@apicity/fireworks

v0.7.3

Published

2 days ago

Fireworks AI provider for chat completions, completions, and embeddings.

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ai fireworks fireworks-ai chat embeddings apicity

@apicity/fireworks

Fireworks AI provider for chat completions, completions, and embeddings.

Runtime dependencies:

zod@^4.4.3 — request schemas attached to every POST endpoint as .schema

Installation

npm install @apicity/fireworks
# or
pnpm add @apicity/fireworks

Quick Start

import { createFireworks } from "@apicity/fireworks";

const fireworks = createFireworks({ apiKey: process.env.FIREWORKS_API_KEY! });

Real-world example: rerank candidate passages, then stream a Llama 3.3 70B answer

Fireworks ships two inference surfaces that compose cleanly into a lightweight RAG pipeline: a hosted cross-encoder reranker (qwen3-reranker-8b) that scores a query against N candidate documents, and OpenAI-compatible streaming chat completions on Llama 3.3 70B Instruct. The rerank is the hard part — most providers only sell embeddings — and the streaming chat is the cheap, fast tail. The two-step flow below mirrors tests/integration/fireworks-rerank.test.ts and tests/integration/fireworks-stream.test.ts, which replay against tests/recordings/fireworks_626462085/rerank-basic_678261817/ and tests/recordings/fireworks_626462085/chat-stream-hello_2801342443/, so every score, token count, and SSE shape below comes straight from the recorded HARs.

import { createFireworks } from "@apicity/fireworks";
import type {
  FireworksRerankResponse,
  FireworksChatStreamChunk,
} from "@apicity/fireworks";

const fireworks = createFireworks({ apiKey: process.env.FIREWORKS_API_KEY! });

// 1. Cross-encoder rerank. The model sees the query and each document
//    jointly (unlike embedding-based retrieval, which scores them
//    independently), so it picks up phrasing-level signal that ANN
//    search misses. `top_n` caps the response length; `return_documents:
//    true` echoes the document text back inline so you can hand the
//    top hit straight to a chat model without keeping a parallel
//    id→text map.
const ranked: FireworksRerankResponse =
  await fireworks.inference.v1.rerank({
    model: "fireworks/qwen3-reranker-8b",
    query: "What is the capital of France?",
    documents: [
      "Berlin is the capital of Germany.",
      "Paris is the capital and largest city of France.",
      "Madrid is the capital of Spain.",
    ],
    top_n: 2,
    return_documents: true,
  });

// 2. The response is sorted descending by relevance_score. The score
//    spread is huge — Paris ~0.96, Berlin ~0.0001, Madrid dropped — a
//    near-four-orders-of-magnitude gap that a cosine-similarity
//    retriever could never produce. `index` points back into the
//    original `documents` array.
const top = ranked.data[0];
console.log(`#1: index=${top.index}, score=${top.relevance_score.toFixed(4)}`);
console.log(`    "${top.document}"`);
// → "#1: index=1, score=0.9579"
// →     "Paris is the capital and largest city of France."

console.log(`model=${ranked.model}, tokens=${ranked.usage.prompt_tokens}`);
// → "model=accounts/fireworks/models/qwen3-reranker-8b, tokens=261"

// 3. Stream a chat answer through Llama 3.3 70B Instruct. The recorded
//    call below is a generic 'say hello' smoke test — independent from
//    the rerank above — but the request shape is exactly what you'd
//    send in production: drop `top.document` into a system message and
//    the user's original question into the user turn.
//    `temperature: 0` makes the output deterministic; `max_tokens: 64`
//    caps the response. The streaming surface lives under
//    `post.stream.*` and yields OpenAI-shaped
//    `chat.completion.chunk` objects.
let answer = "";
let finish: string | null = null;
let totals = { prompt: 0, completion: 0 };

const stream = fireworks.post.stream.inference.v1.chat.completions({
  model: "accounts/fireworks/models/llama-v3p3-70b-instruct",
  messages: [{ role: "user", content: "Say hello in one sentence." }],
  temperature: 0,
  max_tokens: 64,
});

for await (const chunk of stream) {
  // 4. Each delta carries one or two tokens of `content`. The FIRST
  //    delta carries `delta.role` and no content; the LAST delta has
  //    no content but `finish_reason: "stop"` and final `usage`.
  //    Read defensively — both fields are optional on every chunk.
  const choice: FireworksChatStreamChunk["choices"][number] | undefined =
    chunk.choices[0];
  if (choice?.delta?.content) answer += choice.delta.content;
  if (choice?.finish_reason) finish = choice.finish_reason;
  if (chunk.usage) {
    totals = {
      prompt: chunk.usage.prompt_tokens,
      completion: chunk.usage.completion_tokens,
    };
  }
}

console.log(answer);
// → "Hello, it's nice to meet you and I'm here to help with any questions or topics you'd like to discuss."
console.log(
  `finish=${finish}, prompt=${totals.prompt}, completion=${totals.completion}`,
);
// → "finish=stop, prompt=41, completion=26"

Notes

The reranker is a cross-encoder, not an embedding model — it computes query-document interaction directly, so it's substantially more accurate than cosine similarity over independent embeddings, but it doesn't scale to millions of candidates. The standard hybrid is embedding-based ANN to fetch a top-100, then qwen3-reranker-8b to refine to a top-3. Fireworks hosts both halves on the same domain (/v1/embeddings for the first stage, /v1/rerank for the second).
Streaming chat chunks are OpenAI-compatible: chat.completion.chunk with choices[].delta carrying incremental content. Watch finish_reason to know whether you hit "stop", "length", or "content_filter". Only the final chunk has usage populated; intermediate chunks have usage: null. Fireworks also surfaces a per-call fireworks-cached-prompt-tokens response header — at 40 of 41 cached above, the prefix paid for itself almost entirely, visible without parsing the body.
For Anthropic-shaped responses against the same Llama catalog, swap to fireworks.inference.v1.messages({...}). The model id and pricing match, but the response is { role, content: [{ type: "text", text }], ... } instead of { choices: [{ message: { content }}] } — useful when porting code already written against @apicity/anthropic.
For non-streaming completions, drop the post.stream. prefix: fireworks.inference.v1.chat.completions({...}) returns a single FireworksChatResponse with choices[0].message.content populated and full usage in one shot. Same payload schema, exposed at fireworks.inference.v1.chat.completions.schema for runtime validation before the call.
Errors throw FireworksError with status and the parsed body attached. 400 from rerank usually means an empty/missing documents array; 429 is rate-limit; 503 is a deployment cold start. Wrap with withRetry({ retries: 3 }) from @apicity/fireworks to ride out cold deployments without bespoke retry code.

API Reference

101 endpoints across 1 group. Each method mirrors an upstream URL path.

inference

POST https://api.fireworks.ai/v1/accounts/{accountId}/users/{userId}/apiKeys

Cost tier: expensive

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@apicity/fireworks

Installation

Quick Start

Real-world example: rerank candidate passages, then stream a Llama 3.3 70B answer

API Reference

inference

Middleware

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