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Adds llm query bot example (#46)
* Adds llm query bot example * Automated pre-commit update * . * . * . * Automated pre-commit update --------- Co-authored-by: kopekC <[email protected]>
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‎examples/pr_review_bot/README.md‎

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# AI-Powered Pull Request Review Bot
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This example demonstrates how to use Codegen to create an intelligent PR review bot that analyzes code changes and their dependencies to provide comprehensive code reviews. The bot uses GPT-4 to generate contextual feedback based on modified code and its relationships.
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> [!NOTE]
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> This codemod helps development teams by providing automated, context-aware code reviews that consider both direct and indirect code dependencies.
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## How the PR Review Bot Works
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The script analyzes pull requests in several key steps:
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1. **Symbol Analysis**
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```python
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modified_symbols = codebase.get_modified_symbols_in_pr(pr_number)
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for symbol in modified_symbols:
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deps = codebase.get_symbol_dependencies(symbol, max_depth=2)
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rev_deps = codebase.get_symbol_dependents(symbol, max_depth=2)
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```
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- Identifies modified symbols in the PR
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- Analyzes dependencies up to 2 levels deep
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- Tracks reverse dependencies (symbols that depend on changes)
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2. **Context Building**
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```python
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context = {
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"pr_title": pr.title,
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"pr_body": pr.body,
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"modified_symbols": [...],
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"context_symbols": [...]
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}
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```
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- Gathers PR metadata
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- Collects modified code content
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- Includes relevant dependency context
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3. **AI Review Generation**
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```python
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review = codebase.ai_client.llm_query_with_retry(
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messages=[...],
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model="gpt-4",
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max_tokens=2000
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)
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```
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- Uses GPT-4 for analysis
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- Generates comprehensive review feedback
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- Considers full context of changes
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## Why This Makes Code Review Better
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1. **Context-Aware Analysis**
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- Understands code dependencies
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- Considers impact of changes
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- Reviews code in proper context
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2. **Comprehensive Review**
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- Analyzes direct modifications
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- Evaluates dependency impact
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- Suggests improvements
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3. **Consistent Feedback**
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- Structured review format
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- Thorough analysis every time
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- Scalable review process
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## Review Output Format
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The bot provides structured feedback including:
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```
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1. Overall Assessment
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- High-level review of changes
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- Impact analysis
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2. Specific Code Feedback
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- Detailed code comments
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- Style suggestions
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- Best practices
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3. Potential Issues
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- Security concerns
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- Performance impacts
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- Edge cases
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4. Dependency Analysis
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- Impact on dependent code
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- Breaking changes
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- Integration considerations
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```
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## Key Benefits to Note
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1. **Better Code Quality**
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- Thorough code analysis
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- Consistent review standards
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- Early issue detection
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2. **Time Savings**
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- Automated initial review
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- Quick feedback loop
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- Reduced review burden
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3. **Knowledge Sharing**
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- Educational feedback
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- Best practice suggestions
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- Team learning
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## Configuration Options
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You can customize the review by:
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- Adjusting dependency depth
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- Modifying the AI prompt
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- Changing the review focus areas
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- Tuning the GPT-4 parameters
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## Learn More
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- [Codegen Documentation](https://docs.codegen.com)
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- [OpenAI API Documentation](https://platform.openai.com/docs/api-reference)
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- [GitHub API Documentation](https://docs.github.com/en/rest)
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- [Codegen llm integration](https://docs.codegen.com/building-with-codegen/calling-out-to-llms)
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## Contributing
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Feel free to submit issues and enhancement requests! Contributions to improve the review bot's capabilities are welcome.

‎examples/pr_review_bot/run.py‎

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import codegen
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from codegen import Codebase
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from codegen.sdk.enums import ProgrammingLanguage
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from codegen.sdk.codebase.config import CodebaseConfig, GSFeatureFlags, Secrets
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import json
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github_token = "Your github token"
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open_ai_key = "your open ai key"
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pr_number = 0 # Your PR number must be an integer
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codegen.function("pr-review-bot")
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def run(codebase: Codebase):
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context_symbols = set()
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modified_symbols = codebase.get_modified_symbols_in_pr(pr_number)
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for symbol in modified_symbols:
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# Get direct dependencies
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deps = codebase.get_symbol_dependencies(symbol, max_depth=2)
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context_symbols.update(deps)
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# Get reverse dependencies (symbols that depend on this one)
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rev_deps = codebase.get_symbol_dependents(symbol, max_depth=2)
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context_symbols.update(rev_deps)
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# Prepare context for LLM
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context = {
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"modified_symbols": [
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{
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"name": symbol.name,
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"type": symbol.symbol_type.value,
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"filepath": symbol.filepath,
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"content": symbol.content,
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}
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for symbol in modified_symbols
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],
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"context_symbols": [
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{
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"name": symbol.name,
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"type": symbol.symbol_type.value,
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"filepath": symbol.filepath,
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"content": symbol.content,
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}
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for symbol in context_symbols
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],
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}
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system_prompt = """
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You are a helpful assistant that reviews pull requests and provides feedback on the code.
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"""
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# Generate review using AI
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prompt = f"""Please review this pull request based on the following context:
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Title: {context["pr_title"]}
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Description: {context["pr_body"]}
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Modified Symbols:
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{json.dumps(context["modified_symbols"], indent=2)}
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Related Context (Dependencies):
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{json.dumps(context["context_symbols"], indent=2)}
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Please provide a thorough code review that includes:
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1. Overall assessment
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2. Specific feedback on modified code
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3. Potential issues or improvements
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4. Impact on dependencies
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5. Suggestions for testing
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"""
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review = codebase.ai_client.llm_query_with_retry(messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}], model="gpt-4", max_tokens=2000, temperature=0.7)
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return review
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if __name__ == "__main__":
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print("Starting codebase analysis...")
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codebase = Codebase.from_repo(
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"getsentry/sentry",
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shallow=False,
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programming_language=ProgrammingLanguage.PYTHON,
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config=CodebaseConfig(
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secrets=Secrets(openai_key=open_ai_key, github_api_key=github_token),
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feature_flags=GSFeatureFlags(
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sync_enabled=True,
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),
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),
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)
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review = run(codebase)
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print(review)
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print("Codebase analysis complete.")

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