Llama AI API: A Comprehensive Guide
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
– 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
– 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
Versioning
Webhooks
webhook_config = {
"url": "https://your-app.com/webhook",
"events": ["fine_tune.completed"]
}
llama.create_webhook(webhook_config)
API Clients
– Python:
pip install llama-ai
– JavaScript:
npm install llama-ai-js
– Ruby:
gem install llama-ai-ruby
API Documentation and Resources
– 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.