Skip to main content
You can use the OpenAI Python client library to interact with Fireworks. This makes migration of existing applications already using OpenAI particularly easy.

​
Specify endpoint and API key

​
Using the OpenAI client

You can use the OpenAI client by initializing it with your Fireworks configuration: 
from openai import OpenAI

# Initialize with Fireworks parameters
client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="<YOUR_FIREWORKS_API_KEY>",
)
You can also use environment variables with the client: 
import os
from openai import OpenAI

# Initialize using environment variables
client = OpenAI(
    base_url=os.environ.get("OPENAI_API_BASE", "https://api.fireworks.ai/inference/v1"),
    api_key=os.environ.get("OPENAI_API_KEY"),  # Set to your Fireworks API key
)

​
Using environment variables

export OPENAI_API_BASE="https://api.fireworks.ai/inference/v1"
export OPENAI_API_KEY="<YOUR_FIREWORKS_API_KEY>"

​
Alternative approach

import openai

# warning: it has a process-wide effect
openai.api_base = "https://api.fireworks.ai/inference/v1"
openai.api_key = "<YOUR_FIREWORKS_API_KEY>"

​
Usage

Use OpenAI’s SDK how you’d normally would. Just ensure that the model parameter refers to one of Fireworks models.

​
Completion

Simple completion API that doesn’t modify provided prompt in any way: 
from openai import OpenAI

client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="<YOUR_FIREWORKS_API_KEY>",
)

completion = client.completions.create(
    model="accounts/fireworks/models/llama-v3p1-8b-instruct",
    prompt="The quick brown fox",
)
print(completion.choices[0].text)

​
Chat Completion

Works best for models fine-tuned for conversation (e.g. llama*-chat variants): 
from openai import OpenAI

client = OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="<YOUR_FIREWORKS_API_KEY>",
)

chat_completion = client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-8b-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant.",
        },
        {
            "role": "user",
            "content": "Say this is a test",
        },
    ],
)
print(chat_completion.choices[0].message.content)

​
API compatibility

​
Differences

The following options have minor differences:
  • stop: the returned string includes the stop word for Fireworks while it’s omitted for OpenAI (it can be easily truncated on client side)
  • max_tokens: behaves differently if the model context length is exceeded. If the length of prompt or messages plus max_tokens is higher than the model’s context window, max_tokens will be adjusted lower accordingly. OpenAI returns invalid request error in this situation. This behavior can be adjusted by context_length_exceeded_behavior parameter.

​
Token usage for streaming responses

OpenAI API returns usage stats (number of tokens in prompt and completion) for non-streaming responses but doesn’t for the streaming ones (see forum post). Fireworks.ai returns usage stats in both cases. For streaming responses, the usage field is returned in the very last chunk on the response (i.e. the one having finish_reason set). For example:
cURL
curl --request POST \           
     --url https://api.fireworks.ai/inference/v1/completions \
     --header "accept: application/json" \
     --header "authorization: Bearer $API_KEY" \
     --header "content-type: application/json" \
     --data '{"model": "accounts/fireworks/models/starcoder-16b-w8a16", "prompt": "def say_hello_world():", "max_tokens": 100, "stream": true}'

data: {..., "choices":[{"text":"\n  print('Hello,","index":0,"finish_reason":null,"logprobs":null}],"usage":null}
       
data: {..., "choices":[{"text":" World!')\n\n\n","index":0,"finish_reason":null,"logprobs":null}],"usage":null}
       
data: {..., "choices":[{"text":"say_hello_","index":0,"finish_reason":null,"logprobs":null}],"usage":null}
       
data: {..., "choices":[{"text":"world()\n","index":0,"finish_reason":"stop","logprobs":null}],"usage":{"prompt_tokens":7,"total_tokens":24,"completion_tokens":17}}

data: [DONE]
Note, that if you’re using OpenAI SDK, they usage field won’t be listed in the SDK’s structure definition. But it can be accessed directly. For example:
  • In Python SDK, you can access the attribute directly, e.g. for chunk in openai.ChatCompletion.create(...): print(chunk["usage"]).
  • In TypeScript SDK, you need to cast away the typing, e.g. for await (const chunk of await openai.chat.completions.create(...)) { console.log((chunk as any).usage); }.

​
Not supported options

The following options are not yet supported:
  • presence_penalty
  • frequency_penalty
  • best_of: you can use n instead
  • logit_bias
  • functions: you can use our LangChain integration to achieve similar functionality client-side
Please reach out to us on Discord if you have a use case requiring one of these.