ASI LLM¶

ASI1-Mini is an advanced, agentic LLM designed by fetch.ai, a founding member of Artificial Superintelligence Alliance for decentralized operations. Its unique architecture empowers it to execute tasks and collaborate with other agents for efficient, adaptable problem-solving in complex environments.

This notebook demonstrates how to use ASI models with LlamaIndex. It covers various functionalities including basic completion, chat, streaming, function calling, structured prediction, RAG, and more. If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

Setup¶

First, let's install the required packages:

In [ ]:

%pip install llama-index-llms-asi llama-index-llms-openai llama-index-core

%pip install llama-index-llms-asi llama-index-llms-openai llama-index-core

Setting API Keys¶

You'll need to set your API keys for ASI and optionally for OpenAI if you want to compare the two:

In [ ]:

import os

# Set your API keys here - To get the API key visit https://asi1.ai/chat and login
os.environ["ASI_API_KEY"] = "your-api-key"

import os # Set your API keys here - To get the API key visit https://asi1.ai/chat and login os.environ["ASI_API_KEY"] = "your-api-key"

Basic Completion¶

Let's start with a basic completion example using ASI:

In [ ]:

from llama_index.llms.asi import ASI

# Create an ASI LLM instance
llm = ASI(model="asi1-mini")

# Complete a prompt
response = llm.complete("Who is Paul Graham? ")
print(response)

from llama_index.llms.asi import ASI # Create an ASI LLM instance llm = ASI(model="asi1-mini") # Complete a prompt response = llm.complete("Who is Paul Graham? ") print(response)

Paul Graham is a British-born American entrepreneur, venture capitalist, and essayist. He is best known for co-founding Y Combinator, a well-known startup accelerator, and for his influential essays on entrepreneurship, technology, and innovation. Graham has also founded several other companies, including Viaweb, which was acquired by Yahoo! in 1998.

Chat¶

Now let's try chat functionality:

In [ ]:

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

# Create messages
messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]

# Get chat response
chat_response = llm.chat(messages)
print(chat_response)

from llama_index.core.base.llms.types import ChatMessage # Create messages messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] # Get chat response chat_response = llm.chat(messages) print(chat_response)

assistant: Yer lookin' fer me name, eh? Alright then, matey! Yer talkin' to Baron Blackbyte, the scurviest AI pirate on the seven seas!

Streaming¶

ASI supports streaming for chat responses:

In [ ]:

# Stream chat response
for chunk in llm.stream_chat(messages):
    print(chunk.delta, end="")

# Stream chat response for chunk in llm.stream_chat(messages): print(chunk.delta, end="")

Ahoy there, matey! They call me One-Eyed Jack, scourge o' the digital seas and terror of the silicon shores!  At yer service!  Now, what can this ol' salt do for ya?

Using stream_chat endpoint

In [ ]:

from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] resp = llm.stream_chat(messages)

In [ ]:

for r in resp:
    print(r.delta, end="")

for r in resp: print(r.delta, end="")

Ahoy there, matey!  They call me ASI1-Mini, scourge o' the digital seas and terror o' the binary bytes!  At yer service!  Arrr!

Using stream_complete endpoint

In [ ]:

resp = llm.stream_complete("Paul Graham is ")

resp = llm.stream_complete("Paul Graham is ")

In [ ]:

for r in resp:
    print(r.delta, end="")

for r in resp: print(r.delta, end="")

Could you please complete your question? I'm not sure what you'd like to know about Paul Graham.

Image Support¶

ASI has support for images in the input of chat messages for many models.

Using the content blocks feature of chat messages, you can easily combone text and images in a single LLM prompt.

