Content-Type: application/json
Cache-Control: no-cache

    "prompt": "Tell me about NBA"

import urllib.request, json

    url = ""

    hdr ={
    # Request headers
    'Content-Type': 'application/json',
    'Cache-Control': 'no-cache',

    # Request body
    data =  
    data = json.dumps(data)
    req = urllib.request.Request(url, headers=hdr, data = bytes(data.encode("utf-8")))

    req.get_method = lambda: 'POST'
    response = urllib.request.urlopen(req)
    except Exception as e:
// Request body
const body = {
    "prompt": "Tell me about NBA"

fetch('', {
        method: 'POST',
        body: JSON.stringify(body),
        // Request headers
        headers: {
            'Content-Type': 'application/json',
            'Cache-Control': 'no-cache',}
    .then(response => {
    .catch(err => console.error(err));
curl -v -X POST "" -H "Content-Type: application/json" -H "Cache-Control: no-cache" --data-raw "{
    \"prompt\": \"Tell me about NBA\"
import java.util.HashMap;
import java.util.Map;

public class HelloWorld {

  public static void main(String[] args) {
    try {
        String urlString = "";
        URL url = new URL(urlString);
        HttpURLConnection connection = (HttpURLConnection) url.openConnection();

        //Request headers
    connection.setRequestProperty("Content-Type", "application/json");
    connection.setRequestProperty("Cache-Control", "no-cache");

        // Request body
             "{ \"prompt\": \"Tell me about NBA\" }".getBytes()
        int status = connection.getResponseCode();

        BufferedReader in = new BufferedReader(
            new InputStreamReader(connection.getInputStream())
        String inputLine;
        StringBuffer content = new StringBuffer();
        while ((inputLine = in.readLine()) != null) {

    } catch (Exception ex) {
      System.out.print("exception:" + ex.getMessage());
$url = "";
$curl = curl_init($url);

curl_setopt($curl, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($curl, CURLOPT_URL, $url);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, true);

# Request headers
$headers = array(
    'Content-Type: application/json',
    'Cache-Control: no-cache',);
curl_setopt($curl, CURLOPT_HTTPHEADER, $headers);

# Request body
$request_body = '{
    "prompt": "Tell me about NBA"
curl_setopt($curl, CURLOPT_POSTFIELDS, $request_body);

$resp = curl_exec($curl);
Meta Llama 2 Chat API
  • API Documentation for Llama 2 Chat


    The Llama 2 Chat API is a cutting-edge conversational tool developed by Meta AI, leveraging the advanced capabilities of the Llama 2 models. This open-source solution is designed to enhance a wide array of Chat Applications by providing high-quality, context-aware responses. The API is built to handle various user messages and system messages, ensuring seamless and coherent interactions. It excels in chat/completion tasks, generating contextually relevant replies based on the provided input text. The flexibility and robustness of the Llama 2 Chat AI model make it a versatile tool for developers aiming to integrate sophisticated conversational abilities along with chat history into their applications.

    A significant advantage of the Llama 2 Chat API is its comprehensive error handling and content moderation capabilities, ensuring safe and appropriate interactions. This is particularly important for maintaining high standards of Model Performance in applications where quality and reliability are paramount. The ability to manage longer context length allows the model to maintain coherence over extended conversations and chat messages, enhancing user engagement and satisfaction. The API supports a wide range of use cases, from customer support and virtual assistants to content creation and educational tools, making it a valuable asset for developers across various industries. For more details on implementing these features, refer to the Use Guide provided with the API documentation.

    Meta's commitment to Open Source LLM is evident in its release of the Llama 2 models, promoting innovation and collaboration within the AI community. This open approach accelerates development and allows for extensive customization to meet specific needs. Developers can leverage the Llama 2 Chat API through well-documented inference APIs, with detailed API Examples and API endpoint descriptions available. The inclusion of features from models like Mistral 7B and future integrations with Meta Llama 3 ensures that the API remains at the forefront of AI advancements. Overall, the Llama 2 Chat API offers a robust, versatile, and highly customizable solution for creating advanced conversational applications.

  • API Parameters

    The API POST takes the following parameters:


    string, required


    string, optional


    Integration and Implementation

    To use Llama 2 Chat, developers must send POST requests to the specified endpoint, including the appropriate headers and request body. The request body should contain text inputs, task parameters, and additional settings.

