llama-index-integrations/llms/llama-index-llms-everlyai/README.md
Install the required Python packages:
%pip install llama-index-llms-everlyai
!pip install llama-index
Set the EverlyAI API key as an environment variable or pass it directly to the constructor:
import os
os.environ["EVERLYAI_API_KEY"] = "<your-api-key>"
Or use it directly in your Python code:
llm = EverlyAI(api_key="your-api-key")
To send a message and get a response (e.g., a joke):
from llama_index.llms.everlyai import EverlyAI
from llama_index.core.llms import ChatMessage
# Initialize EverlyAI with API key
llm = EverlyAI(api_key="your-api-key")
# Create a message
message = ChatMessage(role="user", content="Tell me a joke")
# Call the chat method
resp = llm.chat([message])
print(resp)
Example output:
Why don't scientists trust atoms?
Because they make up everything!
To stream a response for more dynamic conversations (e.g., storytelling):
message = ChatMessage(role="user", content="Tell me a story in 250 words")
resp = llm.stream_chat([message])
for r in resp:
print(r.delta, end="")
Example output (partial):
As the sun set over the horizon, a young girl named Lily sat on the beach, watching the waves roll in...
To use the complete method for simpler tasks like telling a joke:
resp = llm.complete("Tell me a joke")
print(resp)
Example output:
Why don't scientists trust atoms?
Because they make up everything!
For generating responses like stories using stream_complete:
resp = llm.stream_complete("Tell me a story in 250 words")
for r in resp:
print(r.delta, end="")
Example output (partial):
As the sun set over the horizon, a young girl named Maria sat on the beach, watching the waves roll in...
stream_chat and stream_complete methods allow for real-time response streaming, making them ideal for dynamic and lengthy outputs like stories.