Quickstart with API Endpoints

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Introduction

Welcome to NextAI Documentation! 🎉 We’re thrilled to have you here. This documentation is designed to guide you through the process of getting started with NextAI’s model API endpoints, deploying open-source models with fastest inferencing, fine-tuning model with auto-tune on optimised hardware ensuring a smooth and straightforward experience. For those who prefer visual learning, we also offer concise video tutorials that range from 3-5 minutes. 🚀

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Getting Started with NextAI API’s📑

NextAI allows you to interact with NextAI API through HTTP requests from any programming language. We provide official bindings for Python and Node.js for your convenience.

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Setting up NextAI API Key

1. Login to NextAI: Visit the NextAI website and log in to your account using your credentials.
2. Access API Section: On the homepage, locate and click on the “Use API” button to access the API section.
3. Choose Model: You’ll find various models available in the Model settings section. Choose the specific model you intend to work with based on your requirements.
4. Generate API Key: Look for an option labeled “Generate API Key” specific to the chosen model. Click on the “Generate API Key” button.
5. Create API Key: Create a new API Key by giving it a name and clicking the “Create” button.
6. Copy API Key: Once generated, the API key will be displayed. Copy this API key as it will be necessary for authentication when using the NextAI model in your applications or scripts.

If you want to access premium models you can follow this link to set up the API Key and Model.

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Install Python Bindings:

To begin, install the official Python bindings with the following command:

pip install openai

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API Keys:

To access our API, you’ll need an API key. You can find your API key on your API Keys page or within the details of the model you’ve deployed. Include your API key in an Authorization HTTP header in all your API requests like so:

Authorization: Bearer NextAI_API_KEY

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OpenAI API Compatibility:

Our APIs are designed to be compatible with OpenAI’s API, making it seamless for you to transition from OpenAI to NextAI with minimal code adjustments. Simply update the base URL to the NextAI endpoint of your deployed model and use the provided API key.

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Interacting with Your Model

Here’s how you can interact with your model:

import os
from openai import OpenAI
%env NEXTAI_API_KEY=#add your NextAI api key here

client = OpenAI(
    api_key=os.environ.get("NEXTAI_API_KEY"),
    base_url = "add your end point URL"  #example: https://zephyr.nextai.co.in/v1
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Tell me a hello world joke",
        }
    ],
    model="add your model name", #example: zephyr-7b-alpha
)

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Streaming Interaction:

For streaming interactions, use the following code:

stream = client.chat.completions.create(
    model="add your model name", #example: zephyr-7b-alpha
    messages=[{"role": "user", "content": "Tell a hello world joke"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

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JSON Mode:

You can set response_format to (“type”: “json_object”) enable JSON mode.

response = client.chat.completions.create(
  model="YOUR_MODEL_NAME", # Example: zephyr-7b-alpha
  response_format={ "type": "json_object" },
  messages=[
    {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
    {"role": "user", "content": "Who won the world series in 2023?"}
  ]
)
print(response.choices[0].message.content)

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Using RestAPI with cURL

cURL is another good tool for observing the output of the API

curl --location 'YOUR_MODEL_ENDPOINT_URL/models' \ 
--header 'Authorization: Bearer YOUR_NEXTAI_API_KEY'

curl --location 'YOUR_MODEL_ENDPOINT_URL/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer YOUR_NEXTAI_API_KEY' \
--data '{
    "model": "YOUR_MODEL_NAME", # Example: zephyr-7b-alpha
    "messages": [{"role": "user", "content": "Hello! What is your name?"}]
}'

curl --location 'YOUR_MODEL_ENDPOINT_URL/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer YOUR_NEXTAI_API_KEY' \
--data '{
    "model": "YOUR_MODEL_NAME", # Example: zephyr-7b-alpha
    "prompt": "Once upon a time",
    "max_tokens": 41,
    "temperature": 0.5
}'

curl --location 'YOUR_MODEL_ENDPOINT_URL/embeddings' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer YOUR_NEXTAI_API_KEY' \
--data '{
    "model": "YOUR_MODEL_NAME", # Example: zephyr-7b-alpha
    "input": "Hello world!"
}'

🦙 Please refer to Cookbooks for quick integration with Langchain, Llama index, RAG, Autogen etc..