- 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.
- 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.
- Removed network_mode: "host" from docker-compose.yaml for better isolation.
- Updated execution counts in langgraph_agent_simple.ipynb to reflect new cell order.
- Added OLLAMA_LOCAL_URL to imports in langgraph_agent_simple.ipynb.
- Included base_url parameter for create_chat_model and create_embedding_model functions in langgraph_agent_simple.ipynb.
- Added litellm>=1.82.0 to the development dependencies in pyproject.toml.
- Updated uv.lock to include litellm and its dependencies, along with fastuuid package.
acano
pseco
izapata
Rafael Ruiz