- Implemented `parser v1.py` to run AVAP files in a Docker container using subprocess.
- Created `parser v2.py` to send AVAP code to a local server and handle JSON responses.
- Introduced `mbap_tester.py` as a heuristic mock executor for testing AVAP code against predefined test cases.
- Added functions for transforming AVAP code to Python and executing it in a controlled environment.
- Included error handling and summary reporting for test results in `mbap_tester.py`.
- Added output path parameter to elasticsearch_ingestion command for exporting processed documents.
- Implemented ElasticHandshakeWithMetadata class to preserve chunk metadata during ingestion.
- Updated process_documents function to include extra metadata for each chunk.
- Modified ingest_documents function to return Elasticsearch response for each chunk.
- Introduced export_documents function to save processed documents as JSON files.
- 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.
- Implemented code to utilize OllamaEmbeddings for embedding documents.
- Included example usage with sample text inputs.
- Demonstrated response handling from the Ollama LLM.
- Noted deprecation warning for the Ollama class in LangChain.
- 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 a new script `translate_mbpp.py` to generate synthetic datasets using various LLM providers.
- Integrated the `get_prompt_mbpp` function in `prompts.py` to create prompts tailored for AVAP language conversion.
- Implemented `replace_javascript_with_avap` function to handle text replacement.
- Created `read_concat_files` function to read and concatenate files with a specified prefix, replacing JavaScript markers.
- Added functionality to read files from a specified directory and process their contents.
- Introduced sections on Persistence, Connectors, and Native ORM, detailing the avapConnector, ORM commands, and data access abstraction.
- Documented System Utilities and Transformation, covering time management, string manipulation, and security operations.
- Explained Function Architecture and Scopes, including function definition, invocation, and middleware usage.
- Provided a Master Example that integrates various sections to demonstrate practical application.
- Detailed the dynamic nature of AVAP™ as a programming language, including dynamic typing and memory management.
- Established notation conventions and lexical analysis processes for code clarity and structure.
- Outlined data types and structures available in AVAP™, emphasizing their usage in program development.
- Discussed variable management, including local and global variables, and best practices for comments.
- Explained expressions in AVAP™, including types, operators, and practical examples with lists.
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