diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000..056d62113 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,5 @@ +[run] +source = src +omit = + *_test.py + */__init__.py diff --git a/.eslintrc.json b/.eslintrc.json new file mode 100644 index 000000000..4be52466a --- /dev/null +++ b/.eslintrc.json @@ -0,0 +1,35 @@ +{ + "extends": [ + "eslint:recommended", + // https://khalilstemmler.com/blogs/typescript/eslint-for-typescript/ + "plugin:@typescript-eslint/eslint-recommended", + "plugin:@typescript-eslint/recommended", + "prettier" + ], + "parser": "@babel/eslint-parser", + "plugins": ["prettier"], + "rules": { + "prettier/prettier": ["error"], + "max-len": ["error", {"code": 100}] + }, + "ignorePatterns": ["**/node_modules/**/*", "**/dist/**/*"], + "parserOptions": { + "requireConfigFile": false + }, + "overrides": [ + { + "files": ["*.ts", "*.tsx"], + "parser": "@typescript-eslint/parser", + "plugins": ["@typescript-eslint"], + // See https://github.com/typescript-eslint/typescript-eslint/blob/main/docs/linting/TROUBLESHOOTING.md#i-get-errors-from-the-no-undef-rule-about-global-variables-not-being-defined-even-though-there-are-no-typescript-errors + "rules": { + "no-undef": "off", + "@typescript-eslint/no-non-null-assertion": "off" + } + } + ], + "env": { + "browser": true, + "node": true + } +} diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml new file mode 100644 index 000000000..f408b3c40 --- /dev/null +++ b/.github/workflows/python.yml @@ -0,0 +1,26 @@ +on: + pull_request: + types: [opened, reopened, synchronize] + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Install poetry + run: pipx install poetry + - uses: actions/setup-python@v4 + with: + python-version: '3.9.13' + cache: 'poetry' + - name: Install dependencies + run: | + ./scripts/setup_py.sh + + - name: Lint python + run: | + poetry run ./scripts/lint_py.sh + - name: Test python + run: | + poetry run ./scripts/test_py.sh diff --git a/.github/workflows/typescript.yml b/.github/workflows/typescript.yml new file mode 100644 index 000000000..5da553fb5 --- /dev/null +++ b/.github/workflows/typescript.yml @@ -0,0 +1,31 @@ +on: + pull_request: + types: [opened, reopened, synchronize] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + node-version: [16.x] + + steps: + - uses: actions/checkout@v3 + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v3 + with: + node-version: ${{ matrix.node-version }} + cache: 'npm' + cache-dependency-path: | + package-lock.json + server/package-lock.json + web/package-lock.json + - name: Install dependencies + run: | + ./scripts/setup_ts.sh + - name: Lint and build typescript + run: | + ./scripts/lint_ts.sh + - name: 'Test typescript' + run: | + ./scripts/test_ts.sh diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..daeb6e314 --- /dev/null +++ b/.gitignore @@ -0,0 +1,19 @@ +# Python. +.mypy_cache/ +.venv/ +__pycache__/ +.coverage +cloned_repos/ +.pytest_cache/ +py_coverage_html/ +*.deps.txt + +# Mac OS. +.DS_Store + +# Node. +node_modules/ +dist/ + +# Data. +gcs_cache/ diff --git a/.prettierrc.json b/.prettierrc.json new file mode 100644 index 000000000..04e42cdea --- /dev/null +++ b/.prettierrc.json @@ -0,0 +1,5 @@ +{ + "singleQuote": true, + "printWidth": 100, + "bracketSpacing": false +} diff --git a/.python-version b/.python-version new file mode 100644 index 000000000..21af9507e --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.9.13 diff --git a/.style.yapf b/.style.yapf new file mode 100644 index 000000000..a5aa5a434 --- /dev/null +++ b/.style.yapf @@ -0,0 +1,4 @@ +[style] +based_on_style = google +column_limit = 100 +indent_width = 2 diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 000000000..8718ba487 --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,20 @@ +{ + // See https://go.microsoft.com/fwlink/?LinkId=827846 to learn about workspace recommendations. + // Extension identifier format: ${publisher}.${name}. Example: vscode.csharp + // List of extensions which should be recommended for users of this workspace. + "recommendations": [ + "ms-python.python", + "ms-python.vscode-pylance", + "GitHub.vscode-pull-request-github", + "esbenp.prettier-vscode", + "dbaeumer.vscode-eslint", + "clinyong.vscode-css-modules", + "csstools.postcss", + "bungcip.better-toml", + "breadnaught.vscode-ignore", + "MS-vsliveshare.vsliveshare", + "charliermarsh.ruff" + ], + // List of extensions recommended by VS Code that should not be recommended for users of this workspace. + "unwantedRecommendations": [] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..6a191ab1b --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,66 @@ +{ + "search.exclude": { + "**/node_modules": true, + "**/.venv/": true, + "**/.mypy_cache/": true, + "**/dist/": true, + "**/yarn.lock": true, + "**/poetry.lock": true, + "**/package-lock.json": true + }, + "files.trimTrailingWhitespace": true, + "editor.formatOnSave": true, + "editor.tabSize": 2, + "editor.insertSpaces": true, + "editor.rulers": [100], + "files.insertFinalNewline": true, + "editor.detectIndentation": false, + "editor.wrappingIndent": "none", + "files.exclude": { + "**/__pycache__": true, + "**/.venv/": true, + "**/.mypy_cache/": true, + "**/node_modules": true + }, + "typescript.tsdk": "./node_modules/typescript/lib", + "[typescript]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[typescriptreact]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[html]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[json]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[jsonc]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[python]": { + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.fixAll": true, + "source.organizeImports": true + } + }, + "eslint.workingDirectories": ["auto"], + "eslint.validate": ["typescript"], + "python.envFile": "${workspaceFolder}/.venv", + "python.linting.mypyEnabled": true, + "python.formatting.provider": "yapf", + "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", + "git.enableSmartCommit": true, + "git.confirmSync": false, + "git.autofetch": true, + "git.postCommitCommand": "sync", + "python.testing.pytestArgs": ["src"], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true, + "python.analysis.importFormat": "relative", + "python.analysis.indexing": true, + "python.analysis.useLibraryCodeForTypes": true, + "python.analysis.extraPaths": ["src"], + "python.analysis.include": ["src"] +} diff --git a/README.md b/README.md new file mode 100644 index 000000000..aca9027e7 --- /dev/null +++ b/README.md @@ -0,0 +1,133 @@ +# Lilac + +### Dev setup + +Before you start developing, install the following tools: + +- [Install XCode and sign license](https://apps.apple.com/us/app/xcode/id497799835?mt=12) +- [XCode command line tools](https://mac.install.guide/commandlinetools/4.html) (MacOS) +- [homebrew](https://brew.sh/) (MacOS) +- [pyenv](https://github.com/pyenv/pyenv) (Python version management) +- [Current python version](./.python-version) +- [Python Poetry](https://pypi.org/project/poetry/) +- [GitHub CLI](https://cli.github.com/) +- [VSCode](https://code.visualstudio.com/) +- [DuckDB CLI](https://duckdb.org/docs/installation/index) +- [Refined Github extension](https://github.com/refined-github/refined-github) + +### Setup environment + +```sh +./scripts/setup.sh +``` + +### Source + +The source ingests user data and converts it to parquet files. + +#### Ingesting data + +To run the `source` locally as a binary: + +```sh +poetry run python -m src.datasets.dataset_loader \ + --dataset_name=$DATASET \ + --output_dir=./gcs_cache/ \ + --config_path=./datasets/the_movies_dataset.json +``` + +### Web Server + +#### Development + +To run the web server in dev mode with fast edit-refresh: + +```sh +./run_server_dev.sh +``` + +#### Testing + +Run all the presubmits: + +```sh +./scripts/presubmit.sh +``` + +Test python: + +```sh +./scripts/test_py.sh +``` + +Test JavaScript: + +```sh +./scripts/test_ts.sh +``` + +### Troubleshooting + +#### pyenv install not working on M1 + +If your pyenv does not work on M1 machines after installing xcode, you may need to reinstall xcode command line tools. [Stack Overflow Link](https://stackoverflow.com/questions/65778888/pyenv-configure-error-c-compiler-cannot-create-executables) + +#### No module named `_lzma` + +Follow instructions from [pyenv](https://github.com/pyenv/pyenv/wiki#suggested-build-environment): + +- Uninstall python via `pyenv uninstall` +- Run `brew install openssl readline sqlite3 xz zlib tcl-tk` +- Reinstall python via `pyenv install` + +```sh +$ sudo rm -rf /Library/Developer/CommandLineTools +$ xcode-select --install +``` + +#### Installing TensorFlow on M1 + +M1/M2 chips need a special TF installation. These steps are taken from the official +[Apple docs](https://developer.apple.com/metal/tensorflow-plugin/): + +1. Click [here](https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh) to download Conda env +2. Run: + +``` +chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh +sh ~/Downloads/Miniforge3-MacOSX-arm64.sh +source ~/miniforge3/bin/activate +``` + +3. Install the TensorFlow `2.9.0` dependencies: `conda install -c apple tensorflow-deps=2.9.0` + +#### Too many open files on MacOS + +When downloading and pre-processing TFDS datasets, you might get `too many open files` +error. To fix, increase [the max open files limit](https://superuser.com/a/1679740). + +### Relevant projects + +- [Voxel51](https://voxel51.com/docs/fiftyone/) + - Open-source tool for visualizing datasets. +- [Fastdup](https://github.com/visual-layer/fastdup) + - Easily manage, clean & curate Visual Data + - Can scale to 400M images (low Cost: can process 12M images on a $1 cloud machine budget) + - Enterprise version coming soon: https://www.visual-layer.com/ +- [Scale Nucleus](https://scale.com/nucleus) + - Allows people to upload ML data, label it via UI or by sending it to a pool of labelers. Allows people to train a model and analyze the performance. Nucleus is one of their products for browsing data. Valuation is around $10B! +- [Labelbox](https://labelbox.com/) + - Another largish company, similar to Scale. $190M in funding. +- [Deepnote](https://deepnote.com/) + - Python notebook for data. Integrates with BigQuery, GCS, Snowflake, MySQL, Postgre, and another 20+ sources. +- [Tadviewer](https://www.tadviewer.com/) + - Open source desktop app for browsing CSV, Parquet, SQLite and DuckDB data. +- [Datasette](https://datasette.io/) + - Open source. Similar to Tadviewer. See [a related twitter thread](https://twitter.com/simonw/status/1572285367382061057?s=46&t=6Rc-qn2_pufUx7hwG7z_PQ) from the creator. +- [VisiData](https://www.visidata.org/) + - Open source. Similar to Tadviewer and Datasette, but for the terminal. +- [Amazon Sagemaker Data Wrangler](https://aws.amazon.com/sagemaker/data-wrangler) + - Build a pipeline for preparing data for ML. +- [bit.io](https://bit.io/) + - Drag and drop a file to get a Postgres database and a short link you can share with people. You can also paste a URL to data on the web. +- [Top companies in data technology](https://www.valuer.ai/blog/top-companies-in-data-technology) diff --git a/__init__.py b/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/datasets/the_movies_dataset.json b/datasets/the_movies_dataset.json new file mode 100644 index 000000000..48049853b --- /dev/null +++ b/datasets/the_movies_dataset.json @@ -0,0 +1,6 @@ +{ + "source_name": "csv", + "filepaths": [ + "gs://lilac-data-us-east1/datasets/csv_datasets/the_movies_dataset/the_movies_dataset.csv" + ] +} diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 000000000..c82471ce5 --- /dev/null +++ b/mypy.ini @@ -0,0 +1,37 @@ +# Global options. +[mypy] +check_untyped_defs = True +disallow_untyped_calls = True +# disallow_untyped_decorators = True +disallow_incomplete_defs = True +disallow_untyped_defs = True +ignore_missing_imports = False +no_implicit_optional = True +strict_optional = True +warn_redundant_casts = True 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+python-versions = "~3.9" +content-hash = "d3a17698ca0c3a34da3412ef1f29eed7425688f896a2da99d7d504a600415f26" diff --git a/poetry.toml b/poetry.toml new file mode 100644 index 000000000..5d2eabeeb --- /dev/null +++ b/poetry.toml @@ -0,0 +1,4 @@ +[virtualenvs] +create = true +in-project = true +path = "{project-dir}/.venv" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..20282ba37 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,127 @@ +[tool.poetry] +name = "lilac" +version = "0.0.1" +description = "Index your dataset" +authors = ["Lilac AI Inc. "] +readme = "README.md" +packages = [{include = "src"}] + +[tool.poetry.dependencies] +cohere = "^3.7.0" +datasets = "^2.9.0" +duckdb = "^0.6.0" +fastapi = "^0.92.0" +google-cloud-storage = "^2.5.0" +python = "~3.9" +pyarrow = "^9.0.0" +pydantic = "^1.10.2" +pillow = "^9.3.0" +requests = "^2.28.1" +scikit-learn = "^1.2.1" +tqdm = "^4.65.0" +types-Pillow = "^9.3.0.4" +typing-extensions = "^4.4.0" +uvicorn = {extras = ["standard"], version = "^0.20.0"} +##### Tensorflow ##### +# Install TF on all platforms but Macs with M1/M2 chips. +tensorflow = {version = "~2.9", markers = "platform_machine != 'arm64'"} +# Install special TF packages on Macs with M1/M2 chips. +# https://developer.apple.com/metal/tensorflow-plugin/ +tensorflow-macos = {version = "~2.9.0", markers = "platform_machine == 'arm64'"} +tensorflow-metal = {version = "^0.6.0", markers = "platform_machine == 'arm64'"} +tensorflow-datasets = "^4.6.0" + +# Deps for development. +[tool.poetry.group.dev] +optional = true +[tool.poetry.group.dev.dependencies] +alembic = "^1.8.1" +click = "^8.1.3" +mypy = "^0.971" +pytest = "^7.1.3" +pytest-cov = "^4.0.0" +pytest-mock = "^3.10.0" +ruff = "^0.0.219" +yapf = "^0.32.0" +setuptools = "^65.5.0" +wheel = "^0.37.1" +google-api-python-client-stubs = "^1.13.0" +types-requests = "^2.28.11.5" +pytest-asyncio = "^0.20.2" +types-tqdm = "^4.65.0.0" + +[tool.poetry.scripts] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.pyright] +include = ["src"] +exclude = [ + "**/__pycache__", +] + +[tool.ruff] +line-length = 100 + +# Enable Pyflakes `F`, pycodestyle "W" and `E`, "Q" quotes, "I" imports. +select = ["E", "W", "F", "Q", "I", "D"] +ignore = [ + "D105", # Missing docstring in magic method. + "D106", # Missing docstring in a public nested class. + "D107", # Missing docstring in __init__. + "D203", # 1 blank line required before class docstring. + "D204", # 1 blank line required after class docstring. + "D213", # Multi-line docstring summary should start at the second line. + "D407" # Missing dashed underline after section. +] +fix = true + +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", +] + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +# Assume Python 3.9. +target-version = "py39" + +[tool.ruff.mccabe] +# Unlike Flake8, default to a complexity level of 10. +max-complexity = 10 + +[tool.ruff.flake8-quotes] +inline-quotes = "single" + +[tool.ruff.isort] +force-wrap-aliases = true +combine-as-imports = true + +[tool.ruff.per-file-ignores] +"*_test.py" = ["D101", "D102", "D103", "D104"] +"__init__.py" = ["D104"] + +[tool.ruff.pep8-naming] +# Allow Pydantic's `@validator` decorator to trigger class method treatment. +classmethod-decorators = ["classmethod", "pydantic.validator"] diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 000000000..42abb9d06 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,15 @@ +# Filter deprecation warnings from subpackages so they don't spam the console when testing. +[pytest] +testpaths = + src +filterwarnings = + ignore::DeprecationWarning:google.api_core.*: + ignore::DeprecationWarning:grpc_gcp.*: + ignore::DeprecationWarning:hdfs.*: + ignore::DeprecationWarning:keras.*: + ignore::DeprecationWarning:tensorflow.*: +markers = + largedownload: Marks a test as having a large download. Wont run on github. (deselect with '-m "not largedownload"') +asyncio_mode = auto +# Treat classes that end in "Suite" as test suites. +python_classes = *Suite diff --git a/run_server_dev.sh b/run_server_dev.sh new file mode 100755 index 000000000..876f9549d --- /dev/null +++ b/run_server_dev.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +export NODE_ENV=development + +# Start the webpack devserver. +rm -rf dist/ && npm run --prefix src/web dev --watch & +pid[1]=$! + +# Run the node server. +poetry run uvicorn src.server:app --reload --port 5432 --host 0.0.0.0 --reload-dir src & +pid[0]=$! + +# When control+c is pressed, kill all process ids. +trap "kill ${pid[0]} ${pid[1]}; exit 1" INT +wait diff --git a/scripts/coverage_py.sh b/scripts/coverage_py.sh new file mode 100755 index 000000000..2edc91d08 --- /dev/null +++ b/scripts/coverage_py.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +# Fail if any of the commands below fail. +set -e + +# Activate the current py virtual env. +source $(poetry env info --path)/bin/activate + +pytest --cov --cov-report html:py_coverage_html/ + +open py_coverage_html/index.html diff --git a/scripts/lint.sh b/scripts/lint.sh new file mode 100755 index 000000000..372f11055 --- /dev/null +++ b/scripts/lint.sh @@ -0,0 +1,5 @@ +#!/bin/bash +set -e # Fail if any of the commands below fail. + +./scripts/lint_py.sh +./scripts/lint_ts.sh diff --git a/scripts/lint_py.sh b/scripts/lint_py.sh new file mode 100755 index 000000000..940dc8d26 --- /dev/null +++ b/scripts/lint_py.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +# Fail if any of the commands below fail. +set -e + +# Activate the current py virtual env. +source $(poetry env info --path)/bin/activate + +echo "Checking python types with mypy..." +mypy src + +echo "Linting python with ruff..." +ruff src diff --git a/scripts/lint_ts.sh b/scripts/lint_ts.sh new file mode 100755 index 000000000..4000e7e63 --- /dev/null +++ b/scripts/lint_ts.sh @@ -0,0 +1,9 @@ +#!/bin/bash +set -e # Fail if any of the commands below fail. + +echo "Linting typescript & javascript..." +npx eslint server/ + + +echo "Building typescript..." +cd src/web && npx tsc && cd ../.. diff --git a/scripts/list_py_deps_by_size.sh b/scripts/list_py_deps_by_size.sh new file mode 100755 index 000000000..faf2dbb4e --- /dev/null +++ b/scripts/list_py_deps_by_size.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +# Lists the python dependencies, largest size first. +find .venv/ -maxdepth 4 -mindepth 4 -type d -exec du -hs {} \; | sort -hr diff --git a/scripts/presubmit.sh b/scripts/presubmit.sh new file mode 100755 index 000000000..76e1f249a --- /dev/null +++ b/scripts/presubmit.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +# Presubmit script for local development. + +# Fail if any of the commands below fail. +set -e + +./scripts/lint.sh + +./scripts/test.sh + +GREEN='\033[1;32m' +NC='\033[0m' # No Color +echo -e "${GREEN}Presubmits passed!${NC}" diff --git a/scripts/setup.sh b/scripts/setup.sh new file mode 100755 index 000000000..db498e469 --- /dev/null +++ b/scripts/setup.sh @@ -0,0 +1,5 @@ +#!/bin/bash +set -e + +./scripts/setup_py.sh +./scripts/setup_ts.sh diff --git a/scripts/setup_py.sh b/scripts/setup_py.sh new file mode 100755 index 000000000..d1874e9ab --- /dev/null +++ b/scripts/setup_py.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -e # Fail if any of the commands below fail. + +# Returns "arm64" on m1 devices. +ARCHITECTURE=$(uname -m) + +poetry install --with dev,http_service + +# On arm64 we have to build the tensorflow-io wheel manually. +if [ "$ARCHITECTURE" = "arm64" ]; then + TENSORFLOW_IO_DIRNAME="./cloned_repos/tensorflow-io" + rm -rf "$TENSORFLOW_IO_DIRNAME/" + # We pin the tensorflow-io version to align with the poetry pin in `pyproject.toml`. + git clone --depth 1 https://github.com/tensorflow/io.git --branch v0.27.0 "$TENSORFLOW_IO_DIRNAME" + + pushd $TENSORFLOW_IO_DIRNAME + + # Activate the poetry virtual env to ensure that we reuse the same dependencies and environment. + source $(poetry env info --path)/bin/activate + + python setup.py --project tensorflow_io_gcs_filesystem -q bdist_wheel + python setup.py -q bdist_wheel + python -m pip install --no-deps dist/tensorflow_io* + + deactivate + + popd +fi + diff --git a/scripts/setup_ts.sh b/scripts/setup_ts.sh new file mode 100755 index 000000000..682f31fcd --- /dev/null +++ b/scripts/setup_ts.sh @@ -0,0 +1,7 @@ +#!/bin/bash +set -e # Fail if any of the commands below fail. + +cd server && ./install_cloud_sql_proxy.sh && cd .. + +npm install +cd src/web && npm install && cd ../.. diff --git a/scripts/test.sh b/scripts/test.sh new file mode 100755 index 000000000..61f48a790 --- /dev/null +++ b/scripts/test.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +# Fail if any of the commands below fail. +set -e + +./scripts/test_py.sh +./scripts/test_ts.sh diff --git a/scripts/test_py.sh b/scripts/test_py.sh new file mode 100755 index 000000000..087a8a282 --- /dev/null +++ b/scripts/test_py.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +# Fail if any of the commands below fail. +set -e + +# Activate the current py virtual env. +source $(poetry env info --path)/bin/activate + +echo "Testing python using non-github test script..." + +PYTEST_MARKS="" +if [ "$CI" ]; then + # Turn off large download tests in CI so we don't incur github costs. + PYTEST_MARKS="(not largedownload)" +fi + +# Disables log() statements. +export DISABLE_LOGS=True + +# -vv enables verbose outputs. +# --capture=tee-sys enables printing for passing tests. +# Optional: Add `-v /path/to/test_file.py` to test a single file. +# Optional: Add `-k test_default` to test a single test. +pytest -vv --capture=tee-sys -m "$PYTEST_MARKS" diff --git a/scripts/test_ts.sh b/scripts/test_ts.sh new file mode 100755 index 000000000..ea61040d0 --- /dev/null +++ b/scripts/test_ts.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +# Fail if any of the commands below fail. +set -e + +cd server && npm run test && cd .. diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/concepts/__init__.py b/src/concepts/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/concepts/concept.py b/src/concepts/concept.py new file mode 100644 index 000000000..21b0445f5 --- /dev/null +++ b/src/concepts/concept.py @@ -0,0 +1,87 @@ +"""Defines the concept and the concept models.""" +from typing import Any, Literal, Optional, Sequence, Union + +import numpy as np +from pydantic import BaseModel, Field +from sklearn import linear_model + +from ..embeddings.embedding_registry import get_embed_fn +from ..schema import RichData + +LOCAL_CONCEPT_NAMESPACE = 'local' + + +class ExampleOrigin(BaseModel): + """The origin of an example.""" + # The namespace that holds the dataset. + dataset_namespace: str + + # The name of the dataset. + dataset_name: str + + # The id of row in the dataset that the example was added from. + dataset_row_id: str + + +class ExampleIn(BaseModel): + """An example in a concept without the id (used for adding new examples).""" + label: bool + text: Optional[str] + img: Optional[bytes] + origin: Optional[ExampleOrigin] + + +class Example(ExampleIn): + """A single example in a concept used for training a concept model.""" + id: str + + +class Concept(BaseModel): + """A concept is a collection of examples.""" + # The namespace of the concept. + namespace: str = LOCAL_CONCEPT_NAMESPACE + # The name of the concept. + concept_name: str + # The type of the data format that this concept represents. + type: Union[Literal['text'], Literal['img']] + data: dict[str, Example] + version: int = 0 + + +class ConceptModel(BaseModel): + """A concept model.""" + + class Config: + arbitrary_types_allowed = True + private_attributes_are_hidden = True + + # The concept that this model is for. + namespace: str + concept_name: str + + # The name of the embedding for this model. + embedding_name: str + version: int = -1 + + # The following fields are excluded from JSON serialization, but still pickleable. + embeddings: dict[str, np.ndarray] = Field(exclude=True) + + _model: linear_model.LogisticRegression = Field(exclude=True) + + def __init__(self, **data: Any): + super().__init__(**data) + self._model = linear_model.LogisticRegression(solver='liblinear', class_weight='balanced') + + def fit(self, embeddings: Union[np.ndarray, list[np.ndarray]], labels: list[bool]) -> None: + """Fit the model to the provided data.""" + self._model.fit(embeddings, labels) + + def score_embeddings(self, embeddings: np.ndarray) -> np.ndarray: + """Get the scores for the provided embeddings.""" + return self._model.predict_proba(embeddings)[:, 1] + + def score(self, examples: Sequence[RichData]) -> list[float]: + """Get the scores for the provided examples.""" + _, embed_fn = get_embed_fn(self.embedding_name) + embeddings = embed_fn(examples) + return self.score_embeddings(embeddings).tolist() diff --git a/src/concepts/db_concept.py b/src/concepts/db_concept.py new file mode 100644 index 000000000..ea5f76dd8 --- /dev/null +++ b/src/concepts/db_concept.py @@ -0,0 +1,193 @@ +"""The concept database.""" + +import abc +import os +import pickle +from typing import Optional, cast +from uuid import uuid4 + +from pydantic import BaseModel + +from ..constants import data_path +from ..embeddings.embedding_registry import get_embed_fn +from ..utils import delete_file, file_exists, open_file +from .concept import Concept, ConceptModel, Example, ExampleIn + +CONCEPT_JSON_FILENAME = 'concept.json' + + +class ConceptUpdate(BaseModel): + """An update to a concept.""" + # List of examples to be inserted. + insert: Optional[list[ExampleIn]] + + # List of examples to be updated. + update: Optional[list[Example]] + + # The ids of the examples to be removed. + remove: Optional[list[str]] + + +class ConceptDB(abc.ABC): + """Interface for the concept database.""" + + @abc.abstractmethod + def get(self, namespace: str, name: str) -> Optional[Concept]: + """Return a concept or None if there isn't one.""" + pass + + @abc.abstractmethod + def edit(self, namespace: str, name: str, change: ConceptUpdate) -> Concept: + """Edit a concept.""" + pass + + @abc.abstractmethod + def remove(self, namespace: str, name: str) -> None: + """Remove a concept.""" + pass + + +class ConceptModelDB(abc.ABC): + """Interface for the concept model database.""" + + @abc.abstractmethod + def get(self, namespace: str, concept_name: str, embedding_name: str) -> Optional[ConceptModel]: + """Get the model associated with the provided concept and the embedding, or None.""" + pass + + @abc.abstractmethod + def save(self, concept_model: ConceptModel) -> None: + """Save the concept model.""" + pass + + @abc.abstractmethod + def remove(self, namespace: str, concept_name: str, embedding_name: str) -> None: + """Remove the model of a concept.""" + pass + + +class DiskConceptModelDB(ConceptModelDB): + """Interface for the concept model database.""" + + def get(self, namespace: str, concept_name: str, embedding_name: str) -> Optional[ConceptModel]: + """Get the model associated with the provided concept and the embedding.""" + # Make sure the concept exists. + concept = CONCEPT_DB.get(namespace, concept_name) + if not concept: + return None + + # Make sure that the embedding exists. + if not get_embed_fn(embedding_name): + raise ValueError(f'Embedding "{embedding_name}" is not registered in the registry.') + + concept_model_path = _concept_model_path(namespace, concept_name, embedding_name) + if not file_exists(concept_model_path): + return ConceptModel(namespace=namespace, + concept_name=concept_name, + embedding_name=embedding_name, + embeddings={}, + version=-1) + + with open_file(concept_model_path, 'rb') as f: + return pickle.load(f) + + def save(self, model: ConceptModel) -> None: + """Save the concept model.""" + concept_model_path = _concept_model_path(model.namespace, model.concept_name, + model.embedding_name) + with open_file(concept_model_path, 'wb') as f: + pickle.dump(model, f) + + def remove(self, namespace: str, concept_name: str, embedding_name: str) -> None: + """Remove the model for a concept.""" + concept_model_path = _concept_model_path(namespace, concept_name, embedding_name) + + if not file_exists(concept_model_path): + raise ValueError(f'Concept model {namespace}/{concept_name}/{embedding_name} does not exist.') + + delete_file(concept_model_path) + + +def _concept_output_dir(namespace: str, name: str) -> str: + """Return the output directory for a given concept.""" + return os.path.join(data_path(), 'concept', namespace, name) + + +def _concept_json_path(namespace: str, name: str) -> str: + return os.path.join(_concept_output_dir(namespace, name), CONCEPT_JSON_FILENAME) + + +def _concept_model_path(namespace: str, concept_name: str, embedding_name: str) -> str: + return os.path.join(_concept_output_dir(namespace, concept_name), f'{embedding_name}.pkl') + + +class DiskConceptDB(ConceptDB): + """A concept database.""" + + def get(self, namespace: str, name: str) -> Optional[Concept]: + """Get a concept.""" + concept_json_path = _concept_json_path(namespace, name) + + if not file_exists(concept_json_path): + return None + + with open_file(concept_json_path) as f: + return Concept.parse_raw(f.read()) + + def edit(self, namespace: str, name: str, change: ConceptUpdate) -> Concept: + """Edit a concept.""" + concept_json_path = _concept_json_path(namespace, name) + + inserted_points = change.insert or [] + updated_points = change.update or [] + removed_points = change.remove or [] + + # Create the concept if it doesn't exist. + if not file_exists(concept_json_path): + # Deduce the concept type from the first example. + points = [*inserted_points, *updated_points] + if points: + type = 'text' if points[0].text else 'img' + else: + raise ValueError('Cannot create a concept with no