diff --git a/.darglint2 b/.darglint2
new file mode 100644
index 000000000..4bbb6a31a
--- /dev/null
+++ b/.darglint2
@@ -0,0 +1,3 @@
+[darglint2]
+ignore=DAR003,DAR201,DAR202,DAR301,DAR401
+docstring_style=google
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..cf84e83d7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,34 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System (please complete the following information):**
+ - OS: [e.g. Mac]
+ - Version [e.g. 10.14]
+ - Intelligence Layer Version [e.g. 0.1.0]
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..58c5fa47c
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,22 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 000000000..d71548359
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,37 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+ - package-ecosystem: "pip"
+ directory: "/"
+ schedule:
+ interval: "daily"
+ groups:
+ minor:
+ update-types:
+ - minor
+ - patch
+
+ - package-ecosystem: "github-actions"
+ # Workflow files stored in the default location of `.github/workflows`. (You don't need to specify `/.github/workflows` for `directory`. You can use `directory: "/"`.)
+ directory: "/"
+ schedule:
+ interval: "daily"
+
+ - package-ecosystem: "npm"
+ directory: "/trace-viewer/"
+ schedule:
+ interval: "daily"
+ groups:
+ minor:
+ update-types:
+ - minor
+ - patch
+
+ - package-ecosystem: "docker"
+ directory: "/trace-viewer/"
+ schedule:
+ interval: "daily"
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 000000000..48461561c
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,12 @@
+# Description
+No description.
+
+## Before Merging
+ - [ ] Review the code changes
+ - Unused print / comments / TODOs
+ - Missing docstrings for functions that should have them
+ - Consistent variable names
+ - ...
+ - [ ] Update `changelog.md` if necessary
+ - [ ] Commit messages should contain a semantic [label](https://gist.github.com/joshbuchea/6f47e86d2510bce28f8e7f42ae84c716) and the ticket number
+ - Consider squashing if this is not the case
diff --git a/.github/workflows/artifactory.yml b/.github/workflows/artifactory.yml
new file mode 100644
index 000000000..c44510022
--- /dev/null
+++ b/.github/workflows/artifactory.yml
@@ -0,0 +1,56 @@
+name: Artifactory Deployment of PyPi and trace-viewer
+
+on:
+ workflow_dispatch: {}
+ release:
+ types: [published]
+
+env:
+ ARTIFACTORY_URL: https://alephalpha.jfrog.io
+ ARTIFACTORY_PYPI_REPOSITORY: "intelligence-layer"
+ ARTIFACTORY_DOCKER_REGISTRY: alephalpha.jfrog.io/intelligence-layer-images
+
+jobs:
+ build-and-push-pypi:
+ permissions:
+ contents: read
+ id-token: write
+ runs-on: ubuntu-latest
+ steps:
+ - name: Get Identity Token From Github
+ run: |
+ ID_TOKEN=$(curl -sLS -H "User-Agent: actions/oidc-client" -H "Authorization: Bearer $ACTIONS_ID_TOKEN_REQUEST_TOKEN" \
+ "${ACTIONS_ID_TOKEN_REQUEST_URL}&audience=https://alephalpha.jfrog.io" | jq .value | tr -d '"')
+ echo "ID_TOKEN=${ID_TOKEN}" >> $GITHUB_ENV
+ - name: Get Jfrog Access Token with Github Identity Token
+ env:
+ ID_TOKEN: ${{ env.ID_TOKEN }}
+ run: |
+ JFROG_ACCESS_TOKEN=$(curl \
+ -X POST \
+ -H "Content-type: application/json" \
+ $ARTIFACTORY_URL/access/api/v1/oidc/token \
+ -d \
+ "{\"grant_type\": \"urn:ietf:params:oauth:grant-type:token-exchange\", \"subject_token_type\":\"urn:ietf:params:oauth:token-type:id_token\", \"subject_token\": \"$ID_TOKEN\", \"provider_name\": \"github\"}" \
+ | jq .access_token -r)
+ echo "JFROG_ACCESS_TOKEN=${JFROG_ACCESS_TOKEN}" >> $GITHUB_ENV
+ - name: Checkout
+ uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+ - name: Build package and push to Artifactory
+ env:
+ JFROG_ACCESS_TOKEN: ${{ env.JFROG_ACCESS_TOKEN }}
+ run: |
+ poetry build
+ poetry config repositories.artifactory $ARTIFACTORY_URL/artifactory/api/pypi/$ARTIFACTORY_PYPI_REPOSITORY
+ JFROG_ACCESS_TOKEN_SUBJECT=$(echo $JFROG_ACCESS_TOKEN | awk -F'.' '{print $2}' | sed 's/.\{1,3\}$/&==/' | base64 -d | jq '.sub' -r)
+ poetry config http-basic.artifactory "$JFROG_ACCESS_TOKEN_SUBJECT" "$JFROG_ACCESS_TOKEN"
+ poetry publish -r artifactory
diff --git a/.github/workflows/daily.yml b/.github/workflows/daily.yml
new file mode 100644
index 000000000..9f5654c8d
--- /dev/null
+++ b/.github/workflows/daily.yml
@@ -0,0 +1,88 @@
+name: "os-support-tests"
+
+on:
+ workflow_dispatch:
+ # Scheduled workflows will only run on the default branch.
+ schedule:
+ - cron: '0 0 * * *' # runs once a day at midnight in the timezone of your GitHub repository
+
+defaults:
+ run:
+ shell: bash
+
+jobs:
+ test:
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [macos-latest, windows-latest]
+ runs-on: ${{matrix.os}}
+ # difference to regular test: no docker, no venv caching
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+ - name: Install dependencies
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+
+ - name: Run pytest
+ env:
+ AA_TOKEN: ${{ secrets.AA_TOKEN }}
+ HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+ ARGILLA_API_URL: "http://localhost:6900/"
+ ARGILLA_API_KEY: "argilla.apikey"
+ CLIENT_URL: "https://api.aleph-alpha.com"
+ run: |
+ poetry run python -c "import nltk; nltk.download('punkt')"
+ poetry run pytest -n 10 -m "not docker"
+ run-notebooks:
+ strategy:
+ fail-fast: false
+ matrix:
+ os: [macos-latest, windows-latest]
+ runs-on: ${{matrix.os}}
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+ - name: Install dependencies
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+ - name: Configure Poetry for notebooks and run
+ env:
+ AA_TOKEN: ${{ secrets.AA_TOKEN }}
+ HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+ ARGILLA_API_URL: "http://localhost:6900/"
+ ARGILLA_API_KEY: "argilla.apikey"
+ CLIENT_URL: "https://api.aleph-alpha.com"
+ run: |
+ [ -f .env ] && source .env
+ export AA_TOKEN
+ # Find all .ipynb files in the directory and pass them to xargs for parallel execution
+ rm -rf src/documentation/.ipynb_checkpoints
+ rm -rf src/documentation/how_tos/.ipynb_checkpoints
+
+ find src/documentation -name "*.nbconvert.ipynb" -type f -delete
+ find src/documentation -name "*.ipynb" ! -name "performance_tips.ipynb" ! -name "human_evaluation.ipynb" ! -name "how_to_human_evaluation_via_argilla.ipynb" | xargs -n 1 -P 6 poetry run jupyter nbconvert --to notebook --execute
+ find src/documentation -name "*.nbconvert.ipynb" -type f -delete
diff --git a/.github/workflows/on-push.yml b/.github/workflows/on-push.yml
new file mode 100644
index 000000000..cfb5e83a4
--- /dev/null
+++ b/.github/workflows/on-push.yml
@@ -0,0 +1,14 @@
+name: Intelligence Layer SDK Tests
+
+on:
+ push:
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+jobs:
+ python-tests:
+ uses: ./.github/workflows/sdk-tests.yml
+ with:
+ runner: 'ubuntu-latest'
+ secrets: inherit
diff --git a/.github/workflows/sdk-tests.yml b/.github/workflows/sdk-tests.yml
new file mode 100644
index 000000000..961b92a81
--- /dev/null
+++ b/.github/workflows/sdk-tests.yml
@@ -0,0 +1,212 @@
+on:
+ workflow_call:
+ inputs:
+ runner:
+ type: string
+ default: "ubuntu-latest"
+ secrets:
+ AA_TOKEN:
+ required: true
+ HUGGING_FACE_TOKEN:
+ required: true
+jobs:
+ lint:
+ defaults:
+ run:
+ shell: bash
+ runs-on: ${{inputs.runner}}
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+
+ - name: Load cached venv
+ id: cached-poetry-dependencies
+ uses: actions/cache@v4
+ with:
+ path: .venv
+ key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+
+ - name: Install dependencies
+ if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+
+ - name: set PY for pre-commit
+ run: echo "PY=$(python -VV | sha256sum | cut -d' ' -f1)" >> $GITHUB_ENV
+
+ - uses: actions/cache@v4
+ with:
+ path: ~/.cache/pre-commit
+ key: pre-commit|${{ env.PY }}|${{ hashFiles('.pre-commit-config.yaml') }}
+
+ - name: Run linters
+ run: |
+ ./scripts/lint.sh
+ doctest:
+ defaults:
+ run:
+ shell: bash
+ runs-on: ${{inputs.runner}}
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+
+ - name: Load cached venv
+ id: cached-poetry-dependencies
+ uses: actions/cache@v4
+ with:
+ path: .venv
+ key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+
+ - name: Install dependencies
+ if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+ - name: install Sphinx
+ run: sudo apt-get update -y && sudo apt-get install python3-sphinx
+ - name: run doctest
+ env:
+ AA_TOKEN: ${{ secrets.AA_TOKEN }}
+ HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+ CLIENT_URL: "https://api.aleph-alpha.com"
+ run: ./scripts/doctest.sh
+
+ test:
+ defaults:
+ run:
+ shell: bash
+ runs-on: ${{inputs.runner}}
+ services:
+ argilla-elastic-search:
+ image: docker.elastic.co/elasticsearch/elasticsearch:8.5.3
+ env:
+ ES_JAVA_OPTS: "-Xms512m -Xmx512m"
+ discovery.type: "single-node"
+ xpack.security.enabled: "false"
+ argilla:
+ image: argilla/argilla-server:v1.26.1
+ ports:
+ - "6900:6900"
+ env:
+ ARGILLA_ELASTICSEARCH: "http://argilla-elastic-search:9200"
+ ARGILLA_ENABLE_TELEMETRY: 0
+ open-telemetry-trace-service:
+ image: jaegertracing/all-in-one:1.35
+ env:
+ COLLECTOR_OTLP_ENABLED: "true"
+ ports:
+ - "4317:4317"
+ - "4318:4318"
+ - "16686:16686"
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+
+ - name: Load cached venv
+ id: cached-poetry-dependencies
+ uses: actions/cache@v4
+ with:
+ path: .venv
+ key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+
+ - name: Install dependencies
+ if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+
+ - name: Run pytest
+ env:
+ AA_TOKEN: ${{ secrets.AA_TOKEN }}
+ HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+ ARGILLA_API_URL: "http://localhost:6900/"
+ ARGILLA_API_KEY: "argilla.apikey"
+ CLIENT_URL: "https://api.aleph-alpha.com"
+ run: |
+ ./scripts/test.sh
+ run-notebooks:
+ defaults:
+ run:
+ shell: bash
+ runs-on: ${{inputs.runner}}
+ services:
+ argilla-elastic-search:
+ image: docker.elastic.co/elasticsearch/elasticsearch:8.5.3
+ env:
+ ES_JAVA_OPTS: "-Xms512m -Xmx512m"
+ discovery.type: "single-node"
+ xpack.security.enabled: "false"
+ argilla:
+ image: argilla/argilla-server:v1.26.1
+ ports:
+ - "6900:6900"
+ env:
+ ARGILLA_ELASTICSEARCH: "http://argilla-elastic-search:9200"
+ ARGILLA_ENABLE_TELEMETRY: 0
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.10"
+ - name: Install and configure Poetry
+ uses: snok/install-poetry@v1
+ with:
+ virtualenvs-create: true
+ virtualenvs-in-project: true
+ installer-parallel: true
+
+ - name: Load cached venv
+ id: cached-poetry-dependencies
+ uses: actions/cache@v4
+ with:
+ path: .venv
+ key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+ - name: Install dependencies
+ if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+ run: |
+ poetry config installer.max-workers 10
+ poetry install --no-interaction
+ - name: Configure Poetry for notebooks and run
+ env:
+ AA_TOKEN: ${{ secrets.AA_TOKEN }}
+ HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }}
+ ARGILLA_API_URL: "http://localhost:6900/"
+ ARGILLA_API_KEY: "argilla.apikey"
+ CLIENT_URL: "https://api.aleph-alpha.com"
+ run: |
+ ./scripts/notebook_runner.sh
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..0caf99001
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,250 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+tests/files/data/web_retriever/wikipedia-splitted/*cache_*
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.nbconvert.ipynb
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# IDE
+.vscode/
+Todo.json
+
+# Cython debug symbols
+cython_debug/
+
+# Temporary test files
+tests/files/tmp/
+debug_logs/
+wandb/
+*index_cache*
+
+#environment
+.DS_STORE
+envs*
+scratch
+pyrightconfig.json
+
+# Created by https://www.toptal.com/developers/gitignore/api/intellij+all
+# Edit at https://www.toptal.com/developers/gitignore?templates=intellij+all
+
+### Intellij+all ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn. Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### Intellij+all Patch ###
+# Ignore everything but code style settings and run configurations
+# that are supposed to be shared within teams.
+
+.idea/*
+
+!.idea/codeStyles
+!.idea/runConfigurations
+
+# End of https://www.toptal.com/developers/gitignore/api/intellij+all
+.python-version
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 000000000..26f1c13c2
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,56 @@
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.5.0
+ hooks:
+ - id: check-json
+ - id: pretty-format-json
+ files: .json
+ args:
+ - --autofix
+ - id: end-of-file-fixer
+ - id: trailing-whitespace
+ - repo: https://github.com/pycqa/isort
+ rev: 5.13.2
+ hooks:
+ - id: isort
+ name: isort (python)
+ args: ["--filter-files"]
+ verbose: true
+ - repo: https://github.com/nbQA-dev/nbQA
+ rev: 1.8.5
+ hooks:
+ - id: nbqa-isort
+ name: isort (nbs)
+ args: ["--filter-files"]
+ additional_dependencies: [isort]
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ # Ruff version.
+ rev: v0.3.5
+ hooks:
+ # Run the linter.
+ - id: ruff
+ name: ruff-lint
+ args: [ --fix ]
+ types_or: [ python, pyi, jupyter ]
+ # Run the formatter.
+ - id: ruff-format
+ types_or: [ python, pyi, jupyter ]
+ - repo: https://github.com/kynan/nbstripout
+ rev: 0.7.1
+ hooks:
+ - id: nbstripout
+ files: ".ipynb"
+ - repo: https://github.com/codespell-project/codespell
+ rev: v2.2.6
+ hooks:
+ - id: codespell
+ args:
+ [
+ "-L",
+ "newyorker,te,responde,ist,als,oder,technik,sie,rouge,unter,juli,fiel,couldn,mke, vor,fille,ans",
+ ]
+ exclude: '^(poetry\.lock|tests/connectors/retrievers/test_document_index_retriever\.py|src/intelligence_layer/examples/qa/multiple_chunk_qa.py|src/intelligence_layer/examples/summarize/.*|tests/connectors/retrievers/test_document_index_retriever\.py|src/intelligence_layer/examples/classify/keyword_extract.py|tests/examples/summarize/test_single_chunk_few_shot_summarize.py|tests/examples/summarize/very_long_text.txt)$'
+ - repo: https://github.com/akaihola/darglint2
+ rev: v1.8.2
+ hooks:
+ - id: darglint2
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 000000000..bc03d9581
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,33 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+ os: ubuntu-22.04
+ tools:
+ python: "3.11"
+ # You can also specify other tool versions:
+ # nodejs: "19"
+ # rust: "1.64"
+ # golang: "1.19"
+ jobs:
+ post_create_environment:
+ # Install poetry:
+ - asdf plugin add poetry
+ - asdf install poetry latest
+ - asdf global poetry latest
+ - poetry config virtualenvs.create false
+ post_install:
+ - . "$READTHEDOCS_VIRTUALENV_PATH/bin/activate" && poetry install
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+ configuration: docs/conf.py
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+# - pdf
+# - epub
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 000000000..6fe73e127
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,406 @@
+# Changelog
+
+## Unreleased
+...
+
+### Breaking Changes
+...
+
+### New Features
+...
+
+### Fixes
+...
+
+### Deprecations
+...
+
+## 0.11.0
+
+### Breaking Changes
+- feature: `HuggingFaceDatasetRepository` now has a parameter `caching`, which caches a examples of a dataset once loaded. This is `True` by default. This drastically reduces network traffic. For a non-breaking change, set it to `False`.
+- breaking_change: `MultipleChunkRetrieverQa` does not take `insert_chunk_size`-parameter but instead takes `ExpandChunks`-task
+- breaking_change: the `issue_cassification_user_journey` notebook moved to its own repository
+
+### New Features
+- feature: `Llama2InstructModel` to support llama-2 models in Aleph Alpha API
+- feature: `Llama3InstructModel` to support llama-3 models in Aleph Alpha API
+- feature: `ExpandChunks`-task caches chunked documents by ID
+- feature: `DocumentIndexClient` now supports
+ - `create_index`
+ - `index_configuration`
+ - `assign_index_to_collection`
+ - `delete_index_from_collection`
+ - `list_assigned_index_names`
+- feature: `DocumentIndexRetriever` now supports `index_name`
+- feature: `Runner.run_dataset` now has a configurable number of workers via `max_workers` and defaults to the previous value, which is 10.
+- feature: In case a `BusyError` is raised during a `complete` the `LimitedConcurrencyClient` will retry until `max_retry_time` is reached.
+- feature: `FileTracer` now accepts as `log_file_path` both, a `str` and a `Path`
+
+### Fixes
+- refactor: rename `index` parameter in `DocumentIndex.search()` to `index_name`
+- fix: `HuggingFaceRepository` no longer is a dataset repository. This also means that `HuggingFaceAggregationRepository` no longer is a dataset repository.
+
+
+### Deprecations
+- `RetrieverBasedQa` is now deprecated and will be removed in future versions. We recommend using `MultipleChunkRetrieverQa` instead.
+
+## 0.10.0
+
+### Breaking Changes
+- breaking change: `ExpandChunksOutput` now returns `ChunkWithStartEndIndices` instead of `TextChunk`
+- breaking change: `MultipleChunkRetrieverQa`'s `AnswerSource` now contains `EnrichedChunk` instead of just the `TextChunk`
+- breaking change: `DocumentIndexClient` method `asymmetric_search()` has been removed
+- breaking change: `DocumentIndexRetriever` now additionally needs `index_name`
+
+### New Features
+
+### Fixes
+- fix: `ChunkWithIndices` now additionally returns end_index
+- fix: `DocumentPath` and `CollectionPath` are now immutable
+
+## 0.9.1
+
+### Breaking Changes
+- breaking change: `MultipleChunkRetrieverQaOutput` now return `sources` and `search_results`
+
+### New Features
+- feature: `ExpandChunks` task takes a retriever and some search results to expand the chunks to the desired length
+
+### Fixes
+- fix: `ExpectedSearchOutput` has only relevant fields and supports generic document-`ID` rather than just str
+- fix: `SearchEvaluationLogic` explicitly compares documents by ids
+- fix: In `RecusrsiveSummarize.do_run`, `num_generated_tokens` not uninitialized anymore. [See Issue 743.](https://github.com/Aleph-Alpha/intelligence-layer/issues/743).
+- fix: Reverted pydantic to 2.6.* because of FastAPI incompatibility.
+
+## 0.9.0
+
+### Breaking Changes
+ - breaking change: Renamed the field `chunk` of `AnswerSource` to `search_result` for multi chunk retriever qa.
+ - breaking change: The implementation of the HuggingFace repository creation and deletion got moved to `HuggingFaceRepository`
+
+### New Features
+ - feature: HuggingFaceDataset- & AggregationRepositories now have an explicit `create_repository` function.
+ - feature: Add `MultipleChunkRetrieverBasedQa`, a task that performs better on faster on retriever-QA, especially with longer context models
+
+## 0.8.2
+
+### New Features
+ - feature: Add `SearchEvaluationLogic` and `SearchAggregationLogic` to evaluate `Search`-use-cases
+ - feature: Trace viewer and IL python package are now deployed to artifactory
+
+### Fixes
+ - Documentation
+ - fix: Add missing link to `issue_classification_user_journey` notebook to the tutorials section of README.
+ - fix: Confusion matrix in `issue_classification_user_journey` now have rounded numbers.
+
+## 0.8.1
+
+### Fixes
+- fix: Linting for release version
+
+## 0.8.0
+
+### New Features
+- feature: Expose start and end index in DocumentChunk
+- feature: Add sorted_scores property to `SingleLabelClassifyOutput`.
+- feature: Error information is printed to the console on failed runs and evaluations.
+- feature: The stack trace of a failed run/evaluation is included in the `FailedExampleRun`/`FailedExampleEvaluation` object
+- feature: The `Runner.run_dataset(..)` and `Evaluator.evaluate_run(..)` have an optional flag `abort_on_error` to stop running/evaluating when an error occurs.
+- feature: Add `Runner.failed_runs(..)` and `Evaluator.failed_evaluations(..)` to retrieve all failed run / evaluation lineages
+- feature: Add `.successful_example_outputs(..)` and `.failed_example_outputs(..)` to `RunRepository` to match the evaluation repository
+- feature: Add optional argument to set an id when creating a `Dataset` via `DatasetRepository.create_dataset(..)`
+- feature: Traces now log exceptions using the `ErrorValue` type.
+
+- Documentation:
+ - feature: Add info on how to run tests in VSCode
+ - feature: Add `issue_classification_user_journey` notebook.
+ - feature: Add documentation of newly added data retrieval methods `how_to_retrieve_data_for_analysis`
+ - feature: Add documentation of release workflow
+
+### Fixes
+- fix: Fix version number in pyproject.toml in IL
+- fix: Fix instructions for installing IL via pip.
+
+## 0.7.0
+
+### Breaking Changes
+- breaking change: FScores are now correctly exposed as FScores and no longer as RougeScores
+- breaking change: HuggingFaceAggregationRepository and HuggingFaceDatasetRepository now consistently follow the same folder structure as FileDatasetRepository when creating data sets. This means that datasets will be stored in a folder datasets and additional sub-folders named according to the respective dataset ID.
+- breaking change: Split run_repository into file_run_repository, in_memory_run_repository.
+- breaking change: Split evaluation_repository into argilla_evaluation_repository, file_evaluation_repository and in_memory_evaluation_repository
+- breaking change: Split dataset_repository into file_dataset_repository and in_memory_dataset_respository
+- breaking change: Split aggregation_respository into file_aggragation_repository and in_memory_aggregation_repository
+- breaking change: Renamed evaluation/run.py to evaluation/run_evaluator.py
+- breaking change: Split evaluation/domain and distribute it across aggregation, evaluation, dataset and run packages.
+- breaking change: Split evaluation/argilla and distribute it across aggregation and evaluation packages.
+- breaking change: Split evaluation into separate dataset, run, evaluation and aggregationpackages.
+- breaking change: Split evaluation/hugging_face.py into dataset and aggregation repository files in data_storage package.
+- breaking change: create_dataset now returns the new Dataset type instead of a dataset ID.
+- breaking change: Consistent naming for repository root directories when creating evaluations or aggregations:
+ - .../eval → .../evaluations and .../aggregation → aggregations.
+- breaking change: Core tasks not longer provide defaults for the applied models.
+- breaking change: Methods returning entities from repositories now return the results ordered by their IDs.
+- breaking change: Renamed crashed_during_eval_count to crashed_during_evaluation_count in AggregationOverview.
+- breaking change: Renamed create_evaluation_dataset to initialize_evaluation in EvaluationRepository.
+- breaking change: Renamed to_explanation_response to to_explanation_request in ExplainInput.
+- breaking change: Removed TextHighlight::text in favor of TextHighlight::start and TextHighlight::end
+- breaking change: Removed `IntelligenceApp` and `IntelligenceStarterApp`
+- breaking change: RetrieverBasedQa uses now MultiChunkQa instead of generic task pr SingleChunkQa
+- breaking change: EvaluationRepository failed_example_evaluations no longer abstract
+- breaking change: Elo calculation simplified:
+ - Payoff from elo package has been removed
+ - PayoffMatrix from elo package renamed to MatchOutcome
+ - SingleChunkQa uses logit_bias to promote not answering for German
+- breaking change: Remove ChunkOverlap task.
+- breaking change: Rename Chunk to TextChunk.
+- breaking change: Rename ChunkTask to Chunk .
+- breaking change: Rename EchoTask to Echo.
+- breaking change: Rename TextHighlightTask to TextHighlightbreaking change: Rename ChunkOverlaptTask to ChunkOverlap
+
+### New Features
+- Aggregation:
+ - feature: InstructComparisonArgillaAggregationLogic uses full evaluation set instead of sample for aggregation
+
+- Documentation
+
+ - feature: Added How-To’s (linked in the README):
+ - how to define a task
+ - how to implement a task
+ - how to create a dataset
+ - how to run a task on a dataset
+ - how to perform aggregation
+ - how to evaluate runs
+ - feature: Restructured and cleaned up README for more conciseness.
+ - feature: Add illustrations to Concepts.md.
+ - feature: Added tutorial for adding task to a FastAPI app (linked in README).
+ - feature: Improved and added various DocStrings.
+ - feature: Added a README section about the client URL.
+ - feature: Add python naming convention to README
+
+- Classify
+ - feature: PromptBasedClassify now supports changing of the prompt instruction via the instruction parameter.
+ - feature: Add default model for PromptBasedClassify
+ - feature: Add default task for PromptBasedClassify
+
+- Evaluation
+ - feature: All repositories will return a ValueError when trying to access a dataset that does not exist while also trying to access an entry of the dataset. If only the dataset is retrieved, it will return None.
+ - `ArgillaEvaluationRepository` now handles failed evaluations.
+ - feature: Added SingleHuggingfaceDatasetRepository.
+ - feature: Added HighlightCoverageGrader.
+ - feature: Added LanguageMatchesGrader.
+
+ - feature: Added prettier default printing behavior of repository entities by providing overloads to __str__ and __repr__ methods.
+
+ - feature: Added abstract HuggingFace repository base-class.
+
+ - feature: Refactoring of HuggingFace repository
+
+ - feature: Added HuggingFaceAggregationRepository.
+ - feature: Added template method to individual repository
+ - feature: Added Dataset model to dataset repository. This allows to store a short descriptive name for the dataset for easier identification
+ - feature: SingleChunkQa internally now uses the same model in TextHighlight by default.
+ - feature: MeanAccumulator tracks standard deviation and standard error
+ - feature: EloCalculator now updates ranking after each match
+ - feature: Add data selection methods to repositories:
+ - AggregationRepository::aggregation_overviews
+ - EvaluationRepository::run_overviews
+ - EvaluationRepository::run_overview_ids
+ - EvaluationRepository::example_output
+ - EvaluationRepository::example_outputs
+ - EvaluationRepository::example_output_ids
+ - EvaluationRepository::example_trace
+ - EvaluationRepository::example_tracer
+ - RunRepository::run_overviews
+ - RunRepository::run_overview_ids
+ - RunRepository::example_output
+ - RunRepository::example_outputs
+ - RunRepository::example_output_ids
+ - RunRepository::example_trace
+ - RunRepository::example_tracer
+
+ - feature: Evaluator continues in case of no successful outputs
+
+- Q & A
+
+ - feature: Define default parameters for LongContextQa, SingleChunkQa
+ - feature: Define default task for RetrieverBasedQa
+ - feature: Define default model for KeyWordExtract, MultiChunkQa,
+ - feature: Improved focus of highlights in TextHighlight tasks.
+ - feature: Added filtering for TextHighlight tasks.
+ - feature: Introduce logit_bias to SingleChunkQa
+
+- Summarize
+ - feature: Added RecursiveSummarizeInput.
+ - feature: Define defaults for SteerableSingleChunkSummarize,SteerableLongContexSummarize, RecursiveSummarize
+
+- Tracer
+ - feature: Added better trace viewer integration:
+ - Add trace storage to trace viewer server
+ - added submit_to_tracer_viewer method to InMemoryTracer
+ - UI and navigation improvements for trace viewer
+ - Add exception handling for tracers during log entry writing
+
+- Others
+
+ - feature: The following classes are now exposed:
+ - DocumentChunk
+ - MultipleChunkQaOutput
+ - Subanswer
+ - feature: Simplified internal imports.
+ - feature: Stream lining of __init__-parameters of all tasks
+ - Sub-tasks are typically exposed as `__init__`-parameters with sensible defaults.
+ - Defaults for non-trivial parameters like models or tasks are defined in __init__while the default parameter is None.
+ - Instead of exposing parameters that are passed on to sub-tasks the sub-task themselves are exposed.
+ - feature: Update supported models
+
+### Fixes
+
+- fix: Fixed exception handling in language detection of LanguageMatchesGrader.
+- fix: Fixed a bug that could lead to cut-off highlight ranges in TextHighlight tasks.
+- fix: Fixed list_ids methods to use path_to_str
+- fix: Disallow traces without end in the trace viewer
+- fix: ArgillaClient now correctly uses provided API-URL instead of hard-coded localhost
+
+## 0.6.0
+
+### Breaking Changes
+
+- breaking change: The evaluation module is moved from core to evaluation .
+- breaking change: RetrieverBasedQa task answers now contain document ids in each subanswer
+- breaking change: LongcontextSummarize no longer supports the max_loops parameter
+- breaking change: Rich Mode Representation
+ - The LLM-based tasks no longer accept client, but rather an AlephAlphaModel, which holds the client. The available model classes are AlephAlphaModel and LuminousControlModel
+ - The AlephAlphaModel is responsible for its prompt format, tokenizers, complete task and explain task. These responsibilities were moved into the model classes.
+ - The default client url is now configurable via the environment variable CLIENT_URL
+- breaking change: PromptWithMetadata is removed in favor of RichPrompt . The semantics remain largely unchanged
+- breaking change: The compression-dependent long context summarize classes as well as the few-shot summarize class were removed. Use the better-performing steerable summary classes.
+- breaking change: Runner, Evaluator & Aggregation
+ - The EvaluationRepository has been split up. There is now a total of four repositories: dataset , run, evaluation and aggregation. These repositories save information from their respective steps
+ - The evaluation and evaluation aggregation have been split and are now provided by the classes Evaluator and Aggregator, respectively. These two classes have no abstract methods. The evaluation and aggregation logic is provided by implementing the abstract methods of the classes EvaluationLogic and AggregationLogic which are passed on to an instance of the Evaluator and Aggregator class, respectively. For an example, see the Jupyter notebook xxx.
+
+### New Features
+
+- Documentation
+ - feature: Added an intro to the Intelligence Layer concepts in Concepts.md
+ - feature: Added documentation on how to execute tasks in parallel. See the performance_tips notebook for more information.
+- QA
+ - feature: RetrieverBasedQa task no longer sources its final from all sources, but only the most relevant. This performed better in evaluation.
+ - feature: The notebooks for RetrieverBasedQa have been updated to use SingleChunkQa.
+ - feature: SingleChunkQa now supports a custom no-answer phrase
+ - feature: MultiChunkQA and LongContextQa allow for more configuration of the used qa-task.
+ - feature: Make the distance metric configurable in QdrantInMemoryRetriever.
+ - features: Added list_namespaces to DocumentIndexClient to list all available namespaces in DocumentIndex.
+- Evaluation
+ - feature: The argilla now supports splitting a dataset for multiple people via the split_dataset function
+ - feature: Utilities for ELO score/ranking calculation
+ - The build_tournaments utility function has been added to facilitate the computation of ELO scores when evaluating two models. See InstructComparisonArgillaEvaluator for an example how it can be used to compute the ELO scores.
+ - feature: The Evaluator can run multiple evaluation tasks in parallel.
+- Intelligence app
+ - feature: IntelligenceApp returns 204 if the output is None
+ - feature: Allow registering tasks with a task dependency in IntelligenceApp.
