Llama 3.1 API

Llama AI API: A Comprehensive Guide

The Llama AI API provides developers with access to Meta’s powerful open-source large language models. This API allows integration of advanced natural language processing capabilities into various applications and services.

API Basics

Authentication

The Llama AI API uses API keys for authentication. Developers must obtain an API key from Meta or authorized providers to make API calls.

import os
from llama_api import LlamaAPI
api_key = os.environ.get("LLAMA_API_KEY")
llama = LlamaAPI(api_key)

Base URL

The base URL for API requests is typically:

https://api.llama-ai.com/v1/

Request Format

API requests are made using HTTP POST methods with JSON payloads.

Core API Endpoints

1. Text Generation

Endpoint: /generate
This endpoint allows you to generate text based on a given prompt.

response = llama.generate(
model="llama-3.1-70b",
prompt="Explain quantum computing",
max_tokens=200
)
print(response.generated_text)

2. Chat Completion

Endpoint: /chat/completions
This endpoint is used for conversational AI applications.

messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the capital of France?"}
] response = llama.chat_completion(
model="llama-3.1-70b",
messages=messages
)
print(response.choices[0].message.content)

3. Embeddings

Endpoint: /embeddings
Generate vector representations of text.

text = "The quick brown fox jumps over the lazy dog"
embeddings = llama.get_embeddings(
model="llama-3.1-70b",
input=text
)
print(embeddings.data[0].embedding)

Advanced API Features

1. Function Calling

The API supports function calling, allowing the model to generate structured data or trigger specific actions.

functions = [
{
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"] }
}
] response = llama.chat_completion(
model="llama-3.1-70b",
messages=[{"role": "user", "content": "What's the weather like in New York?"}],
functions=functions,
function_call="auto"
)

2. Streaming Responses

For long-form content generation, the API supports streaming responses.

for chunk in llama.generate_stream(
model="llama-3.1-70b",
prompt="Write a short story about time travel",
max_tokens=1000
):
print(chunk.text, end='', flush=True)

3. Fine-tuning

The API provides endpoints for fine-tuning models on custom datasets.

fine_tune_job = llama.create_fine_tune(
model="llama-3.1-70b",
training_file="path/to/training_data.jsonl"
)

API Parameters

– model: Specifies the Llama model version to use (e.g., “llama-3.1-70b”).
– temperature: Controls randomness (0.0 to 1.0).
– max_tokens: Limits the length of generated text.
– top_p: Alternative to temperature, uses nucleus sampling.
– frequency_penalty: Reduces repetition of token sequences.
– presence_penalty: Encourages the model to talk about new topics.
– stop: Sequences where the API will stop generating further tokens.

Error Handling

The API uses standard HTTP response codes. Common errors include:
– 400: Bad Request
– 401: Unauthorized
– 429: Rate Limit Exceeded
– 500: Internal Server Error

try:
response = llama.generate(...)
except LlamaAPIError as e:
print(f"An error occurred: {e}")

Rate Limits and Quotas

The API imposes rate limits to ensure fair usage. These limits may vary based on the subscription tier and are typically expressed in tokens per minute or requests per day.

Versioning

The API uses semantic versioning. Major version changes may introduce breaking changes, while minor versions add features in a backward-compatible manner.

Webhooks

The API supports webhooks for asynchronous operations, such as receiving notifications when a fine-tuning job is complete.

webhook_config = {
"url": "https://your-app.com/webhook",
"events": ["fine_tune.completed"] }
llama.create_webhook(webhook_config)

API Clients

Official API clients are available for various programming languages:
– Python: pip install llama-ai
– JavaScript: npm install llama-ai-js
– Ruby: gem install llama-ai-ruby

API Documentation and Resources

– Comprehensive API documentation is available at https://docs.llama-ai.com
– API changelog: https://docs.llama-ai.com/changelog
– Developer forum: https://community.llama-ai.com
This extensive overview covers the core functionalities and features of the Llama AI API. Developers can leverage these capabilities to integrate powerful language processing features into their applications, from simple text generation to complex conversational AI systems.