examples.') + concept = Concept(namespace=namespace, concept_name=name, type=type, data=[], version=0) + else: + concept = cast(Concept, self.get(namespace, name)) + + for example_id in removed_points: + if example_id not in concept.data: + raise ValueError(f'Example with id "{example_id}" does not exist.') + concept.data.pop(example_id) + + for example in inserted_points: + id = uuid4().hex + concept.data[id] = Example(id=id, **example.dict()) + + for example in updated_points: + if example.id not in concept.data: + raise ValueError(f'Example with id "{example.id}" does not exist.') + # Remove the old example and make a new one with a new id to keep it functional. + concept.data.pop(example.id) + id = uuid4().hex + concept.data[id] = Example(id=id, **example.dict()) + + concept.version += 1 + + with open_file(concept_json_path, 'w') as f: + f.write(concept.json(exclude_none=True)) + + return concept + + def remove(self, namespace: str, name: str) -> None: + """Remove a concept.""" + concept_json_path = _concept_json_path(namespace, name) + + if not file_exists(concept_json_path): + raise ValueError(f'Concept with namespace "{namespace}" and name "{name}" does not exist.') + + delete_file(concept_json_path) + + +# A singleton concept database. +CONCEPT_DB = DiskConceptDB() +CONCEPT_MODEL_DB = DiskConceptModelDB() diff --git a/src/constants.py b/src/constants.py new file mode 100644 index 000000000..2c59fbfc4 --- /dev/null +++ b/src/constants.py @@ -0,0 +1,59 @@ +"""Pydantic models for data, stored on GCS.""" +import os +from typing import Optional, Union + +from pydantic import BaseModel + +DATA_JSON_FILENAME = 'data.json' +EMBEDDINGS_FILENAME = 'embeddings.npy' + + +def data_path() -> str: + """Return the base path for data.""" + return os.environ['LILAC_DATA_PATH'] if 'LILAC_DATA_PATH' in os.environ else './gcs_cache' + + +# TODO(nsthorat): Delete the following when we remove the old agile server. + + +class DatasetRow(BaseModel): + """A single labeled row.""" + text: str + prediction: Optional[int] + + metadata: dict[str, Union[str, float]] = {} + + +class AddExample(BaseModel): + """A example to be added.""" + text: str + label: int + + +class LabeledExample(BaseModel): + """A single labeled example.""" + # Points to the index in the full dataset. + row_idx: int + label: int + + +class TrainExample(BaseModel): + """A single training example.""" + # Points to the index in the full dataset. + row_idx: int + label: int + + +class TestExample(BaseModel): + """A single test example.""" + # Points to the index in the full dataset. + row_idx: int + label: int + + +class Dataset(BaseModel): + """A dataset for a model.""" + # All of the data. + data: list[DatasetRow] + + labeled_data: list[LabeledExample] diff --git a/src/datasets/__init__.py b/src/datasets/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/datasets/dataset_loader.py b/src/datasets/dataset_loader.py new file mode 100644 index 000000000..5079e9064 --- /dev/null +++ b/src/datasets/dataset_loader.py @@ -0,0 +1,95 @@ +"""The source loader runner which loads data into parquet files for the app. + +To run the source loader as a binary directly: + +poetry run python -m src.datasets.dataset_loader \ + --dataset_name=$DATASET \ + --output_dir=./gcs_cache/ \ + --config_path=./datasets/the_movies_dataset.json +""" + +import asyncio +import json +import os +import time +from concurrent.futures import ProcessPoolExecutor +from typing import Awaitable, Callable + +import click +from pydantic import BaseModel + +from ..schema import MANIFEST_FILENAME, SourceManifest +from ..utils import async_wrap, get_dataset_output_dir, log, open_file +from .sources.default_sources import register_default_sources +from .sources.source import Source +from .sources.source_registry import resolve_source + +REQUEST_TIMEOUT_SEC = 30 * 60 # 30 mins. + +register_default_sources() + + +class ProcessSourceRequest(BaseModel): + """The interface to the /process_source endpoint.""" + username: str + dataset_name: str + + +async def _process_source(base_dir: str, namespace: str, dataset_name: str, source: Source, + process_shard: Callable[[dict], Awaitable[dict]]) -> tuple[str, int]: + + async def shards_loader(shard_infos: list[dict]) -> list[dict]: + return await asyncio.gather(*[process_shard(x) for x in shard_infos]) + + output_dir = get_dataset_output_dir(base_dir, namespace, dataset_name) + start = time.time() + source_process_result = await source.process(output_dir, shards_loader) + elapsed = time.time() - start + log(f'Processing dataset "{dataset_name}" took {elapsed:.2f}sec') + + filenames = [os.path.basename(filepath) for filepath in source_process_result.filepaths] + manifest = SourceManifest(files=filenames, + data_schema=source_process_result.data_schema, + images=source_process_result.images) + with open_file(os.path.join(output_dir, MANIFEST_FILENAME), 'w') as f: + f.write(manifest.json(indent=2, exclude_none=True)) + log(f'Manifest for dataset "{dataset_name}" written to {output_dir}') + return output_dir, source_process_result.num_items + + +@click.command() +@click.option('--output_dir', + required=True, + type=str, + help='The output directory to write the parquet files to.') +@click.option('--config_path', + required=True, + type=str, + help='The path to a json file describing the source configuration.') +@click.option('--dataset_name', + required=True, + type=str, + help='The dataset name, used for binary mode only.') +@click.option('--namespace', + required=False, + default='local', + type=str, + help='The namespace to use. Defaults to "local".') +def main(output_dir: str, config_path: str, dataset_name: str, namespace: str) -> None: + """Run the source loader as a binary.""" + with open_file(config_path) as f: + # Parse the json file in a dict. + source_dict = json.load(f) + source = resolve_source(source_dict) + + pool = ProcessPoolExecutor() + process_shard = async_wrap(source.process_shard, executor=pool) + + # Make the async process_source sync so main() can be synchronous for click. + loop = asyncio.get_event_loop() + loop.run_until_complete( + _process_source(output_dir, namespace, dataset_name, source, process_shard)) + + +if __name__ == '__main__': + main() diff --git a/src/datasets/dataset_utils.py b/src/datasets/dataset_utils.py new file mode 100644 index 000000000..8e9a8b758 --- /dev/null +++ b/src/datasets/dataset_utils.py @@ -0,0 +1,125 @@ +"""Utilities for working with datasets.""" + +from typing import Iterable, Optional, Sequence, Union + +from ..schema import ( + PATH_WILDCARD, + UUID_COLUMN, + Item, + Path, + normalize_path, +) + + +def replace_repeated_wildcards(path: Path, path_repeated_idxs: Optional[list[int]]) -> Path: + """Replace the repeated wildcards in a path with the repeated indexes.""" + if path_repeated_idxs is None: + # No repeated indexes, so just return the path. + return path + + replaced_path: list[Union[str, int]] = [] + path_repeated_idx_i = 0 + for path_part in path: + # Replace wildcards with the actual index. + if path_part == PATH_WILDCARD: + replaced_path.append(path_repeated_idxs[path_repeated_idx_i]) + path_repeated_idx_i += 1 + else: + replaced_path.append(path_part) + + return tuple(replaced_path) + + +def make_enriched_items(source_path: Path, row_ids: Sequence[bytes], + signal_items: Iterable[Optional[Item]], + repeated_idxs: Iterable[Optional[list[int]]]) -> Iterable[Item]: + """Make enriched items from signal outputs. + + NOTE: The iterables that are passed in are not 1:1 with row_ids. The logic of this method will + merge the same UUIDs as long as they are contiguous. + """ + working_enriched_signal_item: Item = {} + num_signal_outputs = 0 + signal_outputs_per_key = 0 + + last_row_id: Optional[bytes] = None + + for row_id, signal_output, path_repeated_idxs in zip(row_ids, signal_items, repeated_idxs): + num_signal_outputs += 1 + + # Duckdb currently just returns a single int as that's all we support. The rest of the code + # supports arrays, so we make it an array for the future when duckdb gives us a list of indices. + if isinstance(path_repeated_idxs, int): + path_repeated_idxs = [path_repeated_idxs] + + if last_row_id is None: + last_row_id = row_id + working_enriched_signal_item = {UUID_COLUMN: row_id} + + # When we're at a new row_id, yield the last working item. + if last_row_id != row_id: + if signal_outputs_per_key > 0: + yield working_enriched_signal_item + last_row_id = row_id + working_enriched_signal_item = {UUID_COLUMN: row_id} + signal_outputs_per_key = 0 + + if not signal_output: + continue + + # Tracking signal outputs per key allows us to know if we have a sparse signal so it will not be + # yielded at all. + signal_outputs_per_key += 1 + + # Apply the list of path indices to the path to replace wildcards. + resolved_path = replace_repeated_wildcards(path=source_path, + path_repeated_idxs=path_repeated_idxs) + + # Now that we have resolves indices, we can modify the enriched item. + enrich_item_from_signal_item(enriched_item=working_enriched_signal_item, + path=resolved_path, + signal_item=signal_output) + + if num_signal_outputs != len(row_ids): + raise ValueError('The signal output and the input data do not have the same length. ' + 'This means the signal either didnt generate a "None" for a sparse signal, ' + 'or generated too many items.') + + if signal_outputs_per_key > 0: + yield working_enriched_signal_item + + +def enrich_item_from_signal_item(enriched_item: Item, path: Path, signal_item: Item) -> None: + """Create an enriched item that holds the output of a signal.""" + path = normalize_path(path) + + signal_subitem = enriched_item + for i in range(len(path) - 1): + path_component = path[i] + next_path_component = path[i + 1] + if isinstance(path_component, str): + if path_component not in signal_subitem: + if isinstance(next_path_component, str): + signal_subitem[path_component] = {} + else: + # This needs to be filled to the length of the index. Since this is the first time we've + # seen path_component, we can fill it exactly to the index we're going to inject. + signal_subitem[path_component] = [None] * (next_path_component + 1) + else: + if isinstance(next_path_component, int): + # We may need to extend the length so we can write the new index. + cur_len = len(signal_subitem[path_component]) # type: ignore + if cur_len <= next_path_component: + signal_subitem[path_component].extend( # type: ignore + [None] * (next_path_component - cur_len + 1)) + elif isinstance(path_component, int): + # The nested list of nones above will fill in integer indices. + if isinstance(next_path_component, str): + signal_subitem[path_component] = {} # type: ignore + elif isinstance(next_path_component, int): + signal_subitem[path_component] = [] # type: ignore + else: + raise ValueError(f'Unknown type {type(path_component)} in path {path}.') + signal_subitem = signal_subitem[path_component] # type: ignore + + signal_subitem[path[-1]] = signal_item # type: ignore diff --git a/src/datasets/db_dataset.py b/src/datasets/db_dataset.py new file mode 100644 index 000000000..93ecccbec --- /dev/null +++ b/src/datasets/db_dataset.py @@ -0,0 +1,199 @@ +"""The interface for the database.""" +import abc +import enum +from typing import Any, Iterable, Iterator, Optional, Sequence, Union + +import pandas as pd +from pydantic import BaseModel + +from ..embeddings.embedding_registry import EmbeddingId +from ..schema import Item, Path, Schema, path_to_alias +from ..signals.signal import Signal + + +class SelectRowsResult(): + """The result of a select rows query.""" + + def __init__(self, rows: Iterable[Item]) -> None: + """Initialize the result.""" + self.rows = rows + + def __iter__(self) -> Iterator: + for row in self.rows: + yield row + + +class Comparison(str, enum.Enum): + """The comparison operator between a column and a feature value.""" + EQUALS = '=' + NOT_EQUAL = '!=' + GREATER = '>' + GREATER_EQUAL = '>=' + LESS = '<' + LESS_EQUAL = '<=' + + +class SortOrder(str, enum.Enum): + """The sort order for a database query.""" + DESC = 'DESC' + ASC = 'ASC' + + +class GroupsSortBy(str, enum.Enum): + """The sort for groups queries. + + Either "count" which sorts by the count of feature value, or "value" which sorts by the + feature value itself. + """ + COUNT = 'count' + VALUE = 'value' + + +class Transform(BaseModel): + """The base class for a feature transform, e.g. bucketization or concept.""" + pass + + +class Column(BaseModel): + """A column in the dataset DB.""" + # The feature points to another column if this is a transformation of that column. + feature: Union[Path, 'Column'] + alias: str # This is the renamed column during querying and response. + + # Defined when the feature is another column. + transform: Optional[Transform] + + def __init__(self, feature: Union[Path, 'Column'], alias: Optional[str] = None, **kwargs: Any): + """Initialize a column. We override __init__ to allow positional arguments for brevity.""" + if not alias: + if isinstance(feature, Column): + raise ValueError('Please define an alias for the column when it has a transform.') + if isinstance(feature, str): + alias = feature + else: + alias = path_to_alias(feature) + + super().__init__(feature=feature, alias=alias, **kwargs) + + +ColumnId = Union[Path, Column] + + +class DatasetManifest(BaseModel): + """The manifest for a dataset.""" + namespace: str + dataset_name: str + data_schema: Schema + + +def column_from_identifier(column: ColumnId) -> Column: + """Create a column from a column identifier.""" + if isinstance(column, Column): + return column + return Column(feature=column) + + +class ConceptTransform(Transform): + """Computes a concept transformation over a field.""" + concept: str + + +class BucketizeTransform(Transform): + """Bucketizes the input float into descrete integer buckets.""" + bins: list[float] + + +def Bucketize(column: Column, bins: list[float]) -> Column: + """Bucketize a column.""" + return Column(feature=column, transform=BucketizeTransform(bins=bins)) + + +FeatureValue = Union[int, float, bool, str] +Filter = tuple[Column, Comparison, FeatureValue] + + +class DatasetDB(abc.ABC): + """The database implementation to query a dataset.""" + + def __init__(self, namespace: str, dataset_name: str): + """Initialize a dataset. + + Args: + namespace: The dataset namespace. + dataset_name: The dataset name. + """ + self.namespace = namespace + self.dataset_name = dataset_name + + @abc.abstractmethod + def manifest(self) -> DatasetManifest: + """Return the manifest for the dataset.""" + pass + + @abc.abstractmethod + def columns(self) -> list[Column]: + """Return the list of columns supported for the dataset.""" + pass + + @abc.abstractmethod + def compute_embedding_index(self, embedding: EmbeddingId, columns: Sequence[ColumnId]) -> None: + """Compute an embedding index for a set of columns.""" + pass + + @abc.abstractmethod + def compute_signal_columns(self, signal: Signal, columns: Sequence[ColumnId]) -> None: + """Compute a signal for a set of columns. + + Args: + signal: The signal to compute over the given columns. + columns: The columns to compute the signal on. + """ + pass + + @abc.abstractmethod + def select_groups(self, + column_ids: Optional[Sequence[ColumnId]] = None, + filters: Optional[Sequence[Filter]] = None, + sort_by: Optional[GroupsSortBy] = None, + sort_order: Optional[SortOrder] = SortOrder.DESC, + limit: Optional[int] = 100) -> pd.DataFrame: + """Select grouped columns to power a histogram. + + Args: + column_ids: The columns to select. A column is an instance of `Column` which can either + define a path to a feature, or a column with an applied Transform, e.g. a Concept. + filters: The filters to apply to the query. + sort_by: What to sort by, either "count" or "value". + sort_order: The sort order. + limit: The maximum number of rows to return. + + Returns + A dataframe with counts for each selected columnm, applying the filters. + """ + pass + + @abc.abstractmethod + def select_rows(self, + columns: Optional[Sequence[ColumnId]] = None, + filters: Optional[Sequence[Filter]] = None, + sort_by: Optional[list[str]] = None, + sort_order: Optional[SortOrder] = SortOrder.DESC, + limit: Optional[int] = 100) -> SelectRowsResult: + """Select grouped columns to power a histogram. + + Args: + columns: The columns to select. A column is an instance of `Column` which can either + define a path to a feature, or a column with an applied Transform, e.g. a Concept. + filters: The filters to apply to the query. + sort_by: An ordered list of what to sort by. When defined, this is a list of aliases of column + names defined by the "alias" field in Column. If no alias is provided for a column, an + automatic alias is generated by combining each path element with a "." + For example: e.g. ('person', 'name') => person.name. For columns that are transform columns, + an alias must be provided explicitly. + sort_order: The sort order. + limit: The maximum number of rows to return. + + Returns + A SelectRowsResult iterator with rows of `Item`s. + """ + pass diff --git a/src/datasets/db_dataset_duckdb.py b/src/datasets/db_dataset_duckdb.py new file mode 100644 index 000000000..ecab539da --- /dev/null +++ b/src/datasets/db_dataset_duckdb.py @@ -0,0 +1,432 @@ +"""The DuckDB implementation of the dataset database.""" +import functools +import itertools +import os +from typing import Iterable, Optional, Sequence, cast + +import duckdb +import pandas as pd +from pydantic import BaseModel, validator +from typing_extensions import override # type: ignore + +from ..constants import data_path +from ..embeddings.embedding_index import EmbeddingIndexer +from ..embeddings.embedding_index_disk import EmbeddingIndexerDisk +from ..embeddings.embedding_registry import EmbeddingId +from ..schema import ( + MANIFEST_FILENAME, + UUID_COLUMN, + Path, + Schema, + SourceManifest, + is_repeated_path_part, + normalize_path, +) +from ..signals.signal import Signal +from ..signals.signal_registry import resolve_signal +from ..signals.signal_utils import create_signal_schema +from ..utils import ( + DebugTimer, + get_dataset_output_dir, + log, + open_file, + write_items_to_parquet, +) +from .dataset_utils import make_enriched_items +from .db_dataset import ( + Column, + ColumnId, + DatasetDB, + DatasetManifest, + Filter, + GroupsSortBy, + SelectRowsResult, + SortOrder, + column_from_identifier, +) + +DEBUG = os.environ['DEBUG'] == 'true' if 'DEBUG' in os.environ else False +UUID_INDEX_FILENAME = 'uuids.npy' + + +class ColumnGroup(BaseModel): + """A group of columns stored in a parquet file.""" + + files: list[str] + + # The signal name that created this group. + signal_name: Optional[str] + # The column path that this group is derived from. + original_column_path: Optional[Path] + + # The schema of this column group. + data_schema: Schema + + +class SelectLeafsResult(BaseModel): + """The result of a select leafs query.""" + + class Config: + arbitrary_types_allowed = True + + duckdb_result: duckdb.DuckDBPyRelation + repeated_idxs_col: Optional[str] + + +class DatasetDuckDB(DatasetDB): + """The DuckDB implementation of the dataset database.""" + + def __init__(self, + namespace: str, + dataset_name: str, + embedding_indexer: Optional[EmbeddingIndexer] = None): + super().__init__(namespace, dataset_name) + + self.dataset_path = get_dataset_output_dir(data_path(), namespace, dataset_name) + + # TODO: Infer the manifest from the parquet files so this is lighter weight. + self._source_manifest = read_source_manifest(self.dataset_path) + + self.con = duckdb.connect(database=':memory:') + self._create_view('t', self._source_manifest.files) + + if not embedding_indexer: + self._embedding_indexer: EmbeddingIndexer = EmbeddingIndexerDisk(self.dataset_path) + else: + self._embedding_indexer = embedding_indexer + + def _create_view(self, view_name: str, files: list[str]) -> None: + parquet_files = [os.path.join(self.dataset_path, filename) for filename in files] + self.con.execute(f""" + CREATE OR REPLACE VIEW {view_name} AS (SELECT * FROM read_parquet({parquet_files})); + """) + + def _column_signal_manifest_files(self) -> tuple[str, ...]: + """Return the signal manifests for all enriched columns.""" + signal_manifest_filepaths: list[str] = [] + for root, _, files in os.walk(self.dataset_path): + for file in files: + if file.endswith('signal_manifest.json'): + signal_manifest_filepaths.append(os.path.join(root, file)) + + return tuple(signal_manifest_filepaths) + + @functools.cache + # NOTE: This is cached, but when the list of filepaths changed the results are invalidated. + def _column_groups(self, manifest_filepaths: list[str]) -> list[ColumnGroup]: + column_groups: list[ColumnGroup] = [] + # Add the source column group. + column_groups.append( + ColumnGroup(files=self._source_manifest.files, + signal_name=None, + original_column_path=None, + data_schema=self._source_manifest.data_schema)) + + # Add the signal column groups. + for signal_manifest_filepath in manifest_filepaths: + with open_file(signal_manifest_filepath) as f: + signal_manifest = SignalManifest.parse_raw(f.read()) + + column_groups.append( + ColumnGroup(files=signal_manifest.files, + signal_name=signal_manifest.signal.signal_name, + original_column_path=signal_manifest.enriched_path, + data_schema=signal_manifest.data_schema)) + + return column_groups + + @override + def manifest(self) -> DatasetManifest: + merged_schema = Schema(fields={**self._source_manifest.data_schema.fields}) + column_groups = self._column_groups(self._column_signal_manifest_files()) + for column_group in column_groups: + # The source schema is already merged. + if column_group.signal_name is None: + continue + if column_group.original_column_path is None: + print(f'Column group {column_group} has no original column path.') + continue + + original_path = column_group.original_column_path + if isinstance(original_path, str): + normalized_path = [original_path] + else: + normalized_path = cast(list[str], original_path) + + original_top_level_column = normalized_path[0] + top_level_column_name = self._top_level_col_name(column_group.original_column_path, + column_group.signal_name) + merged_schema.fields[top_level_column_name] = column_group.data_schema.fields[ + original_top_level_column] + return DatasetManifest(namespace=self.namespace, + dataset_name=self.dataset_name, + data_schema=merged_schema) + + @functools.cache + def columns(self) -> list[Column]: + """Get the columns.""" + columns: list[Column] = [] + for path in self._source_manifest.data_schema.leafs.keys(): + columns.append(Column(feature=path)) + return columns + + def count(self, filters: Optional[list[Filter]] = None) -> int: + """Count the number of rows.""" + raise NotImplementedError + + @override + def compute_embedding_index(self, embedding: EmbeddingId, columns: Sequence[ColumnId]) -> None: + cols = [column_from_identifier(column) for column in columns] + + rows = self.select_rows(columns=[Column(UUID_COLUMN)] + cols, limit=None) + + keys = map(lambda row: row[UUID_COLUMN], rows) + for column in cols: + data = map(lambda row: row[column.alias], rows) + + if isinstance(column.feature, Column): + raise ValueError( + f'Cannot compute an embedding index for {column} as it is not a leaf feature.') + + self._embedding_indexer.compute_embedding_index(column=column.feature, + embedding=embedding, + keys=keys, + data=data) + + @override + def compute_signal_columns(self, signal: Signal, columns: Sequence[ColumnId]) -> None: + cols = [column_from_identifier(column) for column in columns] + + signal_fields = signal.fields() + + for column in cols: + if isinstance(column.feature, Column): + raise ValueError(f'Cannot compute a signal for {column} as it is not a leaf feature.') + + source_path = normalize_path(column.feature) + signal_out_prefix = signal_parquet_prefix(column_name=column.alias, signal_name=signal.name) + signal_schema = create_signal_schema(source_schema=self._source_manifest.data_schema, + signal_enrich_fields=[source_path], + signal_fields=signal_fields) + + if signal.embedding_based: + raise ValueError('Embedding based signal cannot yet be computed.') + else: + leaf_alias = 'text' + + with DebugTimer(f'"_select_leafs" over "{source_path}"'): + select_leafs_result = self._select_leafs(path=source_path, leaf_alias=leaf_alias) + leafs_df = select_leafs_result.duckdb_result.to_df() + + with DebugTimer(f'"compute" for signal "{signal.name}" over "{source_path}"'): + signal_outputs = signal.compute(data=leafs_df[leaf_alias]) + + # Use the repeated indices to generate the correct signal output structure. + if select_leafs_result.repeated_idxs_col: + repeated_idxs = leafs_df[select_leafs_result.repeated_idxs_col] + + # Repeat "None" if there are no repeated indices without allocating an array. This happens + # when the object is a simple structure. + repeated_idxs_iter: Iterable[Optional[list[int]]] = ( + itertools.repeat(None) if not select_leafs_result.repeated_idxs_col else repeated_idxs) + + enriched_signal_items = make_enriched_items(source_path=source_path, + row_ids=leafs_df[UUID_COLUMN], + signal_items=signal_outputs, + repeated_idxs=repeated_idxs_iter) + + parquet_filename, _ = write_items_to_parquet(items=enriched_signal_items, + output_dir=self.dataset_path, + schema=signal_schema, + filename_prefix=signal_out_prefix, + shard_index=0, + num_shards=1) + + signal_manifest = SignalManifest(files=[parquet_filename], + data_schema=signal_schema, + signal=signal, + enriched_path=source_path) + signal_manifest_filepath = os.path.join( + self.dataset_path, + signal_manifest_filename(column_name=column.alias, signal_name=signal.name)) + with open_file(signal_manifest_filepath, 'w') as f: + f.write(signal_manifest.json()) + log(f'Wrote signal manifest to {signal_manifest_filepath}') + + def _select_leafs(self, path: Path, leaf_alias: str) -> SelectLeafsResult: + path = normalize_path(path) + for path_component in path[0:-1]: + if is_repeated_path_part(path_component): + raise ValueError( + f'Outer repeated leafs are not yet supported in _select_leafs. Requested Path: {path}') + + leaf_components: list[str] = [] + for leaf_component in path: + # Ignore repeated paths. They will be added via unnest for inner-most repeated.s + if is_repeated_path_part(leaf_component): + continue + leaf_components.append(f'"{leaf_component}"') + + leaf_col = '.'.join(leaf_components) + + is_inner_repeated = is_repeated_path_part(path[-1]) + + repeated_indices_col: Optional[str] = None + if not is_inner_repeated: + leaf_select = f'{leaf_col} as {leaf_alias}' + else: + repeated_indices_col = 'repeated_indices' + # Zip the values of the array with the indices into the array so we can construct the signal + # item. + leaf_select = f""" + UNNEST({leaf_col}) as {leaf_alias}, + UNNEST(RANGE(ARRAY_LENGTH({leaf_col}))) as {repeated_indices_col} + """ + + query = f""" + SELECT {UUID_COLUMN}, {leaf_select} + FROM t + """ + return SelectLeafsResult(duckdb_result=self._query(query), + repeated_idxs_col=repeated_indices_col) + + @override + def select_groups(self, + columns: Optional[Sequence[ColumnId]] = None, + filters: Optional[Sequence[Filter]] = None, + sort_by: Optional[GroupsSortBy] = None, + sort_order: Optional[SortOrder] = SortOrder.DESC, + limit: Optional[int] = 100) -> pd.DataFrame: + raise NotImplementedError + + @override + def select_rows(self, + columns: Optional[Sequence[ColumnId]] = None, + filters: Optional[Sequence[Filter]] = None, + sort_by: Optional[Sequence[str]] = None, + sort_order: Optional[SortOrder] = SortOrder.DESC, + limit: Optional[int] = 100) -> SelectRowsResult: + cols = [column_from_identifier(column) for column in columns or []] + + select_query, col_aliases = self._create_select(cols) + where_query = self._create_where(filters) + + # Sort. + sort_query = '' + if sort_by: + sort_by_query_parts = [] + for sort_by_alias in sort_by: + if sort_by_alias not in col_aliases: + raise ValueError( + f'Column {sort_by_alias} is not defined as an alias in the given columns. ' + f'Available sort by aliases: {col_aliases}') + sort_by_query_parts.append(sort_by_alias) + + if not sort_order: + raise ValueError( + 'Sort order is undefined but sort by is defined. Please define a sort_order') + sort_order_query = sort_order.value + + sort_by_query = ', '.join(sort_by_query_parts) + sort_query = f'ORDER BY {sort_by_query} {sort_order_query}' + + limit_query = f'LIMIT {limit}' if limit else '' + + query = f""" + SELECT {select_query} + FROM t + {where_query} + {sort_query} + {limit_query} + """ + + query_results = cast(list, self._query(query).fetchall()) + + item_rows = map(lambda row: dict(zip(col_aliases, row)), query_results) + return SelectRowsResult(item_rows) + + def _create_select( + self, + columns: Optional[list[Column]] = None, + ) -> tuple[str, list[str]]: + """Create the select statement.""" + if not columns: + columns = self.columns() + # Always return the UUID column. + columns += [Column(UUID_COLUMN)] + + select_queries: list[str] = [] + aliases: list[str] = [] + + for column in columns: + if isinstance(column.feature, Column): + raise ValueError('Transforms are not yet supported.') + aliases.append(column.alias) + select_queries.append(f'"{self._path_to_col(column.feature)}" AS {column.alias}') + + return ', '.join(select_queries), aliases + + def _create_where(self, filters: Optional[Sequence[Filter]] = None) -> str: + if not filters: + return '' + raise ValueError('Filters are not yet supported for the DuckDB implementation.') + + def _query(self, query: str) -> duckdb.DuckDBPyRelation: + """Execute a query that returns a dataframe.""" + if not DEBUG: + return self.con.query(query) + + # Debug mode. + log('Executing:') + log(query) + with DebugTimer('Query'): + result = self.con.query(query) + + return result + + def _path_to_col(self, path: Path) -> str: + """Convert a path to a column name.""" + if isinstance(path, str): + return path + return '.'