Chroma Go

A simple Chroma Vector Database client written in Go

Works with Chroma Version: v0.4.3 - v0.4.21

Feature Parity with ChromaDB API

✅ Reset
✅ Heartbeat
✅ List 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 API
✅ Cohere API (including Multi-language support)
✅ Sentence Transformers (HuggingFace Inference API)
🚫 PaLM API
🚫 Custom Embedding Function
🚫 Hugging Face Inference Embedding Function

Installation

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

or:

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

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 (
	"fmt"
	"log"
	"os"

	chroma "github.com/amikos-tech/chroma-go"
	openai "github.com/amikos-tech/chroma-go/openai"
	godotenv "github.com/joho/godotenv"
)

func main() {
	client := chroma.NewClient("http://localhost:8000")
	collectionName := "test-collection"
	metadata := map[string]interface{}{}
	err := godotenv.Load(".env")
	if err != nil {
		fmt.Printf("Error loading .env file: %s", err)
		return
	}
	embeddingFunction := openai.NewOpenAIEmbeddingFunction(os.Getenv("OPENAI_API_KEY")) //create a new OpenAI Embedding function
	distanceFunction := chroma.L2
	_, errRest := client.Reset() //reset the database
	if errRest != nil {
		log.Fatalf("Error resetting database: %s \n", errRest.Error())
	}
	col, err := client.CreateCollection(collectionName, metadata, true, embeddingFunction, distanceFunction)
	if err != nil {
		fmt.Printf("Error create collection: %s \n", err.Error())
		return
	}
	documents := []string{
		"This is a document about cats. Cats are great.",
		"this is a document about dogs. Dogs are great.",
	}
	ids := []string{
		"ID1",
		"ID2",
	}

	metadatas := []map[string]interface{}{
		{"key1": "value1"},
		{"key2": "value2"},
	}
	_, addError := col.Add(nil, metadatas, documents, ids)
	if addError != nil {
		log.Fatalf("Error adding documents: %s \n", addError)
	}
	countDocs, qrerr := col.Count()
	if qrerr != nil {
		log.Fatalf("Error counting documents: %s \n", qrerr)
	}
	fmt.Printf("countDocs: %v\n", countDocs) //this should result in 2
	qr, qrerr := col.Query([]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 gotest

Generate ChromaDB API Client

make generate

References

Official Chroma documentation
Chroma Helm chart for cloud-native deployments

scfobao / chroma-go