Embedding Models¶

The following embedding wrappers are available:

Embedding Model	Description
Default Embeddings	The default Chroma embedding function running `all-MiniLM-L6-v2` on Onnx Runtime
OpenAI	OpenAI embeddings API. All models are supported - see OpenAI docs for more info.
Cohere	Cohere embeddings API. All models are supported - see Cohere API docs for more info.
HuggingFace Inference API	HuggingFace Inference API. All models supported by the API.
HuggingFace Embedding Inference Server	HuggingFace Embedding Inference Server. Models supported by the inference server.
Ollama	Ollama embeddings API. All models are supported - see Ollama models lib for more info.
Cloudflare Workers AI	Cloudflare Workers AI Embedding. For more info see CF API Docs.
Together AI	Together AI Embedding. For more info see Together API Docs.
Voyage AI	Voyage AI Embedding. For more info see Together API Docs.
Google Gemini	Google Gemini Embedding. For more info see Gemini Docs.
Mistral AI	Mistral AI Embedding. For more info see Mistral AI API Docs.
Nomic AI	Nomic AI Embedding. For more info see Nomic AI API Docs.
Jina AI	Jina AI Embedding. For more info see Nomic AI API Docs.

Default Embeddings¶

Note: Supported from 0.2.0+

The default embedding function uses the all-MiniLM-L6-v2 model running on Onnx Runtime.

Note: As the EF relies on C bindings to avoid memory leaks make sure to call the close callback

package main

import (
    "context"
    "fmt"

    defaultef "github.com/amikos-tech/chroma-go/pkg/embeddings/default_ef"
)

func main() {
    ef, closeef, efErr := defaultef.NewDefaultEmbeddingFunction()
    defer closeef() // make sure to call this to ensure proper resource release
    if efErr != nil {
        fmt.Printf("Error creating OpenAI embedding function: %s \n", efErr)
    }
    documents := []string{
        "Document 1 content here",
    }
    resp, reqErr := ef.EmbedDocuments(context.Background(), documents)
    if reqErr != nil {
        fmt.Printf("Error embedding documents: %s \n", reqErr)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

OpenAI¶

Supported Embedding Function Options:

WithModel - Set the OpenAI model to use. Default is TextEmbeddingAda002 (text-embedding-ada-002).
WithBaseURL - Set the OpenAI base URL. Default is https://api.openai.com/v1. This allows you to point the EF to a compatible OpenAI API endpoint.
WithDimensions - Set the number of dimensions for the embeddings. Default is None which returns the full embeddings.

package main

import (
    "context"
    "fmt"
    "os"

    openai "github.com/amikos-tech/chroma-go/pkg/embeddings/openai"
)

func main() {
    ef, efErr := openai.NewOpenAIEmbeddingFunction(os.Getenv("OPENAI_API_KEY"), openai.WithModel(openai.TextEmbedding3Large))
    if efErr != nil {
        fmt.Printf("Error creating OpenAI embedding function: %s \n", efErr)
    }
    documents := []string{
        "Document 1 content here",
    }
    resp, reqErr := ef.EmbedDocuments(context.Background(), documents)
    if reqErr != nil {
        fmt.Printf("Error embedding documents: %s \n", reqErr)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Cohere¶

package main

import (
    "context"
    "fmt"
    "os"

    cohere "github.com/amikos-tech/chroma-go/pkg/embeddings/cohere"
)

func main() {
    ef := cohere.NewCohereEmbeddingFunction(os.Getenv("COHERE_API_KEY"))
    documents := []string{
        "Document 1 content here",
    }
    resp, reqErr := ef.EmbedDocuments(context.Background(), documents)
    if reqErr != nil {
        fmt.Printf("Error embedding documents: %s \n", reqErr)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

HuggingFace Inference API¶

package main

import (
    "context"
    "fmt"
    "os"

    huggingface "github.com/amikos-tech/chroma-go/pkg/embeddings/hf"
)

func main() {
    ef := huggingface.NewHuggingFaceEmbeddingFunction(os.Getenv("HUGGINGFACE_API_KEY"), "sentence-transformers/all-MiniLM-L6-v2")
    documents := []string{
        "Document 1 content here",
    }
    resp, reqErr := ef.EmbedDocuments(context.Background(), documents)
    if reqErr != nil {
        fmt.Printf("Error embedding documents: %s \n", reqErr)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

HuggingFace Embedding Inference Server¶

The embedding server allows you to run supported model locally on your machine with CPU and GPU inference. For more information check the HuggingFace Embedding Inference Server repository.

package main

import (
    "context"
    "fmt"

    huggingface "github.com/amikos-tech/chroma-go/hf"
)

func main() {
    ef, err := huggingface.NewHuggingFaceEmbeddingInferenceFunction("http://localhost:8001/embed") //set this to the URL of the HuggingFace Embedding Inference Server
    if err != nil {
        fmt.Printf("Error creating HuggingFace embedding function: %s \n", err)
    }
    documents := []string{
        "Document 1 content here",
    }
    resp, reqErr := ef.EmbedDocuments(context.Background(), documents)
    if reqErr != nil {
        fmt.Printf("Error embedding documents: %s \n", reqErr)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Ollama¶

Assumptions

The below example assumes that you have an Ollama server running locally on http://127.0.0.1:11434.

