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Adding scripts and workflow for release notes generation (#81)
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‎.github/actions/ai-release-notes/action.yml‎

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name: AI Release Notes
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description: Generate AI release notes using git and openai, outputs 'RELEASE_NOTES' and 'OPENAI_PROMPT'
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inputs:
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OPENAI_API_KEY:
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required: true
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type: string
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GHA_PAT:
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required: true
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type: string
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model_name:
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required: false
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type: string
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default: gpt-4o-mini
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repo_path:
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required: false
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type: string
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custom_prompt:
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required: false
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default: ''
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type: string
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git_ref:
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required: false
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type: string
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default: ''
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head_ref:
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required: false
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type: string
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default: main
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base_ref:
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required: false
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type: string
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default: main
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outputs:
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RELEASE_NOTES:
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description: "AI generated release notes"
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value: ${{ steps.ai_release_notes.outputs.RELEASE_NOTES }}
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OPENAI_PROMPT:
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description: "Prompt used to generate release notes"
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value: ${{ steps.ai_prompt.outputs.OPENAI_PROMPT }}
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env:
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GITHUB_REF: ${{ inputs.git_ref == '' && github.event.pull_request.head.ref || inputs.git_ref }}
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BASE_REF: ${{ inputs.base_ref == '' && github.base_ref || inputs.base_ref }}
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HEAD_REF: ${{ inputs.head_ref == '' && github.event.pull_request.head.sha || inputs.head_ref }}
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runs:
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using: "composite"
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steps:
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- uses: actions/checkout@v4
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with:
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repository: ${{ inputs.repo_path }}
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token: ${{ inputs.GHA_PAT }}
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ref: ${{ env.GITHUB_REF }}
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fetch-depth: 0
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- name: Set Workspace
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shell: bash
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run: |
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pip install tiktoken
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pip install pytz
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# Github outputs: 'OPENAI_PROMPT'
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- name: Add Git Info to base prompt
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id: ai_prompt
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shell: bash
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env:
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BASE_REF: ${{ env.BASE_REF }}
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HEAD_SHA: ${{ env.HEAD_SHA }}
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PR_TITLE: ${{ github.event.pull_request.title }}
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PR_BODY: ${{ github.event.pull_request.body }}
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MODEL_NAME: ${{ inputs.model_name }}
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CUSTOM_PROMPT: ${{ inputs.custom_prompt }} # Default: ''
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run: python .github/scripts/release-notes-prompt.py
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# Github outputs: 'RELEASE_NOTES'
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- name: Generate AI release notes
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id: ai_release_notes
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shell: bash
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env:
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OPENAI_API_KEY: ${{ inputs.OPENAI_API_KEY }}
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CUSTOM_PROMPT: ${{ steps.ai_prompt.outputs.OPENAI_PROMPT }}
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MODEL_NAME: ${{ inputs.model_name }}
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run: python .github/scripts/ai-release-notes.py
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‎.github/scripts/ai-release-notes.py‎

