fireworks-ai

// src/llm/fireworks.ts — Fireworks AI inference (OpenAI-compatible)
import OpenAI from "openai";

const fireworks = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY!,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

// Chat completion with open-source models
async function chat(prompt: string, model = "accounts/fireworks/models/llama-v3p3-70b-instruct") {
  const response = await fireworks.chat.completions.create({
    model,
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: prompt },
    ],
    temperature: 0.7,
    max_tokens: 1024,
  });
  return response.choices[0].message.content;
}

// Streaming
async function streamChat(prompt: string, onChunk: (text: string) => void) {
  const stream = await fireworks.chat.completions.create({
    model: "accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages: [{ role: "user", content: prompt }],
    stream: true,
  });
  let full = "";
  for await (const chunk of stream) {
    const text = chunk.choices[0]?.delta?.content ?? "";
    full += text;
    onChunk(text);
  }
  return full;
}

// Force structured JSON output
async function extractData(text: string) {
  const response = await fireworks.chat.completions.create({
    model: "accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages: [
      {
        role: "system",
        content: `Extract product information. Return JSON: { "name": string, "price": number, "category": string, "features": string[] }`,
      },
      { role: "user", content: text },
    ],
    response_format: { type: "json_object" },
    temperature: 0,
  });
  return JSON.parse(response.choices[0].message.content!);
}

// Grammar-constrained generation (Fireworks-specific)
async function generateWithGrammar(prompt: string) {
  const response = await fetch("https://api.fireworks.ai/inference/v1/chat/completions", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.FIREWORKS_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "accounts/fireworks/models/llama-v3p3-70b-instruct",
      messages: [{ role: "user", content: prompt }],
      response_format: {
        type: "json_object",
        schema: {
          type: "object",
          properties: {
            sentiment: { type: "string", enum: ["positive", "negative", "neutral"] },
            confidence: { type: "number", minimum: 0, maximum: 1 },
            keywords: { type: "array", items: { type: "string" } },
          },
          required: ["sentiment", "confidence", "keywords"],
        },
      },
    }),
  });
  return response.json();
}

// Tool use with Fireworks
async function agentWithTools(prompt: string) {
  const response = await fireworks.chat.completions.create({
    model: "accounts/fireworks/models/firefunction-v2",  // Optimized for function calling
    messages: [{ role: "user", content: prompt }],
    tools: [
      {
        type: "function",
        function: {
          name: "search_database",
          description: "Search the product database",
          parameters: {
            type: "object",
            properties: {
              query: { type: "string" },
              category: { type: "string", enum: ["electronics", "clothing", "books"] },
              max_price: { type: "number" },
            },
            required: ["query"],
          },
        },
      },
    ],
    tool_choice: "auto",
  });
  return response;
}

# fine_tune.py — Fine-tune a model on Fireworks
import requests

FIREWORKS_API_KEY = os.environ["FIREWORKS_API_KEY"]
BASE_URL = "https://api.fireworks.ai/inference/v1"

# Upload training data (JSONL format)
def upload_dataset(filepath: str):
    with open(filepath, "rb") as f:
        response = requests.post(
            f"{BASE_URL}/files",
            headers={"Authorization": f"Bearer {FIREWORKS_API_KEY}"},
            files={"file": (filepath, f, "application/jsonl")},
            data={"purpose": "fine-tune"},
        )
    return response.json()["id"]

# Start fine-tuning job
def create_fine_tune(dataset_id: str, base_model: str = "accounts/fireworks/models/llama-v3p1-8b-instruct"):
    response = requests.post(
        f"{BASE_URL}/fine_tuning/jobs",
        headers={
            "Authorization": f"Bearer {FIREWORKS_API_KEY}",
            "Content-Type": "application/json",
        },
        json={
            "model": base_model,
            "training_file": dataset_id,
            "hyperparameters": {
                "n_epochs": 3,
                "learning_rate_multiplier": 1.0,
                "batch_size": 8,
            },
        },
    )
    return response.json()

# Training data format (JSONL):
# {"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

## Popular Models on Fireworks
- **llama-v3p3-70b-instruct** — Best open-source general-purpose model
- **llama-v3p1-8b-instruct** — Fast, cheap, good for simple tasks
- **mixtral-8x22b-instruct** — Strong multilingual, large context
- **qwen2p5-72b-instruct** — Excellent for coding and math
- **firefunction-v2** — Optimized for function calling / tool use
- **deepseek-v3** — Strong reasoning and code generation
- **gemma-2-27b-it** — Google's compact model

# Use any OpenAI-compatible SDK
npm install openai
# Set baseURL to https://api.fireworks.ai/inference/v1

pip install openai
# Set base_url to https://api.fireworks.ai/inference/v1

Add Fireworks Ai to my Next.js app for the AI chat feature. I want streaming responses.

My Fireworks Ai calls are slow and expensive. Help me optimize the setup.