Use the following command to start the Ollama server:

    docker run -d -v ./ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
    docker exec -it ollama ollama run nomic-embed-text # press Ctrl+D to exit after model downloads successfully
    # test it
    curl http://localhost:11434/api/embeddings -d '{"model": "nomic-embed-text","prompt": "Here is an article about llamas..."}'
 ```

```go
package main

import (
    "context"
    "fmt"
    ollama "github.com/amikos-tech/chroma-go/pkg/embeddings/ollama"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // the `/api/embeddings` endpoint is automatically appended to the base URL
    ef, err := ollama.NewOllamaEmbeddingFunction(ollama.WithBaseURL("http://127.0.0.1:11434"), ollama.WithModel("nomic-embed-text"))
    if err != nil {
        fmt.Printf("Error creating Ollama embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Cloudflare Workers AI¶

You will need to register for a Cloudflare account and create a API Token for Workers AI - see docs for more info.

Models can be found in the Cloudflare Workers AI docs. @cf/baai/bge-base-en-v1.5 is the default model.

package main

import (
    "context"
    "fmt"
    cf "github.com/amikos-tech/chroma-go/pkg/embeddings/cloudflare"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `CF_API_TOKEN` and `CF_ACCOUNT_ID` set in your environment
    ef, err := cf.NewCloudflareEmbeddingFunction(cf.WithEnvAPIToken(), cf.WithEnvAccountID(), cf.WithDefaultModel("@cf/baai/bge-small-en-v1.5"))
    if err != nil {
        fmt.Printf("Error creating Cloudflare embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Together AI¶

To use Together AI embeddings, you will need to register for a Together AI account and create an API Key.

Available models can be in Together AI docs. togethercomputer/m2-bert-80M-8k-retrieval is the default model.

package main

import (
    "context"
    "fmt"
    t "github.com/amikos-tech/chroma-go/pkg/embeddings/together"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `TOGETHER_API_KEY` set in your environment
    ef, err := t.NewTogetherEmbeddingFunction(t.WithEnvAPIKey(), t.WithDefaultModel("togethercomputer/m2-bert-80M-2k-retrieval"))
    if err != nil {
        fmt.Printf("Error creating Together embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Voyage AI¶

To use Voyage AI embeddings, you will need to register for a Voyage AI account and create an API Key.

Available models can be in Voyage AI docs. voyage-2 is the default model.

package main

import (
    "context"
    "fmt"
    t "github.com/amikos-tech/chroma-go/pkg/embeddings/voyage"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `VOYAGE_API_KEY` set in your environment
    ef, err := t.NewVoyageAIEmbeddingFunction(t.WithEnvAPIKey(), t.WithDefaultModel("voyage-large-2"))
    if err != nil {
        fmt.Printf("Error creating Together embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Google Gemini¶

To use Google Gemini AI embeddings, you will need to create an API Key.

Available models can be in Gemini Models. text-embedding-004 is the default model.

package main

import (
    "context"
    "fmt"
    g "github.com/amikos-tech/chroma-go/pkg/embeddings/gemini"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `GEMINI_API_KEY` set in your environment
    ef, err := g.NewGeminiEmbeddingFunction(g.WithEnvAPIKey(), g.WithDefaultModel("text-embedding-004"))
    if err != nil {
        fmt.Printf("Error creating Gemini embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Mistral AI¶

To use Mistral AI embeddings, you will need to create an API Key.

Currently, (as of July 2024) only mistral-embed model is available, which is the default model we use.

package main

import (
    "context"
    "fmt"
    mistral "github.com/amikos-tech/chroma-go/pkg/embeddings/mistral"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `MISTRAL_API_KEY` set in your environment
    ef, err := mistral.NewMistralEmbeddingFunction(mistral.WithEnvAPIKey(), mistral.WithDefaultModel("mistral-embed"))
    if err != nil {
        fmt.Printf("Error creating Mistral embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Nomic AI¶

To use Nomic AI embeddings, you will need to create an API Key.

Supported models - https://docs.nomic.ai/atlas/models/text-embedding

package main

import (
    "context"
    "fmt"
    nomic "github.com/amikos-tech/chroma-go/pkg/embeddings/nomic"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `NOMIC_API_KEY` set in your environment
    ef, err := nomic.NewNomicEmbeddingFunction(nomic.WithEnvAPIKey(), nomic.WithDefaultModel(nomic.NomicEmbedTextV1))
    if err != nil {
        fmt.Printf("Error creating Nomic embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}

Jina AI¶

To use Jina AI embeddings, you will need to get an API Key (trial API keys are freely available without any registration, scroll down the page and find the automatically generated API key).

Supported models - https://api.jina.ai/redoc#tag/embeddings/operation/create_embedding_v1_embeddings_post

package main

import (
    "context"
    "fmt"
    jina "github.com/amikos-tech/chroma-go/pkg/embeddings/jina"
)

func main() {
    documents := []string{
        "Document 1 content here",
        "Document 2 content here",
    }
    // Make sure that you have the `JINA_API_KEY` set in your environment
    ef, err := jina.NewJinaEmbeddingFunction(jina.WithEnvAPIKey())
    if err != nil {
        fmt.Printf("Error creating Jina embedding function: %s \n", err)
    }
    resp, err := ef.EmbedDocuments(context.Background(), documents)
    if err != nil {
        fmt.Printf("Error embedding documents: %s \n", err)
    }
    fmt.Printf("Embedding response: %v \n", resp)
}