rag-starter 0.1.0

A lightweight Python starter for retrieval-augmented generation workflows.

rag-starter

     

RAG boilerplate with vector DB adapters.

rag-starter is a lightweight Python starter for retrieval-augmented generation workflows. It gives you a small but clean foundation for:

• chunking documents.
• generating embeddings.
• indexing vectors through adapter classes.
• retrieving relevant context for prompts.
• swapping vector backends without rewriting your pipeline.

This repo is intentionally minimal. It is designed as a starter, not a full framework.

Features

• Small, readable Python package structure.
• Adapter interface for vector stores.
• In-memory adapter included for local development and tests.
• Optional adapter stubs for Chroma, Qdrant, and Pinecone.
• Simple hashing embedder for demos and bootstrapping.
• Retriever and RAG pipeline helpers.
• Example script and tests.

Project structure

rag-starter/
├── examples/
│   └── basic_usage.py
├── src/
│   └── rag_starter/
│       ├── adapters/
│       │   ├── base.py
│       │   ├── chroma.py
│       │   ├── inmemory.py
│       │   ├── pinecone.py
│       │   └── qdrant.py
│       ├── chunking.py
│       ├── document.py
│       ├── embedder.py
│       ├── pipeline.py
│       ├── prompts.py
│       ├── retriever.py
│       └── utils.py
├── tests/
│   ├── test_chunking.py
│   └── test_pipeline.py
├── pyproject.toml
└── README.md

Install

pip install -e .

Optional extras:

pip install -e .[chroma]
pip install -e .[qdrant]
pip install -e .[pinecone]

Quick start

from rag_starter.adapters.inmemory import InMemoryVectorStore
from rag_starter.chunking import chunk_text
from rag_starter.document import Document, Chunk
from rag_starter.embedder import HashingEmbedder
from rag_starter.pipeline import RAGPipeline

source = Document(
    id="doc-1",
    text="RAG combines retrieval with generation. Vector databases help store embeddings.",
    metadata={"title": "RAG Notes"},
)

chunks = [
    Chunk(id=f"chunk-{i}", document_id=source.id, text=text, metadata=source.metadata)
    for i, text in enumerate(chunk_text(source.text, chunk_size=60, overlap=10), start=1)
]

embedder = HashingEmbedder(dimensions=64)
store = InMemoryVectorStore()
pipeline = RAGPipeline(store=store, embedder=embedder)

pipeline.index_chunks(chunks)
result = pipeline.retrieve("What helps store embeddings?", top_k=2)

for item in result.matches:
    print(item.score, item.chunk.text)

Adapter model

All vector database backends follow the same interface defined in VectorStoreAdapter.

Core methods:

• upsert(items)
• query(vector, top_k, filters=None)
• delete(ids)
• clear()

The included InMemoryVectorStore is useful for:

• local development.
• tests.
• learning the architecture.
• quickly bootstrapping a prototype.

The optional adapters are intentionally thin wrappers so you can extend them to fit your preferred backend configuration.

What this starter does not try to do

This starter does not include:

• model serving.
• background ingestion workers.
• file loaders for every format.
• advanced ranking pipelines.
• production auth and tenancy layers.

Those are highly project-specific and are better layered on once your retrieval path is clear.

Development

Run tests:

python -m unittest discover -s tests -v

License

MIT