Open mouimet-infinisoft opened 1 month ago
Setting Up the Environment
Ensure you have the necessary packages installed:
npm install @babel/parser @babel/traverse @supabase/supabase-js
pip install transformers torch radon
Perspective 1: Module/File Overview
This perspective involves analyzing the overall structure of the codebase, including files, folders, imports, and metadata.
const fs = require('fs');
const path = require('path');
const babelParser = require('@babel/parser');
const traverse = require('@babel/traverse').default;
const analyzeModule = (filePath) => {
const content = fs.readFileSync(filePath, 'utf-8');
const ast = babelParser.parse(content, { sourceType: 'module', plugins: ['typescript'] });
const metadata = {
name: path.basename(filePath),
description: '',
responsibilities: [],
dependencies: [],
inputOutput: [],
errorHandling: '',
configuration: '',
testing: '',
versioning: '',
security: '',
performance: '',
changelog: '',
documentation: '',
maintainers: '',
license: ''
};
traverse(ast, {
ImportDeclaration({ node }) {
metadata.dependencies.push(node.source.value);
}
});
return metadata;
};
const moduleToVector = async (moduleMetadata) => {
const textRepresentation = JSON.stringify(moduleMetadata);
const { tokenizer, model } = await loadModel();
const inputs = tokenizer(textRepresentation, { return_tensors: 'pt', truncation: true, padding: true });
const outputs = await model(inputs);
const vector = outputs.last_hidden_state.mean(dim=1).detach().numpy();
return vector;
};
const loadModel = async () => {
const { AutoTokenizer, AutoModel } = require('transformers');
const tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased");
const model = AutoModel.from_pretrained("bert-base-uncased");
return { tokenizer, model };
};
const rootDir = 'path/to/codebase';
const files = fs.readdirSync(rootDir).filter(file => file.endsWith('.js') || file.endsWith('.ts'));
for (const file of files) {
const moduleMetadata = analyzeModule(path.join(rootDir, file));
const moduleVector = await moduleToVector(moduleMetadata);
await storeVector(file, 'module', moduleVector);
}
Perspective 2: Variables, Functions, and Classes
This perspective focuses on the internal components of the code such as functions, classes, variables, and their relationships.
const analyzeCodeComponents = (filePath) => {
const content = fs.readFileSync(filePath, 'utf-8');
const ast = babelParser.parse(content, { sourceType: 'module', plugins: ['typescript'] });
const components = [];
traverse(ast, {
FunctionDeclaration({ node }) {
components.push({
type: 'Function',
name: node.id.name,
startLine: node.loc.start.line,
endLine: node.loc.end.line,
arguments: node.params.map(param => param.name),
returnType: node.returnType ? node.returnType.typeAnnotation.type : 'void'
});
},
ClassDeclaration({ node }) {
components.push({
type: 'Class',
name: node.id.name,
startLine: node.loc.start.line,
endLine: node.loc.end.line,
methods: node.body.body
.filter(member => member.type === 'MethodDefinition')
.map(method => ({
name: method.key.name,
startLine: method.loc.start.line,
endLine: method.loc.end.line,
arguments: method.value.params.map(param => param.name),
returnType: method.value.returnType ? method.value.returnType.typeAnnotation.type : 'void'
}))
});
},
VariableDeclarator({ node }) {
components.push({
type: 'Variable',
name: node.id.name,
startLine: node.loc.start.line,
endLine: node.loc.end.line,
dataType: node.init ? node.init.type : 'undefined'
});
}
});
return components;
};
const componentsToVector = async (components) => {
const componentsMetadata = JSON.stringify(components);
const { tokenizer, model } = await loadModel();
const inputs = tokenizer(componentsMetadata, { return_tensors: 'pt', truncation: true, padding: true });
const outputs = await model(inputs);
const vector = outputs.last_hidden_state.mean(dim=1).detach().numpy();
return vector;
};
const filePath = 'path/to/file.js';
const components = analyzeCodeComponents(filePath);
const componentsVector = await componentsToVector(components);
await storeVector(filePath, 'components', componentsVector);
Perspective 3: Implementation Details
This perspective analyzes control flow, cyclomatic complexity, and dependencies within the code.