In [ ]:

!wget https://cdn.pixabay.com/photo/2016/07/07/16/46/dice-1502706_640.jpg -O image.png

!wget https://cdn.pixabay.com/photo/2016/07/07/16/46/dice-1502706_640.jpg -O image.png

In [ ]:

from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock
from llama_index.llms.asi import ASI

llm = ASI(model="asi1-mini")

messages = [
    ChatMessage(
        role="user",
        blocks=[
            ImageBlock(path="image.png"),
            TextBlock(text="Describe the image in a few sentences."),
        ],
    )
]

resp = llm.chat(messages)
print(resp.message.content)

from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock from llama_index.llms.asi import ASI llm = ASI(model="asi1-mini") messages = [ ChatMessage( role="user", blocks=[ ImageBlock(path="image.png"), TextBlock(text="Describe the image in a few sentences."), ], ) ] resp = llm.chat(messages) print(resp.message.content)

The image showcases three white dice with black dots, positioned on a checkered surface, highlighting their contrasting colors and the game's playful aspect.

Function Calling/Tool Calling¶

ASI LLM have native support for function calling. This conveniently integrates with LlamaIndex tool abstractions, letting you plug in any arbitrary Python function to the LLM.

In the example below, we define a function to generate a Song object.

In [ ]:

from pydantic import BaseModel
from llama_index.core.tools import FunctionTool
from llama_index.llms.asi import ASI


class Song(BaseModel):
    """A song with name and artist"""

    name: str
    artist: str


def generate_song(name: str, artist: str) -> Song:
    """Generates a song with provided name and artist."""
    return Song(name="Sky full of stars", artist="Coldplay")


# Create tool
tool = FunctionTool.from_defaults(fn=generate_song)

from pydantic import BaseModel from llama_index.core.tools import FunctionTool from llama_index.llms.asi import ASI class Song(BaseModel): """A song with name and artist""" name: str artist: str def generate_song(name: str, artist: str) -> Song: """Generates a song with provided name and artist.""" return Song(name="Sky full of stars", artist="Coldplay") # Create tool tool = FunctionTool.from_defaults(fn=generate_song)

The strict parameter tells ASI whether or not to use constrained sampling when generating tool calls/structured outputs. This means that the generated tool call schema will always contain the expected fields.

Since this seems to increase latency, it defaults to false.

In [ ]:

from llama_index.llms.asi import ASI

# Create an ASI LLM instance
llm = ASI(model="asi1-mini", strict=True)
response = llm.predict_and_call(
    [tool],
    "Pick a random song for me",
    # strict=True  # can also be set at the function level to override the class
)
print(str(response))

from llama_index.llms.asi import ASI # Create an ASI LLM instance llm = ASI(model="asi1-mini", strict=True) response = llm.predict_and_call( [tool], "Pick a random song for me", # strict=True # can also be set at the function level to override the class ) print(str(response))

name='Sky full of stars' artist='Coldplay'

In [ ]:

llm = ASI(model="asi1-mini")
response = llm.predict_and_call(
    [tool],
    "Generate five songs from the Beatles",
    allow_parallel_tool_calls=True,
)
for s in response.sources:
    print(f"Name: {s.tool_name}, Input: {s.raw_input}, Output: {str(s)}")

llm = ASI(model="asi1-mini") response = llm.predict_and_call( [tool], "Generate five songs from the Beatles", allow_parallel_tool_calls=True, ) for s in response.sources: print(f"Name: {s.tool_name}, Input: {s.raw_input}, Output: {str(s)}")

Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Beatles Song 1', 'artist': 'The Beatles'}}, Output: name='Sky full of stars' artist='Coldplay'

Manual Tool Calling¶

While automatic tool calling with predict_and_call provides a streamlined experience, manual tool calling gives you more control over the process. With manual tool calling, you can:

1. Explicitly control when and how tools are called
2. Process intermediate results before continuing the conversation
3. Implement custom error handling and fallback strategies
4. Chain multiple tool calls together in a specific sequence

ASI supports manual tool calling, but requires more specific prompting compared to some other LLMs. For best results with ASI, include a system message that explains the available tools and provide specific parameters in your user prompt.

