- 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.
- 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.
- 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
rafa-ruiz
pseco
izapata