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Merge pull request #1 from guardrails-ai/initial-commit
[GR-987]: Package setup and initial implementation
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‎.github/workflows/pr_qc.yml‎

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name: Quality Control
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on:
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workflow_dispatch:
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pull_request: # Trigger the workflow on push events
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jobs:
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quality_control:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.11'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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make install-dev
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- name: Run Type Checks
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run: |
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make type
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- name: Run Linter
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run: |
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make lint

‎.github/workflows/release.yml‎

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name: Publish to PyPI
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permissions:
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id-token: write
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contents: write
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on:
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workflow_dispatch:
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# push:
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# branches:
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# - main
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jobs:
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setup:
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runs-on: ubuntu-latest
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env:
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GUARDRAILS_TOKEN: ${{ secrets.PRIV_PYPI_PUBLISH_TOKEN }}
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PYPI_REPOSITORY_URL: 'https://pypi.guardrailsai.com'
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steps:
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- name: Checkout Repository
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uses: actions/checkout@v4
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with:
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fetch-tags: true
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fetch-depth: 0
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.11'
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- name: Check for version bump
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id: version
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continue-on-error: false
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shell: bash {0}
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run: |
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PYPROJECT_TOML="pyproject.toml"
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# Extract the version using grep and sed
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version=$(grep -m 1 "version" "$PYPROJECT_TOML" | sed -E 's/.*version[[:space:]]*=[[:space:]]*"([^"]*)".*/\1/')
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echo "Project version: $version"
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if [ -z "$version" ]
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then
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echo "Version is missing from pyproject.toml!"
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exit 1
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fi
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echo "Checking if $version already exists..."
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version_commit="$(git rev-parse "$version" 2>/dev/null)"
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if [ ! -z "$version_commit" ] && [ "$version_commit" != "$version" ];
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then
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echo "Version $version already exist on commit $version_commit!"
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echo "Abandoning build..."
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echo "To complete this release update the version field in the pyproject.toml with an appropriate semantic version."
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exit 1
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else
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echo "version=$version" >> "$GITHUB_OUTPUT"
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exit 0
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fi
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- name: Install Twine & Build
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shell: bash
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run: |
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python -m pip install --upgrade pip
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pip install twine build toml
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- name: Create .pypirc
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shell: bash
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run: |
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touch ~/.pypirc
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echo "[distutils]" >> ~/.pypirc
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echo "index-servers =" >> ~/.pypirc
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echo " private-repository" >> ~/.pypirc
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echo "" >> ~/.pypirc
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echo "[private-repository]" >> ~/.pypirc
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echo "repository = $PYPI_REPOSITORY_URL" >> ~/.pypirc
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echo "username = __token__" >> ~/.pypirc
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echo "password = $GUARDRAILS_TOKEN" >> ~/.pypirc
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- name: Build & Upload
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shell: bash
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run: |
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python -m build
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twine upload dist/* -u __token__ -p $GUARDRAILS_TOKEN -r private-repository
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- name: Create .pypirc for PyPI.org
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shell: bash
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env:
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PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: |
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echo "[distutils]" > ~/.pypirc
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echo "index-servers =" >> ~/.pypirc
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echo " pypi" >> ~/.pypirc
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echo "" >> ~/.pypirc
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echo "[pypi]" >> ~/.pypirc
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echo "repository = https://upload.pypi.org/legacy/" >> ~/.pypirc
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echo "username = __token__" >> ~/.pypirc
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echo "password = $PYPI_PASSWORD" >> ~/.pypirc
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- name: Upload to PyPI.org
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shell: bash
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env:
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PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
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run: |
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twine upload dist/* -u __token__ -p $PYPI_PASSWORD -r pypi
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- name: Tag
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id: tag
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continue-on-error: false
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run: |
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version="${{ steps.version.outputs.version }}"
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echo "Configuring github bot"
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git config user.name "github-actions[bot]"
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# Comes from https://api.github.com/users/github-actions%5Bbot%5D
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git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
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echo "Creating github tag: $version"
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git tag "$version"
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echo "Pushing tags"
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git push --tags

‎.gitignore‎

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build
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*.egg-info
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.venv
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.ruff_cache

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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# Ruff version.
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rev: v0.9.4
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hooks:
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# Run the linter.
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- id: ruff
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args: [ --fix ]
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# Run the formatter.
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- id: ruff-format

‎LICENSE‎

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MIT License
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Copyright (c) 2024 Guardrails AI
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎Makefile‎

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.PHONY: install install-dev lint lint-fix type qa test test-cov test-cov-ci
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# Installs production dependencies
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install:
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pip install .;
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# Installs development dependencies
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install-dev:
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pip install ".[dev]";
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lint:
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ruff check .
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ruff format .
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lint-fix:
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ruff check . --fix
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ruff format .
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type:
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pyright
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qa:
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make install-dev
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make lint
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make type
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test:
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python -m unittest discover -s tests --buffer --failfast
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test-cov:
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coverage run -m unittest discover --start-directory tests --buffer --failfast
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coverage report -m
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test-cov-ci:
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coverage run -m unittest discover --start-directory tests --buffer --failfast

