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炼金术师华华
97c1f7a021
* File structure for supporting litellm * more litellm support * feat: Add CachedAPIBackend class and dynamic API backend retrieval function * fix: update benchmark folder path and add default values for architecture and hyperparameters * feat: add LiteLLMAPIBackend and DeprecBackend ; changed structure of the project ; with bus * fix : deprec_backend * feat: Add LiteLLMAPIBackend class and related features; update configuration and test cases. * feat: Enhance LiteLLMAPIBackend with encoder support and dynamic argument handling;Enhance log Colors * lint * fix lint... * fix: Lint * fix:make auto-lint * fix:test oai * fix:redundant _abckend.py * fix: Optimize LiteLLMAPIBackend on token counting functiona, and clean up unused code;add test on this function * feat: Add LiteLLMSettings class and update model settings usage * fix: Update LiteLLMSettings environment variable prefix and model configurations * fix : gitignore * test: Consolidate and relocate test files for litellm backend and oai * fix : lint * fix: lint * auto lint * lint * LINT * lint * chore: remove deprecated backend configuration comments * refactor: Remove unused functions and imports from deprec.py and llm_utils.py * refactor: Move md5_hash function from deprec.py to llm_utils.py * chore: Remove extra newline and add missing import in deprec.py * lint * refactor: Move md5_hash function to utils module * lint * lint * lint --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Yihua Chen <v-yihuachen@microsoft.com>
Tasks
Task Extraction
From paper to task.
# python rdagent/app/model_implementation/task_extraction.py
# It may based on rdagent/document_reader/document_reader.py
python rdagent/components/task_implementation/model_implementation/task_extraction.py ./PaperImpBench/raw_paper/
Complete workflow
From paper to implementation
# Similar to
# rdagent/app/factor_extraction_and_implementation/factor_extract_and_implement.py
Paper benchmark
# TODO: it does not work well now.
python rdagent/app/model_implementation/eval.py
TODO:
- Create reasonable benchmark
- with uniform input
- manually create task
- Create reasonable evaluation metrics