100% Offline RAG

Retrieval-Augmented Generation (RAG) doesn't require sending your private documents to OpenAI or Pinecone. You can build a fully sovereign RAG pipeline using Python.

Local Embedding Models

Instead of hitting an API to convert text to vectors, you can use local embedding models like nomic-embed-text (which can be run via Ollama) or models from Hugging Face's sentence-transformers library.

ChromaDB: The Local Vector Store

ChromaDB is a highly optimized, open-source vector database that can run completely in-memory or save to your local file system, requiring zero cloud infrastructure.

import chromadb
from chromadb.utils import embedding_functions

# Initialize local persistent ChromaDB
client = chromadb.PersistentClient(path="./local_vectors")

# Use a local embedding model
emb_fn = embedding_functions.DefaultEmbeddingFunction()

collection = client.create_collection(
    name="private_docs", 
    embedding_function=emb_fn
)

collection.add(
    documents=["The launch code is 1234", "The server IP is 192.168.1.100"],
    ids=["doc1", "doc2"]
)

# Query the local database
results = collection.query(
    query_texts=["What is the launch code?"],
    n_results=1
)
print(results)