Introduction | Quick Install | Usage | Documentation | Examples | Contributing | modelfusion.dev
Important
ModelFusion has joined Vercel and is being integrated into the Vercel AI SDK. We are bringing the best parts of modelfusion to the Vercel AI SDK, starting with text generation, structured object generation, and tool calls. Please check out the AI SDK for the latest developments.
ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents.
- Vendor-neutral: ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider.
- Multi-modal: ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models.
- Type inference and validation: ModelFusion infers TypeScript types wherever possible and validates model responses.
- Observability and logging: ModelFusion provides an observer framework and logging support.
- Resilience and robustness: ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms.
- Built for production: ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.
npm install modelfusionOr use a starter template:
- ModelFusion terminal app starter
- Next.js, Vercel AI SDK, Llama.cpp & ModelFusion starter
- Next.js, Vercel AI SDK, Ollama & ModelFusion starter
Tip
The basic examples are a great way to get started and to explore in parallel with the documentation. You can find them in the examples/basic folder.
You can provide API keys for the different integrations using environment variables (e.g., OPENAI_API_KEY) or pass them into the model constructors as options.
Generate text using a language model and a prompt. You can stream the text if it is supported by the model. You can use images for multi-modal prompting if the model supports it (e.g. with llama.cpp). You can use prompt styles to use text, instruction, or chat prompts.
import { generateText, openai } from "modelfusion";
const text = await generateText({
model: openai.CompletionTextGenerator({ model: "gpt-3.5-turbo-instruct" }),
prompt: "Write a short story about a robot learning to love:\n\n",
});Providers: OpenAI, OpenAI compatible, Llama.cpp, Ollama, Mistral, Hugging Face, Cohere
import { streamText, openai } from "modelfusion";
const textStream = await streamText({
model: openai.CompletionTextGenerator({ model: "gpt-3.5-turbo-instruct" }),
prompt: "Write a short story about a robot learning to love:\n\n",
});
for await (const textPart of textStream) {
process.stdout.write(textPart);
}Providers: OpenAI, OpenAI compatible, Llama.cpp, Ollama, Mistral, Cohere
Multi-modal vision models such as GPT 4 Vision can process images as part of the prompt.
import { streamText, openai } from "modelfusion";
import { readFileSync } from "fs";
const image = readFileSync("./image.png");
const textStream = await streamText({
model: openai
.ChatTextGenerator({ model: "gpt-4-vision-preview" })
.withInstructionPrompt(),
prompt: {
instruction: [
{ type: "text", text: "Describe the image in detail." },
{ type: "image", image, mimeType: "image/png" },
],
},
});
for await (const textPart of textStream) {
process.stdout.write(textPart);
}Providers: OpenAI, OpenAI compatible, Llama.cpp, Ollama
Generate typed objects using a language model and a schema.
Generate an object that matches a schema.
import {
ollama,
zodSchema,
generateObject,
jsonObjectPrompt,
} from "modelfusion";
const sentiment = await generateObject({
model: ollama
.ChatTextGenerator({
model: "openhermes2.5-mistral",
maxGenerationTokens: 1024,
temperature: 0,
})
.asObjectGenerationModel(jsonObjectPrompt.instruction()),
schema: zodSchema(
z.object({
sentiment: z
.enum(["positive", "neutral", "negative"])
.describe("Sentiment."),
})
),
prompt: {
system:
"You are a sentiment evaluator. " +
"Analyze the sentiment of the following product review:",
instruction:
"After I opened the package, I was met by a very unpleasant smell " +
"that did not disappear even after washing. Never again!",
},
});Providers: OpenAI, Ollama, Llama.cpp
Stream a object that matches a schema. Partial objects before the final part are untyped JSON.
import { zodSchema, openai, streamObject } from "modelfusion";
const objectStream = await streamObject({
model: openai
.ChatTextGenerator(/* ... */)
.asFunctionCallObjectGenerationModel({
fnName: "generateCharacter",
fnDescription: "Generate character descriptions.",
})
.withTextPrompt(),
schema: zodSchema(
z.object({
characters: z.array(
z.object({
name: z.string(),
class: z
.string()
.describe("Character class, e.g. warrior, mage, or thief."),
description: z.string(),
})
),
})
),
prompt: "Generate 3 character descriptions for a fantasy role playing game.",
});
for await (const { partialObject } of objectStream) {
console.clear();
console.log(partialObject);
}Providers: OpenAI, Ollama, Llama.cpp
Generate an image from a prompt.
import { generateImage, openai } from "modelfusion";
const image = await generateImage({
model: openai.ImageGenerator({ model: "dall-e-3", size: "1024x1024" }),
prompt:
"the wicked witch of the west in the style of early 19th century painting",
});Providers: OpenAI (Dall·E), Stability AI, Automatic1111
Synthesize speech (audio) from text. Also called TTS (text-to-speech).
generateSpeech synthesizes speech from text.
