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667 lines
21 KiB
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667 lines
21 KiB
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{
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"cells": [
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"cell_type": "markdown",
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"id": "cf89fbaa",
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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/df_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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"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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"# DataFrame Structured Data Extraction"
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]
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},
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{
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"attachments": {},
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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 demo shows how you can extract tabular DataFrames from raw text.\n",
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"\n",
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"This was directly inspired by jxnl's dataframe example here: https://github.com/jxnl/openai_function_call/blob/main/auto_dataframe.py.\n",
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"\n",
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"We show this with different levels of complexity, all backed by the OpenAI Function API:\n",
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"- (more code) How to build an extractor yourself using our OpenAIPydanticProgram\n",
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"- (less code) Using our out-of-the-box `DFFullProgram` and `DFRowsProgram` objects\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "fb240ba9-2f23-4686-8097-4f1d7bdf02cb",
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"metadata": {},
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"source": [
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"## Build a DF Extractor Yourself (Using OpenAIPydanticProgram)\n",
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"\n",
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"Our OpenAIPydanticProgram is a wrapper around an OpenAI LLM that supports function calling - it will return structured\n",
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"outputs in the form of a Pydantic object.\n",
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"\n",
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"We import our `DataFrame` and `DataFrameRowsOnly` objects.\n",
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"\n",
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"To create an output extractor, you just need to 1) specify the relevant Pydantic object, and 2) Add the right prompt"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bb74e24d",
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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": "bc357ded",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install llama-index-llms-openai\n",
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"%pip install llama-index-program-openai"
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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": "101e1779",
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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": "1c3f2918-7776-4b59-88da-07299cda4f25",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.program.openai import OpenAIPydanticProgram\n",
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"from llama_index.core.program import (\n",
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" DFFullProgram,\n",
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" DataFrame,\n",
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" DataFrameRowsOnly,\n",
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")\n",
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"from llama_index.llms.openai import OpenAI"
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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": "bcb69ed8-7b02-4fac-b6b6-b09c301fc2cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"program = OpenAIPydanticProgram.from_defaults(\n",
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" output_cls=DataFrame,\n",
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" llm=OpenAI(temperature=0, model=\"gpt-4-0613\"),\n",
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" prompt_template_str=(\n",
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" \"Please extract the following query into a structured data according\"\n",
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" \" to: {input_str}.Please extract both the set of column names and a\"\n",
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" \" set of rows.\"\n",
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" ),\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": "420c26b2-f652-436c-a661-d594c56496c8",
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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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"Function call: DataFrame with args: {\n",
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" \"columns\": [\n",
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" {\n",
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" \"column_name\": \"Name\",\n",
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" \"column_desc\": \"Name of the person\"\n",
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" },\n",
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" {\n",
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" \"column_name\": \"Age\",\n",
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" \"column_desc\": \"Age of the person\"\n",
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" },\n",
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" {\n",
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" \"column_name\": \"City\",\n",
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" \"column_desc\": \"City where the person lives\"\n",
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" },\n",
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" {\n",
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" \"column_name\": \"Hobby\",\n",
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" \"column_desc\": \"What the person likes to do\"\n",
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" }\n",
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" ],\n",
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" \"rows\": [\n",
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" {\n",
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" \"row_values\": [\"John\", 25, \"New York\", \"play basketball\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Mike\", 30, \"San Francisco\", \"play baseball\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Sarah\", 20, \"Los Angeles\", \"play tennis\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Mary\", 35, \"Chicago\", \"play tennis\"]\n",
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" }\n",
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" ]\n",
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"}\n"
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]
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},
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{
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"data": {
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"DataFrame(description=None, columns=[DataFrameColumn(column_name='Name', column_desc='Name of the person'), DataFrameColumn(column_name='Age', column_desc='Age of the person'), DataFrameColumn(column_name='City', column_desc='City where the person lives'), DataFrameColumn(column_name='Hobby', column_desc='What the person likes to do')], rows=[DataFrameRow(row_values=['John', 25, 'New York', 'play basketball']), DataFrameRow(row_values=['Mike', 30, 'San Francisco', 'play baseball']), DataFrameRow(row_values=['Sarah', 20, 'Los Angeles', 'play tennis']), DataFrameRow(row_values=['Mary', 35, 'Chicago', 'play tennis'])])"
