- Created `candidate_F_reward_10_coverage_stats.json` with coverage statistics including total cells, filled cells, fill rate, and node type frequency.
- Added `mbpp_avap.json` containing 14 tasks with descriptions, code implementations, test inputs, and expected test results for various endpoints and functionalities.
- Created `n00 Beir Analysis_cosqa.ipynb` for analyzing CoSQA dataset with BEIR.
- Created `n00 first Analysis.ipynb` for initial analysis with embeddings.
- Implemented `evaluate_embeddings_pipeline.py` to evaluate embedding models across CodexGlue, CoSQA, and SciFact benchmarks.
- Added adapters for Ollama and HuggingFace embeddings to ensure compatibility with BEIR.
- Enhanced error handling and data normalization in embedding processes.
- Included functionality to load datasets from local cache or download if not present.
- Introduced a new notebook for generating synthetic datasets for AVAP, including loading AVAP and MBPP data, and creating prompts for LLM interactions.
- Created `n00 Beir Analysis.ipynb` for analyzing BEIR dataset with Ollama embeddings.
- Added `n00 Beir Analysis_cosqa.ipynb` for evaluating the CosQA dataset using similar methods.
- Introduced `n00 first Analysis.ipynb` for initial analysis with Ragas embeddings and semantic similarity evaluation.
- Implemented data loading, processing, and evaluation metrics for each notebook.
- Included functionality to save results to JSON files for further analysis.
- Created `n00 Beir Analysis.ipynb` for analyzing BEIR dataset with Ollama embeddings.
- Added `n00 Beir Analysis_cosqa.ipynb` for evaluating the CosQA dataset using similar embedding techniques.
- Introduced `n00 first Analysis.ipynb` for initial analysis with Ragas embeddings and semantic similarity evaluation.
- Implemented data loading and processing for each notebook, including downloading datasets and saving results.
- Included evaluation metrics such as NDCG, MAP, Recall, and Precision for model performance assessment.
- Changed Elasticsearch index from "avap-docs-test-v3" to "avap-docs-test-v4" in elasticsearch_ingestion.py.
- Removed unused import SystemMessage from langchain_core.messages in translate_mbpp.py.
- Added import for Lark in chunk.py to support new functionality.
- 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.
pseco
acano
rafa-ruiz