ragbucket 0.2.3

Portable executable RAG artifacts for Python

RagBucket

Portable Executable RAG Artifacts for Python

Build once. Load anywhere.

Installation • Quickstart • What is RagBucket? • Providers • Roadmap

---

The Problem

Traditional ML models are portable by default:

model.pt   model.onnx   model.gguf   model.h5

They can be saved, shared, and deployed anywhere. RAG systems can't.

A typical RAG pipeline is a fragile web of:

• vector databases tied to infrastructure
• embedding pipelines that must be re-run
• chunking configs scattered across codebases
• provider-specific integrations with no portability
• metadata that lives nowhere and everywhere

RagBucket solves this. It packages your entire RAG pipeline — vectors, chunks, config, and runtime metadata — into a single portable .rag artifact.

---

Introducing .rag

A .rag artifact is a self-contained, executable unit of retrieval intelligence. It packages:

What	How
Semantic embeddings	via Sentence Transformers
Vector index	via FAISS
Chunked knowledge	via LangChain splitters
Retrieval configuration	embedded in manifest
Runtime metadata	versioned artifact manifest

Build it once. Drop it anywhere. Query it with one line of code.

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Full Architecture

---

✦ Installation

# Using uv (recommended)
uv add ragbucket

# Using pip
pip install ragbucket

---

## ⚡ Quickstart

### Step 1 — Build a Portable `.rag` Artifact

```python
from ragbucket import RagBuilder
from ragbucket import RagConfig

import os

from dotenv import load_dotenv


load_dotenv()


config = RagConfig(

    # ------------------------------------
    # EMBEDDING PROVIDER
    # ------------------------------------
    embedding_provider="cohere",

    embedding_model="embed-english-v3.0",

    embedding_api_key=os.getenv("COHERE_API_KEY"),

    # ------------------------------------
    # CHUNKING
    # ------------------------------------
    chunk_size=512,

    chunk_overlap=50,

    # ------------------------------------
    # RETRIEVAL
    # ------------------------------------
    top_k=3
)


builder = RagBuilder(
    config=config
)


builder.build(
    doc_path="docs/",
    op_path="artifacts/demo.rag"
)
```

This generates:

```text
artifacts/demo.rag
```

The `.rag` artifact contains:

- vector embeddings
- FAISS index
- document chunks
- retrieval configuration
- artifact metadata

Build once. Query anywhere.

---

### Step 2 — Load and Query the Artifact

```python
from ragbucket import RagRuntime

import os

from dotenv import load_dotenv


load_dotenv()


system_prompt = """
You are a helpful assistant.

Keep answers short and crisp.
"""


rag = RagRuntime(

    # ------------------------------------
    # RAG ARTIFACT
    # ------------------------------------
    rag_path="artifacts/demo.rag",

    # ------------------------------------
    # GENERATION PROVIDER
    # ------------------------------------
    provider="groq",

    api_key=os.getenv("GROQ_API_KEY"),

    model="llama-3.1-8b-instant",

    # ------------------------------------
    # EMBEDDING PROVIDER KEY
    # ------------------------------------
    embedding_api_key=os.getenv("COHERE_API_KEY"),

    # ------------------------------------
    # SYSTEM PROMPT
    # ------------------------------------
    system_prompt=system_prompt
)


response = rag.ask(
    "What are Anik's AI/ML skills?"
)

print(response)
```

---

## 🔌 Multi-Provider Runtime

RagBucket separates:

- retrieval embeddings
- response generation

This allows fully modular AI pipelines.

---

### Supported Generation Providers

| Provider    | Example Model             |
| ----------- | ------------------------- |
| `groq`      | `llama-3.1-8b-instant`    |
| `openai`    | `gpt-4o-mini`             |
| `gemini`    | `gemini-1.5-flash`        |
| `anthropic` | `claude-3-haiku-20240307` |

Example:

```python
rag = RagRuntime(

    rag_path="demo.rag",

    provider="openai",

    api_key=os.getenv("OPENAI_API_KEY"),

    model="gpt-4o-mini",

    embedding_api_key=os.getenv("COHERE_API_KEY")
)
```

---

## 🧠 Modular Embedding Providers

RagBucket supports multiple embedding systems.

| Provider | Example Model            |
| -------- | ------------------------ |
| `local`  | `BAAI/bge-small-en-v1.5` |
| `cohere` | `embed-english-v3.0`     |
| `openai` | `text-embedding-3-small` |
| `gemini` | `models/embedding-001`   |
| `voyage` | `voyage-large-2`         |

Example:

```python
config = RagConfig(

    embedding_provider="openai",

    embedding_model="text-embedding-3-small",

    embedding_api_key=os.getenv("OPENAI_API_KEY")
)
```

---

## ⚙️ Dynamic Retrieval Configuration

Customize every stage of the retrieval pipeline:

```python
from ragbucket import RagConfig


config = RagConfig(

    # Embedding system
    embedding_provider="local",

    embedding_model="sentence-transformers/all-MiniLM-L6-v2",

    # Chunking
    chunk_size=1024,

    chunk_overlap=100,

    # Retrieval
    top_k=5
)
```

All missing values are automatically filled using framework defaults.

---

## 🪶 Lightweight by Default

RagBucket no longer forces heavyweight local AI dependencies.

The core package remains lightweight.

Local embedding dependencies are only required when using:

```python
embedding_provider="local"
```

This enables:

- lightweight installations
- faster deployments
- cloud/serverless compatibility
- provider-based embedding pipelines

---

## 📦 What a `.rag` Artifact Contains

```text
demo.rag
│
├── vectors.faiss
├── chunks.json
├── manifest.json
```

The artifact stores:

- semantic vectors
- retrieval memory
- embedding configuration
- retrieval settings
- runtime metadata

making the retrieval system:

- portable
- reusable
- executable
- shareable

---

🏗️ Project Structure

ragbucket/
├── builder/
│   ├── builder.py      ← RagBuilder orchestrator
│   ├── chunker.py      ← LangChain recursive splitter
│   ├── embedder.py     ← Sentence Transformers encoder
│   ├── indexer.py      ← FAISS index builder
│   └── packager.py     ← .rag artifact packaging
├── runtime/
│   ├── runtime.py      ← RagRuntime orchestrator
│   ├── loader.py       ← .rag artifact loader
│   ├── retriever.py    ← Semantic vector retrieval
│   ├── models.py       ← Cached embedding model singleton
│   └── providers/      ← Groq / OpenAI / Gemini / Anthropic
├── schemas/
│   ├── config.py       ← RagConfig Pydantic model
│   └── manifest.py     ← Artifact manifest schema
└── utils/
    ├── file_utils.py   ← Document loading helpers
    └── hashing.py      ← Artifact integrity utilities

---

🧰 Technology Stack

Component	Technology
Embeddings	Sentence Transformers
Vector Search	FAISS
Chunking	LangChain Text Splitters
Artifact Packaging	Python zipfile
Config Validation	Pydantic
Runtime	Pure Python

---

✦ Philosophy

RagBucket treats RAG systems as portable intelligence artifacts — not fragile infrastructure pipelines.

This cleanly separates:

• Retrieval memory (what you built) → lives in the .rag file
• Language generation (how you query it) → any provider, any environment

The result: reusable semantic memory that travels with your code.

---

📄 License

MIT License — see LICENSE for details.

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RagBucket · Built by Anik Chand

The portable runtime layer for Retrieval-Augmented Generation systems.