Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
# Get by IDs
docs = collection.get(
ids=["id1", "id2"]
)
# Get with filters
docs = collection.get(
where={"category": "tutorial"},
limit=10
)
# Get all documents
docs = collection.get()
# Delete by IDs
collection.delete(ids=["id1", "id2"])
# Delete with filter
collection.delete(
where={"source": "outdated"}
)
Persistent storage
# Persist to disk
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.create_collection("my_docs")
collection.add(documents=["Doc 1"], ids=["id1"])
# Data persisted automatically
# Reload later with same path
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection("my_docs")
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
docs = text_splitter.split_documents(documents)
# Create Chroma vector store
vectorstore = Chroma.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(),
persist_directory="./chroma_db"
)
# Query
results = vectorstore.similarity_search("machine learning", k=3)
# As retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
LlamaIndex integration
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
import chromadb
# Initialize Chroma
db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("my_collection")
# Create vector store
vector_store = ChromaVectorStore(chroma_collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# Create index
index = VectorStoreIndex.from_documents(
documents,
storage_context=storage_context
)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is machine learning?")
Server mode
# Run Chroma server
# Terminal: chroma run --path ./chroma_db --port 8000
# Connect to server
import chromadb
from chromadb.config import Settings
client = chromadb.HttpClient(
host="localhost",
port=8000,
settings=Settings(anonymized_telemetry=False)
)
# Use as normal
collection = client.get_or_create_collection("my_docs")
Best practices
Use persistent client - Don't lose data on restart
Add metadata - Enables filtering and tracking
Batch operations - Add multiple docs at once
Choose right embedding model - Balance speed/quality
Use filters - Narrow search space
Unique IDs - Avoid collisions
Regular backups - Copy chroma_db directory
Monitor collection size - Scale up if needed
Test embedding functions - Ensure quality
Use server mode for production - Better for multi-user