+- Others
+ - feature: Runner accepts in run_dataset a new parameter num_examples specifying how many of the first n examples should be run.
+ - feature: Support None as return type in Task
+ - feature: Added a new task: ChunkOverlapTask splits a longer text into overlapping chunks.
+
+## 0.5.1
+
+Failed deploy
+
+## 0.5.0
+
+### Breaking Changes
+
+- Document Index search results now properly return `DocumentChunk`s instead of `Document` objects to make it clear it is only a portion of the document.
+- `Instruct` and `FewShot` tasks now take the model name in the constructor instead of the input.
+- `Dataset`s have now been moved to `DatasetRepository`s, which are responsible for loading and storing datasets. This allows for more flexibility in how datasets are loaded and stored.
+
+### New Features
+
+- Introduced an `OpenTelemetryTracer` to allow for sending trace spans to an OpenTelemetry collector.
+- Notebook walking through how to use Argilla for human evaluation
+- `SteerableLongContextSummarize` task that allows for steering the summarization process by providing a natural language instruction.
+- Document index `SearchResult`s now also return the document ID for each chunk, to make it easier to retrieve the full document.
+- Retrievers now supply a way to retrieve the full document by ID.
+- Introduced the concept of `Accumulator`s to evaluation for incrementally calculating metrics.
+- Added `EloCalculator` metrics for calculating Elo scores in evaluation methods.
+- Introduced new `HuggingFaceDatasetRepository` for loading datasets from the HuggingFace datasets library.
+- Made it easier to evaluate two tasks and or models against each other.
+
+### Fixes
+
+- Argilla client properly handles pagination when retrieving records
+- Ensured file-based repositories are writing and reading in UTF-8
+
+
+## 0.4.1
+
+Fix missing version bump in the packages
+
+## 0.4.0
+
+### Breaking Changes
+
+- `Evaluator` methods changed to support asynchronous processing for human eval. To run everything at once, change `evaluator.evaluate()` calls to `evaluator.run_and_evaluate`
+ - An evaluation also now returns a `EvaluationOverview`, with much more information about the output of the evaluation.
+- `EmbeddingBasedClassify`: init arguments swapped places, from `labels_with_examples, client` to `client, label_with_examples`
+- `PromptOutput` for `Instruct` tasks now inherits from `CompleteOutput` to make it easier to use more information about the raw completion response.
+
+### New Features
+
+- New `IntelligenceApp` builder to quickly spin up a FastAPI server with your `Task`s
+- Integration with [Argilla](https://docs.argilla.io/en/latest/index.html) for human evaluation
+- `CompleteOutput` and `PromptOutput` now support getting the `generated_tokens` in the completion for downstream calculations.
+- Summarization use cases now allow for overriding the default model
+- New `RecursiveSummarizer` allows for recursively calling one of the `LongContextSummarize` tasks until certain thresholds are reached
+
+### Fixes
+
+- `LimitedConcurrencyClient`'s `from_token` method now supports a custom API host
+
+## 0.3.0:
+
+### Breaking Changes
+
+- `Dataset` is now a protocol. `SequenceDataset` replaces the old `Dataset`.
+- The `ident` attribute on `Example` is now `id`.
+- `calculate_bleu` function is removed and instead called from a `BleuGrader`
+- `calculate_rouge` function is removed and instead called from a `RougeGrader`
+- `ClassifyEvaluator` is now called `SingleLabelClassifyEvaluator`
+- `Evaluator`s now take and return `Iterator`s instead of `Sequence`s to allow for streaming datasets
+
+### New Features
+
+- `Evaluators` now have better handling of dataset processing.
+ - Errors are handled for individual examples, so that you don't lose the entire run because of one failed task.
+ - The dataset run now produces an `EvaluationRunOverview` generated by an `EvaluationRepository`, that better captures the aggregated runs and traces.
+ - There is a `FileEvaluationRepository` and an `InMemoryEvaluationRepository` available for storing your evaluation results
+- Support passing `Metadata` field through `DocumentIndexClient` (already supported in the Document Index, new in client only)
+- New `MultiLabelClassifyEvaluator` to evaluate classification use cases that support multi-label classification
+- `Evaluators` can now be called via the CLI
+
+### Fixes
+
+- Fix issue in `EchoTask` regarding concurrent execution causing overrides in the `PromptTemplate`
+
+## 0.2.0
+
+### Breaking Changes
+
+- `SingleLabelClassify` renamed to `PromptBasedClassify` with new `SingleLabelClassifyOutput`
+- `EmbeddingBasedClassify` now outputs `MultiLabelClassifyOutput` to distinguish between the different types of scores produced
+
+### New Features
+
+- New `LimitedConcurrencyClient` to better control how many simultaneous API requests are made concurrently, regardless of where they are called within the Task hierarchy
+- Basic new `SingleChunkSummarizeEvaluator` and `LongContextSummarizeEvaluator` that can calculate Rouge and Bleu scores when compared with a "golden summary"
+
+### Fixes
+
+- Fix issue with Pydantic 2.5 due to ambiguous ordering of types in `PydanticSerializable` type
+- Fixed possible deadlock with nested calls to `Task.run_concurrently`
+
+## 0.1.0
+
+Initial release
diff --git a/Concepts.md b/Concepts.md
new file mode 100644
index 000000000..f45fe4ea2
--- /dev/null
+++ b/Concepts.md
@@ -0,0 +1,227 @@
+# Concepts
+
+The main focus of the Intelligence Layer is to enable developers to
+
+- implement their LLM use cases by building upon and composing existing functionalities
+- obtain insights into the runtime behavior of their implementations
+- iteratively improve their implementations or compare them to existing implementations by evaluating them against
+ a given set of examples
+
+How these focus points are realized in the Intelligence Layer is described in more detail in the following sections.
+
+## Task
+
+At the heart of the Intelligence Layer is a `Task`. A task is actually a pretty generic concept that just
+transforms an input-parameter to an output like a function in mathematics.
+
+```
+Task: Input -> Output
+```
+
+In Python this is realized by an abstract class with type-parameters and the abstract method `do_run`
+in which the actual transformation is implemented:
+
+```Python
+class Task(ABC, Generic[Input, Output]):
+
+ @abstractmethod
+ def do_run(self, input: Input, task_span: TaskSpan) -> Output:
+ ...
+```
+
+`Input` and `Output` are normal Python datatypes that can be serialized from and to JSON. For this the Intelligence
+Layer relies on [Pydantic](https://docs.pydantic.dev/). The used types are defined in form
+of type-aliases PydanticSerializable.
+
+The second parameter `task_span` is used for [tracing](#Trace) which is described below.
+
+`do_run` is the method that implements a concrete task and has to be provided by the user. It will be executed by the external interface method `run` of a
+task:
+
+```Python
+class Task(ABC, Generic[Input, Output]):
+ @final
+ def run(self, input: Input, tracer: Tracer, trace_id: Optional[str] = None) -> Output:
+ ...
+```
+
+The signatures of the `do_run` and `run` methods differ only in the [tracing](#Trace) parameters.
+
+### Levels of abstraction
+
+Even though the concept is so generic the main purpose for a task is of course to make use of an LLM for the
+transformation. Tasks are defined at different levels of abstraction. There are higher level tasks (also called Use Cases)
+that reflect a typical user problem and there are lower level tasks that are more about interfacing
+with an LLM on a very generic or even technical level.
+
+Examples for higher level tasks (Use Cases) are:
+
+- Answering a question based on a given document: `QA: (Document, Question) -> Answer`
+- Generate a summary of a given document: `Summary: Document -> Summary`
+
+Examples for lower level tasks are:
+
+- Let the model generate text based on an instruction and some context: `Instruct: (Context, Instruction) -> Completion`
+- Chunk a text in smaller pieces at optimized boundaries (typically to make it fit into an LLM's context-size): `Chunk: Text -> [Chunk]`
+
+### Composability
+
+Typically you would build higher level tasks from lower level tasks. Given a task you can draw a dependency graph
+that illustrates which sub-tasks it is using and in turn which sub-tasks they are using. This graph typically forms a hierarchy or
+more general a directed acyclic graph. The following drawing shows this graph for the Intelligence Layer's `RecursiveSummarize`
+task:
+
+
+
+
+### Trace
+
+A task implements a workflow. It processes its input, passes it on to sub-tasks, processes the outputs of the sub-tasks
+and builds its own output. This workflow can be represented in a trace. For this a task's `run` method takes a `Tracer`
+that takes care of storing details on the steps of this workflow like the tasks that have been invoked along with their
+input and output and timing information. The following illustration shows the trace of an MultiChunkQa-task:
+
+
+
+To represent this tracing defines the following concepts:
+
+- A `Tracer` is passed to a task's `run` method and provides methods for opening `Span`s or `TaskSpan`s.
+- A `Span` is a `Tracer` and allows to group multiple logs and runtime durations together as a single, logical step in the
+ workflow.
+- A `TaskSpan` is a `Span` that allows to group multiple logs together with the task's specific input and output.
+ An opened `TaskSpan` is passed to `Task.do_run`. Since a `TaskSpan` is a `Tracer` a `do_run` implementation can pass
+ this instance on to `run` methods of sub-tasks.
+
+The following diagram illustrates their relationship:
+
+
+
+Each of these concepts is implemented in form of an abstract base class and the Intelligence Layer provides
+several concrete implementations that store the actual traces in different backends. For each backend each of the
+three abstract classes `Tracer`, `Span` and `TaskSpan` needs to be implemented. Here only the top-level
+`Tracer`-implementations are listed:
+
+- The `NoOpTracer` can be used when tracing information shall not be stored at all.
+- The `InMemoryTracer` stores all traces in an in memory data structure and is most helpful in tests or
+ Jupyter notebooks.
+- The `FileTracer` stores all traces in a json-file.
+- The `OpenTelemetryTracer` uses an OpenTelemetry
+ [`Tracer`](https://opentelemetry-python.readthedocs.io/en/latest/api/trace.html#opentelemetry.trace.Tracer)
+ to store the traces in an OpenTelemetry backend.
+
+
+## Evaluation
+
+An important part of the Intelligence Layer is tooling that helps to evaluate custom tasks. Evaluation helps
+to measure how well the implementation of a task performs given real world examples. The outcome of an entire
+evaluation process is an aggregated evaluation result that consists out of metrics aggregated over all examples.
+
+The evaluation process helps to:
+
+- optimize a task's implementation by comparing and verifying if changes improve the performance.
+- compare the performance of one implementation of a task with that of other (already existing) implementations.
+- compare the performance of models for a given task implementation.
+- verify how changes to the environment (new model version, new finetuning version) affect the
+ performance of a task.
+
+
+### Dataset
+
+The basis of an evaluation is a set of examples for the specific task-type to be evaluated. A single `Example`
+consists of:
+
+- an instance of the `Input` for the specific task and
+- optionally an _expected output_ that can be anything that makes sense in context of the specific evaluation (e.g.
+ in case of classification this could contain the correct classification result, in case of QA this could contain
+ a _golden answer_, but if an evaluation is only about comparing results with other results of other runs this
+ could also be empty)
+
+To enable reproducibility of evaluations datasets are immutable. A single dataset can be used to evaluate all
+tasks of the same type, i.e. with the same `Input` and `Output` types.
+
+
+### Evaluation Process
+
+The Intelligence Layer supports different kinds of evaluation techniques. Most important are:
+
+- Computing absolute metrics for a task where the aggregated result can be compared with results of previous
+ result in a way that they can be ordered. Text classification could be a typical use case for this. In that
+ case the aggregated result could contain metrics like accuracy which can easily compared with other
+ aggregated results.
+- Comparing the individual outputs of different runs (all based on the same dataset)
+ in a single evaluation process and produce a ranking of all runs as an aggregated result. This technique is useful when it is hard to come up with an absolute metrics to evaluate
+ a single output, but it is easier to compare two different outputs and decide which one is better. An example
+ use case could be summarization.
+
+To support these techniques the Intelligence Layer differentiates between 3 consecutive steps:
+
+1. Run a task by feeding it all inputs of a dataset and collecting all outputs
+2. Evaluate the outputs of one or several runs and produce an evaluation result for each example. Typically a single run is evaluated if absolute
+ metrics can be computed and several runs are evaluated when the outputs of runs shall be compared.
+1. Aggregate the evaluation results of one or several evaluation runs into a single object containing the aggregated
+ metrics. Aggregating over several evaluation runs supports amending a previous comparison result with
+ comparisons of new runs without the need to re-execute the previous comparisons again.
+
+The following table shows how these three steps are represented in code:
+
+| Step | Executor | Custom Logic | Repository |
+|---------|----------|--------------|---------------|
+| 1. Run | `Runner` | `Task` | `RunRepository` |
+| 2. Evaluate | `Evaluator` | `EvaluationLogic` | `EvaluationRepository` |
+| 3. Aggregate | `Aggregator` | `AggregationLogic` | `AggregationRepository` |
+
+Columns explained
+- "Executor" lists concrete implementations provided by the Intelligence Layer.
+- "Custom Logic" lists abstract classes that need to be implemented with the custom logic.
+- "Repository" lists abstract classes for storing intermediate results. The Intelligence Layer provides
+ different implementations for these. See the next section for details.
+
+### Data Storage
+
+During an evaluation process a lot of intermediate data is created before the final aggregated result can be produced.
+To avoid that expensive computations have to be repeated if new results are to be produced based on previous ones
+all intermediate results are persisted. For this the different executor-classes make use of repositories.
+
+There are the following Repositories:
+
+- The `DatasetRepository` offers methods to manage datasets. The `Runner` uses it to read all `Example`s of a dataset and feeds them to the `Task`.
+- The `RunRepository` is responsible for storing a task's output (in form of an `ExampleOutput`) for each `Example` of a dataset
+ which are created when a `Runner`
+ runs a task using this dataset. At the end of a run a `RunOverview` is stored containing some metadata concerning the run.
+ The `Evaluator` reads these outputs given a list of runs it should evaluate to create an evaluation
+ result for each `Example` of the dataset.
+- The `EvaluationRepository` enables the `Evaluator` to store the evaluation result (in form of an `ExampleEvaluation`) for each example along with an `EvaluationOverview`. The `Aggregator` uses this repository to read the evaluation results.
+- The `AggregationRepository` stores the `AggregationOverview` containing the aggregated metrics on request of the `Aggregator`.
+
+The following diagrams illustrate how the different concepts play together in case of the different types of evaluations.
+
+
+
+Process of an absolute Evaluation
+
+
+1. The `Runner` reads the `Example`s of a dataset from the `DatasetRepository` and runs a `Task` for each `Example.input` to produce `Output`s.
+2. Each `Output` is wrapped in an `ExampleOutput` and stored in the `RunRepository`.
+3. The `Evaluator` reads the `ExampleOutput`s for a given run from the
+ `RunRepository` and the corresponding `Example` from the `DatasetRepository` and uses the `EvaluationLogic` to compute an `Evaluation`.
+4. Each `Evaluation` gets wrapped in an `ExampleEvaluation` and stored in the `EvaluationRepository`.
+5. The `Aggregator` reads all `ExampleEvaluation`s for a given evaluation and feeds them to the `AggregationLogic` to produce a `AggregatedEvaluation`.
+6. The `AggregatedEvalution` is wrapped in an `AggregationOverview` and stored in the `AggregationRepository`.
+
+The next diagram illustrates the more complex case of a relative evaluation.
+
+
+
+Process of a relative Evaluation
+
+
+1. Multiple `Runner`s read the same dataset and produce the corresponding `Output`s for different `Task`s.
+2. For each run all `Output`s are stored in the `RunRepository`.
+3. The `Evaluator` gets as input previous evaluations (that were produced on basis of the same dataset, but by different `Task`s) and the new runs of the current task.
+4. Given the previous evaluations and the new runs the `Evaluator` can read the `ExampleOutput`s of both the new runs
+ and the runs associated to previous evaluations, collect all that belong to a single `Example` and pass them
+ along with the `Example` to the `EvaluationLogic` to compute an `Evaluation`.
+5. Each `Evaluation` gets wrapped in an `ExampleEvaluation` and is stored in the `EvaluationRepository`.
+6. The `Aggregator` reads all `ExampleEvaluation` from all involved evaluations
+ and feeds them to the `AggregationLogic` to produce a `AggregatedEvaluation`.
+7. The `AggregatedEvalution` is wrapped in an `AggregationOverview` and stored in the `AggregationRepository`.
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 000000000..bb95af133
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,3 @@
+Copyright (C) Aleph Alpha GmbH - All Rights Reserved
+
+This source code, databases, and other material is protected under international copyright law. All rights reserved and protected by the copyright holders. This file is confidential and only available to authorized individuals with the permission of the copyright holders. If you encounter this file and do not have permission, please contact the copyright holder.
diff --git a/README.md b/README.md
new file mode 100644
index 000000000..f7d156c83
--- /dev/null
+++ b/README.md
@@ -0,0 +1,257 @@
+# Aleph Alpha Intelligence Layer
+
+The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs).
+With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment.
+
+The key features of the Intelligence Layer are:
+
+- **Composability:** Streamline your journey from prototyping to scalable deployment. The Intelligence Layer SDK offers seamless integration with diverse evaluation methods, manages concurrency, and orchestrates smaller tasks into complex workflows.
+- **Evaluability:** Continuously evaluate your AI applications against your quantitaive quality requirements. With the Intelligence Layer SDK you can quickly iterate on different solution strategies, ensuring confidence in the performance of your final product. Take inspiration from the provided evaluations for summary and search when building a custom evaluation logic for your own use case.
+- **Traceability:** At the core of the Intelligence Layer is the belief that all AI processes must be auditable and traceable. We provide full observability by seamlessly logging each step of every workflow. This enhances your debugging capabilities and offers greater control post-deployment when examining model responses.
+- **Examples:** Get started by following our hands-on examples, demonstrating how to use the Intelligence Layer SDK and interact with its API.
+
+
+
+# Table of contents
+- [Aleph Alpha Intelligence Layer](#aleph-alpha-intelligence-layer)
+- [Table of contents](#table-of-contents)
+- [Installation](#installation)
+ - [Local installation (for development and tutorials)](#local-installation-for-development-and-tutorials)
+ - [Getting started with the Jupyter Notebooks](#getting-started-with-the-jupyter-notebooks)
+ - [How to use the Intelligence Layer in your project](#how-to-use-the-intelligence-layer-in-your-project)
+ - [How to use the Intelligence Layer in Docker](#how-to-use-the-intelligence-layer-in-docker)
+ - [Via the GitHub repository](#via-the-github-repository)
+- [Getting started](#getting-started)
+ - [Tutorials](#tutorials)
+ - [How-Tos](#how-tos)
+- [Models](#models)
+- [Example index](#example-index)
+- [References](#references)
+- [License](#license)
+- [For Developers](#for-developers)
+ - [Python: Naming Conventions](#python-naming-conventions)
+ - [Executing tests](#executing-tests)
+
+# Installation
+
+## Local installation (for development and tutorials)
+Clone the Intelligence Layer repository from github.
+```bash
+git clone git@github.com:Aleph-Alpha/intelligence-layer.git
+```
+The Intelligence Layer uses `poetry` as a package manager. Follow the [official instructions](https://python-poetry.org/docs/#installation) to install it.
+Afterwards, simply run `poetry install` to install all dependencies in a virtual environment.
+```bash
+poetry install
+```
+The environment can be activated via `poetry shell`. See the official poetry documentation for more information.
+
+
+### Getting started with the Jupyter Notebooks
+
+After running the local installation steps, you can set whether you are using the Aleph-Alpha API or an on-prem setup via the environment variables.
+
+---
+**Using the Aleph-Alpha API** \
+ \
+In the Intelligence Layer the Aleph-Alpha API (`https://api.aleph-alpha.com`) is set as default host URL. However, you will need an [Aleph Alpha access token](https://docs.aleph-alpha.com/docs/account/#create-a-new-token) to run the examples.
+Set your access token with
+
+```bash
+export AA_TOKEN=
+```
+
+---
+
+**Using an on-prem setup** \
+ \
+In case you want to use an on-prem endpoint you will have to change the host URL by setting the `CLIENT_URL` environment variable:
+
+```bash
+export CLIENT_URL=
+```
+
+The program will warn you in case no `CLIENT_URL` is set explicitly set.
+
+---
+After correctly setting up the environment variables you can run the jupyter notebooks.
+For this, run `jupyter lab` inside the virtual environment and go to the [examples](http://localhost:8888/lab/workspaces/auto-C/tree/src/documentation) directory.
+
+```bash
+cd src/documentation && poetry run jupyter lab
+```
+
+## How to use the Intelligence Layer in your project
+To install this as a dependency in your project, you need a [Github access token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token-classic).
+This token needs the following permissions
+
+
+To install the Aleph-Alpha Intelligence Layer from the JFrog artifactory in you project, you have to add this information to your poetry setup via the following four steps. First, add the artifactory as a source to your project via
+```bash
+poetry source add --priority=explicit artifactory https://alephalpha.jfrog.io/artifactory/api/pypi/python/simple
+```
+Second, to install the poetry environment, export your JFrog credentials to the environment
+```bash
+export POETRY_HTTP_BASIC_ARTIFACTORY_USERNAME=your@username.here
+export POETRY_HTTP_BASIC_ARTIFACTORY_PASSWORD=your-token-here
+```
+Third, add the Intelligence Layer to the project
+```bash
+poetry add --source artifactory intelligence-layer
+```
+Fourth, execute
+```bash
+poetry install
+```
+
+Now the Intelligence Layer should be available as a Python package and ready to use.
+
+```py
+from intelligence_layer.core import Task
+```
+
+In VSCode, to enable auto-import up to the second depth, where all symbols are exported, add the following entry to your `./.vscode/settings.json`:
+
+``` json
+"python.analysis.packageIndexDepths": [
+ {
+ "name": "intelligence_layer",
+ "depth": 2
+ }
+]
+```
+## How to use the Intelligence Layer in Docker
+
+### Via the GitHub repository
+
+To use the Intelligence Layer in Docker, a few settings are needed to not leak your Github token.
+
+You will need your Github token set in your environment.
+
+In order to modify the `git config` add the following to your docker container:
+
+```dockerfile
+RUN apt-get -y update
+RUN apt-get -y install git curl gcc python3-dev
+RUN pip install poetry
+
+RUN poetry install --no-dev --no-interaction --no-ansi \
+ && rm -f ~/.gitconfig
+```
+
+# Getting started
+
+Not sure where to start? Familiarize yourself with the Intelligence Layer SDK using the below notebook as interactive tutorials.
+If you prefer you can also read about the [concepts](Concepts.md) first.
+
+## Tutorials
+The tutorials aim to guide you through implementing several common use-cases with the Intelligence Layer SDK. They introduce you to key concepts and enable you to create your own use-cases. In general the tutorials are build in a way that you can simply hop into the topic you are most interested in. However, for starters we recommend to read through the `Summarization` tutorial first. It explains the core concepts of the intelligence layer in more depth while for the other tutorials we assume that these concepts are known.
+
+| Order | Topic | Description | Notebook 📓 |
+| ----- | ------------------ |------------------------------------------------------|-----------------------------------------------------------------|
+| 1 | Summarization | Summarize a document | [summarization.ipynb](./src/documentation/summarization.ipynb) |
+| 2 | Question Answering | Various approaches for QA | [qa.ipynb](./src/documentation/qa.ipynb) |
+| 3 | Classification | Learn about two methods of classification | [classification.ipynb](./src/documentation/classification.ipynb) |
+| 4 | Evaluation | Evaluate LLM-based methodologies | [evaluation.ipynb](./src/documentation/evaluation.ipynb) |
+| 5 | Quickstart Task | Build a custom `Task` for your use case | [quickstart_task.ipynb](./src/documentation/quickstart_task.ipynb) |
+| 6 | Document Index | Connect your proprietary knowledge base | [document_index.ipynb](./src/documentation/document_index.ipynb) |
+| 7 | Human Evaluation | Connect to Argilla for manual evaluation | [human_evaluation.ipynb](./src/documentation/human_evaluation.ipynb) |
+| 8 | Performance tips | Contains some small tips for performance | [performance_tips.ipynb](./src/documentation/performance_tips.ipynb) |
+| 9 | Deployment | Shows how to deploy a Task in a minimal FastAPI app. | [fastapi_tutorial.ipynb](./src/documentation/fastapi_tutorial.ipynb) |
+| 10 | Issue Classification | Deploy a Task in Kubernetes to classify Jira issues | [Found in adjacent repository](https://github.com/Aleph-Alpha/IL-Classification-Journey) |
+
+## How-Tos
+The how-tos are quick lookups about how to do things. Compared to the tutorials, they are shorter and do not explain the concepts they are using in-depth.
+
+| Tutorial | Description |
+| ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- |
+| **Tasks** | |
+| [...define a task](./src/documentation/how_tos/how_to_define_a_task.ipynb) | How to come up with a new task and formulate it |
+| [...implement a task](./src/documentation/how_tos/how_to_implement_a_task.ipynb) | Implement a formulated task and make it run with the Intelligence Layer |
+| [...debug and log a task](./src/documentation/how_tos/how_to_log_and_debug_a_task.ipynb) | Tools for logging and debugging in tasks |
+| [...run the trace viewer](./src/documentation/how_tos/how_to_run_the_trace_viewer.ipynb) | Downloading and running the trace viewer for debugging traces |
+| **Analysis Pipeline** | |
+| [...implement a simple evaluation and aggregation logic](./src/documentation/how_tos/how_to_implement_a_simple_evaluation_and_aggregation_logic.ipynb) | Basic examples of evaluation and aggregation logic |
+| [...create a dataset](./src/documentation/how_tos/how_to_create_a_dataset.ipynb) | Create a dataset used for running a task |
+| [...run a task on a dataset](./src/documentation/how_tos/how_to_run_a_task_on_a_dataset.ipynb) | Run a task on a whole dataset instead of single examples |
+| [...evaluate multiple runs](./src/documentation/how_tos/how_to_evaluate_runs.ipynb) | Evaluate (multiple) runs in a single evaluation |
+| [...aggregate multiple evaluations](./src/documentation/how_tos/how_to_aggregate_evaluations.ipynb) | Aggregate (multiple) evaluations in a single aggregation |
+| [...retrieve data for analysis](./src/documentation/how_tos/how_to_retrieve_data_for_analysis.ipynb) | Retrieve experiment data in multiple different ways |
+| [...implement a custom human evaluation](./src/documentation/how_tos/how_to_human_evaluation_via_argilla.ipynb) | Necessary steps to create an evaluation with humans as a judge via Argilla |
+
+# Models
+
+Currently, we support a bunch of models accessible via the Aleph Alpha API. Depending on your local setup, you may even have additional models available.
+
+| Model | Description |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [LuminousControlModel](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.core.html#intelligence_layer.core.LuminousControlModel) | Any control-type model based on the first Luminous generation, specifically `luminous-base-control`, `luminous-extended-control` and `luminous-supreme-control`. Multilingual support. |
+| [Llama2InstructModel](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.core.html#intelligence_layer.core.Llama2InstructModel) | Llama-2 based models prompted for one-turn instruction answering. Includes `llama-2-7b-chat`, `llama-2-13b-chat` and `llama-2-70b-chat`. Best suited for English tasks. |
+| [Llama3InstructModel](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.core.html#intelligence_layer.core.Llama3InstructModel) | Llama-3 based models prompted for one-turn instruction answering. Includes `llama-3-8b-instruct` and `llama-3-70b-instruct`. Best suited for English tasks and recommended over llama-2 models. |
+
+# Example index
+
+To give you a starting point for using the Intelligence Layer, we provide some pre-configured `Task`s that are ready to use out-of-the-box, as well as an accompanying "Getting started" guide in the form of Jupyter Notebooks.
+
+| Type | Task | Description |
+| --------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Classify | [EmbeddingBasedClassify](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.EmbeddingBasedClassify) | Classify a short text by computing its similarity with example texts for each class. |
+| Classify | [PromptBasedClassify](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.PromptBasedClassify) | Classify a short text by assessing each class' probability using zero-shot prompting. |
+| Classify | [PromptBasedClassifyWithDefinitions](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.PromptBasedClassifyWithDefinitions) | Classify a short text by assessing each class' probability using zero-shot prompting. Each class is defined by a natural language description. |
+| Classify | [KeywordExtract](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.KeywordExtract) | Generate matching labels for a short text. |
+| QA | [MultipleChunkRetrieverQa](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.MultipleChunkRetrieverQa) | Answer a question based on an entire knowledge base. Recommended for most RAG-QA use-cases. |
+| QA | [LongContextQa](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.LongContextQa) | Answer a question based on one document of any length. |
+| QA | [MultipleChunkQa](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.MultipleChunkQa) | Answer a question based on a list of short texts. |
+| QA | [SingleChunkQa](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.SingleChunkQa) | Answer a question based on a short text. |
+| QA | [RetrieverBasedQa (deprecated)](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.RetrieverBasedQa) | Answer a question based on a document base using a [BaseRetriever](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.connectors.html#intelligence_layer.connectors.BaseRetriever) implementation. |
+| Search | [Search](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.Search) | Search for texts in a document base using a [BaseRetriever](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.connectors.html#intelligence_layer.connectors.BaseRetriever) implementation. |
+| Search | [ExpandChunks](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.ExpandChunks) | Expand chunks retrieved with a [BaseRetriever](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.connectors.html#intelligence_layer.connectors.BaseRetriever) implementation. |
+| Summarize | [SteerableLongContextSummarize](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.SteerableLongContextSummarize) | Condense a long text into a summary with a natural language instruction. |
+| Summarize | [SteerableSingleChunkSummarize](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.SteerableSingleChunkSummarize) | Condense a short text into a summary with a natural language instruction. |
+| Summarize | [RecursiveSummarize](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.use_cases.html#intelligence_layer.use_cases.RecursiveSummarize) | Recursively condense a text into a summary. |
+
+Note that we do not expect the above use cases to solve all of your issues.
+Instead, we encourage you to think of our pre-configured use cases as a foundation to fast-track your development process.
+By leveraging these tasks, you gain insights into the framework's capabilities and best practices.
+
+We encourage you to copy and paste these use cases directly into your own project.
+From here, you can customize everything, including the prompt, model, and more intricate functional logic.
+For more information, check the [tutorials](#tutorials) and the [how-tos](#how-tos)
+
+
+
+# References
+The full code documentation can be found in our read-the-docs [here](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/)
+
+# License
+
+This project can only be used after signing the agreement with Aleph Alpha®. Please refer to the [LICENSE](LICENSE.md) file for more details.
+
+# For Developers
+
+## Python: Naming Conventions
+
+We follow the [PEP 8 – Style Guide for Python Code](https://peps.python.org/pep-0008/).
+In addition, there are the following naming conventions:
+* Class method names:
+ * Use only substantives for a method name having no side effects and returning some objects
+ * E.g., `evaluation_overview` which returns an evaluation overview object
+ * Use a verb for a method name if it has side effects and return nothing
+ * E.g., `store_evaluation_overview` which saves a given evaluation overview (and returns nothing)
+
+
+
+## Executing tests
+**In VSCode**
+1. Sidebar > Testing
+2. Select pytest as framework for the tests
+3. Select `intelligence_layer/tests` as source of the tests
+
+You can then run the tests from the sidebar.
+
+**In a terminal**
+In order to run a local proxy w.r.t. to the CI pipeline (required to merge) you can run
+> scripts/all.sh
+
+This will run linters and all tests.
+The scripts to run single steps can also be found in the `scripts` folder.
diff --git a/RELEASE.md b/RELEASE.md
new file mode 100644
index 000000000..8fa31699e
--- /dev/null
+++ b/RELEASE.md
@@ -0,0 +1,13 @@
+# Release cycle TODOs
+
+- Update CHANGELOG.md
+ - We committed to updating the changelog with every relevant merge into main. Check the new entries of the changelog and perform adjustments where necessary.
+- Update the "version" field of the project in `pyproject.toml`
+ - We use [semantic versioning](https://semver.org/)
+- Commit the changes and merge to main
+- Tag the latest commit on main with the new release number (e.g. v0.6.0)
+ - `git checkout main, git tag , git push origin `
+- Create a new release draft in GitHub (Tags -> Releases -> Draft a new release) and save it as draft
+ - Copy the changelog into the release description. Also add a link to the commits since the last release at the bottom of the description.
