LlamaIndex LLMs Integration: AI21 Labs

Installation

First, you need to install the package. You can do this using pip:

bash

pip install llama-index-llms-ai21

Usage

Here's a basic example of how to use the AI21 class to generate text completions and handle chat interactions.

Initializing the AI21 Client

You need to initialize the AI21 client with the appropriate model and API key.

python

from llama_index.llms.ai21 import AI21

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)

Chat Completions

python

from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)

messages = [ChatMessage(role="user", content="What is the meaning of life?")]
response = llm.chat(messages)
print(response.message.content)

Chat Streaming

python

from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)

messages = [ChatMessage(role="user", content="What is the meaning of life?")]

for chunk in llm.stream_chat(messages):
    print(chunk.message.content)

Text Completion

python

from llama_index.llms.ai21 import AI21

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)

response = llm.complete(prompt="What is the meaning of life?")
print(response.text)

Stream Text Completion

python

from llama_index.llms.ai21 import AI21

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)

response = llm.stream_complete(prompt="What is the meaning of life?")

for chunk in response:
    print(response.text)

Other Models Support

You could also use more model types. For example the j2-ultra and j2-mid

These models support chat and complete methods only.

Chat

python

from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage

api_key = "your_api_key"
llm = AI21(model="j2-chat", api_key=api_key)

messages = [ChatMessage(role="user", content="What is the meaning of life?")]
response = llm.chat(messages)
print(response.message.content)

Complete

python

from llama_index.llms.ai21 import AI21

api_key = "your_api_key"
llm = AI21(model="j2-ultra", api_key=api_key)

response = llm.complete(prompt="What is the meaning of life?")
print(response.text)

Tokenizer

The type of the tokenizer is determined by the name of the model

python

from llama_index.llms.ai21 import AI21

api_key = "your_api_key"
llm = AI21(model="jamba-1.5-mini", api_key=api_key)
tokenizer = llm.tokenizer

tokens = tokenizer.encode("What is the meaning of life?")
print(tokens)

text = tokenizer.decode(tokens)
print(text)

Async Support

You can also use the async functionalities

async chat

python

from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage


async def main():
    api_key = "your_api_key"
    llm = AI21(model="jamba-1.5-mini", api_key=api_key)

    messages = [
        ChatMessage(role="user", content="What is the meaning of life?")
    ]
    response = await llm.achat(messages)
    print(response.message.content)

async stream_chat

python

from llama_index.llms.ai21 import AI21
from llama_index.core.base.llms.types import ChatMessage


async def main():
    api_key = "your_api_key"
    llm = AI21(model="jamba-1.5-mini", api_key=api_key)

    messages = [
        ChatMessage(role="user", content="What is the meaning of life?")
    ]
    response = await llm.astream_chat(messages)

    async for chunk in response:
        print(chunk.message.content)

Tool Calling

python

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.ai21 import AI21
from llama_index.core.tools import FunctionTool


def multiply(a: int, b: int) -> int:
    """Multiply two integers and returns the result integer"""
    return a * b


def subtract(a: int, b: int) -> int:
    """Subtract two integers and returns the result integer"""
    return a - b


def divide(a: int, b: int) -> float:
    """Divide two integers and returns the result float"""
    return a - b


def add(a: int, b: int) -> int:
    """Add two integers and returns the result integer"""
    return a + b


multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
subtract_tool = FunctionTool.from_defaults(fn=subtract)
divide_tool = FunctionTool.from_defaults(fn=divide)

api_key = "your_api_key"

llm = AI21(model="jamba-1.5-mini", api_key=api_key)

agent = FunctionAgent(
    tools=[multiply_tool, add_tool, subtract_tool, divide_tool],
    llm=llm,
)

response = await agent.run(
    "My friend Moses had 10 apples. He ate 5 apples in the morning. Then he found a box with 25 apples."
    "He divided all his apples between his 5 friends. How many apples did each friend get?"
)