.join([str(path_comp) for path_comp in path]) + + def _top_level_col_name(self, path: Path, signal_name: str) -> str: + column_name = self._path_to_col(path) + if column_name.endswith('.*'): + # Remove the trailing .* from the column name. + column_name = column_name[:-2] + + return f'{column_name}.{signal_name}' + + +def read_source_manifest(dataset_path: str) -> SourceManifest: + """Read the manifest file.""" + with open_file(os.path.join(dataset_path, MANIFEST_FILENAME), 'r') as f: + return SourceManifest.parse_raw(f.read()) + + +def signal_parquet_prefix(column_name: str, signal_name: str) -> str: + """Get the filename prefix for a signal parquet file.""" + return f'{column_name}.{signal_name}' + + +def signal_manifest_filename(column_name: str, signal_name: str) -> str: + """Get the filename for a signal output.""" + return f'{column_name}.{signal_name}.signal_manifest.json' + + +class SignalManifest(BaseModel): + """The manifest that describes the dataset run, including schema and parquet files.""" + # List of a parquet filepaths storing the data. The paths can be relative to `manifest.json`. + files: list[str] + + data_schema: Schema + signal: Signal + + # The column path that this signal is derived from. + enriched_path: Path + + @validator('signal', pre=True) + def parse_signal(cls, signal: dict) -> Signal: + """Parse a signal to its specific subclass instance.""" + return resolve_signal(signal) diff --git a/src/datasets/db_dataset_duckdb_test.py b/src/datasets/db_dataset_duckdb_test.py new file mode 100644 index 000000000..c779eb00d --- /dev/null +++ b/src/datasets/db_dataset_duckdb_test.py @@ -0,0 +1,271 @@ +"""Tests implementations of db_dataset_duckdb.""" + +import os +import pathlib +from typing import Iterable, Optional + +import pyarrow.parquet as pq +import pytest +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import GetEmbeddingIndexFn +from ..schema import ( + PATH_WILDCARD, + UUID_COLUMN, + DataType, + EnrichmentType, + Field, + Item, + RichData, + Schema, +) +from ..signals.signal import Signal +from ..signals.signal_registry import clear_signal_registry, register_signal +from ..utils import get_dataset_output_dir, open_file +from .db_dataset import DatasetManifest +from .db_dataset_duckdb import ( + DatasetDuckDB, + SignalManifest, + signal_manifest_filename, +) +from .db_dataset_test_utils import TEST_DATASET_NAME, TEST_NAMESPACE, make_db + + +class TestSignal(Signal): + name = 'test_signal' + enrichment_type = EnrichmentType.TEXT + embedding_based = False + + @override + def fields(self) -> dict[str, Field]: + return {'len': Field(dtype=DataType.INT32), 'flen': Field(dtype=DataType.FLOAT32)} + + @override + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[GetEmbeddingIndexFn] = None) -> Iterable[Optional[Item]]: + if data is None: + raise ValueError('data is not defined') + return [{'len': len(text_content), 'flen': float(len(text_content))} for text_content in data] + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + # Setup. + register_signal(TestSignal) + + # Unit test runs. + yield + + # Teardown. + clear_signal_registry() + + +def test_compute_signal_columns_simple(tmp_path: pathlib.Path) -> None: + test_signal = TestSignal() + + db = make_db(db_cls=DatasetDuckDB, + tmp_path=tmp_path, + items=[{ + UUID_COLUMN: b'1' * 16, + 'text': 'hello', + 'text2': 'hello2', + }, { + UUID_COLUMN: b'2' * 16, + 'text': 'hello world', + 'text2': 'hello world2', + }], + schema=Schema( + fields={ + UUID_COLUMN: Field(dtype=DataType.BINARY), + 'text': Field(dtype=DataType.STRING), + 'text2': Field(dtype=DataType.STRING), + })) + + db.compute_signal_columns(signal=test_signal, columns=['text', 'text2']) + + # Check the enriched dataset manifest. + dataset_manifest = db.manifest() + expected_dataset_manifest = DatasetManifest(namespace=TEST_NAMESPACE, + dataset_name=TEST_DATASET_NAME, + data_schema=Schema( + fields={ + UUID_COLUMN: + Field(dtype=DataType.BINARY), + 'text': + Field(dtype=DataType.STRING), + 'text2': + Field(dtype=DataType.STRING), + 'text.test_signal': + Field(fields={ + 'len': Field(dtype=DataType.INT32), + 'flen': Field(dtype=DataType.FLOAT32) + }, + enriched=True), + 'text2.test_signal': + Field(fields={ + 'len': Field(dtype=DataType.INT32), + 'flen': Field(dtype=DataType.FLOAT32) + }, + enriched=True) + })) + assert dataset_manifest == expected_dataset_manifest + + dataset_path = get_dataset_output_dir(tmp_path, db.namespace, db.dataset_name) + + # Make sure the signal manifest for 'text' is correct. + expected_signal_schema = Schema( + fields={ + UUID_COLUMN: + Field(dtype=DataType.BINARY), + 'text': + Field(fields={ + 'len': Field(dtype=DataType.INT32), + 'flen': Field(dtype=DataType.FLOAT32) + }, + enriched=True), + }) + + text_parquet_filename = 'text.test_signal-00000-of-00001.parquet' + expected_manifest = SignalManifest(files=[text_parquet_filename], + data_schema=expected_signal_schema, + signal=test_signal, + enriched_path=('text',)) + manifest_path = os.path.join(dataset_path, signal_manifest_filename('text', test_signal.name)) + with open_file(manifest_path) as f: + signal_manifest = SignalManifest.parse_raw(f.read()) + assert signal_manifest == expected_manifest + + # Make sure the parquet file for 'text' was written and does not include information about the + # second column. + text_stats_text_filepath = os.path.join(dataset_path, text_parquet_filename) + signal_items = pq.read_table(text_stats_text_filepath).to_pylist() + + assert signal_items == [{ + UUID_COLUMN: b'1' * 16, + 'text': { + 'len': len('hello'), + 'flen': float(len('hello')) + } + }, { + UUID_COLUMN: b'2' * 16, + 'text': { + 'len': len('hello world'), + 'flen': float(len('hello world')) + } + }] + + # Make sure the signal manifest for 'text2' is correct. + expected_signal_schema = Schema( + fields={ + UUID_COLUMN: + Field(dtype=DataType.BINARY), + 'text2': + Field(fields={ + 'len': Field(dtype=DataType.INT32), + 'flen': Field(dtype=DataType.FLOAT32) + }, + enriched=True), + }) + text2_parquet_filename = 'text2.test_signal-00000-of-00001.parquet' + expected_manifest = SignalManifest(files=[text2_parquet_filename], + data_schema=expected_signal_schema, + signal=test_signal, + enriched_path=('text2',)) + manifest_path = os.path.join(dataset_path, signal_manifest_filename('text2', test_signal.name)) + with open_file(manifest_path) as f: + signal_manifest = SignalManifest.parse_raw(f.read()) + assert signal_manifest == expected_manifest + + # Make sure the parquet file for 'text2' was written and does not include information about the + # first column. + text_stats_text2_filepath = os.path.join(dataset_path, text2_parquet_filename) + signal_items = pq.read_table(text_stats_text2_filepath).to_pylist() + + assert signal_items == [{ + UUID_COLUMN: b'1' * 16, + 'text2': { + 'len': len('hello2'), + 'flen': float(len('hello2')) + } + }, { + UUID_COLUMN: b'2' * 16, + 'text2': { + 'len': len('hello world2'), + 'flen': float(len('hello world2')) + } + }] + + +def test_compute_signal_columns_repeated(tmp_path: pathlib.Path) -> None: + test_signal = TestSignal() + + db = make_db(db_cls=DatasetDuckDB, + tmp_path=tmp_path, + items=[{ + UUID_COLUMN: b'1' * 16, + 'text': ['hello', 'hello hello'], + }, { + UUID_COLUMN: b'2' * 16, + 'text': ['hello2', 'hello2 hello2'], + }], + schema=Schema( + fields={ + UUID_COLUMN: Field(dtype=DataType.BINARY), + 'text': Field(repeated_field=Field(dtype=DataType.STRING)), + })) + + db.compute_signal_columns(signal=test_signal, columns=[('text', PATH_WILDCARD)]) + + # Check the enriched dataset manifest. + manifest = db.manifest() + expected_manifest = DatasetManifest( + namespace=TEST_NAMESPACE, + dataset_name=TEST_DATASET_NAME, + data_schema=Schema( + fields={ + UUID_COLUMN: + Field(dtype=DataType.BINARY), + 'text': + Field(repeated_field=Field(dtype=DataType.STRING)), + 'text.test_signal': + Field(repeated_field=Field(fields={ + 'len': Field(dtype=DataType.INT32), + 'flen': Field(dtype=DataType.FLOAT32) + }, + enriched=True), + enriched=True) + })) + assert manifest == expected_manifest + + text_parquet_filename = 'text.*.test_signal-00000-of-00001.parquet' + dataset_path = get_dataset_output_dir(tmp_path, db.namespace, db.dataset_name) + + # Make sure the parquet file for 'text' was written and does not include information about the + # second column. + text_stats_text_filepath = os.path.join(dataset_path, text_parquet_filename) + signal_items = pq.read_table(text_stats_text_filepath).to_pylist() + + assert signal_items == [{ + UUID_COLUMN: + b'1' * 16, + 'text': [{ + 'len': len('hello'), + 'flen': float(len('hello')) + }, { + 'len': len('hello hello'), + 'flen': float(len('hello hello')) + }] + }, { + UUID_COLUMN: + b'2' * 16, + 'text': [{ + 'len': len('hello2'), + 'flen': float(len('hello2')) + }, { + 'len': len('hello2 hello2'), + 'flen': float(len('hello2 hello2')) + }] + }] diff --git a/src/datasets/db_dataset_test.py b/src/datasets/db_dataset_test.py new file mode 100644 index 000000000..5f7ab6446 --- /dev/null +++ b/src/datasets/db_dataset_test.py @@ -0,0 +1,171 @@ +"""Tests implentations of db_dataset.""" + +import pathlib +from typing import Iterable, Optional, Type + +import pytest +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import GetEmbeddingIndexFn +from ..schema import UUID_COLUMN, DataType, EnrichmentType, Field, Item, RichData, Schema +from ..signals.signal import Signal +from .db_dataset import DatasetDB, SortOrder +from .db_dataset_duckdb import DatasetDuckDB +from .db_dataset_test_utils import make_db + +ALL_DBS = [DatasetDuckDB] + +SIMPLE_DATASET_NAME = 'simple' + +SIMPLE_ITEMS: list[Item] = [{ + UUID_COLUMN: b'1' * 16, + 'str': 'a', + 'int': 1, + 'bool': False, + 'float': 3.0 +}, { + UUID_COLUMN: b'2' * 16, + 'str': 'b', + 'int': 2, + 'bool': True, + 'float': 2.0 +}, { + UUID_COLUMN: b'2' * 16, + 'str': 'b', + 'int': 2, + 'bool': True, + 'float': 1.0 +}] + +SIMPLE_SCHEMA = Schema( + fields={ + UUID_COLUMN: Field(dtype=DataType.BINARY), + 'str': Field(dtype=DataType.STRING), + 'int': Field(dtype=DataType.INT64), + 'bool': Field(dtype=DataType.BOOLEAN), + 'float': Field(dtype=DataType.FLOAT64), + }) + + +class TestSelectRows: + + @pytest.mark.parametrize('db_cls', ALL_DBS) + def test_default(self, tmp_path: pathlib.Path, db_cls: Type[DatasetDB]) -> None: + db = make_db(db_cls, tmp_path, SIMPLE_ITEMS, SIMPLE_SCHEMA) + + result = db.select_rows() + + assert list(result) == SIMPLE_ITEMS + + @pytest.mark.parametrize('db_cls', ALL_DBS) + def test_columns(self, tmp_path: pathlib.Path, db_cls: Type[DatasetDB]) -> None: + db = make_db(db_cls, tmp_path, SIMPLE_ITEMS, SIMPLE_SCHEMA) + + result = db.select_rows(columns=['str', 'float']) + + assert list(result) == [{ + UUID_COLUMN: b'1' * 16, + 'str': 'a', + 'float': 3.0 + }, { + UUID_COLUMN: b'2' * 16, + 'str': 'b', + 'float': 2.0 + }, { + UUID_COLUMN: b'2' * 16, + 'str': 'b', + 'float': 1.0 + }] + + @pytest.mark.parametrize('db_cls', ALL_DBS) + def test_sort(self, tmp_path: pathlib.Path, db_cls: Type[DatasetDB]) -> None: + db = make_db(db_cls, tmp_path, SIMPLE_ITEMS, SIMPLE_SCHEMA) + + result = db.select_rows(columns=[UUID_COLUMN, 'float'], + sort_by=['float'], + sort_order=SortOrder.ASC) + + assert list(result) == [{ + UUID_COLUMN: b'2' * 16, + 'float': 1.0 + }, { + UUID_COLUMN: b'2' * 16, + 'float': 2.0 + }, { + UUID_COLUMN: b'1' * 16, + 'float': 3.0 + }] + + result = db.select_rows(columns=[UUID_COLUMN, 'float'], + sort_by=['float'], + sort_order=SortOrder.DESC) + + assert list(result) == [{ + UUID_COLUMN: b'1' * 16, + 'float': 3.0 + }, { + UUID_COLUMN: b'2' * 16, + 'float': 2.0 + }, { + UUID_COLUMN: b'2' * 16, + 'float': 1.0 + }] + + @pytest.mark.parametrize('db_cls', ALL_DBS) + def test_limit(self, tmp_path: pathlib.Path, db_cls: Type[DatasetDB]) -> None: + db = make_db(db_cls, tmp_path, SIMPLE_ITEMS, SIMPLE_SCHEMA) + + result = db.select_rows(columns=[UUID_COLUMN, 'float'], + sort_by=['float'], + sort_order=SortOrder.ASC, + limit=2) + assert list(result) == [{ + UUID_COLUMN: b'2' * 16, + 'float': 1.0 + }, { + UUID_COLUMN: b'2' * 16, + 'float': 2.0 + }] + + +class TestInvalidSignal(Signal): + name = 'test_invalid_signal' + enrichment_type = EnrichmentType.TEXT + + @override + def fields(self) -> dict[str, Field]: + return {'len': Field(dtype=DataType.INT32)} + + @override + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[GetEmbeddingIndexFn] = None) -> Iterable[Optional[Item]]: + # Return an invalid output that doesn't match the input length. + return [] + + +class TestComputeSignalItems: + + @pytest.mark.parametrize('db_cls', ALL_DBS) + def test_signal_output_validation(self, tmp_path: pathlib.Path, db_cls: Type[DatasetDB]) -> None: + signal = TestInvalidSignal() + + db = make_db(db_cls=db_cls, + tmp_path=tmp_path, + items=[{ + UUID_COLUMN: b'1' * 16, + 'text': 'hello', + }, { + UUID_COLUMN: b'2' * 16, + 'text': 'hello world', + }], + schema=Schema(fields={ + UUID_COLUMN: Field(dtype=DataType.BINARY), + 'text': Field(dtype=DataType.STRING), + })) + + with pytest.raises(ValueError, + match='The signal output and the input data do not have the same length'): + db.compute_signal_columns(signal=signal, columns=[('text',)]) diff --git a/src/datasets/db_dataset_test_utils.py b/src/datasets/db_dataset_test_utils.py new file mode 100644 index 000000000..0830389ff --- /dev/null +++ b/src/datasets/db_dataset_test_utils.py @@ -0,0 +1,41 @@ +"""Tests utils of for db_dataset_test.""" +import os +import pathlib +from typing import Type + +from ..schema import MANIFEST_FILENAME, PARQUET_FILENAME_PREFIX, Item, Schema, SourceManifest +from ..utils import get_dataset_output_dir, open_file, write_items_to_parquet +from .db_dataset import DatasetDB + +TEST_NAMESPACE = 'test_namespace' +TEST_DATASET_NAME = 'test_dataset' + + +def make_db(db_cls: Type[DatasetDB], tmp_path: pathlib.Path, items: list[Item], + schema: Schema) -> DatasetDB: + """Create a test database.""" + _write_items(tmp_path, TEST_DATASET_NAME, items, schema) + + # Override the data base path to point to the temp directory so the database knows where to read + # files. This is only needed for the DuckDB file basedimplementation. + olddata_path = os.environ['LILAC_DATA_PATH'] if 'LILAC_DATA_PATH' in os.environ else None + os.environ['LILAC_DATA_PATH'] = str(tmp_path) + db = db_cls(TEST_NAMESPACE, TEST_DATASET_NAME) + os.environ['LILAC_DATA_PATH'] = olddata_path or '' + return db + + +def _write_items(tmpdir: pathlib.Path, dataset_name: str, items: list[Item], + schema: Schema) -> None: + """Write the items JSON to the dataset format: manifest.json and parquet files.""" + source_dir = get_dataset_output_dir(str(tmpdir), TEST_NAMESPACE, dataset_name) + os.makedirs(source_dir) + simple_parquet_files, _ = write_items_to_parquet(items, + source_dir, + schema, + filename_prefix=PARQUET_FILENAME_PREFIX, + shard_index=0, + num_shards=1) + manifest = SourceManifest(files=[simple_parquet_files], data_schema=schema) + with open_file(os.path.join(source_dir, MANIFEST_FILENAME), 'w') as f: + f.write(manifest.json(indent=2, exclude_none=True)) diff --git a/src/datasets/sources/__init__.py b/src/datasets/sources/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/datasets/sources/csv_source.py b/src/datasets/sources/csv_source.py new file mode 100644 index 000000000..ce4b9ca75 --- /dev/null +++ b/src/datasets/sources/csv_source.py @@ -0,0 +1,189 @@ +"""CSV source.""" +import os +import time +import uuid +from typing import Optional + +import duckdb +import pyarrow.parquet as pq +import requests +from pydantic import BaseModel +from typing_extensions import override # type: ignore + +from ...constants import data_path +from ...schema import PARQUET_FILENAME_PREFIX, ImageInfo, Path, arrow_schema_to_schema +from ...utils import ( + GCS_REGEX, + UUID_COLUMN, + CopyRequest, + copy_files, + delete_file, + file_exists, + get_image_path, + log, + makedirs, + open_file, +) +from .source import ShardsLoader, Source, SourceProcessResult + + +class ImageColumn(BaseModel): + """Info about the column that contains a link to the image file.""" + name: str + subdir = '' + path_suffix = '' + + +class ShardInfo(BaseModel): + """Info about an individual csv file shard. Each shard is processed in parallel.""" + filepath: str + shard_index: int + num_shards: int + output_dir: str + delim: str + image_columns: Optional[list[ImageColumn]] + + +class CSVSource(Source): + """CSV source.""" + name = 'csv' + + filepaths: list[str] + delim: Optional[str] = ',' + image_columns: Optional[list[ImageColumn]] + + async def process(self, output_dir: str, shards_loader: ShardsLoader) -> SourceProcessResult: + """Process the source upload request.""" + start_time = time.time() + gcs_filepaths: list[str] = [] + temp_files_to_delete = [] + for filepath in self.filepaths: + if filepath.startswith(('http://', 'https://')): + tmp_filename = uuid.uuid4().bytes.hex() + gcs_filepath = f'{data_path()}/local_cache/{tmp_filename}' + if not file_exists(gcs_filepath): + log(f'Downloading CSV from url {filepath} to {gcs_filepath}') + dl = requests.get(filepath, timeout=10000) + with open_file(gcs_filepath, 'wb') as f: + f.write(dl.content) + temp_files_to_delete.append(gcs_filepath) + filepath = gcs_filepath + else: + if not file_exists(filepath): + raise ValueError(f'CSV file {filepath} was not found.') + gcs_filepaths.append(filepath) + + elapsed = time.time() - start_time + log(f'[CSV Source] Checking input CSV file existence took {elapsed:.2f} seconds.') + num_shards = len(self.filepaths) + delim = self.delim or ',' + shard_infos = [ + ShardInfo(filepath=path, + shard_index=i, + num_shards=num_shards, + output_dir=output_dir, + delim=delim, + image_columns=self.image_columns) for i, path in enumerate(gcs_filepaths) + ] + shard_info_dicts = [x.dict(exclude_none=True) for x in shard_infos] + + start_time = time.time() + shard_outs = await shards_loader(shard_info_dicts) + elapsed = time.time() - start_time + log(f'[CSV Source] Converting files {gcs_filepaths} to parquet took {elapsed:.2f} seconds.') + + filepaths: list[str] = [x['filepath'] for x in shard_outs] + num_items = sum(x['num_items'] for x in shard_outs) + arrow_schema = pq.read_schema(open_file(filepaths[0], mode='rb')) + schema = arrow_schema_to_schema(arrow_schema) + images: Optional[list[ImageInfo]] = None + if self.image_columns: + images = [ + ImageInfo(path=_csv_column_to_path(image_column.name)) + for image_column in self.image_columns + ] + + # Clean up the temporary files that we created for http CSV requests. + for temp_filename in temp_files_to_delete: + delete_file(temp_filename) + return SourceProcessResult(filepaths=filepaths, + data_schema=schema, + images=images, + num_items=num_items) + + @override + def process_shard(self, shard_info_dict: dict) -> dict: + shard_info = ShardInfo(**shard_info_dict) + filepath = shard_info.filepath + con = duckdb.connect(database=':memory:') + con.install_extension('httpfs') + con.load_extension('httpfs') + delim = shard_info.delim + # DuckDB expects s3 protocol: https://duckdb.org/docs/guides/import/s3_import.html. + s3_filepath = filepath.replace('gs://', 's3://') + # The sample size here is just used to infer the schema. + # TODO(https://github.com/lilacml/lilac/issues/1): Remove this workaround when duckdb + # supports direct casting uuid --> blob. + blob_uuid = "regexp_replace(replace(uuid(), '-', ''), '.{2}', '\\\\x\\0', 'g')::BLOB" + csv_sql = f"SELECT {blob_uuid} as {UUID_COLUMN}, * FROM read_csv_auto(\ + '{s3_filepath}', SAMPLE_SIZE=500000, DELIM='{delim}', IGNORE_ERRORS=true)" + + prefix = os.path.join(shard_info.output_dir, PARQUET_FILENAME_PREFIX) + shard_index = shard_info.shard_index + num_shards = shard_info.num_shards + out_filepath = f'{prefix}-{shard_index:05d}-of-{num_shards:05d}.parquet' + + # DuckDB won't create the parent dirs so we have to create it. + out_dirname = os.path.dirname(out_filepath) + makedirs(out_dirname) + gcs_setup = '' + if 'GCS_REGION' in os.environ: + gcs_setup = f""" + SET s3_region='{os.environ['GCS_REGION']}'; + SET s3_access_key_id='{os.environ['GCS_ACCESS_KEY']}'; + SET s3_secret_access_key='{os.environ['GCS_SECRET_KEY']}'; + SET s3_endpoint='storage.googleapis.com'; + """ + # DuckDB expects s3 protocol: https://duckdb.org/docs/guides/import/s3_import.html. + s3_out_filepath = out_filepath.replace('gs://', 's3://') + con.execute(f""" + {gcs_setup} + SET preserve_insertion_order=false; + SET experimental_parallel_csv=true; + COPY ({csv_sql}) TO '{s3_out_filepath}' + """) + + shard_num_items = con.execute(f""" + CREATE VIEW csv_view AS (SELECT * FROM read_parquet('{s3_out_filepath}')); + SELECT COUNT({UUID_COLUMN}) FROM csv_view; + """).fetchall()[0][0] + + # Copy the images from the source to the output dir. + if shard_info.image_columns: + image_col_names = ', '.join([column.name for column in shard_info.image_columns]) + result = con.execute(f""" + SELECT {image_col_names}, {UUID_COLUMN} FROM csv_view; + """).fetchall() + + copy_image_infos: list[CopyRequest] = [] + for row in result: + row_id = row[-1] + for i, image_column in enumerate(shard_info.image_columns): + input_image_path = os.path.join(os.path.dirname(shard_info.filepath), image_column.subdir, + f'{row[i]}{image_column.path_suffix}') + copy_image_infos.append( + CopyRequest(from_path=input_image_path, + to_path=get_image_path(output_dir=shard_info.output_dir, + path=_csv_column_to_path(image_column.name), + row_id=row_id))) + + log(f'[CSV Source] Copying {len(copy_image_infos)} images...') + copy_files(copy_image_infos, + input_gcs=GCS_REGEX.match(shard_info.filepath) is not None, + output_gcs=GCS_REGEX.match(out_filepath) is not None) + con.close() + return {'filepath': out_filepath, 'num_items': int(shard_num_items)} + + +def _csv_column_to_path(column_name: str) -> Path: + return (column_name,) diff --git a/src/datasets/sources/default_sources.py b/src/datasets/sources/default_sources.py new file mode 100644 index 000000000..6e2b9997a --- /dev/null +++ b/src/datasets/sources/default_sources.py @@ -0,0 +1,12 @@ +"""Registers all available default sources.""" +from .csv_source import CSVSource +from .pandas_source import PandasSource +from .source_registry import register_source +from .tfds_source import TFDSSource + + +def register_default_sources() -> None: + """Register all the default sources.""" + register_source(CSVSource) + register_source(TFDSSource) + register_source(PandasSource) diff --git a/src/datasets/sources/pandas_source.py b/src/datasets/sources/pandas_source.py new file mode 100644 index 000000000..1080573ec --- /dev/null +++ b/src/datasets/sources/pandas_source.py @@ -0,0 +1,166 @@ +"""Pandas source.""" +import os +import time +from typing import Any, Optional + +import duckdb +import pandas as pd +import pyarrow.parquet as pq +from pydantic import BaseModel +from typing_extensions import override # type: ignore + +from ...schema import PARQUET_FILENAME_PREFIX, ImageInfo, Path, arrow_schema_to_schema +from ...utils import ( + GCS_REGEX, + UUID_COLUMN, + CopyRequest, + copy_files, + get_image_path, + log, + makedirs, + open_file, +) +from .source import ShardsLoader, Source, SourceProcessResult + + +class ImageColumn(BaseModel): + """Info about the column that contains a link to the image file.""" + name: str + subdir = '' + path_suffix = '' + + +class ShardInfo(BaseModel): + """Info about an individual file shard. Each shard is processed in parallel.""" + output_dir: str + image_base_path: Optional[str] + image_columns: Optional[list[ImageColumn]] + + +class ShardOut(BaseModel): + """Output of processing a single shard.""" + filepath: str + num_items: int + + +class PandasSource(Source): + """Pandas source.""" + name = 'pandas' + + _df: pd.DataFrame + + # The base path of the images files. We prepend this to the image path column in the dataframe. + image_base_path: Optional[str] + image_columns: Optional[list[ImageColumn]] + + class Config: + underscore_attrs_are_private = True + + def __init__(self, df: pd.DataFrame, **kwargs: Any): + super().__init__(**kwargs) + self._df = df + + async def process(self, output_dir: str, shards_loader: ShardsLoader) -> SourceProcessResult: + """Process the source upload request.""" + shard_info = ShardInfo(image_base_path=self.image_base_path, + output_dir=output_dir, + image_columns=self.image_columns).dict(exclude_none=True) + + start_time = time.time() + shard_outs = [ShardOut(**x) for x in (await shards_loader([shard_info]))] + elapsed = time.time() - start_time + log(f'[Pandas Source] Converting pandas to parquet took {elapsed:.2f} seconds.') + + filepaths: list[str] = [x.filepath for x in shard_outs] + num_items = sum(x.num_items for x in shard_outs) + arrow_schema = pq.read_schema(open_file(filepaths[0], mode='rb')) + schema = arrow_schema_to_schema(arrow_schema) + images: Optional[list[ImageInfo]] = None + if self.image_columns: + images = [ + ImageInfo(path=_pandas_column_to_path(image_column.name)) + for image_column in self.image_columns + ] + + return SourceProcessResult(filepaths=filepaths, + data_schema=schema, + images=images, + num_items=num_items) + + @override + def process_shard(self, shard_info_dict: dict) -> dict: + shard_info = ShardInfo(**shard_info_dict) + con = duckdb.connect(database=':memory:') + con.install_extension('httpfs') + con.load_extension('httpfs') + + # Register the dataframe as a duckdb view. + con.register('df', self._df) + + # TODO(https://github.com/lilacml/lilac/issues/1): Remove this workaround when duckdb + # supports direct casting uuid --> blob. + blob_uuid = "regexp_replace(replace(uuid(), '-', ''), '.{2}', '\\\\x\\0', 'g')::BLOB" + pd_sql = f'SELECT {blob_uuid} as {UUID_COLUMN}, * FROM df' + + prefix = os.path.join(shard_info.output_dir, PARQUET_FILENAME_PREFIX) + shard_index = 0 + num_shards = 1 + out_filepath = f'{prefix}-{shard_index:05d}-of-{num_shards:05d}.parquet' + + # DuckDB won't create the parent dirs so we have to create it. + out_dirname = os.path.dirname(out_filepath) + makedirs(out_dirname) + gcs_setup = '' + if 'GCS_REGION' in os.environ: + gcs_setup = f""" + SET s3_region='{os.environ['GCS_REGION']}'; + SET s3_access_key_id='{os.environ['GCS_ACCESS_KEY']}'; + SET s3_secret_access_key='{os.environ['GCS_SECRET_KEY']}'; + SET s3_endpoint='storage.googleapis.com'; + """ + # DuckDB expects s3 protocol: https://duckdb.org/docs/guides/import/s3_import.html. + s3_out_filepath = out_filepath.replace('gs://', 's3://') + con.execute(f""" + {gcs_setup} + SET preserve_insertion_order=false; + COPY ({pd_sql}) TO '{s3_out_filepath}' + """) + + shard_num_items = con.execute(f""" + CREATE VIEW pd_view AS (SELECT * FROM read_parquet('{s3_out_filepath}')); + SELECT COUNT({UUID_COLUMN}) FROM pd_view; + """).fetchall()[0][0] + + # Copy the images from the source to the output dir. + if shard_info.image_columns: + if not shard_info.image_base_path: + raise ValueError('image_base_path must be set if image_columns is set.') + + image_base_path = shard_info.image_base_path + image_col_names = ', '.join([column.name for column in shard_info.image_columns]) + result = con.execute(f""" + SELECT {image_col_names}, {UUID_COLUMN} FROM pd_view; + """).fetchall() + + copy_image_infos: list[CopyRequest] = [] + for row in result: + row_id = row[-1] + for i, image_column in enumerate(shard_info.image_columns): + input_image_path = os.path.join(os.path.dirname(image_base_path), image_column.subdir, + f'{row[i]}{image_column.path_suffix}') + copy_image_infos.append( + CopyRequest(from_path=input_image_path, + to_path=get_image_path(output_dir=shard_info.output_dir, + path=_pandas_column_to_path(image_column.name), + row_id=row_id))) + + log(f'[Pandas Source] Copying {len(copy_image_infos)} images...') + copy_files(copy_image_infos, + input_gcs=GCS_REGEX.match(image_base_path) is not None, + output_gcs=GCS_REGEX.match(out_filepath) is not None) + con.close() + return {'filepath': out_filepath, 'num_items': int(shard_num_items)} + + +def _pandas_column_to_path(column_name: str) -> Path: + return (column_name,) diff --git a/src/datasets/sources/pandas_source_test.py b/src/datasets/sources/pandas_source_test.py new file mode 100644 index 000000000..f58e44aa1 --- /dev/null +++ b/src/datasets/sources/pandas_source_test.py @@ -0,0 +1,31 @@ +"""Tests for the pandas source.""" +import pathlib + +import pandas as pd + +from ...schema import UUID_COLUMN, Field, Schema +from .pandas_source import PandasSource +from .source import SourceProcessResult + + +async def test_simple_dataframe(tmp_path: pathlib.Path) -> None: + df = pd.DataFrame({'name': ['a', 'b', 'c'], 'age': [1, 2, 3]}) + source = PandasSource(df) + + async def shards_loader(shard_infos: list[dict]) -> list[dict]: + return [source.process_shard(x) for x in shard_infos] + + result = await source.process(str(tmp_path), shards_loader) + expected_result = SourceProcessResult(data_schema=Schema( + fields={ + UUID_COLUMN: Field(dtype='binary'), + 'name': Field(dtype='string'), + 'age': Field(dtype='int64') + }), + num_items=3, + filepaths=[]) + + # Validate except for the filepaths, which are not deterministic. + expected_result.filepaths = result.filepaths + assert result == expected_result + assert len(result.filepaths) == 1 diff --git a/src/datasets/sources/source.py b/src/datasets/sources/source.py new file mode 100644 index 000000000..cfab707ca --- /dev/null +++ b/src/datasets/sources/source.py @@ -0,0 +1,50 @@ +"""Interface for implementing a source.""" + +import abc +from typing import Any, Awaitable, Callable, ClassVar, Optional + +from pydantic import BaseModel + +from ...schema import ImageInfo, Schema + +# Loads an input shard and returns the output of `process_shard`. +ShardsLoader = Callable[[list[dict]], Awaitable[list[dict]]] + + +class SourceProcessResult(BaseModel): + """The result after processing all the shards of a source dataset.""" + filepaths: list[str] + data_schema: Schema + images: Optional[list[ImageInfo]] + num_items: int + + +class Source(abc.ABC, BaseModel): + """Interface for sources to implement. A source processes a set of shards and writes files.""" + # ClassVars do not get serialized with pydantic. + name: ClassVar[str] + + # The source_name will get populated in init automatically from the class name so it gets + # serialized and the source author doesn't have to define both the static property and the field. + source_name: str = 'source_base' + + class Config: + underscore_attrs_are_private = True + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + if 'name' not in self.