import { generateSpeech, lmnt } from "modelfusion";
// `speech` is a Uint8Array with MP3 audio data
const speech = await generateSpeech({
model: lmnt.SpeechGenerator({
voice: "034b632b-df71-46c8-b440-86a42ffc3cf3", // Henry
}),
text:
"Good evening, ladies and gentlemen! Exciting news on the airwaves tonight " +
"as The Rolling Stones unveil 'Hackney Diamonds,' their first collection of " +
"fresh tunes in nearly twenty years, featuring the illustrious Lady Gaga, the " +
"magical Stevie Wonder, and the final beats from the late Charlie Watts.",
});Providers: Eleven Labs, LMNT, OpenAI
generateSpeech generates a stream of speech chunks from text or from a text stream. Depending on the model, this can be fully duplex.
import { streamSpeech, elevenlabs } from "modelfusion";
const textStream: AsyncIterable<string>;
const speechStream = await streamSpeech({
model: elevenlabs.SpeechGenerator({
model: "eleven_turbo_v2",
voice: "pNInz6obpgDQGcFmaJgB", // Adam
optimizeStreamingLatency: 1,
voiceSettings: { stability: 1, similarityBoost: 0.35 },
generationConfig: {
chunkLengthSchedule: [50, 90, 120, 150, 200],
},
}),
text: textStream,
});
for await (const part of speechStream) {
// each part is a Uint8Array with MP3 audio data
}Providers: Eleven Labs
Transcribe speech (audio) data into text. Also called speech-to-text (STT).
import { generateTranscription, openai } from "modelfusion";
import fs from "node:fs";
const transcription = await generateTranscription({
model: openai.Transcriber({ model: "whisper-1" }),
mimeType: "audio/mp3",
audioData: await fs.promises.readFile("data/test.mp3"),
});Providers: OpenAI (Whisper), Whisper.cpp
Create embeddings for text and other values. Embeddings are vectors that represent the essence of the values in the context of the model.
import { embed, embedMany, openai } from "modelfusion";
// embed single value:
const embedding = await embed({
model: openai.TextEmbedder({ model: "text-embedding-ada-002" }),
value: "At first, Nox didn't know what to do with the pup.",
});
// embed many values:
const embeddings = await embedMany({
model: openai.TextEmbedder({ model: "text-embedding-ada-002" }),
values: [
"At first, Nox didn't know what to do with the pup.",
"He keenly observed and absorbed everything around him, from the birds in the sky to the trees in the forest.",
],
});Providers: OpenAI, OpenAI compatible, Llama.cpp, Ollama, Mistral, Hugging Face, Cohere
Classifies a value into a category.
import { classify, EmbeddingSimilarityClassifier, openai } from "modelfusion";
const classifier = new EmbeddingSimilarityClassifier({
embeddingModel: openai.TextEmbedder({ model: "text-embedding-ada-002" }),
similarityThreshold: 0.82,
clusters: [
{
name: "politics" as const,
values: [
"they will save the country!",
// ...
],
},
{
name: "chitchat" as const,
values: [
"how's the weather today?",
// ...
],
},
],
});
// strongly typed result:
const result = await classify({
model: classifier,
value: "don't you love politics?",
});Classifiers: EmbeddingSimilarityClassifier
Split text into tokens and reconstruct the text from tokens.
const tokenizer = openai.Tokenizer({ model: "gpt-4" });
const text = "At first, Nox didn't know what to do with the pup.";
const tokenCount = await countTokens(tokenizer, text);
const tokens = await tokenizer.tokenize(text);
const tokensAndTokenTexts = await tokenizer.tokenizeWithTexts(text);
const reconstructedText = await tokenizer.detokenize(tokens);Providers: OpenAI, Llama.cpp, Cohere
Tools are functions (and associated metadata) that can be executed by an AI model. They are useful for building chatbots and agents.
ModelFusion offers several tools out-of-the-box: Math.js, MediaWiki Search, SerpAPI, Google Custom Search. You can also create custom tools.
With runTool, you can ask a tool-compatible language model (e.g. OpenAI chat) to invoke a single tool. runTool first generates a tool call and then executes the tool with the arguments.
const { tool, toolCall, args, ok, result } = await runTool({
model: openai.ChatTextGenerator({ model: "gpt-3.5-turbo" }),
too: calculator,
prompt: [openai.ChatMessage.user("What's fourteen times twelve?")],
});
console.log(`Tool call:`, toolCall);
console.log(`Tool:`, tool);
console.log(`Arguments:`, args);
console.log(`Ok:`, ok);
console.log(`Result or Error:`, result);With runTools, you can ask a language model to generate several tool calls as well as text. The model will choose which tools (if any) should be called with which arguments. Both the text and the tool calls are optional. This function executes the tools.
const { text, toolResults } = await runTools({
model: openai.ChatTextGenerator({ model: "gpt-3.5-turbo" }),
tools: [calculator /* ... */],
prompt: [openai.ChatMessage.user("What's fourteen times twelve?")],
});You can use runTools to implement an agent loop that responds to user messages and executes tools. Learn more.
const texts = [
"A rainbow is an optical phenomenon that can occur under certain meteorological conditions.",
"It is caused by refraction, internal reflection and dispersion of light in water droplets resulting in a continuous spectrum of light appearing in the sky.",
// ...