    Base URL

    POST /Get Data

    This endpoint generates text based on the prompts provided.

    • URL:
    • Method: POST
    • Headers:
      • Content-Type: application/json
      • Cache-Control: no-cache
      • Ocp-Apim-Subscription-Key: {subscription_key}
    • Body:


        "prompt": "Tell me about the NBA"
  • Responses
    • HTTP Status Codes:
      • 200 OK: The request was successful, and the generated text is included in the response body.
      • 400 Bad Request: The request must be corrected or include some arguments.
      • 401 Unauthorized: The API key provided in the header is invalid.
      • 500 Internal Server Error: An error occurred on the server while processing the request.
    • Sample Response:


        "status": 200,
        "content-type": "application/json"
    Error Handling

    The Llama 2 Chat API features robust error-handling mechanisms to ensure seamless operation. Common status codes encountered include:

    • Error Field Contract:
      • code: An integer that indicates the HTTP status code (e.g., 400, 401, 500).
      • message: A clear and concise description of what the error is about.
      • traceId: A unique identifier that can be used to trace the request in case of issues.
    • AI Model: Refers to the underlying machine learning model used to interpret the text prompts and generate corresponding texts.
    • Changelog: Document detailing any updates, bug fixes, or improvements made to the API in each version.

    Use Cases of Llama 2 Chat

    • Customer Support Automation: Implement the Llama 2 Chat API to handle common customer inquiries through automated API calls, enhancing response times and customer satisfaction with its advanced conversational capabilities.
    • Virtual Assistant Development: Utilize prompt engineering to create sophisticated virtual assistants. By crafting effective system prompts and user inputs, the assistants can provide accurate and contextually relevant replies.
    • Code Generation: Integrate Llama 2 Chat API with development tools to assist in code generation. Developers can use API requests to submit user input and receive a generated response that includes code snippets or debugging advice.
    • Content Creation: Leverage the API's capabilities for creative writing and art projects. Generative AI Models like Llama 2 can produce creative text and assist in writing, using a broad context window to maintain coherence over longer pieces. Developers can also consider the output price when integrating the API into commercial projects, ensuring cost-effectiveness alongside creative flexibility. This combination makes the Llama 2 API a valuable tool for both artistic exploration and cost-conscious content creation initiatives.
    • Educational Tools: Develop interactive tutoring systems using Llama 2 Chat API. By analyzing API Reference materials and using effective system prompts, these tools can answer student questions and explain complex topics.
    • Integration with OpenAI APIs: Combine the Llama 2 Chat API with OpenAI API services to enhance functionality. This allows for a richer set of features and improved generated responses by leveraging the strengths of both Generative AI Models.
    • Chatbot Enhancement: Improve existing chatbots by incorporating Run Llama 2 for more accurate and contextually aware conversations. This can be especially useful in applications requiring a large context window to maintain conversation continuity.

    Advanced Features of the Llama 2 Chat API

    • Fine-tuned Model: The Llama 2 Chat API offers the ability to use a fine-tuned model that is customized for specific conversational contexts. This enhances the relevance and accuracy of the responses, making it ideal for specialized applications.
    • Integration with Deepinfra: Developers can integrate the Llama 2 Chat API with Deepinfra for scalable and efficient deployment, ensuring high-performance API interactions and robust handling of large volumes of requests.
    • Python Code Examples: The Llama 2 Chat API documentation provides Python code snippets to help developers get started quickly. These examples demonstrate how to set up and make API calls using the following code and libraries.
    • Art AI Capabilities: The API supports art AI applications, enabling the generation of creative and artistic text outputs. This is particularly useful for projects that require a high degree of creativity and originality.
    • Parameter Language Model Customization: The API allows for the customization of the parameter language model, enabling fine-tuning of various parameters to optimize performance for different use cases and applications.
    • Secure API Access: Access to the Llama 2 Chat API requires secure authentication methods, including the use of an email address and API keys. This ensures that only authorized users can make API requests and access the full capabilities of the service.

