{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 2, "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": 3, "metadata": {}, "outputs": [], "source": [ "misformatted = \"{'name': \\\"Tom Hanks\\\", \\\"film_names\\\": [\\\"Forrest Gump\\\"]}\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# 将报解析错误。\n", "# parser.parse(misformatted)" ] }, { "cell_type": "code", "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Actor(name='Tom Hanks', film_names=['Forrest Gump'])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_parser.parse(misformatted)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan'])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test_str = '{\"name\": \"Tom Hanks\", \"film_names\": [\"Forrest Gump\"]}'\n", "test_str = '{\"name\": \"Tom Hanks\"}'\n", "# parser.parse(test_str)\n", "\n", "new_parser.parse(test_str)" ] } ], "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 }