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"""
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AI-powered release notes generator that creates concise and informative release notes from git changes.
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This script uses OpenAI's API to analyze git changes (summary, diff, and commit log) and generate
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well-formatted release notes in markdown. It focuses on important changes and their impact,
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particularly highlighting new types and schemas while avoiding repetitive information.
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Environment Variables Required:
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OPENAI_API_KEY: OpenAI API key for authentication
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CHANGE_SUMMARY: Summary of changes made (optional if CUSTOM_PROMPT provided)
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CHANGE_DIFF: Git diff of changes (optional if CUSTOM_PROMPT provided)
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CHANGE_LOG: Git commit log (optional if CUSTOM_PROMPT provided)
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GITHUB_OUTPUT: Path to GitHub output file
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CUSTOM_PROMPT: Custom prompt to override default (optional)
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"""
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import os
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import requests # type: ignore
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import json
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import tiktoken # type: ignore
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OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
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CHANGE_SUMMARY = os.environ.get('CHANGE_SUMMARY', '')
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CHANGE_DIFF = os.environ.get('CHANGE_DIFF', '')
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CHANGE_LOG = os.environ.get('CHANGE_LOG', '')
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GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT")
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OPEN_AI_BASE_URL = "https://api.openai.com/v1"
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OPEN_API_HEADERS = {"Authorization": f"Bearer {OPENAI_API_KEY}", "Content-Type": "application/json"}
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CUSTOM_PROMPT = os.environ.get('CUSTOM_PROMPT', '')
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MODEL_NAME = os.environ.get('MODEL_NAME', 'gpt-3.5-turbo-16k')
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def num_tokens_from_string(string: str, model_name: str) -> int:
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"""
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Calculate the number of tokens in a text string for a specific model.
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Args:
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string: The input text to count tokens for
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model_name: Name of the OpenAI model to use for token counting
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Returns:
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int: Number of tokens in the input string
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"""
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encoding = tiktoken.encoding_for_model(model_name)
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num_tokens = len(encoding.encode(string))
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return num_tokens
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def truncate_to_token_limit(text, max_tokens, model_name):
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"""
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Truncate text to fit within a maximum token limit for a specific model.
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Args:
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text: The input text to truncate
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max_tokens: Maximum number of tokens allowed
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model_name: Name of the OpenAI model to use for tokenization
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Returns:
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str: Truncated text that fits within the token limit
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"""
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encoding = tiktoken.encoding_for_model(model_name)
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encoded = encoding.encode(text)
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truncated = encoded[:max_tokens]
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return encoding.decode(truncated)
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def generate_release_notes(model_name):
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"""
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Generate release notes using OpenAI's API based on git changes.
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Uses the GPT-3.5-turbo model to analyze change summary, commit log, and code diff
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to generate concise and informative release notes in markdown format. The notes
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focus on important changes and their impact, with sections for new types/schemas
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and other updates.
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Returns:
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str: Generated release notes in markdown format
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Raises:
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requests.exceptions.RequestException: If the OpenAI API request fails
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"""
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max_tokens = 14000 # Reserve some tokens for the response
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# Truncate inputs if necessary to fit within token limits
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change_summary = '' if CUSTOM_PROMPT else truncate_to_token_limit(CHANGE_SUMMARY, 1000, model_name)
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change_log = '' if CUSTOM_PROMPT else truncate_to_token_limit(CHANGE_LOG, 2000, model_name)
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change_diff = '' if CUSTOM_PROMPT else truncate_to_token_limit(CHANGE_DIFF, max_tokens - num_tokens_from_string(change_summary, model_name) - num_tokens_from_string(change_log, model_name) - 1000, model_name)
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url = f"{OPEN_AI_BASE_URL}/chat/completions"
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# Construct prompt for OpenAI API
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openai_prompt = CUSTOM_PROMPT if CUSTOM_PROMPT else f"""Based on the following summary of changes, commit log and code diff, please generate concise and informative release notes:
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Summary of changes:
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{change_summary}
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Commit log:
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{change_log}
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Code Diff:
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{json.dumps(change_diff)}
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"""
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data = {
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"model": model_name,
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"messages": [{"role": "user", "content": openai_prompt}],
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"temperature": 0.7,
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"max_tokens": 1000,
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}
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print("----------------------------------------------------------------------------------------------------------")
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print("POST request to OpenAI")
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print("----------------------------------------------------------------------------------------------------------")
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ai_response = requests.post(url, headers=OPEN_API_HEADERS, json=data)
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print(f"Status Code: {str(ai_response.status_code)}")
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print(f"Response: {ai_response.text}")
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ai_response.raise_for_status()
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return ai_response.json()["choices"][0]["message"]["content"]
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release_notes = generate_release_notes(MODEL_NAME)
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print("----------------------------------------------------------------------------------------------------------")
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print("OpenAI generated release notes")
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print("----------------------------------------------------------------------------------------------------------")
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print(release_notes)
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# Write the release notes to GITHUB_OUTPUT
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with open(GITHUB_OUTPUT, "a") as outputs_file:
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outputs_file.write(f"RELEASE_NOTES<<EOF\n{release_notes}\nEOF")