    Technical Specifications of Llama 2 Chat

    • Model Parameters: The Llama 2 Chat model is equipped with extensive model parameters, allowing it to handle complex conversational tasks with high accuracy and efficiency. These parameters govern the behavior of the model during inference, dictating how it processes input text and generates output tokens.
    • Context Size: The model supports a large context size, enabling it to maintain coherent and contextually relevant conversations over extended interactions. This feature is critical for applications like customer support systems and virtual assistants, where understanding the context of previous interactions is essential for providing accurate responses.
    • Inference Endpoints: Developers can deploy Llama 2 Chat using Inference Endpoints on cloud platforms such as Microsoft Azure and Google Colab, ensuring scalable and high-performance API access. These endpoints allow users to send input text to the model and receive output tokens, facilitating seamless integration into various applications. Additionally, developers can configure notification settings within the API, allowing for customized handling of notifications based on specific events or conditions.
    • REST API: Llama 2 Chat API is accessible via a REST API, allowing for seamless integration into various applications. This makes it easy to incorporate advanced conversational capabilities into existing systems. Developers can send HTTP requests to the API endpoint, providing input text and receiving output tokens in return.
    • Model Weights: The model weights are optimized for performance, ensuring that the model delivers quick and accurate responses. These weights can be loaded and executed with the following command in supported environments. By fine-tuning these weights, developers can further enhance the model's ability to generate relevant output tokens.
    • Performance Metrics: Performance Metrics: Detailed performance metrics, including response times, accuracy rates, resource utilization, and input tokens length, are provided to help developers monitor and optimize the model's efficiency and accuracy. By analyzing these metrics, developers can identify areas for improvement and fine-tune the model to generate more accurate output tokens. This comprehensive approach ensures that the Llama 2 Chat API delivers consistent and reliable performance across various applications and use cases with new tokens.
    • Chat Interface: Our API URL supports a chat interface, allowing for interactive and dynamic conversations. This can be integrated into web and mobile applications to enhance user engagement. Users can input text through the interface, and the model will generate output tokens in response, creating a seamless conversational experience. The Llama 2 Chat API supports a GET method, enabling developers to seamlessly integrate advanced conversational capabilities into existing systems.
    • Google Colab and Streamlit Integration: The AI model can be easily tested and deployed in Google Colab notebooks, and developers can use Streamlit to import json_body and create interactive web applications. These integrations provide developers with flexible tools for experimenting with the model and showcasing its capabilities to users, facilitating the generation of relevant output tokens. Additionally, developers can perform API Provider Benchmarking & Analysis to assess performance metrics, ensuring optimal integration and functionality of the AI model within various applications.

    What are the Benefits of Using Llama 2 Chat?

    • Open-Source Model: Llama 2 is an open-source model, making it accessible for developers and researchers to customize and fine-tune it to meet the specific needs of the chatbot. This fosters innovation and collaboration within the AI community, driving continuous improvement.
    • Superior Performance: Llama 2 outperforms other open-source models, such as ChatGPT chatbot, in terms of helpfulness and safety, making it a suitable alternative for closed models. Its superior performance ensures reliable and accurate responses, enhancing user satisfaction.
    • Large Context Length: Llama 2 has maximum context length, allowing it to handle longer conversations and providing more flexibility in chat applications like chatbots. This enables more coherent and contextually relevant interactions, improving the overall user experience.
    • Fine-Tuning: Llama 2 models have been heavily fine-tuned to align with human preferences, enhancing their usability and safety. This fine-tuning process ensures that the model generates responses that are both accurate and appropriate for a wide range of contexts with import requests.
    • Customization: Users can customize Llama 2 models to suit their specific needs and preferences, making it a versatile tool for various applications. Whether it's adjusting parameters or fine-tuning the model for specific use cases, Llama 2 offers flexibility and adaptability.
    • Cost-Effective: Llama 2 is free for both research and commercial use, eliminating the need for expensive API tokens like OpenAI GPTs. This makes it accessible to a wide range of users and organizations, regardless of budget constraints.
    • Community Collaboration: The open-source nature of Llama 2 fosters community collaboration and ensures that the model is constantly improved and updated. Developers can contribute to the model's development and benefit from collective insights and expertise.
    • Real-Time AI Integration: Llama 2 can be integrated with platforms like Jina and DocArray to create real-time AI applications, enabling seamless interactions with users. This integration opens up possibilities for creating interactive and dynamic experiences that respond to user input instantaneously.

Top APIs for Generative AI Models


Unlock the full potential of your projects with our Generative AI APIs. from video generation APIs to image creation, text generation, animation, 3D models, prompt generation, image restoration, and code generation, we offer advanced APIs for all your generative AI needs.