6.16. Large Language Model (LLM)#

6.16.1. Simplify LLM Integration with Magentic’s @prompt Decorator#

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!pip install magentic

To enhance your code’s natural language skills with LLM effortlessly, try magentic.

With magentic, you can use the @prompt decorator to create functions that return organized LLM results, keeping your code neat and easy to read.

import openai

openai.api_key = "sk-..."
from magentic import prompt

@prompt('Add more "dude"ness to: {phrase}')
def dudeify(phrase: str) -> str:
    ...  # No function body as this is never executed

dudeify("Hello, how are you?")
# "Hey, dude! What's up? How's it going, my man?"
"Yo dude, how's it going?"

The @prompt decorator will consider the return type annotation, including those supported by pydantic.

from magentic import prompt, FunctionCall
from pydantic import BaseModel
from typing import Literal

class MilkTea(BaseModel):
    tea: str
    sweetness_percentage: float
    topping: str

@prompt("Create a milk tea with the following tea {tea}.")
def create_milk_tea(tea: str) -> MilkTea:

create_milk_tea("green tea")
MilkTea(tea='green tea', sweetness_percentage=100.0, topping='boba')

The @prompt decorator also considers a function call.

def froth_milk(temperature: int, texture: Literal["foamy", "hot", "cold"]) -> str:
    """Froth the milk to the desired temperature and texture."""
    return f"Frothing milk to {temperature} F with texture {texture}"

    "Prepare the milk for my {coffee_type}",
def configure_coffee(coffee_type: str) -> FunctionCall[str]:

output = configure_coffee("latte!")
'Frothing milk to 60 F with texture foamy'

Link to magentic.

6.16.2. Outlines: Ensuring Consistent Outputs from Language Models#

The Outlines library enables controlling the outputs of language models. This makes the outputs more predictable, ensuring the reliability of systems using large language models.

import outlines

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = """You are a sentiment-labelling assistant.
Is the following review positive or negative?

Review: This restaurant is just awesome!
# Only return a choice between multiple possibilities
answer = outlines.generate.choice(model, ["Positive", "Negative"])(prompt)
# Only return integers or floats
model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = "1+1="
answer = outlines.generate.format(model, int)(prompt)

prompt = "sqrt(2)="
answer = outlines.generate.format(model, float)(prompt)

Link to Outlines.

6.16.3. Mirascope: Extract Structured Data Extraction From LLM Outputs#

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!pip install mirascope

Large Language Models (LLMs) are powerful at producing human-like text, but their outputs lack structure, which can limit their usefulness in many practical applications that require organized data.

Mirascope offers a solution by enabling the extraction of structured information from LLM outputs reliably.

The following code uses Mirascope to extract meeting details such as topic, date, time, and participants.

import os

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from typing import List, Type
from pydantic import BaseModel
from mirascope.openai import OpenAIExtractor

class MeetingDetails(BaseModel):
    topic: str
    date: str
    time: str
    participants: List[str]

class MeetingExtractor(OpenAIExtractor[MeetingDetails]):
    extract_schema: Type[MeetingDetails] = MeetingDetails
    prompt_template = """
    Extract the meeting details from the following description:

    description: str

# Example usage:
description = "Discuss the upcoming product launch on June 15th at 3 PM with John, Sarah, and Mike."
meeting_details = MeetingExtractor(description=description).extract()
assert isinstance(meeting_details, MeetingDetails)
topic='Upcoming product launch' date='June 15th' time='3 PM' participants=['John', 'Sarah', 'Mike']

Link to Mirascope.

6.16.4. Maximize Accuracy and Relevance with External Data and LLMs#

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!pip install -U mirascope

Combining external data and an LLM offers the best of both worlds: accuracy and relevance. External data provides up-to-date information, while an LLM can generate text based on input prompts. Together, they enable a system to respond helpfully to a wider range of queries.

Mirascope simplifies this combination with Pythonic code. In the example below, we use an LLM to process natural language prompts and query the database for data.

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import sqlite3

# Set up database and table for the example below
conn = sqlite3.connect("grocery.db")
cursor = conn.cursor()

# Create the 'grocery_items' table
    CREATE TABLE IF NOT EXISTS grocery_items (
        name TEXT NOT NULL,
        category TEXT NOT NULL,
        price REAL NOT NULL

# Insert some sample data
items = [
    ("apple", "Fruits", 0.75),
    ("banana", "Fruits", 0.50),
    ("carrot", "Vegetables", 1.20),

    "INSERT INTO grocery_items (name, category, price) VALUES (?, ?, ?)", items

# Commit the changes and close the connection
print("Database created with sample data.")
Database created with sample data.
import os

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from mirascope.openai import OpenAICall, OpenAICallParams
import sqlite3

# Assume you have a SQLite database with a 'grocery_items' table
conn = sqlite3.connect("grocery.db")

def get_item_info(table: str, item: str, info: str) -> dict:
    """Get `info` from the `table` table based on `item`."""
    cursor = conn.cursor()
        row = cursor.execute(
            f"SELECT {info} FROM {table} WHERE name = ?", (item,)
        return f"The {info} for {item} is {row[0]}."
    except TypeError:
        return f"Sorry but {item} doesn't exist in the database."

class GroceryItemQuery(OpenAICall):
    prompt_template = """
    Your task is to query a database given a user's input.

    input: str
    call_params = OpenAICallParams(tools=[get_item_info])

text = "What's the price for banana in the grocery_items table?"
query_tool = GroceryItemQuery(input=text).call().tool
result = query_tool.fn(**query_tool.args)
'The price for banana is 0.5.'

Link to Mirascope.