‎.github/scripts/release-notes-prompt.py‎

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"""
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This script generates a base prompt for OpenAI to create release notes.
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"""
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#!/usr/bin/env python3
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import os
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import subprocess
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import json
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import re
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import tiktoken # type: ignore
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from datetime import datetime;
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from pytz import timezone
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GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT")
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BASE_REF = os.getenv("BASE_REF", "main")
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HEAD_SHA = os.environ["HEAD_SHA"]
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PR_TITLE = os.environ["PR_TITLE"]
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PR_BODY = os.environ["PR_BODY"]
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EXISTING_NOTES = os.environ.get("EXISTING_NOTES", "null")
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MODEL_NAME = os.environ.get('MODEL_NAME', 'gpt-3.5-turbo-16k')
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CUSTOM_PROMPT = os.environ.get('CUSTOM_PROMPT', '')
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def extract_description_section(pr_body):
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# Find content between ## Description and the next ## or end of text
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description_match = re.search(r'## Description\s*\n(.*?)(?=\n##|$)', pr_body, re.DOTALL)
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if description_match:
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content = description_match.group(1).strip()
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# Remove the comment line if it exists
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comment_pattern = r'\[comment\]:.+?\n'
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content = re.sub(comment_pattern, '', content)
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return content.strip()
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return ""
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def extract_ellipsis_important(pr_body):
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# Find content between <!-- ELLIPSIS_HIDDEN --> and <!-- ELLIPSIS_HIDDEN --> that contains [!IMPORTANT]
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ellipsis_match = re.search(r'<!--\s*ELLIPSIS_HIDDEN\s*-->(.*?)<!--\s*ELLIPSIS_HIDDEN\s*-->', pr_body, re.DOTALL)
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if ellipsis_match:
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content = ellipsis_match.group(1).strip()
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important_match = re.search(r'\[!IMPORTANT\](.*?)(?=\[!|$)', content, re.DOTALL)
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if important_match:
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important_text = important_match.group(1).strip()
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important_text = re.sub(r'^-+\s*', '', important_text)
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return important_text.strip()
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return ""
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def extract_coderabbit_summary(pr_body):
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# Find content between ## Summary by CodeRabbit and the next ## or end of text
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summary_match = re.search(r'## Summary by CodeRabbit\s*\n(.*?)(?=\n##|$)', pr_body, re.DOTALL)
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return summary_match.group(1).strip() if summary_match else ""
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def num_tokens_from_string(string: str, model_name: str) -> int:
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"""
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Calculate the number of tokens in a text string for a specific model.
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Args:
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string: The input text to count tokens for
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model_name: Name of the OpenAI model to use for token counting
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Returns:
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int: Number of tokens in the input string
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"""
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encoding = tiktoken.encoding_for_model(model_name)
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num_tokens = len(encoding.encode(string))
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return num_tokens
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def truncate_to_token_limit(text, max_tokens, model_name):
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"""
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Truncate text to fit within a maximum token limit for a specific model.
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Args:
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text: The input text to truncate
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max_tokens: Maximum number of tokens allowed
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model_name: Name of the OpenAI model to use for tokenization
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Returns:
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str: Truncated text that fits within the token limit
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"""
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encoding = tiktoken.encoding_for_model(model_name)
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encoded = encoding.encode(text)
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truncated = encoded[:max_tokens]
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return encoding.decode(truncated)
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# Extract sections and combine into PR_OVERVIEW
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description = extract_description_section(PR_BODY)
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important = extract_ellipsis_important(PR_BODY)
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summary = extract_coderabbit_summary(PR_BODY)
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PR_OVERVIEW = "\n\n".join(filter(None, [description, important, summary]))
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# Get git information
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base_sha = subprocess.getoutput(f"git rev-parse origin/{BASE_REF}") if BASE_REF == 'main' else BASE_REF
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diff_overview = subprocess.getoutput(f"git diff {base_sha}..{HEAD_SHA} --name-status | awk '{{print $2}}' | sort | uniq -c | awk '{{print $2 \": \" $1 \" files changed\"}}'")
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git_log = subprocess.getoutput(f"git log {base_sha}..{HEAD_SHA} --pretty=format:'%h - %s (%an)' --reverse | head -n 50")
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git_diff = subprocess.getoutput(f"git diff {base_sha}..{HEAD_SHA} --minimal --abbrev --ignore-cr-at-eol --ignore-space-at-eol --ignore-space-change --ignore-all-space --ignore-blank-lines --unified=0 --diff-filter=ACDMRT")
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max_tokens = 14000 # Reserve some tokens for the response
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changes_summary = truncate_to_token_limit(diff_overview, 1000, MODEL_NAME)
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git_logs = truncate_to_token_limit(git_log, 2000, MODEL_NAME)
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changes_diff = truncate_to_token_limit(git_diff, max_tokens - num_tokens_from_string(changes_summary, MODEL_NAME) - num_tokens_from_string(git_logs, MODEL_NAME) - 1000, MODEL_NAME)
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# Get today's existing changelog if any
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existing_changelog = EXISTING_NOTES if EXISTING_NOTES != "null" else None
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existing_changelog_text = f"\nAdditional context:\n{existing_changelog}" if existing_changelog else ""
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TODAY = datetime.now(timezone('US/Eastern')).isoformat(sep=' ', timespec='seconds')
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BASE_PROMPT = f"""Based on the following 'PR Information', please generate concise and informative release notes to be read by developers.
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BASE_PROMPT = CUSTOM_PROMPT if CUSTOM_PROMPT else f"""Based on the following 'PR Information', please generate concise and informative release notes to be read by developers.
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Format the release notes with markdown, and always use this structure: a descriptive and very short title (no more than 8 words) with heading level 2, a paragraph with a summary of changes (no header), and if applicable, sections for '🚀 New Features & Improvements', '🐛 Bugs Fixed' and '🔧 Other Updates', with heading level 3, skip respectively the sections if not applicable.
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Finally include the following markdown comment with the PR merged date: <!-- PR_DATE: {TODAY} -->.
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Avoid being repetitive and focus on the most important changes and their impact, discard any mention of version bumps/updates, changeset files, environment variables or syntax updates.
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PR Information:"""
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OPENAI_PROMPT = f"""{BASE_PROMPT}
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Git log summary:
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{changes_summary}
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Commit Messages:
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{git_logs}
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PR Title:
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{PR_TITLE}
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PR Overview:
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{PR_OVERVIEW}{existing_changelog_text}
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Code Diff:
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{json.dumps(changes_diff)}"""
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print("OpenAI Prompt")
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print("----------------------------------------------------------------")
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print(OPENAI_PROMPT)
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# Write the prompt to GITHUB_OUTPUT
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with open(GITHUB_OUTPUT, "a") as outputs_file:
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outputs_file.write(f"BASE_PROMPT<<EOF\n{BASE_PROMPT}\nEOF")
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outputs_file.write(f"OPENAI_PROMPT<<EOF\n{OPENAI_PROMPT}\nEOF")

