langchain/docs/modules/prompts/output_parsers/examples/structured.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "91871002",
"metadata": {},
"source": [
"# Structured Output Parser\n",
"\n",
"While the Pydantic/JSON parser is more powerful, we initially experimented data structures having text fields only."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b492997a",
"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": "markdown",
"id": "09473dce",
"metadata": {},
"source": [
"Here we define the response schema we want to receive."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "432ac44a",
"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": "markdown",
"id": "7b92ce96",
"metadata": {},
"source": [
"We now get a string that contains instructions for how the response should be formatted, and we then insert that into our prompt."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "593cfc25",
"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": "markdown",
"id": "0943e783",
"metadata": {},
"source": [
"We can now use this to format a prompt to send to the language model, and then parse the returned result."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "106f1ba6",
"metadata": {},
"outputs": [],
"source": [
"model = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "86d9d24f",
"metadata": {},
"outputs": [],
"source": [
"_input = prompt.format_prompt(question=\"what's the capital of france\")\n",
"output = model(_input.to_string())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "956bdc99",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_parser.parse(output)"
]
},
{
"cell_type": "markdown",
"id": "da639285",
"metadata": {},
"source": [
"And here's an example of using this in a chat model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8f483d7d",
"metadata": {},
"outputs": [],
"source": [
"chat_model = ChatOpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f761cbf1",
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate(\n",
" messages=[\n",
" HumanMessagePromptTemplate.from_template(\"answer the users question as best as possible.\\n{format_instructions}\\n{question}\") \n",
" ],\n",
" input_variables=[\"question\"],\n",
" partial_variables={\"format_instructions\": format_instructions}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "edd73ae3",
"metadata": {},
"outputs": [],
"source": [
"_input = prompt.format_prompt(question=\"what's the capital of france\")\n",
"output = chat_model(_input.to_messages())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a3c8b91e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_parser.parse(output.content)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}