+- Make sure the changes have been merged into the main branch.
+- Publish the release.
diff --git a/assets/AbsoluteEvaluation.drawio.svg b/assets/AbsoluteEvaluation.drawio.svg
new file mode 100644
index 000000000..d7f36f871
--- /dev/null
+++ b/assets/AbsoluteEvaluation.drawio.svg
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/assets/RecursiveSummary.drawio.svg b/assets/RecursiveSummary.drawio.svg
new file mode 100644
index 000000000..dc7384ac9
--- /dev/null
+++ b/assets/RecursiveSummary.drawio.svg
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/assets/RelativeEvaluation.drawio.svg b/assets/RelativeEvaluation.drawio.svg
new file mode 100644
index 000000000..c7aa089f4
--- /dev/null
+++ b/assets/RelativeEvaluation.drawio.svg
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/assets/TraceViewer.png b/assets/TraceViewer.png
new file mode 100644
index 000000000..31e08c7c8
Binary files /dev/null and b/assets/TraceViewer.png differ
diff --git a/assets/Tracer.drawio.svg b/assets/Tracer.drawio.svg
new file mode 100644
index 000000000..ed6fd5465
--- /dev/null
+++ b/assets/Tracer.drawio.svg
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/assets/Tracing.drawio.svg b/assets/Tracing.drawio.svg
new file mode 100644
index 000000000..a9571a4d7
--- /dev/null
+++ b/assets/Tracing.drawio.svg
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/assets/argilla_interface.png b/assets/argilla_interface.png
new file mode 100644
index 000000000..33fdf7a0a
Binary files /dev/null and b/assets/argilla_interface.png differ
diff --git a/assets/argilla_splits.png b/assets/argilla_splits.png
new file mode 100644
index 000000000..ddf4c1e80
Binary files /dev/null and b/assets/argilla_splits.png differ
diff --git a/assets/fork.png b/assets/fork.png
new file mode 100644
index 000000000..be08e1d58
Binary files /dev/null and b/assets/fork.png differ
diff --git a/docker-compose.yaml b/docker-compose.yaml
new file mode 100644
index 000000000..8d288a0c9
--- /dev/null
+++ b/docker-compose.yaml
@@ -0,0 +1,25 @@
+version: "3"
+services:
+ argilla-elastic-search:
+ image: docker.elastic.co/elasticsearch/elasticsearch:8.5.3
+ environment:
+ ES_JAVA_OPTS: "-Xms512m -Xmx512m"
+ discovery.type: "single-node"
+ xpack.security.enabled: "false"
+ argilla:
+ image: argilla/argilla-server:v1.26.1
+ ports:
+ - "6900:6900"
+ environment:
+ ARGILLA_ELASTICSEARCH: "http://argilla-elastic-search:9200"
+ ARGILLA_ENABLE_TELEMETRY: 0
+
+ open-telemetry-trace-service:
+ container_name: jaeger_1_35
+ environment:
+ COLLECTOR_OTLP_ENABLED: "true"
+ ports:
+ - "4317:4317"
+ - "4318:4318"
+ - "16686:16686"
+ image: jaegertracing/all-in-one:1.35
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 000000000..3e4b5f71d
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,24 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS ?= # -nvT
+SPHINXBUILD ?= sphinx-build
+SOURCEDIR = .
+BUILDDIR = _build
+
+export SPHINX_APIDOC_OPTIONS=members,show-inheritance
+
+# Put it first so that "make" without argument is like "make help".
+help:
+ @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+ # sphinx-apidoc -o . ../src
+ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+ # rm --force `ls *.rst | grep --fixed-strings --invert-match index.rst`
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 000000000..0ee2b4f85
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,38 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "Intelligence Layer"
+copyright = "2023, Aleph Alpha"
+author = "Aleph Alpha"
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+ "sphinx.ext.autodoc",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.viewcode",
+ "sphinx.ext.doctest",
+]
+
+autodoc_default_options = {
+ "members": True,
+ "show-inheritance": True,
+ "inherited-members": "BaseModel,RuntimeError",
+ # BaseModel attributes where the documentation does not add a lot of value
+ "exclude-members": "model_config,model_fields,model_computed_fields",
+}
+
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "sphinx_rtd_theme"
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 000000000..68eb2fb3f
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,18 @@
+.. Intelligence Layer documentation master file, created by
+ sphinx-quickstart on Fri Oct 27 14:17:00 2023.
+ You can adapt this file completely to your liking, but it should at least
+ contain the root `toctree` directive.
+
+Welcome to Intelligence Layer's documentation!
+==============================================
+
+.. toctree::
+ :maxdepth: 1
+ :caption: Contents:
+
+ intelligence_layer
+
+Indices and tables
+==================
+
+* :ref:`genindex`
diff --git a/docs/intelligence_layer.connectors.rst b/docs/intelligence_layer.connectors.rst
new file mode 100644
index 000000000..0e9570b37
--- /dev/null
+++ b/docs/intelligence_layer.connectors.rst
@@ -0,0 +1,7 @@
+intelligence\_layer.connectors package
+======================================
+
+Module contents
+---------------
+
+.. automodule:: intelligence_layer.connectors
diff --git a/docs/intelligence_layer.core.rst b/docs/intelligence_layer.core.rst
new file mode 100644
index 000000000..cce640699
--- /dev/null
+++ b/docs/intelligence_layer.core.rst
@@ -0,0 +1,9 @@
+intelligence\_layer.core package
+================================
+
+Module contents
+---------------
+
+.. automodule:: intelligence_layer.core
+
+ .. autoclass:: TextChunk
diff --git a/docs/intelligence_layer.evaluation.rst b/docs/intelligence_layer.evaluation.rst
new file mode 100644
index 000000000..a871a800a
--- /dev/null
+++ b/docs/intelligence_layer.evaluation.rst
@@ -0,0 +1,8 @@
+intelligence\_layer.evaluation package
+======================================
+
+
+Module contents
+---------------
+
+.. automodule:: intelligence_layer.evaluation
diff --git a/docs/intelligence_layer.rst b/docs/intelligence_layer.rst
new file mode 100644
index 000000000..0e4ba8eef
--- /dev/null
+++ b/docs/intelligence_layer.rst
@@ -0,0 +1,18 @@
+intelligence\_layer package
+===========================
+
+Subpackages
+-----------
+
+.. toctree::
+ :maxdepth: 3
+
+ intelligence_layer.connectors
+ intelligence_layer.core
+ intelligence_layer.evaluation
+ intelligence_layer.use_cases
+
+Module contents
+---------------
+
+.. automodule:: intelligence_layer
diff --git a/docs/intelligence_layer.use_cases.rst b/docs/intelligence_layer.use_cases.rst
new file mode 100644
index 000000000..b620902f5
--- /dev/null
+++ b/docs/intelligence_layer.use_cases.rst
@@ -0,0 +1,8 @@
+intelligence\_layer.use\_cases package
+======================================
+
+
+Module contents
+---------------
+
+.. automodule:: intelligence_layer.examples
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 000000000..32bb24529
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.https://www.sphinx-doc.org/
+ exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/env.sample b/env.sample
new file mode 100644
index 000000000..c687dc0f8
--- /dev/null
+++ b/env.sample
@@ -0,0 +1,7 @@
+AA_TOKEN=token
+CLIENT_URL="https://api.aleph-alpha.com"
+ARGILLA_API_URL="http://localhost:6900/"
+ARGILLA_API_KEY="argilla.apikey"
+HUGGING_FACE_TOKEN=token
+# local dev builds run on 5173
+TRACE_VIEWER_URL="http://localhost:3000"
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 000000000..cd914a8ed
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,5551 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "aiodns"
+version = "3.2.0"
+description = "Simple DNS resolver for asyncio"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+pycares = ">=4.0.0"
+
+[[package]]
+name = "aiohttp"
+version = "3.9.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
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+ {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zipp"
+version = "3.18.1"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+ {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.12"
+content-hash = "16effe1ca63608828a41caab5434ecbcdcaa08947c0140bdd87f6e26b29cd6be"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..c15a56ba8
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,95 @@
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "intelligence-layer"
+version = "0.11.0"
+description = ""
+authors = ["Aleph Alpha Engineering "]
+readme = "README.md"
+license = "Aleph Alpha Licensed"
+packages = [{ include = "intelligence_layer", from = "src" }]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+pydantic = "2.7.*"
+fastapi = "*"
+uvicorn = "*"
+aleph-alpha-client = ">=7.1.0"
+python-dotenv = "*"
+semantic-text-splitter = "^0.12.0"
+qdrant-client = "^1.9.0"
+rich = "^13.7.1"
+nbconvert = "^7.16.3"
+datasets = "^2.19.0"
+jupyter = "^1.0.0"
+requests = "^2.31.0"
+langdetect = "^1.0.9"
+nltk = "^3.8.1"
+pycountry = "23.12.11"
+rouge = "^1.0.1"
+opentelemetry-api = "^1.22.0"
+opentelemetry-sdk = "^1.22.0"
+huggingface-hub = "^0.22.2"
+opentelemetry-exporter-otlp-proto-http = "1.23.0"
+
+[tool.poetry.group.dev.dependencies]
+# lint & format
+mypy = "^1.10.0"
+nbqa = "^1.8.5"
+ruff = "^0.4.2"
+pre-commit = "*"
+pylama = { extras = ["radon", "vulture", "toml"], version = "^8.4.1" }
+faker = "^24.14.0"
+
+# tests
+pytest = "*"
+pytest-xdist = "^3.5.0"
+hypercorn = "0.16.0"
+
+# typing
+httpx = "*"
+types-Pygments = "*"
+types-colorama = "*"
+types-docutils = "*"
+types-setuptools = "*"
+types-decorator = "*"
+types-requests = "*"
+types-tqdm = "*"
+pandas-stubs = "^2.2.1.240316"
+# docs
+sphinx-rtd-theme = "^2.0.0"
+sphinx = "^7.3.7"
+matplotlib = "^3.8.4"
+
+[tool.mypy]
+files = "src,tests"
+strict = "True"
+
+[tool.pytest.ini_options]
+markers = [
+ "internal: marks tests as internal (deselect with '-k \"not internal\"')",
+ "docker: the test depends on having a docker container running."
+]
+addopts = "--capture=tee-sys"
+filterwarnings = [
+ 'ignore:.*\`general_plain_validator_function\` is deprecated.*',
+]
+
+[tool.pylama]
+skip = "*/__init__.py,.venv/*,*/node_modules/*"
+ignore = "E501,E203"
+
+[tool.ruff]
+extend-include = ["*.ipynb"]
+
+[tool.ruff.lint.mccabe]
+max-complexity = 11
+
+# we double check this, maybe pylama isn't necessary anymore
+[tool.pylama.linter.mccabe]
+max-complexity = "11"
+
+[tool.isort]
+profile = "black"
diff --git a/run.py b/run.py
new file mode 100644
index 000000000..c85dfa0bf
--- /dev/null
+++ b/run.py
@@ -0,0 +1,31 @@
+"""Fastapi server to run predictions."""
+
+from dotenv import load_dotenv
+from fastapi import Depends, FastAPI
+
+from intelligence_layer.core import ControlModel, LuminousControlModel, NoOpTracer
+from intelligence_layer.examples.classify.classify import (
+ ClassifyInput,
+ SingleLabelClassifyOutput,
+)
+from intelligence_layer.examples.classify.prompt_based_classify import (
+ PromptBasedClassify,
+)
+
+app = FastAPI()
+
+load_dotenv()
+
+
+def model() -> ControlModel:
+ return LuminousControlModel("luminous-base-control")
+
+
+@app.post("/classify")
+async def classify(
+ classify_input: ClassifyInput,
+ luminous_control_model: LuminousControlModel = Depends(model),
+) -> SingleLabelClassifyOutput:
+ classify = PromptBasedClassify(luminous_control_model)
+ classify_output = classify.run(classify_input, NoOpTracer())
+ return classify_output
diff --git a/scripts/all.sh b/scripts/all.sh
new file mode 100644
index 000000000..50028c751
--- /dev/null
+++ b/scripts/all.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+
+ProjectRoot="$(cd $(dirname "$0")/.. && pwd -P)"
+
+cd "$ProjectRoot"
+
+# see https://stackoverflow.com/questions/43267413/how-to-set-environment-variables-from-env-file
+set -a # automatically export all variables
+source .env
+set +a
+
+./scripts/lint.sh
+./scripts/doctest.sh
+./scripts/notebook_runner.sh
+./scripts/test.sh
diff --git a/scripts/doctest.sh b/scripts/doctest.sh
new file mode 100644
index 000000000..288e013e8
--- /dev/null
+++ b/scripts/doctest.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+
+ProjectRoot="$(cd $(dirname "$0")/.. && pwd -P)"
+
+cd "$ProjectRoot"
+
+if [ -f .env ]; then
+ # Export environment variables from .env file
+ set -a # automatically export all variables
+ source .env
+ set +a
+fi
+(cd docs && poetry run make doctest)
diff --git a/scripts/fastapi_example_test.sh b/scripts/fastapi_example_test.sh
new file mode 100644
index 000000000..b554ef47c
--- /dev/null
+++ b/scripts/fastapi_example_test.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+
+# start the server in the background
+hypercorn src/documentation/fastapi_example:app --bind localhost:8000 &
+server_pid=$!
+
+attempt_counter=0
+max_attempts=10
+
+trap 'kill $server_pid' EXIT SIGINT
+# waiting for server startup
+until $(curl -X GET http://localhost:8000 --fail-with-body --output /dev/null --silent --head); do
+ if [ ${attempt_counter} -eq ${max_attempts} ];then
+ echo "Max attempts reached"
+ exit 1
+ fi
+
+ printf '.'
+ attempt_counter=$(($attempt_counter+1))
+ sleep 1
+done
+
+curl -X GET http://localhost:8000 --fail-with-body
+curl -X POST http://localhost:8000/summary --fail-with-body -H "Content-Type: application/json" -d '{"chunk": "", "language": {"iso_639_1": "en"}}'
+
+# kill happens at the end with the trap command
+exit 0
diff --git a/scripts/lint.sh b/scripts/lint.sh
new file mode 100644
index 000000000..97899bbfd
--- /dev/null
+++ b/scripts/lint.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+
+poetry run pre-commit run --all-files
+poetry run mypy
diff --git a/scripts/notebook_runner.sh b/scripts/notebook_runner.sh
new file mode 100644
index 000000000..6bfd344db
--- /dev/null
+++ b/scripts/notebook_runner.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+# next line loads AA_TOKEN from .env file when running bash script locally. In CI this is not necessary since AA_TOKEN is environment variable.
+[ -f .env ] && source .env
+export AA_TOKEN
+# Find all .ipynb files in the directory and pass them to xargs for parallel execution
+rm -rf src/documentation/.ipynb_checkpoints
+rm -rf src/documentation/how_tos/.ipynb_checkpoints
+
+find src/documentation -name "*.nbconvert.ipynb" -type f -delete
+find src/documentation -name "*.ipynb" ! -name "performance_tips.ipynb" | xargs --max-args 1 --max-procs 6 poetry run jupyter nbconvert --to notebook --execute
+find src/documentation -name "*.nbconvert.ipynb" -type f -delete
+
+poetry run ./scripts/fastapi_example_test.sh
diff --git a/scripts/test.sh b/scripts/test.sh
new file mode 100644
index 000000000..82abaac1e
--- /dev/null
+++ b/scripts/test.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env -S bash -eu -o pipefail
+
+poetry run python3 -c "import nltk; nltk.download('punkt')"
+poetry run pytest -n 10
diff --git a/src/documentation/classification.ipynb b/src/documentation/classification.ipynb
new file mode 100644
index 000000000..b64750213
--- /dev/null
+++ b/src/documentation/classification.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "from intelligence_layer.core import InMemoryTracer, LogEntry, TextChunk\n",
+ "from intelligence_layer.examples import (\n",
+ " ClassifyInput,\n",
+ " EmbeddingBasedClassify,\n",
+ " LabelWithExamples,\n",
+ " PromptBasedClassify,\n",
+ " TreeNode,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Classification\n",
+ "\n",
+ "Language models offer unprecedented capabilities in understanding and generating human-like text.\n",
+ "One of the pressing issues in their application is the classification of vast amounts of data.\n",
+ "Traditional methods often require manual labeling and can be time-consuming and prone to errors.\n",
+ "LLMs, on the other hand, can swiftly process and categorize enormous datasets with minimal human intervention.\n",
+ "By leveraging LLMs for classification tasks, organizations can unlock insights from their data more efficiently, streamline their workflows, and harness the full potential of their information assets.\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "This notebook is designed to showcase two different approaches and ways of classifying text using Aleph Alpha's Luminous models.\n",
+ "To make proper use of the classification task, it is necessary to evaluate the results in an iterative way, to ensure it satisfies your requirements.\n",
+ "For an example of how such an evaluation can look like, refer to [evaluation.ipynb](./evaluation.ipynb).\n",
+ "
\n",
+ "\n",
+ "First, let's have a look at single-label classification using prompting.\n",
+ "\n",
+ "### Prompt-based single-label classification\n",
+ "\n",
+ "Single-label classification refers to the task of categorizing data points into one of n distinct categories or classes.\n",
+ "In this type of classification, each input is assigned to only one class, ensuring that no overlap exists between categories.\n",
+ "Common applications of single-label classification include email spam detection, where emails are classified as either \"spam\" or \"not spam\", or sentiment classification, where a text can be \"positive\", \"negative\" or \"neutral\".\n",
+ "When trying to solve this issue in a prompt-based manner, our primary goal is to construct a prompt that instructs the model to accurately predict the correct class for any given input.\n",
+ "\n",
+ "### When should you use prompt-based classification?\n",
+ "\n",
+ "We recommend using this type of classification when...\n",
+ "- ...the labels are easily understood (they don't require explanation or examples).\n",
+ "- ...the labels cannot be recognized purely by their semantic meaning.\n",
+ "- ...many examples for each label aren't readily available.\n",
+ "\n",
+ "### Example snippet\n",
+ "\n",
+ "Running the following code will instantiate a `PromptBasedClassify`-task that leverages a prompt for classification.\n",
+ "We can pass any `ClassifyInput` to the task and it returns each label along with its probability.\n",
+ "In addition, note the `tracer`, which will give a comprehensive overview of the result."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text_to_classify = TextChunk(\n",
+ " \"In the distant future, a space exploration party embarked on a thrilling journey to the uncharted regions of the galaxy. \\n\\\n",
+ "With excitement in their hearts and the cosmos as their canvas, they ventured into the unknown, discovering breathtaking celestial wonders. \\n\\\n",
+ "As they gazed upon distant stars and nebulas, they forged unforgettable memories that would forever bind them as pioneers of the cosmos.\"\n",
+ ")\n",
+ "labels = [\"happy\", \"angry\", \"sad\"]\n",
+ "input = ClassifyInput(chunk=text_to_classify, labels=labels)\n",
+ "\n",
+ "task = PromptBasedClassify()\n",
+ "tracer = InMemoryTracer()\n",
+ "output = task.run(input, tracer)\n",
+ "\n",
+ "for label, score in output.scores.items():\n",
+ " print(f\"{label}: {round(score, 4)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How does this implementation work?\n",
+ "\n",
+ "We prompt the model multiple times, each time supplying the text, or chunk, and one label at a time.\n",
+ "Note that we also supply each label, rather than letting the model generate it.\n",
+ "\n",
+ "To further explain this, let's start with a more familiar case.\n",
+ "Intuitively, one would probably prompt a model like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt = PromptBasedClassify.INSTRUCTION\n",
+ "print(prompt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model would then complete our instruction, thus generating a matching label.\n",
+ "\n",
+ "In case of single-label classification, however, we already know all possible classes beforehand.\n",
+ "Thus, all we are interested in is the probability that the model would have generated our specific class for the given input.\n",
+ "To get this probability, we modify the model such that it does not generate any token but returns the logarithmic probabilities (logprops) of the completion instead. From this we then extract the probability with which our class would have been selected. This process is called an `EchoTask`.\n",
+ "\n",
+ "Let's have a look at just one of these tasks triggered by our classification run.\n",
+ "\n",
+ "Feel free to ignore the big `Complete` task dump in the middle.\n",
+ "Instead, focus on the `expected_completion` in the `Input` and the `prob` for the token \" angry\" in the `Output`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In particular, note the `expected_completion` in the `Input` and the `prob` for the token \" angry\" in the `Output`.\n",
+ "Feel free to ignore the big `Complete` task dump in the middle."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tracer.entries[-1].entries[0].entries[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have the logprobs, we just need to do some calculations to turn them into a final score.\n",
+ "\n",
+ "To turn the logprobs into our end scores, we first normalize our probabilities.\n",
+ "For this, we utilize a probability tree."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "task_log = tracer.entries[-1]\n",
+ "normalized_probs_logs = [\n",
+ " log_entry.value\n",
+ " for log_entry in task_log.entries\n",
+ " if isinstance(log_entry, LogEntry) and log_entry.message == \"Normalized Probs\"\n",
+ "]\n",
+ "log = normalized_probs_logs[-1]\n",
+ "\n",
+ "root = TreeNode()\n",
+ "for probs in log.values():\n",
+ " root.insert_without_calculation(probs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we take the product of all the paths to get the following results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for label, score in output.scores.items():\n",
+ " print(f\"{label}: {round(score, 5)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Embedding-based multi-label classification\n",
+ "\n",
+ "Large language model embeddings offer a powerful approach to text classification.\n",
+ "In particular, such embeddings can be seen as a numerical representation of the meaning of a text.\n",
+ "Utilizing this, we can provide textual examples for each label and embed them to create a representations for each label in vector space.\n",
+ "\n",
+ "**Or, in more detail**:\n",
+ "In this method, each example from various classes is transformed into a vector representation using the embeddings from the language model.\n",
+ "These embedded vectors capture the semantic essence of the text.\n",
+ "Once this is done, clusters of embeddings are formed for each class, representing the centroid or the average meaning of the examples within that class.\n",
+ "When a new piece of text needs to be classified, it is first embedded using the same language model.\n",
+ "This new embedded vector is then compared to the pre-defined clusters for each class using a cosine similarity.\n",
+ "The class whose cluster is closest to the new text's embedding is then assigned to the text, thereby achieving classification.\n",
+ "This method leverages the deep semantic understanding of large language models to classify texts with high accuracy and nuance.\n",
+ "\n",
+ "### When should you use embedding-based classification?\n",
+ "\n",
+ "We recommend using this type of classification when...\n",
+ "- ...proper classification requires fine-grained control over the classes' definitions.\n",
+ "- ...the labels can be defined mostly or purely by the semantic meaning of the examples.\n",
+ "- ...examples for each label are readily available.\n",
+ "\n",
+ "### Example snippet\n",
+ "\n",
+ "Let's start by instantiating a classifier for sentiment classification."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels_with_examples = [\n",
+ " LabelWithExamples(\n",
+ " name=\"positive\",\n",
+ " examples=[\n",
+ " \"I really like this.\",\n",
+ " \"Wow, your hair looks great!\",\n",
+ " \"We're so in love.\",\n",
+ " \"That truly was the best day of my life!\",\n",
+ " \"What a great movie.\",\n",
+ " ],\n",
+ " ),\n",
+ " LabelWithExamples(\n",
+ " name=\"negative\",\n",
+ " examples=[\n",
+ " \"I really dislike this.\",\n",
+ " \"Ugh, Your hair looks horrible!\",\n",
+ " \"We're not in love anymore.\",\n",
+ " \"My day was very bad, I did not have a good time.\",\n",
+ " \"They make terrible food.\",\n",
+ " ],\n",
+ " ),\n",
+ "]\n",
+ "\n",
+ "classify = EmbeddingBasedClassify(labels_with_examples)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are several things to note here, in particular:\n",
+ "- This time, we instantiated our classification task with a number of `LabelWithExamples`.\n",
+ "- The examples provided should reflect the spectrum of texts expected in the intended usage domain of this classifier.\n",
+ "- This cell took some time to run.\n",
+ "This is because we instantiate a retriever in the background, which also requires us to embed the provided examples.\n",
+ "\n",
+ "With that being said, let's run an unknown example!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classify_input = ClassifyInput(\n",
+ " chunk=\"It was very awkward with him, I did not enjoy it.\",\n",
+ " labels=frozenset(label.name for label in labels_with_examples),\n",
+ ")\n",
+ "tracer = InMemoryTracer()\n",
+ "result = classify.run(classify_input, tracer)\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nice, we correctly identified the new example.\n",
+ "\n",
+ "Again, let's appreciate the difference of this result compared to `PromptBasedClassify`'s result.\n",
+ "- The probabilities do not add up to 1.\n",
+ "In fact, we have no way of predicting what the sum of all scores will be.\n",
+ "We only know they will be in the range from 0 to 1.\n",
+ "All we know is that the highest score is likely to correspond to the best fitting label, provided we delivered good examples.\n",
+ "- We were much quicker to obtain a result.\n",
+ "\n",
+ "Because all examples are pre-embedded, this classifier is much cheaper to operate as it only requires a single embedding-task to be sent to the Aleph Alpha API.\n",
+ "\n",
+ "Let's try another example. This time, we expect the outcome to be positive.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classify_input = ClassifyInput(\n",
+ " chunk=\"We used to be not like each other, but this changed a lot.\",\n",
+ " labels=frozenset(label.name for label in labels_with_examples),\n",
+ ")\n",
+ "tracer = InMemoryTracer()\n",
+ "result = classify.run(classify_input, tracer)\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Unfortunately, we wrongly classify this text as negative.\n",
+ "To be fair, it is a difficult example.\n",
+ "But no worries, let's simply include this failing example in our list of label examples and try again!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels_with_examples = [\n",
+ " LabelWithExamples(\n",
+ " name=\"positive\",\n",
+ " examples=[\n",
+ " \"I really like this.\",\n",
+ " \"Wow, your hair looks great!\",\n",
+ " \"We're so in love.\",\n",
+ " \"That truly was the best day of my life!\",\n",
+ " \"What a great movie.\",\n",
+ " \"We used to be not like each other, but this changed a lot.\", # failing example\n",
+ " ],\n",
+ " ),\n",
+ " LabelWithExamples(\n",
+ " name=\"negative\",\n",
+ " examples=[\n",
+ " \"I really dislike this.\",\n",
+ " \"Ugh, Your hair looks horrible!\",\n",
+ " \"We're not in love anymore.\",\n",
+ " \"My day was very bad, I did not have a good time.\",\n",
+ " \"They make terrible food.\",\n",
+ " ],\n",
+ " ),\n",
+ "]\n",
+ "classify = EmbeddingBasedClassify(labels_with_examples)\n",
+ "\n",
+ "tracer = InMemoryTracer()\n",
+ "result = classify.run(classify_input, tracer)\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nice, we now correctly classify this example!\n",
+ "\n",
+ "One advantage of using the `EmbeddingBasedClassify`-approach is that we can easily tweak our labels by adding new examples.\n",
+ "In essence, this guarantees that we never make the same mistake twice.\n",
+ "As we increase the number of examples, this makes the method evermore precise.\n",
+ "\n",
+ "You now have an overview of these two main methods of classification!\n",
+ "Feel free to tweak these method and play around with their parameters to finetune them to our specific use-case."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/data/classify_examples.json b/src/documentation/data/classify_examples.json
new file mode 100644
index 000000000..cd2be7297
--- /dev/null
+++ b/src/documentation/data/classify_examples.json
@@ -0,0 +1,98 @@
+[
+ {
+ "label": "Finance",
+ "message": "I just traveled to Paris for a conference, where can I get the train ride refunded?"
+ },
+ {
+ "label": "Sales",
+ "message": "Hello, we would like to get in contact with your sales team, because we are interested in your solution."
+ },
+ {
+ "label": "Communications",
+ "message": "We are working on a documentation on AI and would like to film a piece about you. Would you be interested?"
+ },
+ {
+ "label": "Research",
+ "message": "I am working with Stanford and was hoping to win you over for a research collaboration."
+ },
+ {
+ "label": "IT Support",
+ "message": "My laptop is broken"
+ },
+ {
+ "label": "Communications",
+ "message": "Can you send your models via email?"
+ },
+ {
+ "label": "Research",
+ "message": "We should do a research collaboration."
+ },
+ {
+ "label": "Research",
+ "message": "My company has been working on time series and signal processing for a long time. It would make sense to define a joint go to market and research strategy."
+ },
+ {
+ "label": "Human Resources",
+ "message": "Full stack developer in your area available now."
+ },
+ {
+ "label": "Product",
+ "message": "Hi,\n\nI recently bought your offering. I am having trouble running your docker container in my environment. It fails to start. Can you help?"
+ },
+ {
+ "label": "Product",
+ "message": "Hello,\n\nI am getting strange errors from your API. It is saying the queue is full, but I am only sending one task at a time. Why is this happening?"
+ },
+ {
+ "label": "Product",
+ "message": "Can you show me a demo of different use cases your offering can solve?"
+ },
+ {
+ "label": "Human Resources",
+ "message": "Hey, I did not get a t-shirt in the onboarding. Could I still get one?"
+ },
+ {
+ "label": "Customer",
+ "message": "Hi, can you name me a couple of timeslots for a first call? Would be really interested in learning more about the product?"
+ },
+ {
+ "label": "Product",
+ "message": "Hi Jan, is your product ISO 37301 compliant?"
+ },
+ {
+ "label": "IT Support",
+ "message": "I can\u2019t login to Mattermost or Sharepoint, how can I gain access?"
+ },
+ {
+ "label": "Finance",
+ "message": "I did not get paid last month, when do I get paid? What is going on?"
+ },
+ {
+ "label": "Security",
+ "message": "Hi, I want to get a new badge, the photo of me looks ugly and I just got new glasses so it does not look like me. "
+ },
+ {
+ "label": "Marketing",
+ "message": "I have a question concerning your marketing strategy, would you have time to hop on a call?"
+ },
+ {
+ "label": "CEO Office",
+ "message": "Dear Jonas Andrulis,\n\nWe have met each other at the event in N\u00fcrnberg, can we meet for a follow up in your Office in Heidelberg?"
+ },
+ {
+ "label": "Security",
+ "message": "Your hTTPs Certificate is not valid on your www.aleph-alpha.de"
+ },
+ {
+ "label": "Human Resources",
+ "message": "I want to take a week off immediately"
+ },
+ {
+ "label": "Human Resources",
+ "message": "I want to take a sabbatical"
+ },
+ {
+ "label": "Human Resources",
+ "message": "How can I work more, I want to work weekends, can I get paid overtime?"