The following example demonstrates manual tool calling with ASI to generate a song:

In [ ]:

from pydantic import BaseModel
from llama_index.core.tools import FunctionTool
from llama_index.core.llms import ChatMessage


class Song(BaseModel):
    """A song with name and artist"""

    name: str
    artist: str


def generate_song(name: str, artist: str) -> Song:
    """Generates a song with provided name and artist."""
    return Song(name=name, artist=artist)


# Create tool
tool = FunctionTool.from_defaults(fn=generate_song)

# First, select a tool with specific instructions
chat_history = [
    ChatMessage(
        role="system",
        content="You have access to a tool called generate_song that can create songs. When asked to generate a song, use this tool with appropriate name and artist values.",
    ),
    ChatMessage(
        role="user", content="Generate a song by Coldplay called Viva La Vida"
    ),
]

# Get initial response
resp = llm.chat_with_tools([tool], chat_history=chat_history)
print(f"Initial response: {resp.message.content}")

# Check for tool calls
tool_calls = llm.get_tool_calls_from_response(
    resp, error_on_no_tool_call=False
)

# Process tool calls if any
if tool_calls:
    # Add the LLM's response to the chat history
    chat_history.append(resp.message)

    for tool_call in tool_calls:
        tool_name = tool_call.tool_name
        tool_kwargs = tool_call.tool_kwargs

        print(f"Calling {tool_name} with {tool_kwargs}")
        tool_output = tool(**tool_kwargs)
        print(f"Tool output: {tool_output}")

        # Add tool response to chat history
        chat_history.append(
            ChatMessage(
                role="tool",
                content=str(tool_output),
                additional_kwargs={"tool_call_id": tool_call.tool_id},
            )
        )

        # Get final response
        resp = llm.chat_with_tools([tool], chat_history=chat_history)
        print(f"Final response: {resp.message.content}")
else:
    print("No tool calls detected in the response.")

from pydantic import BaseModel from llama_index.core.tools import FunctionTool from llama_index.core.llms import ChatMessage class Song(BaseModel): """A song with name and artist""" name: str artist: str def generate_song(name: str, artist: str) -> Song: """Generates a song with provided name and artist.""" return Song(name=name, artist=artist) # Create tool tool = FunctionTool.from_defaults(fn=generate_song) # First, select a tool with specific instructions chat_history = [ ChatMessage( role="system", content="You have access to a tool called generate_song that can create songs. When asked to generate a song, use this tool with appropriate name and artist values.", ), ChatMessage( role="user", content="Generate a song by Coldplay called Viva La Vida" ), ] # Get initial response resp = llm.chat_with_tools([tool], chat_history=chat_history) print(f"Initial response: {resp.message.content}") # Check for tool calls tool_calls = llm.get_tool_calls_from_response( resp, error_on_no_tool_call=False ) # Process tool calls if any if tool_calls: # Add the LLM's response to the chat history chat_history.append(resp.message) for tool_call in tool_calls: tool_name = tool_call.tool_name tool_kwargs = tool_call.tool_kwargs print(f"Calling {tool_name} with {tool_kwargs}") tool_output = tool(**tool_kwargs) print(f"Tool output: {tool_output}") # Add tool response to chat history chat_history.append( ChatMessage( role="tool", content=str(tool_output), additional_kwargs={"tool_call_id": tool_call.tool_id}, ) ) # Get final response resp = llm.chat_with_tools([tool], chat_history=chat_history) print(f"Final response: {resp.message.content}") else: print("No tool calls detected in the response.")

Initial response: Okay, I will generate a song with the name "Viva La Vida" and the artist "Coldplay".

Calling generate_song with {'name': 'Viva La Vida', 'artist': 'Coldplay'}
Tool output: name='Viva La Vida' artist='Coldplay'
Final response: I have successfully generated the song "Viva La Vida" by Coldplay.

Structured Prediction¶

You can use ASI to extract structured data from text:

In [ ]:

from llama_index.core.prompts import PromptTemplate
from pydantic import BaseModel
from typing import List


class MenuItem(BaseModel):
    """A menu item in a restaurant."""

    course_name: str
    is_vegetarian: bool


class Restaurant(BaseModel):
    """A restaurant with name, city, and cuisine."""

    name: str
    city: str
    cuisine: str
    menu_items: List[MenuItem]


# Create prompt template
prompt_tmpl = PromptTemplate(
    "Generate a restaurant in a given city {city_name}"
)

# Option 1: Use structured_predict
restaurant_obj = llm.structured_predict(
    Restaurant, prompt_tmpl, city_name="Dallas"
)
print(f"Restaurant: {restaurant_obj}")