‎README.md‎

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# snowglobe-telemetry-openinference
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Telemetry instrumentation for the snowglobe client using OpenInference semantics.
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# Snowlgobe Telemetry Instrumentation for OpenInference
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Instrument your Snowglobe connected app with OpenInference and start sending traces to popular OpenInference compatible sinks like Arize or Arize Phoenix.
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## Installation
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```
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pip install snowglobe-telemetry-openinference
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```
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If using uv, set the `--prerelease=allow` flag
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```
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uv pip install --prerelease=allow snowglobe-telemetry-openinference
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```
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## Add the OpenInferenceInstrumentor to your agent file
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Reminder: Each agent wrapper file resides in the root directory of your project, and is named after the agent (e.g. `My Agent Name` becomes `my_agent_name.py`).
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```python
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from snowglobe.client import CompletionRequest, CompletionFunctionOutputs
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from openai import OpenAI
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import os
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### Add these two lines to your agent file and watch context rich traces come in!
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from snowglobe.telemetry.openinference import OpenInferenceInstrumentor
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OpenInferenceInstrumentor().instrument()
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client = OpenAI(api_key=os.getenv("SNOWGLOBE_API_KEY"))
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def completion_fn(request: CompletionRequest) -> CompletionFunctionOutputs:
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"""
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Process a scenario request from Snowglobe.
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This function is called by the Snowglobe client to process requests. It should return a
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CompletionFunctionOutputs object with the response content.
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Example CompletionRequest:
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CompletionRequest(
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messages=[
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SnowglobeMessage(role="user", content="Hello, how are you?", snowglobe_data=None),
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]
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)
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Example CompletionFunctionOutputs:
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CompletionFunctionOutputs(response="This is a string response from your application")
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Args:
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request (CompletionRequest): The request object containing the messages.
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Returns:
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CompletionFunctionOutputs: The response object with the generated content.
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"""
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# Process the request using the messages. Example:
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messages = request.to_openai_messages()
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages
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)
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return CompletionFunctionOutputs(response=response.choices[0].message.content)
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```
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## Enhancing Snowglobe Connect SDK's Traces with OpenInference Integrations
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You can add more rich context to the traces the Snowglobe Connect SDK captures by installing additional OpenInference instrumentors and registering the appropriate tracer provider in your agent wrapper file.
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The below examples shows how to add OpenAI instrumentation for either Arize or Arize Phoenix in addition to Snowglobe's OpenInference instrumentation:
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### Arize
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Install the Arize OpenTelemetry pacakge and the OpenAI specific instrumentor.
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```sh
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pip install openinference-instrumentation-openai arize-otel
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```
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Then register the tracer provider and use the OpenAI instrumentator in your agent file:
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```py
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import os
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from openai import OpenAI
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from snowglobe.client import CompletionRequest, CompletionFunctionOutputs
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from arize.otel import register
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# Setup OTel via our convenience function
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tracer_provider = register(
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space_id = "your-space-id", # in app space settings page
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api_key = "your-api-key", # in app space settings page
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project_name = "your-project-name", # name this to whatever you would like
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)
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# Import the OpenAI instrumentor from OpenInference
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from openinference.instrumentation.openai import OpenAIInstrumentor
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# Instrument OpenAI
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OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
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# Import the OpenInference instrumentor from Snowglobe
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from snowglobe.telemetry.openinference import OpenInferenceInstrumentor
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# Instrument the Snowglobe client
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OpenInferenceInstrumentor().instrument(tracer_provider=tracer_provider)
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def completion_fn(request: CompletionRequest) -> CompletionFunctionOutputs:
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messages = request.to_openai_messages()
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages
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)
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return CompletionFunctionOutputs(response=response.choices[0].message.content)
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```
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### Arize Phoenix
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Install the Arize Phoenix OpenTelemetry pacakge and the OpenAI specific instrumentor.
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```sh
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pip install openinference-instrumentation-openai arize-phoenix-otel
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```
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Then register the tracer provider and use the OpenAI instrumentator in your agent file:
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```py
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import os
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from openai import OpenAI
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from snowglobe.client import CompletionRequest, CompletionFunctionOutputs
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from phoenix.otel import register
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os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
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# configure the Phoenix tracer
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tracer_provider = register(
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project_name="my-llm-app", # Default is 'default'
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)
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# Import the OpenAI instrumentor from OpenInference
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from openinference.instrumentation.openai import OpenAIInstrumentor
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# Instrument OpenAI
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OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
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# Import the OpenInference instrumentor from Snowglobe
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from snowglobe.telemetry.openinference import OpenInferenceInstrumentor
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# Instrument the Snowglobe client
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OpenInferenceInstrumentor().instrument(tracer_provider=tracer_provider)
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def completion_fn(request: CompletionRequest) -> CompletionFunctionOutputs:
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messages = request.to_openai_messages()
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages
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)
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return CompletionFunctionOutputs(response=response.choices[0].message.content)
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```

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