OpenAI

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

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="gpt-4o-mini",
    # api_key="some key",  # uses OPENAI_API_KEY env var by default
)

from llama_index.llms.openai import OpenAI

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

print(resp)

a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work on Lisp, a programming language. Graham has also written several influential essays on startups, technology, and entrepreneurship.

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.chat(messages)

print(resp)

assistant: Ahoy matey! The name's Rainbow Roger, the most colorful pirate on the seven seas! What can I do for ye today?

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

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

a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work on programming languages and web development. Graham is also a prolific writer and has published several influential essays on technology, startups, and entrepreneurship.

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)

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

Ahoy matey! The name's Captain Rainbowbeard! Aye, I be a pirate with a love for all things colorful and bright. Me beard be as vibrant as a rainbow, and me ship be the most colorful vessel on the seven seas! What can I do for ye today, me hearty?

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o")

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

print(resp)

a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work on Lisp, a programming language. Graham has also written several influential essays on startups, technology, and entrepreneurship.

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

print(resp)

assistant: Ahoy matey! The name's Captain Rainbowbeard, the most colorful pirate on the seven seas! What can I do for ye today? Arrr!

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

from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock
from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o")

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 is a black and white photograph featuring three dice in mid-air above a checkered surface, likely a chessboard. The dice are captured in motion, with one showing the number five prominently. The background is dark, with a subtle light beam creating a triangular shape above the dice, adding a dramatic effect to the composition.

from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI


llm = OpenAI(
    model="gpt-4o-audio-preview",
    modalities=["text", "audio"],
    audio_config={"voice": "alloy", "format": "wav"},
)

messages = [
    ChatMessage(role="user", content="Hello! My name is Logan."),
]

resp = llm.chat(messages)

import base64
from IPython.display import Audio

Audio(base64.b64decode(resp.message.blocks[0].audio), rate=16000)

# Add the response to the chat history and ask for the user's name
messages.append(resp.message)
messages.append(ChatMessage(role="user", content="What is my name?"))

resp = llm.chat(messages)
Audio(base64.b64decode(resp.message.blocks[0].audio), rate=16000)

!wget AUDIO_URL = "https://science.nasa.gov/wp-content/uploads/2024/04/sounds-of-mars-one-small-step-earth.wav" -O audio.wav

from llama_index.core.llms import ChatMessage, AudioBlock, TextBlock

messages = [
    ChatMessage(
        role="user",
        blocks=[
            AudioBlock(path="audio.wav", format="wav"),
            TextBlock(
                text="Describe the audio in a few sentences. What is it from?"
            ),
        ],
    )
]

llm = OpenAI(
    model="gpt-4o-audio-preview",
    modalities=["text"],
)

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

assistant: The audio is a famous quote from astronaut Neil Armstrong during the Apollo 11 moon landing in 1969. As he became the first human to step onto the moon's surface, he said, "That's one small step for man, one giant leap for mankind." This moment marked a significant achievement in space exploration and human history.

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


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)


tool = FunctionTool.from_defaults(fn=generate_song)

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o-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='Random Vibes' artist='DJ Chill'

llm = OpenAI(model="gpt-3.5-turbo")
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': 'Hey Jude', 'artist': 'The Beatles'}}, Output: name='Hey Jude' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Let It Be', 'artist': 'The Beatles'}}, Output: name='Let It Be' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Yesterday', 'artist': 'The Beatles'}}, Output: name='Yesterday' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Come Together', 'artist': 'The Beatles'}}, Output: name='Come Together' artist='The Beatles'
Name: generate_song, Input: {'args': (), 'kwargs': {'name': 'Help!', 'artist': 'The Beatles'}}, Output: name='Help!' artist='The Beatles'

from llama_index.core.llms import ChatMessage

chat_history = [ChatMessage(role="user", content="Pick a random song for me")]

resp = llm.chat_with_tools([tool], chat_history=chat_history)

tools_by_name = {t.metadata.name: t for t in [tool]}
tool_calls = llm.get_tool_calls_from_response(
    resp, error_on_no_tool_call=False
)

while 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)
        chat_history.append(
            ChatMessage(
                role="tool",
                content=str(tool_output),
                # most LLMs like OpenAI need to know the tool call id
                additional_kwargs={"tool_call_id": tool_call.tool_id},
            )
        )

        resp = llm.chat_with_tools([tool], chat_history=chat_history)
        tool_calls = llm.get_tool_calls_from_response(
            resp, error_on_no_tool_call=False
        )

Calling generate_song with {'name': 'Random Vibes', 'artist': 'DJ Chill'}

print(resp.message.content)

Here's a random song for you: **"Random Vibes"** by **DJ Chill**. Enjoy!