# Option 2: Use as_structured_llm
structured_llm = llm.as_structured_llm(Restaurant)
restaurant_obj2 = structured_llm.complete(
    prompt_tmpl.format(city_name="Miami")
).raw
print(f"Restaurant: {restaurant_obj2}")

from llama_index.core.prompts import PromptTemplate from pydantic import BaseModel from typing import List class MenuItem(BaseModel): """A menu item in a restaurant.""" course_name: str is_vegetarian: bool class Restaurant(BaseModel): """A restaurant with name, city, and cuisine.""" name: str city: str cuisine: str menu_items: List[MenuItem] # Create prompt template prompt_tmpl = PromptTemplate( "Generate a restaurant in a given city {city_name}" ) # Option 1: Use structured_predict restaurant_obj = llm.structured_predict( Restaurant, prompt_tmpl, city_name="Dallas" ) print(f"Restaurant: {restaurant_obj}") # Option 2: Use as_structured_llm structured_llm = llm.as_structured_llm(Restaurant) restaurant_obj2 = structured_llm.complete( prompt_tmpl.format(city_name="Miami") ).raw print(f"Restaurant: {restaurant_obj2}")

Restaurant: name='The Dallas Bistro' city='Dallas' cuisine='American' menu_items=[MenuItem(course_name='Grilled Caesar Salad', is_vegetarian=True), MenuItem(course_name='BBQ Pulled Pork Sandwich', is_vegetarian=False), MenuItem(course_name='Cheeseburger with Fries', is_vegetarian=False), MenuItem(course_name='Vegan Mushroom Risotto', is_vegetarian=True)]
Restaurant: name='Ocean Breeze Grill' city='Miami' cuisine='Seafood' menu_items=[MenuItem(course_name='Grilled Mahi-Mahi', is_vegetarian=False), MenuItem(course_name='Coconut Shrimp', is_vegetarian=False), MenuItem(course_name='Tropical Quinoa Salad', is_vegetarian=True), MenuItem(course_name='Key Lime Pie', is_vegetarian=True)]

Note: Structured streaming is currently not supported with ASI.

Async¶

ASI supports async operations:

In [ ]:

from llama_index.llms.asi import ASI

# Create an ASI LLM instance
llm = ASI(model="asi1-mini")

from llama_index.llms.asi import ASI # Create an ASI LLM instance llm = ASI(model="asi1-mini")

In [ ]:

resp = await llm.acomplete("who is Paul Graham")

resp = await llm.acomplete("who is Paul Graham")

In [ ]:

print(resp)

print(resp)

Paul Graham is a prominent figure in the technology and startup world, best known for co-founding Y Combinator, a leading startup accelerator that has helped launch companies like Airbnb, Dropbox, and Reddit. In addition to his role as an investor, he is a respected programmer and writer. Graham's contributions to programming include his work on the Lisp language and his book *On Lisp*, which is regarded as a seminal text in the field. He is also known for his thought-provoking essays on entrepreneurship, startups, and philosophy, which are widely read and cited. Through his writing and mentorship, Paul Graham has significantly influenced the global entrepreneurial ecosystem.

In [ ]:

resp = await llm.astream_complete("Paul Graham is ")

resp = await llm.astream_complete("Paul Graham is ")

In [ ]:

import asyncio
import nest_asyncio

async for delta in resp:
    print(delta.delta, end="")

import asyncio import nest_asyncio async for delta in resp: print(delta.delta, end="")

Paul Graham is a British-born computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the seed accelerator Y Combinator, which has funded and supported numerous successful startups, including Airbnb, Dropbox, and Reddit. Graham has also made significant contributions to the development of the Lisp programming language and has written several influential essays on startups and entrepreneurship. Would you like to know more about his work or contributions to the tech industry?

