retrievify 0.1.3

Lightweight Retrieval-Augmented Generation (RAG) toolkit in 3 lines.

retrievify

Lightweight Retrieval-Augmented Generation (RAG) toolkit in 3 lines.

from retrievify import RAG
rag = RAG().fit("docs/", patterns=["*.pdf","*.md","*.txt"])
print(rag.ask("What are the core contributions?"))

Why retrievify?

• ⚡ Fast local embeddings (MiniLM) by default
• 🧱 Smart chunking & FAISS/Annoy vector stores (Windows-friendly)
• 🧩 Optional LLM generation hook (OpenAI/Ollama)
• 🛠️ CLI for quick indexing and querying

Install

pip install retrievify
# If FAISS is tricky on Windows, use Annoy:
pip install annoy

Quickstart

from retrievify import RAG
rag = RAG().fit("docs/")
res = rag.ask("What are the key limitations?", k=5)
print(res["evidence"][0])

CLI

retrievify index ./docs --pattern "*.pdf,*.md"
retrievify query ./docs -q "evaluation pipeline" -k 5 --generate

LLM (optional)

Set env var for OpenAI first:

$env:OPENAI_API_KEY="sk-..."

Then:

from retrievify import RAG
rag = RAG({"generation": True, "llm_backend": "openai"}).fit("docs/")
print(rag.ask("Summarize the paper")["answer"])

Roadmap

• Cross-encoder re-ranking
• HTML/URL loaders & deduplication
• Simple retrieval eval (Recall@k, MRR, NDCG)