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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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"# NOTE: the test example is taken from jxnl's repo\n",
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"\n",
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"response_obj = program(\n",
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" input_str=\"\"\"My name is John and I am 25 years old. I live in \n",
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" New York and I like to play basketball. His name is \n",
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" Mike and he is 30 years old. He lives in San Francisco \n",
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" and he likes to play baseball. Sarah is 20 years old \n",
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" and she lives in Los Angeles. She likes to play tennis.\n",
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" Her name is Mary and she is 35 years old. \n",
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" She lives in Chicago.\"\"\"\n",
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")\n",
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"response_obj"
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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": "fe28dc6d-98f9-4530-a5c4-255ae68d6d9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"program = OpenAIPydanticProgram.from_defaults(\n",
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" output_cls=DataFrameRowsOnly,\n",
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" llm=OpenAI(temperature=0, model=\"gpt-4-0613\"),\n",
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" prompt_template_str=(\n",
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" \"Please extract the following text into a structured data:\"\n",
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" \" {input_str}. The column names are the following: ['Name', 'Age',\"\n",
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" \" 'City', 'Favorite Sport']. Do not specify additional parameters that\"\n",
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" \" are not in the function schema. \"\n",
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" ),\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": "4ac6260c-c06a-48e6-9f7e-1892dd988eac",
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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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"Function call: DataFrameRowsOnly with args: {\n",
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" \"rows\": [\n",
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" {\n",
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" \"row_values\": [\"John\", 25, \"New York\", \"basketball\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Mike\", 30, \"San Francisco\", \"baseball\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Sarah\", 20, \"Los Angeles\", \"tennis\"]\n",
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" },\n",
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" {\n",
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" \"row_values\": [\"Mary\", 35, \"Chicago\", \"\"]\n",
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" }\n",
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" ]\n",
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"}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"DataFrameRowsOnly(rows=[DataFrameRow(row_values=['John', 25, 'New York', 'basketball']), DataFrameRow(row_values=['Mike', 30, 'San Francisco', 'baseball']), DataFrameRow(row_values=['Sarah', 20, 'Los Angeles', 'tennis']), DataFrameRow(row_values=['Mary', 35, 'Chicago', ''])])"
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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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"program(\n",
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" input_str=\"\"\"My name is John and I am 25 years old. I live in \n",
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" New York and I like to play basketball. His name is \n",
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" Mike and he is 30 years old. He lives in San Francisco \n",
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" and he likes to play baseball. Sarah is 20 years old \n",
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" and she lives in Los Angeles. She likes to play tennis.\n",
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" Her name is Mary and she is 35 years old. \n",
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" She lives in Chicago.\"\"\"\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "acbcbca8-78f1-47cd-9507-81b2c78ba6fe",
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"metadata": {},
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"source": [
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"## Use our DataFrame Programs\n",
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"\n",
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"We provide convenience wrappers for `DFFullProgram` and `DFRowsProgram`. This allows a simpler object creation interface than specifying all details through the `OpenAIPydanticProgram`."
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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": "e9b4c40f-5426-4b6f-9b97-b612c63f772d",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"from llama_index.program.openai import OpenAIPydanticProgram\n",
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"from llama_index.core.program import DFFullProgram, DFRowsProgram\n",
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"import pandas as pd\n",
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"\n",
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"# initialize empty df\n",
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"df = pd.DataFrame(\n",
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" {\n",
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" \"Name\": pd.Series(dtype=\"str\"),\n",
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" \"Age\": pd.Series(dtype=\"int\"),\n",
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" \"City\": pd.Series(dtype=\"str\"),\n",
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" \"Favorite Sport\": pd.Series(dtype=\"str\"),\n",
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" }\n",
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")\n",
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"\n",
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"# initialize program, using existing df as schema\n",
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"df_rows_program = DFRowsProgram.from_defaults(\n",
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" pydantic_program_cls=OpenAIPydanticProgram, df=df\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": "d7eeba46-25bd-499b-a7c4-d7f475c4de21",
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"metadata": {},
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"outputs": [],
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"source": [
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"# parse text, using existing df as schema\n",
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"result_obj = df_rows_program(\n",
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" input_str=\"\"\"My name is John and I am 25 years old. I live in \n",
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" New York and I like to play basketball. His name is \n",
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" Mike and he is 30 years old. He lives in San Francisco \n",
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" and he likes to play baseball. Sarah is 20 years old \n",
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" and she lives in Los Angeles. She likes to play tennis.\n",
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" Her name is Mary and she is 35 years old. \n",
