Complete ChromaDB Tutorial: Simple Vector Database for AI

ChromaDB is an open-source vector database designed to store and query embeddings easily. With its intuitive API, ChromaDB is perfect for building RAG applications, semantic search, and recommendation systems.

Why ChromaDB?

• Simple API: Easy to learn and use
• Embedded mode: Can run in-memory or persistent
• Multi-modal: Support text, images, embeddings
• Integrations: LangChain, LlamaIndex, OpenAI
• No infrastructure: No server setup required

Installation

pip install chromadb
pip install chromadb-client  # For client mode

With sentence-transformers
pip install sentence-transformers

Quick Start

1. Basic Usage

import chromadb

Create client (in-memory)
client = chromadb.Client()

Or persistent storage
client = chromadb.PersistentClient(path="./chromadb")


Create collection
collection = client.createcollection(name="mycollection")


Add documents
collection.add(
    documents=["Python is a programming language", "Machine learning is AI"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)

Query
results = collection.query(
    querytexts=["What is Python?"],
    nresults=2

)
print(results)

import chromadb
from chromadb.utils import embeddingfunctions

Setup embedding function
sentencetransformeref = embeddingfunctions.SentenceTransformerEmbeddingFunction(

    modelname="all-MiniLM-L6-v2"
)

Create collection with embedding function
collection = client.createcollection(

    name="docs",
    embeddingfunction=sentencetransformeref
)

Add documents (embeddings auto-generated)
collection.add(
    documents=["Doc 1 content", "Doc 2 content"],
    ids=["1", "2"]
)

Query
results = collection.query(
    querytexts=["search query"],

    nresults=5
)

Collections

1. Collection Operations

# Create
collection = client.createcollection("mycollection")

Get existing
collection = client.getcollection("mycollection")

Get or create
collection = client.getorcreatecollection("mycollection")

Delete
client.deletecollection("mycollection")

List all
collections = client.listcollections()


Count items
count = collection.count()

from chromadb.utils import embeddingfunctions

OpenAI embeddings
openaief = embeddingfunctions.OpenAIEmbeddingFunction(
    apikey="your-api-key",

    modelname="text-embedding-3-small"
)

Sentence Transformers
stef = embeddingfunctions.SentenceTransformerEmbeddingFunction(
    modelname="all-mpnet-base-v2"

)

Hugging Face
hfef = embeddingfunctions.HuggingFaceEmbeddingFunction(

    apikey="your-hf-token",
    modelname="sentence-transformers/all-MiniLM-L6-v2"

)

collection = client.createcollection(
    name="mydocs",

    embeddingfunction=openaief,

    metadata={"hnsw:space": "cosine"}  # Distance metric
)

# Add with documents (auto-embed)
collection.add(
    documents=["text 1", "text 2", "text 3"],
    metadatas=[{"source": "a"}, {"source": "b"}, {"source": "c"}],
    ids=["1", "2", "3"]
)

Add with pre-computed embeddings
collection.add(
    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    metadatas=[{"key": "value"}],
    ids=["1", "2"]
)

Add with documents and embeddings
collection.add(
    documents=["text"],
    embeddings=[[0.1, 0.2, ...]],
    metadatas=[{"key": "value"}],
    ids=["1"]
)

# Basic query
results = collection.query(