];
const vectorIndex = new MemoryVectorIndex<string>();
const embeddingModel = openai.TextEmbedder({
model: "text-embedding-ada-002",
});
// update an index - usually done as part of an ingestion process:
await upsertIntoVectorIndex({
vectorIndex,
embeddingModel,
objects: texts,
getValueToEmbed: (text) => text,
});
// retrieve text chunks from the vector index - usually done at query time:
const retrievedTexts = await retrieve(
new VectorIndexRetriever({
vectorIndex,
embeddingModel,
maxResults: 3,
similarityThreshold: 0.8,
}),
"rainbow and water droplets"
);Available Vector Stores: Memory, SQLite VSS, Pinecone
You can use different prompt styles (such as text, instruction or chat prompts) with ModelFusion text generation models. These prompt styles can be accessed through the methods .withTextPrompt(), .withChatPrompt() and .withInstructionPrompt():
const text = await generateText({
model: openai
.ChatTextGenerator({
// ...
})
.withTextPrompt(),
prompt: "Write a short story about a robot learning to love",
});const text = await generateText({
model: llamacpp
.CompletionTextGenerator({
// run https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF with llama.cpp
promptTemplate: llamacpp.prompt.Llama2, // Set prompt template
contextWindowSize: 4096, // Llama 2 context window size
maxGenerationTokens: 512,
})
.withInstructionPrompt(),
prompt: {
system: "You are a story writer.",
instruction: "Write a short story about a robot learning to love.",
},
});const textStream = await streamText({
model: openai
.ChatTextGenerator({
model: "gpt-3.5-turbo",
})
.withChatPrompt(),
prompt: {
system: "You are a celebrated poet.",
messages: [
{
role: "user",
content: "Suggest a name for a robot.",
},
{
role: "assistant",
content: "I suggest the name Robbie",
},
{
role: "user",
content: "Write a short story about Robbie learning to love",
},
],
},
});You an use prompt templates with image models as well, e.g. to use a basic text prompt. It is available as a shorthand method:
const image = await generateImage({
model: stability
.ImageGenerator({
//...
})
.withTextPrompt(),
prompt:
"the wicked witch of the west in the style of early 19th century painting",
});| Prompt Template | Text Prompt |
|---|---|
| Automatic1111 | ✅ |
| Stability | ✅ |
ModelFusion model functions return rich responses that include the raw (original) response and metadata when you set the fullResponse argument to true.
// access the raw response (needs to be typed) and the metadata:
const { text, rawResponse, metadata } = await generateText({
model: openai.CompletionTextGenerator({
model: "gpt-3.5-turbo-instruct",
maxGenerationTokens: 1000,
n: 2, // generate 2 completions
}),
prompt: "Write a short story about a robot learning to love:\n\n",
fullResponse: true,
});
console.log(metadata);
// cast to the raw response type:
for (const choice of (rawResponse as OpenAICompletionResponse).choices) {
console.log(choice.text);
}ModelFusion provides an observer framework and logging support. You can easily trace runs and call hierarchies, and you can add your own observers.
import { generateText, openai } from "modelfusion";
const text = await generateText({
model: openai.CompletionTextGenerator({ model: "gpt-3.5-turbo-instruct" }),
prompt: "Write a short story about a robot learning to love:\n\n",
logging: "detailed-object",
});Examples for almost all of the individual functions and objects. Highly recommended to get started.
multi-modal, object streaming, image generation, text to speech, speech to text, text generation, object generation, embeddings
StoryTeller is an exploratory web application that creates short audio stories for pre-school kids.
Next.js app, OpenAI GPT-3.5-turbo, streaming, abort handling
A web chat with an AI assistant, implemented as a Next.js app.
terminal app, PDF parsing, in memory vector indices, retrieval augmented generation, hypothetical document embedding
Ask questions about a PDF document and get answers from the document.
Next.js app, image generation, transcription, object streaming, OpenAI, Stability AI, Ollama
Examples of using ModelFusion with Next.js 14 (App Router):
- image generation
- voice recording & transcription
- object streaming
Speech Streaming, OpenAI, Elevenlabs streaming, Vite, Fastify, ModelFusion Server
Given a prompt, the server returns both a text and a speech stream response.
terminal app, agent, BabyAGI
TypeScript implementation of the BabyAGI classic and BabyBeeAGI.
terminal app, ReAct agent, GPT-4, OpenAI functions, tools
Get answers to questions from Wikipedia, e.g. "Who was born first, Einstein or Picasso?"
terminal app, agent, tools, GPT-4
Small agent that solves middle school math problems. It uses a calculator tool to solve the problems.
terminal app, PDF parsing, recursive information extraction, in memory vector index, _style example retrieval, OpenAI GPT-4, cost calculation
Extracts information about a topic from a PDF and writes a tweet in your own style about it.
Cloudflare, OpenAI
Generate text on a Cloudflare Worker using ModelFusion and OpenAI.
Read the ModelFusion contributing guide to learn about the development process, how to propose bugfixes and improvements, and how to build and test your changes.