__class__.__dict__: + raise ValueError('Signal attribute "name" must be defined.') + + self.source_name = self.__class__.name + + @abc.abstractmethod + async def process(self, output_dir: str, shards_loader: ShardsLoader) -> SourceProcessResult: + """Process the source upload request.""" + pass + + @abc.abstractmethod + def process_shard(self, shard_info_dict: dict) -> dict: + """Process an individual file shard from an input dataset.""" + pass diff --git a/src/datasets/sources/source_registry.py b/src/datasets/sources/source_registry.py new file mode 100644 index 000000000..ba67056e2 --- /dev/null +++ b/src/datasets/sources/source_registry.py @@ -0,0 +1,42 @@ +"""Source registry for the dataset sources.""" + +from typing import Type, Union + +from .source import Source + +SOURCE_REGISTRY: dict[str, Type[Source]] = {} + + +def register_source(source_cls: Type[Source]) -> None: + """Register a source configuration globally.""" + if source_cls.name in SOURCE_REGISTRY: + raise ValueError(f'Source "{source_cls.name}" has already been registered!') + + SOURCE_REGISTRY[source_cls.name] = source_cls + + +def get_source_cls(source_name: str) -> Type[Source]: + """Return a registered source given the name in the registry.""" + if source_name not in SOURCE_REGISTRY: + raise ValueError(f'Source "{source_name}" not found in the registry') + + return SOURCE_REGISTRY[source_name] + + +def resolve_source(source: Union[dict, Source]) -> Source: + """Resolve a generic source base class to a specific source class.""" + if isinstance(source, Source): + # The source is already parsed. + return source + + source_name = source.get('source_name') + if not source_name: + raise ValueError('"source_name" needs to be defined in the json dict.') + + source_cls = get_source_cls(source_name) + return source_cls(**source) + + +def clear_source_registry() -> None: + """Clear the source registry.""" + SOURCE_REGISTRY.clear() diff --git a/src/datasets/sources/source_registry_test.py b/src/datasets/sources/source_registry_test.py new file mode 100644 index 000000000..dd793618e --- /dev/null +++ b/src/datasets/sources/source_registry_test.py @@ -0,0 +1,49 @@ +"""A source to compute semantic search for a document.""" +from typing import Iterable + +import pytest +from typing_extensions import override # type: ignore + +from .source import ShardsLoader, Source, SourceProcessResult +from .source_registry import clear_source_registry, get_source_cls, register_source, resolve_source + + +class TestSource(Source): + """A test source.""" + name = 'test_source' + + @override + def process(self, output_dir: str, shards_loader: ShardsLoader) -> SourceProcessResult: + pass + + @override + def process_shard(self, shard_info_dict: dict) -> dict: + pass + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + # Setup. + register_source(TestSource) + + # Unit test runs. + yield + + # Teardown. + clear_source_registry() + + +def test_get_source_cls() -> None: + """Test getting a source.""" + assert TestSource == get_source_cls('test_source') + + +def test_resolve_source() -> None: + """Test resolving a source.""" + test_source = TestSource() + + # sources pass through. + assert resolve_source(test_source) == test_source + + # Dicts resolve to the base class. + assert resolve_source(test_source.dict()) == test_source diff --git a/src/datasets/sources/tfds_source.py b/src/datasets/sources/tfds_source.py new file mode 100644 index 000000000..5798ceeec --- /dev/null +++ b/src/datasets/sources/tfds_source.py @@ -0,0 +1,185 @@ +"""TFDS source.""" +import time +from typing import Optional, Union + +import numpy as np +import tensorflow as tf +import tensorflow_datasets as tfds +from pydantic import BaseModel + +from ...schema import PARQUET_FILENAME_PREFIX, UUID_COLUMN, DataType, Field, Item, Schema +from ...utils import log, write_items_to_parquet +from .source import ShardsLoader, Source, SourceProcessResult + +TFDSElement = Union[dict, tf.RaggedTensor, tf.Tensor] + + +class ShardInfo(BaseModel): + """Information about an individual source file shard.""" + dataset_name: str + split: str + data_schema: Schema + shard_index: int + num_shards: int + output_dir: str + + +def _convert_to_item(tfds_element: TFDSElement) -> Item: + if isinstance(tfds_element, dict): + return {name: _convert_to_item(sub_element) for name, sub_element in tfds_element.items()} + elif isinstance(tfds_element, tf.RaggedTensor): + return tfds_element.to_list() + elif isinstance(tfds_element, tf.Tensor): + rank = tf.rank(tfds_element).numpy() + if rank > 1: + # Flatten the tensor. + tfds_element = tf.reshape(tfds_element, [-1]) + return tfds_element.numpy() + else: + raise ValueError( + f'Failed to convert TFDS element to py object: unknown type: "{type(tfds_element)}"') + + +def _tf_dtype_to_dtype(dtype: tf.DType) -> DataType: + """Convert a TF dtype to pyarrow dtype.""" + # Floats. + if dtype == tf.float16: + return DataType.FLOAT16 + elif dtype == tf.float32: + return DataType.FLOAT32 + elif dtype == tf.float64: + return DataType.FLOAT64 + # Ints. + elif dtype == tf.int8: + return DataType.INT8 + elif dtype == tf.int16: + return DataType.INT16 + elif dtype == tf.int32: + return DataType.INT32 + elif dtype == tf.int64: + return DataType.INT64 + elif dtype == tf.uint8: + return DataType.UINT8 + elif dtype == tf.uint16: + return DataType.UINT16 + elif dtype == tf.uint32: + return DataType.UINT32 + elif dtype == tf.uint64: + return DataType.UINT64 + elif dtype.is_bool: + return DataType.BOOLEAN + elif dtype == tf.string: + return DataType.STRING + else: + raise ValueError(f'Cannot convert TF dtype to dtype. Unsupported TF dtype: "{dtype}"') + + +def _tfds_subschema_to_field(feature: tfds.features.FeatureConnector) -> Union[Field, None]: + # Handle composite features. + if isinstance(feature, tfds.features.FeaturesDict): + fields: dict[str, Field] = {} + for name, sub_feature in feature.items(): + field = _tfds_subschema_to_field(sub_feature) + if field: + fields[name] = field + return Field(fields=fields) + elif isinstance(feature, tfds.features.Sequence): + field = _tfds_subschema_to_field(feature.feature) + if field: + return Field(repeated_field=field) + else: + return None + # Handle primitives. + elif isinstance(feature, tfds.features.Tensor): + rank = len(feature.shape) + if rank > 1: + log(f'Feature "{feature}" has a higher rank tensor with shape {feature.shape}: will flatten') + # TODO(smilkov): Store the original shape somewhere in parquet column metadata. + dtype = _tf_dtype_to_dtype(feature.dtype) + size = np.prod(feature.shape) + field = Field(dtype=dtype) + if size > 1: + return Field(repeated_field=field) + else: + return field + else: + log(f'Skipping feature "{feature}": type "{type(feature)}" is not supported.') + return None + + +def _tfds_schema_to_schema(feature: tfds.features.FeaturesDict) -> Schema: + """Convert the TFDS schema to our schema.""" + # Handle composite features. + fields: dict[str, Field] = {} + for name, sub_feature in feature.items(): + field = _tfds_subschema_to_field(sub_feature) + if field: + fields[name] = field + + # Add UUID to the Schema. + fields[UUID_COLUMN] = Field(dtype=DataType.BINARY) + + return Schema(fields=fields) + + +class TFDSSource(Source): + """TFDS source.""" + name = 'tfds' + + dataset_name: str + split: Optional[str] = None + + async def process(self, output_dir: str, shards_loader: ShardsLoader) -> SourceProcessResult: + """Process the source upload request.""" + builder = tfds.builder(self.dataset_name) + split = list(builder.info.splits.keys())[0] + if self.split: + split = self.split + elif 'train' in builder.info.splits: + split = 'train' + log(f'TFDS({self.dataset_name}): Using split "{split}"') + + if not builder.info.splits.total_num_examples: + raise ValueError(f'No examples found in {builder.name!r}. Was the dataset generated ?') + + if not tf.io.gfile.exists(builder._data_dir): + log(f'Downloading and preparing "{builder.name}"...') + start_time = time.time() + builder.download_and_prepare() + elapsed = time.time() - start_time + log(f'Download and prepare of "{builder.name}" took {elapsed:.2f} seconds.') + + schema = _tfds_schema_to_schema(builder.info.features) + num_shards = builder.info.splits[split].num_shards + shard_infos = [ + ShardInfo(dataset_name=self.dataset_name, + split=split, + data_schema=schema, + shard_index=shard_index, + num_shards=num_shards, + output_dir=output_dir) for shard_index in range(num_shards) + ] + shard_outs = await shards_loader([x.dict(exclude_none=True) for x in shard_infos]) + filepaths: list[str] = [x['filepath'] for x in shard_outs] + num_items = sum(x['num_items'] for x in shard_outs) + + return SourceProcessResult(filepaths=filepaths, + data_schema=schema, + images=None, + num_items=num_items) + + def process_shard(self, shard_info_dict: dict) -> dict: + """Process an input file shard. Each shard is processed in parallel by different workers.""" + shard_info = ShardInfo(**shard_info_dict) + ds = tfds.load(shard_info.dataset_name, + split=f'{shard_info.split}[{shard_info.shard_index}shard]') + + ds = ds.prefetch(10_000) + items = (_convert_to_item(tfds_element) for tfds_element in ds) + filepath, num_items = write_items_to_parquet(items=items, + output_dir=shard_info.output_dir, + schema=shard_info.data_schema, + filename_prefix=PARQUET_FILENAME_PREFIX, + shard_index=shard_info.shard_index, + num_shards=shard_info.num_shards) + return {'filepath': filepath, 'num_items': num_items} diff --git a/src/db_deprecated.py b/src/db_deprecated.py new file mode 100644 index 000000000..fd8b90b8a --- /dev/null +++ b/src/db_deprecated.py @@ -0,0 +1,274 @@ +"""Methods for interfacing with the db.""" +import itertools +import os +import re +import time +from typing import Iterable, Optional, TypeVar, cast + +import cohere +import numpy as np +import pandas as pd +from datasets import DatasetDict, load_dataset +from sklearn.linear_model import LogisticRegression +from sklearn.preprocessing import normalize + +from .constants import ( + DATA_JSON_FILENAME, + EMBEDDINGS_FILENAME, + Dataset, + DatasetRow, + LabeledExample, + data_path, +) +from .server_api import ( + AddDatasetOptions, + AddExamplesOptions, + GetModelInfoOptions, + ListModelsResponse, + LoadModelOptions, + LoadModelResponse, + ModelInfo, + SaveModelOptions, + SearchExamplesOptions, + SearchExamplesResult, + SearchExamplesRowResult, +) +from .utils import get_output_dir, log, open_file + +PREFIX = '' +co = cohere.Client(os.environ['COHERE_API_KEY']) + +BATCH_SIZE = 96 +DATA_DIR = '/Users/niku/Code/datasets/jigsaw-toxic-comment-classification-challenge' + + +def list_models() -> ListModelsResponse: + """List the models found in gcs_cache.""" + model_infos: list[ModelInfo] = [] + for root, _, files in os.walk(data_path()): + if len(files) > 0: + subdir = root[len(data_path() + '/'):] + split = list(filter(lambda path: path != '', os.path.split(subdir))) + if len(split) == 2: # The subdir is a (username, model_name) + username, model_name = split + model_infos.append(ModelInfo(username=username, name=model_name, description='')) + + return ListModelsResponse(models=model_infos) + + +def get_model_info(options: GetModelInfoOptions) -> ModelInfo: + """Read the model info.""" + return ModelInfo(username=options.username, name=options.name, description='') + + +def _fix_text(text: str) -> str: + # Fix extra spacing before punctuation. E.g. "I like it ." -> "I like it.". + text = re.sub(' ([,.;?!:])', '\\1', text) + # Fix extra spacing around quotes. E.g. 'He said: " I like it "' -> 'He said: "I like it"'. + text = re.sub('" (.*?) "', '"\\1"', text) + text = re.sub("' (.*?) '", "'\\1'", text) + # Remove brackets. E.g. '[I] like it' -> 'I like it'. + text = re.sub('\\[ ?(.*?) ?\\]', '\\1', text) + # Remove parentheses. E.g. '(I) like it' -> 'I like it'. + text = re.sub('\\( ?(.*?) ?\\)', '\\1', text) + return text + + +def _compute_embedding(texts: list[str]) -> np.ndarray: + texts = [_fix_text(PREFIX + text) for text in texts] + cohere_embeddings = co.embed(texts, truncate='START').embeddings + return normalize(np.array(cohere_embeddings)).astype(np.float16) + + +def _save_dataset(model_info: ModelInfo, dataset: Dataset, embeddings: np.ndarray) -> None: + dataset_json = dataset.json() + + gcs_dir = get_output_dir(data_path(), model_info.username, model_info.name) + gcs_path = os.path.join(gcs_dir, DATA_JSON_FILENAME) + with open_file(gcs_path, 'w') as f: + f.write(dataset_json) + + embeddings_gcs_path = os.path.join(gcs_dir, EMBEDDINGS_FILENAME) + with open_file(embeddings_gcs_path, 'wb') as f: + np.save(f, embeddings) + + +Tchunk = TypeVar('Tchunk') + + +def chunks(iterable: Iterable[Tchunk], size: int) -> Iterable[list[Tchunk]]: + """Split a list of items into equal-sized chunks. The last chunk might be smaller.""" + it = iter(iterable) + chunk = list(itertools.islice(it, size)) + while chunk: + yield chunk + chunk = list(itertools.islice(it, size)) + + +def add_df(df: pd.DataFrame, username: str, model_name: str, text_col: str, + metadata_cols: list[str]) -> None: + """Add a pandas dataframe to a model.""" + text_series = df[text_col] + metadata_series = [df[metadata_col] for metadata_col in metadata_cols] + dataset_rows: list[DatasetRow] = [] + for text, *metadata in zip(text_series, *metadata_series): + metadata_dict: dict[str, str] = {} + for metadata, metadata_col in zip(metadata, metadata_cols): + metadata_dict[metadata_col] = metadata + dataset_rows.append(DatasetRow(text=text, metadata=metadata_dict, prediction=0)) + + chunked_embeddings = [ + np.array(_compute_embedding([row.text + for row in chunk])) + for chunk in chunks(dataset_rows, size=96) + ] + embeddings = np.concatenate(chunked_embeddings) + _save_dataset(ModelInfo(username=username, name=model_name, description=''), + Dataset(data=dataset_rows, labeled_data=[]), embeddings) + + +def add_dataset(options: AddDatasetOptions) -> None: + """Add a dataset to a model.""" + model_info = get_model_info( + GetModelInfoOptions(username=options.username, name=options.model_name)) + start_time = time.time() + + hf_dataset = cast( + DatasetDict, + load_dataset(options.hf_dataset, split=options.hf_split, data_dir=DATA_DIR).shuffle()) + + # Set the prediction to 0 when there's no model so all data is in "out of filter". + dataset = [DatasetRow(text=text, prediction=0) for text in hf_dataset[options.hf_text_field]] + + def _process_embeddings(batch: dict) -> dict: + return {'embeddings': _compute_embedding(batch[options.hf_text_field])} + + # Compute embeddings. + print('Computing embeddings...') + dataset_embeddings = hf_dataset.map(_process_embeddings, batched=True, batch_size=BATCH_SIZE) + + all_embeddings = np.array(dataset_embeddings['embeddings']) + + _save_dataset(model_info, Dataset(data=dataset, labeled_data=[]), all_embeddings) + + log(f'[DB] Writing data and embeddings took {time.time() - start_time}s.') + + +# Maps a username/model_name to a [dataset, embeddings] matrix. +MODEL_CACHE: dict[str, tuple[Dataset, np.ndarray]] = {} + + +def load_model(options: LoadModelOptions) -> LoadModelResponse: + """Load a model and returns all the data to the frontend.""" + dataset: Optional[Dataset] = None + + cache_key = f'{options.username}/{options.name}' + if cache_key in MODEL_CACHE: + dataset, _ = MODEL_CACHE[cache_key] + return LoadModelResponse(dataset=dataset, has_data=True) + + try: + gcs_dir = get_output_dir(data_path(), options.username, options.name) + gcs_path = os.path.join(gcs_dir, DATA_JSON_FILENAME) + with open_file(gcs_path) as f: + dataset = Dataset.parse_raw(f.read()) + + embeddings_gcs_path = os.path.join(gcs_dir, EMBEDDINGS_FILENAME) + with open_file(embeddings_gcs_path, 'rb') as f: + embeddings = np.load(f) + + MODEL_CACHE[cache_key] = (dataset, embeddings) + has_data = True + except Exception as e: + print('Error loading model:', e) + has_data = False + + return LoadModelResponse(dataset=dataset, has_data=has_data) + + +def save_model(options: SaveModelOptions) -> None: + """Load a model and returns all the data to the frontend.""" + model_info = get_model_info(GetModelInfoOptions(username=options.username, name=options.name)) + + dataset: Optional[Dataset] = None + + cache_key = f'{model_info.username}/{model_info.name}' + if cache_key in MODEL_CACHE: + dataset, embeddings = MODEL_CACHE[cache_key] + dataset.labeled_data = options.labeled_data + + # Train the new linear model with the training data. + model = LogisticRegression(solver='liblinear', warm_start=False, class_weight='balanced') + train_embedding_idxs: list[int] = [] + labels: list[int] = [] + for train_data_row in dataset.labeled_data: + train_embedding_idxs.append(train_data_row.row_idx) + labels.append(train_data_row.label) + + train_embeddings = embeddings[train_embedding_idxs] + predictions = [] + prediction_probs = [] + if len(train_embeddings) > 0: + model.fit(train_embeddings, labels) + # Add predictions to every point. + predictions = model.predict(embeddings) + prediction_probs = model.predict_proba(embeddings) + + # Add sortable metadata. + for i, example in enumerate(dataset.data): + if len(predictions): + prediction, confidence = int(predictions[i]), prediction_probs[i][1] + else: + # Zero out the confidence and the prediction. + prediction, confidence = 0, 0.0 + + example.prediction, example.metadata['confidence'] = prediction, confidence + + _save_dataset(model_info, dataset, embeddings) + else: + raise ValueError(f'{cache_key} not in cache!') + + +def add_examples(options: AddExamplesOptions) -> None: + """Add examples (currently training) to the dataset.""" + model_info = get_model_info(GetModelInfoOptions(username=options.username, name=options.name)) + + dataset: Optional[Dataset] = None + + cache_key = f'{model_info.username}/{model_info.name}' + if cache_key in MODEL_CACHE: + dataset, embeddings = MODEL_CACHE[cache_key] + + new_embeddings = _compute_embedding([example.text for example in options.examples]) + new_example_idx = len(dataset.data) + for i, example in enumerate(options.examples): + dataset.data.append(DatasetRow(text=example.text)) + dataset.labeled_data.append(LabeledExample(row_idx=i + new_example_idx, label=example.label)) + # Add the new embeddings. + MODEL_CACHE[cache_key] = (dataset, np.vstack([embeddings, new_embeddings])) + else: + raise ValueError(f'{cache_key} not in cache!') + + +def search_examples(options: SearchExamplesOptions) -> SearchExamplesResult: + """Search examples from the dataset using embedding similarity.""" + search_embedding = _compute_embedding([options.query]).flatten() + cache_key = f'{options.username}/{options.model_name}' + if cache_key in MODEL_CACHE: + _, embeddings = MODEL_CACHE[cache_key] + similarities = np.dot(embeddings, search_embedding) + + k = 500 + idx = np.argpartition(similarities, -k)[-k:] # Indices not sorted + + idx = idx[np.argsort( + similarities[idx])][::-1] # Indices sorted by value from largest to smallest + + topk_similarities = similarities[idx] + else: + raise ValueError('No model!') + + return SearchExamplesResult(row_results=[ + SearchExamplesRowResult(row_idx=row_idx, similarity=similarity) + for (row_idx, similarity) in zip(idx, topk_similarities) + ]) diff --git a/src/db_manager.py b/src/db_manager.py new file mode 100644 index 000000000..41b0f8503 --- /dev/null +++ b/src/db_manager.py @@ -0,0 +1,14 @@ +"""Manages mapping the dataset name to the database instance.""" + +from .datasets.db_dataset import DatasetDB +from .datasets.db_dataset_duckdb import DatasetDuckDB + +_DATASET_DBS: dict[str, DatasetDB] = {} + + +def get_dataset_db(namespace: str, dataset_name: str) -> DatasetDB: + """Get the dataset database instance for the dataset.""" + cache_key = f'{namespace}/{dataset_name}' + if cache_key not in _DATASET_DBS: + _DATASET_DBS[cache_key] = DatasetDuckDB(namespace=namespace, dataset_name=dataset_name) + return _DATASET_DBS[cache_key] diff --git a/src/embeddings/__init__.py b/src/embeddings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/embeddings/cohere.py b/src/embeddings/cohere.py new file mode 100644 index 000000000..1c865b6fa --- /dev/null +++ b/src/embeddings/cohere.py @@ -0,0 +1,28 @@ +"""Cohere embeddings.""" +import os +from typing import Iterable + +import cohere +import numpy as np +from sklearn.preprocessing import normalize +from tqdm import tqdm + +from ..schema import RichData +from ..utils import chunks +from .embedding_registry import register_embed_fn + +co = cohere.Client(os.environ['COHERE_API_KEY']) + +# Cohere only accepts 96 inputs at a time. +COHERE_EXAMPLE_LIMIT = 96 + + +@register_embed_fn('cohere') +def embed(examples: Iterable[RichData]) -> np.ndarray: + """Embed the examples using cohere.""" + embeddings = np.concatenate([ + np.array(co.embed(chunk, truncate='START').embeddings) + for chunk in tqdm(list(chunks(examples, size=COHERE_EXAMPLE_LIMIT))) + ]) + + return normalize(embeddings).astype(np.float16) diff --git a/src/embeddings/default_embeddings.py b/src/embeddings/default_embeddings.py new file mode 100644 index 000000000..255bc6200 --- /dev/null +++ b/src/embeddings/default_embeddings.py @@ -0,0 +1,3 @@ +"""Default embeddings registered in the global registry.""" + +from . import cohere # noqa diff --git a/src/embeddings/embedding_index.py b/src/embeddings/embedding_index.py new file mode 100644 index 000000000..6b76658f0 --- /dev/null +++ b/src/embeddings/embedding_index.py @@ -0,0 +1,59 @@ +"""Utils for writing the embedding index for a column.""" +import abc +from typing import Callable, Iterable, Optional + +import numpy as np +from pydantic import BaseModel + +from ..embeddings.embedding_registry import EmbeddingId +from ..schema import Path, RichData + + +class EmbeddingIndex(BaseModel): + """The result of an embedding index query.""" + + class Config: + arbitrary_types_allowed = True + + embeddings: np.ndarray + + +GetEmbeddingIndexFn = Callable[[Optional[Iterable[bytes]]], EmbeddingIndex] + + +class EmbeddingIndexer(abc.ABC): + """An interface for embedding indexers.""" + + @abc.abstractmethod + def get_embedding_index(self, + column: Path, + embedding: EmbeddingId, + keys: Optional[Iterable[bytes]] = None) -> EmbeddingIndex: + """Get an embedding index for a column, throw if it doesn't exist. + + Args: + column: The column to get the embedding index for. + embedding: The embedding to use. + keys: The keys to get the embedding index for. If None, get the embedding index for all + keys. + + Returns + The embedding index for the given column and embedding. + """ + pass + + @abc.abstractmethod + def compute_embedding_index(self, column: Path, embedding: EmbeddingId, keys: Iterable[bytes], + data: Iterable[RichData]) -> None: + """Get an embedding index for a column, throw if it doesn't exist. + + Args: + column: The column to get the embedding index for. + embedding: The embedding to use. + keys: The keys to use for the embedding index. This should align with data. + data: The rich data to compute the embedding index for. + + Returns + The embedding index for the given column and embedding. + """ + pass diff --git a/src/embeddings/embedding_index_disk.py b/src/embeddings/embedding_index_disk.py new file mode 100644 index 000000000..d708db169 --- /dev/null +++ b/src/embeddings/embedding_index_disk.py @@ -0,0 +1,79 @@ +"""An embedding indexer that stores the embedding index on disk.""" +import os +import pathlib +from typing import Iterable, Optional, Union + +import numpy as np +import pandas as pd +from typing_extensions import override # type: ignore + +from ..schema import Path, RichData, path_to_alias +from ..utils import file_exists, open_file +from .embedding_index import EmbeddingIndex, EmbeddingIndexer +from .embedding_registry import EmbeddingId, get_embed_fn + +NP_INDEX_KEYS_KWD = 'keys' +NP_EMBEDDINGS_KWD = 'embeddings' + + +class EmbeddingIndexerDisk(EmbeddingIndexer): + """An embedding indexer that stores the embedding index on disk.""" + + def __init__(self, dataset_path: Union[str, pathlib.Path]) -> None: + self.dataset_path = dataset_path + + @override + def get_embedding_index(self, + column: Path, + embedding: EmbeddingId, + keys: Optional[Iterable[bytes]] = None) -> EmbeddingIndex: + embedding_name, _ = get_embed_fn(embedding) + + index_filename = embedding_index_filename(column, embedding_name) + index_path = os.path.join(self.dataset_path, index_filename) + + if not file_exists(index_path): + raise ValueError( + F'Embedding index for column "{column}" and embedding "{embedding_name}" does not exist. ' + 'Please run "db.compute_embedding_index" on the database first.') + + keys_ndarray: Optional[np.ndarray] = None + if keys is not None: + keys_ndarray = np.array(keys, dtype=bytes) + + # Read the embedding index from disk. + with open_file(index_path, 'rb') as f: + np_index: dict[str, np.ndarray] = np.load(f) + if keys_ndarray is not None: + # NOTE: Calling tolist() here is necessary because we can't put the entire matrix into the + # dataframe. This will store each embedding a list. This could be sped up if we write our + # own implementation, or use something faster. + df = pd.DataFrame({NP_EMBEDDINGS_KWD: np_index[NP_EMBEDDINGS_KWD].tolist()}, + index=np_index[NP_INDEX_KEYS_KWD]) + embeddings = np.stack(df.loc[keys_ndarray][NP_EMBEDDINGS_KWD]) + else: + embeddings = np_index[NP_EMBEDDINGS_KWD] + + return EmbeddingIndex(path=index_path, embeddings=embeddings) + + @override + def compute_embedding_index(self, column: Path, embedding: EmbeddingId, keys: Iterable[bytes], + data: Iterable[RichData]) -> None: + embedding_name, embed_fn = get_embed_fn(embedding) + + index_filename = embedding_index_filename(column, embedding_name) + index_path = os.path.join(self.dataset_path, index_filename) + + if file_exists(index_path): + # The embedding index has already been computed. + return + + embeddings = embed_fn(data) + # Write the embedding index and the ordered UUID column to disk so they can be joined later. + with open_file(index_path, 'wb') as f: + np.savez(f, **{NP_INDEX_KEYS_KWD: np.array(keys, dtype=bytes), NP_EMBEDDINGS_KWD: embeddings}) + + +def embedding_index_filename(column: Path, embedding_name: str) -> str: + """Return the filename for the embedding index.""" + return f'{path_to_alias(column)}.{embedding_name}.embedding_index.npy' diff --git a/src/embeddings/embedding_index_test.py b/src/embeddings/embedding_index_test.py new file mode 100644 index 000000000..fcfb264cc --- /dev/null +++ b/src/embeddings/embedding_index_test.py @@ -0,0 +1,105 @@ +"""Tests for embedding indexers.""" + +import pathlib +import sys +from typing import Iterable, Type, cast + +import numpy as np +import pytest +from pytest_mock import MockerFixture + +from ..schema import RichData +from .embedding_index import EmbeddingIndexer +from .embedding_index_disk import EmbeddingIndexerDisk +from .embedding_registry import clear_embedding_registry, register_embed_fn + +ALL_INDEXERS: list[Type[EmbeddingIndexer]] = [EmbeddingIndexerDisk] + +TEST_EMBEDDING_NAME = 'test_embedding' + +EMBEDDINGS: list[tuple[bytes, str, list[float]]] = [(b'1', 'hello', [1.0, 0.0, 0.0]), + (b'2', 'hello world', [0.9, 0.1, 0.0]), + (b'3', 'far', [0.0, 0.0, 1.0])] + +STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for _, text, embedding in EMBEDDINGS} +KEY_EMBEDDINGS: dict[bytes, list[float]] = {key: embedding for key, _, embedding in EMBEDDINGS} + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + + # We register the embed function like this so we can mock it and assert how many times its called. + register_embed_fn(TEST_EMBEDDING_NAME)(lambda examples: embed(examples)) + + # Unit test runs. + yield + + # Teardown. + clear_embedding_registry() + + +def embed(examples: Iterable[RichData]) -> np.ndarray: + """Embed the examples, use a hashmap to the vector for simplicity.""" + return np.array([STR_EMBEDDINGS[cast(str, example)] for example in examples]) + + +def _make_indexer(embedding_indexer_cls: Type[EmbeddingIndexer], + tmp_path: pathlib.Path) -> EmbeddingIndexer: + if embedding_indexer_cls == EmbeddingIndexerDisk: + return EmbeddingIndexerDisk(tmp_path) + raise ValueError('Cant create embedding indexer class ', embedding_indexer_cls) + + +class TestEmbeddingIndexer: + + @pytest.mark.parametrize('indexer_cls', ALL_INDEXERS) + def test_get_full_index(self, tmp_path: pathlib.Path, mocker: MockerFixture, + indexer_cls: Type[EmbeddingIndexer]) -> None: + embed_mock = mocker.spy(sys.modules[__name__], embed.