✅ Create Tenant
✅ Get Tenant
✅ Create Database
✅ Get Database
✅ Reset
✅ Heartbeat
✅ List Collections
✅ Count Collections
✅ Get Version
✅ Create Collection
✅ Delete Collection
✅ Collection Add
✅ Collection Get (partial without additional parameters)
✅ Collection Count
✅ Collection Query
✅ Collection Modify Embeddings
✅ Collection Update
✅ Collection Upsert
✅ Collection Delete - delete documents in collection
✅ Authentication (Basic, Token with Authorization header, Token with X-Chroma-Token header)

Embedding Functions Support

✅ OpenAI Embedding Support
✅ Cohere API (including Multi-language support)
✅ Sentence Transformers (HuggingFace Inference API)
✅ Google Gemini Embedding Support
🚫 Custom Embedding Function
✅ HuggingFace Embedding Inference Server Function
✅ Ollama Embedding Support
✅ Cloudflare Workers AI Embedding Support
✅ TogetherAI Embedding Support
✅ VoyageAI Embedding Support

Installation

go get github.com/amikos-tech/chroma-go

Import:

import (
chroma "github.com/amikos-tech/chroma-go"
)

Usage

Ensure you have a running instance of Chroma running. We recommend one of the two following options:

Official documentation
If you are a fan of Kubernetes, you can use the Helm chart (Note: You will need Docker, minikube and kubectl installed)

The Setup (Cloud-native):

minikube start --profile chromago
minikube profile chromago
helm repo add chroma https://amikos-tech.github.io/chromadb-chart/
helm repo update
helm install chroma chroma/chromadb --set chromadb.allowReset=true,chromadb.apiVersion=0.4.5

|Note: To delete the minikube cluster: minikube delete --profile chromago

Getting Started

Consider the following example where:

We create a new collection
Add documents using OpenAI embedding function
Query the collection using the same embedding function

package main

import (
	"context"
	"fmt"
	"log"
	"os"

	chroma "github.com/amikos-tech/chroma-go"
	"github.com/amikos-tech/chroma-go/collection"
	openai "github.com/amikos-tech/chroma-go/openai"
	"github.com/amikos-tech/chroma-go/types"
)

func main() {
	// Create new OpenAI embedding function

	openaiEf, err := openai.NewOpenAIEmbeddingFunction(os.Getenv("OPENAI_API_KEY"))
	if err != nil {
		log.Fatalf("Error creating OpenAI embedding function: %s \n", err)
	}
	// Create a new Chroma client
	client := chroma.NewClient("localhost:8000")

	// Create a new collection with options
	newCollection, err := client.NewCollection(
		context.TODO(),
		collection.WithName("test-collection"),
		collection.WithMetadata("key1", "value1"),
		collection.WithEmbeddingFunction(openaiEf),
		collection.WithHNSWDistanceFunction(types.L2),
	)
	if err != nil {
		log.Fatalf("Error creating collection: %s \n", err)
	}

	// Create a new record set with to hold the records to insert
	rs, err := types.NewRecordSet(
		types.WithEmbeddingFunction(openaiEf),
		types.WithIDGenerator(types.NewULIDGenerator()),
	)
	if err != nil {
		log.Fatalf("Error creating record set: %s \n", err)
	}
	// Add a few records to the record set
	rs.WithRecord(types.WithDocument("My name is John. And I have two dogs."), types.WithMetadata("key1", "value1"))
	rs.WithRecord(types.WithDocument("My name is Jane. I am a data scientist."), types.WithMetadata("key2", "value2"))

	// Build and validate the record set (this will create embeddings if not already present)
	_, err = rs.BuildAndValidate(context.TODO())
	if err != nil {
		log.Fatalf("Error validating record set: %s \n", err)
	}

	// Add the records to the collection
	_, err = newCollection.AddRecords(context.Background(), rs)
	if err != nil {
		log.Fatalf("Error adding documents: %s \n", err)
	}

	// Count the number of documents in the collection
	countDocs, qrerr := newCollection.Count(context.TODO())
	if qrerr != nil {
		log.Fatalf("Error counting documents: %s \n", qrerr)
	}

	// Query the collection
	fmt.Printf("countDocs: %v\n", countDocs) //this should result in 2
	qr, qrerr := newCollection.Query(context.TODO(), []string{"I love dogs"}, 5, nil, nil, nil)
	if qrerr != nil {
		log.Fatalf("Error querying documents: %s \n", qrerr)
	}
	fmt.Printf("qr: %v\n", qr.Documents[0][0]) //this should result in the document about dogs
}

Development

Build

make build

Test

make test

Generate ChromaDB API Client

make generate

Lint

make lint-fix

Local Server

Note: Docker must be installed

make server

References

Official Chroma documentation
Chroma Helm chart for cloud-native deployments
Chroma Cookbook for examples and recipes