fecf6decfb
* base requirements * autofix * Configure ruff for Python linting and formatting - Set up minimal ruff configuration with basic checks (E, W, F, I) - Add temporary ignores for common issues during migration - Configure pre-commit hooks to use ruff with pass_filenames - This enables gradual migration from black to ruff * Delete sdj * autofixed only * migrate lint action * more autofixed * more fixes * change precommit * try changing the hook * try this stuff
Nested K/V (nested_kv_task)
This task runs K/V lookups on synthetic data. You can run it with icml_experiments/nested_kv_task/run.sh.
Document Q/A (doc_qa_task)
This task runs question answering on a set of embedded wikipedia passages.
Setup
You need a a running postgres database to run this experiment and an OpenAI account. Set your enviornment variables:
export PGVECTOR_TEST_DB_URL=postgresql+pg8000://{username}:{password}@localhost:8888/{db}
export OPENAI_API_KEY={key}
Download data
Download the wikipedia embedding at:
huggingface-cli download nlpkevinl/wikipedia_openai_embeddings --repo-type dataset
Loading embeddings
Run the script ./0_load_embeddings.sh.
This step will take a while. You can check the status of the loading by connecting to psql:
> psql -h localhost -p {password} -U {username} -d {db}
> SELECT COUNT(*) from letta_passages;
Once completed, there will be ~19 million rows in the database.
Creating an index
To avoid extremeley slow queries, you need to create an index:
CREATE INDEX ON letta_passages USING hnsw (embedding vector_l2_ops);
You can check to see if the index was created successfully with:
> SELECT indexname, indexdef FROM pg_indexes WHERE tablename = 'letta_passages';
letta_passages_embedding_idx | CREATE INDEX letta_passages_embedding_idx ON public.letta_passages USING hnsw (embedding vector_cosine_ops) WITH (m='24', ef_construction='100')
Running Document Q/A
Run the script ./1_run_docqa.sh {model_name} {n_docs} {letta/model_name}.
Evaluation
Run the script ./2_run_eval.sh.