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378 lines
11 KiB
Plaintext
378 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0d7688a7",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/output_parsing/openai_pydantic_program.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "530c973e-916d-4c9e-9365-e2d5306d7e3d",
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"metadata": {},
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"source": [
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"# LLM Pydantic Program"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18461ba1-6978-4b5b-861e-6dceec36857b",
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"metadata": {},
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"source": [
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"This guide shows you how to generate structured data with our `LLMTextCompletionProgram`. Given an LLM as well as an output Pydantic class, generate a structured Pydantic object.\n",
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"\n",
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"In terms of the target object, you can choose to directly specify `output_cls`, or specify a `PydanticOutputParser` or any other BaseOutputParser that generates a Pydantic object.\n",
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"\n",
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"in the examples below, we show you different ways of extracting into the `Album` object (which can contain a list of Song objects)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8fcefc79-68b4-4481-b1ef-a902fc12e4c8",
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"metadata": {},
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"source": [
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"## Extract into `Album` class\n",
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"\n",
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"This is a simple example of parsing an output into an `Album` schema, which can contain multiple songs.\n",
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"\n",
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"Just pass `Album` into the `output_cls` property on initialization of the `LLMTextCompletionProgram`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "81e5dde0",
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"metadata": {},
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"source": [
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"If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b2833cea",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install llama-index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f7a83b49-5c34-45d5-8cf4-62f348fb1299",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pydantic import BaseModel\n",
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"from typing import List\n",
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"\n",
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"from llama_index.core.program import LLMTextCompletionProgram"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0563f1ba-8086-4dcc-ba35-bfda31c45ae4",
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"metadata": {},
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"source": [
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"Define output schema"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "42053ea8-2580-4639-9dcf-566e8427c44e",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Song(BaseModel):\n",
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" \"\"\"Data model for a song.\"\"\"\n",
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"\n",
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" title: str\n",
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" length_seconds: int\n",
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"\n",
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"\n",
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"class Album(BaseModel):\n",
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" \"\"\"Data model for an album.\"\"\"\n",
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"\n",
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" name: str\n",
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" artist: str\n",
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" songs: List[Song]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4afff44e-a746-4b9f-85a9-72058bcdd29f",
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"metadata": {},
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"source": [
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"Define LLM pydantic program"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "64145300-0f02-474e-8d61-74490fe4702b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core.program import LLMTextCompletionProgram"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fe756697-c299-4f9a-a108-944b6693f824",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt_template_str = \"\"\"\\\n",
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"Generate an example album, with an artist and a list of songs. \\\n",
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"Using the movie {movie_name} as inspiration.\\\n",
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"\"\"\"\n",
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"program = LLMTextCompletionProgram.from_defaults(\n",
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" output_cls=Album,\n",
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" prompt_template_str=prompt_template_str,\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7be01dc-433e-4485-bab0-36a04c3afbcb",
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"metadata": {},
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"source": [
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"Run program to get structured output. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "25d02228-2907-4810-932e-83ec9fc71f6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"output = program(movie_name=\"The Shining\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "27ec0777-28d5-494b-b419-daf6bce2b20e",
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"metadata": {},
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"source": [
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"The output is a valid Pydantic object that we can then use to call functions/APIs. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3e51bcf4-e7df-47b9-b380-8e5b900a31e1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Album(name='The Overlook', artist='Jack Torrance', songs=[Song(title='Redrum', length_seconds=240), Song(title=\"Here's Johnny\", length_seconds=180), Song(title='Room 237', length_seconds=300), Song(title='All Work and No Play', length_seconds=210), Song(title='The Maze', length_seconds=270)])"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3c3a009-2bfd-4d30-a982-133e4ff41c62",
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"metadata": {},
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"source": [
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"### Initialize with Pydantic Output Parser\n",
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"\n",
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"The above is equivalent to defining a Pydantic output parser and passing that in instead of the `output_cls` directly."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5741f8e9-1b1f-4f57-b6dd-9c8681383686",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core.output_parsers import PydanticOutputParser\n",
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"\n",