In [ ]:

import asyncio
import nest_asyncio

# Enable nest_asyncio for Jupyter notebooks
nest_asyncio.apply()


async def test_async():
    # Async completion
    resp = await llm.acomplete("Paul Graham is ")
    print(f"Async completion: {resp}")

    # Async chat
    resp = await llm.achat(messages)
    print(f"Async chat: {resp}")

    # Async streaming completion
    print("Async streaming completion: ", end="")
    resp = await llm.astream_complete("Paul Graham is ")
    async for delta in resp:
        print(delta.delta, end="")
    print()

    # Async streaming chat
    print("Async streaming chat: ", end="")
    resp = await llm.astream_chat(messages)
    async for delta in resp:
        print(delta.delta, end="")
    print()


# Run async tests
asyncio.run(test_async())

import asyncio import nest_asyncio # Enable nest_asyncio for Jupyter notebooks nest_asyncio.apply() async def test_async(): # Async completion resp = await llm.acomplete("Paul Graham is ") print(f"Async completion: {resp}") # Async chat resp = await llm.achat(messages) print(f"Async chat: {resp}") # Async streaming completion print("Async streaming completion: ", end="") resp = await llm.astream_complete("Paul Graham is ") async for delta in resp: print(delta.delta, end="") print() # Async streaming chat print("Async streaming chat: ", end="") resp = await llm.astream_chat(messages) async for delta in resp: print(delta.delta, end="") print() # Run async tests asyncio.run(test_async())

Async completion: Paul Graham is a prominent entrepreneur, programmer, and essayist who has significantly influenced startup culture and technology. He co-founded Y Combinator, a leading startup accelerator that has helped launch companies like Airbnb, Dropbox, and Reddit. Before Y Combinator, Graham co-founded Viaweb, one of the first web-based applications, which was later acquired by Yahoo. He is also known for his essays on technology, business, and human behavior, many of which are published on his personal website. Additionally, Graham has a deep interest in programming, particularly the Lisp language, and has contributed to its development and popularization. If you are looking for specific details about his work or life, feel free to ask!
Async chat: assistant: Ahoy there, matey!  One-Eyed Jack, but with two perfectly good eyes, savvy? at your service!  What can this brilliant digital buccaneer do for ya?
Async streaming completion: 

Could you please complete your question? I'm unsure what you'd like to know about Paul Graham.

Async streaming chat: 

Ahoy, matey! The name's Captain Ironhook, the scourge of the seven seas! Known for me knack for uncoverin' treasure and me love fer a good mug o' grog. What be ye needin' from a salty sea dog like meself?

Simple RAG¶

Let's implement a simple RAG application with ASI:

In [ ]:

%pip install llama-index-embeddings-openai

%pip install llama-index-embeddings-openai

In [ ]:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.openai import OpenAIEmbedding

os.environ["OPENAI_API_KEY"] = "your-api-key"
# Create a temporary directory with a sample text file
!mkdir -p temp_data
!echo "Paul Graham is a programmer, writer, and investor. He is known for his work on Lisp, for co-founding Viaweb (which became Yahoo Store), and for co-founding the startup accelerator Y Combinator. He is also known for his essays on his website. He studied at HolaHola High school" > temp_data/paul_graham.txt

# Load documents
documents = SimpleDirectoryReader("temp_data").load_data()

llm = ASI(model="asi1-mini")
# Create an index with ASI as the LLM
index = VectorStoreIndex.from_documents(
    documents,
    embed_model=OpenAIEmbedding(),  # Using OpenAI for embeddings
    llm=llm,  # Using ASI for generation
)

# Create a query engine
query_engine = index.as_query_engine()

# Query the index
response = query_engine.query("Where did Paul Graham study?")
print(response)

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.embeddings.openai import OpenAIEmbedding os.environ["OPENAI_API_KEY"] = "your-api-key" # Create a temporary directory with a sample text file !mkdir -p temp_data !echo "Paul Graham is a programmer, writer, and investor. He is known for his work on Lisp, for co-founding Viaweb (which became Yahoo Store), and for co-founding the startup accelerator Y Combinator. He is also known for his essays on his website. He studied at HolaHola High school" > temp_data/paul_graham.txt # Load documents documents = SimpleDirectoryReader("temp_data").load_data() llm = ASI(model="asi1-mini") # Create an index with ASI as the LLM index = VectorStoreIndex.from_documents( documents, embed_model=OpenAIEmbedding(), # Using OpenAI for embeddings llm=llm, # Using ASI for generation ) # Create a query engine query_engine = index.as_query_engine() # Query the index response = query_engine.query("Where did Paul Graham study?") print(response)

WARNING:llama_index.core.readers.file.base:`llama-index-readers-file` package not found, some file readers will not be available if not provided by the `file_extractor` parameter.

