ragbox-core 2.1.0

RAG in 3 lines. Auto-GraphRAG, auto-reranking, auto-streaming. No LangChain required.

RAGBox

RAG that works in 3 lines. Not 50.

---

The Problem With LangChain

This is a LangChain RAG app:

from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

loader = DirectoryLoader("./docs", glob="**/*.txt")
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory="./chroma")
vectorstore.persist()

llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
prompt = PromptTemplate(
    input_variables=["context", "question"],
    template="Answer from context:\n{context}\n\nQuestion: {question}"
)

chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt}
)

result = chain({"query": "What is the vacation policy?"})
print(result["result"])

That's 30+ lines. And it doesn't include GraphRAG, reranking, or streaming.

---

This is RAGBox

from ragbox import RAGBox

rag = RAGBox("./docs")
print(rag.query("What is the vacation policy?"))

3 lines. Includes GraphRAG, reranking, streaming, and self-healing — out of the box.

---

Install

pip install ragbox-core

Set one API key:

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

That's it. No config files. No boilerplate. Point it at a folder and ask questions.

---

What You Get For Free

Feature	LangChain	LlamaIndex	RAGBox
Works in 3 lines	❌	❌	✅
Auto-detects LLM provider	❌	❌	✅
Built-in GraphRAG (Leiden)	❌	❌	✅
Dual-Mode Retrieval (Fast Factual vs Deep Graph)	manual	manual	✅ auto
Cross-Encoder Reranking	manual	manual	✅ auto
Streaming (astream())	complex	complex	✅ built-in
Self-healing watchdog	❌	❌	✅
Cost estimation before indexing	❌	❌	✅
Multi-query expansion	manual	manual	✅ auto
Default install size	~500MB	~300MB	~80MB

---

Real Examples

Ask questions about your company docs

from ragbox import RAGBox

rag = RAGBox("./company-docs")
print(rag.query("What's the oncall escalation policy?"))
print(rag.query("Who does Maria Santos report to?"))     # cross-doc GraphRAG
print(rag.query("What was Q4 revenue and who drove it?"))  # multi-hop

Stream answers token-by-token

import asyncio
from ragbox import RAGBox

async def main():
    rag = RAGBox("./docs")
    async for chunk in rag.astream("Summarize all findings"):
        print(chunk, end="", flush=True)

asyncio.run(main())

Chat with a codebase

from ragbox import RAGBox

rag = RAGBox("./my-python-project")  # auto-parses .py files with AST
print(rag.query("How does the auth middleware work?"))
print(rag.query("Which functions call the database?"))  # graph traversal

Estimate cost before indexing

from ragbox import RAGBox

rag = RAGBox("./large-docs")
estimate = rag.estimate_cost()
print(f"Indexing will cost ~${estimate.total_cost_usd:.4f}")
# Indexing will cost ~$0.0023

Docker — instant RAG server

docker run \
  -v ./docs:/data \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -p 8000:8000 \
  ragbox/ragbox

# Query via HTTP
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What is the vacation policy?"}'

---

How It Works

RAGBox wires up 7 components automatically — you never touch them:

Your Documents
     │
     ▼
Document Processor   ← auto-routes PDF / TXT / PPTX / Code
     │
     ├──▶ Chunking Engine  ← late chunking with context awareness
     │
     ├──▶ Vector Store     ← ChromaDB, auto-configured
     │
     └──▶ Knowledge Graph  ← Leiden/Louvain entity extraction
                                          │
Your Question ────────────────────────────▼
     │                          Agentic Orchestrator
     │                       (classifies: vector / graph / multi-query)
     │                                    │
     └────────────────────────────────────▼
                               Retrieval Fusion + Reranking
                                          │
                                     Your Answer

---

CLI

# Index your documents
ragbox init ./docs

# Ask a question
ragbox query "What is the vacation policy?" -d ./docs

---

When RAGBox Wins

RAGBox is not for everyone. Here's exactly when to use it:

✅ Use RAGBox if:

• You want a working RAG system today, not next week
• You're tired of wiring together 10 LangChain components
• You need cross-document reasoning (GraphRAG) without a PhD
• You're building internal tools, demos, or MVPs

❌ Don't use RAGBox if:

• You need highly custom retrieval pipelines
• You're building a commercial RAG product with specific SLAs
• You want to control every single component manually

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Benchmarks

See BENCHMARKS.md — reproducible comparisons against vanilla vector search.

RAGBox features Dual-Mode Retrieval:

• Fast Path (Factual): Bypasses the graph and reranker for simple queries. ~1500ms latency.
• Deep Path (GraphRAG): Activates multi-query, graph context, and cross-encoder reranking for complex queries.

RAGBox wins heavily on cross-document reasoning. For example, on the query: "What is the relationship between the deployment strategy and the SEV1 incident?":

• Vanilla Vector: 0.802
• RAGBox GraphRAG: 0.891 (+0.089 Delta)

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Contributing

PRs welcome. See CONTRIBUTING.md.

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License

MIT — use it for anything.