- Implemented parser for executing AVAP files within a Docker container (parser v1.py).
- Created a script to send AVAP code to a local server and handle responses (parser v2.py).
- Introduced a mock MBAP test harness to validate AVAP code against expected outputs (mbap_tester.py).
- Added transformation logic to convert AVAP code into Python-like syntax for testing purposes.
- Enhanced error handling and output formatting in the testing harness.
- 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 `export_documents` function to save processed documents to JSON.
- Extended `ElasticHandshake` to include chunk metadata during ingestion.
- Updated `process_documents` to include extra metadata for each chunk.
- Modified `ingest_documents` to return Elasticsearch responses for further processing.
- Adjusted `elasticsearch_ingestion` command to accept output path for exported JSON.
- 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.
Rafael Ruiz
acano
pseco