Local RAG with ChromaDB — Applied Local AI & RAG

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Local RAG with ChromaDB

🧠 Applied Local AI & RAG⏱ 25 min⭐ 250 BASE XP⌨ HANDS-ON LAB

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)
⌨ HANDS-ON LABIndex and Query Local Vectors in ChromaDB
⭐ +200 XP

Initialize an in-memory ChromaDB vector store, embed documents, and execute semantic similarity search.

1Execute a Python script adding documents to a Chroma collection and performing nearest-neighbor search.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
KNOWLEDGE CHECK
QUERY 1 // 1
What is the primary benefit of using a local embedding model and ChromaDB for RAG?
It is faster than cloud APIs for all workloads.
It ensures your private documents are never sent over the internet.
It automatically fine-tunes the LLM on your data.
It prevents LLM hallucinations.