forked from Archives/langchain
83eea230f3
changed height in the example to a more reasonable number (from 9 feet to 6 feet)
255 lines
6.5 KiB
Plaintext
255 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6605e7f7",
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"metadata": {},
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"source": [
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"# Extraction\n",
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"\n",
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"The extraction chain uses the OpenAI `functions` parameter to specify a schema to extract entities from a document. This helps us make sure that the model outputs exactly the schema of entities and properties that we want, with their appropriate types.\n",
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"\n",
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"The extraction chain is to be used when we want to extract several entities with their properties from the same passage (i.e. what people were mentioned in this passage?)"
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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": 1,
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"id": "34f04daf",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import create_extraction_chain, create_extraction_chain_pydantic\n",
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"from langchain.prompts import ChatPromptTemplate"
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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": 2,
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"id": "a2648974",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ef034ce",
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"metadata": {},
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"source": [
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"## Extracting entities"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78ff9df9",
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"metadata": {},
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"source": [
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"To extract entities, we need to create a schema like the following, were we specify all the properties we want to find and the type we expect them to have. We can also specify which of these properties are required and which are optional."
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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": 3,
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"id": "4ac43eba",
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"metadata": {},
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"outputs": [],
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"source": [
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"schema = {\n",
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" \"properties\": {\n",
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" \"person_name\": {\"type\": \"string\"},\n",
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" \"person_height\": {\"type\": \"integer\"},\n",
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" \"person_hair_color\": {\"type\": \"string\"},\n",
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" \"dog_name\": {\"type\": \"string\"},\n",
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" \"dog_breed\": {\"type\": \"string\"},\n",
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" },\n",
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" \"required\": [\"person_name\", \"person_height\"],\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": 6,
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"id": "640bd005",
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"metadata": {},
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"outputs": [],
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"source": [
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"inp = \"\"\"\n",
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"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
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"Alex's dog Frosty is a labrador and likes to play hide and seek.\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": 7,
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"id": "64313214",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain(schema, llm)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17c48adb",
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"metadata": {},
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"source": [
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"As we can see, we extracted the required entities and their properties in the required format:"
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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": 8,
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"id": "cc5436ed",
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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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"[{'person_name': 'Alex',\n",
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" 'person_height': 5,\n",
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" 'person_hair_color': 'blonde',\n",
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" 'dog_name': 'Frosty',\n",
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" 'dog_breed': 'labrador'},\n",
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" {'person_name': 'Claudia',\n",
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" 'person_height': 6,\n",
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" 'person_hair_color': 'brunette'}]"
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]
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},
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"execution_count": 8,
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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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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "698b4c4d",
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"metadata": {},
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"source": [
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"## Pydantic example"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6504a6d9",
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"metadata": {},
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"source": [
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"We can also use a Pydantic schema to choose the required properties and types and we will set as 'Optional' those that are not strictly required.\n",
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"\n",
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"By using the `create_extraction_chain_pydantic` function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. \n",
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"\n",
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"In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types."
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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": 9,
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"id": "6792866b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Optional, List\n",
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"from pydantic import BaseModel, Field"
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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": 10,
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"id": "36a63761",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Properties(BaseModel):\n",
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" person_name: str\n",
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" person_height: int\n",
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" person_hair_color: str\n",
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" dog_breed: Optional[str]\n",
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" dog_name: Optional[str]"
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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": 11,
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"id": "8ffd1e57",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)"
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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": 12,
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"id": "24baa954",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"inp = \"\"\"\n",
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"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
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"Alex's dog Frosty is a labrador and likes to play hide and seek.\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": "84e0a241",
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"metadata": {},
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"source": [
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"As we can see, we extracted the required entities and their properties in the required format:"
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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": 13,
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"id": "f771df58",
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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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"[Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed='labrador', dog_name='Frosty'),\n",
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" Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)]"
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]
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},
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"execution_count": 13,
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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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"chain.run(inp)"
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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": "general",
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"language": "python",
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"name": "general"
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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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"version": "3.10.6"
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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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