+ }
+]
diff --git a/src/documentation/document_index.ipynb b/src/documentation/document_index.ipynb
new file mode 100644
index 000000000..e3ea71c9b
--- /dev/null
+++ b/src/documentation/document_index.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from os import getenv\n",
+ "\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "from intelligence_layer.connectors import (\n",
+ " CollectionPath,\n",
+ " DocumentContents,\n",
+ " DocumentIndexClient,\n",
+ " DocumentIndexRetriever,\n",
+ " DocumentPath,\n",
+ " LimitedConcurrencyClient,\n",
+ ")\n",
+ "from intelligence_layer.core import InMemoryTracer\n",
+ "from intelligence_layer.examples import RetrieverBasedQa, RetrieverBasedQaInput\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Document Index\n",
+ "\n",
+ "Answering questions given a known text may not be sufficient for your use case.\n",
+ "At some point, you will probably want to search through, or answer questions about, your own knowledge base.\n",
+ "You can leverage Aleph Alpha's DocumentIndex (DI) – a robust semantic search tool – to pinpoint sections in documents that align closely with your query.\n",
+ "Simply upload your knowledge base, internal documents, manuals, SharePoint, and more to the DI for efficient searching.\n",
+ "\n",
+ "The DI splits documents into smaller parts called 'chunks', generates semantic embedding vectors for each, and stores them alongside each other.\n",
+ "To find segments that closely match your query, the system identifies chunks with embedding vectors that best align semantically with your question.\n",
+ "The DI seamlessly manages document updates (using document names), determines the ideal chunk size, and optimizes the vector space search process.\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "In this notebook, we will show you how to upload your own documents to the DI, how to search through your documents, and how to build a question-answering system based on your DI-knowledge base.\n",
+ "To make proper use of the search and question-answering task, it is necessary to evaluate the results in an iterative way, to ensure it satisfies your requirements.\n",
+ "For an example of how such an evaluation can look like, refer to [evaluation.ipynb](./evaluation.ipynb).\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Upload documents to the Document Index\n",
+ "\n",
+ "To search through the DI, you'll first need to upload the documents to it.\n",
+ "For now, we'll use the DI instance stored in the Aleph Alpha cloud.\n",
+ "We assume you have an assigned namespace and possess a token to access it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# specify this for your own namespace\n",
+ "NAMESPACE = \"aleph-alpha\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "document_index = DocumentIndexClient(\n",
+ " token=getenv(\"AA_TOKEN\"),\n",
+ " base_document_index_url=\"https://document-index.aleph-alpha.com\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, to start working with the DI, you need to execute four simple steps:\n",
+ "\n",
+ "1. Create a collection.\n",
+ "2. Define a few documents we will put in our collection.\n",
+ "3. Upload the documents to the collection.\n",
+ "4. Verify whether the documents are successfully uploaded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# change this value if you want to use a collection of a different name\n",
+ "COLLECTION = \"demo\"\n",
+ "\n",
+ "collection_path = CollectionPath(namespace=NAMESPACE, collection=COLLECTION)\n",
+ "\n",
+ "document_index.create_collection(collection_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define three documents based on Wikipedia articles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# \"https://en.wikipedia.org/wiki/Robert_Moses\"\n",
+ "document_1 = {\n",
+ " \"name\": \"robert_moses\",\n",
+ " \"content\": \"\"\"Robert Moses''' (December 18, 1888 – July 29, 1981) was an American [[urban planner]] and public official who worked in the [[New York metropolitan area]] during the early to mid 20th century. Despite never being elected to any office, Moses is regarded as one of the most powerful and influential individuals in the history of New York City and New York State. The grand scale of his infrastructural projects and his philosophy of urban development influenced a generation of engineers, architects, and urban planners across the United States.\n",
+ "\n",
+ "Moses held various positions throughout his more than forty-year long career. He at times held up to 12 titles simultaneously, including [[New York City Parks Commissioner]] and chairman of the [[Long Island State Park Commission]].{{Cite web|url=https://www.pbs.org/wnet/need-to-know/environment/the-legacy-of-robert-moses/16018/|title=The legacy of Robert Moses|last=Sarachan|first=Sydney|date=January 17, 2013|website=Need to Know {{!}} PBS|language=en-US|access-date=December 3, 2019}} Having worked closely with New York governor [[Al Smith]] early in his career, Moses became expert in writing laws and navigating and manipulating the inner workings of state government. He created and led numerous semi-autonomous [[Public authority|public authorities]], through which he controlled millions of dollars in revenue and directly issued [[Bond (finance)|bonds]] to fund new ventures with little outside input or oversight.\n",
+ "\n",
+ "Moses's projects transformed the New York area and revolutionized the way cities in the U.S. were designed and built. As Long Island State Park Commissioner, Moses oversaw the construction of [[Jones Beach State Park]], the most visited public beach in the United States,{{cite news |url=http://www.longislandexchange.com/jones-beach.html |website=Long Island Exchange |title=Jones Beach |access-date=November 21, 2012 |archive-url=https://web.archive.org/web/20130121130008/http://www.longislandexchange.com/jones-beach.html |archive-date=January 21, 2013 |url-status=dead }} and was the primary architect of the [[Parkways in New York|New York State Parkway System]]. As head of the [[MTA Bridges and Tunnels|Triborough Bridge Authority]], Moses had near-complete control over bridges and tunnels in New York City as well as the tolls collected from them, and built, among others, the [[Robert F. Kennedy Bridge|Triborough Bridge]], the [[Brooklyn–Battery Tunnel]], and the [[Throgs Neck Bridge]], as well as several major highways. These roadways and bridges, alongside [[urban renewal]] efforts that saw the destruction of huge swaths of tenement housing and their replacement with large [[New York City Housing Authority|public housing projects]], transformed the physical fabric of New York and inspired other cities to undertake similar development endeavors.\n",
+ "\n",
+ "Moses's reputation declined following the publication of [[Robert Caro]]'s [[Pulitzer Prize]]-winning biography ''[[The Power Broker]]'' (1974), which cast doubt on the purported benefits of many of Moses's projects and further cast Moses as racist. In large part because of ''The Power Broker'', Moses is today considered a controversial figure in the history of New York City.\n",
+ "\n",
+ "==Early life and career==\n",
+ "Moses was born in [[New Haven, Connecticut]], on December 18, 1888, to [[German Jewish]] parents, Bella (Silverman) and Emanuel Moses.{{cite news | url=https://www.nytimes.com/learning/general/onthisday/bday/1218.html | title=Robert Moses, Master Builder, is Dead at 92| newspaper=The New York Times |archive-url=https://web.archive.org/web/20160305003155/https://www.nytimes.com/learning/general/onthisday/bday/1218.html |archive-date=March 5, 2016 |url-status=dead}}{{sfn|Caro|1974|p=25}} He spent the first nine years of his life living at 83 Dwight Street in New Haven, two blocks from [[Yale University]]. In 1897, the Moses family moved to New York City,{{sfn|Caro|1974|pp=29}} where they lived on East 46th Street off Fifth Avenue.{{cite web |url=http://www.newsday.com/community/guide/lihistory/ny-history-hs722a,0,7092161.story |title=The Master Builder |access-date=April 4, 2007 |last=DeWan |first=George |year=2007 |website=Long Island History |publisher=Newsday |archive-url=https://web.archive.org/web/20061211045554/http://www.newsday.com/community/guide/lihistory/ny-history-hs722a%2C0%2C7092161.story |archive-date=December 11, 2006 |url-status=dead }} Moses's father was a successful department store owner and [[real estate]] speculator in New Haven. In order for the family to move to New York City, he sold his real estate holdings and store, then retired.{{sfn|Caro|1974|pp=29}} Moses's mother was active in the [[settlement movement]], with her own love of building. Robert Moses and his brother Paul attended several schools for their elementary and [[secondary education]], including the [[Ethical Culture Fieldston School|Ethical Culture School]], the [[Dwight School]] and the [[Mohegan Lake, New York#Historic places|Mohegan Lake School]], a military academy near [[Peekskill, New York|Peekskill]].{{sfn|Caro|1974|pp=35}}\n",
+ "\n",
+ "After graduating from [[Yale College]] (B.A., 1909) and [[Wadham College]], [[Oxford University|Oxford]] (B.A., Jurisprudence, 1911; M.A., 1913), and earning a Ph.D. in [[political science]] from [[Columbia University]] in 1914, Moses became attracted to New York City reform politics.{{Cite web|url=http://c250.columbia.edu/c250_celebrates/remarkable_columbians/robert_moses.html|title = Robert Moses}} A committed [[idealism|idealist]], he developed several plans to rid New York of [[Patronage#Politics|patronage hiring]] practices, including being the lead author of a 1919 proposal to reorganize the New York state government. None went very far, but Moses, due to his intelligence, caught the notice of [[Belle Moskowitz]], a friend and trusted advisor to Governor [[Al Smith]].{{sfn|Caro|1974}} When the state [[Secretary of State of New York|Secretary of State's]] position became appointive rather than elective, Smith named Moses. He served from 1927 to 1929.{{cite news |date=December 19, 1928 |title=Moses Resigns State Position |url=http://cdsun.library.cornell.edu/cgi-bin/cornell?a=d&d=CDS19281219.2.63.7# |newspaper=Cornell Daily Sun |location=Ithaca, NY |page=8}}\n",
+ "\n",
+ "Moses rose to power with Smith, who was elected as governor in 1918, and then again in 1922. With Smith's support, Moses set in motion a sweeping consolidation of the New York State government. During that period Moses began his first foray into large-scale public work initiatives, while drawing on Smith's political power to enact legislation. This helped create the new [[Long Island State Park Commission]] and the State Council of Parks.{{cite web|last=Gutfreund|first=Owen|title=Moses, Robert|url=http://www.anb.org/articles/07/07-00375.html|publisher=Anb.org|access-date=December 24, 2014}} In 1924, Governor Smith appointed Moses chairman of the State Council of Parks and president of the Long Island State Park Commission.{{Cite book|title=Encyclopedia of the City|url=https://archive.org/details/encyclopediacity00cave|url-access=limited|last=Caves|first=R. W.|publisher=Routledge|year=2004|isbn=978-0-415-25225-6|pages=[https://archive.org/details/encyclopediacity00cave/page/n512 472]}} This centralization allowed Smith to run a government later used as a model for Franklin D. Roosevelt's [[New Deal]] federal government.{{or|date=October 2022}} Moses also received numerous commissions that he carried out efficiently, such as the development of [[Jones Beach State Park]].{{cn|date=October 2022}} Displaying a strong command of [[law]] as well as matters of [[engineering]], Moses became known for his skill in drafting legislation, and was called \"the best bill drafter in [[Albany, New York|Albany]]\".{{cite news |title=Annals of Power |first=Robert A. |last=Caro |author-link=Robert Caro |url=http://archives.newyorker.com/?i=1974-07-22#folio=032 |magazine=[[The New Yorker]] |date=July 22, 1974 |access-date=September 1, 2011}} At a time when the public was accustomed to [[Tammany Hall]] corruption and incompetence, Moses was seen as a savior of government.{{sfn|Caro|1974}}\n",
+ "\n",
+ "Shortly after [[President of the United States|President]] [[Franklin Delano Roosevelt|Franklin D. Roosevelt's]] [[First inauguration of Franklin D. Roosevelt|inauguration]] in 1933, the [[United States federal government|federal government]] found itself with millions of [[New Deal]] dollars to spend, yet states and cities had few projects ready. Moses was one of the few local officials who had projects [[shovel ready]]. For that reason, New York City was able to obtain significant [[Works Progress Administration]] (WPA), [[Civilian Conservation Corps]] (CCC), and other Depression-era funding. One of his most influential and longest-lasting positions was that of Parks Commissioner of New York City, a role he served from January 18, 1934, to May 23, 1960.{{Cite web|url=https://www.nycgovparks.org/about/history/commissioners|title=New York City Parks Commissioners : NYC Parks|website=www.nycgovparks.org|language=en|access-date=March 29, 2018}}\n",
+ "\n",
+ "==Offices held==\n",
+ "The many offices and professional titles that Moses held gave him unusually broad power to shape urban development in the New York metropolitan region. These include, according to the New York Preservation Archive Project:{{Cite web|url=http://www.nypap.org/preservation-history/robert-moses/|title=Robert Moses {{!}}|website=www.nypap.org|language=en-US|access-date=March 29, 2018}}\n",
+ "*[[Long Island State Park Commission]] (President, 1924–1963)\n",
+ "* New York State Council of Parks (Chairman, 1924–1963)\n",
+ "*[[Secretary of State of New York|New York Secretary of State]] (1927–1928)\n",
+ "* Bethpage State Park Authority (President, 1933–1963)\n",
+ "* Emergency Public Works Commission (Chairman, 1933–1934)\n",
+ "* Jones Beach Parkway Authority (President, 1933–1963)\n",
+ "*[[New York City Department of Parks and Recreation|New York City Department of Parks]] (Commissioner, 1934–1960)\n",
+ "* [[Triborough Bridge]] and Tunnel Authority (Chairman, 1934–1968)\n",
+ "* New York City Planning Commission (Commissioner, 1942–1960)\n",
+ "* New York State Power Authority (Chairman, 1954–1962)\n",
+ "* [[1964 New York World's Fair|New York's World Fair]] (President, 1960–1966)\n",
+ "* Office of the Governor of New York (Special Advisor on Housing, 1974–1975)\n",
+ "\n",
+ "==Influence==\n",
+ "During the 1920s, Moses sparred with [[Franklin D. Roosevelt]], then head of the Taconic State Park Commission, who favored the prompt construction of a [[parkway]] through the [[Hudson Valley]]. Moses succeeded in diverting funds to his Long Island parkway projects (the [[Northern State Parkway]], the [[Southern State Parkway]] and the [[Wantagh State Parkway]]), although the [[Taconic State Parkway]] was later completed as well.{{cite web|url=http://www.nycroads.com/roads/taconic/ |title=Taconic State Parkway |website=NYCRoads.com |access-date=May 25, 2006}} Moses helped build Long Island's [[Meadowbrook State Parkway]]. It was the first fully divided limited access highway in the world.{{cite book|last=Leonard|first=Wallock|title=The Myth of The Master Builder|year=1991|publisher=Journal of Urban History|page=339}}\n",
+ "\n",
+ "Moses was a highly influential figure in the initiation of many of the reforms that restructured New York state's government during the 1920s. A 'Reconstruction Commission' headed by Moses produced a highly influential report that provided recommendations that would largely be adopted, including the consolidation of 187 existing agencies under 18 departments, a new executive budget system, and the four-year term limit for the governorship.{{sfn|Caro|1974|pp=106, 260}}\"\"\",\n",
+ "}\n",
+ "\n",
+ "# \"https://en.wikipedia.org/wiki/Jane_Jacobs\"\n",
+ "document_2 = {\n",
+ " \"name\": \"jane_jacobs\",\n",
+ " \"content\": \"\"\"Jane Jacobs OC OOnt (née Butzner; 4 May 1916 – 25 April 2006) was an American-Canadian journalist, author, theorist, and activist who influenced urban studies, sociology, and economics. Her book The Death and Life of Great American Cities (1961) argued that \"urban renewal\" and \"slum clearance\" did not respect the needs of city-dwellers.[1][2]\n",
+ "\n",
+ "Jacobs organized grassroots efforts to protect neighborhoods from urban renewal and slum clearance – in particular plans by Robert Moses to overhaul her own Greenwich Village neighborhood. She was instrumental in the eventual cancellation of the Lower Manhattan Expressway,[3] which would have passed directly through the area of Manhattan that would later become known as SoHo, as well as part of Little Italy and Chinatown.[4] She was arrested in 1968 for inciting a crowd at a public hearing on that project.[5] After moving to Toronto in 1968, she joined the opposition to the Spadina Expressway and the associated network of expressways in Toronto that were planned and under construction.[6][7]\n",
+ "\n",
+ "As a woman and a writer who criticized experts in the male-dominated field of urban planning,[8][9] Jacobs endured scorn from established figures.[who?] Routinely, she was described first as a housewife,[10] as she did not have a college degree or any formal training in urban planning; as a result, her lack of credentials was seized upon as grounds for criticism.[11][12] However, the influence of her concepts eventually was acknowledged by highly respected professionals such as Richard Florida and Robert Lucas.[13] \"\"\",\n",
+ "}\n",
+ "\n",
+ "# \"https://en.wikipedia.org/wiki/Nelson_Rockefeller\"\n",
+ "document_3 = {\n",
+ " \"name\": \"nelson_rockefeller\",\n",
+ " \"content\": \"\"\"Nelson Aldrich Rockefeller (July 8, 1908 – January 26, 1979), sometimes referred to by his nickname Rocky,[1] was an American businessman and politician who served as the 41st vice president of the United States from 1974 to 1977 under President Gerald Ford. A member of the Republican Party and the wealthy Rockefeller family, he previously served as the 49th governor of New York from 1959 to 1973. Rockefeller also served as assistant secretary of State for American Republic Affairs for Presidents Franklin D. Roosevelt and Harry S. Truman (1944–1945) as well as under secretary of Health, Education and Welfare (HEW) under Dwight D. Eisenhower from 1953 to 1954. A son of John D. Rockefeller Jr. and Abby Aldrich Rockefeller as well as a grandson of Standard Oil co-founder John D. Rockefeller, he was a noted art collector and served as administrator of Rockefeller Center in Manhattan, New York City.\n",
+ "\n",
+ "Rockefeller was often considered to be liberal, progressive,[2] or moderate. In an agreement that was termed the Treaty of Fifth Avenue, he persuaded Richard Nixon to alter the Republican Party platform just before the 1960 Republican Convention. In his time, liberals in the Republican Party were called \"Rockefeller Republicans\". As Governor of New York from 1959 to 1973, Rockefeller's achievements included the expansion of the State University of New York (SUNY), efforts to protect the environment, the construction of the Empire State Plaza in Albany, increased facilities and personnel for medical care, and the creation of the New York State Council on the Arts.\n",
+ "\n",
+ "After unsuccessfully seeking the Republican presidential nomination in 1960, 1964, and 1968, he was appointed vice president of the United States under President Gerald Ford, who was appointed Vice President by President Richard Nixon after the resignation of Spiro Agnew, and who ascended to the presidency following Nixon's August 1974 resignation. Rockefeller was the second vice president appointed to the position under the 25th Amendment, following Ford himself. Rockefeller did not seek a full term on the 1976 Republican ticket with Ford. He retired from politics in 1977 and died two years later.\n",
+ "\n",
+ "As a businessman, Rockefeller was president and later chair of Rockefeller Center, Inc., and he formed the International Basic Economy Corporation in 1947. Rockefeller assembled a significant art collection and promoted public access to the arts. He served as trustee, treasurer, and president of the Museum of Modern Art and founded the Museum of Primitive Art in 1954. In the area of philanthropy, he founded the Rockefeller Brothers Fund in 1940 with his four brothers and established the American International Association for Economic and Social Development in 1946. \"\"\",\n",
+ "}\n",
+ "\n",
+ "documents = [document_1, document_2, document_3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's upload the documents:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for doc in documents:\n",
+ " document_path = DocumentPath(\n",
+ " collection_path=collection_path, document_name=doc[\"name\"]\n",
+ " )\n",
+ " document_index.add_document(\n",
+ " document_path, contents=DocumentContents.from_text(doc[\"content\"])\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And let's check if the documents are in the collection:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "document_index.documents(collection_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Search\n",
+ "\n",
+ "Now that we have uploaded our documents, we can search through them using the semantic similarities between a given query and each chunk.\n",
+ "\n",
+ "To do so, let's use the `DocumentIndexRetriever`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "document_index_retriever = DocumentIndexRetriever(\n",
+ " document_index=document_index,\n",
+ " index_name=\"asymmetric\",\n",
+ " namespace=NAMESPACE,\n",
+ " collection=COLLECTION,\n",
+ " k=5,\n",
+ " threshold=0.5,\n",
+ ")\n",
+ "\n",
+ "document_index_retriever.get_relevant_documents_with_scores(\n",
+ " query=\"The influence of Robert Moses\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Question Answering\n",
+ "\n",
+ "Finally, since we have a ready-to use 'Retriever', we can employ it to do something more complicated than just search; it can serve as the basis for a question-answering system.\n",
+ "\n",
+ "To do so, let's run a `RetrieverBasedQa` task:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client = LimitedConcurrencyClient.from_env()\n",
+ "retriever_qa = RetrieverBasedQa(document_index_retriever)\n",
+ "\n",
+ "\n",
+ "input = RetrieverBasedQaInput(\n",
+ " question=\"What is the name of the book about Robert Moses?\"\n",
+ ")\n",
+ "tracer = InMemoryTracer()\n",
+ "\n",
+ "output = retriever_qa.run(input, tracer)\n",
+ "\n",
+ "output.answer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want to learn how the answer was produced, you can see it step-by-step in the `tracer`.\n",
+ "Here, we record the input and output of every individual step up to the final answer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tracer"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/evaluation.ipynb b/src/documentation/evaluation.ipynb
new file mode 100644
index 000000000..75eda7620
--- /dev/null
+++ b/src/documentation/evaluation.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from collections import defaultdict\n",
+ "from typing import Any, Mapping, Sequence\n",
+ "\n",
+ "from datasets import load_dataset\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "from intelligence_layer.connectors import LimitedConcurrencyClient\n",
+ "from intelligence_layer.core import NoOpTracer, TextChunk\n",
+ "from intelligence_layer.evaluation import (\n",
+ " Aggregator,\n",
+ " Evaluator,\n",
+ " Example,\n",
+ " InMemoryAggregationRepository,\n",
+ " InMemoryDatasetRepository,\n",
+ " InMemoryEvaluationRepository,\n",
+ " InMemoryRunRepository,\n",
+ " RepositoryNavigator,\n",
+ " Runner,\n",
+ " evaluation_lineages_to_pandas,\n",
+ ")\n",
+ "from intelligence_layer.examples import (\n",
+ " ClassifyInput,\n",
+ " EmbeddingBasedClassify,\n",
+ " LabelWithExamples,\n",
+ " MultiLabelClassifyAggregationLogic,\n",
+ " MultiLabelClassifyEvaluationLogic,\n",
+ " PromptBasedClassify,\n",
+ " SingleLabelClassifyAggregationLogic,\n",
+ " SingleLabelClassifyEvaluation,\n",
+ " SingleLabelClassifyEvaluationLogic,\n",
+ " SingleLabelClassifyOutput,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Evaluating LLM-based tasks\n",
+ "\n",
+ "Evaluating LLM-based use cases is pivotal for several reasons.\n",
+ "First, with the myriad of methods available, comparability becomes essential.\n",
+ "By systematically evaluating different approaches, we can discern which techniques are more effective or suited for specific tasks, fostering a deeper understanding of their strengths and weaknesses.\n",
+ "Secondly, optimization plays a significant role. Without proper evaluation metrics and rigorous testing, it becomes challenging to fine-tune methods and/or models to achieve their maximum potential.\n",
+ "Moreover, drawing comparisons with state-of-the-art (SOTA) and open-source methods is crucial.\n",
+ "Such comparisons not only provide benchmarks but also enable users to determine the value-added by proprietary or newer models over freely available counterparts.\n",
+ "\n",
+ "However, evaluating LLMs, especially in the domain of text generation, presents unique challenges.\n",
+ "Text generation is inherently subjective, and what one evaluator deems coherent and relevant, another might find disjointed or off-topic. This subjectivity complicates the establishment of universal evaluation standards, making it imperative to approach LLM evaluation with a multifaceted and comprehensive strategy.\n",
+ "\n",
+ "### Evaluating classification use-cases\n",
+ "\n",
+ "To (at least for now) evade the elusive issue described in the last paragraph, let's have a look at an easier to evaluate methodology: classification.\n",
+ "Why is this easier?\n",
+ "Well, unlike other tasks such as QA, the result of a classification task is more or less binary (true/false).\n",
+ "There are very few grey areas, as it is unlikely that a classification result is somewhat or \"half\" correct.\n",
+ "\n",
+ "Make sure that you have familiarized yourself with the [PromptBasedClassify](classification.ipynb#prompt-based-single-label-classification) prior to starting this notebook.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we need to instantiate our task, as well as, a runner, an evaluator and an aggregator for it. Furthermore, we need the corresponding repositories that store the results of each step along with tracing information.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "task = PromptBasedClassify()\n",
+ "dataset_repository = InMemoryDatasetRepository()\n",
+ "run_repository = InMemoryRunRepository()\n",
+ "evaluation_repository = InMemoryEvaluationRepository()\n",
+ "evaluation_logic = SingleLabelClassifyEvaluationLogic()\n",
+ "aggregation_repository = InMemoryAggregationRepository()\n",
+ "aggregation_logic = SingleLabelClassifyAggregationLogic()\n",
+ "\n",
+ "\n",
+ "runner = Runner(task, dataset_repository, run_repository, \"prompt-based-classify\")\n",
+ "evaluator = Evaluator(\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " evaluation_repository,\n",
+ " \"single-label-classify\",\n",
+ " evaluation_logic,\n",
+ ")\n",
+ "aggregator = Aggregator(\n",
+ " evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"single-label-classify\",\n",
+ " aggregation_logic,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's run a single example and see what comes of it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classify_input = ClassifyInput(\n",
+ " chunk=TextChunk(\"This is good\"),\n",
+ " labels=frozenset({\"positive\", \"negative\"}),\n",
+ ")\n",
+ "\n",
+ "single_example_dataset = dataset_repository.create_dataset(\n",
+ " examples=[Example(input=classify_input, expected_output=\"positive\")],\n",
+ " dataset_name=\"ClassifyDataset\",\n",
+ ")\n",
+ "\n",
+ "run_overview = runner.run_dataset(single_example_dataset.id, NoOpTracer())\n",
+ "evaluation_overview = evaluator.evaluate_runs(run_overview.id)\n",
+ "aggregation_overview = aggregator.aggregate_evaluation(evaluation_overview.id)\n",
+ "\n",
+ "print(\"Statistics: \", aggregation_overview.statistics)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Perfect! The example was classified correctly.\n",
+ "\n",
+ "Next, we will have a look at this pre-defined [dataset of tweets](https://huggingface.co/cardiffnlp/tweet-topic-21-multi) for more elaborate evaluation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset = load_dataset(\"cardiffnlp/tweet_topic_multi\")\n",
+ "test_set_name = \"validation_random\"\n",
+ "all_data = list(dataset[test_set_name])\n",
+ "data = all_data[:25] # this has 573 datapoints, let's take a look at 25 for now"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need to transform our dataset into the required format. \n",
+ "Therefore, let's check out what it looks like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accordingly, this must be translated into the interface of our `Evaluator`.\n",
+ "\n",
+ "This is the target structure:\n",
+ "\n",
+ "``` python\n",
+ "class Example(BaseModel, Generic[Input, ExpectedOutput]):\n",
+ " input: Input\n",
+ " expected_output: ExpectedOutput\n",
+ " id: Optional[str] = Field(default_factory=lambda: str(uuid4()))\n",
+ "\n",
+ "```\n",
+ "\n",
+ "We want the `input` in each `Example` to mimic the input of an actual task. Therefore, we have to always include the text (chunk) and all possible labels.\n",
+ "The `expected_output` shall correspond to anything we wish to compare our generated output to.\n",
+ "In this case, that means the correct class(es), i.e., the label name(s)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_labels = list(set(label_name for item in data for label_name in item[\"label_name\"]))\n",
+ "dataset = dataset_repository.create_dataset(\n",
+ " examples=[\n",
+ " Example(\n",
+ " input=ClassifyInput(chunk=TextChunk(item[\"text\"]), labels=all_labels),\n",
+ " expected_output=item[\"label_name\"][0],\n",
+ " )\n",
+ " for item in data\n",
+ " ],\n",
+ " dataset_name=\"tweet_topic_multi\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ok, let's run this!\n",
+ "\n",
+ "Note that this may take a while as we parallelise the tasks in a way that accommodates the inference API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "run_overview = runner.run_dataset(dataset.id)\n",
+ "evaluation_overview = evaluator.evaluate_runs(run_overview.id)\n",
+ "aggregation_overview = aggregator.aggregate_evaluation(evaluation_overview.id)\n",
+ "aggregation_overview.raise_on_evaluation_failure()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Checking out the results..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluation_overview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Percentage correct:\", aggregation_overview.statistics.percentage_correct)\n",
+ "\n",
+ "# You can also use evaluator.evaluation_lineages for an easier use, but that only works if the evaluator is still in memory.\n",
+ "navigator = RepositoryNavigator(\n",
+ " dataset_repository=dataset_repository,\n",
+ " run_repository=run_repository,\n",
+ " evaluation_repository=evaluation_repository,\n",
+ ")\n",
+ "lineages = navigator.evaluation_lineages(\n",
+ " next(iter(aggregation_overview.evaluation_overviews)).id,\n",
+ " input_type=ClassifyInput,\n",
+ " expected_output_type=SingleLabelClassifyOutput,\n",
+ " output_type=Sequence[str],\n",
+ " evaluation_type=SingleLabelClassifyEvaluation,\n",
+ ")\n",
+ "evaluation_lineages_to_pandas(lineages).head(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As an alternative to the `PromptBasedClassify` we now gonne use the `EmbeddingBasedClassify` for multi label classifications.\n",
+ "In this case, we have to provide some example for each class.\n",
+ "\n",
+ "We can even reuse our data repositories:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_labels_and_examples(hf_data: Any) -> Mapping[str, Sequence[str]]:\n",
+ " examples = defaultdict(list)\n",
+ " for item in hf_data:\n",
+ " labels = item[\"label_name\"]\n",
+ " for label in labels:\n",
+ " if len(examples[label]) < 20:\n",
+ " examples[label].append(item[\"text\"])\n",
+ " return examples\n",
+ "\n",
+ "\n",
+ "client = LimitedConcurrencyClient.from_env()\n",
+ "embedding_based_classify = EmbeddingBasedClassify(\n",
+ " client=client,\n",
+ " labels_with_examples=[\n",
+ " LabelWithExamples(name=name, examples=examples)\n",
+ " for name, examples in build_labels_and_examples(all_data[25:]).items()\n",
+ " ],\n",
+ ")\n",
+ "eval_logic = MultiLabelClassifyEvaluationLogic(threshold=0.60)\n",
+ "aggregation_logic = MultiLabelClassifyAggregationLogic()\n",
+ "\n",
+ "embedding_based_classify_runner = Runner(\n",
+ " embedding_based_classify,\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " \"embedding-based-classify\",\n",
+ ")\n",
+ "embedding_based_classify_evaluator = Evaluator(\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " evaluation_repository,\n",
+ " \"multi-label-classify\",\n",
+ " eval_logic,\n",
+ ")\n",
+ "embedding_based_classify_aggregator = Aggregator(\n",
+ " evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"multi-label-classify\",\n",
+ " aggregation_logic,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "embedding_based_classify_run_result = embedding_based_classify_runner.run_dataset(\n",
+ " dataset.id\n",
+ ")\n",
+ "embedding_based_classify_evaluation_result = (\n",
+ " embedding_based_classify_evaluator.evaluate_runs(\n",
+ " embedding_based_classify_run_result.id\n",
+ " )\n",
+ ")\n",
