codecanon/dist/lib/query.js

CLI tool that downloads documentation from 3rd party libraries, converts them to Markdown, and optimizes for LLMs

import { promises as fs } from "fs";
import { join } from "path";
import { getWorkspacePaths, isWorkspaceInitialized, loadWorkspaceConfig, } from "./workspace.js";
/**
 * Query the local documentation context using natural language
 */
export async function queryContext(options) {
    // Ensure workspace is initialized
    if (!(await isWorkspaceInitialized(options.cwd))) {
        throw new Error("No CodeCanon workspace found. Run 'canon init' first.");
    }
    const config = await loadWorkspaceConfig(options.cwd);
    if (config.packages.length === 0) {
        throw new Error("No packages in workspace. Use 'canon add <package>' to add documentation first.");
    }
    // Filter packages if specified
    const targetPackages = options.packages
        ? config.packages.filter((p) => options.packages.includes(p.name))
        : config.packages;
    if (targetPackages.length === 0) {
        throw new Error("No matching packages found in workspace.");
    }
    // TODO: Implement actual LLM querying with RAG
    // For now, return a placeholder response
    const contextChunks = await loadRelevantContext(options, targetPackages);
    const answer = await generateAnswer(options, contextChunks);
    return {
        answer,
        sources: contextChunks.map((chunk) => ({
            package: chunk.package,
            section: chunk.section,
            url: chunk.url,
        })),
    };
}
/**
 * Load relevant context chunks for the query (placeholder implementation)
 */
async function loadRelevantContext(options, packages) {
    const paths = getWorkspacePaths(options.cwd);
    const chunks = [];
    // TODO: Implement vector similarity search
    // For now, just load some content from each package
    for (const pkg of packages.slice(0, options.contextLimit)) {
        try {
            const chunkPath = join(paths.cache, pkg.name, pkg.version, "chunks", "README.chunk.md");
            const content = await fs.readFile(chunkPath, "utf-8");
            chunks.push({
                package: pkg.name,
                section: "README",
                content: content.slice(0, 1000), // Limit content length
                url: pkg.documentationUrl,
            });
        }
        catch (error) {
            console.warn(`Warning: Could not load content for ${pkg.name}`);
        }
    }
    return chunks;
}
/**
 * Generate an answer using the context chunks (placeholder implementation)
 */
async function generateAnswer(options, contextChunks) {
    // TODO: Implement actual LLM API calls
    // For now, return a mock response that incorporates the context
    const packageNames = contextChunks.map((c) => c.package).join(", ");
    return `This is a placeholder answer for your question: "${options.question}"

Based on the documentation for packages: ${packageNames}

The actual LLM integration will be implemented later. This would normally:
1. Use the selected model (${options.model || "default"})
2. Include relevant context from ${contextChunks.length} documentation chunks
3. Generate a comprehensive answer using RAG (Retrieval-Augmented Generation)

Context loaded from: ${contextChunks.map((c) => `${c.package}/${c.section}`).join(", ")}`;
}