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" She lives in Chicago.\"\"\"\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": "455a96c0-b4dd-46cc-a8f0-b70241733e2e",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/jerryliu/Programming/gpt_index/llama_index/program/predefined/df.py:65: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
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" return existing_df.append(new_df, ignore_index=True)\n"
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]
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},
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"<div>\n",
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"<style scoped>\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Name</th>\n",
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" <th>Age</th>\n",
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" <th>City</th>\n",
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" <th>Favorite Sport</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>John</td>\n",
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" <td>25</td>\n",
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" <td>New York</td>\n",
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" <td>Basketball</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Mike</td>\n",
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" <td>30</td>\n",
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" <td>San Francisco</td>\n",
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" <td>Baseball</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Sarah</td>\n",
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" <td>20</td>\n",
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" <td>Los Angeles</td>\n",
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" <td>Tennis</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Mary</td>\n",
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" <td>35</td>\n",
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" <td>Chicago</td>\n",
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" <td></td>\n",
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],
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"text/plain": [
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" Name Age City Favorite Sport\n",
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"0 John 25 New York Basketball\n",
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"1 Mike 30 San Francisco Baseball\n",
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"2 Sarah 20 Los Angeles Tennis\n",
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"3 Mary 35 Chicago "
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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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"result_obj.to_df(existing_df=df)"
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]
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},
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{
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"cell_type": "code",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"# initialize program that can do joint schema extraction and structured data extraction\n",
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"df_full_program = DFFullProgram.from_defaults(\n",
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||
" pydantic_program_cls=OpenAIPydanticProgram,\n",
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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": "dda44cc0-a3f4-4ba8-9304-a6f511722e07",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"result_obj = df_full_program(\n",
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" input_str=\"\"\"My name is John and I am 25 years old. I live in \n",
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" New York and I like to play basketball. His name is \n",
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" Mike and he is 30 years old. He lives in San Francisco \n",
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" and he likes to play baseball. Sarah is 20 years old \n",
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" and she lives in Los Angeles. She likes to play tennis.\n",
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" Her name is Mary and she is 35 years old. \n",
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" She lives in Chicago.\"\"\"\n",
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")"
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]
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},
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{
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"<div>\n",
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"<style scoped>\n",
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|
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Mike</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Los Angeles</td>\n",
|
||
" <td>Tennis</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" Name Age Location Hobby\n",
|
||
"0 John 25 New York Basketball\n",
|
||
"1 Mike 30 San Francisco Baseball\n",
|
||
"2 Sarah 20 Los Angeles Tennis\n",
|
||
"3 Mary 35 Chicago "
|
||
]
|
||
},
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
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|
||
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|
||
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|
||
"result_obj.to_df()"
|
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|
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|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# initialize empty df\n",
|
||
"df = pd.DataFrame(\n",
|
||
" {\n",
|
||
" \"City\": pd.Series(dtype=\"str\"),\n",
|
||
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" \"Population\": pd.Series(dtype=\"int\"),\n",
|
||
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|
||
")\n",
|
||
"\n",
|
||
"# initialize program, using existing df as schema\n",
|
||
"df_rows_program = DFRowsProgram.from_defaults(\n",
|
||
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|
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")"
|
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},
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{
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|
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"metadata": {},
|
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|
||
"source": [
|
||
"input_text = \"\"\"San Francisco is in California, has a population of 800,000. \n",
|
||
"New York City is the most populous city in the United States. \\\n",
|
||
"With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), \\\n",
|
||
"New York City is the most densely populated major city in the United States.\n",
|
||
"New York City is in New York State.\n",
|
||
"Boston (US: /ˈbɔːstən/),[8] officially the City of Boston, is the capital and largest city of the Commonwealth of Massachusetts \\\n",
|
||
"and the cultural and financial center of the New England region of the Northeastern United States. \\\n",
|
||
"The city boundaries encompass an area of about 48.4 sq mi (125 km2)[9] and a population of 675,647 as of 2020.[4]\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"# parse text, using existing df as schema\n",
|
||
"result_obj = df_rows_program(input_str=input_text)"
|
||
]
|
||
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|
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{
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"id": "16fa5b01-e12c-4151-b554-c7de470b4255",
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"name": "stderr",
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||
"/Users/jerryliu/Programming/gpt_index/llama_index/program/predefined/df.py:65: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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"text/plain": [
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|
||
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|
||
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|
||
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|
||
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