API for VectorStores (#138).
  • Overrides for prompt discovery configurable in pyproject.toml file (#101).
  • Default LLM factory configurable in pyproject.toml file (#101).

Changed

  • Fixed bug in chromadb while returning multiple records (#117).
  • Fixed bug in prompt rendering for some pydantic models (#137).

0.1.0 (2024-10-08)

Added

  • Initial release of the package.
  • Introduce core components: Prompts, LLMs, Embedder and VectorStores.
  • Prompt class integration with promptfoo.
  • LiteLLM integration.
  • ChromaDB integration.
  • Prompts lab.
  • Prompts autodiscovery.