+ "embedding_based_classify_aggregation_result = (\n",
+ " embedding_based_classify_aggregator.aggregate_evaluation(\n",
+ " embedding_based_classify_evaluation_result.id\n",
+ " )\n",
+ ")\n",
+ "embedding_based_classify_aggregation_result.raise_on_evaluation_failure()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "embedding_based_classify_aggregation_result.statistics.macro_avg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Apparently, our method has a great recall value, i.e. all relevant labels are retrieved. However, the low precision value indicates that we tend to falsely predict labels at times.\n",
+ "\n",
+ "Note, that the evaluation criteria for the multiple label approach are a lot harsher; we evaluate whether we correctly predict all labels & not just one of the correct ones!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Wrap up\n",
+ "\n",
+ "There you go, this is how to evaluate any task using the 'Intelligence Layer'-framework.\n",
+ "Simply define an `Evaluator` that takes the target `Task` as input and customize the `do_evaluate` as well as `aggregate` methods."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/fastapi_example.py b/src/documentation/fastapi_example.py
new file mode 100644
index 000000000..16563dafe
--- /dev/null
+++ b/src/documentation/fastapi_example.py
@@ -0,0 +1,94 @@
+import http
+import os
+from http import HTTPStatus
+from typing import Annotated, Sequence
+
+from aleph_alpha_client import Client
+from dotenv import load_dotenv
+from fastapi import Depends, FastAPI, HTTPException, Request, Response
+from fastapi.datastructures import URL
+
+from intelligence_layer.connectors import AlephAlphaClientProtocol
+from intelligence_layer.core import LuminousControlModel, NoOpTracer, Task
+from intelligence_layer.examples import (
+ SingleChunkSummarizeInput,
+ SteerableSingleChunkSummarize,
+ SummarizeOutput,
+)
+
+# Minimal FastAPI app ##########################################################
+
+app = FastAPI()
+
+
+@app.get("/")
+def root() -> Response:
+ return Response(content="Hello World", status_code=HTTPStatus.OK)
+
+
+# Authentication ###############################################################
+
+
+class AuthService:
+ def is_valid_token(self, token: str, permissions: Sequence[str], url: URL) -> bool:
+ # Add your authentication logic here
+ print(f"Checking permission for route: {url.path}")
+ return True
+
+
+class PermissionChecker:
+ def __init__(self, permissions: Sequence[str] = []):
+ self.permissions = permissions
+
+ def __call__(
+ self,
+ request: Request,
+ auth_service: Annotated[AuthService, Depends(AuthService)],
+ ) -> None:
+ token = request.headers.get("Authorization") or ""
+ try:
+ if not auth_service.is_valid_token(token, self.permissions, request.url):
+ raise HTTPException(HTTPStatus.UNAUTHORIZED)
+ except RuntimeError:
+ raise HTTPException(HTTPStatus.INTERNAL_SERVER_ERROR)
+
+
+permission_checker_for_user = PermissionChecker(["User"])
+
+
+# Intelligence Layer Task ######################################################
+
+load_dotenv()
+
+
+def client() -> Client:
+ return Client(
+ token=os.environ["AA_TOKEN"],
+ host=os.getenv("AA_CLIENT_BASE_URL", "https://api.aleph-alpha.com"),
+ )
+
+
+def default_model(
+ app_client: Annotated[AlephAlphaClientProtocol, Depends(client)],
+) -> LuminousControlModel:
+ return LuminousControlModel(client=app_client)
+
+
+def summary_task(
+ model: Annotated[LuminousControlModel, Depends(default_model)],
+) -> SteerableSingleChunkSummarize:
+ return SteerableSingleChunkSummarize(model)
+
+
+@app.post(
+ "/summary",
+ dependencies=[Depends(PermissionChecker(["User"]))],
+ status_code=http.HTTPStatus.OK,
+)
+def summary_task_route(
+ input: SingleChunkSummarizeInput,
+ task: Annotated[
+ Task[SingleChunkSummarizeInput, SummarizeOutput], Depends(summary_task)
+ ],
+) -> SummarizeOutput:
+ return task.run(input, NoOpTracer())
diff --git a/src/documentation/fastapi_tutorial.ipynb b/src/documentation/fastapi_tutorial.ipynb
new file mode 100644
index 000000000..0f5078f24
--- /dev/null
+++ b/src/documentation/fastapi_tutorial.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tutorial: Extending a FastAPI App with the Aleph-Alpha Intelligence Layer\n",
+ "\n",
+ "In this tutorial, a basic [FastAPI](https://fastapi.tiangolo.com) app is extended with a new route at which a summary for a given text can be retrieved, using the _Aleph-Alpha Intelligence Layer_, and it's _Luminous_ control models.\n",
+ "\n",
+ "The full source code for this example app can be found at the end of this tutorial and in [src/examples/fastapi_example.py](./fastapi_example.py).\n",
+ "\n",
+ "## Basic FastAPI App\n",
+ "\n",
+ "The foundation for this tutorial is a minimal [FastAPI](https://fastapi.tiangolo.com) application with a root endpoint:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from http import HTTPStatus\n",
+ "\n",
+ "from fastapi import FastAPI, Response\n",
+ "\n",
+ "app = FastAPI()\n",
+ "\n",
+ "\n",
+ "@app.get(\"/\")\n",
+ "def root() -> Response:\n",
+ " return Response(content=\"Hello World\", status_code=HTTPStatus.OK)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This application can be started from the command line with the [Hypercorn](https://github.com/pgjones/hypercorn/) server as follows:\n",
+ "\n",
+ "```bash\n",
+ "hypercorn fastapi_example:app --bind localhost:8000\n",
+ "```\n",
+ "\n",
+ "If the start-up was successful, you should see a message similar to\n",
+ "```cmd\n",
+ "[2024-03-07 14:00:55 +0100] [6468] [INFO] Running on http://:8000 (CTRL + C to quit)\n",
+ "```\n",
+ "\n",
+ "Now that the server is running, we can perform a `GET` request via `cURL`:\n",
+ "```bash\n",
+ "curl -X GET http://localhost:8000\n",
+ "```\n",
+ "You should get\n",
+ "```\n",
+ "Hello World\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After successfully starting the basic FastAPI app, the next step is to add a route that makes use of the Intelligence Layer.\n",
+ "\n",
+ "## Adding the Intelligence Layer to the application\n",
+ "\n",
+ "The building blocks of the Intelligence Layer for applications are `Tasks`. In general, a task implements the `Task`\n",
+ "interface and defines an `Input` and an `Output`. Multiple tasks can be chained to create more complex applications.\n",
+ "Here, we will make use of the pre-built task `SteerableSingleChunkSummarize`. This task defines `SingleChunkSummarizeInput`\n",
+ "as it's input, and `SummarizeOutput` as it's output.\n",
+ "Like many other tasks, the `SteerableSingleChunkSummarize` task makes use of a `ControlModel`. The\n",
+ "`ControlModel` itself needs access to the Aleph-Alpha backend via a `AlephAlphaClientProtocol` client.\n",
+ "In short, the hierarchy is as follows:\n",
+ "\n",
+ "\n",
+ "\n",
+ "We make use of the built-in [Dependency Injection](https://fastapi.tiangolo.com/reference/dependencies/) of FastAPI to\n",
+ "resolve this hierarchy automatically. In this framework, the defaults for the parameters are dynamically created with\n",
+ "the `Depends(func)` annotation, where `func` is a function that returns the default value.\n",
+ "\n",
+ "So, first, we define our client-generating function. For that, we provide the host URL and a valid Aleph-Alpha token,\n",
+ "which are stored in an `.env`-file.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "from aleph_alpha_client import Client\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "\n",
+ "def client() -> Client:\n",
+ " return Client(\n",
+ " token=os.environ[\"AA_TOKEN\"],\n",
+ " host=os.getenv(\"AA_CLIENT_BASE_URL\", \"https://api.aleph-alpha.com\"),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we create a `ControlModel`. In this case, we make use of the `LuminousControlModel`, which takes\n",
+ "an `AlephAlphaClientProtocol` that we let default to the previously defined `client`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Annotated\n",
+ "\n",
+ "from fastapi import Depends\n",
+ "\n",
+ "from intelligence_layer.connectors import AlephAlphaClientProtocol\n",
+ "from intelligence_layer.core import LuminousControlModel\n",
+ "\n",
+ "\n",
+ "def default_model(app_client: Annotated[AlephAlphaClientProtocol, Depends(client)]):\n",
+ " return LuminousControlModel(client=app_client)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we create the actual `Task`. For our example, we choose the `SteerableSingleChunkSummarize`.\n",
+ "The `Input` of this task is a `SingleChunkSummarizeInput`, consisting of the text to summarize as the `chunk` field,\n",
+ "and the desired `Language` as the `language` field.\n",
+ "The `Output` of this task is a `SummarizeOutput` and contains the `summary` as text,\n",
+ "and number of generated tokens for the `summary` as the `generated_tokens` field."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from intelligence_layer.examples import SteerableSingleChunkSummarize\n",
+ "\n",
+ "\n",
+ "def summary_task(\n",
+ " model: Annotated[LuminousControlModel, Depends(default_model)],\n",
+ ") -> SteerableSingleChunkSummarize:\n",
+ " return SteerableSingleChunkSummarize(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can then provide a `POST` endpoint on `/summary` to run the task.\n",
+ "The default for `task` will be set by `summary_task`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from intelligence_layer.core import NoOpTracer, Task\n",
+ "from intelligence_layer.examples import SingleChunkSummarizeInput, SummarizeOutput\n",
+ "\n",
+ "\n",
+ "@app.post(\"/summary\")\n",
+ "def summary_task_route_without_permissions(\n",
+ " input: SingleChunkSummarizeInput,\n",
+ " task: Annotated[\n",
+ " Task[SingleChunkSummarizeInput, SummarizeOutput], Depends(summary_task)\n",
+ " ],\n",
+ ") -> SummarizeOutput:\n",
+ " return task.run(input, NoOpTracer())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This concludes the addition of an Intelligence-Layer task to the FastAPI app. After restarting the server, we can call\n",
+ "our newly created endpoint via a command such as the following:\n",
+ "```bash\n",
+ "\n",
+ "curl -X POST http://localhost:8000/summary -H \"Content-Type: application/json\" -d '{\"chunk\": \"\", \"language\": {\"iso_639_1\": \"en\"}}'\n",
+ "```\n",
+ "\n",
+ "## Add Authorization to the Routes\n",
+ "\n",
+ "Typically, authorization is needed to control access to endpoints.\n",
+ "Here, we will give a minimal example of how a per-route authorization system could be implemented in the minimal example app.\n",
+ "\n",
+ "The authorization system makes use of two parts: An `AuthService` that checks whether the user is allowed to access a\n",
+ "given site, and a `PermissionsChecker` that is called on each route access and in turn calls the `AuthService`.\n",
+ "\n",
+ "For this minimal example, the `AuthService` is simply a stub. You will want to implement a concrete authorization service\n",
+ "tailored to your needs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Sequence\n",
+ "\n",
+ "from fastapi.datastructures import URL\n",
+ "\n",
+ "\n",
+ "class AuthService:\n",
+ " def is_valid_token(self, token: str, permissions: Sequence[str], url: URL):\n",
+ " # Add your authentication logic here\n",
+ " print(f\"Checking permission for route: {url.path}\")\n",
+ " return True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this `PermissionsChecker`, `permissions` can be passed in to define which roles, e.g. \"user\" or \"admin\",\n",
+ "are allowed to access which endpoints. The `PermissionsChecker` implements the `__call__` function, so that it can be\n",
+ "used as a function in the `dependencies` argument of each route via `Depends`. For more details see the extended\n",
+ "definition of the `summary_task_route` further below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastapi import HTTPException, Request\n",
+ "\n",
+ "\n",
+ "class PermissionChecker:\n",
+ " def __init__(self, permissions: Sequence[str] = []):\n",
+ " self.permissions = permissions\n",
+ "\n",
+ " def __call__(\n",
+ " self,\n",
+ " request: Request,\n",
+ " auth_service: Annotated[AuthService, Depends(AuthService)],\n",
+ " ) -> None:\n",
+ " token = request.headers.get(\"Authorization\")\n",
+ " try:\n",
+ " if not auth_service.is_valid_token(token, self.permissions, request.url):\n",
+ " raise HTTPException(HTTPStatus.UNAUTHORIZED)\n",
+ " except RuntimeError:\n",
+ " raise HTTPException(HTTPStatus.INTERNAL_SERVER_ERROR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A specific `PermissionChecker` with `\"User\"` permissions is created which will be called for the `/summary` route to check, whether a \"User\" is allowed to access it.\n",
+ "\n",
+ "The permission checker can then be added to any route via the `dependencies` argument in the decorator. Here, we add it to the `summary_task_route`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "permission_checker_for_user = PermissionChecker([\"User\"])\n",
+ "\n",
+ "\n",
+ "@app.post(\"/summary\", dependencies=[Depends(permission_checker_for_user)])\n",
+ "def summary_task_route(\n",
+ " input: SingleChunkSummarizeInput,\n",
+ " task: Annotated[\n",
+ " Task[SingleChunkSummarizeInput, SummarizeOutput], Depends(summary_task)\n",
+ " ],\n",
+ ") -> SummarizeOutput:\n",
+ " return task.run(input, NoOpTracer())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Complete Source\n",
+ "The final source can be found in the [accompanying python file](./fastapi_example.py)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-rp3__H-P-py3.11",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/__init__.py b/src/documentation/how_tos/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/documentation/how_tos/example_data.py b/src/documentation/how_tos/example_data.py
new file mode 100644
index 000000000..c9d60dd33
--- /dev/null
+++ b/src/documentation/how_tos/example_data.py
@@ -0,0 +1,112 @@
+from typing import Iterable, Sequence
+
+from pydantic import BaseModel
+
+from intelligence_layer.core import Task, TaskSpan
+from intelligence_layer.evaluation import (
+ Dataset,
+ EvaluationLogic,
+ EvaluationOverview,
+ Evaluator,
+ Example,
+ InMemoryDatasetRepository,
+ InMemoryEvaluationRepository,
+ InMemoryRunRepository,
+ Runner,
+ RunOverview,
+ SuccessfulExampleOutput,
+)
+from intelligence_layer.evaluation.aggregation.aggregator import AggregationLogic
+
+
+class DummyExample(Example[str, str]):
+ data: str
+
+
+class DummyTask(Task[str, str]):
+ def do_run(self, input: str, task_span: TaskSpan) -> str:
+ return f"{input} -> output"
+
+
+class DummyEvaluation(BaseModel):
+ eval: str
+
+
+class DummyEvaluationLogic(EvaluationLogic[str, str, str, DummyEvaluation]):
+ def do_evaluate(
+ self, example: Example[str, str], *output: SuccessfulExampleOutput[str]
+ ) -> DummyEvaluation:
+ output_str = "(" + (", ".join(o.output for o in output)) + ")"
+ return DummyEvaluation(
+ eval=f"{example.input}, {example.expected_output}, {output_str} -> evaluation"
+ )
+
+
+class DummyAggregation(BaseModel):
+ num_evaluations: int
+
+
+class DummyAggregationLogic(AggregationLogic[DummyEvaluation, DummyAggregation]):
+ def aggregate(self, evaluations: Iterable[DummyEvaluation]) -> DummyAggregation:
+ return DummyAggregation(num_evaluations=len(list(evaluations)))
+
+
+class ExampleData:
+ examples: Sequence[DummyExample]
+ dataset_repository: InMemoryDatasetRepository
+ run_repository: InMemoryRunRepository
+ evaluation_repository: InMemoryEvaluationRepository
+ runner: Runner[str, str]
+ evaluator: Evaluator[str, str, str, DummyEvaluation]
+ dataset: Dataset
+ run_overview_1: RunOverview
+ run_overview_2: RunOverview
+ evaluation_overview_1: EvaluationOverview
+ evaluation_overview_2: EvaluationOverview
+
+
+def example_data() -> ExampleData:
+ examples = [
+ DummyExample(input="input0", expected_output="expected_output0", data="data0"),
+ DummyExample(input="input1", expected_output="expected_output1", data="data1"),
+ ]
+
+ dataset_repository = InMemoryDatasetRepository()
+ dataset = dataset_repository.create_dataset(
+ examples=examples, dataset_name="my-dataset"
+ )
+
+ run_repository = InMemoryRunRepository()
+ runner = Runner(DummyTask(), dataset_repository, run_repository, "my-runner")
+ run_overview_1 = runner.run_dataset(dataset.id)
+ run_overview_2 = runner.run_dataset(dataset.id)
+
+ evaluation_repository = InMemoryEvaluationRepository()
+ evaluator = Evaluator(
+ dataset_repository,
+ run_repository,
+ evaluation_repository,
+ "my-evaluator",
+ DummyEvaluationLogic(),
+ )
+ evaluation_overview_1 = evaluator.evaluate_runs(
+ run_overview_1.id, run_overview_2.id
+ )
+ evaluation_overview_2 = evaluator.evaluate_runs(
+ run_overview_1.id, run_overview_2.id
+ )
+
+ example_data = ExampleData()
+ example_data.examples = examples
+ example_data.dataset_repository = dataset_repository
+ example_data.run_repository = run_repository
+ example_data.evaluation_repository = evaluation_repository
+ example_data.runner = runner
+ example_data.evaluator = evaluator
+ example_data.dataset = dataset
+ example_data.run_overview_1 = run_overview_1
+ example_data.run_overview_2 = run_overview_2
+ example_data.evaluation_overview_1 = evaluation_overview_1
+ example_data.evaluation_overview_2 = evaluation_overview_2
+
+ return example_data
diff --git a/src/documentation/how_tos/how_to_aggregate_evaluations.ipynb b/src/documentation/how_tos/how_to_aggregate_evaluations.ipynb
new file mode 100644
index 000000000..2dadcce47
--- /dev/null
+++ b/src/documentation/how_tos/how_to_aggregate_evaluations.ipynb
@@ -0,0 +1,88 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from example_data import DummyAggregationLogic, example_data\n",
+ "\n",
+ "from intelligence_layer.evaluation.aggregation.aggregator import Aggregator\n",
+ "from intelligence_layer.evaluation.aggregation.in_memory_aggregation_repository import (\n",
+ " InMemoryAggregationRepository,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to aggregate evaluations\n",
+ "0. Run the evaluations of all your tasks and datasets (see [here](./how_to_evaluate_runs.ipynb)).\n",
+ " - When aggregating multiple evaluations, all of them need the same data types \n",
+ "1. Initialize all necessary repositories for the `Aggregator`, and an `AggregationLogic`\n",
+ "2. Run the `Aggregator` to aggregate all examples and create a single `AggregationOverview`\n",
+ "3. (Optional) Save the `AggregationOverview.id` for later retrieval\n",
+ "\n",
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0\n",
+ "\n",
+ "\n",
+ "my_example_data = example_data()\n",
+ "print()\n",
+ "\n",
+ "evaluation_ids = [\n",
+ " my_example_data.evaluation_overview_1.id,\n",
+ " my_example_data.evaluation_overview_2.id,\n",
+ "]\n",
+ "\n",
+ "# Step 1\n",
+ "evaluation_repository = my_example_data.evaluation_repository\n",
+ "aggregation_repository = InMemoryAggregationRepository()\n",
+ "aggregation_logic = DummyAggregationLogic()\n",
+ "\n",
+ "# Step 2\n",
+ "aggregator = Aggregator(\n",
+ " evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"MyAggregationDescription\",\n",
+ " aggregation_logic,\n",
+ ")\n",
+ "aggregation_overview = aggregator.aggregate_evaluation(*evaluation_ids)\n",
+ "\n",
+ "# Step 3\n",
+ "print(aggregation_overview.id)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_create_a_dataset.ipynb b/src/documentation/how_tos/how_to_create_a_dataset.ipynb
new file mode 100644
index 000000000..498c21bce
--- /dev/null
+++ b/src/documentation/how_tos/how_to_create_a_dataset.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Sequence\n",
+ "\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.evaluation import Example, InMemoryDatasetRepository"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to create a dataset\n",
+ "\n",
+ "0. Collect data for examples.\n",
+ "1. Convert data to `Example`s.\n",
+ "1. Create a `DatasetRepository`.\n",
+ "2. Store `Example`s to `DatasetRepository`.\n",
+ "3. Remember the dataset id."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class StoryTaskInput(BaseModel): # Should already be implemented in your task\n",
+ " topic: str\n",
+ " targeted_word_count: int\n",
+ "\n",
+ "\n",
+ "class StoryTaskExpectedOutput(BaseModel): # Should already be implemented in your task\n",
+ " keywords: Sequence[str]\n",
+ "\n",
+ "\n",
+ "# Step 1\n",
+ "examples = [\n",
+ " Example(\n",
+ " input=StoryTaskInput(topic=\"rain\", targeted_word_count=42),\n",
+ " expected_output=StoryTaskExpectedOutput(keywords=[\"wet\"]),\n",
+ " ),\n",
+ " # ...\n",
+ "]\n",
+ "\n",
+ "# Step 2 - Use FileDatasetRepository or HuggingFaceDatasetRepository for persistence\n",
+ "dataset_repository = InMemoryDatasetRepository()\n",
+ "\n",
+ "# Step 3\n",
+ "dataset = dataset_repository.create_dataset(\n",
+ " examples=examples,\n",
+ " dataset_name=\"StoryDataset\",\n",
+ ")\n",
+ "\n",
+ "# Step 4\n",
+ "print(dataset.id)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-dgcJwC7l-py3.11",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_define_a_task.ipynb b/src/documentation/how_tos/how_to_define_a_task.ipynb
new file mode 100644
index 000000000..f3d803091
--- /dev/null
+++ b/src/documentation/how_tos/how_to_define_a_task.ipynb
@@ -0,0 +1,70 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to define a task\n",
+ "\n",
+ "1. Think about what you want to do and define the requirements for your task\n",
+ "2. Define the corresponding input and output in the form of Python classes\n",
+ "3. Check if any existing task can be used to fulfill these requirements (see the [Use-case index](../../../README.md#use-case-index))\n",
+ "4. Implement the task with the defined input and output types, see [How to implement a task](how_to_implement_a_task.ipynb) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example\n",
+ "\n",
+ "**Step 1**\n",
+ "- I want the LLM to tell a joke about a specific topic\n",
+ "- It should work for any topic\n",
+ "- It should fail if there is no topic given by the user\n",
+ "\n",
+ "\n",
+ "**Step 2**\n",
+ "\n",
+ "```python\n",
+ "class TellAJokeTaskInput(BaseModel):\n",
+ " topic: str\n",
+ "\n",
+ "class TellAJokeTaskOutput(BaseModel):\n",
+ " joke: str\n",
+ "```\n",
+ "\n",
+ "**Step 3**\n",
+ "On first glance any of the QA tasks seem to fulfill the requirements. However, here only the topic for the joke should be specified by the user and the request to tell a joke should be handled by the task itself. \n",
+ "\n",
+ "\n",
+ "**Step 4**\n",
+ "```python\n",
+ "class TellAJokeTask(Task[TellAJokeTaskInput, TellAJokeTaskOutput]):\n",
+ " ...\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_evaluate_runs.ipynb b/src/documentation/how_tos/how_to_evaluate_runs.ipynb
new file mode 100644
index 000000000..403ebcb1a
--- /dev/null
+++ b/src/documentation/how_tos/how_to_evaluate_runs.ipynb
@@ -0,0 +1,90 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from example_data import DummyEvaluationLogic, example_data\n",
+ "\n",
+ "from intelligence_layer.evaluation.evaluation.evaluator import Evaluator\n",
+ "from intelligence_layer.evaluation.evaluation.in_memory_evaluation_repository import (\n",
+ " InMemoryEvaluationRepository,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to evaluate runs\n",
+ "0. Run your tasks on the datasets where you want to evaluate them on (see [here](./how_to_run_a_task_on_a_dataset.ipynb))\n",
+ " - When evaluating multiple runs, all of them need the same data types \n",
+ "2. Initialize all necessary repositories for the `Evaluator`, and an `EvaluationLogic`.\n",
+ "3. Run the evaluator to evaluate all examples and create a single `EvaluationOverview`\n",
+ "4. (Optional) Save the evaluation id for later use"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0\n",
+ "my_example_data = example_data()\n",
+ "print()\n",
+ "run_ids = [my_example_data.run_overview_1.id, my_example_data.run_overview_2.id]\n",
+ "\n",
+ "# Step 1\n",
+ "dataset_repository = my_example_data.dataset_repository\n",
+ "run_repository = my_example_data.run_repository\n",
+ "evaluation_repository = InMemoryEvaluationRepository()\n",
+ "evaluation_logic = DummyEvaluationLogic()\n",
+ "\n",
+ "# Step 3\n",
+ "evaluator = Evaluator(\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " evaluation_repository,\n",
+ " \"My dummy evaluation\",\n",
+ " evaluation_logic,\n",
+ ")\n",
+ "\n",
+ "evaluation_overview = evaluator.evaluate_runs(*run_ids)\n",
+ "\n",
+ "# Step 4\n",
+ "print(evaluation_overview.id)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_human_evaluation_via_argilla.ipynb b/src/documentation/how_tos/how_to_human_evaluation_via_argilla.ipynb
new file mode 100644
index 000000000..b7de9e085
--- /dev/null
+++ b/src/documentation/how_tos/how_to_human_evaluation_via_argilla.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Iterable\n",
+ "\n",
+ "from dotenv import load_dotenv\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.connectors import (\n",
+ " ArgillaEvaluation,\n",
+ " DefaultArgillaClient,\n",
+ " Field,\n",
+ " Question,\n",
+ " RecordData,\n",
+ ")\n",
+ "from intelligence_layer.evaluation import (\n",
+ " AggregationLogic,\n",
+ " ArgillaAggregator,\n",
+ " ArgillaEvaluationLogic,\n",
+ " ArgillaEvaluationRepository,\n",
+ " Example,\n",
+ " InMemoryAggregationRepository,\n",
+ " InMemoryEvaluationRepository,\n",
+ " RecordDataSequence,\n",
+ " SuccessfulExampleOutput,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to evaluate with human evaluation via Argilla\n",
+ "1. Initialize an Argilla client with the correct settings for your setup\n",
+ " - By default, the url and api key are read from the environment variables `ARGILLA_API_URL` and `ARGILLA_API_KEY`\n",
+ "2. Create `Question`s and `Field`s to structure the data that will be displayed in Argilla\n",
+ "3. Choose an Argilla workspace and get its ID\n",
+ "4. Create an `ArgillaEvaluationRepository`\n",
+ "5. Implement an `ArgillaEvaluationLogic`\n",
+ "6. Submit tasks to the Argilla instance by running the `ArgillaEvaluator`\n",
+ " - Make sure to save the `EvaluationOverview.id`, as it is needed to retrieve the results later\n",
+ "7. **Use the Argilla web platform to evaluate** "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1\n",
+ "client = DefaultArgillaClient(\n",
+ " # api_url=\"your url here\", # not necessary if ARGILLA_API_URL is set in environment\n",
+ " # api_key=\"your api key here\", # not necessary if ARGILLA_API_KEY is set in environment\n",
+ ")\n",
+ "\n",
+ "# Step 2\n",
+ "questions = [\n",
+ " Question(\n",
+ " name=\"rating\",\n",
+ " title=\"Funniness\",\n",
+ " description=\"How funny do you think is the joke? Rate it from 1-5.\",\n",
+ " options=range(1, 6),\n",
+ " )\n",
+ "]\n",
+ "fields = [\n",
+ " Field(name=\"input\", title=\"Topic\"),\n",
+ " Field(name=\"output\", title=\"Joke\"),\n",
+ "]\n",
+ "\n",
+ "# Step 3\n",
+ "workspace_id = client.ensure_workspace_exists(\"my-workspace-name\")\n",
+ "\n",
+ "# Step 4\n",
+ "data_storage = (\n",
+ " InMemoryEvaluationRepository()\n",
+ ") # Use FileEvaluationRepository for persistent results\n",
+ "evaluation_repository = ArgillaEvaluationRepository(\n",
+ " data_storage, client, workspace_id, fields, questions\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# Step 5\n",
+ "class StoryTaskInput(BaseModel): # Should already be implemented in your task\n",
+ " topic: str\n",
+ " targeted_word_count: int\n",
+ "\n",
+ "\n",
+ "class StoryTaskOutput(BaseModel): # Should already be implemented in your task\n",
+ " story: str\n",
+ "\n",
+ "\n",
+ "class CustomArgillaEvaluationLogic(\n",
+ " ArgillaEvaluationLogic[\n",
+ " StoryTaskInput, StoryTaskOutput, None\n",
+ " ] # No expected output, therefore \"None\"\n",
+ "):\n",
+ " def _to_record(\n",
+ " self,\n",
+ " example: Example[StoryTaskInput, None],\n",
+ " *output: SuccessfulExampleOutput[StoryTaskOutput],\n",
+ " ) -> RecordDataSequence:\n",
+ " return RecordDataSequence(\n",
+ " records=[\n",
+ " RecordData(\n",
+ " content={\n",
+ " # labels as defined in Field.name\n",
+ " \"input\": example.input.topic,\n",
+ " \"output\": run_output.output.story,\n",
+ " },\n",
+ " example_id=example.id,\n",
+ " )\n",
+ " for run_output in output\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "evaluation_logic = CustomArgillaEvaluationLogic()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%script false --no-raise-error\n",
+ "# we skip this as we do not have a dataset or run in this example\n",
+ "\n",
+ "# Step 6\n",
+ "runs_to_evaluate = [\"your_run_id_of_interest\", \"other_run_id_of_interest\"]\n",
+ "\n",
+ "evaluator = ArgillaEvaluator(\n",
+ " ..., evaluation_repository, description=\"My evaluation description\", evaluation_logic=evaluation_logic\n",
+ ")\n",
+ "evaluation_overview = evaluator.evaluate_runs(*runs_to_evaluate)\n",
+ "print(\"ID to retrieve results later: \", evaluation_overview.id)\n",
+ "\n",
+ "# Step 7\n",
+ "\n",
+ "####################################\n",
+ "# Evaluate via the Argilla UI here #\n",
+ "####################################"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "```python\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to aggregate an Argilla evaluation\n",
+ "0. Submit tasks to Argilla and perform an evaluation (see [here](#how-to-evaluate-with-human-evaluation-via-argilla)).\n",
+ "1. Implement an `AggregationLogic` that takes `ArgillaEvaluation`s as input.\n",
+ "2. Remember the ID of the evaluation and the name of the Argilla workspace that you want to aggregate.\n",
+ "3. Initialize the `ArgillaEvaluationRepository` and an aggregation repository.\n",
+ "4. Aggregate the results with an `ArgillaAggregator`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1\n",
+ "\n",
+ "\n",
+ "class CustomArgillaAggregation(BaseModel):\n",
+ " avg_funniness: float\n",
+ "\n",
+ "\n",
+ "class CustomArgillaAggregationLogic(\n",
+ " AggregationLogic[ArgillaEvaluation, CustomArgillaAggregation]\n",
+ "):\n",
+ " def aggregate(\n",
+ " self, evaluations: Iterable[ArgillaEvaluation]\n",
+ " ) -> CustomArgillaAggregation:\n",
+ " evaluation_list = list(evaluations)\n",
+ " total_score = sum(\n",
+ " evaluation.metadata[\n",
+ " \"rating\"\n",
+ " ] # This name is defined by the `Question`s given to the Argilla repository during submission\n",
+ " for evaluation in evaluation_list\n",
+ " )\n",
+ " return CustomArgillaAggregation(\n",
+ " avg_funniness=total_score / len(evaluation_list)\n",
+ " )\n",
+ "\n",
+ "\n",
+ "aggregation_logic = CustomArgillaAggregationLogic()\n",
+ "\n",
+ "# Step 2 - See the first example for more info\n",
+ "eval_id = \"my-previous-eval-id\"\n",
+ "client = DefaultArgillaClient()\n",
+ "workspace_id = client.ensure_workspace_exists(\"my-workspace-name\")\n",
+ "\n",
+ "# Step 3\n",
+ "evaluation_repository = ArgillaEvaluationRepository(\n",
+ " InMemoryEvaluationRepository(), client, workspace_id\n",
+ ")\n",
+ "aggregation_repository = InMemoryAggregationRepository()\n",
+ "\n",
+ "# Step 4\n",
+ "aggregator = ArgillaAggregator(\n",
+ " evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"My aggregation description\",\n",
+ " aggregation_logic,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%script false --no-raise-error\n",
+ "# we skip this as we do not have a dataset or run in this example\n",
+ "\n",
+ "aggregation = aggregator.aggregate_evaluation(eval_id)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_implement_a_simple_evaluation_and_aggregation_logic.ipynb b/src/documentation/how_tos/how_to_implement_a_simple_evaluation_and_aggregation_logic.ipynb
new file mode 100644
index 000000000..2d90e7781
--- /dev/null
+++ b/src/documentation/how_tos/how_to_implement_a_simple_evaluation_and_aggregation_logic.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Iterable\n",
+ "\n",
+ "import numpy as np\n",
+ "from dotenv import load_dotenv\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.evaluation.aggregation.aggregator import AggregationLogic\n",
+ "from intelligence_layer.evaluation.dataset.domain import Example\n",
+ "from intelligence_layer.evaluation.evaluation.evaluator import (\n",
+ " SingleOutputEvaluationLogic,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to implement logic for the evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Determine the data types you need for the evaluation:\n",
+ " - An `Example` of the dataset you are using defines \"`Input`\" and \"`ExpectedOutput`\" data types\n",
+ " - The task you are using defines the `Output` data type.\n",
+ "2. Create an `Evaluation` type that will contain the domain-specific evaluation result for a single `Example`.\n",
+ "3. Decide if you want to use a single `Output` per `Example`, or multiple outputs per example, during your evaluation to generate your evaluation results.\n",
+ " - For a single output, we recommend to implement a `SingleOutputEvaluationLogic`.\n",
+ " - For multiple outputs, implement an `EvaluationLogic`.\n",
+ "4. Implement the evaluation logic in the `do_evaluate_single_output` method for `SingleOutputEvaluationLogic` or in the `do_evaluate` method for `EvaluationLogic`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example\n",
+ "In the following example we want to evaluate a story-generating task that generates a story of a topic with a targeted word count"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1 - This is only redefined here for completeness. Normally these would be imported.\n",
+ "# Note that we do not have an ExpectedOutput here.\n",
+ "\n",
+ "\n",
+ "class StoryTaskInput(BaseModel):\n",
+ " topic: str\n",
