Directory Indexer FAQs

Question 1

What file types are supported for indexing?

Accepted Answer

It supports a wide range including text (.md, .txt), code (.rs, .py, .js, etc.), data (.json, .yaml, .csv), and configuration files (.env, .conf).

Question 2

How does Directory Indexer perform semantic search?

Accepted Answer

It leverages a vector database (like Qdrant) and embedding providers (Ollama/OpenAI) to understand the meaning of your files, allowing concept-based searching beyond just keywords.

Question 3

What is Directory Indexer?

Accepted Answer

Directory Indexer transforms your local directories into an AI-powered knowledge base, enabling semantic search across your documents, code, and data files based on meaning.

Question 4

Can Directory Indexer integrate with AI assistants?

Accepted Answer

Yes, it seamlessly integrates with AI assistants such as Claude Desktop via an MCP server, allowing you to use natural language queries to search your indexed local files.

Question 5

What are the core requirements to use Directory Indexer?

Accepted Answer

You need a Qdrant vector database and an embedding provider like self-hosted Ollama (recommended) or OpenAI to generate file embeddings for semantic search.

Directory Indexer

Key Features

Use Cases

Directory Indexer

Key Features

Use Cases