| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Georgia Grant Opportunities</title> | |
| <style> | |
| * { box-sizing: border-box; margin: 0; padding: 0; } | |
| body { | |
| font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; |
AGENTS.md files are short, directory-scoped “orientation docs” intended for an LLM (or a new human contributor) to quickly understand:
The goal is to reduce repeated repo-wide exploration by making architecture and responsibilities discoverable from the filesystem.
| type QueryResult = { | |
| score: number, | |
| func: FunctionInfo | |
| } | |
| export async function runQuery( | |
| query: string, | |
| files: FileInfo[] | |
| ): Promise<QueryResult[]> { | |
| // convert input query to embedding | |
| const queryEmbedding = await createEmbedding(query); |
| // math -> https://mathjs.org/index.html | |
| export function cosineSimilarity(embedding1: number[], embedding2: number[]): number { | |
| const dotProduct = math.dot(embedding1, embedding2) | |
| const magnitude1 = math.norm(embedding1) | |
| const magnitude2 = math.norm(embedding2) | |
| return dotProduct / (magnitude1 * magnitude2) | |
| } |
| export const createEmbedding = async ( | |
| model: string, | |
| text: string | |
| ) => { | |
| const resp = await ollama.embeddings({ | |
| model: model, | |
| prompt: text | |
| }) | |
| return resp.embedding |
| const collectFileData = async (dir: string) => { | |
| // collect all the files in the directory | |
| const files = findTsFiles(dir) | |
| for (const file of files) { | |
| // extract functions from each file | |
| file.functions = extractFunctions(file.content) | |
| for (const func of file.functions) { | |
| // get the description for each function | |
| const description = await getFunctionDescription(file, func.functionName) | |
| if (description) { |
| export const getFunctionDescription = async (file: FileInfo, functionName: string, model: string = "llama3"): Promise<string | null> => { | |
| const prompt = `given the following file ${file!.path}/${file!.filename}: | |
| \`\`\`typescript | |
| ${file!.content} | |
| \`\`\` | |
| Provide a concise description for the function \`${functionName}\`. focusing on its specific role within the larger codebase. | |
| The description should briefly explain: | |
| - The purpose of the function | |
| - The function's inputs, outputs, and any notable side effects or dependencies |
| export function extractFunctions(fileContents: string): ExtractFunctionResponse[] { | |
| const functions: ExtractFunctionResponse[] = []; | |
| try { | |
| const ast = parser.parse(fileContents, { | |
| sourceType: 'module', | |
| plugins: ['typescript', 'jsx'], | |
| allowImportExportEverywhere: true, | |
| allowAwaitOutsideFunction: true, | |
| allowReturnOutsideFunction: true, |
| type FileInfo { | |
| content: string | |
| path: string | |
| filename: string | |
| } | |
| function findTsFiles(directory: string, exclude_dirs: string[]): void { | |
| const fileList: FileInfo[] = [] | |
| function traverseDirectory(currentDir: string) { |
| const evaluateCodeSearch = async ( | |
| files: FileInfo[], | |
| numSamples: number = 15, | |
| k: number = 5 | |
| ) => { | |
| // create validation set | |
| const validationSet: ValidationQuery[] = await generateValidationQueries(files, numSamples) | |
| const resultSet: QueryResult[][] = [] | |
| // run each validation query against the code search system |