+ " targeted_word_count: int\n",
+ "\n",
+ "\n",
+ "class StoryTaskOutput(BaseModel):\n",
+ " story: str\n",
+ "\n",
+ "\n",
+ "# Step 2 - We want to analyze if the word count is accurate\n",
+ "class StoryEvaluation(BaseModel):\n",
+ " word_count_off_by: int\n",
+ "\n",
+ "\n",
+ "class StoryEvaluationLogic(\n",
+ " # Step 3 - We only need a single output to analyze the word count\n",
+ " SingleOutputEvaluationLogic[\n",
+ " StoryTaskInput, StoryTaskOutput, None, StoryEvaluation\n",
+ " ] # We pass None here as we do not have an ExpectedOutput\n",
+ "):\n",
+ " def do_evaluate_single_output(\n",
+ " self, example: Example[StoryTaskInput, None], output: StoryTaskOutput\n",
+ " ) -> StoryEvaluation:\n",
+ " # Step 4 - Implement the domain specific logic\n",
+ " output_word_count = len(output.story.split())\n",
+ " word_count_off_by = output_word_count - example.input.targeted_word_count\n",
+ " return StoryEvaluation(word_count_off_by=word_count_off_by)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to implement a logic for an aggregation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "0. Implement the evaluation logic for your use-case. (see [above](#how-to-implement-logic-for-the-evaluation))\n",
+ "1. Create an `AggregatedEvaluation` type that will contain the domain specific data aggregated from evaluations.\n",
+ "2. Implement an `AggregationLogic` for your data types\n",
+ " 1. Implement the domain-specific logic in the `aggregate` method"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example\n",
+ "In the following example, we calculate basic statistics on the word count differences of the previous evaluation example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0 - See the example above\n",
+ "\n",
+ "\n",
+ "# Step 1\n",
+ "class StoryAggregation(BaseModel):\n",
+ " wc_off_mean: float\n",
+ " wc_off_median: int\n",
+ " wc_off_std: float\n",
+ "\n",
+ "\n",
+ "# Step 2\n",
+ "class StoryAggregationLogic(AggregationLogic[StoryEvaluation, StoryAggregation]):\n",
+ " def aggregate(self, evaluations: Iterable[StoryEvaluation]) -> StoryAggregation:\n",
+ " # Step 2.1\n",
+ " word_counts = np.array(\n",
+ " [evaluation.word_count_off_by for evaluation in evaluations]\n",
+ " )\n",
+ " wc_off_mean = np.mean(word_counts)\n",
+ " wc_off_median = np.median(word_counts)\n",
+ " wc_off_std = np.std(word_counts)\n",
+ " return StoryAggregation(\n",
+ " wc_off_mean=wc_off_mean, wc_off_median=wc_off_median, wc_off_std=wc_off_std\n",
+ " )"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_implement_a_task.ipynb b/src/documentation/how_tos/how_to_implement_a_task.ipynb
new file mode 100644
index 000000000..4c17ac6cc
--- /dev/null
+++ b/src/documentation/how_tos/how_to_implement_a_task.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.core import (\n",
+ " CompleteInput,\n",
+ " LuminousControlModel,\n",
+ " NoOpTracer,\n",
+ " Task,\n",
+ " TaskSpan,\n",
+ ")\n",
+ "from intelligence_layer.examples import SingleChunkQa, SingleChunkQaInput\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to implement a task using an Aleph Alpha model\n",
+ "0. Define the task (see [here](./how_to_define_a_task.ipynb))\n",
+ "1. Decide which model best suits your use case (for a list of Aleph Alpha control models see [here](https://aleph-alpha-intelligence-layer.readthedocs-hosted.com/en/latest/intelligence_layer.core.html#intelligence_layer.core.LuminousControlModel))\n",
+ "2. Create a `Task` subclass\n",
+ " 1. Pass the Model to the constructor \n",
+ " 2. Implement your domain logic in `do_run()`\n",
+ " 1. Generate a `Prompt`. Examples for generating prompts are `ControlModel.to_instruct_prompt()`, `PromptTemplate.to_rich_prompt()` or `Prompt.from_text()`\n",
+ " 2. Run the model with the prompt\n",
+ " 3. Map the prompt output to the task output class\n",
+ "3. Run and test it"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0 - Define the input and output types for your task\n",
+ "\n",
+ "\n",
+ "class TellAJokeTaskInput(BaseModel):\n",
+ " topic: str\n",
+ "\n",
+ "\n",
+ "class TellAJokeTaskOutput(BaseModel):\n",
+ " joke: str\n",
+ "\n",
+ "\n",
+ "# Step 1 - we want a control model but do not care otherwise. Therefore we use the default.\n",
+ "\n",
+ "\n",
+ "# Step 2\n",
+ "class TellAJokeTask(Task[TellAJokeTaskInput, TellAJokeTaskOutput]):\n",
+ " PROMPT_TEMPLATE: str = \"\"\"Tell me a joke about the following topic:\"\"\"\n",
+ "\n",
+ " # Step 2.1\n",
+ " def __init__(self, model: LuminousControlModel = LuminousControlModel()) -> None:\n",
+ " self._model = model\n",
+ "\n",
+ " # Step 2.2\n",
+ " def do_run(\n",
+ " self, input: TellAJokeTaskInput, task_span: TaskSpan\n",
+ " ) -> TellAJokeTaskOutput:\n",
+ " # Step 2.2.1\n",
+ " prompt = self._model.to_instruct_prompt(self.PROMPT_TEMPLATE, input.topic)\n",
+ " completion_input = CompleteInput(prompt=prompt)\n",
+ " # Step 2.2.2\n",
+ " completion = self._model.complete(completion_input, task_span)\n",
+ " return TellAJokeTaskOutput(joke=completion.completions[0].completion)\n",
+ "\n",
+ "\n",
+ "TellAJokeTask().run(TellAJokeTaskInput(topic=\"Software Engineers\"), NoOpTracer())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to use subtasks in your task\n",
+ " - Follow [How to implement a task using an Aleph Alpha model](#how-to-implement-a-task-using-an-aleph-alpha-model) to create a task and replace the `Model` class with a subtask of your choosing."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PeopleExtractorInput(BaseModel):\n",
+ " text_passage: str\n",
+ "\n",
+ "\n",
+ "class PeopleExtractorOutput(BaseModel):\n",
+ " answer: str | None\n",
+ "\n",
+ "\n",
+ "class PeopleExtractor(Task[PeopleExtractorInput, PeopleExtractorOutput]):\n",
+ " QUESTION: str = \"\"\"Who are the people involved in the text?\"\"\"\n",
+ "\n",
+ " # Step 2.1 - pass the task into the init function\n",
+ " def __init__(self, task: SingleChunkQa = SingleChunkQa()) -> None:\n",
+ " self._task = task\n",
+ "\n",
+ " def do_run(\n",
+ " self, input: PeopleExtractorInput, task_span: TaskSpan\n",
+ " ) -> PeopleExtractorOutput:\n",
+ " # Step 2.2.1 - create the required input for the task\n",
+ " question_input = SingleChunkQaInput(\n",
+ " chunk=input.text_passage, question=self.QUESTION\n",
+ " )\n",
+ " # Step 2.2.2 - use the task in the run\n",
+ " completion = self._task.run(\n",
+ " question_input,\n",
+ " task_span,\n",
+ " )\n",
+ " return PeopleExtractorOutput(answer=completion.answer)\n",
+ "\n",
+ "\n",
+ "task_input = PeopleExtractorInput(\n",
+ " text_passage=\"Peter ate Sarahs Lunch, their teacher Mr. Meyers was very angry with him.'\"\n",
+ ")\n",
+ "PeopleExtractor().run(task_input, NoOpTracer()).answer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_log_and_debug_a_task.ipynb b/src/documentation/how_tos/how_to_log_and_debug_a_task.ipynb
new file mode 100644
index 000000000..9072bfdbe
--- /dev/null
+++ b/src/documentation/how_tos/how_to_log_and_debug_a_task.ipynb
@@ -0,0 +1,116 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "\n",
+ "from aleph_alpha_client import Prompt\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "from intelligence_layer.core import (\n",
+ " CompleteInput,\n",
+ " InMemoryTracer,\n",
+ " LuminousControlModel,\n",
+ " Task,\n",
+ " TaskSpan,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to log and debug a task\n",
+ "The Intelligence Layer offers logging and debugging via a `Tracer`. \n",
+ "Here are several steps you can use to debug tasks with the trace feature:\n",
+ "\n",
+ "-----\n",
+ "Most logging of a task (input, output, time) is done simply by inheriting from `Task`. This logs to a trace.\n",
+ "\n",
+ " - If you don't care about logging and tracing, use the `NoOpTracer`.\n",
+ " - To create custom logging messages in a trace use `task_span.log()`.\n",
+ " - To map a complex execution flow of a task into a single trace, pass the `task_span` of the `do_run` to other execution methods (e.g. `Task.run()` or `model.complete()`). \n",
+ " - If the execution method is not provided by the intelligence layer, the tracing of input and output has to happen manually. See the implementation of `Task.run()` for an example.\n",
+ " - Use the [trace viewer](./how_to_run_the_trace_viewer.ipynb) to view and inspect a trace\n",
+ " - Use and display an `InMemoryTracer` in a notebook to automatically send the trace data to the trace viewer.\n",
+ " - Note: This also works for traces of the `Runner` and the `Evaluator`.\n",
+ " - To create persistent traces, use the `FileTracer` instead. This creates files which can manually be uploaded in the trace viewer UI."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DummyTask(Task[str, str]):\n",
+ " def __init__(self, model: LuminousControlModel = LuminousControlModel()) -> None:\n",
+ " self._model = model\n",
+ "\n",
+ " def do_run(self, input: str, task_span: TaskSpan) -> str:\n",
+ " should_output = random.random()\n",
+ " # log a custom message and value\n",
+ " task_span.log(\n",
+ " \"My very important log message that logs a random value\", should_output\n",
+ " )\n",
+ " if should_output > 0.5:\n",
+ " model_input = CompleteInput(prompt=Prompt.from_text(input), temperature=0.2)\n",
+ " # Create a trace tree by passing task_span to .run or .complete methods.\n",
+ " completion = self._model.complete(model_input, task_span)\n",
+ " return completion.completions[0].completion\n",
+ " else:\n",
+ " return \"Nope!\"\n",
+ "\n",
+ "\n",
+ "tracer = InMemoryTracer()\n",
+ "DummyTask().run(\"\", tracer)\n",
+ "# ! make sure to run the trace viewer docker container to get the improved display !\n",
+ "# display an InMemoryTracer in a notebook and send the data to the trace viewer\n",
+ "display(tracer)\n",
+ "\n",
+ "pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_retrieve_data_for_analysis.ipynb b/src/documentation/how_tos/how_to_retrieve_data_for_analysis.ipynb
new file mode 100644
index 000000000..57875aa8a
--- /dev/null
+++ b/src/documentation/how_tos/how_to_retrieve_data_for_analysis.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from example_data import DummyEvaluation, example_data\n",
+ "\n",
+ "from intelligence_layer.evaluation import (\n",
+ " RepositoryNavigator,\n",
+ " evaluation_lineages_to_pandas,\n",
+ ")\n",
+ "\n",
+ "example_data = example_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to retrieve data for analysis\n",
+ "0. Initialize the relevant repositories for the data of interest\n",
+ "1. Retrieve the data\n",
+ " - from the repositories directly for a quick overview of specific data of interest\n",
+ " - via the `RepositoryNavigator` to join data from different repositories together.\n",
+ " - via the `Runner`/`Evaluator`\n",
+ "\n",
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0\n",
+ "dataset_repository = example_data.dataset_repository\n",
+ "run_repository = example_data.run_repository\n",
+ "evaluation_repository = example_data.evaluation_repository\n",
+ "\n",
+ "# Step 1 - direct access of data\n",
+ "my_example_id = example_data.examples[0].id\n",
+ "my_dataset_id = example_data.dataset.id\n",
+ "\n",
+ "# retrieve a dataset with all examples, and a single example\n",
+ "my_dataset = dataset_repository.dataset(my_dataset_id)\n",
+ "my_example = dataset_repository.example(\n",
+ " my_dataset_id, my_example_id, input_type=str, expected_output_type=str\n",
+ ")\n",
+ "\n",
+ "# retrieve all outputs, and an output for an example\n",
+ "my_run_id = example_data.run_overview_1.id\n",
+ "my_outputs = run_repository.example_outputs(my_run_id, output_type=str)\n",
+ "my_example_output = run_repository.example_output(\n",
+ " my_run_id, my_example_id, output_type=str\n",
+ ")\n",
+ "\n",
+ "# retrieve all evaluations, and an evaluation for an example\n",
+ "my_evaluation_id = example_data.evaluation_overview_1.id\n",
+ "my_evaluations = evaluation_repository.example_evaluations(\n",
+ " my_evaluation_id, evaluation_type=DummyEvaluation\n",
+ ")\n",
+ "my_example_evaluation = evaluation_repository.example_evaluation(\n",
+ " my_evaluation_id, my_example_id, evaluation_type=DummyEvaluation\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1 - retrieve linked data across all repositories\n",
+ "\n",
+ "navigator = RepositoryNavigator(\n",
+ " dataset_repository=dataset_repository,\n",
+ " run_repository=run_repository,\n",
+ " evaluation_repository=evaluation_repository,\n",
+ ")\n",
+ "\n",
+ "my_lineages = navigator.evaluation_lineages(\n",
+ " my_evaluation_id,\n",
+ " input_type=str,\n",
+ " expected_output_type=str,\n",
+ " output_type=str,\n",
+ " evaluation_type=DummyEvaluation,\n",
+ ")\n",
+ "display(evaluation_lineages_to_pandas(my_lineages))\n",
+ "\n",
+ "my_lineage = navigator.evaluation_lineage(\n",
+ " my_evaluation_id,\n",
+ " my_example_id,\n",
+ " input_type=str,\n",
+ " expected_output_type=str,\n",
+ " output_type=str,\n",
+ " evaluation_type=DummyEvaluation,\n",
+ ")\n",
+ "display(my_lineage)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 1 - retrieve linked data via the `Runner`/`Evaluator`\n",
+ "my_evaluator = example_data.evaluator\n",
+ "\n",
+ "my_lineages = my_evaluator.evaluation_lineages(my_evaluation_id)\n",
+ "display(evaluation_lineages_to_pandas(my_lineages))\n",
+ "\n",
+ "my_lineage = my_evaluator.evaluation_lineage(my_evaluation_id, my_example_id)\n",
+ "display(my_lineage)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_run_a_task_on_a_dataset.ipynb b/src/documentation/how_tos/how_to_run_a_task_on_a_dataset.ipynb
new file mode 100644
index 000000000..8b9358268
--- /dev/null
+++ b/src/documentation/how_tos/how_to_run_a_task_on_a_dataset.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from example_data import DummyTask, example_data\n",
+ "\n",
+ "from intelligence_layer.evaluation.run.in_memory_run_repository import (\n",
+ " InMemoryRunRepository,\n",
+ ")\n",
+ "from intelligence_layer.evaluation.run.runner import Runner"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to run a task on a dataset\n",
+ "0. Create a suitable dataset (see [here](./how_to_create_a_dataset.ipynb)) and a task (see [here](./how_to_implement_a_task.ipynb)).\n",
+ "1. Initialize the task and a `RunRepository`, and open the correct `DatasetRepository`\n",
+ " - The `DatasetRepository` needs to contain the dataset.\n",
+ " - The `RunRepository` stores results.\n",
+ "2. Use the `Runner` to run the task on the given dataset via `run_dataset`\n",
+ "3. Save the id of the resulting `RunOverview`\n",
+ "\n",
+ "### Example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Step 0\n",
+ "my_example_data = example_data()\n",
+ "print()\n",
+ "\n",
+ "# Step 1\n",
+ "dataset_repository = my_example_data.dataset_repository\n",
+ "run_repository = InMemoryRunRepository()\n",
+ "task = DummyTask()\n",
+ "\n",
+ "# Step 2\n",
+ "runner = Runner(task, dataset_repository, run_repository, \"MyRunDescription\")\n",
+ "run_overview = runner.run_dataset(my_example_data.dataset.id)\n",
+ "\n",
+ "# Step 3\n",
+ "print(run_overview.id)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "intelligence-layer-d3iSWYpm-py3.10",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/how_tos/how_to_run_the_trace_viewer.ipynb b/src/documentation/how_tos/how_to_run_the_trace_viewer.ipynb
new file mode 100644
index 000000000..4daf1fed6
--- /dev/null
+++ b/src/documentation/how_tos/how_to_run_the_trace_viewer.ipynb
@@ -0,0 +1,35 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Running the Trace Viewer\n",
+ "\n",
+ "Make sure you have your access to the Jfrog instance at https://alephalpha.jfrog.io. \n",
+ "Then login to the container registry with docker with your JFrog user name and a JFrog token as the password with the following command:\n",
+ "\n",
+ "```bash\n",
+ "docker login alephalpha.jfrog.io\n",
+ "```\n",
+ "\n",
+ "Note: If you do not already have a JFrog token, you can find it on the website under the \"Set me up\" option, either in the resource of interest or under your profile name.\n",
+ "\n",
+ "Afterwards, run the container locally to start the trace viewer:\n",
+ "\n",
+ "```bash\n",
+ "docker run -p 3000:3000 alephalpha.jfrog.io/container-images/trace-viewer:latest\n",
+ "```\n",
+ "\n",
+ "Finally, visit `http://localhost:3000`, where you can upload a trace to interact with the data."
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/documentation/human_evaluation.ipynb b/src/documentation/human_evaluation.ipynb
new file mode 100644
index 000000000..486a1c56b
--- /dev/null
+++ b/src/documentation/human_evaluation.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import shutil\n",
+ "from pathlib import Path\n",
+ "from typing import Iterable, cast\n",
+ "\n",
+ "from datasets import load_dataset\n",
+ "from dotenv import load_dotenv\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.connectors import (\n",
+ " ArgillaEvaluation,\n",
+ " DefaultArgillaClient,\n",
+ " Field,\n",
+ " LimitedConcurrencyClient,\n",
+ " Question,\n",
+ " RecordData,\n",
+ ")\n",
+ "from intelligence_layer.core import (\n",
+ " CompleteOutput,\n",
+ " Instruct,\n",
+ " InstructInput,\n",
+ " LuminousControlModel,\n",
+ ")\n",
+ "from intelligence_layer.evaluation import (\n",
+ " AggregationLogic,\n",
+ " ArgillaAggregator,\n",
+ " ArgillaEvaluationLogic,\n",
+ " ArgillaEvaluationRepository,\n",
+ " ArgillaEvaluator,\n",
+ " Example,\n",
+ " FileAggregationRepository,\n",
+ " FileDatasetRepository,\n",
+ " FileEvaluationRepository,\n",
+ " FileRunRepository,\n",
+ " RecordDataSequence,\n",
+ " Runner,\n",
+ " SuccessfulExampleOutput,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "client = LimitedConcurrencyClient.from_env()\n",
+ "\n",
+ "REPOSITORY_ROOT_PATH = Path(\"human-eval-data\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Human Evaluation using the Intelligence Layer\n",
+ "\n",
+ "Although there are a variety of ways to automate the evaluation of LLM-based tasks, sometimes it is still necessary to get a human opinion.\n",
+ "To make this as painless as possible, we have integrated an [Argilla](https://argilla.io/)-Evaluator into the intelligence layer.\n",
+ "This notebook serves as a quick start guide."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment setup\n",
+ "This notebook expects that you have added your Aleph Alpha token to your .env file.\n",
+ "Additionally you need to add the `ARGILLA_API_URL` and `ARGILLA_API_KEY` from env.sample to your .env file. \n",
+ "Next, run\n",
+ "\n",
+ "```bash\n",
+ "docker-compose up -d\n",
+ "``` \n",
+ "\n",
+ "from the intelligence layer base directory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Once you go to `localhost:6900` and you are prompted to enter a username and password, use:\n",
+ "- username: `argilla`\n",
+ "- password: `1234`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "\n",
+ "This notebook is designed such that the creation of the dataset, the submission to Argilla and the aggregation of the Argilla evaluations do not have to be done in a single session.\n",
+ "\n",
+ "As a result, the data repositories are redefined for each step and we use file-based repositories that persist the data. If you run all steps in a single session, you can use InMemory-based repositories and reuse the same repository object for multiple steps.\n",
+ "\n",
+ "Running this notebook creates a `human-eval-data` folder, which will be deleted if you run the whole notebook to completion. It also creates the `test-human-eval` Argilla workspace, which will also be deleted afterwards.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dataset Repository definition\n",
+ "First we need to define our dataset. Here we use an [Instruction Dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset?row=0) from Huggingface. Before we can use it for human eval, we need to make an intelligence layer dataset repository."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset = load_dataset(\"HuggingfaceH4/instruction-dataset\")[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us explore the dataset a bit. It consists of prompts, example completions and metadata for 327 examples. Since we are doing human eval, for now we only need the prompt and corresponding id."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(dataset)\n",
+ "print(dataset[\"meta\"][0].keys())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We could now build a single `Example` like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "example = Example(\n",
+ " input=InstructInput(instruction=dataset[\"prompt\"][0], input=None),\n",
+ " expected_output=None,\n",
+ " id=str(dataset[\"meta\"][0][\"id\"]),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For our dataset repository, we can either use a `FileDatasetRepository` or an `InMemoryDatasetRepository`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_examples = 5\n",
+ "assert num_examples <= len(dataset)\n",
+ "dataset_repository = FileDatasetRepository(REPOSITORY_ROOT_PATH)\n",
+ "dataset_id = dataset_repository.create_dataset(\n",
+ " examples=[\n",
+ " Example(\n",
+ " input=InstructInput(instruction=dataset[\"prompt\"][i], input=None),\n",
+ " expected_output=None,\n",
+ " id=str(dataset[\"meta\"][i][\"id\"]),\n",
+ " )\n",
+ " for i in range(num_examples)\n",
+ " ],\n",
+ " dataset_name=\"human-evaluation-dataset\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_id.name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Task Setup\n",
+ "\n",
+ "We use an `Instruction` task to run the examples in our dataset.\n",
+ "In addition, we define a `Runner` to generate the completions from the model for our dataset\n",
+ "and a `RunRepository` to save the results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = LuminousControlModel(name=\"luminous-base-control\", client=client)\n",
+ "task = Instruct(model=model)\n",
+ "\n",
+ "dataset_repository = FileDatasetRepository(REPOSITORY_ROOT_PATH)\n",
+ "# either remember the id from before (dataset.id) or retrieve as below\n",
+ "dataset_id = [\n",
+ " dataset.id\n",
+ " for dataset in dataset_repository.datasets()\n",
+ " if dataset.name == \"human-evaluation-dataset\"\n",
+ "][0]\n",
+ "dataset_repository.datasets()\n",
+ "run_repository = FileRunRepository(REPOSITORY_ROOT_PATH)\n",
+ "runner = Runner(task, dataset_repository, run_repository, \"instruct-run\")\n",
+ "\n",
+ "run_overview = runner.run_dataset(dataset_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Evaluator Definition\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At the end of our evaluation we want a float score $s \\in [1,5]$ describing the model performance.\n",
+ "We define this as an `InstructAggregatedEvaluation`, which will be used in our aggregation later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InstructAggregatedEvaluation(BaseModel):\n",
+ " general_rating: float | None\n",
+ " fluency: float | None\n",
+ " evaluated_examples: int"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now start to define our human evaluation. This is done with `Questions` and `Fields`. \n",
+ "`Fields` define what a user has to evaluate. In our example, this will be the model input (Instruction) and output (Model Completion). Note that the field names have to match the content keys from the `RecordData` which we will define later in our `InstructArgillaEvaluationLogic`. \n",
+ "`Questions` are what a user has to answer in order to evaluate the `Fields`. The `name` property will later be used to access the human ratings in the aggregation step. In our case we ask how complete and how fluent the completions are."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "questions = [\n",
+ " Question(\n",
+ " name=\"general_rating\", # name of the field in program, used for retrieval later\n",
+ " title=\"Rating\", # name shown to the user\n",
+ " description=\"Rate this completion on a scale from 1 to 5\",\n",
+ " options=range(1, 6),\n",
+ " ),\n",
+ " Question(\n",
+ " name=\"fluency\",\n",
+ " title=\"Fluency\",\n",
+ " description=\"How fluent is the completion?\",\n",
+ " options=range(1, 6),\n",
+ " ),\n",
+ "]\n",
+ "\n",
+ "fields = [\n",
+ " Field(name=\"input\", title=\"Instruction\"),\n",
+ " Field(name=\"output\", title=\"Model Completion\"),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our defined fields and questions will look like this:\n",
+ "\n",
+ "\n",
+ "We can now define our `InstructArgillaEvaluationLogic` and `InstructArgillaAggregationLogic`.\n",
+ "They have to implement the two abstract methods `_to_record` and `aggregate` respectively.\n",
+ "Lets look at the documentation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "help(ArgillaEvaluationLogic._to_record)\n",
+ "print(\"-\" * 100)\n",
+ "help(AggregationLogic.aggregate)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Instead of performing the evaluation, the `ArgillaEvaluationLogic` is responsible for converting the evaluation data to a format that is accepted by Argilla. During the evaluation, these records will simply be submitted to Argilla. \n",
+ "We will now create everything we need to submit these evaluations to our Argilla instance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InstructArgillaEvaluationLogic(\n",
+ " ArgillaEvaluationLogic[\n",
+ " InstructInput,\n",
+ " CompleteOutput,\n",
+ " None,\n",
+ " ]\n",
+ "):\n",
+ " def _to_record(\n",
+ " self,\n",
+ " example: Example[InstructInput, None],\n",
+ " *example_outputs: SuccessfulExampleOutput[CompleteOutput],\n",
+ " ) -> RecordDataSequence:\n",
+ " return RecordDataSequence(\n",
+ " records=[\n",
+ " RecordData(\n",
+ " content={\n",
+ " \"input\": example.input.instruction,\n",
+ " \"output\": example_outputs[0].output.completion,\n",
+ " },\n",
+ " example_id=example.id,\n",
+ " )\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "argilla_client = DefaultArgillaClient()\n",
+ "workspace_id = argilla_client.ensure_workspace_exists(\"test-human-eval\")\n",
+ "\n",
+ "dataset_repository = FileDatasetRepository(REPOSITORY_ROOT_PATH)\n",
+ "run_repository = FileRunRepository(REPOSITORY_ROOT_PATH)\n",
+ "evaluation_repository = FileEvaluationRepository(\n",
+ " REPOSITORY_ROOT_PATH\n",
+ ") # this is only used to store failed evaluations and the evaluation overview\n",
+ "argilla_evaluation_repository = ArgillaEvaluationRepository(\n",
+ " evaluation_repository, argilla_client, workspace_id, fields, questions\n",
+ ")\n",
+ "\n",
+ "eval_logic = InstructArgillaEvaluationLogic()\n",
+ "evaluator = ArgillaEvaluator(\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " argilla_evaluation_repository,\n",
+ " \"instruct-evaluation\",\n",
+ " eval_logic,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After setting up the `ArgillaEvaluator`, the `evaluate_runs` methods posts the records to the Argilla instance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# either remember the id from before (run_overview.id) or retrieve as below\n",
+ "run_id = [\n",
+ " overview.id\n",
+ " for overview in run_repository.run_overviews()\n",
+ " if overview.description == \"instruct-run\"\n",
+ "][0]\n",
+ "\n",
+ "try:\n",
+ " eval_overview = evaluator.evaluate_runs(run_id)\n",
+ " print(eval_overview)\n",
+ "\n",
+ "except Exception as e:\n",
+ " eval_overview = None\n",
+ " print(str(e))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "While the evaluation says that 5 examples were successfully evaluated, no real evaluation has happened yet. \n",
+ "If we try to perform an aggregation right now, it will have no evaluations, as none of the submitted records were evaluated by humans through Argilla yet. \n",
+ "The aggregation fetches only the results that were already evaluated.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "**Note:** Sometimes it is best to split up the human evaluation effort into multiple people. To best facilitate this, it is possible to split up the dataset by giving them labels.\n",
+ "Our Argilla client offers an easy way to do this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eval_id = eval_overview.id\n",
+ "argilla_client.split_dataset(eval_id, n_splits=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These splits can then be filered by, as shown below. \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ "For the Aggregation, we first need to define our `AggregationLogic` that has to take an `ArgillaEvaluation` as an input. As output, we use the `InstructAggregatedEvaluation` we defined earlier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InstructArgillaAggregationLogic(\n",
+ " AggregationLogic[ArgillaEvaluation, InstructAggregatedEvaluation]\n",
+ "):\n",
+ " def aggregate(\n",
+ " self,\n",
+ " evaluations: Iterable[ArgillaEvaluation],\n",
+ " ) -> InstructAggregatedEvaluation:\n",
+ " evaluations = list(evaluations)\n",
+ "\n",
+ " if len(evaluations) == 0: # if no evaluations were submitted, return\n",
+ " return InstructAggregatedEvaluation(\n",
+ " general_rating=None,\n",
+ " fluency=None,\n",
+ " evaluated_examples=0,\n",
+ " )\n",
+ "\n",
+ " general_rating = sum(\n",
+ " cast(float, evaluation.responses[\"general_rating\"])\n",
+ " for evaluation in evaluations\n",
+ " ) / len(evaluations)\n",
+ "\n",
+ " fluency = sum(\n",
+ " cast(float, evaluation.responses[\"fluency\"]) for evaluation in evaluations\n",
+ " ) / len(evaluations)\n",
+ "\n",
+ " return InstructAggregatedEvaluation(\n",
+ " general_rating=general_rating,\n",
+ " fluency=fluency,\n",
+ " evaluated_examples=len(evaluations),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "aggregation_logic = InstructArgillaAggregationLogic()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this, we can define our `ArgillaAggregator` and retrieve the aggregation of all records that have been evaluated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluation_repository = FileEvaluationRepository(REPOSITORY_ROOT_PATH)\n",
+ "argilla_evaluation_repository = ArgillaEvaluationRepository(\n",
+ " evaluation_repository,\n",
+ " argilla_client,\n",
+ " workspace_id, # we do not need to set questions and fields here\n",
+ ")\n",
+ "aggregation_repository = FileAggregationRepository(REPOSITORY_ROOT_PATH)\n",
+ "# either remember the id from before (eval_overview.id) or retrieve as below\n",
+ "eval_id = [\n",
+ " overview.id\n",
+ " for overview in argilla_evaluation_repository.evaluation_overviews()\n",
+ " if overview.description == \"instruct-evaluation\"\n",
+ "][0]\n",
+ "\n",
+ "\n",
+ "aggregator = ArgillaAggregator(\n",
+ " argilla_evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"instruct-aggregation\",\n",
+ " aggregation_logic,\n",
+ ")\n",
+ "\n",
+ "if eval_overview:\n",
+ " output = aggregator.aggregate_evaluation(eval_id)\n",
+ " print(output.statistics)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Cleanup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ! WARNING ! This deletes the \"test-human-eval\" argilla workspace and the \"human-eval-data\" folder.\n",
+ "argilla_client.delete_workspace(workspace_id)\n",
+ "\n",
+ "shutil.rmtree(REPOSITORY_ROOT_PATH)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/performance_tips.ipynb b/src/documentation/performance_tips.ipynb
new file mode 100644
index 000000000..ca065a9ce
--- /dev/null
+++ b/src/documentation/performance_tips.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from itertools import repeat\n",
+ "from typing import Any\n",
+ "\n",
+ "from intelligence_layer.core import NoOpTracer, Task, TaskSpan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "# How to get more done in less time\n",
+ "The following notebook contains tips for the following problems:\n",
+ " - A single task that takes very long to complete\n",
+ " - Running one task multiple times\n",
+ " - Running several different tasks at the same time\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## A single long running task\n",
+ "With a single long running task, consider the following:\n",
+ " - If there are other calculations to do, consider using `ThreadPool.submit`, together with `result`\n",
+ " - See [here](#submit_example) for an example\n",
+ " - If this is not the case consider:\n",
+ " - Choosing a faster model. The `base` model is faster than `extended`, `extended` is faster than `supreme`\n",
+ " - Choosing tasks that perform fewer LLM operations. E.g.: `MultiChunkQa` usually takes longer than `SingleChunkQa`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Running one task multiple times\n",
+ "When a single task should process multiple inputs, one can use `task.run_concurrently` to easily process the inputs at the same time \n",
+ "\n",
+ "**Example:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DummyTask(Task):\n",
+ " def do_run(self, input: Any, task_span: TaskSpan) -> Any:\n",
+ " time.sleep(2)\n",
+ " print(\"Task1 complete\")\n",
+ " return input\n",
+ "\n",
+ "\n",
+ "tracer = NoOpTracer()\n",
+ "\n",
+ "task_input = [\"A\", \"B\", \"C\", \"D\"]\n",
+ "task = DummyTask()\n",
+ "\n",
+ "\n",
+ "result = task.run_concurrently(\n",
+ " task_input, tracer\n",
+ ") # this finishes in 2 seconds instead of 8\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Running several tasks at the same time\n",
+ "When having to run multiple distinct tasks at the same time, one can leverage the existing `concurrent.futures` python library.\n",
+ "The following shows some examples on how this can be done"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Second long running task\n",
+ "\n",
+ "\n",
+ "class DummyTask2(Task):\n",
+ " def do_run(self, input: Any, task_span: TaskSpan) -> Any:\n",