from llama_index.llms.openai import OpenAI
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]


llm = OpenAI(model="gpt-3.5-turbo")
prompt_tmpl = PromptTemplate(
    "Generate a restaurant in a given city {city_name}"
)
# Option 1: Use `as_structured_llm`
restaurant_obj = (
    llm.as_structured_llm(Restaurant)
    .complete(prompt_tmpl.format(city_name="Dallas"))
    .raw
)
# Option 2: Use `structured_predict`
# restaurant_obj = llm.structured_predict(Restaurant, prompt_tmpl, city_name="Miami")

restaurant_obj

Restaurant(name='Tasty Bites', city='Dallas', cuisine='Italian', menu_items=[MenuItem(course_name='Appetizer', is_vegetarian=True), MenuItem(course_name='Main Course', is_vegetarian=False), MenuItem(course_name='Dessert', is_vegetarian=True)])

from llama_index.core.llms import ChatMessage
from IPython.display import clear_output
from pprint import pprint

input_msg = ChatMessage.from_str("Generate a restaurant in Boston")

sllm = llm.as_structured_llm(Restaurant)
stream_output = sllm.stream_chat([input_msg])
for partial_output in stream_output:
    clear_output(wait=True)
    pprint(partial_output.raw.dict())
    restaurant_obj = partial_output.raw

restaurant_obj

{'city': 'Boston',
 'cuisine': 'American',
 'menu_items': [{'course_name': 'Appetizer', 'is_vegetarian': True},
                {'course_name': 'Main Course', 'is_vegetarian': False},
                {'course_name': 'Dessert', 'is_vegetarian': True}],
 'name': 'Boston Bites'}





Restaurant(name='Boston Bites', city='Boston', cuisine='American', menu_items=[MenuItem(course_name='Appetizer', is_vegetarian=True), MenuItem(course_name='Main Course', is_vegetarian=False), MenuItem(course_name='Dessert', is_vegetarian=True)])

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo")

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

print(resp)

a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work as an essayist and author on topics related to technology, startups, and entrepreneurship. Graham is also the co-founder of Viaweb, one of the first web-based applications, which was acquired by Yahoo in 1998. He has been a prominent figure in the tech industry for many years and is known for his insightful and thought-provoking writings on a wide range of subjects.

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

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

Paul Graham is an entrepreneur, venture capitalist, and computer scientist. He is best known for his work in the startup world, having co-founded the accelerator Y Combinator and investing in many successful startups such as Airbnb, Dropbox, and Stripe. He is also a prolific writer, having authored several books on topics such as startups, programming, and technology.

llm = OpenAI(model="gpt-3.5-turbo")
response = await llm.apredict_and_call([tool], "Generate a song")
print(str(response))

name='Sunshine' artist='John Smith'

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo", api_key="BAD_KEY")
resp = llm.complete("Paul Graham is ")
print(resp)

a computer scientist, entrepreneur, and venture capitalist. He is best known as the co-founder of the startup accelerator Y Combinator. Graham has also written several influential essays on startups and entrepreneurship, which have gained a wide following in the tech industry. He has been involved in the founding and funding of numerous successful startups, including Reddit, Dropbox, and Airbnb. Graham is known for his insightful and often controversial opinions on various topics, including education, inequality, and the future of technology.

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo", additional_kwargs={"user": "your_user_id"})
resp = llm.complete("Paul Graham is ")
print(resp)

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo", additional_kwargs={"user": "your_user_id"})
messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)

%pip install llama-cloud-services

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

os.environ["LLAMA_CLOUD_API_KEY"] = "llx-..."

from llama_cloud.client import LlamaCloud

client = LlamaCloud(token=os.environ["LLAMA_CLOUD_API_KEY"])

# Embedding config
embedding_config = {
    "type": "OPENAI_EMBEDDING",
    "component": {
        "api_key": os.environ["OPENAI_API_KEY"],
        "model_name": "text-embedding-ada-002",  # You can choose any OpenAI Embedding model
    },
}

# Transformation auto config
transform_config = {
    "mode": "auto",
    "config": {
        "chunk_size": 1024,  # editable
        "chunk_overlap": 20,  # editable
    },
}

pipeline = {
    "name": "openai-rag-pipeline",  # Change the name if needed
    "embedding_config": embedding_config,
    "transform_config": transform_config,
    "data_sink_id": None,
}

pipeline = client.pipelines.upsert_pipeline(request=pipeline)

with open("../data/10k/uber_2021.pdf", "rb") as f:
    file = client.files.upload_file(upload_file=f)

files = [{"file_id": file.id}]

pipeline_files = client.pipelines.add_files_to_pipeline(
    pipeline.id, request=files
)

jobs = client.pipelines.list_pipeline_jobs(pipeline.id)

jobs[0].status

<ManagedIngestionStatus.SUCCESS: 'SUCCESS'>

from llama_cloud_services import LlamaCloudIndex

index = LlamaCloudIndex(
    name="openai-rag-pipeline",
    project_name="Default",
    organization_id="YOUR ORG ID",
    api_key=os.environ["LLAMA_CLOUD_API_KEY"],
)

query = "What is the revenue of Uber in 2021?"

retriever = index.as_retriever(
    dense_similarity_top_k=3,
    sparse_similarity_top_k=3,
    alpha=0.5,
    enable_reranking=True,
)

retrieved_nodes = retriever.retrieve(query)

from llama_index.core.response.notebook_utils import display_source_node

for retrieved_node in retrieved_nodes:
    display_source_node(retrieved_node, source_length=1000)

query_engine = index.as_query_engine(
    dense_similarity_top_k=3,
    sparse_similarity_top_k=3,
    alpha=0.5,
    enable_reranking=True,
)

response = query_engine.query(query)

print(response)

The revenue of Uber in 2021 was $17.5 billion.