Struggling to understand enterprise-scale codebase?

Understanding complex codebases is a universal challenge in software engineering. Whether you're diving into legacy systems, exploring open-source projects, or onboarding to a new team, making sense of large codebases can be time-consuming and mentally taxing. Today, we're excited to introduce KnowLang, an open-source tool that makes codebases more accessible through semantic search and intelligent Q&A capabilities.

Why KnowLang?

While Large Language Models (LLMs) have revolutionized code understanding, current solutions often struggle with:

• Lacking awareness of confidential or up-to-date code
• Providing accurate answers with proper context
• Answering the inter-repository dependencies

KnowLang addresses these challenges through:

1. Smart code chunking that preserves semantic meaning
2. Advanced retrieval techniques combining embeddings and summaries
3. Context-aware responses that reference specific code sections

How It Works

KnowLang uses a two-stage pipeline to process and interact with codebases:

Stage 1: Code Processing Pipeline

The code processing pipeline:

1. Clones and identifies relevant files
2. Uses Tree-sitter for robust parsing
3. Intelligently chunks code while preserving context
4. Generates summaries and embeddings
5. Stores in a vector database for efficient retrieval

Stage 2: Query Pipeline

Example: Understanding Hugging Face Transformers

Let's use KnowLang to explore the Hugging Face Transformers library. Here's a sample interaction:

User Question:

what are the key components required to implement a new quantization method??

KnowLang Answer:

Based on the provided code context, to implement a new quantization method, you need to create a new class that inherits from HfQuantizer and implement these key required components:

1. Required Class Attributes:

• requires_calibration: Boolean indicating if calibration is needed
• required_packages: List of required pip packages
• requires_parameters_quantization: Boolean indicating if new parameters need to be created

2. Mandatory Abstract Methods (must be implemented):

• _process_model_before_weight_loading(self, model, **kwargs): For pre-processing model before loading weights
• _process_model_after_weight_loading(self, model, **kwargs): For post-processing model after loading weights
• is_serializable(self, safe_serialization=None): To define serialization behavior

3. Essential Method Overrides:

• validate_environment(self, *args, **kwargs): To check for required dependencies and potential conflicts
• update_torch_dtype(self, torch_dtype): To handle specific dtype requirements
• check_quantized_param(self, model, param_value, param_name, state_dict, **kwargs): If parameter quantization is required

4. Configuration:

• Must accept a quantization_config in the constructor that defines the quantization parameters

This can be seen in the implementation examples of QuantoHfQuantizer and AqlmHfQuantizer classes, which follow this pattern. For instance, in QuantoHfQuantizer (lines 1-10):

class QuantoHfQuantizer(HfQuantizer):
    required_packages = ["quanto", "accelerate"]
    requires_parameters_quantization = True
    requires_calibration = False

    def __init__(self, quantization_config: QuantoConfig, **kwargs):
        super().__init__(quantization_config, **kwargs)

Performance and Evaluation

A significant challenge in code-focused RAG systems is the lack of standardized evaluation methods. Traditional metrics often fail to capture the nuanced understanding required for code comprehension. KnowLang takes a novel approach by leveraging LLMs themselves as evaluators. Currently, our evaluation framework employs GPT-4 and Claude 3.5 Sonnet to assess three critical dimensions:

• Chunk Relevance (7.19/10): How well the retrieved code chunks align with the query
• Answer Correctness (6.05/10): Accuracy and completeness of the generated responses
• Code Reference (5.9/10): Proper attribution and contextual use of the source code

Through this automated evaluation process, we discovered several interesting insights:

• Adding code summaries to chunks improved overall performance across all metrics
• Surprisingly, embedding-only approaches outperformed more complex reranking strategies for code retrieval.
• Different embedding models showed unexpected performance variations, with smaller models sometimes outperforming larger ones. Showing the model trained for specific usage is much powerful than the one trained for generic purpose.

While these early results are promising, we acknowledge that LLM-based evaluation is still an emerging field. We're actively working on improving the evaluation framework's reliability and reproducibility.

Future Directions

We're actively working on:

1. Inter-repository semantic search
2. Support for additional programming languages
3. Automatic documentation maintenance
4. IDE integrations

Try It Out

KnowLang is open-source and available on GitHub. You can:

• Try the demo in our Hugging Face Space
• Install via pip: pip install knowlang
• Visit our GitHub repository

Join the Community

We believe in the power of community-driven development. You can contribute by:

• Trying KnowLang and providing feedback
• Opening issues or submitting pull requests

Conclusion

Understanding complex codebases shouldn't be a bottleneck in software development. KnowLang aims to make code comprehension more accessible and efficient through the power of LLMs and advanced retrieval techniques.

We're excited to see how the community uses and improves KnowLang. Try it out and let us know what you think!