Building a self-corrective coding assistant from scratch

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AI Summary

Summary: Using Lang Graph for Code Generation

• Introduction
  • Presenter: Lan from Lang chain
  • Topic: Code generation using Lang graph and LLMs (Large Language Models)
  • Reference: GitHub Co-pilot and Alpha Codium paper
• Alpha Codium Paper Highlights
  • Introduces flow engineering for code generation
  • Generates solutions, ranks them, and iteratively improves based on tests
• Lang Graph
  • Tool for building arbitrary graphs representing logical flows
  • Allows defining steps in a specific order for execution
• Implementation Example
  • Goal: Answer coding questions using Lang chain expression language docs
  • Steps:
    1. Create a node to take a question and output an answer
    2. Parse the answer into components (Preamble, Imports, Code)
    3. Use Pydantic for structured output
    4. Implement tests for Imports and Code execution
    5. If tests fail, loop back to generation node with error trace
• Practical Demonstration
  • Setup: Ingest Lang chain expression language docs, define environment variables
  • Define graph state with relevant problem information
  • Create functions for each node in the graph that modify the state
  • Implement conditional edges based on the state to direct the flow
  • Define workflow by stitching together nodes and edges
  • Run the graph with a sample question and observe the process
• Results
  • Evaluated 20 questions related to Lang chain expression language
  • Compared performance with and without Lang graph
  • Significant improvement in code execution success rate with Lang graph
• Conclusion
  • Simple idea of using feedback loops can significantly improve performance
  • Encourages experimentation with the provided code and concepts
• Resources
  • Code and examples will be available for experimentation
  • Upcoming blog post with more details and results

Markdown Outline

## Using Lang Graph for Code Generation  
  
### Introduction  
- Lan from Lang chain discusses code generation with LLMs.  
- References GitHub Co-pilot and Alpha Codium paper.  
  
### Alpha Codium Paper Highlights  
- Introduces flow engineering for iterative code generation improvement.  
  
### Lang Graph  
- A tool for creating logical flows with LLMs in a structured manner.  
  
### Implementation Example  
- **Objective**: Answer coding questions using Lang chain expression language docs.  
- **Steps**:  
  1. Node creation for question input and answer output.  
  2. Parsing answer into Preamble, Imports, and Code.  
  3. Structured output using Pydantic.  
  4. Testing for Imports and Code execution.  
  5. Error feedback loop for generation node.  
  
### Practical Demonstration  
- Setup with Lang chain expression language docs and environment variables.  
- Graph state definition and node function creation.  
- Conditional edges for flow direction based on state.  
- Workflow definition by combining nodes and edges.  
- Sample question run through the graph with process observation.  
  
### Results  
- Evaluation of 20 questions related to Lang chain expression language.  
- Performance comparison with and without Lang graph.  
- Notable improvement in code execution success with Lang graph.  
  
### Conclusion  
- Feedback loops can greatly enhance performance.  
- Experimentation with provided code is encouraged.  
  
### Resources  
- Availability of code for experimentation.  
- Upcoming blog post with detailed results.