fastify-lm

fastify-lm

What is fastify-lm?

fastify-lm is a Fastify plugin that simplifies integration with multiple language model (LM) providers, such as:

Provider	Description
Test	Test provider, always returns "test" and the input parameters
OpenAI	GPT models, including GPT-4o, GPT-3.5
Google	Gemini models, such as Gemini 1.5
Claude	Anthropic’s Claude models (Claude 3, etc.)
Deepseek	Deepseek AI language models
Llama	Llama AI language models
Mistral	Mistral AI language models

It provides a unified interface, allowing you to switch providers without modifying your application code.

🔥 Why use fastify-lm?

Developing applications that interact with language models usually requires direct API integration, which can lead to:

• 🔗 Dependency on a single provider
• 🔄 Difficulty switching models without refactoring code
• ❌ Inconsistencies in how different APIs are used

With fastify-lm, you can:
✅ Define multiple providers in a single configuration
✅ Switch models just by changing environment variables
✅ Use a consistent query system without worrying about API differences
✅ Easily run A/B tests with different models to find the best fit for your use case

🛠 Use Cases

• Chatbots and virtual assistants: Seamlessly integrate multiple AI models to enhance user experience.
• Natural Language Processing (NLP): Analyze text using different models without modifying your code.
• Model comparison: Evaluate different LMs within the same application with minimal changes.
• Flexible infrastructure: Switch providers based on availability, cost, or technological improvements.
• Analyze requests: Moderate or analyze requests using language models.

🚀 Ready to get started? Continue with the installation guide and start using fastify-lm in just a few minutes.

Installation

To install the plugin, on existing Fastify project, just run:

npm install fastify-lm

Compatibility

fastify-lm (plugin)	Fastify
^1.x	^3.x, ^4.x, ^5.x

Please note that if a Fastify version is out of support, then so are the corresponding versions of this plugin in the table above. See Fastify's LTS policy for more details.

Quick start

Start by creating a Fastify instance and registering the plugin.

npm i fastify fastify-lm

Create a file src/server.js and add following code:

// Import the framework and instantiate it
import Fastify from "fastify";
import LmPlugin from "fastify-lm";

const fastify = Fastify({
  logger: true,
});

// Register the lm-plugin
fastify.register(LmPlugin, {
  models: [
    {
      name: "lm", // the name of the model instance on your app
      provider: "openai", // openai, google, claude, deepseek or any available provider
      model: "gpt-4o-mini",
      apiKey: "your-api-key",
    },
  ],
});

// Declare a route / that returns the models
fastify.get("/", async function handler(request, reply) {
  const models = await fastify.lm.models();
  return { models };
});

// Run the server!
try {
  await fastify.listen({ port: 3000 });
  await fastify.lm.models();
} catch (err) {
  fastify.log.error(err);
  process.exit(1);
}

Remember to replace your-api-key with your actual API key.

Finally, launch the server with:

node src/server.js

and test it with:

curl http://localhost:3000/

Usage

Registering the Plugin

Register the plugin in your Fastify instance by specifying the models and providers to use.

Basic Usage

import Fastify from "fastify";
import lmPlugin from "fastify-lm";

// Create a Fastify instance and register the plugin
const app = Fastify();
app.register(lmPlugin, {
  models: [
    {
      name: "lm",
      provider: process.env.LM_PROVIDER,
      model: process.env.LM_MODEL,
      apiKey: process.env.LM_API_KEY,
    },
  ],
});

const response = await app.lm.chat({
  messages: [{ role: "user", content: "How are you?" }],
});

💡 Change the environment variables to switch the provider.

Multiple Providers with Query Parameter Selection

import Fastify, { FastifyRequest, FastifyReply } from "fastify";
import lmPlugin from "fastify-lm";

// Create a Fastify instance and register the plugin
const app = Fastify();
app.register(lmPlugin, {
  models: [
    {
      name: "openai",
      provider: "openai",
      model: "gpt-3.5-turbo",
      apiKey: process.env.OPENAI_API_KEY,
    },
    {
      name: "google",
      provider: "google",
      model: "gemini-2.0-flash-lite",
      apiKey: process.env.GOOGLE_API_KEY,
    },
    {
      name: "claude",
      provider: "claude",
      model: "claude-3-5-sonnet-20240620",
      apiKey: process.env.CLAUDE_API_KEY,
    },
    {
      name: "deepseek",
      provider: "deepseek",
      model: "deepseek-chat",
      apiKey: process.env.DEEPSEEK_API_KEY,
    },
    {
      name: "mistral",
      provider: "mistral",
      model: "mistral-medium",
      apiKey: process.env.MISTRAL_API_KEY,
    },
  ],
});

// Route that receives the query and optional model parameter
app.get<{ Querystring: QueryParams }>(
  "/chat",
  {
    schema: {
      querystring: {
        type: 'object',
        required: ['query'],
        properties: {
          query: { type: 'string' },
          model: { 
            type: 'string', 
            enum: ['openai', 'google', 'claude', 'deepseek', 'mistral'],
            default: 'openai'
          }
        }
      }
    }
  },
  async (
    request: FastifyRequest<{ Querystring: QueryParams }>,
    reply: FastifyReply
  ) => {
    const { query, model = "openai" } = request.query;

    try {
      const response = await app[model].chat({
        messages: [{ role: "user", content: query }],
      });
      
      return { response };
    } catch (error: any) {
      reply.status(500).send({ error: error.message });
    }
  }
);

// Start the server
app.listen({ port: 3000 }, (err, address) => {
  if (err) {
    console.error(err);
    process.exit(1);
  }
  console.log(`Server running at ${address}`);
});

interface QueryParams {
  query: string;
  model?: "openai" | "google" | "claude" | "deepseek" | "mistral"; // Optional, defaults to "openai"
}

Advanced Use Cases

Beyond simple model queries, you can leverage fastify-lm for more advanced functionalities:

🤖 Automated Customer Support Responses

Use AI to generate instant answers for common support queries.
📖 Read the full guide →

🎫 AI-Powered Support Ticket Prioritization

Automatically classify and prioritize support tickets based on urgency and sentiment.
📖 Read the full guide →

📢 AI-Driven Sentiment Analysis

Analyze user feedback, reviews, or messages to determine sentiment trends.
📖 Read the full guide →

📌 Automatic Content Moderation

Detect and block inappropriate messages before processing them.
📖 Read the full guide →

🔍 Semantic Search & Query Expansion

Improve search relevance by understanding intent and expanding queries intelligently.
📖 Read the full guide →

✨ Smart Autocomplete for Forms

Enhance user input by automatically generating text suggestions.
📖 Read the full guide →

📄 Automatic Text Summarization

Summarize long text passages using AI models.
📖 Read the full guide →

🌍 Real-Time Text Translation

Translate user input dynamically with multi-provider support.
📖 Read the full guide →

📊 AI-Powered Data Extraction

Extract structured information from unstructured text, such as invoices, legal documents, or reports.
📖 Read the full guide →

🚀 Check out more examples in the /docs/ folder!

Contributing

We need a lot of hands to implement other providers you can help us by submitting a pull request.

📖 Adding a New Adapter

License

MIT