LlamaIndex Llms Integration: Litellm
Installation
-
Install the required Python packages:
%pip install llama-index-llms-litellm !pip install llama-index
Usage
Import Required Libraries
import os
from llama_index.llms.litellm import LiteLLM
from llama_index.core.llms import ChatMessage
Set Up Environment Variables
Set your API keys as environment variables:
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["COHERE_API_KEY"] = "your-api-key"
Example: OpenAI Call
To interact with the OpenAI model:
message = ChatMessage(role="user", content="Hey! how's it going?")
llm = LiteLLM("gpt-3.5-turbo")
chat_response = llm.chat([message])
print(chat_response)
Example: Cohere Call
To interact with the Cohere model:
llm = LiteLLM("command-nightly")
chat_response = llm.chat([message])
print(chat_response)
Example: Chat with System Message
To have a chat with a system role:
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = LiteLLM("gpt-3.5-turbo").chat(messages)
print(resp)
Streaming Responses
To use the streaming feature with stream_complete:
llm = LiteLLM("gpt-3.5-turbo")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
Streaming Chat Example
To stream chat messages:
llm = LiteLLM("gpt-3.5-turbo")
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
Asynchronous Example
For asynchronous calls, use:
llm = LiteLLM("gpt-3.5-turbo")
resp = await llm.acomplete("Paul Graham is ")
print(resp)
LLM Implementation example
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