vunone

vunone / ennoia

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Declarative Document Indexing (DDI) framework for Python. Define schemas, extract structured indices, search smarter.

ai document-indexing python rag rag-pipeline
19
3
100% credibility
Found Apr 19, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Ennoia is a Python library for using AI to extract structured data from documents into searchable indexes supporting hybrid filtering and vector search.

How It Works

1
🔍 Discover Ennoia

You find a smart tool that organizes your documents by pulling out key details like dates, names, and terms using AI.

2
📦 Set it up easily

Download and prepare the tool on your computer with a simple command.

3
🤖 Connect your AI helper

Link a free local AI or a quick online service so it can read and understand your files.

4
📝 Describe what to extract

Write simple notes on what info to grab from each document, like payment details or contract dates.

5
📄 Feed in your documents

Upload your files, and watch the tool automatically pull out and organize all the important facts.

6
🔍 Search with smart filters

Ask natural questions like 'contracts with late fees in Delaware' and get precise matches.

✅ Everything organized!

Your documents are now searchable by details, saving you hours of manual reading.

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Star Growth

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AI-Generated Review

What is ennoia?

Ennoia is a Python DDI framework for declarative document indexing. Define schemas as Python classes to extract structured data—like dates, parties, or clauses—from docs using LLMs, then persist for hybrid filter + vector search. Turns messy text into searchable indices, skipping plain chunking for RAG apps.

Why is it gaining traction?

Declarative pipelines on GitHub make LLM extraction typed and diff-visible, not buried in prompts. CLI indexes folders (`ennoia index ./docs`), searches with filters (`ennoia search "late fees" --filter "law=Delaware"`), plus REST/MCP servers for agents. Benchmarks beat LangChain baselines on legal QA recall.

Who should use this?

RAG devs indexing contracts, leases, or MSAs where structure (governing law, terms) enables precise filters. Teams building agent tools over docs, ditching naive embeddings for schema-driven extraction.

Verdict

Alpha (0.3.x) at 19 stars and 1.0% credibility—API converging, but expect shifts. Strong docs, CLI, PyPI extras, full test coverage; grab for ennoia ai experiments on structured search.

(178 words)

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