const { default: complexity } = require('escomplex');
const analyzeComplexity = (filePath) => {
const content = fs.readFileSync(filePath, 'utf-8');
const analysis = complexity.analyzeModule(content);
const complexityMetrics = {
startLine: 1,
endLine: content.split('\n').length,
complexity: analysis.aggregate.complexity.cyclomatic,
maintainability: analysis.aggregate.maintainability,
dependencies: analysis.dependencies.map(dep => dep.path)
};
return complexityMetrics;
};
const complexityToVector = async (complexityMetrics) => {
const complexityMetadata = JSON.stringify(complexityMetrics);
const { tokenizer, model } = await loadModel();
const inputs = tokenizer(complexityMetadata, { return_tensors: 'pt', truncation: true, padding: true });
const outputs = await model(inputs);
const vector = outputs.last_hidden_state.mean(dim=1).detach().numpy();
return vector;
};
const complexityMetrics = analyzeComplexity(filePath);
const complexityVector = await complexityToVector(complexityMetrics);
await storeVector(filePath, 'complexity', complexityVector);
Set Up pg_vector on Supabase
Ensure pg_vector
is enabled on your Supabase instance:
CREATE EXTENSION IF NOT EXISTS vector;
Create a Table for Vectors
Create a table to store the code vectors:
CREATE TABLE code_vectors (
id SERIAL PRIMARY KEY,
file_path TEXT,
perspective TEXT,
vector vector(768) -- Adjust the dimension based on the vector size from your model
);
Store Vectors in Database
Insert the vectors into the database using Supabase:
const { createClient } = require('@supabase/supabase-js');
const supabaseUrl = 'https://your-supabase-url.supabase.co';
const supabaseKey = 'your-supabase-key';
const supabase = createClient(supabaseUrl, supabaseKey);
const storeVector = async (filePath, perspective, vector) => {
const { data, error } = await supabase
.from('code_vectors')
.insert([
{ file_path: filePath, perspective: perspective, vector: vector }
]);
if (error) console.error(error);
else console.log('Vector stored:', data);
};
// Store vectors for each perspective
await storeVector('path/to/file.js', 'structure', structureVector);
await storeVector('path/to/file.js', 'components', componentsVector);
await storeVector('path/to/file.js', 'complexity', complexityVector);
Search Using Vectors
Implement a search functionality using pg_vector for similarity queries:
SELECT * FROM code_vectors
ORDER BY vector <-> '[your_vector_representation]'
LIMIT 5;
Including detailed metadata from the AST for each perspective enhances the vector representations and captures more context about the codebase. This method makes the vectors more informative, improving the AI's ability to provide context-aware suggestions and collaborations.
Objective: Enhance the analysis and representation of code by incorporating comprehensive metadata and detailed context from the Abstract Syntax Tree (AST), along with the entire Git history, including commits, pull requests (PRs), change requests, merge discussions, and treating this as a time series data. This will provide a richer, more contextual understanding of the codebase evolution over time.
Commits:
Pull Requests (PRs):
Change Requests:
Merge Discussions:
Time Series Data:
Set Up pg_vector on Supabase:
pg_vector
on Supabase instance.Store Vectors and Git History:
Search Functionality:
pg_vector
to find relevant code snippets, modules, or historical changes based on vector representations and time series analysis.
Specification for Enriching Code Analysis and Representation
Objective: Enhance the analysis and representation of code by including comprehensive metadata and detailed context from the Abstract Syntax Tree (AST). This will improve the ability to query and understand the codebase effectively.
Perspective 1: Module/File Overview
Perspective 2: Variables, Functions, and Classes
Perspective 3: Implementation Details
Integration with Supabase and pg_vector
pg_vector
on Supabase instance.pg_vector
to find relevant code snippets or modules based on vector representations.By following this comprehensive approach, you ensure that the codebase analysis is rich in context and detail, improving the system's ability to provide accurate and helpful insights.