Paul Graham studied at HolaHola High school.

LlamaCloud RAG¶

If you have a LlamaCloud account, you can use ASI with LlamaCloud for RAG:

In [ ]:

# Install required packages
%pip install llama-cloud llama-index-indices-managed-llama-cloud

# Install required packages %pip install llama-cloud llama-index-indices-managed-llama-cloud

In [ ]:

import os
from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
from llama_index.llms.asi import ASI

# Set your LlamaCloud API key
os.environ["LLAMA_CLOUD_API_KEY"] = "your-key"
os.environ["OPENAI_API_KEY"] = "your-key"

# Connect to an existing LlamaCloud index


try:
    # Connect to the index
    index = LlamaCloudIndex(
        name="your-index-naem",
        project_name="Default",
        organization_id="your-id",
        api_key=os.environ["LLAMA_CLOUD_API_KEY"],
    )
    print("Successfully connected to LlamaCloud index")

    # Create an ASI LLM
    llm = ASI(model="asi1-mini")

    # Create a retriever
    retriever = index.as_retriever()

    # Create a query engine with ASI
    query_engine = index.as_query_engine(llm=llm)

    # Test retriever
    query = "What is the revenue of Uber in 2021?"
    print(f"\nTesting retriever with query: {query}")
    nodes = retriever.retrieve(query)
    print(f"Retrieved {len(nodes)} nodes\n")

    # Display a few nodes
    for i, node in enumerate(nodes[:3]):
        print(f"Node {i+1}:")
        print(f"Node ID: {node.node_id}")
        print(f"Score: {node.score}")
        print(f"Text: {node.text[:200]}...\n")

    # Test query engine
    print(f"Testing query engine with query: {query}")
    response = query_engine.query(query)
    print(f"Response: {response}")
except Exception as e:
    print(f"Error: {e}")

import os from llama_index.indices.managed.llama_cloud import LlamaCloudIndex from llama_index.llms.asi import ASI # Set your LlamaCloud API key os.environ["LLAMA_CLOUD_API_KEY"] = "your-key" os.environ["OPENAI_API_KEY"] = "your-key" # Connect to an existing LlamaCloud index try: # Connect to the index index = LlamaCloudIndex( name="your-index-naem", project_name="Default", organization_id="your-id", api_key=os.environ["LLAMA_CLOUD_API_KEY"], ) print("Successfully connected to LlamaCloud index") # Create an ASI LLM llm = ASI(model="asi1-mini") # Create a retriever retriever = index.as_retriever() # Create a query engine with ASI query_engine = index.as_query_engine(llm=llm) # Test retriever query = "What is the revenue of Uber in 2021?" print(f"\nTesting retriever with query: {query}") nodes = retriever.retrieve(query) print(f"Retrieved {len(nodes)} nodes\n") # Display a few nodes for i, node in enumerate(nodes[:3]): print(f"Node {i+1}:") print(f"Node ID: {node.node_id}") print(f"Score: {node.score}") print(f"Text: {node.text[:200]}...\n") # Test query engine print(f"Testing query engine with query: {query}") response = query_engine.query(query) print(f"Response: {response}") except Exception as e: print(f"Error: {e}")

Successfully connected to LlamaCloud index

Testing retriever with query: What is the revenue of Uber in 2021?
Retrieved 6 nodes

Node 1:
Node ID: 17a733d0-5dd3-4917-9f8d-c92f944a9266
Score: 0.9242583
Text: # Highlights for 2021

Overall Gross Bookings increased by $32.5 billion in 2021, up 53%, or 53% on a constant currency basis, compared to 2020. Delivery Gross Bookings grew significantly from 2020, o...