+ " time.sleep(2)\n",
+ " print(\"Task2 complete\")\n",
+ " return input\n",
+ "\n",
+ "\n",
+ "# initialize all tasks and inputs\n",
+ "task_1 = DummyTask()\n",
+ "task_2 = DummyTask2()\n",
+ "\n",
+ "task_input_1 = list(range(10))\n",
+ "task_input_2 = list(range(10, 20))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "The individual tasks can then be submitted to a ThreadPool. \n",
+ "This is especially useful when there are other things to do while running tasks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+ " task_1_result = executor.submit(task_1.run_concurrently, task_input_1, tracer)\n",
+ " task_2_result = executor.submit(task_2.run_concurrently, task_input_2, tracer)\n",
+ " # ...other important code here\n",
+ " print(\"Task 1 result:\", task_1_result.result())\n",
+ " print(\"Task 2 result:\", task_2_result.result())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "`ThreadPool` can easily be used via the function `.map`. This processes a list of jobs in order and outputs the results once all jobs are done. \n",
+ "This is especially useful if there are many diverse jobs that take a varying amount of time. \n",
+ "However, since `map` only takes a single parameter, the input has to be bundled into a list of tuples beforehand."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jobs = list(zip(repeat(task_1), task_input_1)) + list(zip(repeat(task_2), task_input_2))\n",
+ "\n",
+ "with ThreadPoolExecutor(max_workers=20) as executor:\n",
+ " result = list(executor.map(lambda job: job[0].run(job[1], tracer), jobs))\n",
+ " print(\"Task 1 result:\", result[: len(task_input_1)])\n",
+ " print(\"Task 2 result:\", result[len(task_input_1) :])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "`ThreadPool.map` can also be used with `Task.run_concurrently()` in which case the creation of the jobs becomes slightly easier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+ " results = list(\n",
+ " executor.map(\n",
+ " lambda job: job[0].run_concurrently(job[1], tracer),\n",
+ " [(task_1, task_input_1), (task_2, task_input_2)],\n",
+ " )\n",
+ " )\n",
+ " print(\"Task 1 result:\", result[: len(task_input_1)])\n",
+ " print(\"Task 2 result:\", result[len(task_input_1) :])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Note\n",
+ "
\n",
+ "\n",
+ "If tasks are CPU bound, the abovementioned code will not help. In that case, replace the `ThreadPoolExecutor` with a `ProcessPoolExecutor`."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/documentation/qa.ipynb b/src/documentation/qa.ipynb
new file mode 100644
index 000000000..37955bc52
--- /dev/null
+++ b/src/documentation/qa.ipynb
@@ -0,0 +1,428 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "from IPython.display import Pretty\n",
+ "\n",
+ "from intelligence_layer.core import (\n",
+ " DetectLanguage,\n",
+ " DetectLanguageInput,\n",
+ " InMemoryTracer,\n",
+ " Language,\n",
+ " LuminousControlModel,\n",
+ " NoOpTracer,\n",
+ ")\n",
+ "from intelligence_layer.examples import (\n",
+ " LongContextQa,\n",
+ " LongContextQaInput,\n",
+ " MultipleChunkQa,\n",
+ " MultipleChunkQaInput,\n",
+ " SingleChunkQa,\n",
+ " SingleChunkQaInput,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Question and Answer\n",
+ "\n",
+ "A common use case for using large language models is to generate answers to questions based on a given piece of text.\n",
+ "\n",
+ "
\n",
+ "\n",
+ "This notebook we will be focusing on the open-book Q&A use case, where we provide the model with a piece of text we think is relevant to the question and ask the model to answer the question based on the given text.\n",
+ "To make proper use of the classification task, it is necessary to evaluate the results in an iterative way, to ensure it satisfies your requirements.\n",
+ "For an example of how such an evaluation can look like, refer to [evaluation.ipynb](./evaluation.ipynb).\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's grab a piece of text we want to ask a question about. We can start with a random Wikipedia article about [\"Surface micromachining\"](https://en.wikipedia.org/wiki/Surface_micromachining)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text = \"\"\"Surface micromachining\n",
+ "\n",
+ "Surface micromachining builds microstructures by deposition and etching structural layers over a substrate.[1] This is different from Bulk micromachining, in which a silicon substrate wafer is selectively etched to produce structures.\n",
+ "\n",
+ "Layers\n",
+ "\n",
+ "Generally, polysilicon is used as one of the substrate layers while silicon dioxide is used as a sacrificial layer. The sacrificial layer is removed or etched out to create any necessary void in the thickness direction. Added layers tend to vary in size from 2-5 micrometres. The main advantage of this machining process is the ability to build electronic and mechanical components (functions) on the same substrate. Surface micro-machined components are smaller compared to their bulk micro-machined counterparts.\n",
+ "\n",
+ "As the structures are built on top of the substrate and not inside it, the substrate's properties are not as important as in bulk micro-machining. Expensive silicon wafers can be replaced by cheaper substrates, such as glass or plastic. The size of the substrates may be larger than a silicon wafer, and surface micro-machining is used to produce thin-film transistors on large area glass substrates for flat panel displays. This technology can also be used for the manufacture of thin film solar cells, which can be deposited on glass, polyethylene terepthalate substrates or other non-rigid materials.\n",
+ "\n",
+ "Fabrication process\n",
+ "\n",
+ "Micro-machining starts with a silicon wafer or other substrate upon which new layers are grown. These layers are selectively etched by photo-lithography; either a wet etch involving an acid, or a dry etch involving an ionized gas (or plasma). Dry etching can combine chemical etching with physical etching or ion bombardment. Surface micro-machining involves as many layers as are needed with a different mask (producing a different pattern) on each layer. Modern integrated circuit fabrication uses this technique and can use as many as 100 layers. Micro-machining is a younger technology and usually uses no more than 5 or 6 layers. Surface micro-machining uses developed technology (although sometimes not enough for demanding applications) which is easily repeatable for volume production.\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the `SingleChunkQa`-task to answer questions about this text!\n",
+ "This particular task is optimized for shorter texts that fit into the model's context window.\n",
+ "The main things of interest are that you can provide a `QaInput`, which consists of a `question` you want to ask and a `text` to base that answer upon.\n",
+ "\n",
+ "The output will be a `QaOutput`, which will include an `answer` (if it can find one in the text) and `highlights` which mark the most relevant sections of the input text for the generated answer.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define some question you want to ask about the input text\n",
+ "question = \"What are some benefits of surface micro-machining?\"\n",
+ "\n",
+ "# Pass both the input text and the question to the SingleChunkQaInput-task\n",
+ "input = SingleChunkQaInput(chunk=text, question=question)\n",
+ "\n",
+ "# Define a LuminousControlModel and instantiate a SingleChunkQa task\n",
+ "model = LuminousControlModel(name=\"luminous-supreme-control\")\n",
+ "single_chunk_qa = SingleChunkQa(model=model)\n",
+ "\n",
+ "output = single_chunk_qa.run(input, NoOpTracer())\n",
+ "\n",
+ "Pretty(output.answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nice, we extracted some advantages!\n",
+ "\n",
+ "If you want to investigate based on which part of the input text the answer was produced, you can use the `highlights` property of the `SingleChunkQaOutput`.\n",
+ "Under the hood, is uses the explainability feature of the Aleph Alpha inference stack. Each highlight in the `highlights` list contains the start and end courser position of the relevant text section and a score indicating its degree of relevance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output.highlights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " f\"Highlight 1 (Score {round(output.highlights[0].score,2)}): {text[output.highlights[0].start:output.highlights[0].end]}\"\n",
+ ")\n",
+ "print(\n",
+ " f\"Highlight 2 (Score {round(output.highlights[1].score,2)}): {text[output.highlights[1].start:output.highlights[1].end]}\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What if we ask a question that cannot be answered on the basis of the text?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"Who plays Gandalf in LOTR?\"\n",
+ "input = SingleChunkQaInput(chunk=text, question=question)\n",
+ "output = single_chunk_qa.run(input, NoOpTracer())\n",
+ "\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, our QA-task returns *None*, because there is no answer to our question within the source document.\n",
+ "\n",
+ "Thus, we prevented a potential hallucination."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Language detection\n",
+ "\n",
+ "You can also ask questions about documents in languages other than English.\n",
+ "Our models support 5 European languages:\n",
+ "- English - 'en'\n",
+ "- German - 'de'\n",
+ "- Spanish - 'es'\n",
+ "- French - 'fr'\n",
+ "- Italian - 'it'\n",
+ " \n",
+ "We provide you with some tools making it easier to detect the language in the document."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "document_with_unknown_language = \"\"\"Rom begann ab dem 5. Jahrhundert v. Chr. mit einer immer rascheren Expansion in Mittelitalien (Eroberung von Veji 396 v. Chr.), musste dabei aber auch schwere Rückschläge verkraften. Der „Galliersturm“ unter Brennus hinterließ psychologisch tiefe Spuren, wobei die Schlacht an der Allia am 18. Juli (wahrscheinlich) 387 v. Chr. als „dies ater“ („schwarzer Tag“) in die Geschichte Roms einging. Es folgten die Samnitenkriege (343–341 v. Chr.; 326–304 v. Chr.; 298–290 v. Chr.) und der Latinerkrieg (um 340–338 v. Chr.). Rom schuf schließlich ein weitverzweigtes Bündnisgeflecht. So wurden an strategisch wichtigen Orten Kolonien angelegt und Bündnisse mit mehreren italischen Stämmen geschlossen, die jedoch nicht das römische Bürgerrecht erhielten.\n",
+ "\n",
+ "Aus dieser Zeit seiner Geschichte ging Rom als straffes Staatswesen mit schlagkräftiger Armee und starkem Drang zur Ausdehnung hervor. Damit waren die Grundlagen für seinen weiteren Aufstieg geschaffen. Konkurrierende Mächte stellten auf der Italischen Halbinsel die Stadtstaaten der Etrusker nördlich von Rom, die Kelten in der Po-Ebene und die griechischen Kolonien in Süditalien dar.\n",
+ "\n",
+ "Im 3. Jahrhundert v. Chr. setzte sich Rom gegen die Samniten und andere italische Stämme durch. Nach und nach fiel die gesamte Halbinsel an Rom (außer Oberitalien, welches erst später annektiert wurde). Im Süden verleibte sich die Republik um 275 v. Chr. die dortigen griechischen Stadtstaaten ein, nachdem es während des Pyrrhischen Krieges gelungen war, den hellenistischen Hegemon Pyrrhos I. von Epiros abzuwehren. Mit dieser Expansion kam Rom allerdings in Konflikt mit der bisher Rom freundlich gesinnten Handelsrepublik Karthago (im heutigen Tunesien), was zu den Punischen Kriegen führte.\"\"\"\n",
+ "\n",
+ "lang_detection_input = DetectLanguageInput(\n",
+ " text=document_with_unknown_language,\n",
+ " possible_languages=[\n",
+ " Language(language) for language in [\"en\", \"de\", \"es\", \"fr\", \"it\"]\n",
+ " ],\n",
+ ")\n",
+ "language = DetectLanguage().run(lang_detection_input, NoOpTracer())\n",
+ "language"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ah, it seems that this document is written in German!\n",
+ "Let's generate a German answer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"Wie viele Samnitenkriege gab es & wann fanden sie statt?\"\n",
+ "\n",
+ "input = SingleChunkQaInput(\n",
+ " chunk=document_with_unknown_language,\n",
+ " question=question,\n",
+ " language=language.best_fit,\n",
+ ")\n",
+ "output = single_chunk_qa.run(input, NoOpTracer())\n",
+ "\n",
+ "print(output.answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-chunk QA\n",
+ "\n",
+ "Some times you might have multiple texts you want to provide as context for your question. In this case the `MultipleChunkQa`-task might be the better option. The workflow of this task consists of the following steps:\n",
+ "1. The tasks takes multiple text chunks and a question as input.\n",
+ "2. It runs the model for each chunk generating an individual answer per chunk.\n",
+ "3. It generates a final answer based on the combination of the intermediate answers.\n",
+ "\n",
+ "Note, that for the `MultipleChunkQa` the combined length of all input chunks is **not** limited by the context window of the model. Thus, `MultipleChunkQa` provides one option to deal with long input texts by splitting them into multiple chunks. However, below in the section [Long context QA](#long-context-qa) we will present a more sophisticated approache on how to handle QA-tasks for long input texts. \n",
+ "\n",
+ "Now let's have a look at an example where two chunks lead to different parts of the final answer.\n",
+ "\n",
+ "This time, let's also use a proper debug log, so that we can see what happens under the hood!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chunks = [\n",
+ " 'Around 1997, Goldenvoice was struggling to book concerts against larger companies, and they were unable to offer guarantees as high as their competitors, such as SFX Entertainment. Tollett said, \"We were getting our ass kicked financially. We were losing a lot of bands. And we couldn\\'t compete with the money.\" As a result, the idea of a music festival was conceived, and Tollett began to brainstorm ideas for one with multiple venues. His intent was to book trendy artists who were not necessarily chart successes: \"Maybe if you put a bunch of them together, that might be a magnet for a lot of people.\" While attending the 1997 Glastonbury Festival, Tollett handed out pamphlets to artists and talent managers that featured pictures of the Empire Polo Club and pitched a possible festival there. In contrast to the frequently muddy conditions at Glastonbury caused by rain, he recalled, \"We had this pamphlet... showing sunny Coachella. Everyone was laughing.\"',\n",
+ " \"Rock am Ring wurde erstmals 1985 veranstaltet und war ursprünglich als ein einmaliges Ereignis geplant. Aufgrund des großen Erfolges mit 75.000 Zuschauern entschloss man sich jedoch, diese Veranstaltung jedes Jahr stattfinden zu lassen. Der Einbruch der Zuschauerzahlen 1988 hatte eine zweijährige Pause zur Folge. 1991 startete das größte deutsche Rockfestival mit einem überarbeiteten Konzept erneut. Ein neues Hauptaugenmerk wurde darauf gelegt, dem Publikum mehr Newcomer vorzustellen. So traten unter anderem die zu diesem Zeitpunkt eher unbekannten INXS oder Alanis Morissette bei Rock am Ring vor großem Publikum auf.\",\n",
+ "]\n",
+ "\n",
+ "question = \"What festival is the text about?\"\n",
+ "input = MultipleChunkQaInput(chunks=chunks, question=question)\n",
+ "\n",
+ "multi_chunk_qa = MultipleChunkQa(merge_answers_model=model)\n",
+ "tracer = InMemoryTracer()\n",
+ "output = multi_chunk_qa.run(input, tracer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(output.answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice how information from each chunk made it into the final combined answer; despite the texts being written in different languages.\n",
+ "\n",
+ "Furthermore, it is also possible to examine the inner workings of the QA mechanism by looking at the `tracer`. There, we track the inputs, outputs, and internal states used to calculate the mentioned outputs. You can also take a look at the parameters of the request sent to the API."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tracer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the trace we can see that the `MultipleChunkQa`-task runs the the `SingleChunkQa` twice, once for each chunk and then combines both answeres in a final `Complete`. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Long context QA\n",
+ "\n",
+ "Sometimes you want to execute a QA request against a document that is really, really long.\n",
+ "In such cases, the document will not fit into the context window of the model (e.g., 2048 tokens for Luminous) and not all parts of it will be relevant for the question at hand.\n",
+ "We will first need to split the document into chunks and use semantic search to find the most relevant chunks (i.e., those most similar to the question).\n",
+ "Then, we use these chunks to answer the question using the `MultipleChunkQa` functionality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://en.wikipedia.org/wiki/Robert_Moses\n",
+ "long_text = \"\"\"Robert Moses''' (December 18, 1888 – July 29, 1981) was an American [[urban planner]] and public official who worked in the [[New York metropolitan area]] during the early to mid 20th century. Despite never being elected to any office, Moses is regarded as one of the most powerful and influential individuals in the history of New York City and New York State. The grand scale of his infrastructural projects and his philosophy of urban development influenced a generation of engineers, architects, and urban planners across the United States.\n",
+ "\n",
+ "Moses held various positions throughout his more than forty-year long career. He at times held up to 12 titles simultaneously, including [[New York City Parks Commissioner]] and chairman of the [[Long Island State Park Commission]].{{Cite web|url=https://www.pbs.org/wnet/need-to-know/environment/the-legacy-of-robert-moses/16018/|title=The legacy of Robert Moses|last=Sarachan|first=Sydney|date=January 17, 2013|website=Need to Know {{!}} PBS|language=en-US|access-date=December 3, 2019}} Having worked closely with New York governor [[Al Smith]] early in his career, Moses became expert in writing laws and navigating and manipulating the inner workings of state government. He created and led numerous semi-autonomous [[Public authority|public authorities]], through which he controlled millions of dollars in revenue and directly issued [[Bond (finance)|bonds]] to fund new ventures with little outside input or oversight.\n",
+ "\n",
+ "Moses's projects transformed the New York area and revolutionized the way cities in the U.S. were designed and built. As Long Island State Park Commissioner, Moses oversaw the construction of [[Jones Beach State Park]], the most visited public beach in the United States,{{cite news |url=http://www.longislandexchange.com/jones-beach.html |website=Long Island Exchange |title=Jones Beach |access-date=November 21, 2012 |archive-url=https://web.archive.org/web/20130121130008/http://www.longislandexchange.com/jones-beach.html |archive-date=January 21, 2013 |url-status=dead }} and was the primary architect of the [[Parkways in New York|New York State Parkway System]]. As head of the [[MTA Bridges and Tunnels|Triborough Bridge Authority]], Moses had near-complete control over bridges and tunnels in New York City as well as the tolls collected from them, and built, among others, the [[Robert F. Kennedy Bridge|Triborough Bridge]], the [[Brooklyn–Battery Tunnel]], and the [[Throgs Neck Bridge]], as well as several major highways. These roadways and bridges, alongside [[urban renewal]] efforts that saw the destruction of huge swaths of tenement housing and their replacement with large [[New York City Housing Authority|public housing projects]], transformed the physical fabric of New York and inspired other cities to undertake similar development endeavors.\n",
+ "\n",
+ "Moses's reputation declined following the publication of [[Robert Caro]]'s [[Pulitzer Prize]]-winning biography ''[[The Power Broker]]'' (1974), which cast doubt on the purported benefits of many of Moses's projects and further cast Moses as racist. In large part because of ''The Power Broker'', Moses is today considered a controversial figure in the history of New York City.\n",
+ "\n",
+ "==Early life and career==\n",
+ "Moses was born in [[New Haven, Connecticut]], on December 18, 1888, to [[German Jewish]] parents, Bella (Silverman) and Emanuel Moses.{{cite news | url=https://www.nytimes.com/learning/general/onthisday/bday/1218.html | title=Robert Moses, Master Builder, is Dead at 92| newspaper=The New York Times |archive-url=https://web.archive.org/web/20160305003155/https://www.nytimes.com/learning/general/onthisday/bday/1218.html |archive-date=March 5, 2016 |url-status=dead}}{{sfn|Caro|1974|p=25}} He spent the first nine years of his life living at 83 Dwight Street in New Haven, two blocks from [[Yale University]]. In 1897, the Moses family moved to New York City,{{sfn|Caro|1974|pp=29}} where they lived on East 46th Street off Fifth Avenue.{{cite web |url=http://www.newsday.com/community/guide/lihistory/ny-history-hs722a,0,7092161.story |title=The Master Builder |access-date=April 4, 2007 |last=DeWan |first=George |year=2007 |website=Long Island History |publisher=Newsday |archive-url=https://web.archive.org/web/20061211045554/http://www.newsday.com/community/guide/lihistory/ny-history-hs722a%2C0%2C7092161.story |archive-date=December 11, 2006 |url-status=dead }} Moses's father was a successful department store owner and [[real estate]] speculator in New Haven. In order for the family to move to New York City, he sold his real estate holdings and store, then retired.{{sfn|Caro|1974|pp=29}} Moses's mother was active in the [[settlement movement]], with her own love of building. Robert Moses and his brother Paul attended several schools for their elementary and [[secondary education]], including the [[Ethical Culture Fieldston School|Ethical Culture School]], the [[Dwight School]] and the [[Mohegan Lake, New York#Historic places|Mohegan Lake School]], a military academy near [[Peekskill, New York|Peekskill]].{{sfn|Caro|1974|pp=35}}\n",
+ "\n",
+ "After graduating from [[Yale College]] (B.A., 1909) and [[Wadham College]], [[Oxford University|Oxford]] (B.A., Jurisprudence, 1911; M.A., 1913), and earning a Ph.D. in [[political science]] from [[Columbia University]] in 1914, Moses became attracted to New York City reform politics.{{Cite web|url=http://c250.columbia.edu/c250_celebrates/remarkable_columbians/robert_moses.html|title = Robert Moses}} A committed [[idealism|idealist]], he developed several plans to rid New York of [[Patronage#Politics|patronage hiring]] practices, including being the lead author of a 1919 proposal to reorganize the New York state government. None went very far, but Moses, due to his intelligence, caught the notice of [[Belle Moskowitz]], a friend and trusted advisor to Governor [[Al Smith]].{{sfn|Caro|1974}} When the state [[Secretary of State of New York|Secretary of State's]] position became appointive rather than elective, Smith named Moses. He served from 1927 to 1929.{{cite news |date=December 19, 1928 |title=Moses Resigns State Position |url=http://cdsun.library.cornell.edu/cgi-bin/cornell?a=d&d=CDS19281219.2.63.7# |newspaper=Cornell Daily Sun |location=Ithaca, NY |page=8}}\n",
+ "\n",
+ "Moses rose to power with Smith, who was elected as governor in 1918, and then again in 1922. With Smith's support, Moses set in motion a sweeping consolidation of the New York State government. During that period Moses began his first foray into large-scale public work initiatives, while drawing on Smith's political power to enact legislation. This helped create the new [[Long Island State Park Commission]] and the State Council of Parks.{{cite web|last=Gutfreund|first=Owen|title=Moses, Robert|url=http://www.anb.org/articles/07/07-00375.html|publisher=Anb.org|access-date=December 24, 2014}} In 1924, Governor Smith appointed Moses chairman of the State Council of Parks and president of the Long Island State Park Commission.{{Cite book|title=Encyclopedia of the City|url=https://archive.org/details/encyclopediacity00cave|url-access=limited|last=Caves|first=R. W.|publisher=Routledge|year=2004|isbn=978-0-415-25225-6|pages=[https://archive.org/details/encyclopediacity00cave/page/n512 472]}} This centralization allowed Smith to run a government later used as a model for Franklin D. Roosevelt's [[New Deal]] federal government.{{or|date=October 2022}} Moses also received numerous commissions that he carried out efficiently, such as the development of [[Jones Beach State Park]].{{cn|date=October 2022}} Displaying a strong command of [[law]] as well as matters of [[engineering]], Moses became known for his skill in drafting legislation, and was called \"the best bill drafter in [[Albany, New York|Albany]]\".{{cite news |title=Annals of Power |first=Robert A. |last=Caro |author-link=Robert Caro |url=http://archives.newyorker.com/?i=1974-07-22#folio=032 |magazine=[[The New Yorker]] |date=July 22, 1974 |access-date=September 1, 2011}} At a time when the public was accustomed to [[Tammany Hall]] corruption and incompetence, Moses was seen as a savior of government.{{sfn|Caro|1974}}\n",
+ "\n",
+ "Shortly after [[President of the United States|President]] [[Franklin Delano Roosevelt|Franklin D. Roosevelt's]] [[First inauguration of Franklin D. Roosevelt|inauguration]] in 1933, the [[United States federal government|federal government]] found itself with millions of [[New Deal]] dollars to spend, yet states and cities had few projects ready. Moses was one of the few local officials who had projects [[shovel ready]]. For that reason, New York City was able to obtain significant [[Works Progress Administration]] (WPA), [[Civilian Conservation Corps]] (CCC), and other Depression-era funding. One of his most influential and longest-lasting positions was that of Parks Commissioner of New York City, a role he served from January 18, 1934, to May 23, 1960.{{Cite web|url=https://www.nycgovparks.org/about/history/commissioners|title=New York City Parks Commissioners : NYC Parks|website=www.nycgovparks.org|language=en|access-date=March 29, 2018}}\n",
+ "\n",
+ "==Offices held==\n",
+ "The many offices and professional titles that Moses held gave him unusually broad power to shape urban development in the New York metropolitan region. These include, according to the New York Preservation Archive Project:{{Cite web|url=http://www.nypap.org/preservation-history/robert-moses/|title=Robert Moses {{!}}|website=www.nypap.org|language=en-US|access-date=March 29, 2018}}\n",
+ "*[[Long Island State Park Commission]] (President, 1924–1963)\n",
+ "* New York State Council of Parks (Chairman, 1924–1963)\n",
+ "*[[Secretary of State of New York|New York Secretary of State]] (1927–1928)\n",
+ "* Bethpage State Park Authority (President, 1933–1963)\n",
+ "* Emergency Public Works Commission (Chairman, 1933–1934)\n",
+ "* Jones Beach Parkway Authority (President, 1933–1963)\n",
+ "*[[New York City Department of Parks and Recreation|New York City Department of Parks]] (Commissioner, 1934–1960)\n",
+ "* [[Triborough Bridge]] and Tunnel Authority (Chairman, 1934–1968)\n",
+ "* New York City Planning Commission (Commissioner, 1942–1960)\n",
+ "* New York State Power Authority (Chairman, 1954–1962)\n",
+ "* [[1964 New York World's Fair|New York's World Fair]] (President, 1960–1966)\n",
+ "* Office of the Governor of New York (Special Advisor on Housing, 1974–1975)\n",
+ "\n",
+ "==Influence==\n",
+ "During the 1920s, Moses sparred with [[Franklin D. Roosevelt]], then head of the Taconic State Park Commission, who favored the prompt construction of a [[parkway]] through the [[Hudson Valley]]. Moses succeeded in diverting funds to his Long Island parkway projects (the [[Northern State Parkway]], the [[Southern State Parkway]] and the [[Wantagh State Parkway]]), although the [[Taconic State Parkway]] was later completed as well.{{cite web|url=http://www.nycroads.com/roads/taconic/ |title=Taconic State Parkway |website=NYCRoads.com |access-date=May 25, 2006}} Moses helped build Long Island's [[Meadowbrook State Parkway]]. It was the first fully divided limited access highway in the world.{{cite book|last=Leonard|first=Wallock|title=The Myth of The Master Builder|year=1991|publisher=Journal of Urban History|page=339}}\n",
+ "\n",
+ "Moses was a highly influential figure in the initiation of many of the reforms that restructured New York state's government during the 1920s. A 'Reconstruction Commission' headed by Moses produced a highly influential report that provided recommendations that would largely be adopted, including the consolidation of 187 existing agencies under 18 departments, a new executive budget system, and the four-year term limit for the governorship.{{sfn|Caro|1974|pp=106, 260}}\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "question = \"What is the name of the book about Robert Moses?\"\n",
+ "input = LongContextQaInput(text=long_text, question=question)\n",
+ "\n",
+ "long_context_qa = LongContextQa()\n",
+ "tracer = InMemoryTracer()\n",
+ "output = long_context_qa.run(input, tracer=tracer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(output.answer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the source chunk for this answer!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " \"Number of subanswers that contributed to the final answer:\", len(output.subanswers)\n",
+ ")\n",
+ "print(\"Subanswer:\", output.subanswers[0].answer)\n",
+ "print(\"Chunk:\", output.subanswers[0].chunk)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/quickstart_task.ipynb b/src/documentation/quickstart_task.ipynb
new file mode 100644
index 000000000..c8f75aacf
--- /dev/null
+++ b/src/documentation/quickstart_task.ipynb
@@ -0,0 +1,603 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from statistics import mean\n",
+ "from typing import Iterable\n",
+ "\n",
+ "from aleph_alpha_client import Prompt\n",
+ "from dotenv import load_dotenv\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "from intelligence_layer.core import (\n",
+ " AlephAlphaModel,\n",
+ " CompleteInput,\n",
+ " InMemoryTracer,\n",
+ " NoOpTracer,\n",
+ " Task,\n",
+ " TaskSpan,\n",
+ ")\n",
+ "from intelligence_layer.evaluation import (\n",
+ " AggregationLogic,\n",
+ " Aggregator,\n",
+ " Evaluator,\n",
+ " Example,\n",
+ " InMemoryAggregationRepository,\n",
+ " InMemoryDatasetRepository,\n",
+ " InMemoryEvaluationRepository,\n",
+ " InMemoryRunRepository,\n",
+ " Runner,\n",
+ " SingleOutputEvaluationLogic,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Setting up your own custom task\n",
+ "\n",
+ "If the available task methodologies are not suitable for your use case, this guide explains how to set up your own task from scratch.\n",
+ "Using the task interface gives us the added benefit of getting built-in input and output logging and gives us the possibility of using the evaluation framework.\n",
+ "\n",
+ "For the purpose of this tutorial, we will set up a simple keyword extraction task.\n",
+ "To do so, we will leverage `luminous-base` and a few-shot prompt to generate matching keywords for variable input texts.\n",
+ "Next, we will build an evaluator to check how well our extractor performs.\n",
+ "\n",
+ "## Initial task setup\n",
+ "\n",
+ "Let's start with the interface of any generic task. The full `Task` interface can be found here: [../intelligence_layer/task.py](../intelligence_layer/task.py).\n",
+ "However, to initially set up a `Task`, there are only a few parts relevant to us. For now, we shall only care about the following part of the interface:\n",
+ "\n",
+ "```python\n",
+ "Input = TypeVar(\"Input\", bound=PydanticSerializable)\n",
+ "Output = TypeVar(\"Output\", bound=PydanticSerializable)\n",
+ "\n",
+ "class Task(ABC, Generic[Input, Output]):\n",
+ " @abstractmethod\n",
+ " def do_run(self, input: Input, task_span: TaskSpan) -> Output:\n",
+ " \"\"\"Executes the process for this use-case.\"\"\"\n",
+ " ...\n",
+ "```\n",
+ "\n",
+ "For every task, we have to define an `Input`, an `Output` and how we would like to run it. Since these can vary so much, we make no assumptions about a `Task`'s implementation. \n",
+ "We only require both input and output to be `PydanticSerializable`. The best way to guarantee this is to make them pydantic `BaseModel`s. For our keyword extraction task, we will define `Input` and `Output` as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KeywordExtractionInput(BaseModel):\n",
+ " \"\"\"This is the text we will extract keywords from\"\"\"\n",
+ "\n",
+ " text: str\n",
+ "\n",
+ "\n",
+ "class KeywordExtractionOutput(BaseModel):\n",
+ " \"\"\"The matching set of keywords we aim to extract\"\"\"\n",
+ "\n",
+ " keywords: frozenset[str]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have our input and output defined, we will implement the actual task.\n",
+ "\n",
+ "The steps that the task consists of are:\n",
+ "- Create a `Prompt` using the input text.\n",
+ "- Have `luminous-base` complete the prompt.\n",
+ "- Extract keywords from said completion.\n",
+ "\n",
+ "When a task is executed, we offer the possibility to log all intermediate steps and outputs.\n",
+ "This is crucial because large language models are inherently probabilistic.\n",
+ "Therefore, we might get unexpected answers.\n",
+ "This logging allows us to check the results afterwards and find out what went wrong.\n",
+ "\n",
