rag-titan 0.6.0

📖 Documentation · Getting started · Supported RAG types · API reference · PyPI

Why rag-titan?

• Many RAG types, one API. Two today (simple, adaptive), with more on the way. Switch with one word: rag_type="adaptive".
• Runs 100% locally with Ollama, so your documents never leave your machine.
• Grounded answers. The model answers only from your files and cites the chunks it used.
• Chunking your way. Eight chunkers: by words, characters or model tokens (tiktoken or your own tokenizer), recursive separators, and structure-aware chunkers that keep sentences, paragraphs, heading sections or pages whole. Try them live in the chunk playground →
• PDF, Word, Markdown and text files, with paragraphs, headings and pages kept for the chunkers.
• Measure, don't guess. Compare any chunker with any RAG type on your own questions in one command (rag-titan evaluate): recall, MRR, nDCG, answered rate and p95 time, plus every step of a run with --trace. Evaluating RAG →
• Easy to read, easy to change. Short files, plain Python, and a docstring on every class. Swap any part by inheriting one base class.

Install

pip install rag-titan
ollama pull gemma3:4b && ollama pull nomic-embed-text

For token-based chunking, add the extra: pip install "rag-titan[tokens]".

Needs Python 3.10+ and Ollama. → Full setup guide

Quick start

from rag_titan import RAGTitan

rag = RAGTitan(rag_type="simple")            # or "adaptive"
rag.index(["handbook.pdf", "policy.docx"])

print(rag.ask("How many leave days do I get?"))
# You get 20 paid leave days every year [chunk_0].

Or from the terminal:

rag-titan ask --files handbook.pdf --question "How many leave days do I get?"

Supported RAG types

rag-titan 0.1 supports 2 RAG types. Pick one with rag_type:

from rag_titan import RAG_TYPES
print(RAG_TYPES)        # ['simple', 'adaptive']

Coming next (not available yet): Corrective RAG · Multi-query RAG · HyDE · Hybrid RAG · Self-RAG · Parent-document RAG · Graph RAG · Agentic RAG. → Roadmap

Also included: a tool-calling agent loop. It's a separate feature, not a rag_type.

Explore the docs

Click any picture to open its page.

Chunking methods All 8 chunkers explained, with formulas and animated diagrams.	Chunk playground Paste your text and watch the chunkers cut it, live in the browser.
Building blocks Load → chunk → embed → store, with the data at every step.	Extending rag-titan Add your own LLM, vector store, chunker or RAG type.
Agents Let the LLM call your Python functions as tools.	Command line rag-titan ask and rag-titan chat.

Contributing

Contributions are welcome! Read CONTRIBUTING.md to get set up. The tests run without Ollama.

License

MIT © Kumar Nishant