__name__) + + indexer = _make_indexer(indexer_cls, tmp_path) + + indexer.compute_embedding_index('test_column', + TEST_EMBEDDING_NAME, + keys=[key for key, _, _ in EMBEDDINGS], + data=[text for _, text, _ in EMBEDDINGS]) + + # Embed should only be called once. + assert embed_mock.call_count == 1 + + index = indexer.get_embedding_index('test_column', TEST_EMBEDDING_NAME) + + np.testing.assert_array_equal(index.embeddings, + np.array([embedding for _, _, embedding in EMBEDDINGS])) + + # Embed should not be called again. + assert embed_mock.call_count == 1 + + @pytest.mark.parametrize('indexer_cls', ALL_INDEXERS) + def test_get_partial_index(self, tmp_path: pathlib.Path, mocker: MockerFixture, + indexer_cls: Type[EmbeddingIndexer]) -> None: + embed_mock = mocker.spy(sys.modules[__name__], embed.__name__) + + indexer = _make_indexer(indexer_cls, tmp_path) + + indexer.compute_embedding_index('test_column', + TEST_EMBEDDING_NAME, + keys=[key for key, _, _ in EMBEDDINGS], + data=[text for _, text, _ in EMBEDDINGS]) + + # Embed should only be called once. + assert embed_mock.call_count == 1 + + index = indexer.get_embedding_index( + 'test_column', + TEST_EMBEDDING_NAME, + # Keys are partial. + keys=[b'1', b'2']) + + np.testing.assert_array_equal( + index.embeddings, + # Results should be partial. + np.array([KEY_EMBEDDINGS[b'1'], KEY_EMBEDDINGS[b'2']])) + + # Embed should not be called again. + assert embed_mock.call_count == 1 diff --git a/src/embeddings/embedding_registry.py b/src/embeddings/embedding_registry.py new file mode 100644 index 000000000..e58187dc2 --- /dev/null +++ b/src/embeddings/embedding_registry.py @@ -0,0 +1,56 @@ +"""Embedding registry.""" +from typing import Callable, Iterable, Union + +import numpy as np + +from ..schema import RichData + +EmbedFnType = Callable[[Iterable[RichData]], np.ndarray] + + +class EmbedFn: + """A function that embeds text or images.""" + + def __init__(self, embed_fn: EmbedFnType): + self.embed_fn = embed_fn + + def __call__(self, data: Iterable[RichData]) -> np.ndarray: + """Call the embedding function.""" + return self.embed_fn(data) + + +EmbeddingId = Union[str, EmbedFnType] +EMBEDDING_REGISTRY: dict[str, EmbedFn] = {} + + +def get_embed_fn(embedding_identifier: EmbeddingId) -> tuple[str, EmbedFn]: + """Get an embedding name and function from an embedding identifier.""" + if isinstance(embedding_identifier, str): + if embedding_identifier not in EMBEDDING_REGISTRY: + raise ValueError(f'Embedding "{embedding_identifier}" not found in the registry') + embed_fn = EMBEDDING_REGISTRY[embedding_identifier] + embedding_name = embedding_identifier + else: # The embedding identifier is a function itself, so use the name as the embedding name. + embed_fn = EmbedFn(embedding_identifier) + embedding_name = embedding_identifier.__name__ + + return embedding_name, embed_fn + + +def register_embed_fn(embedding_name: str) -> Callable[[EmbedFnType], EmbedFnType]: + """Register an embedding function.""" + + def decorator(embed_fn: EmbedFnType) -> EmbedFnType: + if embedding_name in EMBEDDING_REGISTRY: + raise ValueError(f'Embedding "{embedding_name}" already exists in the registry') + + EMBEDDING_REGISTRY[embedding_name] = EmbedFn(embed_fn) + + return embed_fn + + return decorator + + +def clear_embedding_registry() -> None: + """Clear the embedding registry.""" + EMBEDDING_REGISTRY.clear() diff --git a/src/integration_tests/db_integration_test.py b/src/integration_tests/db_integration_test.py new file mode 100644 index 000000000..76f241050 --- /dev/null +++ b/src/integration_tests/db_integration_test.py @@ -0,0 +1,74 @@ +# type: ignore +"""Currently fake integration tests for end to end functionality. + +NOTE: This code does not run! It is just what we're building towards. That's why everything is +ignored. +""" +# pylint: disable-all +# mypy: ignore-errors +# flake8: noqa + + +def db_integration_test_fake(): + location = DatasetLocation(path='/local/path') + db = DatasetDuckDB(namespace='local', name='my_dataset', location=location) + + # Yells because there is no dataset in this location. + db.select_rows() + + # Load data from a dataframe, or a source config to find data. + db.load_from(df) + db.load_from(SourceConfig()) + + # Works because we've loaded a dataset. + db.select_rows() + + # Loading the db again, from the same directory, gives a cache-hit beacuse we've loaded something + # to that location already with that dataset name. + db = DatasetDuckDB(namespace='local', name='my_dataset', location=location) + + # Works because we've loaded a dataset, and it's a cache hit. + db.select_rows() + + ## Simple signals. + # Both of these yell, there is not yet a Signal column computed for text. + db.select_rows(columns=[Signal(Column('text'), signal='word_count')]) + db.select_rows(columns=[Signal(Column('text'), signal=WordCount)]) + + # Only use one of these, the first uses a registry, the second is your own function. This writes + # the signal output to a file next to the dataset. + db.compute_signal_column(Column('text'), signal='word_count') + db.compute_signal_column(Column('text'), signal=WordCount) + + # Either of these work now because we've written another parquet file to disk for the signal over + # the text column. + db.select_rows(columns=[Signal(Column('text'), signal='word_count')]) + db.select_rows(columns=[Signal(Column('text'), signal=WordCount)]) + + ## Concept signals. + + # Yells because the concept needs to be precomputed on the column. + db.select_rows(columns=[Signal(Column('text'), signal='toxicity_cohere_embeddings')]) + + # Precompute the concept on the column. + db.compute_signal_column(Column('text'), signal='toxicity_cohere_embeddings') + toxicity_cohere_embeddings = ConceptModel() + db.compute_signal_column(Column('text'), signal=toxicity_cohere_embeddings) + # Now the concept parquet file has been written to disk, with a version in the filename. + + # Now works because we've written the concept parquet file to disk. + db.select_rows(columns=[Signal(Column('text'), signal='toxicity_cohere_embeddings')]) + + ## Nearest neighbors & semantic similarity. + + # Yells because the embedding index has not been computed (or it doesnt yell if the embedding + # happens to be shared from a model before for that column) + db.select_rows( + columns=[SemanticSimilarity(Column('text'), text='hello world', embedding='cohere')]) + + # This line may share embeddings the toxicity model from before. + db.compute_semantic_index(Column('text'), embedding='cohere') + + # This now works because we've computed the index. + db.select_rows( + columns=[SemanticSimilarity(Column('text'), text='hello world', embedding='cohere')]) diff --git a/src/parquet_writer.py b/src/parquet_writer.py new file mode 100644 index 000000000..2f3889242 --- /dev/null +++ b/src/parquet_writer.py @@ -0,0 +1,70 @@ +"""A Parquet file writer that wraps the pyarrow writer.""" +from typing import IO + +import pyarrow as pa +import pyarrow.parquet as pq + +from .schema import Item, ItemValue, Schema, schema_to_arrow_schema + + +class ParquetWriter: + """A writer to parquet.""" + + def __init__(self, + schema: Schema, + codec: str = 'snappy', + row_group_buffer_size: int = 128 * 1024 * 1024, + record_batch_size: int = 10_000): + self._schema = schema_to_arrow_schema(schema) + self._codec = codec + self._row_group_buffer_size = row_group_buffer_size + self._buffer: list[list[ItemValue]] = [[] for _ in range(len(self._schema.names))] + self._buffer_size = record_batch_size + self._record_batches: list[pa.RecordBatch] = [] + self._record_batches_byte_size = 0 + self.writer: pq.ParquetWriter = None + + def open(self, file_handle: IO) -> None: + """Open the destination file for writing.""" + self.writer = pq.ParquetWriter(file_handle, self._schema, compression=self._codec) + + def write(self, record: Item) -> None: + """Write the record to the destination file.""" + if len(self._buffer[0]) >= self._buffer_size: + self._flush_buffer() + + if self._record_batches_byte_size >= self._row_group_buffer_size: + self._write_batches() + + # reorder the data in columnar format. + for i, n in enumerate(self._schema.names): + self._buffer[i].append(record[n]) + + def close(self) -> None: + """Flushes the write buffer and closes the destination file.""" + if len(self._buffer[0]) > 0: + self._flush_buffer() + if self._record_batches_byte_size > 0: + self._write_batches() + + self.writer.close() + + def _write_batches(self) -> None: + table = pa.Table.from_batches(self._record_batches, schema=self._schema) + self._record_batches = [] + self._record_batches_byte_size = 0 + self.writer.write_table(table) + + def _flush_buffer(self) -> None: + arrays: list[pa.array] = [[] for _ in range(len(self._schema.names))] + for x, y in enumerate(self._buffer): + arrays[x] = pa.array(y, type=self._schema.types[x]) + self._buffer[x] = [] + rb = pa.RecordBatch.from_arrays(arrays, schema=self._schema) + self._record_batches.append(rb) + size = 0 + for x in arrays: + for b in x.buffers(): # type: ignore + if b is not None: + size = size + b.size + self._record_batches_byte_size = self._record_batches_byte_size + size diff --git a/src/router_concept.py b/src/router_concept.py new file mode 100644 index 000000000..356962267 --- /dev/null +++ b/src/router_concept.py @@ -0,0 +1,131 @@ +"""Router for the concept database.""" + +from typing import Optional + +import numpy as np +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel + +from .concepts.concept import Concept, ConceptModel +from .concepts.db_concept import ( + CONCEPT_DB, + CONCEPT_MODEL_DB, + ConceptUpdate, +) +from .embeddings.embedding_registry import get_embed_fn + +router = APIRouter() + + +@router.get('/{namespace}/{concept_name}', response_model_exclude_none=True) +def get_concept(namespace: str, concept_name: str) -> Concept: + """Get a concept from a database.""" + concept = CONCEPT_DB.get(namespace, concept_name) + if not concept: + raise HTTPException(status_code=404, + detail=f'Concept "{namespace}/{concept_name}" was not found') + return concept + + +@router.post('/{namespace}/{concept_name}', response_model_exclude_none=True) +def post_concept(namespace: str, concept_name: str, change: ConceptUpdate) -> Concept: + """Edit a concept in the database.""" + return CONCEPT_DB.edit(namespace, concept_name, change) + + +class ScoreExample(BaseModel): + """Example to score along a specific concept.""" + text: Optional[str] + img: Optional[bytes] + + +class ScoreBody(BaseModel): + """Request body for the score endpoint.""" + examples: list[ScoreExample] + + +class ScoreResponse(BaseModel): + """Response body for the score endpoint.""" + scores: list[float] + model_updated: bool + + +class ConceptModelResponse(BaseModel): + """Response body for the get_concept_model endpoint.""" + model: ConceptModel + model_updated: bool + + +@router.get('/{namespace}/{concept_name}/{embedding_name}') +def get_concept_model(namespace: str, concept_name: str, embedding_name: str) -> dict: + """Get a concept model from a database.""" + concept = CONCEPT_DB.get(namespace, concept_name) + if not concept: + raise HTTPException(status_code=404, + detail=f'Concept "{namespace}/{concept_name}" was not found') + + model = CONCEPT_MODEL_DB.get(namespace, concept_name, embedding_name) + if not model: + raise HTTPException( + status_code=404, + detail=f'Concept model "{namespace}/{concept_name}/{embedding_name}" was not found') + + model, model_updated = _sync_model_with_concept(concept, model) + return ConceptModelResponse(model=model, model_updated=model_updated).dict(exclude_none=True) + + +@router.post('/{namespace}/{concept_name}/{embedding_name}/score', response_model_exclude_none=True) +def score(namespace: str, concept_name: str, embedding_name: str, body: ScoreBody) -> ScoreResponse: + """Score examples along the specified concept.""" + concept = CONCEPT_DB.get(namespace, concept_name) + if not concept: + raise HTTPException(status_code=404, + detail=f'Concept "{namespace}/{concept_name}" was not found') + model = CONCEPT_MODEL_DB.get(namespace, concept_name, embedding_name) + if not model: + raise HTTPException( + status_code=404, + detail=f'Concept model "{namespace}/{concept_name}/{embedding_name}" was not found') + + model, model_updated = _sync_model_with_concept(concept, model) + + # TODO(smilkov): Support images. + texts = [example.text or '' for example in body.examples] + return ScoreResponse(scores=model.score(texts), model_updated=model_updated) + + +def _sync_model_with_concept(concept: Concept, model: ConceptModel) -> tuple[ConceptModel, bool]: + """Update the model with the latest labeled concept data.""" + if concept.version == model.version: + # Already in sync. + return model, False + + concept_embeddings: dict[str, np.ndarray] = {} + _, embed_fn = get_embed_fn(model.embedding_name) + + # Compute the embeddings for the examples with cache miss. + texts_of_missing_embeddings: dict[str, str] = {} + for id, example in concept.data.items(): + if id in model.embeddings: + # Cache hit. + concept_embeddings[id] = model.embeddings[id] + else: + # Cache miss. + # TODO(smilkov): Support images. + texts_of_missing_embeddings[id] = example.text or '' + missing_ids = texts_of_missing_embeddings.keys() + missing_embeddings = embed_fn(list(texts_of_missing_embeddings.values())) + + for id, embedding in zip(missing_ids, missing_embeddings): + concept_embeddings[id] = embedding + + embedding_matrix = list(concept_embeddings.values()) + new_labels = [concept.data[id].label for id in concept_embeddings.keys()] + + model.fit(embedding_matrix, new_labels) + model.embeddings = concept_embeddings + # Synchronize the model version with the concept version. + model.version = concept.version + CONCEPT_MODEL_DB.save(model) + + return model, True diff --git a/src/router_dataset.py b/src/router_dataset.py new file mode 100644 index 000000000..51a65e741 --- /dev/null +++ b/src/router_dataset.py @@ -0,0 +1,84 @@ +"""Router for the dataset database.""" +from typing import Optional + +from fastapi import APIRouter, Depends + +from .datasets.db_dataset import ColumnId +from .db_manager import get_dataset_db +from .embeddings import default_embeddings # noqa # pylint: disable=unused-import +from .schema import UUID_COLUMN +from .server_api import ( + ComputeEmbeddingIndexOptions, + ComputeSignalOptions, + SelectDatasetRowsOptions, + WebColumnInfo, + WebManifest, +) +from .signals.default_signals import register_default_signals + +router = APIRouter() + +register_default_signals() + + +@router.get('/{namespace}/{dataset_name}/manifest') +def manifest(namespace: str, dataset_name: str) -> dict: + """Get the web manifest for the dataset.""" + dataset_db = get_dataset_db(namespace, dataset_name) + web_manifest = WebManifest( + columns=[WebColumnInfo(name=column.alias) for column in dataset_db.columns()]) + return web_manifest.dict() + + +@router.post('/{namespace}/{dataset_name}/compute_embedding_index') +def compute_embedding_index(namespace: str, dataset_name: str, + options: ComputeEmbeddingIndexOptions) -> dict: + """Compute a signal for a dataset.""" + dataset_db = get_dataset_db(namespace, dataset_name) + + dataset_db.compute_embedding_index(options.embedding, [options.column]) + + return {} + + +@router.post('/{namespace}/{dataset_name}/compute_signal_column') +def compute_signal_column(namespace: str, dataset_name: str, options: ComputeSignalOptions) -> dict: + """Compute a signal for a dataset.""" + dataset_db = get_dataset_db(namespace, dataset_name) + dataset_db.compute_signal_columns(options.signal, [options.column]) + + return {} + + +@router.get('/{namespace}/{dataset_name}/select_rows') +def dataset_select_rows(namespace: str, + dataset_name: str, + options: SelectDatasetRowsOptions = Depends()) -> list: + """Select rows from the dataset database.""" + dataset_db = get_dataset_db(namespace, dataset_name) + + # Split the columns by ',' to generate the columns. FastAPI does not support parsing pydantic + # lists directly. + # See: https://stackoverflow.com/questions/63881885/fastapi-get-request-with-pydantic-list-field + columns: Optional[list[ColumnId]] = None + if options.columns is not None: + query_columns = options.columns.split(',') + columns = [column for column in dataset_db.columns() if column.alias in query_columns] + + sort_by = None + if options.sort_by is not None: + sort_by = options.sort_by.split(',') + + items = list( + dataset_db.select_rows( + columns=columns, + # TODO(nsthorat): Support filters in the GET request. + filters=[], + sort_by=sort_by, + sort_order=options.sort_order, + limit=options.limit)) + + for item in items: + item[UUID_COLUMN] = item[UUID_COLUMN].hex() + + return list(items) diff --git a/src/schema.py b/src/schema.py new file mode 100644 index 000000000..66b23e01d --- /dev/null +++ b/src/schema.py @@ -0,0 +1,453 @@ +"""Item: an individual entry in the dataset.""" + +from collections import deque +from datetime import datetime +from enum import Enum +from typing import Any, Optional, Union, cast + +import numpy as np +import pyarrow as pa +from pydantic import ( + BaseModel, + Field as pydantic_Field, + validator, +) + +MANIFEST_FILENAME = 'manifest.json' +PARQUET_FILENAME_PREFIX = 'data' + +# We choose `__rowid__` inspired by the standard `rowid` pseudocolumn in DBs: +# https://docs.oracle.com/cd/B19306_01/server.102/b14200/pseudocolumns008.htm +UUID_COLUMN = '__rowid__' +PATH_WILDCARD = '*' + +# Python doesn't work with recursive types. These types provide some notion of type-safety. +Scalar = Union[bool, datetime, int, float, str, bytes] +ItemValue = Union[dict, list, np.ndarray, Scalar] +Item = dict[str, ItemValue] +RowKeyedItem = tuple[bytes, Item] + +# Contains a string field name, a wildcard for repeateds, or a specific integer index for repeateds. +# This path represents a path to a particular column. +# Examples: +# ['article', 'field'] represents {'article': {'field': VALUES}} +# ['article', '*', 'field'] represents {'article': [{'field': VALUES}, {'field': VALUES}]} +# ['article', 0, 'field'] represents {'article': [{'field': VALUES}, {'field': UNRELATED}]} +Path = Union[str, tuple[Union[str, int], ...]] + +PathKeyedItem = tuple[Path, Item] + +# These fields are for for python only and not written to a schema. +RichData = Union[str, bytes] +RowKeyedPath = tuple[bytes, Path] +PathKeyedRichData = tuple[Path, Union[str, bytes]] +RowPathKeyedItem = tuple[RowKeyedPath, Item] +# The str is the signal name. +PathKeyedSignalItem = tuple[str, Path, Item] + + +class DataType(str, Enum): + """Enum holding the dtype for a field.""" + STRING = 'string' + BOOLEAN = 'boolean' + + # Ints. + INT8 = 'int8' + INT16 = 'int16' + INT32 = 'int32' + INT64 = 'int64' + UINT8 = 'uint8' + UINT16 = 'uint16' + UINT32 = 'uint32' + UINT64 = 'uint64' + + # Floats. + FLOAT16 = 'float16' + FLOAT32 = 'float32' + FLOAT64 = 'float64' + + ### Time ### + # Time of day (no time zone). + TIME = 'time' + # Calendar date (year, month, day), no time zone. + DATE = 'date' + # An "Instant" stored as number of microseconds (µs) since 1970-01-01 00:00:00+00 (UTC time zone). + TIMESTAMP = 'timestamp' + # Time span, stored as microseconds. + INTERVAL = 'interval' + + STRUCT = 'struct' + LIST = 'list' + BINARY = 'binary' + + def __repr__(self) -> str: + return self.value + + +class EnrichmentType(str, Enum): + """Enum holding the enrichment type for a signal.""" + TEXT = 'text' + IMAGE = 'image' + + def __repr__(self) -> str: + return self.value + + +class Field(BaseModel): + """Holds information for a field in the schema.""" + repeated_field: Optional['Field'] + fields: Optional[dict[str, 'Field']] + dtype: Optional[DataType] + enriched: Optional[bool] + + @validator('fields') + def either_fields_or_repeated_field_is_defined(cls, fields: dict[str, 'Field'], + values: dict[str, Any]) -> dict[str, 'Field']: + """Error if both `fields` and `repeated_fields` are defined.""" + if fields and values.get('repeated_field'): + raise ValueError('Both "fields" and "repeated_field" should not be defined') + return fields + + @validator('dtype', always=True) + def infer_default_dtype(cls, dtype: Optional[DataType], values: dict[str, Any]) -> DataType: + """Infers the default value for dtype if not explicitly provided.""" + if dtype: + if values.get('fields') and dtype != DataType.STRUCT: + raise ValueError('dtype needs to be STRUCT when fields is defined') + if values.get('repeated_field') and dtype != DataType.LIST: + raise ValueError('dtype needs to be LIST when repeated_field is defined') + return dtype + elif values.get('repeated_field'): + return DataType.LIST + else: + return DataType.STRUCT + + def __str__(self) -> str: + return _str_field(self, indent=0) + + def __repr__(self) -> str: + return f' {self.__class__.__name__}::{self.json(exclude_none=True, indent=2)}' + + +class Schema(BaseModel): + """Database schema.""" + fields: dict[str, Field] + # We exclude the computed property `leafs` from the dict() and json() serialization. + leafs: dict[Path, Field] = pydantic_Field(exclude=True, default={}) + + @validator('leafs', pre=True, always=True) + def compute_leafs(cls, leafs: dict[Path, Field], values: dict[str, Any]) -> dict[Path, Field]: + """Return all the leaf fields in the schema (a leaf holds a primitive value).""" + if leafs: + return leafs + fields = cast(dict[str, Field], values.get('fields')) + result: dict[Path, Field] = {} + q: deque[tuple[Path, Field]] = deque([((), Field(fields=fields))]) + while q: + path, field = q.popleft() + if field.fields: + for name, child_field in field.fields.items(): + child_path = (*path, name) + q.append((child_path, child_field)) + elif field.repeated_field: + child_path = (*path, PATH_WILDCARD) + q.append((child_path, field.repeated_field)) + else: + result[path] = field + return result + + def __str__(self) -> str: + return _str_fields(self.fields, indent=0) + + def __repr__(self) -> str: + return self.json(exclude_none=True, indent=2) + + +def get_leaf_vals(item: ItemValue, emit_repeated_indices: bool) -> dict[Path, list[Scalar]]: + """Return a dictionary that maps a "leaf path" to all flatten values for that leaf. + + Args: + item: The item. + emit_repeated_indices: When True emits integer indices for repeated values. When False emits + a "*" meaning all values for that repeated index. + """ + result: dict[Path, list[Scalar]] = {} + q: deque[tuple[Path, ItemValue]] = deque([((), item)]) + while q: + path, item = q.popleft() + if isinstance(item, dict): + for name, child_item in item.items(): + child_path = (*path, name) + q.append((child_path, child_item)) + elif isinstance(item, (tuple, list)): + if emit_repeated_indices: + for i, child_item in enumerate(item): + child_path = (*path, i) + q.append((child_path, child_item)) + else: + for child_item in item: + child_path = (*path, PATH_WILDCARD) + q.append((child_path, child_item)) + else: + if path not in result: + result[path] = [] + result[path].append(cast(Scalar, item)) + return result + + +def child_item_from_column_path(item: Item, path: Path) -> Item: + """Return the last (child) item from a column path.""" + child_item_value = item + for path_part in path: + if path_part == PATH_WILDCARD: + raise ValueError( + 'child_item_from_column_path cannot be called with a path that contains a repeated ' + f'wildcard: "{path}"') + # path_part can either be an integer or a string for a dictionary, both of which we can + # directly index with. + child_item_value = child_item_value[path_part] # type: ignore + return child_item_value + + +def validate_path_against_schema(path: Path, schema: Schema, msg: str) -> None: + """Raise an error if the path is invalid for this schema.""" + if len(path) == 0: + raise ValueError(f'The length of path must not be 0. {msg}') + + # Wrap in a field for convenience. + field = Field(fields=schema.fields) + for i, path_part in enumerate(path): + if isinstance(path_part, int) or path_part == PATH_WILDCARD: + if field.dtype != DataType.LIST: + raise ValueError( + f'Path part "{path_part}" for path "{path}" at index {i} represents an list, but the ' + f'field at this path is "{field}". ' + f'{msg}') + field = cast(Field, field.repeated_field) + elif isinstance(path_part, str): + if field.dtype != DataType.STRUCT: + raise ValueError( + f'Path part "{path_part}" for path "{path}" at index {i} represents a field of a ' + f'struct, but the field at this path is "{field}". ' + f'{msg}') + + if path_part not in cast(dict, field.fields): + raise ValueError(f'Path part "{path_part}" for path "{path}" at index {i} represents a ' + 'field of struct that does not exist. Schema field: {field}. {msg}') + field = cast(dict[str, Field], field.fields)[path_part] + + if field.dtype in (DataType.STRUCT, DataType.LIST): + raise ValueError( + f'The path "{path}" specifies {field} but should specify a leaf primitive. {msg}') + + +def column_paths_match(path_match: Path, specific_path: Path) -> bool: + """Test whether two column paths match. + + Args: + path_match: A column path that contains wildcards, and sub-paths. This path will be used for + testing the second specific path. + specific_path: A column path that specifically identifies an field. + + Returns + Whether specific_path matches the path_match. This will only match when the + paths are equal length. If a user wants to enrich everything with an array, they must use the + path wildcard '*' in their patch match. + """ + if isinstance(path_match, str): + path_match = (path_match,) + if isinstance(specific_path, str): + specific_path = (specific_path,) + + if len(path_match) != len(specific_path): + return False + + for path_match_p, specific_path_p in zip(path_match, specific_path): + if path_match_p == PATH_WILDCARD: + continue + + if path_match_p != specific_path_p: + return False + + return True + + +def path_to_alias(path: Path) -> str: + """Convert a path to a string.""" + return '.'