navidoc 0.1.0

A lightweight, completely local, zero-API, tree-based RAG framework

🗺️ NaviDoc

NaviDoc is a lightweight, completely local, zero-API, tree-based RAG framework designed to navigate document structures intelligently. Instead of blindly chopping your files into vector chunks, NaviDoc maps your documents into a logical structural tree hierarchy and uses local LLMs to precisely steer and navigate to answers.

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✨ Features

• 🔒 100% Private & Offline: Your documents never leave your machine. Zero cloud APIs, zero telemetry.
• 🌳 Tree-Based Navigation: Mimics human navigation by following document structures (headers, font sizes) instead of standard proximity vector chunks.
• ⚡ High Precision: Pinpoints specific structural sections, avoiding context contamination or context blowouts.
• 📄 Multi-Format Support: Supports Markdown, PDF (with font-size analysis), DOCX (with style detection), and PPTX.
• 💾 Index Persistence: Save your indexed tree structures to JSON and reload them instantly.
• 💬 Chat SDK: Maintain conversation history with your documents SDK-style.

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🚀 Getting Started

1. Prerequisites

NaviDoc requires Ollama to host your local LLM engine.

1. Download and install Ollama from ollama.com.
2. Pull a smart, small model (we recommend phi3 or llama3):

   ollama pull phi3
   

3. Ensure the Ollama service is running in the background before running NaviDoc.

2. Installation

Install NaviDoc via pip:

pip install navidoc

Or using uv:

uv add navidoc

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💡 Usage Examples

🔍 One-off Query

from navidoc import NaviDoc

# Initialize (defaults to phi3 or NAVIDOC_MODEL_NAME env var)
engine = NaviDoc()

# Ingest and structurally index any local document
status = engine.ingest("your_document.pdf")
print(status)

# Query your document offline
response = engine.query("What are the exact system requirements?")
print(response)

💬 Multi-turn Chat (SDK Style)

from navidoc import NaviDoc

engine = NaviDoc()
engine.ingest("manual.docx")

# First turn
print(engine.chat("How do I install the battery?"))

# Second turn (remembers context and history!)
print(engine.chat("Where can I buy a replacement?"))

# Clear history if needed
engine.clear_history()

💾 Save & Fast Load Index

Avoid re-parsing large documents by saving the tree index.

from navidoc import NaviDoc

engine = NaviDoc()

# First time: Parse and Save
engine.ingest("massive_report.pdf")
engine.save_index("storage/indices/massive_report.json")

# Second time: Instant Load in milliseconds
engine.load_index("storage/indices/massive_report.json")
response = engine.query("What is the revenue?")

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⚙️ Configuration

Environment Variables

You can configure NaviDoc without changing your code by setting environment variables:

• NAVIDOC_MODEL_NAME: Set the default Ollama model to use (Default: phi3).

How to change it:

• Windows (PowerShell): $env:NAVIDOC_MODEL_NAME="llama3"
• Linux/Mac: export NAVIDOC_MODEL_NAME="llama3"

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📜 License

NaviDoc is open-source software distributed completely free under the MIT License.