Ollama Embeddings

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Python

JavaScript

Chroma provides a convenient wrapper around Ollama' s embeddings API. You can use the OllamaEmbeddingFunction embedding function to generate embeddings for your documents with a model of your choice.

Python

import chromadb.utils.embedding_functions as embedding_functions

ollama_ef = embedding_functions.OllamaEmbeddingFunction(
    url="http://localhost:11434/api/embeddings",
    model_name="llama2",
)

embeddings = ollama_ef(["This is my first text to embed",
                        "This is my second document"])

JavaScript

// const {OllamaEmbeddingFunction} = require('chromadb'); //CJS import
import {OllamaEmbeddingFunction}  from "chromadb"; //ESM import
const embedder = new OllamaEmbeddingFunction({
    url: "http://127.0.0.1:11434/api/embeddings",
    model: "llama2"
})

// use directly 
const embeddings = embedder.generate(["document1", "document2"])

// pass documents to query for .add and .query
const collection = await client.createCollection({
    name: "name",
    embeddingFunction: embedder
})
const collection = await client.getCollection({
    name: "name",
    embeddingFunction: embedder
})

You can pass in an optional model_name argument, which lets you choose which OpenAI embeddings model to use. By default, Chroma uses text-embedding-ada-002. You can see a list of all available models here.