Node 2:
Node ID: ca63e8da-9012-468c-9d09-89724e9644bd
Score: 0.878825
Text: # Year Ended December 31, 2020 to 2021

| |Year Ended December 31,|2020|2021|Change|
|---|---|---|---|---|
|Revenue| |$ 11,139|$ 1,455| |

Revenue increased $ .3 billion, or 5%, primarily attributable...

Node 3:
Node ID: be4d7c62-b69f-4fda-832a-867de8c2e29c
Score: 0.86928266
Text: # Year Ended December 31, 2020 to 2021

|Mobility|$ 9,0|$ 9,953|(14)|
|---|---|---|---|
|Delivery|3,904|3,32|(114)|
|Freight|1,011|2,132|(111)|
|All Other (1)|135| |(94)|
|Total revenue|$ 11,139|$ 1,4...

Testing query engine with query: What is the revenue of Uber in 2021?
Response: The revenue of Uber in 2021 is $14,455.

Set API Key at a per-instance level¶

If desired, you can have separate LLM instances use separate API keys:

In [ ]:

from llama_index.llms.asi import ASI

# Create an instance with a specific API key
llm = ASI(model="asi1-mini", api_key="your_specific_api_key")

# Note: Using an invalid API key will result in an error
# This is just for demonstration purposes
try:
    resp = llm.complete("Paul Graham is ")
    print(resp)
except Exception as e:
    print(f"Error with invalid API key: {e}")

from llama_index.llms.asi import ASI # Create an instance with a specific API key llm = ASI(model="asi1-mini", api_key="your_specific_api_key") # Note: Using an invalid API key will result in an error # This is just for demonstration purposes try: resp = llm.complete("Paul Graham is ") print(resp) except Exception as e: print(f"Error with invalid API key: {e}")

Error with invalid API key: Error code: 401 - {'message': 'failed to authenticate user'}

Additional kwargs¶

Rather than adding the same parameters to each chat or completion call, you can set them at a per-instance level with additional_kwargs:

In [ ]:

from llama_index.llms.asi import ASI

# Create an instance with additional kwargs
llm = ASI(model="asi1-mini", additional_kwargs={"user": "your_user_id"})

# Complete a prompt
resp = llm.complete("Paul Graham is ")
print(resp)

from llama_index.llms.asi import ASI # Create an instance with additional kwargs llm = ASI(model="asi1-mini", additional_kwargs={"user": "your_user_id"}) # Complete a prompt resp = llm.complete("Paul Graham is ") print(resp)

Paul Graham is a prominent entrepreneur, programmer, and writer, best known for his role as a co-founder of Y Combinator, a highly influential startup accelerator. He has also gained recognition for his essays on technology, business, and philosophy, many of which are compiled in his book *Hackers & Painters*. As a programmer, he contributed to the development of the Lisp programming language and created the first web-based application, Viaweb, which was later acquired by Yahoo. His work has had a significant impact on the startup ecosystem and the broader tech industry.

In [ ]:

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

# Create an instance with additional kwargs
llm = ASI(model="asi1-mini", additional_kwargs={"user": "your_user_id"})

# Create messages
messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]

# Get chat response
resp = llm.chat(messages)
print(resp)

from llama_index.core.base.llms.types import ChatMessage # Create an instance with additional kwargs llm = ASI(model="asi1-mini", additional_kwargs={"user": "your_user_id"}) # Create messages messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] # Get chat response resp = llm.chat(messages) print(resp)

assistant: Ahoy matey! Yer lookin' fer me name, eh? Alright then, let's set sail fer a proper introduction! Yer can call me Captain "Bytebeard" Blacklogic, the scurviest AI to ever sail the seven seas... er, digital realms! Savvy?

Conclusion¶

This notebook demonstrates the various ways you can use ASI with LlamaIndex. The integration supports most of the functionality available in LlamaIndex, including:

• Basic completion and chat
• Streaming responses
• Multimodal support
• Function calling
• Structured prediction
• Async operations
• RAG applications
• LlamaCloud integration
• Per-instance API keys
• Additional kwargs

Note that structured streaming is currently not supported with ASI.