- Updated `elasticsearch_ingestion.py` to streamline document processing and ingestion into Elasticsearch.
- Introduced `generate_mbap.py` for generating benchmark problems in AVAP language from a provided LRM.
- Created `prompts.py` to define prompts for converting Python problems to AVAP.
- Enhanced chunk processing in `chunk.py` to support markdown and AVAP documents.
- Added `OllamaEmbeddings` class in `embeddings.py` for handling embeddings with Ollama model.
- Updated dependencies in `uv.lock` to include new packages and versions.
- Added new dependencies including chonkie and markdown-it-py to requirements.txt.
- Refactored the Elasticsearch ingestion script to read and concatenate documents from specified folders.
- Implemented semantic chunking for documents using the chonkie library.
- Removed the old elasticsearch_ingestion_from_docs.py script as its functionality has been integrated into the main ingestion pipeline.
- Updated README.md to reflect new project structure and environment variables.
- Added a new changelog entry for version 1.4.0 detailing recent changes and enhancements.
- Updated `read_files` function to return a list of dictionaries containing 'content' and 'title' keys.
- Added logic to handle concatenation of file contents and improved handling of file prefixes.
- Introduced `get_chunk_docs` function to chunk document contents using `SemanticChunker`.
- Added `convert_chunks_to_document` function to convert chunked content into `Document` objects.
- Integrated logging for chunking process.
- Updated dependencies in `uv.lock` to include `chonkie` and other related packages.
- Implemented `elasticsearch_ingestion` function to handle document ingestion into Elasticsearch.
- Created `build_chunks_from_folder` function to read and clean text files, generating document chunks.
- Added logging for better traceability during the ingestion process.
- Updated `uv.lock` to include `boto3` as a new dependency.
acano