Function semanticNavigate

src/tools/semantic-navigate.ts:252–334

(options: SemanticNavigateOptions)

}

export async function semanticNavigate(options: SemanticNavigateOptions): Promise<string> {
  const maxClusters = options.maxClusters ?? 20;
  const maxDepth = options.maxDepth ?? 3;

  const entries = await walkDirectory({ rootDir: options.rootDir, depthLimit: 0 });
  const fileEntries = entries.filter((e) => !e.isDirectory && isNavigableSourceCandidate(e.path));

  if (fileEntries.length === 0) return "No supported source files found in the project.";

  const files: FileInfo[] = [];
  for (const entry of fileEntries) {
    try {
      const content = await readFile(entry.path, "utf-8");
      let header = extractHeader(content);
      let symbolPreview: string[] = [];
      try {
        const analysis = await analyzeFile(entry.path);
        if (analysis.header) header = analysis.header;
        symbolPreview = flattenSymbols(analysis.symbols)
          .slice(0, 3)
          .map((s) => `${s.name}@${formatLineRange(s.line, s.endLine)}`);
      } catch {
      }
      files.push({
        relativePath: entry.relativePath,
        header,
        content: content.substring(0, 500),
        symbolPreview,
      });
    } catch {
    }
  }

  if (files.length === 0) return "Could not read any source files.";

  let embeddableFiles: FileInfo[] = files;
  let vectors: number[][] = [];
  let skippedForEmbedding = 0;
  try {
    const embedded = await embedFilesWithFallback(files);
    embeddableFiles = embedded.files;
    vectors = embedded.vectors;
    skippedForEmbedding = embedded.skipped;
  } catch (err) {
    const providerHint = EMBED_PROVIDER === "openai"
      ? `Check CONTEXTPLUS_OPENAI_API_KEY and CONTEXTPLUS_OPENAI_BASE_URL.`
      : `Make sure Ollama is running (check OLLAMA_HOST) and that the embedding model configured in OLLAMA_EMBED_MODEL is available.`;
    return `Embedding provider (${EMBED_PROVIDER}) not available: ${err instanceof Error ? err.message : String(err)}\n${providerHint}`;
  }

  if (embeddableFiles.length === 0) return "No embeddable source files found in the project.";

  if (embeddableFiles.length <= MAX_FILES_PER_LEAF) {
    let fileLabels: string[];
    try {
      const prompt = `For each file below, produce a 3-7 word description. Return ONLY a JSON array of strings.\n\n${embeddableFiles.map((f) => `${f.relativePath}: ${f.header}`).join("\n")}`;
      const response = await chatCompletion(prompt);
      const match = response.match(/\[[\s\S]*\]/);