Free AI tools for RAG over your documents

No-code RAG app builder—upload docs, configure chunking and embedding, connect an LLM, and deploy a chat interface or API endpoint in under an hour.

Self-hosted setup required for full data control; cloud version stores data on Dify servers.

Self-hosted chat UI with built-in RAG over local documents—connects to Ollama or any OpenAI-compatible API with zero data leaving your server.

Requires Docker and a local or private LLM; not a no-code option for non-technical teams.

Mature Python/JS framework for building RAG pipelines—composable loaders, splitters, vector stores, and retrieval chains with full production flexibility.

Code-first; requires Python experience. More boilerplate than visual builders like Dify or Flowise.

Simplest Python path to RAG—index a folder of PDFs in five lines, query with natural language, and integrate with any LLM or vector store.

Code-first like LangChain; steeper than no-code tools but less boilerplate than raw API calls.

Upload PDFs directly in ChatGPT Plus and query them in chat—quickest zero-setup option for one-off document Q&A without building a pipeline.

File uploads are session-scoped; not a scalable or programmable RAG solution for production apps.

Embeds and retrieves documents locally with no data leaving your infrastructure.

Needs engineering effort to scale beyond a single machine.

Handles ingestion, indexing, and retrieval with strong anti-hallucination scoring.

Proprietary platform limits customization of the retrieval pipeline.

Enterprise-scale vector database supporting billions of vectors with GPU acceleration.

Heavyweight setup; overkill for small document collections.