.join([str(path_comp) for path_comp in path]) + + +def normalize_path(path: Path) -> Path: + """Normalize a path.""" + if isinstance(path, str): + path = (path,) + return path + + +def is_repeated_path_part(path_component: Union[str, int]) -> bool: + """Return True if the path component is a repeated path part.""" + return isinstance(path_component, int) or path_component == PATH_WILDCARD + + +class ImageInfo(BaseModel): + """Info about an individual image.""" + path: Path + + +class SourceManifest(BaseModel): + """The manifest that describes the dataset run, including schema and parquet files.""" + # List of a parquet filepaths storing the data. The paths can be relative to `manifest.json`. + files: list[str] + # The data schema. + data_schema: Schema + + # Image information for the dataset. + images: Optional[list[ImageInfo]] + + +def _str_fields(fields: dict[str, Field], indent: int) -> str: + prefix = ' ' * indent + out: list[str] = [] + for name, field in fields.items(): + out.append(f'{prefix}{name}:{_str_field(field, indent=indent + 2)}') + return '\n'.join(out) + + +def _str_field(field: Field, indent: int) -> str: + if field.fields: + prefix = '\n' if indent > 0 else '' + return f'{prefix}{_str_fields(field.fields, indent)}' + if field.repeated_field: + return f' list({_str_field(field.repeated_field, indent)})' + return f' {cast(DataType, field.dtype)}' + + +def dtype_to_arrow_dtype(dtype: DataType) -> pa.DataType: + """Convert the dtype to an arrow dtype.""" + if dtype == DataType.STRING: + return pa.string() + elif dtype == DataType.BOOLEAN: + return pa.bool_() + elif dtype == DataType.FLOAT16: + return pa.float16() + elif dtype == DataType.FLOAT32: + return pa.float32() + elif dtype == DataType.FLOAT64: + return pa.float64() + elif dtype == DataType.INT8: + return pa.int8() + elif dtype == DataType.INT16: + return pa.int16() + elif dtype == DataType.INT32: + return pa.int32() + elif dtype == DataType.INT64: + return pa.int64() + elif dtype == DataType.UINT8: + return pa.uint8() + elif dtype == DataType.UINT16: + return pa.uint16() + elif dtype == DataType.UINT32: + return pa.uint32() + elif dtype == DataType.UINT64: + return pa.uint64() + elif dtype == DataType.BINARY: + return pa.binary() + elif dtype == DataType.TIME: + return pa.time64() + elif dtype == DataType.DATE: + return pa.date64() + elif dtype == DataType.TIMESTAMP: + return pa.timestamp('us') + elif dtype == DataType.INTERVAL: + return pa.duration('us') + else: + raise ValueError(f'Can not convert dtype "{dtype}" to arrow dtype') + + +def schema_to_arrow_schema(schema: Schema) -> pa.Schema: + """Convert our schema to arrow schema.""" + arrow_schema = cast(pa.Schema, _schema_to_arrow_schema_impl(schema)) + arrow_fields = {field.name: field.type for field in arrow_schema} + # Since UUID is a fixed size binary of 16 bytes, we mark it as such in the schema -- leads to + # optimizations by DuckDB and aligns with DuckDB's own UUID dtype. + if UUID_COLUMN in arrow_fields: + arrow_fields[UUID_COLUMN] = pa.binary(16) + return pa.schema(arrow_fields) + + +def _schema_to_arrow_schema_impl(schema: Union[Schema, Field]) -> Union[pa.Schema, pa.DataType]: + """Convert a schema to an apache arrow schema.""" + if schema.fields: + arrow_fields = { + name: _schema_to_arrow_schema_impl(field) for name, field in schema.fields.items() + } + return pa.schema(arrow_fields) if isinstance(schema, Schema) else pa.struct(arrow_fields) + field = cast(Field, schema) + if field.repeated_field: + return pa.list_(_schema_to_arrow_schema_impl(field.repeated_field)) + return dtype_to_arrow_dtype(cast(DataType, field.dtype)) + + +def arrow_dtype_to_dtype(arrow_dtype: pa.DataType) -> DataType: + """Convert arrow dtype to our dtype.""" + # Ints. + if arrow_dtype == pa.int8(): + return DataType.INT8 + elif arrow_dtype == pa.int16(): + return DataType.INT16 + elif arrow_dtype == pa.int32(): + return DataType.INT32 + elif arrow_dtype == pa.int64(): + return DataType.INT64 + elif arrow_dtype == pa.uint8(): + return DataType.UINT8 + elif arrow_dtype == pa.uint16(): + return DataType.UINT16 + elif arrow_dtype == pa.uint32(): + return DataType.UINT32 + elif arrow_dtype == pa.uint64(): + return DataType.UINT64 + # Floats. + elif arrow_dtype == pa.float16(): + return DataType.FLOAT16 + elif arrow_dtype == pa.float32(): + return DataType.FLOAT32 + elif arrow_dtype == pa.float64(): + return DataType.FLOAT64 + # Time. + elif pa.types.is_time(arrow_dtype): + return DataType.TIME + elif pa.types.is_date(arrow_dtype): + return DataType.DATE + elif pa.types.is_timestamp(arrow_dtype): + return DataType.TIMESTAMP + elif pa.types.is_duration(arrow_dtype): + return DataType.INTERVAL + # Others. + elif arrow_dtype == pa.string(): + return DataType.STRING + elif pa.types.is_binary(arrow_dtype) or pa.types.is_fixed_size_binary(arrow_dtype): + return DataType.BINARY + elif pa.types.is_boolean(arrow_dtype): + return DataType.BOOLEAN + else: + raise ValueError(f'Can not convert arrow dtype "{arrow_dtype}" to our dtype') + + +def arrow_schema_to_schema(schema: pa.Schema) -> Schema: + """Convert arrow schema to our schema.""" + return cast(Schema, _arrow_schema_to_schema_impl(schema)) + + +def _arrow_schema_to_schema_impl(schema: Union[pa.Schema, pa.DataType]) -> Union[Schema, Field]: + """Convert an apache arrow schema to our schema.""" + if isinstance(schema, (pa.Schema, pa.StructType)): + fields: dict[str, Field] = { + field.name: cast(Field, _arrow_schema_to_schema_impl(field.type)) for field in schema + } + return Schema(fields=fields) if isinstance(schema, pa.Schema) else Field(fields=fields) + elif isinstance(schema, pa.ListType): + return Field(repeated_field=cast(Field, _arrow_schema_to_schema_impl(schema.value_field.type))) + else: + + return Field(dtype=arrow_dtype_to_dtype(schema)) diff --git a/src/server.py b/src/server.py new file mode 100644 index 000000000..976102f91 --- /dev/null +++ b/src/server.py @@ -0,0 +1,92 @@ +"""Serves the agile model server.""" + +import os + +from fastapi import Depends, FastAPI +from fastapi.responses import HTMLResponse +from fastapi.staticfiles import StaticFiles + +from . import ( + db_deprecated, + router_concept, + router_dataset, +) +from .server_api import ( + AddDatasetOptions, + AddExamplesOptions, + GetModelInfoOptions, + LoadModelOptions, + SaveModelOptions, + SearchExamplesOptions, +) + +DIST_PATH = os.path.abspath(os.path.join('dist')) + +app = FastAPI() +app.include_router(router_dataset.router, prefix='/dataset', tags=['datasets']) +app.include_router(router_concept.router, prefix='/concept', tags=['concepts']) + +# For static files generated by webpack, including bundle.js. +# NOTE: We use check_dir=False because the server can start before files exist. +app.mount('/static', + StaticFiles(directory=os.path.join(DIST_PATH, 'static'), check_dir=False), + name='static') +# For hot updated with webpack dev server. +app.mount('/hot', + StaticFiles(directory=os.path.join(DIST_PATH, 'hot'), check_dir=False), + name='hot') + + +@app.get('/db/list_models') +def list_models() -> dict: + """List the models.""" + model_infos = db_deprecated.list_models() + return model_infos.dict() + + +@app.get('/db/model_info') +def model_info(options: GetModelInfoOptions = Depends()) -> dict: + """List the models.""" + model_infos = db_deprecated.get_model_info(options) + return model_infos.dict() + + +@app.get('/db/load_model') +def load_model(options: LoadModelOptions = Depends()) -> dict: + """List the models.""" + load_model_response = db_deprecated.load_model(options) + return load_model_response.dict() + + +@app.post('/db/save_model') +def save_model(options: SaveModelOptions) -> dict: + """Save cached model to GCS.""" + db_deprecated.save_model(options) + return {} + + +@app.post('/db/add_examples') +def add_examples(options: AddExamplesOptions) -> dict: + """Save cached model to GCS.""" + db_deprecated.add_examples(options) + return {} + + +@app.get('/db/search_examples') +def search_examples(options: SearchExamplesOptions = Depends()) -> dict: + """Search exmaples.""" + return db_deprecated.search_examples(options).dict() + + +@app.post('/db/add_dataset') +def add_data(options: AddDatasetOptions) -> dict: + """Add data to a model.""" + db_deprecated.add_dataset(options) + return {} + + +@app.get('/{full_path:path}', response_class=HTMLResponse) +def read_index() -> str: + """Return the index.html file.""" + with open(os.path.join(DIST_PATH, 'index.html')) as f: + return f.read() diff --git a/src/server_api.py b/src/server_api.py new file mode 100644 index 000000000..1f34e29f3 --- /dev/null +++ b/src/server_api.py @@ -0,0 +1,158 @@ +"""The REST API for the server.""" + +from typing import Optional + +from pydantic import BaseModel, validator + +from .constants import AddExample, Dataset, LabeledExample +from .datasets.db_dataset import SortOrder +from .signals.signal import Signal +from .signals.signal_registry import resolve_signal + + +# Mirrors the CreateModelOptions in server_api.ts. +class CreateModelOptions(BaseModel): + """Options to create a model. Body of the POST request to /db/create_model.""" + username: str + name: str + description: str + + +class GetModelInfoOptions(BaseModel): + """Options to get a single model.""" + username: str + name: str + + +class CreateModelResponse(BaseModel): + """Options to create a model. Body of the POST request to /db/create_model.""" + username: str + name: str + + +class ModelInfo(BaseModel): + """Information about a model.""" + username: str + name: str + description: str + + +class ListModelsResponse(BaseModel): + """The response object of /db/list_models.""" + models: list[ModelInfo] + + +class AddDatasetOptions(BaseModel): + """The request for the create model spec endpoint.""" + username: str + model_name: str + + # Huggingface data loading. + hf_dataset: str + hf_split: str = 'train' + hf_text_field: str = 'text' + + +class LoadModelOptions(BaseModel): + """The request for the create model spec endpoint.""" + username: str + name: str + + +class SaveModelOptions(BaseModel): + """The request for the save model to GCS endpoint.""" + username: str + name: str + labeled_data: list[LabeledExample] + + +class AddExamplesOptions(BaseModel): + """The request for the add examples endpoint.""" + username: str + name: str + examples: list[AddExample] + + +class SearchExamplesOptions(BaseModel): + """The request for the search examples endpoint.""" + username: str + model_name: str + query: str + + +class SearchExamplesRowResult(BaseModel): + """The response of the search examples endpoint.""" + row_idx: int + similarity: float + + +class SearchExamplesResult(BaseModel): + """The response of the search examples endpoint.""" + row_results: list[SearchExamplesRowResult] + + +class LoadModelResponse(BaseModel): + """The request for the create model spec endpoint.""" + dataset: Optional[Dataset] + has_data: bool + + +class ModelPredictOptions(BaseModel): + """The request for the model predict endpoint.""" + username: str + model_name: str + text: str + + +class ModelPredictResponse(BaseModel): + """The response for the model predict endpoint.""" + prediction: int + + +class WebEnrichmentInfo(BaseModel): + """Information about an enrichmed column.""" + signal_name: str + column_enriched: str + + +class WebColumnInfo(BaseModel): + """Information about a column.""" + name: str + enrichment: Optional[WebEnrichmentInfo] + + +class WebManifest(BaseModel): + """Information about a dataset.""" + columns: list[WebColumnInfo] + + +# Dataset DB methods. +class SelectDatasetRowsOptions(BaseModel): + """The request for the select dataset rows endpoint.""" + + columns: Optional[str] = None + filters: Optional[str] = None + sort_by: Optional[str] = None + sort_order: Optional[SortOrder] = SortOrder.DESC + limit: Optional[int] = None + + +class ComputeSignalOptions(BaseModel): + """The request for the compute signal endpoint.""" + signal: Signal + + # The columns to compute the signal on. + column: str + + @validator('signal', pre=True) + def parse_signal(cls, signal: dict) -> Signal: + """Parse a signal to its specific subclass instance.""" + return resolve_signal(signal) + + +class ComputeEmbeddingIndexOptions(BaseModel): + """The request for the compute embedding index endpoint.""" + embedding: str + + # The columns to compute the signal on. + column: str diff --git a/src/signals/__init__.py b/src/signals/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/signals/default_signals.py b/src/signals/default_signals.py new file mode 100644 index 000000000..78ed01e42 --- /dev/null +++ b/src/signals/default_signals.py @@ -0,0 +1,9 @@ +"""Registers all available default signals.""" +from .signal_registry import register_signal +from .text_statistics import TextStatisticsSignal + + +def register_default_signals() -> None: + """Register all the default signals.""" + # Text. + register_signal(TextStatisticsSignal) diff --git a/src/signals/semantic_search.py b/src/signals/semantic_search.py new file mode 100644 index 000000000..7757f332a --- /dev/null +++ b/src/signals/semantic_search.py @@ -0,0 +1,64 @@ +"""A signal to compute semantic search for a document.""" +from typing import Any, Callable, Iterable, Optional, Union + +import numpy as np +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import EmbeddingIndex +from ..embeddings.embedding_registry import EmbeddingId, EmbedFn +from ..schema import DataType, EnrichmentType, Field, Item, RichData +from .signal import Signal + +SIMILARITY_FEATURE_NAME = 'semantic_similarity' + + +class SemanticSearchSignal(Signal): + """A signal to compute semantic search for a document.""" + + name = 'semantic_search' + enrichment_type = EnrichmentType.TEXT + embedding_based = True + + query: Union[str, bytes] + embedding: EmbeddingId + + _embed_fn: EmbedFn + _search_text_embedding: Optional[np.ndarray] = None + + def __init__(self, query: Union[str, bytes], embedding: EmbeddingId, **kwargs: dict[Any, Any]): + if isinstance(query, bytes): + raise ValueError('Image queries are not yet supported for SemanticSearch.') + + super().__init__(query=query, embedding=embedding, **kwargs) + + @override + def fields(self) -> dict[str, Field]: + return {SIMILARITY_FEATURE_NAME: Field(dtype=DataType.FLOAT32)} + + def _get_search_embedding(self) -> np.ndarray: + """Return the embedding for the search text.""" + if self._search_text_embedding is None: + self._search_text_embedding = self._embed_fn([self.query]).flatten() + return self._search_text_embedding + + @override + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[Callable[[EmbeddingId, Iterable[bytes]], EmbeddingIndex]] = None + ) -> Iterable[Optional[Item]]: + if data and keys: + raise ValueError('"data" and "keys" cannot both be provided for SemanticSearch.compute().') + + if data: + text_embeddings = self._embed_fn(data) + elif keys: + if not get_embedding_index: + raise ValueError( + '"get_embedding_index" is required for SemanticSearch.compute() when passing "keys".') + text_embeddings = get_embedding_index(self.embedding, keys).embeddings + + scores = text_embeddings.dot(self._get_search_embedding()).flatten() + + return [{SIMILARITY_FEATURE_NAME: score} for score in scores] diff --git a/src/signals/semantic_search_test.py b/src/signals/semantic_search_test.py new file mode 100644 index 000000000..af1474873 --- /dev/null +++ b/src/signals/semantic_search_test.py @@ -0,0 +1,104 @@ +"""Test the semantic search signal.""" + +import sys +from typing import Iterable, Optional, cast + +import numpy as np +import pytest +from pytest_mock import MockerFixture +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import EmbeddingIndex, EmbeddingIndexer +from ..embeddings.embedding_registry import EmbeddingId, clear_embedding_registry, register_embed_fn +from ..schema import RichData +from .semantic_search import SIMILARITY_FEATURE_NAME, SemanticSearchSignal + +TEST_EMBEDDING_NAME = 'test_embedding' + +EMBEDDINGS: dict[bytes, list[float]] = { + b'1': [1.0, 0.0, 0.0], + b'2': [0.9, 0.1, 0.0], + b'3': [0.0, 0.0, 1.0] +} + +STR_EMBEDDINGS: dict[str, list[float]] = { + 'hello': [1.0, 0.0, 0.0], + 'hello world': [0.9, 0.1, 0.0], + 'far': [0.0, 0.0, 1.0] +} + + +class TestEmbeddingIndexer(EmbeddingIndexer): + """A test embedding indexer with fixed embeddings.""" + + @override + def get_embedding_index(self, column: str, embedding: EmbeddingId, + row_ids: Iterable[bytes]) -> EmbeddingIndex: + return EmbeddingIndex(embeddings=np.array([EMBEDDINGS[row_id] for row_id in row_ids])) + + @override + def compute_embedding_index(self, + column: str, + embedding: EmbeddingId, + rows: Optional[Iterable[bytes]] = None) -> None: + pass + + +def embed(examples: Iterable[RichData]) -> np.ndarray: + """Embed the examples, use a hashmap to the vector for simplicity.""" + return np.array([STR_EMBEDDINGS[cast(str, example)] for example in examples]) + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + + # We register the embed function like this so we can mock it and assert how many times its called. + register_embed_fn(TEST_EMBEDDING_NAME)(lambda examples: embed(examples)) + + # Unit test runs. + yield + + # Teardown. + clear_embedding_registry() + + +def test_semantic_search_compute_keys(mocker: MockerFixture) -> None: + embedding_indexer = TestEmbeddingIndexer() + + embed_mock = mocker.spy(sys.modules[__name__], embed.__name__) + + signal = SemanticSearchSignal(query='hello', embedding=TEST_EMBEDDING_NAME) + scores = signal.compute( + keys=[b'1', b'2', b'3'], + get_embedding_index=lambda embedding, row_ids: embedding_indexer.get_embedding_index( + column='test_col', embedding=embedding, row_ids=row_ids)) + + # Embeddings should be called only 1 time for the search. + assert embed_mock.call_count == 1 + + assert scores == [{ + SIMILARITY_FEATURE_NAME: 1.0 + }, { + SIMILARITY_FEATURE_NAME: 0.9 + }, { + SIMILARITY_FEATURE_NAME: 0.0 + }] + + +def test_semantic_search_compute_data(mocker: MockerFixture) -> None: + embed_mock = mocker.spy(sys.modules[__name__], embed.__name__) + + signal = SemanticSearchSignal(query='hello', embedding=TEST_EMBEDDING_NAME) + # Compute over the text. + scores = signal.compute(data=STR_EMBEDDINGS.keys()) + + # Embeddings should be called only 2 times, once for the search, once for the query itself. + assert embed_mock.call_count == 2 + + assert scores == [{ + SIMILARITY_FEATURE_NAME: 1.0 + }, { + SIMILARITY_FEATURE_NAME: 0.9 + }, { + SIMILARITY_FEATURE_NAME: 0.0 + }] diff --git a/src/signals/signal.py b/src/signals/signal.py new file mode 100644 index 000000000..2e7e847aa --- /dev/null +++ b/src/signals/signal.py @@ -0,0 +1,86 @@ +"""Interface for implementing a signal.""" + +import abc +from typing import Any, Callable, ClassVar, Iterable, Optional + +import pandas as pd +from pydantic import BaseModel + +from ..embeddings.embedding_index import EmbeddingIndex +from ..embeddings.embedding_registry import EmbeddingId, EmbedFn, get_embed_fn +from ..schema import EnrichmentType, Field, Item, RichData + + +class SignalScoredDataset(BaseModel): + """The result of a call to score dataset for a signal.""" + + class Config: + arbitrary_types_allowed = True + + df: pd.DataFrame + + # Maps the original column name to a list of the new columns that were added. + score_columns: dict[str, list[str]] + + +class Signal(abc.ABC, BaseModel): + """Interface for signals to implement. A signal can score documents and a dataset column.""" + # ClassVars do not get serialized with pydantic. + name: ClassVar[str] + enrichment_type: ClassVar[EnrichmentType] + embedding_based: ClassVar[bool] = False + + # The signal_name will get populated in init automatically from the class name so it gets + # serialized and the signal author doesn't have to define both the static property and the field. + signal_name: str = 'signal_base' + embedding: Optional[EmbeddingId] = None + + class Config: + underscore_attrs_are_private = True + + _embed_fn: Optional[EmbedFn] = None + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + if 'name' not in self.__class__.__dict__: + raise ValueError('Signal attribute "name" must be defined.') + + if self.__class__.embedding_based and not self.embedding: + raise ValueError( + 'Signal attribute "embedding" must be defined for "embedding_based" signals.') + + if self.embedding: + _, embed_fn = get_embed_fn(self.embedding) + self._embed_fn = embed_fn + + self.signal_name = self.__class__.name + + @abc.abstractmethod + def fields(self) -> dict[str, Field]: + """Return the fields schema for this signal.""" + pass + + @abc.abstractmethod + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[Callable[[EmbeddingId, Iterable[bytes]], EmbeddingIndex]] = None + ) -> Iterable[Optional[Item]]: + """Compute the signal for an iterable of row-keyed documents or images. + + Args: + data: An iterable of rich data to compute the signal over + keys: An iterable of row-uuids. These are used to lookup pre-computed embeddings. + get_embedding_index: A method to get embeddings from a set of keys. + + Returns + An iterable of items. The signal should return "None" if the signal is sparse for the input. + """ + pass + + +def signal_column_name(column: str, signal_feature_name: str) -> str: + """Get the name of a signal feature column.""" + return f'{column}.{signal_feature_name}' diff --git a/src/signals/signal_registry.py b/src/signals/signal_registry.py new file mode 100644 index 000000000..7309102d3 --- /dev/null +++ b/src/signals/signal_registry.py @@ -0,0 +1,41 @@ +"""Embedding registry.""" +from typing import Type, Union + +from .signal import Signal + +SIGNAL_REGISTRY: dict[str, Type[Signal]] = {} + + +def register_signal(signal_cls: Type[Signal]) -> None: + """Register a signal in the global registry.""" + if signal_cls.name in SIGNAL_REGISTRY: + raise ValueError(f'Signal "{signal_cls.name}" has already been registered!') + + SIGNAL_REGISTRY[signal_cls.name] = signal_cls + + +def get_signal_cls(signal_name: str) -> Type[Signal]: + """Return a registered signal given the name in the registry.""" + if signal_name not in SIGNAL_REGISTRY: + raise ValueError(f'Signal "{signal_name}" not found in the registry') + + return SIGNAL_REGISTRY[signal_name] + + +def resolve_signal(signal: Union[dict, Signal]) -> Signal: + """Resolve a generic signal base class to a specific signal class.""" + if isinstance(signal, Signal): + # The signal config is already parsed. + return signal + + signal_name = signal.get('signal_name') + if not signal_name: + raise ValueError('"signal_name" needs to be defined in the json dict.') + + signal_cls = get_signal_cls(signal_name) + return signal_cls(**signal) + + +def clear_signal_registry() -> None: + """Clear the signal registry.""" + SIGNAL_REGISTRY.clear() diff --git a/src/signals/signal_registry_test.py b/src/signals/signal_registry_test.py new file mode 100644 index 000000000..5c3d2f8b7 --- /dev/null +++ b/src/signals/signal_registry_test.py @@ -0,0 +1,61 @@ +"""A signal to compute semantic search for a document.""" +from typing import Iterable, Optional + +import pytest +from typing_extensions import override # type: ignore + +from ..schema import EnrichmentType, Field, Item +from ..signals.signal_registry import ( + clear_signal_registry, + get_signal_cls, + register_signal, + resolve_signal, +) +from .signal import Signal + + +class TestSignal(Signal): + """A test signal.""" + + # Pydantic fields + name = 'test_signal' + enrichment_type = EnrichmentType.TEXT + + query: str + + @override + def fields(self) -> dict[str, Field]: + return {} + + @override + def compute(self, data: Iterable[str]) -> list[Optional[Item]]: + del data + return [] + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + # Setup. + register_signal(TestSignal) + + # Unit test runs. + yield + + # Teardown. + clear_signal_registry() + + +def test_get_signal_cls() -> None: + """Test getting a signal.""" + assert TestSignal == get_signal_cls('test_signal') + + +def test_resolve_signal() -> None: + """Test resolving a signal.""" + test_signal = TestSignal(query='hello') + + # Signals pass through. + assert resolve_signal(test_signal) == test_signal + + # Dicts resolve to the base class. + assert resolve_signal(test_signal.dict()) == test_signal diff --git a/src/signals/signal_test.py b/src/signals/signal_test.py new file mode 100644 index 000000000..b2cbec9bc --- /dev/null +++ b/src/signals/signal_test.py @@ -0,0 +1,86 @@ +"""Test signal base class.""" +from typing import Iterable, Optional + +import numpy as np +import pytest +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import GetEmbeddingIndexFn +from ..embeddings.embedding_registry import ( + clear_embedding_registry, + register_embed_fn, +) +from ..schema import EnrichmentType, Field, Item, RichData +from .signal import Signal + +TEST_EMBEDDING_NAME = 'test_embedding' + + +def embed(examples: Iterable[RichData]) -> np.ndarray: + """Embed the examples, use a hashmap to the vector for simplicity.""" + return np.array([1.0]) + + +@pytest.fixture(scope='module', autouse=True) +def setup_teardown() -> Iterable[None]: + + # We register the embed function like this so we can mock it and assert how many times its called. + register_embed_fn(TEST_EMBEDDING_NAME)(lambda examples: embed(examples)) + + # Unit test runs. + yield + + # Teardown. + clear_embedding_registry() + + +class TestSignal(Signal): + """A test signal.""" + + # Pydantic fields + name = 'test_signal' + enrichment_type = EnrichmentType.TEXT + + query: str + + @override + def fields(self) -> dict[str, Field]: + return {} + + @override + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[GetEmbeddingIndexFn] = None) -> Iterable[Optional[Item]]: + del data + return [] + + +class TestEmbeddingSignal(TestSignal): + """A test embedding signal.""" + name = 'test_embedding_signal' + embedding_based = True + + +def test_embedding_signal_throws_no_embedding() -> None: + with pytest.raises(ValueError, match='Signal attribute "embedding" must be defined'): + TestEmbeddingSignal(query='test') + + +def test_embedding_signal_serialization() -> None: + signal = TestEmbeddingSignal(query='test', embedding='test_embedding') + + # The class variables should not be included. The embedding name should be included. + assert signal.dict(exclude_unset=True) == { + 'signal_name': 'test_embedding_signal', + 'query': 'test', + 'embedding': TEST_EMBEDDING_NAME + } + + +def test_non_embedding_signal_serialization() -> None: + signal = TestSignal(query='test') + + # The class variables should not be included. + assert signal.dict(exclude_unset=True) == {'signal_name': 'test_signal', 'query': 'test'} diff --git a/src/signals/signal_utils.py b/src/signals/signal_utils.py new file mode 100644 index 000000000..fff8ff8ea --- /dev/null +++ b/src/signals/signal_utils.py @@ -0,0 +1,94 @@ +"""Utils for signals.""" + +from typing import Optional, cast + +from ..schema import ( + PATH_WILDCARD, + UUID_COLUMN, + DataType, + Field, + Path, + Schema, + column_paths_match, + normalize_path, +) + + +def create_signal_schema(source_schema: Schema, signal_enrich_fields: list[Path], + signal_fields: dict[str, Field]) -> Schema: + """Create a schema describing the enriched fields added by the signal.""" + signal_schema = Schema(fields={UUID_COLUMN: Field(dtype=DataType.BINARY)}) + return _add_signal_fields_to_schema(source_schema, signal_schema, signal_fields, + signal_enrich_fields) + + +def _add_signal_fields_to_schema(source_schema: Schema, signal_schema: Schema, + signal_fields: dict[str, + Field], enrich_fields: list[Path]) -> Schema: + enrich_fields = [normalize_path(field) for field in enrich_fields] + source_leafs = source_schema.leafs + # Validate that the enrich fields are actually a valid leaf path. + for signal_enrich_field in enrich_fields: + if signal_enrich_field not in source_leafs: + raise ValueError(f'Field for enrichment {signal_enrich_field} is not a valid leaf path. ' + f'Leaf paths: {source_leafs.keys()}') + + for leaf_path, _ in source_leafs.items(): + field_is_enriched = False + for signal_enrich_field in enrich_fields: + if column_paths_match(signal_enrich_field, leaf_path): + field_is_enriched = True + if not field_is_enriched: + continue + + # Find the inner most repeated subpath as we will put the signal schema next to its parent. + inner_struct_path_idx = len(leaf_path) - 1 + for i, path_part in reversed(list(enumerate(leaf_path))): + if path_part == PATH_WILDCARD: + inner_struct_path_idx = i - 1 + else: + break + + repeated_depth = len(leaf_path) - 1 - inner_struct_path_idx + + inner_field = Field(fields=signal_fields, enriched=True) + + # Wrap in a list to mirror the input structure. + for i in range(repeated_depth): + inner_field = Field(repeated_field=inner_field, enriched=True) + + signal_path: Path = leaf_path[0:inner_struct_path_idx + 1] + + # Merge the source schema into the signal schema up until inner struct parent. + for i in reversed(range(inner_struct_path_idx + 1)): + path_component = cast(str, signal_path[i]) + + if path_component == PATH_WILDCARD: + inner_field = Field(repeated_field=inner_field) + else: + field = get_field_if_exists(signal_schema, signal_path[0:i]) + if field and field.fields: + field.fields[path_component] = inner_field + break + else: + inner_field = Field(fields={path_component: inner_field}) + + return signal_schema + + +def get_field_if_exists(schema: Schema, path: Path) -> Optional[Field]: + """Return a field from a path if it exists in the schema.""" + # Wrap in a field for convenience. + field = cast(Field, schema) + for path_part in path: + if isinstance(path_part, int) or path_part == PATH_WILDCARD: + if not field.repeated_field: + return None + field = field.repeated_field + elif isinstance(path_part, str): + if not field.fields: + return None + if path_part not in cast(dict, field.fields): + return None + field = field.fields[path_part] + return field diff --git a/src/signals/text_statistics.py b/src/signals/text_statistics.py new file mode 100644 index 000000000..6caff7f39 --- /dev/null +++ b/src/signals/text_statistics.py @@ -0,0 +1,35 @@ +"""A signal to compute semantic search for a document.""" +from typing import Iterable, Optional + +from typing_extensions import override # type: ignore + +from ..embeddings.embedding_index import GetEmbeddingIndexFn +from ..schema import DataType, EnrichmentType, Field, Item, RichData +from .signal import Signal + +NUM_CHARS_FEATURE_NAME = 'num_characters' + + +class TextStatisticsSignal(Signal): + """A signal to compute text statistics for a document.""" + name = 'text_statistics' + enrichment_type = EnrichmentType.TEXT + + @override + def fields(self) -> dict[str, Field]: + return {NUM_CHARS_FEATURE_NAME: Field(dtype=DataType.INT32)} + + @override + def compute( + self, + data: Optional[Iterable[RichData]] = None, + keys: Optional[Iterable[bytes]] = None, + get_embedding_index: Optional[GetEmbeddingIndexFn] = None) -> Iterable[Optional[Item]]: + if data is None: + raise ValueError('"data" is required for TextStatistics.compute().') + if keys: + raise ValueError('"keys" is not supported for TextStatistics.compute().') + + return [{ + NUM_CHARS_FEATURE_NAME: len(text_content) + } if isinstance(text_content, str) else None for text_content in data] diff --git a/src/signals/text_statistics_test.py b/src/signals/text_statistics_test.py new file mode 100644 index 000000000..5d90fa3e4 --- /dev/null +++ b/src/signals/text_statistics_test.py @@ -0,0 +1,21 @@ +"""Test the semantic search signal.""" + +from ..schema import DataType, Field +from .text_statistics import NUM_CHARS_FEATURE_NAME, TextStatisticsSignal + + +def test_text_statistics_fields() -> None: + signal = TextStatisticsSignal() + assert signal.fields() == {NUM_CHARS_FEATURE_NAME: Field(dtype=DataType.INT32)} + + +def test_text_statistics_compute() -> None: + signal = TextStatisticsSignal() + + scores = signal.compute(data=['hello', 'hello world']) + + assert scores == [{ + NUM_CHARS_FEATURE_NAME: len('hello') + }, { + NUM_CHARS_FEATURE_NAME: len('hello world') + }] diff --git a/src/splitters/__init__.py b/src/splitters/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 000000000..eed85b836 --- /dev/null +++ b/src/utils.py @@ -0,0 +1,269 @@ +"""Utils for the python server.""" +import asyncio +import functools +import itertools +import logging +import os +import pathlib +import pprint +import re +import shutil +import threading +import time +import uuid +from asyncio import AbstractEventLoop +from concurrent.futures import Executor, ThreadPoolExecutor +from functools import partial, wraps +from typing import IO, Any, Awaitable, Callable, Iterable, Optional, TypeVar, Union + +import pyarrow as pa +from google.cloud.storage import Blob, Client +from pydantic import BaseModel + +from .parquet_writer import ParquetWriter +from .schema import UUID_COLUMN, Item, Path, Schema, schema_to_arrow_schema + +GCS_PROTOCOL = 'gs://' +GCS_REGEX = re.compile(f'{GCS_PROTOCOL}(.*?)/(.