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Runtime error
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{
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
"from langchain.llms import OpenAI\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.output_parsers import PydanticOutputParser\n",
"from pydantic import BaseModel, Field, validator\n",
"from typing import List"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class Actor(BaseModel):\n",
" name: str = Field(description=\"name of an actor\")\n",
" film_names: List[str] = Field(description=\"list of names of films they starred in\")\n",
" \n",
"actor_query = \"Generate the filmography for a random actor.\"\n",
"\n",
"parser = PydanticOutputParser(pydantic_object=Actor)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"misformatted = \"{'name': \\\"Tom Hanks\\\", \\\"film_names\\\": [\\\"Forrest Gump\\\"]}\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# 将报解析错误。\n",
"# parser.parse(misformatted)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from langchain.output_parsers import OutputFixingParser\n",
"\n",
"new_parser = OutputFixingParser.from_llm(parser=parser, llm=OpenAI())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Actor(name='Tom Hanks', film_names=['Forrest Gump'])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_parser.parse(misformatted)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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