ragbox-core 1.0.6

RAG-in-a-Box: Zero-Configuration Self-Building Agentic RAG System

RAGBox-Core

📚 Read the Official Documentation

RAG-in-a-Box: Zero-Configuration Self-Building Agentic RAG System

RAGBox is a production-ready, auto-configuring, async-first RAG engine that combines Vector Search, Agentic Orchestration, and Graph Retrieval natively.

Installation

pip install ragbox

Note on Dependencies: Advanced document processing features like OCR and complex PDF parsing require system-level dependencies. Depending on your OS, you may need to install standard C++ build tools or Tesseract for paddleocr and pdfplumber to function optimally.

Configuration (API Keys)

RAGBox auto-detects cloud providers. For the best experience, set one of the following environment variables before running:

export OPENAI_API_KEY="sk-..."
# OR
export ANTHROPIC_API_KEY="sk-ant-..."
# OR
export GROQ_API_KEY="gsk_..."

If no keys are found, RAGBox falls back to a local LLaMA model (requires manual model download to models/llama-3.1-8b-instruct.gguf).

Quick Start (3-Line API)

from ragbox import RAGBox

# Automatically ingests, builds graphs, configures vector db, and chunks
rag = RAGBox("./company-docs")

# Intelligent routing via query classification
answer = rag.query("What's our vacation policy?")
print(answer)

CLI Interface

RAGBox provides a dead-simple CLI for running locally without writing code:

# Point to your documents. RAGBox will self-build the index and graph.
ragbox init ./company-docs

# Query the active index
ragbox query "What's our vacation policy?" -d ./company-docs

Architecture

graph TD
    A[Local Documents] --> B{Document Processor Auto-Router}
    B --> C[AST / OCR / PDF Parsing]
    C --> D[Chunking Engine]
    D --> E[(Vector Store)]
    C --> F[(Knowledge Graph)]
    
    Q[User Query] --> G[Agentic Orchestrator]
    G --> H[Retrieval Fusion Engine]
    E --> H
    F --> H
    H --> G
    G --> I[Final Answer]

Risk Surface Analysis

• Temporal Edges (T=0 vs T=Scale): At T=0, ragbox init is blocking to guarantee index availability. At T=scale, the background daemon handles delta updates (via watchdog) to prevent index staleness and thundering herds.
• Adversarial Edges: Subject to standard prompt injection if queries are exposed raw to external users. The Orchestrator currently assumes trusted inputs.
• Resource Edges: High concurrency read/write spikes memory due to dual maintenance of the local Vector DB and the Knowledge Graph.

Features

• Self-Healing Infrastructure: Watchdog auto-detects changes and updates vector stores & knowledge graphs incrementally, preventing index staleness or storms.
• Auto-Document Intelligence: Automatically detects PDF, Text, Images, and Code to use AST, OCR (paddleocr), or structural layouts (pdfplumber).
• Cost Estimator: See the expected USD cost of indexing before it runs.
• Auto-Knowledge-Graph (GraphRAG): Extracts entities and communities automatically using the Leiden algorithm for structured reasoning.
• Retrieval Fusion & Reranking: Merges Dense Vectors and Graph Search using Reciprocal Rank Fusion, then reranks the massive candidate pool using a highly accurate ms-marco Cross-Encoder.
• Late Chunking: Contextual sequence embeddings! Vectors are calculated over the full document bounds before being pooled into chunks, preserving global semantic context within local tokens.
• Agentic Orchestrator & Intelligent Routing: Automatically routes incoming queries into 6 distinct pipelines: Vector, Keyword, Graph, Multi-Query, Time-Based, and Agentic.
• Multi-Query Expansion: Broad intent queries are dynamically expanded into multiple variations by the LLM, retrieving and fusing results across all variations for unparalleled recall.

Contributing

We welcome contributions to RAGBox-Core! Please see our CONTRIBUTING.md for details on how to set up your development environment, run the test suite, and submit Pull Requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.