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"program = LLMTextCompletionProgram.from_defaults(\n",
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" output_parser=PydanticOutputParser(output_cls=Album),\n",
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" prompt_template_str=prompt_template_str,\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "66c4fe3c-f60b-4b7e-bf1a-5f3c33397d7e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Album(name='The Fellowship of the Ring', artist='Middle-earth Ensemble', songs=[Song(title='The Shire', length_seconds=240), Song(title='Concerning Hobbits', length_seconds=180), Song(title='The Ring Goes South', length_seconds=300), Song(title='A Knife in the Dark', length_seconds=270), Song(title='Flight to the Ford', length_seconds=210), Song(title='Many Meetings', length_seconds=240), Song(title='The Council of Elrond', length_seconds=330), Song(title='The Great Eye', length_seconds=180), Song(title='The Breaking of the Fellowship', length_seconds=360)])"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"output = program(movie_name=\"Lord of the Rings\")\n",
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"output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f72755ed-c460-4615-bf65-f3c288391618",
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"metadata": {},
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"source": [
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"## Define a Custom Output Parser\n",
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"\n",
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"Sometimes you may want to parse an output your own way into a JSON object. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b72fb2a3-0501-4671-8aa0-7e5ad0e3b225",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core.output_parsers import BaseOutputParser\n",
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"\n",
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"\n",
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"class CustomAlbumOutputParser(BaseOutputParser):\n",
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" \"\"\"Custom Album output parser.\n",
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"\n",
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" Assume first line is name and artist.\n",
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"\n",
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" Assume each subsequent line is the song.\n",
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"\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, verbose: bool = False):\n",
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" self.verbose = verbose\n",
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"\n",
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" def parse(self, output: str) -> Album:\n",
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" \"\"\"Parse output.\"\"\"\n",
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" if self.verbose:\n",
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" print(f\"> Raw output: {output}\")\n",
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" lines = output.split(\"\\n\")\n",
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" name, artist = lines[0].split(\",\")\n",
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" songs = []\n",
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" for i in range(1, len(lines)):\n",
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" title, length_seconds = lines[i].split(\",\")\n",
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" songs.append(Song(title=title, length_seconds=length_seconds))\n",
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"\n",
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" return Album(name=name, artist=artist, songs=songs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa128fd1-93ca-4983-b0c8-9190d4dc5162",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt_template_str = \"\"\"\\\n",
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"Generate an example album, with an artist and a list of songs. \\\n",
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"Using the movie {movie_name} as inspiration.\\\n",
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"\n",
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"Return answer in following format.\n",
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"The first line is:\n",
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"<album_name>, <album_artist>\n",
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"Every subsequent line is a song with format:\n",
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"<song_title>, <song_length_seconds>\n",
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"\n",
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"\"\"\"\n",
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"program = LLMTextCompletionProgram.from_defaults(\n",
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" output_parser=CustomAlbumOutputParser(verbose=True),\n",
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" output_cls=Album,\n",
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" prompt_template_str=prompt_template_str,\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51115206-b781-4b66-965d-ebe3765504c8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"> Raw output: Gotham's Reckoning, The Dark Knight\n",
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"A Dark Knight Rises, 240\n",
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"The Joker's Symphony, 180\n",
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"Harvey Dent's Lament, 210\n",
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"Gotham's Guardian, 195\n",
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"The Batmobile Chase, 225\n",
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"The Dark Knight's Theme, 150\n",
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"The Joker's Mind Games, 180\n",
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"Rachel's Tragedy, 210\n",
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"Gotham's Last Stand, 240\n",
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"The Dark Knight's Triumph, 180\n"
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]
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}
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],
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"source": [
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"output = program(movie_name=\"The Dark Knight\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cca8432e-a6cb-4fc7-8120-0b7b84b0e755",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Album(name=\"Gotham's Reckoning\", artist=' The Dark Knight', songs=[Song(title='A Dark Knight Rises', length_seconds=240), Song(title=\"The Joker's Symphony\", length_seconds=180), Song(title=\"Harvey Dent's Lament\", length_seconds=210), Song(title=\"Gotham's Guardian\", length_seconds=195), Song(title='The Batmobile Chase', length_seconds=225), Song(title=\"The Dark Knight's Theme\", length_seconds=150), Song(title=\"The Joker's Mind Games\", length_seconds=180), Song(title=\"Rachel's Tragedy\", length_seconds=210), Song(title=\"Gotham's Last Stand\", length_seconds=240), Song(title=\"The Dark Knight's Triumph\", length_seconds=180)])"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"output"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llama_index_v2",
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"language": "python",
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"name": "llama_index_v2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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