‎.github/workflows/changeset-ai-releases.yml‎

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echo "version=$VERSION"
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echo "prev_version=$PREV_VERSION"
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# Generate base prompt for OpenAI, GITHUB_OUTPUT: 'BASE_PROMPT'
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- name: Release Notes Prompt
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id: ai_prompt
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run: python .github/scripts/release-notes-prompt.py
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# Get previous version refs, GITHUB_OUTPUT: 'BASE_REF' and 'HEAD_REF'
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- name: Get Previous Version Refs
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id: version_refs
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# Generate release notes using OpenAI if not already edited, GITHUB_OUTPUT: 'RELEASE_NOTES' and 'OPENAI_PROMPT'
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- name: AI Release Notes
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if: ${{ !contains(github.event.pull_request.labels.*.name, 'openai-edited') }}
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uses: RooVetGit/Roo-GHA/.github/actions/ai-release-notes@main
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uses: ./.github/actions/ai-release-notes
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id: ai_release_notes
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with:
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GHA_PAT: ${{ secrets.CROSS_REPO_ACCESS_TOKEN }}
@@ -118,7 +113,6 @@ jobs:
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repo_path: ${{ env.REPO_PATH }}
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base_ref: ${{ steps.version_refs.outputs.base_ref }}
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head_ref: ${{ steps.version_refs.outputs.head_ref }}
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custom_prompt: ${{ steps.ai_prompt.outputs.BASE_PROMPT }}
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# Update CHANGELOG.md with AI-generated notes
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- name: Update Changeset Changelog

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