*)') +GCS_COPY_CHUNK_SIZE = 1_000 +IMAGES_DIR_NAME = 'images' + + +@functools.cache +def _get_storage_client(thread_id: Optional[int] = None) -> Client: + # The storage client is not thread safe so we use a thread_id to make sure each thread gets a + # separate storage client. + del thread_id + return Client() + + +def _parse_gcs_path(filepath: str) -> tuple[str, str]: + # match a regular expression to extract the bucket and filename + if matches := GCS_REGEX.match(filepath): + bucket_name, object_name = matches.groups() + return bucket_name, object_name + raise ValueError(f'Failed to parse GCS path: {filepath}') + + +def _get_gcs_blob(filepath: str) -> Blob: + bucket_name, object_name = _parse_gcs_path(filepath) + storage_client = _get_storage_client(threading.get_ident()) + bucket = storage_client.bucket(bucket_name) + return bucket.blob(object_name) + + +def open_file(filepath: str, mode: str = 'r') -> IO: + """Open a file handle. It works with both GCS and local paths.""" + if filepath.startswith(GCS_PROTOCOL): + blob = _get_gcs_blob(filepath) + return blob.open(mode) + + write_mode = 'w' in mode + binary_mode = 'b' in mode + + if write_mode: + base_path = os.path.dirname(filepath) + os.makedirs(base_path, exist_ok=True) + + encoding = None if binary_mode else 'utf-8' + return open(filepath, mode=mode, encoding=encoding) + + +def makedirs(dir_path: str) -> None: + """Recursively makes the directories. It works with both GCS and local paths.""" + if dir_path.startswith(GCS_PROTOCOL): + return + os.makedirs(dir_path, exist_ok=True) + + +def get_output_dir(base_dir: str, username: str, model_name: str) -> str: + """Return the output directory for a given model.""" + return os.path.join(base_dir, username, model_name) + + +def get_dataset_output_dir(base_dir: Union[str, pathlib.Path], namespace: str, + model_name: str) -> str: + """Return the output directory for a given model.""" + return os.path.join(base_dir, 'datasets', namespace, model_name) + + +class CopyRequest(BaseModel): + """A request to copy a file from source to destination path. Used to copy media files to GCS.""" + from_path: str + to_path: str + + +def copy_batch(copy_requests: list[CopyRequest]) -> None: + """Copy a single item from a CopyRequest.""" + storage_client = _get_storage_client(threading.get_ident()) + with storage_client.batch(): + for copy_request in copy_requests: + from_gcs = False + if GCS_REGEX.match(copy_request.from_path): + from_gcs = True + to_gcs = False + if GCS_REGEX.match(copy_request.to_path): + to_gcs = True + + makedirs(os.path.dirname(copy_request.to_path)) + + # When both source and destination are local, use the shutil copy. + if not from_gcs and not to_gcs: + shutil.copyfile(copy_request.from_path, copy_request.to_path) + continue + + if from_gcs: + from_bucket_name, from_object_name = _parse_gcs_path(copy_request.from_path) + from_bucket = storage_client.bucket(from_bucket_name) + from_gcs_blob = from_bucket.blob(from_object_name) + + if to_gcs: + to_bucket_name, to_object_name = _parse_gcs_path(copy_request.to_path) + to_bucket = storage_client.bucket(to_bucket_name) + + if from_gcs and to_gcs: + from_bucket.copy_blob(from_gcs_blob, from_bucket, to_object_name) + elif from_gcs and not to_gcs: + from_gcs_blob.download_to_filename(copy_request.to_path) + elif not from_gcs and to_gcs: + to_gcs_blob = to_bucket.blob(to_object_name) + to_gcs_blob.upload_from_filename(copy_request.from_path) + + +def copy_files(copy_requests: Iterable[CopyRequest], input_gcs: bool, output_gcs: bool) -> None: + """Copy media files from an input gcs path to an output gcs path.""" + start_time = time.time() + + chunk_size = 1 + if output_gcs and input_gcs: + # When downloading or uploading locally, batching greatly slows down the parallelism as GCS + # batching with storage.batch() has no effect. + # When copying files locally, storage.batch() has no effect and it's better to run each copy in + # separate thread. + chunk_size = GCS_COPY_CHUNK_SIZE + + batched_copy_requests = chunks(copy_requests, chunk_size) + with ThreadPoolExecutor() as executor: + executor.map(copy_batch, batched_copy_requests) + + log(f'Copy took {time.time() - start_time} seconds.') + + +def delete_file(filepath: str) -> None: + """Delete a file. It works for both GCS and local paths.""" + if filepath.startswith(GCS_PROTOCOL): + blob = _get_gcs_blob(filepath) + blob.delete() + return + + os.remove(filepath) + + +def file_exists(filepath: str) -> bool: + """Return true if the file exists. It works with both GCS and local paths.""" + if filepath.startswith(GCS_PROTOCOL): + return _get_gcs_blob(filepath).exists() + return os.path.exists(filepath) + + +def get_image_path(output_dir: str, path: Path, row_id: bytes) -> str: + """Return the GCS file path to an image associated with a specific row.""" + path_subdir = '_'.join([str(p) for p in path]) + filename = row_id.hex() + return os.path.join(output_dir, IMAGES_DIR_NAME, path_subdir, filename) + + +def _validate(item: Item, schema: pa.Schema) -> None: + # Try to parse the item using the inferred schema. + try: + pa.RecordBatch.from_pylist([item], schema=schema) + except pa.ArrowTypeError: + log('Failed to parse arrow item using the arrow schema.') + log('Item:') + log(pprint.pformat(item, indent=2)) + log('Arrow schema:') + log(schema) + raise # Re-raise the same exception, same stacktrace. + + +def write_items_to_parquet(items: Iterable[Item], output_dir: str, schema: Schema, + filename_prefix: str, shard_index: int, + num_shards: int) -> tuple[str, int]: + """Write a set of items to a parquet file, in columnar format.""" + arrow_schema = schema_to_arrow_schema(schema) + parquet_filename = f'{filename_prefix}-{shard_index:05d}-of-{num_shards:05d}.parquet' + filepath = os.path.join(output_dir, parquet_filename) + f = open_file(filepath, mode='wb') + writer = ParquetWriter(schema) + writer.open(f) + num_items = 0 + for item in items: + # Add a UUID column. + if UUID_COLUMN not in item: + item[UUID_COLUMN] = uuid.uuid4().bytes + if os.getenv('DEBUG'): + _validate(item, arrow_schema) + writer.write(item) + num_items += 1 + writer.close() + f.close() + log(f'[Parquet writer] Wrote shard {shard_index + 1} of {num_shards} to {output_dir}') + return parquet_filename, num_items + + +Tout = TypeVar('Tout') + + +def async_wrap(func: Callable[..., Tout], + loop: Optional[AbstractEventLoop] = None, + executor: Optional[Executor] = None) -> Callable[..., Awaitable[Tout]]: + """Wrap a sync function into an async function.""" + + @wraps(func) + async def run(*args: Any, **kwargs: Any) -> Any: + current_loop = loop or asyncio.get_running_loop() + pfunc: Callable = partial(func, *args, **kwargs) + return await current_loop.run_in_executor(executor, pfunc) + + return run + + +Tchunk = TypeVar('Tchunk') + + +def chunks(iterable: Iterable[Tchunk], size: int) -> Iterable[list[Tchunk]]: + """Split a list of items into equal-sized chunks. The last chunk might be smaller.""" + it = iter(iterable) + chunk = list(itertools.islice(it, size)) + while chunk: + yield chunk + chunk = list(itertools.islice(it, size)) + + +# TODO(nsthorat): Centralize environment flags. +DISABLE_LOGS = os.getenv('DISABLE_LOGS', False) + + +def log(log_str: str) -> None: + """Print and logs a message so it shows up in the logs on cloud.""" + if DISABLE_LOGS: + return + + print(log_str) + logging.info(log_str) + + +class DebugTimer: + """A context manager that prints the time elapsed in a block of code.""" + + def __init__(self, name: str) -> None: + self.name = name + + def __enter__(self) -> 'DebugTimer': + """Start a timer.""" + self.start = time.time() + return self + + def __exit__(self, *args: list[Any]) -> None: + """Stop the timer and print the elapsed time.""" + log(f'{self.name} took {(time.time() - self.start):.3f}s.') diff --git a/src/web/.babelrc.json b/src/web/.babelrc.json new file mode 100644 index 000000000..0967ef424 --- /dev/null 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"write-file-webpack-plugin": "~4.5.1" + }, + "dependencies": { + "@reduxjs/toolkit": "~1.8.5", + "@shoelace-style/shoelace": "2.1.0", + "@types/react-table": "~7.7.14", + "@types/react-window": "~1.8.5", + "react": "~18.2.0", + "react-dom": "~18.1.0", + "react-redux": "~8.0.4", + "react-router-dom": "~6.5.0", + "react-table": "~7.8.0", + "react-window": "~1.8.8" + }, + "scripts": { + "dev": "npx webpack-dev-server", + "build": "npx webpack" + }, + "private": true +} diff --git a/src/web/postcss.config.js b/src/web/postcss.config.js new file mode 100644 index 000000000..15e32e8eb --- /dev/null +++ b/src/web/postcss.config.js @@ -0,0 +1,3 @@ +module.exports = { + plugins: [require('tailwindcss'), require('autoprefixer'), require('postcss-nested')], +}; diff --git a/src/web/src/@types/index.d.ts b/src/web/src/@types/index.d.ts new file mode 100644 index 000000000..35306c6fc --- /dev/null +++ b/src/web/src/@types/index.d.ts @@ -0,0 +1 @@ +declare module '*.css'; diff --git a/src/web/src/create_model.module.css b/src/web/src/create_model.module.css new file mode 100644 index 000000000..de261a2f7 --- /dev/null +++ b/src/web/src/create_model.module.css @@ -0,0 +1,23 @@ +.create_container { + width: 36rem; + margin: auto; +} + +.create_row { + border-bottom-width: 1px; + padding: 16px 4px; + width: 100%; +} + +sl-spinner.create_step_loading, +sl-icon.create_step_complete { + position: relative; + margin-top: auto; + margin-bottom: auto; + padding: 0; + font-size: 20px; +} +sl-icon.create_step_complete { + color: white; + background: var(--sl-color-green-500); +} diff --git a/src/web/src/create_model.tsx b/src/web/src/create_model.tsx new file mode 100644 index 000000000..93f090776 --- /dev/null +++ b/src/web/src/create_model.tsx @@ -0,0 +1,124 @@ +import { + SlButton, + SlIcon, + SlInput, + SlOption, + SlSelect, + SlSpinner, + SlTextarea, +} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {Link} from 'react-router-dom'; +import styles from './create_model.module.css'; +import {useCreateModelMutation} from './store'; +import {getModelLink, renderError} from './utils'; + +export const CreateModel = React.memo(function CreateModel(): JSX.Element { + const [username, setUsername] = React.useState('nikhil'); + const [modelName, setModelName] = React.useState(''); + const [description, setDescription] = React.useState(''); + + const [ + createModel, + { + isLoading: isCreateLoading, + isError: isCreateError, + error: createError, + isSuccess: isCreateSuccess, + }, + ] = useCreateModelMutation(); + // TODO: Use authentication tokens to populate the user. + const users = ['nikhil', 'daniel'].map((user) => { + return ( + + {user} + + ); + }); + + const createClicked = () => { + createModel({username: username, name: modelName, description}); + }; + + return ( + <> +
+
+
Create a new model
+
+
+
+
+ setUsername((e.target as HTMLInputElement).value)} + > + {users} + +
+
+ / +
+
+ setModelName((e.target as HTMLInputElement).value)} + /> +
+
+
+
+ setDescription((e.target as HTMLInputElement).value)} + /> +
+
+ createClicked()} + > + Create model + + + {isCreateLoading || isCreateSuccess ? ( +
+
+ {isCreateLoading ? ( + + ) : ( + + )} +
+
Creating model...
+
+ ) : ( + <> + )} + {isCreateSuccess ? ( +
+
+ + + View model + + +
+
+ ) : ( + <> + )} + +
{isCreateError ? <>{renderError(createError)} : <>}
+
+
+ + ); +}); diff --git a/src/web/src/header.module.css b/src/web/src/header.module.css new file mode 100644 index 000000000..37d5b0225 --- /dev/null +++ b/src/web/src/header.module.css @@ -0,0 +1,9 @@ +.app_header { + height: 50px; +} + +.app_header_title { + vertical-align: middle; + font-size: 20px; + padding: 10px 14px 12px; +} diff --git a/src/web/src/header.tsx b/src/web/src/header.tsx new file mode 100644 index 000000000..5398fc019 --- /dev/null +++ b/src/web/src/header.tsx @@ -0,0 +1,23 @@ +import {SlButton} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {Link} from 'react-router-dom'; +import styles from './header.module.css'; + +export const Header = React.memo(function Header(): JSX.Element { + return ( +
+
+
+ Lilac +
+
+
+ + + New model + + +
+
+ ); +}); diff --git a/src/web/src/home.tsx b/src/web/src/home.tsx new file mode 100644 index 000000000..b95562c4d --- /dev/null +++ b/src/web/src/home.tsx @@ -0,0 +1,29 @@ +import {SlSpinner} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {Link} from 'react-router-dom'; +import {useListModelsQuery} from './store'; +import {getModelLink, renderError} from './utils'; + +export const Home = React.memo(function Home(): JSX.Element { + const models = useListModelsQuery(); + + return ( + <> +
+
+ {models.isFetching ? ( + + ) : models.error || models.currentData == null ? ( + renderError(models.error) + ) : ( + models.currentData.models.map((model, i) => ( + + {model.username}/{model.name} + + )) + )} +
+
+ + ); +}); diff --git a/src/web/src/hooks.ts b/src/web/src/hooks.ts new file mode 100644 index 000000000..806ade443 --- /dev/null +++ b/src/web/src/hooks.ts @@ -0,0 +1,7 @@ +import {TypedUseSelectorHook, useDispatch, useSelector} from 'react-redux'; +import type {AppDispatch, RootState} from './store'; + +// Use throughout the app instead of plain `useDispatch` and `useSelector`. +// See: https://react-redux.js.org/tutorials/typescript-quick-start +export const useAppDispatch: () => AppDispatch = useDispatch; +export const useAppSelector: TypedUseSelectorHook = useSelector; diff --git a/src/web/src/index.css b/src/web/src/index.css new file mode 100644 index 000000000..f42b270d1 --- /dev/null +++ b/src/web/src/index.css @@ -0,0 +1,2 @@ +@tailwind base; +@tailwind utilities; diff --git a/src/web/src/index.html b/src/web/src/index.html new file mode 100644 index 000000000..9900d893b --- /dev/null +++ b/src/web/src/index.html @@ -0,0 +1 @@ +
diff --git a/src/web/src/index.tsx b/src/web/src/index.tsx new file mode 100644 index 000000000..c838e7412 --- /dev/null +++ b/src/web/src/index.tsx @@ -0,0 +1,58 @@ +import '@shoelace-style/shoelace/dist/themes/light.css'; +import {setBasePath} from '@shoelace-style/shoelace/dist/utilities/base-path'; +import * as React from 'react'; +import {createRoot} from 'react-dom/client'; +import {Provider} from 'react-redux'; +import { + createBrowserRouter, + createRoutesFromElements, + Outlet, + Route, + RouterProvider, +} from 'react-router-dom'; +import {CreateModel} from './create_model'; +import {Header} from './header'; +import {Home} from './home'; +import './index.css'; +import {FilterMaker} from './model_viewer/filter_maker'; +import {store} from './store'; +setBasePath('/static/shoelace'); + +export const PageNotFound = React.memo(function PageNotFound(): JSX.Element { + return <>Error: Page not found!; +}); + +export const AppContainer = React.memo(function App(): JSX.Element { + return ( + <> +
+
+
+ +
+
+ + ); +}); + +const router = createBrowserRouter( + createRoutesFromElements( + }> + } /> + } /> + } /> + } /> + + ) +); + +// Create the react render tree. +const root = createRoot(document.getElementById('root') as HTMLDivElement); +root.render( + // Make the redux store available to the whole app. + + + + + +); diff --git a/src/web/src/model_viewer/dataset_loader.module.css b/src/web/src/model_viewer/dataset_loader.module.css new file mode 100644 index 000000000..533fb5563 --- /dev/null +++ b/src/web/src/model_viewer/dataset_loader.module.css @@ -0,0 +1,4 @@ +.add_dataset_row { + padding: 4px 4px; + width: 100%; +} diff --git a/src/web/src/model_viewer/dataset_loader.tsx b/src/web/src/model_viewer/dataset_loader.tsx new file mode 100644 index 000000000..a2b893aeb --- /dev/null +++ b/src/web/src/model_viewer/dataset_loader.tsx @@ -0,0 +1,82 @@ +import {SlButton, SlInput, SlSpinner} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {useAddModelDataMutation} from '../store'; +import {renderError} from '../utils'; +import styles from './dataset_loader.module.css'; + +export interface DatasetLoaderProps { + username: string; + modelName: string; +} +export const DatasetLoader = React.memo(function DatasetLoader({ + username, + modelName, +}: DatasetLoaderProps): JSX.Element { + // For huggingface data loading. + const [hfDataset, setHfDataset] = React.useState('rotten_tomatoes'); + const [hfSplit, setHfSplit] = React.useState('train'); + const [hfTextField, setHfTextField] = React.useState('text'); + const [ + addModelData, + { + isLoading: isAddModelLoading, + isError: isAddModelDataError, + error: addModelDataError, + isSuccess: isAddModelDataSuccess, + }, + ] = useAddModelDataMutation(); + + const addDataClicked = () => { + addModelData({ + username: username!, + model_name: modelName!, + hf_dataset: hfDataset, + hf_split: hfSplit, + hf_text_field: hfTextField, + }); + }; + return ( + <> +
+
No data found for this model. Please add data.
+
+
+ setHfDataset((e.target as HTMLInputElement).value)} + /> +
+
+ setHfSplit((e.target as HTMLInputElement).value)} + /> +
+
+ setHfTextField((e.target as HTMLInputElement).value)} + /> +
+
+
+
+ addDataClicked()} + > + Add data + + {isAddModelLoading ? : <>} +
+
+ {isAddModelDataError ? <>{renderError(addModelDataError)} : <>} +
+ + ); +}); diff --git a/src/web/src/model_viewer/example_viewer.tsx b/src/web/src/model_viewer/example_viewer.tsx new file mode 100644 index 000000000..e22fee5cd --- /dev/null +++ b/src/web/src/model_viewer/example_viewer.tsx @@ -0,0 +1,61 @@ +import {SlIcon} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; + +export interface Example { + text: string; + id: string; +} + +interface ExampleViewerProps { + examples: Example[]; + labeledData?: LabeledData; + exampleLabeled: (labeledData: LabeledData) => void; +} + +export interface LabeledData { + [exampleIdx: string]: number; +} + +export const ExampleViewer = React.memo(function ExampleViewer({ + examples, + labeledData, + exampleLabeled, +}: ExampleViewerProps): JSX.Element { + if (examples.length === 0) { + return
No examples to show...
; + } + const exampleLabelClicked = (exampleId: string, label: number) => { + const newLabeledData = {...labeledData}; + if (exampleId in newLabeledData && newLabeledData[exampleId] === label) { + delete newLabeledData[exampleId]; + } else { + newLabeledData[exampleId] = label; + } + exampleLabeled(newLabeledData); + }; + const exampleCards = examples.map((example) => { + labeledData = labeledData || {}; + const checkedName = + example.id in labeledData && labeledData[example.id] ? 'check-square-fill' : 'check-square'; + const uncheckedName = + example.id in labeledData && !labeledData[example.id] ? 'x-square-fill' : 'x-square'; + return ( +
+
{example.text}
+
+ exampleLabelClicked(example.id, 1)} + className="cursor-pointer text-blue-400 text-2xl mr-2" + name={checkedName} + /> + exampleLabelClicked(example.id, 0)} + className="cursor-pointer text-rose-400 text-2xl" + name={uncheckedName} + /> +
+
+ ); + }); + return
{exampleCards}
; +}); diff --git a/src/web/src/model_viewer/filter_builder.module.css b/src/web/src/model_viewer/filter_builder.module.css new file mode 100644 index 000000000..6ed71d192 --- /dev/null +++ b/src/web/src/model_viewer/filter_builder.module.css @@ -0,0 +1,76 @@ +.table { + display: flex; + flex-direction: row; + margin-right: 12px; +} + +.table .body { + max-height: 500px; + overflow: auto; +} + +.row { + display: flex; + align-items: center; + border-bottom: 1px solid rgb(167, 167, 167); + min-height: 42px; +} + +.statusRow { + justify-content: space-between; +} + +.header { + font-weight: 500; + color: rgb(107 114 128); +} + +.cell { + display: flex; + align-items: center; + height: 100%; + border-right: 1px solid rgb(238, 238, 237); + padding: 6px 8px; +} + +.lastCell { + border-right: none; +} + +.textCell { + flex: 1; + overflow: hidden; + text-overflow: ellipsis; +} + +.predictionIcon { + margin-left: 10px; + font-size: 22px; +} + +.predictionCell { + width: 100px; +} +.labelCell { + width: 180px; +} +.correctCell { + width: 40px; +} +.incorrect { + color: red; +} + +.labelPill { + cursor: pointer; + display: flex; + align-items: center; + border-radius: 10px; + padding: 0 9px; + font-size: 14px; + line-height: 120%; + height: 20px; + min-width: 0px; + margin-left: 2px; + margin-right: 2px; +} diff --git a/src/web/src/model_viewer/filter_builder.tsx b/src/web/src/model_viewer/filter_builder.tsx new file mode 100644 index 000000000..46cd7bf38 --- /dev/null +++ b/src/web/src/model_viewer/filter_builder.tsx @@ -0,0 +1,391 @@ +import {SlButton, SlIcon, SlInput, SlSpinner} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {useParams} from 'react-router-dom'; +import {LabeledExample} from '../server_api'; +import { + useAddExamplesMutation, + useLazySearchExamplesQuery, + useLoadModelQuery, + useModelInfoQuery, + useSaveModelMutation, +} from '../store'; +import {renderError} from '../utils'; +import {DatasetLoader} from './dataset_loader'; +import {Label, Table, TableExample} from './table'; + +// TODO: Generalize this. +const LABEL_SET = ['out filter', 'in filter']; + +export const FilterBuilder = React.memo(function ModelViewer(): JSX.Element { + const {username, modelName} = useParams<{username: string; modelName: string}>(); + // Search. + const [searchQuery, setSearchQuery] = React.useState(''); + + const [newInputText, setNewInputText] = React.useState(''); + const [newInputLabel, setNewInputLabel] = React.useState(null); + + // Labeled data. + const [labeledData, setLabeledData] = React.useState([]); + + const { + currentData: modelInfo, + error: modelError, + isError: isModelError, + isFetching: isModelFetching, + } = useModelInfoQuery({ + username, + name: modelName, + }); + + const { + currentData: modelData, + error: loadModelError, + isError: isLoadModelError, + isFetching: isLoadModelFetching, + } = useLoadModelQuery({ + username, + name: modelName, + }); + + React.useEffect(() => { + if (modelData?.dataset?.labeled_data != null) { + setLabeledData(modelData?.dataset.labeled_data); + } + }, [modelData?.dataset?.labeled_data]); + + const [ + saveModel, + { + isLoading: isSaveModelLoading, + isError: isSaveModelError, + error: saveModelError, + isSuccess: isSaveModelSuccess, + }, + ] = useSaveModelMutation(); + + const saveModelClicked = () => { + saveModel({ + username: username!, + name: modelName!, + labeled_data: labeledData, + }); + }; + + const [ + addExamples, + { + isLoading: isAddExamplesLoading, + isError: isAddExamplesError, + error: addExamplesError, + isSuccess: isAddExamplesSuccess, + }, + ] = useAddExamplesMutation(); + [isAddExamplesError, isAddExamplesLoading, isAddExamplesSuccess, addExamplesError]; + + const [ + searchExamples, + { + currentData: searchExamplesData, + error: searchExamplesError, + isError: isSearchExamplesError, + isFetching: isSearchExamplesLoading, + }, + ] = useLazySearchExamplesQuery(); + [isSearchExamplesError, isSearchExamplesLoading, searchExamplesError]; + + const labelSelected = (rowIdx: number, label: number) => { + const newLabeledData = [...labeledData]; + + let foundExample = false; + for (const [i, labeledExample] of newLabeledData.entries()) { + if (labeledExample.row_idx === rowIdx) { + newLabeledData[i] = {row_idx: rowIdx, label}; + foundExample = true; + } + } + + if (!foundExample) { + newLabeledData.push({row_idx: rowIdx, label}); + } + //setTrainData(newTrainingData); + saveModel({ + username: username!, + name: modelName!, + labeled_data: newLabeledData, + }); + }; + const removeExampleLabel = (trainRowIdx: number) => { + const newTrainData = labeledData.filter((example) => example.row_idx !== trainRowIdx); + saveModel({ + username: username!, + name: modelName!, + labeled_data: newTrainData, + }); + //setTrainData(trainData.filter((example) => example.row_idx !== trainRowIdx)); + }; + + const {negativeTableExamples, positiveTableExamples, labelCountMap} = React.useMemo(() => { + const negativeTableExamples: TableExample[] = []; + const positiveTableExamples: TableExample[] = []; + const labelCountMap: {[label: number]: number} = {}; + + const rowIdxSearchSimilarity: {[rowIdx: number]: number} = {}; + for (const rowResult of searchExamplesData?.row_results || []) { + rowIdxSearchSimilarity[rowResult.row_idx] = rowResult.similarity; + } + + if (modelData != null && modelData?.has_data) { + const labelMap: {[rowIdx: number]: number} = {}; + for (const labeledExample of labeledData || []) { + const dataRow = modelData.dataset!.data[labeledExample.row_idx]; + + const labeledTableExample = { + text: dataRow.text, + rowIdx: labeledExample.row_idx, + prediction: dataRow.prediction, + label: labeledExample.label, + metadata: dataRow.metadata, + }; + if (labeledExample.label === 0) { + negativeTableExamples.push(labeledTableExample); + } else if (labeledExample.label === 1) { + positiveTableExamples.push(labeledTableExample); + } + + labelMap[labeledExample.row_idx] = labeledExample.label; + + // Balance the training data by sorting the tables by prediction. + if (labelCountMap[labeledExample.label] == null) { + labelCountMap[labeledExample.label] = 0; + } + labelCountMap[labeledExample.label]++; + } + + for (const [i, dataRow] of modelData.dataset!.data.entries()) { + // Don't add to the full dataset when it exists in train or test. + if (labelMap[i] == null) { + const metadata = + rowIdxSearchSimilarity[i] != null + ? {similarity: rowIdxSearchSimilarity[i], ...dataRow.metadata} + : dataRow.metadata; + + const predictionTableExample = { + text: dataRow.text, + rowIdx: i, + prediction: dataRow.prediction, + metadata, + }; + if (dataRow.prediction === 0) { + negativeTableExamples.push(predictionTableExample); + } else if (dataRow.prediction === 1) { + positiveTableExamples.push(predictionTableExample); + } + } + } + } + return { + negativeTableExamples, + positiveTableExamples, + labelCountMap, + }; + }, [modelData, labeledData, searchExamplesData]); + + const isLoading = + isLoadModelFetching || + isModelFetching || + isSaveModelLoading || + isSearchExamplesLoading || + isAddExamplesLoading; + return ( + <> + {modelError || isModelError ? renderError(modelError) : <>} + {loadModelError || isLoadModelError ? renderError(modelError) : <>} + +
+ {modelInfo != null || !isModelFetching ? ( +
+
+ {modelInfo?.username} / {modelInfo?.name} +
+
{modelInfo?.description}
+
+ ) : ( + + )} + + {modelData != null && !modelData?.has_data && username != null && modelName != null ? ( + + ) : ( + <> + )} + + {modelData?.dataset != null ? ( + <> +
+
Prefix
+
Movie review:
{/** TODO: Move this to server */} +
+
+
+
+ setNewInputText((e.target as HTMLInputElement).value)} + /> +
+
+ +
+
+ + addExamples({ + username: username!, + name: modelName!, + examples: [{text: newInputText, label: newInputLabel!}], + }) + } + > + Add + +
+
+
+
+ setSearchQuery((e.target as any).value)} + > + + searchExamples({ + username: username!, + modelName: modelName!, + query: searchQuery, + }) + } + > + Search + +
+
+ + saveModel({ + username: username!, + name: modelName!, + labeled_data: [], + }) + } + > + Clear all labels + +
+ {/* Out of filter table */} +
+
+ = labelCountMap[1]}, + {id: 'confidence', desc: false}, + ] + : [{id: 'similarity', desc: true}] + } + rowIdxFilter={searchExamplesData?.row_results.map( + (rowResult) => rowResult.row_idx + )} + >
+
+ {/* In filter table */} + +
+ = labelCountMap[1]}, + {id: 'confidence', desc: false}, + ] + : [{id: 'similarity', desc: true}] + } + rowIdxFilter={searchExamplesData?.row_results.map( + (rowResult) => rowResult.row_idx + )} + addExample={(text: string, label: number) => + addExamples({username: username!, name: modelName!, examples: [{text, label}]}) + } + >
+
+
+ {/* Save data */} +
+
+ saveModelClicked()} + > + Save + +
+ {isSaveModelLoading ? ( +
+ +
+ ) : ( + <> + )} +
+ {isSaveModelSuccess ? : <>} +
+
+ {isSaveModelError ? <>{renderError(saveModelError)} : <>} +
+
+ + ) : ( + <> + )} +
+ + ); +}); diff --git a/src/web/src/model_viewer/filter_maker.tsx b/src/web/src/model_viewer/filter_maker.tsx new file mode 100644 index 000000000..7d5db3dc2 --- /dev/null +++ b/src/web/src/model_viewer/filter_maker.tsx @@ -0,0 +1,311 @@ +import { + SlButton, + SlIcon, + SlInput, + SlRadioButton, + SlRadioGroup, + SlSpinner, +} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {useParams} from 'react-router-dom'; +import { + useLazySearchExamplesQuery, + useLoadModelQuery, + useModelInfoQuery, + useSaveModelMutation, +} from '../store'; +import {renderError} from '../utils'; +import {DatasetLoader} from './dataset_loader'; +import {Example, ExampleViewer, LabeledData} from './example_viewer'; + +const TOP_K = 100; + +type FilterView = 'labeled' | 'semantic' | 'ranked' | 'random'; + +export const FilterMaker = React.memo(function FilterMaker(): JSX.Element { + const {username, modelName} = useParams<{username: string; modelName: string}>(); + // Search. + const [searchQuery, setSearchQuery] = React.useState('Kids go on a treasure hunt'); + const [filterView, setFilterView] = React.useState('random'); + + const { + currentData: modelInfo, + error: modelError, + isError: isModelError, + isFetching: isModelFetching, + } = useModelInfoQuery({ + username, + name: modelName, + }); + + const { + currentData: modelData, + error: loadModelError, + isError: isLoadModelError, + isFetching: isLoadModelFetching, + } = useLoadModelQuery({ + username, + name: modelName, + }); + + const [ + saveModel, + { + isLoading: isSaveModelLoading, + isError: isSaveModelError, + error: saveModelError, + isSuccess: isSaveModelSuccess, + }, + ] = useSaveModelMutation(); + + const saveModelClicked = () => { + const labeledExamples = Object.entries(labeledData).map(([rowIdx, label]) => ({ + row_idx: +rowIdx, + label, + })); + setFilterView('ranked'); + saveModel({ + username: username!, + name: modelName!, + labeled_data: labeledExamples, + }); + }; + + const [ + searchExamples, + { + currentData: searchExamplesData, + error: searchExamplesError, + isError: isSearchExamplesError, + isFetching: isSearchExamplesLoading, + }, + ] = useLazySearchExamplesQuery(); + [isSearchExamplesError, isSearchExamplesLoading, searchExamplesError]; + + React.useEffect(() => { + if (modelData?.dataset?.labeled_data == null) { + return; + } + const newLabeledData: LabeledData = {}; + for (const labeledExample of modelData.dataset.labeled_data) { + newLabeledData[labeledExample.row_idx.toString()] = labeledExample.label; + } + setLabeledData(newLabeledData); + }, [modelData?.dataset?.labeled_data]); + + const [rankedExamples, randomExamples] = React.useMemo(() => { + if (modelData?.dataset?.data == null) { + return [[], []]; + } + const serverLabeledData: LabeledData = {}; + for (const labeledExample of modelData.dataset.labeled_data) { + serverLabeledData[labeledExample.row_idx.toString()] = labeledExample.label; + } + const modelExamples = modelData.dataset.data + .map((row, idx) => ({ + text: row.text, + id: idx.toString(), + confidence: row.metadata['confidence'] as number, + })) + .filter((row) => !(row.id in serverLabeledData)); + const rankedExamples = modelExamples + .sort((a, b) => b.confidence - a.confidence) + .slice(0, TOP_K); + const randomExamples = modelExamples.sort(() => Math.random() - 0.5).slice(0, TOP_K); + return [rankedExamples, randomExamples]; + }, [modelData?.dataset?.data]); + + const [labeledData, setLabeledData] = React.useState({}); + + let semanticSearchExamples: Example[] = []; + if (searchExamplesData != null) { + semanticSearchExamples = searchExamplesData.row_results.slice(0, TOP_K).map((row) => { + const text = modelData && modelData.dataset ? modelData.dataset.data[row.row_idx].text : ''; + return {text, id: row.row_idx.toString()}; + }); + } + const isLoading = + isLoadModelFetching || isModelFetching || isSaveModelLoading || isSearchExamplesLoading; + + const labeledExamples = Object.entries(labeledData).map(([rowIdx, label]) => ({ + text: modelData?.dataset?.data[+rowIdx].text ?? '', + id: rowIdx, + })); + const labeledTable = + filterView === 'labeled' ? ( + <> + setLabeledData(labeledData)} + > + + ) : null; + const semanticTable = + filterView === 'semantic' ? ( + <> + {semanticSearchExamples.length > 0 ? ( + setLabeledData(labeledData)} + > + ) : ( + 'Please enter a search query' + )} + + ) : null; + + const rankedTable = + filterView === 'ranked' ? ( + <> + setLabeledData(labeledData)} + > + + ) : null; + + const randomTable = + filterView === 'random' ? ( + setLabeledData(labeledData)} + > + ) : null; + return ( + <> + {modelError || isModelError ? renderError(modelError) : <>} + {loadModelError || isLoadModelError ? renderError(modelError) : <>} + +
