ChromaDB Flashcards

Posted on by uplatzblog

This page gives you a fast, practical overview of ChromaDB and how it supports modern AI search.
You’ll learn what the engine does, where it fits in an LLM stack, and how to start building with it today.

In brief, it’s an open-source vector database that runs in-process.
Because of that design, developers can prototype quickly, keep everything local, and avoid extra services.
Moreover, the store keeps both embeddings and metadata together, which makes retrieval simple and predictable.

The diagram below shows a typical RAG flow from text to embeddings and then to nearest-neighbour results.
It clarifies how documents move through ingestion, indexing, and querying.

ChromaDB vector database in a RAG pipeline: embed, index, query, return context
Typical RAG pipeline: create embeddings, store vectors with metadata, and retrieve relevant context quickly.

If you want a full LLM workflow, connect this store to a framework that manages chunking, prompts, and evaluation.
For example, read our guide on
LangChain vector stores for RAG.
In addition, the official repository provides reference code and persistence tips.

ChromaDB collections example: documents, vectors, and metadata fields
Collections store documents, embeddings, and metadata together for simple queries and updates.

Scroll to the flashcards for a structured recap.
Then, try the links below for deeper examples and next steps.

🧠 ChromaDB Flashcards
🔍 What is ChromaDB?
An open-source vector database for LLM apps, designed for fast, in-process search.
🧠 What makes it unique?
It runs in your process and stores embeddings plus metadata for rapid local development.
📦 What data does it keep?
Documents, metadata, and vectors—either in memory or on disk for persistence.
⚙️ How do you use it?
Install via pip and integrate with Python or LangChain through a straightforward API.
📚 Is it production-ready?
Great for prototypes and small-scale production that needs low-latency searches.
🔧 How does it persist data?
Use a persistent directory to save and reload collections between sessions.
🧪 Is it good for RAG?
Yes—many chatbots and assistants rely on it for retrieval.
🔌 Does it work with LangChain?
It’s a supported vector store, so integration is simple.
💻 Can you use it offline?
Absolutely. In-process design lets you build local apps without a network.
🧰 What’s the API like?
Create collections, add or update items, and query vectors with a clean Python interface.

For best results, start small, measure recall and latency, then tune query parameters.
Next, add persistence before you ship anything important.
Finally, test your prompts and chunking so retrieval stays relevant.

Explore our hands-on tutorial: LangChain vector stores for RAG.
For code samples and releases, visit the
ChromaDB GitHub repository.