Building Your AI Coding Assistant: A Comprehensive Guide
With the rising prominence of generative AI in 2023, organizations have increasingly integrated AI into coding support, building on previous innovations like GitHub Copilot released in 2021. However, AI's role has evolved beyond code completion to include full code generation and code review, significantly enhancing development efficiency. Thoughtworks Open Source Community has responded by releasing various AI-assisted tools, supporting organizations in creating their own AI coding assistants.
AI-Enhanced Tools Released
The Thoughtworks Open Source Community has developed several tools to support AI-assisted coding:
- AutoDev for IntelliJ: A comprehensive AI-assisted coding tool based on the JetBrains platform.
- AutoDev for VSCode: A similar tool tailored for the VSCode editor.
- Unit Eval: A tool for building high-quality datasets in code completion scenarios.
- Unit Minions: A tool for constructing datasets in scenarios like requirement generation and test generation, using data distillation.
These tools are continuously evolving with advancements in open source models, involving the following steps:
- Building IDE Plugins and Developing a Measurement System: This involves expanding IDE plugin features using public model APIs.
- Model Evaluation System and Fine-Tuning Experiments: To continually refine the AI's performance.
- Data Engineering and Model Evolution Centered Around Intent: This forms the core of the development process.
The project uses a tech stack including IntelliJ IDEA as the main plugin framework, Llama 2-based models such as DeepSeek Coder, and tools for model fine-tuning and evaluation.
Defining Your AI Assistant Features
Based on JetBrains' 2023 Developer Ecosystem Report, key scenarios where generative AI can enhance development are identified:
- Code Auto-completion: AI can analyze context and learn code patterns to suggest intelligent code completions.
- Code Explanation: Helping developers understand the function and implementation of specific code snippets.
- Code Generation: Quickly producing required code by learning from extensive code libraries.
- Code Review: Offering high-quality suggestions to improve code quality and adherence to best practices.
- Natural Language Query: Allowing developers to interact with AI using natural language for information retrieval.
The AutoDev tool also supports custom scenarios, enabling developers to define their unique AI capabilities.
Scenario-Driven Architecture Design
Different coding scenarios demand varying levels of AI response speed and quality. For instance:
- Code Completion: Requires quick responses with moderate quality from a small-to-medium model.
- Code Refactoring: Prioritizes quality, allowing for slower response times with a large model.
- Natural Language Code Search and Explanation: Requires high-quality responses from larger models.
This framework uses a combination of large, medium, and micro models to provide comprehensive AI-assisted coding:
- High-Quality Large Models: For complex tasks like code refactoring.
- High-Speed Medium Models: For everyday tasks like code completion and review.
- Vectorized Micro Models: For tasks such as code similarity analysis within an IDE.
Key Scenario: Code Completion
AI code completion tools like GitHub Copilot offer different modes:
- Inline Completion: Filling in code within the current line based on context.
- InBlock Completion: Completing code within the current function block.
- AfterBlock Completion: Adding code beyond the current block.
Developers need to consider appropriate mode datasets to improve completion quality and user experience.
Key Scenario: Code Explanation
This feature helps developers manage large codebases by providing answers to code-related questions, documentation, and error identification, thus enhancing efficiency and reducing error rates. It usually involves larger models for higher quality results, broken down into:
- Understanding User Intent: Using large models to interpret intent.
- Intent-Based Searches: Transforming intent into code snippets or documentation using search technologies.
- Output: Summarizing results through large models and presenting to users.
Architecture Design: Contextual Engineering
In developing AutoDev, two context models were identified:
- Relevant Contexts: Generated through static code analysis for better-quality outcomes.
- Similar Contexts: Based on similarity searches for a broader code generation.
Similar Context Architecture: GitHub Copilot Example
GitHub Copilot uses a similar context architecture, capturing user actions within IDEs to build prompts sent to AI models. Using Jaccard Similarity and other techniques, it constructs relevant prompts and ensures efficient code completion.
Ultimately, building an AI coding assistant involves thoughtful integration of AI-based tools, scenario-driven architectural decisions, and context engineering to significantly benefit developers and enhance coding productivity.