+ {modelInfo != null || !isModelFetching ? ( +
+
+ {modelInfo?.username} / {modelInfo?.name} +
+
{modelInfo?.description}
+
+ ) : ( + + )} + + {modelData != null && !modelData?.has_data && username != null && modelName != null ? ( + + ) : ( + <> + )} + {modelData?.dataset != null ? ( + <> +
+
Prefix
+
Movie review:
{/** TODO: Move this to server */} +
+
+ { + setSearchQuery((e.target as HTMLInputElement).value); + }} + > + { + setFilterView('semantic'); + searchExamples({ + username: username!, + modelName: modelName!, + query: searchQuery, + }); + }} + > + Search + +
+
+ saveModelClicked()} + > + Save + + + saveModel({ + username: username!, + name: modelName!, + labeled_data: [], + }) + } + > + Clear all labels + + {/* Save data */} +
+ {isSaveModelLoading ? ( +
+ +
+ ) : ( + <> + )} +
+ {isSaveModelSuccess ? : <>} +
+
+ {isSaveModelError ? <>{renderError(saveModelError)} : <>} +
+
+
+
+ + setFilterView((e.target as HTMLInputElement).value as FilterView) + } + > + + Labeled ({labeledExamples.length} examples) + + + Semantic ({semanticSearchExamples.length} examples) + + + Ranked ({rankedExamples.length} examples) + + + Random ({randomExamples.length} examples) + + +
+ {labeledTable} + {semanticTable} + {rankedTable} + {randomTable} +
+
+ + ) : ( + <> + )} +
+ + ); +}); diff --git a/src/web/src/model_viewer/table.module.css b/src/web/src/model_viewer/table.module.css new file mode 100644 index 000000000..80b65ea0c --- /dev/null +++ b/src/web/src/model_viewer/table.module.css @@ -0,0 +1,97 @@ +.table { + display: flex; + flex-direction: row; + margin-right: 12px; +} + +.table .body { + max-height: 500px; + overflow: auto; +} + +.header { + font-weight: 500; + color: rgb(107 114 128); +} + +.row { + display: flex; + align-items: center; + border-bottom: 1px solid rgb(200, 200, 200); + min-height: 42px; + width: 100%; +} + +.cell { + display: flex; + align-items: center; + border-right: 1px solid rgb(238, 238, 237); + padding: 6px 8px; + height: 100%; + width: 140px; +} + +.clearRowCell { + width: 20px; + padding: 0; +} + +.textCell { + min-width: 330px; + flex: 1 1 1px; + overflow: hidden; + text-overflow: ellipsis; + + display: -webkit-box; + -webkit-box-orient: vertical; + //-webkit-line-clamp: 6; + overflow: hidden; + text-overflow: ellipsis; + /* mask-image: linear-gradient(#000, #000, rgba(0, 0, 0, 0.4)); */ +} + +.predictionCell { + width: 110px; + justify-content: center; + align-items: center; +} + +.labelCell { + width: 80px; + justify-content: center; + align-items: center; +} + +.correctCell { + width: 40px; + justify-content: center; + align-items: center; +} + +.labelPill { + cursor: pointer; + display: flex; + align-items: center; + border-radius: 10px; + padding: 0 9px; + font-size: 14px; + line-height: 120%; + height: 20px; + min-width: 0px; +} + +.statusRow { + justify-content: space-between; +} + +.predictionIcon { + font-size: 22px; +} + +.correct { + color: green; +} + +.incorrect { + color: red; +} diff --git a/src/web/src/model_viewer/table.tsx b/src/web/src/model_viewer/table.tsx new file mode 100644 index 000000000..34a236357 --- /dev/null +++ b/src/web/src/model_viewer/table.tsx @@ -0,0 +1,493 @@ +import { + SlIcon, + SlIconButton, + SlOption, + SlRadioButton, + SlRadioGroup, + SlSelect, +} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; +import {useSortBy, useTable} from 'react-table'; +import {VariableSizeList as List} from 'react-window'; +import styles from './table.module.css'; + +const TABLE_HEIGHT_PX = 300; +enum Colors { + ORANGE = 'rgb(250, 222, 201)', + YELLOW = 'rgb(253, 236, 200)', + GREEN = 'rgb(219, 237, 219)', + BLUE = 'rgb(211, 229, 239)', + PURPLE = 'rgb(232, 222, 238)', + RED = 'rgb(255, 226, 221)', + GRAY = 'rgba(227, 226, 224, 0.5)', +} + +const LABEL_COLORS = [ + Colors.ORANGE, + Colors.BLUE, + Colors.RED, + Colors.GREEN, + Colors.YELLOW, + Colors.PURPLE, +]; + +const NO_LABEL = 'No label'; +const NO_LABEL_COLOR = Colors.GRAY; + +type FILTER_VIEW = 'all' | 'labeled' | 'unlabeled'; + +export interface TableExample { + text: string; + + // Original data. + rowIdx: number; + prediction: number; + + label?: number; + + // Extra sortable metadata. + metadata: {[columnName: string]: string | number}; +} + +interface TableProps { + title: string; + + examples: TableExample[]; + labelSet: {[label: number]: string}; + + tableHeightPx?: number; + + exampleRemoved?: (rowIdx: number) => void; + labelSelected?: (rowIdx: number, label: number) => void; + addExample?: (text: string, label: number) => void; + sortBy?: Array<{ + id: string; + desc: boolean; + }>; + rowIdxFilter?: number[]; +} + +const COLUMN_ID_CELL_CLASS_MAP: {[columnId: string]: string} = { + text: styles.textCell, + prediction: styles.predictionCell, + label: styles.labelCell, + correct: styles.correctCell, + clear_row: styles.clearRowCell, +}; + +export const Table = React.memo(function Table({ + title, + examples, + labelSet, + tableHeightPx, + exampleRemoved, + labelSelected, + addExample, + sortBy, + rowIdxFilter, +}: TableProps): JSX.Element { + // TODO: Switch this when switching the tab. + const isLabeled = true; + const [filterView, setFilterView] = React.useState('all'); + + const rowIdxFilterSet = new Set(rowIdxFilter); + const {data, extraColumns, correct, numLabeledExamples, numTotalExamples} = React.useMemo(() => { + let correct = 0; + let numLabeledExamples = 0; + + let numTotalExamples = 0; + + let data = examples.filter((example) => { + if (rowIdxFilter == null) { + return true; + } + return rowIdxFilterSet.has(example.rowIdx); + }); + + numTotalExamples = data.length; + data.forEach((example) => { + if (example.prediction === example.label) { + correct++; + } + if (example.label != null) { + numLabeledExamples++; + } + }); + + if (filterView === 'labeled') { + data = data.filter((example) => example.label != null); + } else if (filterView === 'unlabeled') { + data = data.filter((example) => example.label == null); + } + + const extraColumns = new Set(); + data = data.map((example) => { + Object.keys(example.metadata).forEach((key) => extraColumns.add(key)); + return { + rowIdx: example.rowIdx, + text: example.text, + prediction: example.prediction, + label: example.label, + metadata: example.metadata, + }; + }); + return { + data, + extraColumns: Array.from(extraColumns) as string[], + correct, + numTotalExamples, + numLabeledExamples, + }; + }, [examples, rowIdxFilter, filterView]); + + const numUnlabeledExamples = numTotalExamples - numLabeledExamples; + + sortBy = sortBy || []; + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const initialState: any = React.useMemo(() => { + return {sortBy}; + }, [sortBy]); + + const {getTableProps, getTableBodyProps, headerGroups, rows, prepareRow} = useTable( + { + columns: React.useMemo( + () => [ + { + id: 'clear_row', + accessor: 'rowIdx', + Cell: ({ + cell: { + value, + row: {index}, + }, + }: { + cell: {value: number; row: {index: number}}; + }) => { + const rowIdx = value; + return data[index].label != null ? ( +
+ exampleRemoved!(rowIdx)}> +
+ ) : ( + <> + ); + }, + }, + + { + Header: 'Text', + accessor: 'text', + Cell: ({cell: {value}}: {cell: {value: string}}) => { + return ( +
+
{value}
+
+ ); + }, + }, + ...extraColumns.map((columnName) => { + return { + Header: columnName, + id: columnName, + accessor: ({metadata}: {metadata: {[columnName: string]: string | number}}) => { + return metadata![columnName] || -1; + }, + sortType: 'basic', + Cell: ({cell: {value}}: {cell: {value: number | string}}) => { + return ( +
+ {typeof value === 'number' ? Math.floor(100 * value) : value} +
+ ); + }, + }; + }), + { + Header: 'Prediction', + accessor: 'prediction', + Cell: ({ + cell: { + value, + row: {index}, + }, + }: { + cell: {value: number; row: {index: number}}; + }) => { + const predictionColor = LABEL_COLORS[value]; + const predictionLabel = labelSet[value]; + return ( + <> +
+ {predictionLabel} +
+ + ); + }, + }, + { + Header: 'Label', + accessor: 'label', + Cell: ({ + cell: { + value, + row: {index}, + }, + }: { + cell: {value: number; row: {index: number}}; + }) => { + return ( +
+ labelSelected!(data[index].rowIdx, 0)} + /> + labelSelected!(data[index].rowIdx, 1)} + /> +
+ ); + }, + }, + + ...(isLabeled + ? [ + { + id: 'correct', + accessor: ({label, prediction}: {label: number; prediction: number}) => { + return label === prediction; + }, + sortType: 'basic', + Cell: ({ + cell: { + value, + row: {index}, + }, + }: { + cell: {value: boolean; row: {index: number}}; + }) => { + return data[index].label != null ? ( + + ) : ( + <> + ); + }, + }, + ] + : []), + ], + [examples, rowIdxFilter, extraColumns] + ), + data, + initialState: initialState, + }, + useSortBy + ); + + // Compute the sizes of the rows dynamically as a function of the content. + const sizeMap = React.useRef<{[idx: number]: number}>({}); + const listRef = React.useRef(null); + const setSize = React.useCallback((index: number, size: number) => { + sizeMap.current = {...sizeMap.current, [index]: size}; + listRef.current!.resetAfterIndex(index); + }, []); + const getSize = (index: number) => sizeMap.current[index] || 50; + + const RenderRow = React.useCallback( + ({index, style}: {index: number; style: React.CSSProperties}) => { + const row = rows[index]; + prepareRow(row); + + const rowRef = React.useRef(null); + return ( +
+ + <> + {row.cells.map((cell) => { + const cellClasses = [styles.cell]; + if (COLUMN_ID_CELL_CLASS_MAP[cell.column.id] != null) { + cellClasses.push(COLUMN_ID_CELL_CLASS_MAP[cell.column.id]); + } + // TODO(nsthorat): Figure this out... + if (cell.column.id != 'text') { + cellClasses.push('text-center'); + } + return ( +
+ {cell.render('Cell')} +
+ ); + })} + + +
+ ); + }, + [prepareRow, rows] + ); + + const accuracy = + isLabeled && numLabeledExamples > 0 ? ((100 * correct) / numLabeledExamples).toFixed(2) : null; + return ( +
+

{title}

+

{accuracy != null ? `Accuracy: ${accuracy}%` : ''}

+
+ setFilterView((e.target as HTMLInputElement).value as FILTER_VIEW)} + > + All ({numTotalExamples}) + Labeled ({numLabeledExamples}) + Unlabeled ({numUnlabeledExamples}) + +
+
+
+
+
+ {headerGroups.map((headerGroup) => ( +
+ {headerGroup.headers.map((column) => { + const cellClasses = [styles.cell]; + if (COLUMN_ID_CELL_CLASS_MAP[column.id] != null) { + cellClasses.push(COLUMN_ID_CELL_CLASS_MAP[column.id]); + } + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const icon = (column as any).isSorted + ? // eslint-disable-next-line @typescript-eslint/no-explicit-any + (column as any).isSortedDesc + ? 'sort-down' + : 'sort-down-alt' + : ''; + return ( +
+ {column.render('Header')} +
+ +
+
+ ); + })} +
+ ))} +
+ +
+ + {RenderRow} + +
+
+
+
+
+ ); +}); + +interface VariableHeightRowProps { + index: number; + setSize: (index: number, height: number) => void; + children: JSX.Element; +} +// The job of this component is to just measure the height of the row for the react table +// virtualizer, and use it to set the dynamic size of the row after computing the real size. +export const VariableHeightRow = React.memo(function Row({ + index, + setSize, + children, +}: VariableHeightRowProps): JSX.Element { + const rowRef = React.useRef(null); + + React.useEffect(() => { + if (rowRef.current != null) { + setSize(index, rowRef.current.getBoundingClientRect().height); + } + }, [setSize, index]); + + return ( +
+ {children} +
+ ); +}); +interface LabelProps { + labelId: number | null; + labels: {[labelId: number]: string}; + onLabelChange: (labelId: number) => void; +} + +export const Label = React.memo(function Label({ + labelId, + labels, + onLabelChange, +}: LabelProps): JSX.Element { + const [focused, setFocused] = React.useState(false); + + const selectChanged = (e: Event) => { + onLabelChange(Number((e.target as HTMLInputElement).value)); + setFocused(false); + }; + + const selectMenuCloses = () => { + if (labelId != null && labelId >= 0) { + setFocused(false); + } + }; + + const showSelect = (labelId != null && labelId < 0) || focused; + return ( + <> +
+ selectMenuCloses()} + placeholder="Select label" + onSlChange={(e) => selectChanged(e)} + value={labelId == null ? NO_LABEL : labelId.toString()} + > + {Object.entries(labels).map(([labelId, label]) => ( + + {label} + + ))} + +
+ +
setFocused(true)}> +
+ {labelId == null ? NO_LABEL : labels[labelId]} +
+
+ + ); +}); diff --git a/src/web/src/server_api.ts b/src/web/src/server_api.ts new file mode 100644 index 000000000..c59fd9f0b --- /dev/null +++ b/src/web/src/server_api.ts @@ -0,0 +1,90 @@ +// Request interfaces. +export interface ModelOptions { + username?: string; + name?: string; +} + +export interface CreateModelOptions { + username: string; + name: string; + description: string; +} + +export interface SaveModelOptions { + username: string; + name: string; + labeled_data: LabeledExample[]; +} + +export interface AddExamplesOptions { + username: string; + name: string; + examples: Array<{text: string; label: number}>; +} + +export interface SearchExamplesOptions { + username: string; + modelName: string; + query: string; +} + +// Response interfaces. +export interface CreateModelResponse { + name: string; +} + +// Database interfaces. +export interface UserInfo { + id: number; + username: string; + name: string; + date_joined: string; +} + +export interface ModelInfo { + id: number; + username: string; + name: string; + description: string; + date_created: string; +} + +export interface ListModelsResponse { + models: ModelInfo[]; +} + +export interface DatasetRow { + text: string; + prediction: number; + + // Extra sortable metadata. + metadata: {[columnName: string]: string | number}; +} + +export interface LabeledExample { + row_idx: number; + label: number; +} + +export interface Dataset { + data: DatasetRow[]; + labeled_data: LabeledExample[]; +} + +export interface AddDatasetOptions { + username: string; + model_name: string; + + hf_dataset: string; + hf_split: string; + hf_text_field: string; +} + +export interface LoadModelResponse { + dataset?: Dataset; + has_data: boolean; +} + +export interface SearchExamplesResponse { + row_results: Array<{row_idx: number; similarity: number}>; +} diff --git a/src/web/src/store.ts b/src/web/src/store.ts new file mode 100644 index 000000000..1882e58a7 --- /dev/null +++ b/src/web/src/store.ts @@ -0,0 +1,199 @@ +import {configureStore} from '@reduxjs/toolkit'; +import {createApi} from '@reduxjs/toolkit/query/react'; + +import {createSlice} from '@reduxjs/toolkit'; +import { + AddDatasetOptions, + AddExamplesOptions, + CreateModelOptions, + CreateModelResponse, + ListModelsResponse, + LoadModelResponse, + ModelInfo, + ModelOptions, + SaveModelOptions, + SearchExamplesOptions, + SearchExamplesResponse, +} from './server_api'; + +// eslint-disable-next-line @typescript-eslint/no-empty-interface +interface AppState {} + +// Define the initial state using that type +const initialState: AppState = {}; + +// NOTE: We might want to split the state into multiple slices in the future. +const appSlice = createSlice({ + name: 'app', + initialState, + reducers: {}, +}); + +const MODELS_TAG = 'models'; +const MODEL_DATA_TAG = 'model_data'; +export const dbApi = createApi({ + reducerPath: 'dpiApi', + baseQuery: () => { + return {error: 'baseQuery should never be called.'}; + }, + tagTypes: [MODELS_TAG, MODEL_DATA_TAG], + endpoints: (builder) => ({ + modelInfo: builder.query({ + queryFn: async (options) => { + const {username, name} = options; + if (username == null || name == null) { + return {data: null}; + } + const response = await fetch(`/db/model_info?username=${username}&name=${name}`); + return {data: await response.json()}; + }, + }), + loadModel: builder.query({ + queryFn: async (options) => { + const {username, name} = options; + if (username == null || name == null) { + return {data: null}; + } + const response = await fetch(`/db/load_model?username=${username}&name=${name}`); + return {data: await response.json()}; + }, + providesTags: [MODEL_DATA_TAG], + }), + searchExamples: builder.query({ + queryFn: async (options) => { + const {username, modelName, query} = options; + if (username == null || modelName == null || query == '') { + return {data: null}; + } + const response = await fetch( + `/db/search_examples?username=${username}&model_name=${modelName}&query=${query}` + ); + return {data: await response.json()}; + }, + }), + listModels: builder.query< + // A row of the 'model' table. + ListModelsResponse, + void + >({ + queryFn: async () => { + const response = await fetch(`/db/list_models`); + return {data: await response.json()}; + }, + providesTags: [MODELS_TAG], + }), + createModel: builder.mutation({ + queryFn: async (options) => { + const createModelResponse = await fetch(`/db/create_model`, { + method: 'POST', + headers: { + Accept: 'application/json', + 'Content-Type': 'application/json', + }, + body: JSON.stringify(options), + }); + if (createModelResponse.status != 200) { + return {error: `/db/create_model returned status code ${createModelResponse.status}.`}; + } + + const createModelResponseJson: CreateModelResponse = await createModelResponse.json(); + return {data: createModelResponseJson}; + }, + // Invalidate the list of datasets to force a refetch. + invalidatesTags: [MODELS_TAG], + }), + saveModel: builder.mutation({ + queryFn: async (options) => { + const saveModelResponse = await fetch(`/db/save_model`, { + method: 'POST', + headers: { + Accept: 'application/json', + 'Content-Type': 'application/json', + }, + body: JSON.stringify(options), + }); + if (saveModelResponse.status != 200) { + return { + error: `/db/save_model returned status code ${saveModelResponse.status}. + ${await saveModelResponse.text()}`, + }; + } + + return {data: null}; + }, + // Invalidate the model data. + // NOTE: This is very inneficient as it causes *all* data to be invalidated. We can update + // this later as we figure out the data loading story. + invalidatesTags: [MODEL_DATA_TAG], + }), + addExamples: builder.mutation({ + queryFn: async (options) => { + const addExamplesResponse = await fetch(`/db/add_examples`, { + method: 'POST', + headers: { + Accept: 'application/json', + 'Content-Type': 'application/json', + }, + body: JSON.stringify(options), + }); + if (addExamplesResponse.status != 200) { + return { + error: `/db/save_model returned status code ${addExamplesResponse.status}. + ${await addExamplesResponse.text()}`, + }; + } + + return {data: null}; + }, + // Invalidate the model data. + // NOTE: This is very inneficient as it causes *all* data to be invalidated. We can update + // this later as we figure out the data loading story. + invalidatesTags: [MODEL_DATA_TAG], + }), + addModelData: builder.mutation({ + queryFn: async (options) => { + const addDatasetResponse = await fetch(`/db/add_dataset`, { + method: 'POST', + headers: { + Accept: 'application/json', + 'Content-Type': 'application/json', + }, + body: JSON.stringify(options), + }); + if (addDatasetResponse.status != 200) { + return {error: `/db/add_dataset returned status code ${addDatasetResponse.status}.`}; + } + + return {data: null}; + }, + // Invalidate the model data. + // NOTE: This is very inneficient as it causes *all* data to be invalidated. We can update + // this later as we figure out the data loading story. + invalidatesTags: [MODEL_DATA_TAG], + }), + }), +}); + +export const store = configureStore({ + reducer: { + [appSlice.name]: appSlice.reducer, + [dbApi.reducerPath]: dbApi.reducer, + }, + middleware: (getDefaultMiddleware) => getDefaultMiddleware().concat([dbApi.middleware]), + devTools: process.env.NODE_ENV !== 'production', +}); + +export const { + useCreateModelMutation, + useModelInfoQuery, + useListModelsQuery, + useLoadModelQuery, + useAddModelDataMutation, + useSaveModelMutation, + useAddExamplesMutation, + useLazySearchExamplesQuery, +} = dbApi; + +// See: https://react-redux.js.org/tutorials/typescript-quick-start +export type RootState = ReturnType; +export type AppDispatch = typeof store.dispatch; diff --git a/src/web/src/utils.tsx b/src/web/src/utils.tsx new file mode 100644 index 000000000..aa7eafd26 --- /dev/null +++ b/src/web/src/utils.tsx @@ -0,0 +1,35 @@ +import {SerializedError} from '@reduxjs/toolkit'; +import {SlTooltip} from '@shoelace-style/shoelace/dist/react'; +import * as React from 'react'; + +export function renderError(error: string | SerializedError | undefined): JSX.Element { + if (error == null) { + return <>; + } + if (typeof error === 'string') { + return <>{error}; + } + // Custom css property exposed by the SlTooltip component. + const cssProps = {'--max-width': '800px'} as React.CSSProperties; + return ( + + {error.message} + + 
{error.stack}
+ + + ); +} + +export function getModelLink(username: string, modelName: string): string { + return `/${username}/${modelName}`; +} + +export function roundNumber(val: number, precision: number): number { + return Number(val.toFixed(precision)); +} + +export function formatDatetime(datetime: string): string { + const date = new Date(datetime); + return date.toLocaleDateString('en-US', {year: 'numeric', month: 'long', day: 'numeric'}); +} diff --git a/src/web/tailwind.config.js b/src/web/tailwind.config.js new file mode 100644 index 000000000..64ddc7904 --- /dev/null +++ b/src/web/tailwind.config.js @@ -0,0 +1,8 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: ['./src/**/*.{ts,tsx,html}'], + theme: { + extend: {}, + }, + plugins: [], +}; diff --git a/src/web/tsconfig.json b/src/web/tsconfig.json new file mode 100644 index 000000000..00254dca8 --- /dev/null +++ b/src/web/tsconfig.json @@ -0,0 +1,17 @@ +{ + "extends": "../../tsconfig.json", + "compilerOptions": { + "target": "ES2020", + // So webpack can tree-shake. + "module": "es6", + "moduleResolution": "node", + "sourceMap": true, + "inlineSources": true, + "jsx": "react", + "lib": ["es6", "dom", "es2015"], + "outDir": "dist", + "declaration": true, + // Type roots are defined here to add CSS modules. + "typeRoots": ["./node_modules/@types/", "./src/@types"] + } +} diff --git a/src/web/webpack.config.ts b/src/web/webpack.config.ts new file mode 100644 index 000000000..22ca35d2f --- /dev/null +++ b/src/web/webpack.config.ts @@ -0,0 +1,101 @@ +import CopyPlugin from 'copy-webpack-plugin'; +import HtmlWebpackPlugin from 'html-webpack-plugin'; +import open from 'open'; +import * as path from 'path'; +import {Compiler, Configuration as WebpackConfiguration} from 'webpack'; +import {Configuration as WebpackDevServerConfiguration} from 'webpack-dev-server'; + +// We use require() here because this module has no typings. +// eslint-disable-next-line @typescript-eslint/no-var-requires +const WriteFilePlugin = require('write-file-webpack-plugin'); + +const WEBPACK_DEVSERVER_PORT = 9000; + +const SERVER_PORT = 5432; + +const DIST_PATH = path.join(__dirname, '..', '..', 'dist'); +const DIST_STATIC_PATH = path.join(DIST_PATH, 'static'); + +export const INDEX_HTML_OPTIONS: HtmlWebpackPlugin.Options = { + title: 'Lilac', + publicPath: '/', + template: path.join(__dirname, 'src/index.html'), +}; + +export const WEBPACK_DEVSERVER_CONFIG: WebpackDevServerConfiguration = { + port: WEBPACK_DEVSERVER_PORT, + // Gzip compress all assets. + compress: true, + // Automatically refresh pages when typescript changes. + hot: true, + devMiddleware: { + // Writes files to dist so we can serve them statically from express. + writeToDisk: true, + }, + watchFiles: [path.join(__dirname, 'web', 'src')], +}; + +interface Configuration extends WebpackConfiguration { + devServer?: WebpackDevServerConfiguration; +} + +let hasOpened = false; +export const WEBPACK_CONFIG: Configuration = { + mode: 'development', + devtool: 'source-map', + module: { + rules: [ + { + test: /\.(ts|tsx)$/, + // Only compile the browser typescript code. + include: [path.resolve('src/'), path.resolve('../shared/src/')], + use: [{loader: 'ts-loader'}], + }, + { + test: /\.css$/i, + use: ['style-loader', 'css-loader', 'postcss-loader'], + }, + ], + }, + resolve: { + modules: ['node_modules'], + extensions: ['.ts', '.tsx', '.js', '.jsx'], + }, + entry: { + demo: './src/index.tsx', + }, + output: { + path: DIST_PATH, + filename: 'static/bundle.js', + hotUpdateChunkFilename: 'hot/hot-update.js', + hotUpdateMainFilename: 'hot/hot-update.json', + }, + plugins: [ + new HtmlWebpackPlugin(INDEX_HTML_OPTIONS), + new WriteFilePlugin(), + // Manually copy the duckdb wasm files to the static folder. + new CopyPlugin({ + patterns: [ + // Copy Shoelace assets to dist/shoelace + { + from: path.resolve(__dirname, 'node_modules/@shoelace-style/shoelace/dist/assets'), + to: path.resolve(DIST_STATIC_PATH, 'shoelace/assets'), + }, + ], + }), + { + apply: (compiler: Compiler) => { + compiler.hooks.afterCompile.tap('OpenAfterBuild', () => { + if (!hasOpened) { + // Opens the browser window to this URL automatically when the first build completes. + open(`http://localhost:${SERVER_PORT}`); + hasOpened = true; + } + }); + }, + }, + ], + devServer: WEBPACK_DEVSERVER_CONFIG, +}; + +export default WEBPACK_CONFIG; diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 000000000..6571cf24b --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,35 @@ +// Shared compiler options across all ts packages (express server and react app). +{ + "compilerOptions": { + // To allow default imports such as import y from 'y'. + "allowSyntheticDefaultImports": true, + "allowUnreachableCode": false, + "alwaysStrict": true, + // To allow const a = require('module'); + "esModuleInterop": true, + "incremental": true, + "noFallthroughCasesInSwitch": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": false, + "preserveConstEnums": true, + "pretty": true, + "removeComments": false, + "strictNullChecks": true, + "target": "es2020" + }, + // Shared options for ts-node across all packages (express server and react app). + "ts-node": { + // Tell ts-node to look at all files included by tsconfig.json. + "files": true, + "compilerOptions": { + // ts-node can only work with commonjs. + "module": "commonjs" + }, + // Skip type checking (slow) and only transpile (fast) via "swc", a 10x faster Rust transpiler. + // https://github.com/TypeStrong/ts-node#swc-1. + "swc": true + } +} diff --git a/tslint.json b/tslint.json new file mode 100644 index 000000000..4f41ed6bd --- /dev/null +++ b/tslint.json @@ -0,0 +1,53 @@ +{ + "extends": ["tslint-no-circular-imports"], + "rulesDirectory": ".tslint", + "rules": { + "array-type": [true, "array-simple"], + "arrow-return-shorthand": true, + "no-return-await": true, + "class-name": true, + "curly": true, + "interface-name": [true, "never-prefix"], + "jsdoc-format": true, + "forin": false, + "label-position": true, + "max-line-length": { + "options": {"limit": 80, "ignore-pattern": "^import |^export |https?://"} + }, + "new-parens": true, + "no-angle-bracket-type-assertion": true, + "no-any": true, + "no-construct": true, + "no-consecutive-blank-lines": true, + "no-debugger": true, + "no-default-export": true, + "no-imports-from-dist": true, + "no-imports-with-regex": [true, "src"], + "no-inferrable-types": true, + "no-namespace": [true, "allow-declarations"], + "no-reference": true, + "no-require-imports": true, + "no-string-throw": true, + "no-unused-expression": true, + "no-var-keyword": true, + "no-unnecessary-type-assertion": true, + "object-literal-shorthand": true, + "only-arrow-functions": [true, "allow-declarations", "allow-named-functions"], + "prefer-const": true, + "quotemark": [true, "single"], + "radix": true, + "restrict-plus-operands": true, + "semicolon": [true, "always", "ignore-bound-class-methods"], + "switch-default": true, + "triple-equals": [true, "allow-null-check"], + "use-isnan": true, + "use-default-type-parameter": true, + "variable-name": [ + true, + "check-format", + "ban-keywords", + "allow-leading-underscore", + "allow-trailing-underscore" + ] + } +}