add ResponseSchema and fix output。

parser_fix_output.ipynb

@@ -0,0 +1,105 @@
+{
+ "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
+}

parser_reponse_schema.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers import StructuredOutputParser, ResponseSchema\n",
+    "from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_schemas = [\n",
+    "    ResponseSchema(name=\"answer\", description=\"answer to the user's question\"),\n",
+    "    ResponseSchema(name=\"source\", description=\"source used to answer the user's question, should be a website.\")\n",
+    "]\n",
+    "output_parser = StructuredOutputParser.from_response_schemas(response_schemas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "format_instructions = output_parser.get_format_instructions()\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"answer the users question as best as possible.\\n{format_instructions}\\n{question}\",\n",
+    "    input_variables=[\"question\"],\n",
+    "    partial_variables={\"format_instructions\": format_instructions}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = OpenAI(temperature=0, n=2, best_of=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_input = prompt.format_prompt(question=\"周杰伦有哪些歌？\")\n",
+    "output = model(_input.to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# output_parser.parse(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': '周杰伦的歌曲有《稻香》、《发如雪》、《青花瓷》、《七里香》、《简单爱》、《等你下课》、《菊花台》、《夜曲》、《不能说的秘密》、《回到过去》等。',\n",
+       " 'source': '百度百科 https://baike.baidu.com/item/%E5%91%A8%E6%9D%B0%E4%BC%A6/109983'}"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.output_parsers import OutputFixingParser\n",
+    "new_parser = OutputFixingParser.from_llm(parser=output_parser, llm=OpenAI())\n",
+    "new_parser.parse(output)"
+   ]
+  }
+ ],
+ "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
+}