+ "For this, we shall inject an `InMemoryTracer` into the task. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KeywordExtractionTask(Task[KeywordExtractionInput, KeywordExtractionOutput]):\n",
+ " PROMPT_TEMPLATE: str = \"\"\"Identify matching keywords for each text.\n",
+ "###\n",
+ "Text: The \"Whiskey War\" is an ongoing conflict between Denmark and Canada over ownership of Hans Island. The dispute began in 1973, when Denmark and Canada reached an agreement on Greenland's borders. However, no settlement regarding Hans Island could be reached by the time the treaty was signed. Since then both countries have used peaceful means - such as planting their national flag or burying liquor - to draw attention to the disagreement.\n",
+ "Keywords: Conflict, Whiskey War, Denmark, Canada, Treaty, Flag, Liquor\n",
+ "###\n",
+ "Text: I really like pizza and sushi.\n",
+ "Keywords: Pizza, Sushi\n",
+ "###\n",
+ "Text: NASA launched the Discovery program to explore the solar system. It comprises a series of expeditions that have continued from the program's launch in the 1990s to the present day. In the course of the 16 expeditions launched so far, the Moon, Mars, Mercury and Venus, among others, have been explored. Unlike other space programs, the Discovery program places particular emphasis on cost efficiency, true to the motto: \"faster, better, cheaper\".\n",
+ "Keywords: Space program, NASA, Expedition, Cost efficiency, Moon, Mars, Mercury, Venus\n",
+ "###\n",
+ "Text: {text}\n",
+ "Keywords:\"\"\"\n",
+ " MODEL: str = \"luminous-base\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, model: AlephAlphaModel = AlephAlphaModel(name=\"luminous-base\")\n",
+ " ) -> None:\n",
+ " super().__init__()\n",
+ " self._model = model\n",
+ "\n",
+ " def _create_complete_input(self, text: str) -> Prompt:\n",
+ " prompt = Prompt.from_text(self.PROMPT_TEMPLATE.format(text=text))\n",
+ " # 'stop_sequences' is a list of strings which will stop generation if they're generated.\n",
+ " model_input = CompleteInput(\n",
+ " prompt=prompt,\n",
+ " stop_sequences=[\"\\n\", \"###\"],\n",
+ " frequency_penalty=0.25,\n",
+ " model=self._model.name,\n",
+ " )\n",
+ " return model_input\n",
+ "\n",
+ " def do_run(\n",
+ " self, input: KeywordExtractionInput, task_span: TaskSpan\n",
+ " ) -> KeywordExtractionOutput:\n",
+ " completion_input = self._create_complete_input(input.text)\n",
+ " completion = self._model.complete(completion_input, task_span)\n",
+ " return KeywordExtractionOutput(\n",
+ " keywords=set(\n",
+ " k.strip().lower() for k in completion.completion.split(\",\") if k.strip()\n",
+ " )\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we can run this `KeywordExtractionTask` like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "task = KeywordExtractionTask()\n",
+ "text = \"Computer vision describes the processing of an image by a machine using external devices (e.g., a scanner) into a digital description of that image for further processing. An example of this is optical character recognition (OCR), the recognition and processing of images containing text. Further processing and final classification of the image is often done using artificial intelligence methods. The goal of this field is to enable computers to process visual tasks that were previously reserved for humans.\"\n",
+ "\n",
+ "tracer = InMemoryTracer()\n",
+ "output = task.run(KeywordExtractionInput(text=text), tracer)\n",
+ "\n",
+ "print(output)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looks great!\n",
+ "\n",
+ "## Evaluation\n",
+ "\n",
+ "Now that our task is set up, we can start evaluating its performance.\n",
+ "\n",
+ "For this, we will have to set up an evaluator. The evaluator requires an `EvaluationLogic` and an `AggregationLogic` object. \n",
+ "The logic objects are responsible for how single examples are evaluated and how a list of examples are aggregated. \n",
+ "How these single examples are put together is the job of the `Evaluator`. This typically does not need to be changed and can just be used.\n",
+ "\n",
+ "```python\n",
+ "class EvaluationLogic(ABC, Generic[Input, Output, ExpectedOutput, Evaluation]):\n",
+ " @abstractmethod\n",
+ " def do_evaluate(\n",
+ " self,\n",
+ " example: Example[Input, ExpectedOutput],\n",
+ " *output: SuccessfulExampleOutput[Output],\n",
+ " ) -> Evaluation:\n",
+ " ...\n",
+ "\n",
+ "class AggregationLogic(ABC, Generic[Evaluation, AggregatedEvaluation]):\n",
+ " @abstractmethod\n",
+ " def aggregate(self, evaluations: Iterable[Evaluation]) -> AggregatedEvaluation:\n",
+ " ...\n",
+ "```\n",
+ "\n",
+ "Notice that, just like our `Task`, the `EvaluationLogic` takes an `Input`. This input is the same as our task input.\n",
+ "However, we don't just want to run a task; we also want to evaluate the result. \n",
+ "Therefore, our evaluation logic also depends on some `ExpectedOutput`, as well as `Evaluation`.\n",
+ "We will come back to the `AggregatedEvaluation` of the `AggregationLogic` at a later stage.\n",
+ "\n",
+ "Let's build an evaluation that can check the performance of our keyword extraction methodology. For this, we need four things:\n",
+ "- An implementation of the task to be run (we suggest supplying this in the `Evaluator`'s `__init__`)\n",
+ "- An interface for our `ExpectedOutput`\n",
+ "- Some `Evaluation`, i.e., the output of the `do_evaluate` method\n",
+ "- An implementation of the `do_evaluate` function in form of an `EvaluationLogic`.\n",
+ "\n",
+ "In our case, we will measure the performance of our keyword extraction by calculating the proportion of correctly generated keywords compared to all expected keywords. \n",
+ "This is also known as the \"true positive rate\". \n",
+ "To calculate this, our evaluate function will need a set of the expected keywords.\n",
+ "Also, we will add the missing keywords and keywords that are generated that we don't expect. \n",
+ "This way, we can see how our task performs for a specific example, and we can check for unexpected results.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KeywordExtractionExpectedOutput(BaseModel):\n",
+ " \"\"\"This is the expected output for an example run. This is used to compare the output of the task with.\n",
+ "\n",
+ " We will be evaluating our keyword extraction based on the expected keywords.\"\"\"\n",
+ "\n",
+ " keywords: frozenset[str]\n",
+ "\n",
+ "\n",
+ "class KeywordExtractionEvaluation(BaseModel):\n",
+ " \"\"\"This is the interface for the metrics that are generated for each evaluation case\"\"\"\n",
+ "\n",
+ " true_positive_rate: float\n",
+ " true_positives: frozenset[str]\n",
+ " false_positives: frozenset[str]\n",
+ " false_negatives: frozenset[str]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accordingly, our evaluate function will take a `KeywordExtractionInput`, and run the task with this.\n",
+ "Next, we shall compare the generated output with the `KeywordExtractionExpectedOutput` to create the `KeywordExtractionEvaluation`.\n",
+ "\n",
+ "```python\n",
+ "def do_evaluate(\n",
+ " self,\n",
+ " input: KeywordExtractionInput,\n",
+ " output: KeywordExtractionOutput,\n",
+ " expected_output: KeywordExtractionExpectedOutput,\n",
+ ") -> KeywordExtractionEvaluation:\n",
+ " true_positives = output.keywords & expected_output.keywords\n",
+ " false_positives = output.keywords - expected_output.keywords\n",
+ " false_negatives = expected_output.keywords - output.keywords\n",
+ " return KeywordExtractionEvaluation(\n",
+ " true_positive_rate=len(true_positives) / len(expected_output.keywords),\n",
+ " true_positives=true_positives,\n",
+ " false_positives=false_positives,\n",
+ " false_negatives=false_negatives,\n",
+ " )\n",
+ "```\n",
+ "\n",
+ "However, to quantitatively evaluate the performance of a task, we will need to run many different examples and calculate the metrics for each. \n",
+ "To do this, we can use the `eval_and_aggregate_runs` function provided by the `Evaluator` base class. This takes a dataset, runs all the examples, and aggregates the metrics generated from the evaluation.\n",
+ "\n",
+ "To set this up, we will first need to create an interface for the `AggregatedEvaluation` and implement the `aggregate` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"This is the interface for the aggregated metrics that are generated from running a number of examples\"\"\"\n",
+ "\n",
+ "\n",
+ "class KeywordExtractionAggregatedEvaluation(BaseModel):\n",
+ " average_true_positive_rate: float"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have all parts in place, let's run our task which will produce the results for evaluation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_repository = InMemoryDatasetRepository()\n",
+ "run_repository = InMemoryRunRepository()\n",
+ "\n",
+ "runner = Runner(task, dataset_repository, run_repository, \"keyword-extraction\")\n",
+ "model_input = KeywordExtractionInput(text=\"This is a text about dolphins and sharks.\")\n",
+ "expected_output = KeywordExtractionExpectedOutput(keywords=[\"dolphins\", \"sharks\"])\n",
+ "\n",
+ "single_example_dataset = dataset_repository.create_dataset(\n",
+ " examples=[Example(input=model_input, expected_output=expected_output)],\n",
+ " dataset_name=\"quickstart-task-single-example-dataset\",\n",
+ ").id\n",
+ "\n",
+ "run_overview = runner.run_dataset(single_example_dataset, NoOpTracer())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's build an evaluator.\n",
+ "For this, we need to implement a method doing the actual evaluation in a `EvaluationLogic` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KeywordExtractionEvaluationLogic(\n",
+ " SingleOutputEvaluationLogic[\n",
+ " KeywordExtractionInput,\n",
+ " KeywordExtractionOutput,\n",
+ " KeywordExtractionExpectedOutput,\n",
+ " KeywordExtractionEvaluation,\n",
+ " ]\n",
+ "):\n",
+ " def do_evaluate_single_output(\n",
+ " self,\n",
+ " example: Example[KeywordExtractionInput, KeywordExtractionOutput],\n",
+ " output: KeywordExtractionExpectedOutput,\n",
+ " ) -> KeywordExtractionEvaluation:\n",
+ " true_positives = output.keywords & example.expected_output.keywords\n",
+ " false_positives = output.keywords - example.expected_output.keywords\n",
+ " false_negatives = example.expected_output.keywords - output.keywords\n",
+ " return KeywordExtractionEvaluation(\n",
+ " true_positive_rate=len(true_positives) / len(output.keywords),\n",
+ " true_positives=true_positives,\n",
+ " false_positives=false_positives,\n",
+ " false_negatives=false_negatives,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now, we can create an evaluator and run it on our data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluation_repository = InMemoryEvaluationRepository()\n",
+ "evaluation_logic = KeywordExtractionEvaluationLogic()\n",
+ "evaluator = Evaluator(\n",
+ " dataset_repository,\n",
+ " run_repository,\n",
+ " evaluation_repository,\n",
+ " \"keyword-extraction\",\n",
+ " evaluation_logic,\n",
+ ")\n",
+ "\n",
+ "evaluation_overview = evaluator.evaluate_runs(run_overview.id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To aggregate the evaluation results, we have to implement a method doing this in an `AggregationLogic` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KeywordExtractionAggregationLogic(\n",
+ " AggregationLogic[\n",
+ " KeywordExtractionEvaluation,\n",
+ " KeywordExtractionAggregatedEvaluation,\n",
+ " ]\n",
+ "):\n",
+ " def aggregate(\n",
+ " self, evaluations: Iterable[KeywordExtractionEvaluation]\n",
+ " ) -> KeywordExtractionAggregatedEvaluation:\n",
+ " evaluation_list = list(evaluations)\n",
+ " true_positive_rate = (\n",
+ " mean(evaluation.true_positive_rate for evaluation in evaluation_list)\n",
+ " if evaluation_list\n",
+ " else 0\n",
+ " )\n",
+ " return KeywordExtractionAggregatedEvaluation(\n",
+ " average_true_positive_rate=true_positive_rate\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's now create an aggregator and generate evaluation statistics from the previously generated evaluation results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aggregation_repository = InMemoryAggregationRepository()\n",
+ "aggregation_logic = KeywordExtractionAggregationLogic()\n",
+ "aggregator = Aggregator(\n",
+ " evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"keyword-extraction\",\n",
+ " aggregation_logic,\n",
+ ")\n",
+ "\n",
+ "aggregation_overview = aggregator.aggregate_evaluation(evaluation_overview.id)\n",
+ "\n",
+ "print(\"Statistics: \", aggregation_overview.statistics)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have implemented all required methods, let's run a dataset with some more examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_id = dataset_repository.create_dataset(\n",
+ " examples=[\n",
+ " Example(input=model_input, expected_output=expected_output),\n",
+ " Example(\n",
+ " input=KeywordExtractionInput(\n",
+ " text=\"Clinical psychology is an integration of human science, behavioral science, theory, and clinical knowledge for the purpose of understanding, preventing, and relieving psychologically-based distress or dysfunction and to promote subjective well-being and personal development.\"\n",
+ " ),\n",
+ " expected_output=KeywordExtractionExpectedOutput(\n",
+ " keywords={\"clinical psychology\", \"well-being\", \"personal development\"}\n",
+ " ),\n",
+ " ),\n",
+ " Example(\n",
+ " input=KeywordExtractionInput(\n",
+ " text=\"Prospect theory is a theory of behavioral economics, judgment and decision making that was developed by Daniel Kahneman and Amos Tversky in 1979.[1] The theory was cited in the decision to award Kahneman the 2002 Nobel Memorial Prize in Economics.[2]Based on results from controlled studies, it describes how individuals assess their loss and gain perspectives in an asymmetric manner (see loss aversion).\"\n",
+ " ),\n",
+ " expected_output=KeywordExtractionExpectedOutput(\n",
+ " keywords={\n",
+ " \"prospect theory\",\n",
+ " \"behavioural economics\",\n",
+ " \"decision making\",\n",
+ " \"losses and gains\",\n",
+ " }\n",
+ " ),\n",
+ " ),\n",
+ " ],\n",
+ " dataset_name=\"human-evaluation-multiple-examples-dataset\",\n",
+ ").id\n",
+ "\n",
+ "run = runner.run_dataset(dataset_id)\n",
+ "evaluation_overview = evaluator.evaluate_runs(run.id)\n",
+ "aggregation_overview = aggregator.aggregate_evaluation(evaluation_overview.id)\n",
+ "\n",
+ "print(aggregation_overview)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have now run our first evaluation on this tiny dataset.\n",
+ "Let's take a more detailed look at the debug log of one of the example runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "examples = list(\n",
+ " dataset_repository.examples(\n",
+ " dataset_id, evaluator.input_type(), evaluator.expected_output_type()\n",
+ " )\n",
+ ")\n",
+ "print(examples[1].input.text)\n",
+ "examples.sort(key=lambda x: x.input.text)\n",
+ "last_example_result = run_repository.example_trace(\n",
+ " next(iter(aggregation_overview.run_overviews())).id, examples[1].id\n",
+ ")\n",
+ "last_example_result.trace"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's inspect this debug log from top to bottom to try and figure out what happened here.\n",
+ "\n",
+ "1. **Input**: This corresponds to the `Input` we supplied for our task. In this case, it's just the text of the provided example.\n",
+ "\n",
+ "2. **Completion request**: The request sent to the Aleph Alpha API. Here you can see the formatted prompt.\n",
+ "\n",
+ "3. **The output of the `CompletionTask`**: This is the original completion created by the API.\n",
+ "\n",
+ "4. **The output of our `KeywordExtractionTask`**: The output of our task. Here this is just a list of stripped, lowercase keywords.\n",
+ "\n",
+ "5. **Metrics**: Several metrics generated by our `KeywordExtractionTaskEvaluationLogic`.\n",
+ "\n",
+ "Let's have a look at the evaluation results.\n",
+ "Here, we can see that the model returned \"behavi*o*ral economics\" as a keyword.\n",
+ "However, in the `false_negatives`, we can see that we did indeed expect this phrase, but with a different spelling: \"behavi*ou*ral economics\".\n",
+ "Thus, the debug log helped us easily identify this misalignment between our dataset and the model's generation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "last_example_result = evaluation_repository.example_evaluation(\n",
+ " next(iter(aggregation_overview.evaluation_overviews)).id,\n",
+ " examples[1].id,\n",
+ " KeywordExtractionEvaluation,\n",
+ ")\n",
+ "print(last_example_result.result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, we predicted \"behavioural economics\" but expected \"behavioral economics\"...\n",
+ "\n",
+ "**What does this tell us?**\n",
+ "\n",
+ "Why did the British \"ou\" and the American \"o\" go to therapy?\n",
+ "\n",
+ "They had behavioural differences!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/summarization.ipynb b/src/documentation/summarization.ipynb
new file mode 100644
index 000000000..96f6d59f0
--- /dev/null
+++ b/src/documentation/summarization.ipynb
@@ -0,0 +1,361 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "from intelligence_layer.core import (\n",
+ " CompleteInput,\n",
+ " InMemoryTracer,\n",
+ " Language,\n",
+ " LuminousControlModel,\n",
+ " NoOpTracer,\n",
+ " TextChunk,\n",
+ ")\n",
+ "from intelligence_layer.examples import (\n",
+ " RecursiveSummarize,\n",
+ " RecursiveSummarizeInput,\n",
+ " SingleChunkSummarizeInput,\n",
+ " SteerableLongContextSummarize,\n",
+ " SteerableSingleChunkSummarize,\n",
+ ")\n",
+ "\n",
+ "load_dotenv()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Summarization\n",
+ "\n",
+ "Summarizing and compressing information, whether from a text, a book or freely from previous experience, is something that is inherently useful for many different types of knowledge work.\n",
+ "Large language models are adept at summarizing due to their sophisticated understanding of language structure, semantics, and context derived from the vast amounts of text they have been trained on.\n",
+ "\n",
+ "
\n",
+ "\n",
+ "This notebook is designed to showcase a summarization task.\n",
+ "To make proper use of such a summarization example, it is necessary to evaluate the results in an iterative way, to ensure it satisfies your requirements.\n",
+ "For an example of how such an evaluation can look like, refer to [evaluation.ipynb](./evaluation.ipynb).\n",
+ "
+
+
+
+### Trace
+
+A task implements a workflow. It processes its input, passes it on to sub-tasks, processes the outputs of the sub-tasks
+and builds its own output. This workflow can be represented in a trace. For this a task's `run` method takes a `Tracer`
+that takes care of storing details on the steps of this workflow like the tasks that have been invoked along with their
+input and output and timing information. The following illustration shows the trace of an MultiChunkQa-task:
+
+
+
+To represent this tracing defines the following concepts:
+
+- A `Tracer` is passed to a task's `run` method and provides methods for opening `Span`s or `TaskSpan`s.
+- A `Span` is a `Tracer` and allows to group multiple logs and runtime durations together as a single, logical step in the
+ workflow.
+- A `TaskSpan` is a `Span` that allows to group multiple logs together with the task's specific input and output.
+ An opened `TaskSpan` is passed to `Task.do_run`. Since a `TaskSpan` is a `Tracer` a `do_run` implementation can pass
+ this instance on to `run` methods of sub-tasks.
+
+The following diagram illustrates their relationship:
+
+
+
+Each of these concepts is implemented in form of an abstract base class and the Intelligence Layer provides
+several concrete implementations that store the actual traces in different backends. For each backend each of the
+three abstract classes `Tracer`, `Span` and `TaskSpan` needs to be implemented. Here only the top-level
+`Tracer`-implementations are listed:
+
+- The `NoOpTracer` can be used when tracing information shall not be stored at all.
+- The `InMemoryTracer` stores all traces in an in memory data structure and is most helpful in tests or
+ Jupyter notebooks.
+- The `FileTracer` stores all traces in a json-file.
+- The `OpenTelemetryTracer` uses an OpenTelemetry
+ [`Tracer`](https://opentelemetry-python.readthedocs.io/en/latest/api/trace.html#opentelemetry.trace.Tracer)
+ to store the traces in an OpenTelemetry backend.
+
+
+## Evaluation
+
+An important part of the Intelligence Layer is tooling that helps to evaluate custom tasks. Evaluation helps
+to measure how well the implementation of a task performs given real world examples. The outcome of an entire
+evaluation process is an aggregated evaluation result that consists out of metrics aggregated over all examples.
+
+The evaluation process helps to:
+
+- optimize a task's implementation by comparing and verifying if changes improve the performance.
+- compare the performance of one implementation of a task with that of other (already existing) implementations.
+- compare the performance of models for a given task implementation.
+- verify how changes to the environment (new model version, new finetuning version) affect the
+ performance of a task.
+
+
+### Dataset
+
+The basis of an evaluation is a set of examples for the specific task-type to be evaluated. A single `Example`
+consists of:
+
+- an instance of the `Input` for the specific task and
+- optionally an _expected output_ that can be anything that makes sense in context of the specific evaluation (e.g.
+ in case of classification this could contain the correct classification result, in case of QA this could contain
+ a _golden answer_, but if an evaluation is only about comparing results with other results of other runs this
+ could also be empty)
+
+To enable reproducibility of evaluations datasets are immutable. A single dataset can be used to evaluate all
+tasks of the same type, i.e. with the same `Input` and `Output` types.
+
+
+### Evaluation Process
+
+The Intelligence Layer supports different kinds of evaluation techniques. Most important are:
+
+- Computing absolute metrics for a task where the aggregated result can be compared with results of previous
+ result in a way that they can be ordered. Text classification could be a typical use case for this. In that
+ case the aggregated result could contain metrics like accuracy which can easily compared with other
+ aggregated results.
+- Comparing the individual outputs of different runs (all based on the same dataset)
+ in a single evaluation process and produce a ranking of all runs as an aggregated result. This technique is useful when it is hard to come up with an absolute metrics to evaluate
+ a single output, but it is easier to compare two different outputs and decide which one is better. An example
+ use case could be summarization.
+
+To support these techniques the Intelligence Layer differentiates between 3 consecutive steps:
+
+1. Run a task by feeding it all inputs of a dataset and collecting all outputs
+2. Evaluate the outputs of one or several runs and produce an evaluation result for each example. Typically a single run is evaluated if absolute
+ metrics can be computed and several runs are evaluated when the outputs of runs shall be compared.
+1. Aggregate the evaluation results of one or several evaluation runs into a single object containing the aggregated
+ metrics. Aggregating over several evaluation runs supports amending a previous comparison result with
+ comparisons of new runs without the need to re-execute the previous comparisons again.
+
+The following table shows how these three steps are represented in code:
+
+| Step | Executor | Custom Logic | Repository |
+|---------|----------|--------------|---------------|
+| 1. Run | `Runner` | `Task` | `RunRepository` |
+| 2. Evaluate | `Evaluator` | `EvaluationLogic` | `EvaluationRepository` |
+| 3. Aggregate | `Aggregator` | `AggregationLogic` | `AggregationRepository` |
+
+Columns explained
+- "Executor" lists concrete implementations provided by the Intelligence Layer.
+- "Custom Logic" lists abstract classes that need to be implemented with the custom logic.
+- "Repository" lists abstract classes for storing intermediate results. The Intelligence Layer provides
+ different implementations for these. See the next section for details.
+
+### Data Storage
+
+During an evaluation process a lot of intermediate data is created before the final aggregated result can be produced.
+To avoid that expensive computations have to be repeated if new results are to be produced based on previous ones
+all intermediate results are persisted. For this the different executor-classes make use of repositories.
+
+There are the following Repositories:
+
+- The `DatasetRepository` offers methods to manage datasets. The `Runner` uses it to read all `Example`s of a dataset and feeds them to the `Task`.
+- The `RunRepository` is responsible for storing a task's output (in form of an `ExampleOutput`) for each `Example` of a dataset
+ which are created when a `Runner`
+ runs a task using this dataset. At the end of a run a `RunOverview` is stored containing some metadata concerning the run.
+ The `Evaluator` reads these outputs given a list of runs it should evaluate to create an evaluation
+ result for each `Example` of the dataset.
+- The `EvaluationRepository` enables the `Evaluator` to store the evaluation result (in form of an `ExampleEvaluation`) for each example along with an `EvaluationOverview`. The `Aggregator` uses this repository to read the evaluation results.
+- The `AggregationRepository` stores the `AggregationOverview` containing the aggregated metrics on request of the `Aggregator`.
+
+The following diagrams illustrate how the different concepts play together in case of the different types of evaluations.
+
+
+![\"drawing\"](\"../../assets/argilla_splits.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ "For the Aggregation, we first need to define our `AggregationLogic` that has to take an `ArgillaEvaluation` as an input. As output, we use the `InstructAggregatedEvaluation` we defined earlier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InstructArgillaAggregationLogic(\n",
+ " AggregationLogic[ArgillaEvaluation, InstructAggregatedEvaluation]\n",
+ "):\n",
+ " def aggregate(\n",
+ " self,\n",
+ " evaluations: Iterable[ArgillaEvaluation],\n",
+ " ) -> InstructAggregatedEvaluation:\n",
+ " evaluations = list(evaluations)\n",
+ "\n",
+ " if len(evaluations) == 0: # if no evaluations were submitted, return\n",
+ " return InstructAggregatedEvaluation(\n",
+ " general_rating=None,\n",
+ " fluency=None,\n",
+ " evaluated_examples=0,\n",
+ " )\n",
+ "\n",
+ " general_rating = sum(\n",
+ " cast(float, evaluation.responses[\"general_rating\"])\n",
+ " for evaluation in evaluations\n",
+ " ) / len(evaluations)\n",
+ "\n",
+ " fluency = sum(\n",
+ " cast(float, evaluation.responses[\"fluency\"]) for evaluation in evaluations\n",
+ " ) / len(evaluations)\n",
+ "\n",
+ " return InstructAggregatedEvaluation(\n",
+ " general_rating=general_rating,\n",
+ " fluency=fluency,\n",
+ " evaluated_examples=len(evaluations),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "aggregation_logic = InstructArgillaAggregationLogic()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this, we can define our `ArgillaAggregator` and retrieve the aggregation of all records that have been evaluated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluation_repository = FileEvaluationRepository(REPOSITORY_ROOT_PATH)\n",
+ "argilla_evaluation_repository = ArgillaEvaluationRepository(\n",
+ " evaluation_repository,\n",
+ " argilla_client,\n",
+ " workspace_id, # we do not need to set questions and fields here\n",
+ ")\n",
+ "aggregation_repository = FileAggregationRepository(REPOSITORY_ROOT_PATH)\n",
+ "# either remember the id from before (eval_overview.id) or retrieve as below\n",
+ "eval_id = [\n",
+ " overview.id\n",
+ " for overview in argilla_evaluation_repository.evaluation_overviews()\n",
+ " if overview.description == \"instruct-evaluation\"\n",
+ "][0]\n",
+ "\n",
+ "\n",
+ "aggregator = ArgillaAggregator(\n",
+ " argilla_evaluation_repository,\n",
+ " aggregation_repository,\n",
+ " \"instruct-aggregation\",\n",
+ " aggregation_logic,\n",
+ ")\n",
+ "\n",
+ "if eval_overview:\n",
+ " output = aggregator.aggregate_evaluation(eval_id)\n",
+ " print(output.statistics)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Cleanup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ! WARNING ! This deletes the \"test-human-eval\" argilla workspace and the \"human-eval-data\" folder.\n",
+ "argilla_client.delete_workspace(workspace_id)\n",
+ "\n",
+ "shutil.rmtree(REPOSITORY_ROOT_PATH)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/documentation/performance_tips.ipynb b/src/documentation/performance_tips.ipynb
new file mode 100644
index 000000000..ca065a9ce
--- /dev/null
+++ b/src/documentation/performance_tips.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from itertools import repeat\n",
+ "from typing import Any\n",
+ "\n",
+ "from intelligence_layer.core import NoOpTracer, Task, TaskSpan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "# How to get more done in less time\n",
+ "The following notebook contains tips for the following problems:\n",
+ " - A single task that takes very long to complete\n",
+ " - Running one task multiple times\n",
+ " - Running several different tasks at the same time\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## A single long running task\n",
+ "With a single long running task, consider the following:\n",
+ " - If there are other calculations to do, consider using `ThreadPool.submit`, together with `result`\n",
+ " - See [here](#submit_example) for an example\n",
+ " - If this is not the case consider:\n",
+ " - Choosing a faster model. The `base` model is faster than `extended`, `extended` is faster than `supreme`\n",
+ " - Choosing tasks that perform fewer LLM operations. E.g.: `MultiChunkQa` usually takes longer than `SingleChunkQa`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Running one task multiple times\n",
+ "When a single task should process multiple inputs, one can use `task.run_concurrently` to easily process the inputs at the same time \n",
+ "\n",
+ "**Example:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DummyTask(Task):\n",
+ " def do_run(self, input: Any, task_span: TaskSpan) -> Any:\n",
+ " time.sleep(2)\n",
+ " print(\"Task1 complete\")\n",
+ " return input\n",
+ "\n",
+ "\n",
+ "tracer = NoOpTracer()\n",
+ "\n",
+ "task_input = [\"A\", \"B\", \"C\", \"D\"]\n",
+ "task = DummyTask()\n",
+ "\n",
+ "\n",
+ "result = task.run_concurrently(\n",
+ " task_input, tracer\n",
+ ") # this finishes in 2 seconds instead of 8\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Running several tasks at the same time\n",
+ "When having to run multiple distinct tasks at the same time, one can leverage the existing `concurrent.futures` python library.\n",
+ "The following shows some examples on how this can be done"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Second long running task\n",
+ "\n",
+ "\n",
+ "class DummyTask2(Task):\n",
+ " def do_run(self, input: Any, task_span: TaskSpan) -> Any:\n",
+ " time.sleep(2)\n",
+ " print(\"Task2 complete\")\n",
+ " return input\n",
+ "\n",
+ "\n",
+ "# initialize all tasks and inputs\n",
+ "task_1 = DummyTask()\n",
+ "task_2 = DummyTask2()\n",
+ "\n",
+ "task_input_1 = list(range(10))\n",
+ "task_input_2 = list(range(10, 20))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "The individual tasks can then be submitted to a ThreadPool. \n",
+ "This is especially useful when there are other things to do while running tasks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+ " task_1_result = executor.submit(task_1.run_concurrently, task_input_1, tracer)\n",
+ " task_2_result = executor.submit(task_2.run_concurrently, task_input_2, tracer)\n",
+ " # ...other important code here\n",
+ " print(\"Task 1 result:\", task_1_result.result())\n",
+ " print(\"Task 2 result:\", task_2_result.result())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "`ThreadPool` can easily be used via the function `.map`. This processes a list of jobs in order and outputs the results once all jobs are done. \n",
+ "This is especially useful if there are many diverse jobs that take a varying amount of time. \n",
+ "However, since `map` only takes a single parameter, the input has to be bundled into a list of tuples beforehand."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jobs = list(zip(repeat(task_1), task_input_1)) + list(zip(repeat(task_2), task_input_2))\n",
+ "\n",
+ "with ThreadPoolExecutor(max_workers=20) as executor:\n",
+ " result = list(executor.map(lambda job: job[0].run(job[1], tracer), jobs))\n",
+ " print(\"Task 1 result:\", result[: len(task_input_1)])\n",
+ " print(\"Task 2 result:\", result[len(task_input_1) :])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "`ThreadPool.map` can also be used with `Task.run_concurrently()` in which case the creation of the jobs becomes slightly easier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with ThreadPoolExecutor(max_workers=2) as executor:\n",
+ " results = list(\n",
+ " executor.map(\n",
+ " lambda job: job[0].run_concurrently(job[1], tracer),\n",
+ " [(task_1, task_input_1), (task_2, task_input_2)],\n",
+ " )\n",
+ " )\n",
+ " print(\"Task 1 result:\", result[: len(task_input_1)])\n",
+ " print(\"Task 2 result:\", result[len(task_input_1) :])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {},
+ "source": [
+ "