- Changed Elasticsearch index from "avap-docs-test-v3" to "avap-docs-test-v4" in elasticsearch_ingestion.py.
- Added Lark parser for AVAP code processing in chunk.py.
- Enhanced metadata extraction for processed documents, including AST for AVAP files.
- Improved error handling for AVAP code parsing.
- 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.
- 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.
acano
izapata
rafa-ruiz
pseco