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720f6dbaac
**Description** Adds new output parser, this time enabling the output of LLM to be of an XML format. Seems to be particularly useful together with Claude model. Addresses [issue 9820](https://github.com/langchain-ai/langchain/issues/9820). **Twitter handle** @deepsense_ai @matt_wosinski
359 lines
10 KiB
Plaintext
359 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "181b5b6d",
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"metadata": {},
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"source": [
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"# XML parser\n",
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"This output parser allows users to obtain results from LLM in the popular XML format. \n",
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"\n",
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"Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed XML. \n",
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"\n",
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"In the following example we use Claude model (https://docs.anthropic.com/claude/docs) which works really well with XML tags."
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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": "3b10fc55",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.llms import Anthropic\n",
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"from langchain.output_parsers import XMLOutputParser"
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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": "909161d1",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mateusz/Documents/Projects/langchain/libs/langchain/langchain/llms/anthropic.py:170: UserWarning: This Anthropic LLM is deprecated. Please use `from langchain.chat_models import ChatAnthropic` instead\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"model = Anthropic(model=\"claude-2\", max_tokens_to_sample=512, temperature=0.1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "da312f86-0d2a-4aef-a09d-1e72bd0ea9b1",
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"metadata": {},
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"source": [
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"Let's start with the simple request to the model."
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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": "b03785af-69fc-40a1-a1be-c04ed6fade70",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Here is the shortened filmography for Tom Hanks enclosed in <movie> tags:\n",
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"\n",
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"<movie>Splash (1984)</movie>\n",
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"<movie>Big (1988)</movie> \n",
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"<movie>A League of Their Own (1992)</movie>\n",
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"<movie>Sleepless in Seattle (1993)</movie> \n",
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"<movie>Forrest Gump (1994)</movie>\n",
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"<movie>Apollo 13 (1995)</movie>\n",
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"<movie>Toy Story (1995)</movie>\n",
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"<movie>Saving Private Ryan (1998)</movie>\n",
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"<movie>Cast Away (2000)</movie>\n",
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"<movie>The Da Vinci Code (2006)</movie>\n",
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"<movie>Toy Story 3 (2010)</movie>\n",
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"<movie>Captain Phillips (2013)</movie>\n",
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"<movie>Bridge of Spies (2015)</movie>\n",
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"<movie>Toy Story 4 (2019)</movie>\n"
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]
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}
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],
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"source": [
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"actor_query = \"Generate the shortened filmography for Tom Hanks.\"\n",
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"output = model(\n",
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" f\"\"\"\n",
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"\n",
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"Human:\n",
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"{actor_query}\n",
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"Please enclose the movies in <movie></movie> tags\n",
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"Assistant:\n",
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"\"\"\"\n",
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")\n",
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"print(output)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4db65781-3d54-4ba6-ae26-5b4ead47a4c8",
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"metadata": {},
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"source": [
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"Now we will use the XMLOutputParser in order to get the structured output."
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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": "87ba8d11",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"<filmography>\n",
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" <movie>\n",
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" <title>Splash</title>\n",
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" <year>1984</year>\n",
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" </movie>\n",
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" \n",
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" <movie>\n",
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" <title>Big</title> \n",
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" <year>1988</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>A League of Their Own</title>\n",
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" <year>1992</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Sleepless in Seattle</title>\n",
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" <year>1993</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Forrest Gump</title>\n",
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" <year>1994</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Toy Story</title>\n",
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" <year>1995</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Apollo 13</title>\n",
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" <year>1995</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Saving Private Ryan</title>\n",
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" <year>1998</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Cast Away</title>\n",
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" <year>2000</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Catch Me If You Can</title>\n",
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" <year>2002</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>The Polar Express</title>\n",
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" <year>2004</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Charlie Wilson's War</title>\n",
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" <year>2007</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Toy Story 3</title>\n",
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" <year>2010</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Captain Phillips</title>\n",
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" <year>2013</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>Bridge of Spies</title>\n",
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" <year>2015</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>The Post</title>\n",
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" <year>2017</year>\n",
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" </movie>\n",
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"\n",
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" <movie>\n",
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" <title>A Beautiful Day in the Neighborhood</title> \n",
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" <year>2019</year>\n",
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" </movie>\n",
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"</filmography>\n"
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]
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}
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],
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"source": [
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"parser = XMLOutputParser()\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" template=\"\"\"\n",
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" \n",
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" Human:\n",
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" {query}\n",
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" {format_instructions}\n",
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" Assistant:\"\"\",\n",
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" input_variables=[\"query\"],\n",
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" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
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")\n",
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"\n",
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"_input = prompt.format_prompt(query=actor_query)\n",
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"\n",
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"output = model(_input.to_string())\n",
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"print(output)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c4c47ee",
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"metadata": {},
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"source": [
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"And here parsed output is shown:"
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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": "4c864dc9",
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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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"{'filmography': [{'movie': [{'title': 'Splash'}, {'year': '1984'}]},\n",
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" {'movie': [{'title': 'Big'}, {'year': '1988'}]},\n",
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" {'movie': [{'title': 'A League of Their Own'}, {'year': '1992'}]},\n",
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" {'movie': [{'title': 'Sleepless in Seattle'}, {'year': '1993'}]},\n",
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" {'movie': [{'title': 'Forrest Gump'}, {'year': '1994'}]},\n",
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" {'movie': [{'title': 'Toy Story'}, {'year': '1995'}]},\n",
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" {'movie': [{'title': 'Apollo 13'}, {'year': '1995'}]},\n",
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" {'movie': [{'title': 'Saving Private Ryan'}, {'year': '1998'}]},\n",
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" {'movie': [{'title': 'Cast Away'}, {'year': '2000'}]},\n",
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" {'movie': [{'title': 'Catch Me If You Can'}, {'year': '2002'}]},\n",
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" {'movie': [{'title': 'The Polar Express'}, {'year': '2004'}]},\n",
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" {'movie': [{'title': \"Charlie Wilson's War\"}, {'year': '2007'}]},\n",
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" {'movie': [{'title': 'Toy Story 3'}, {'year': '2010'}]},\n",
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" {'movie': [{'title': 'Captain Phillips'}, {'year': '2013'}]},\n",
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" {'movie': [{'title': 'Bridge of Spies'}, {'year': '2015'}]},\n",
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" {'movie': [{'title': 'The Post'}, {'year': '2017'}]},\n",
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" {'movie': [{'title': 'A Beautiful Day in the Neighborhood'},\n",
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" {'year': '2019'}]}]}"
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]
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},
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"execution_count": 9,
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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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"parser.parse(output)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "327f5479-77e0-4549-8393-2cd7a286d491",
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"metadata": {},
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"source": [
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"Finally, let's add some tags to tailor the output to our needs."
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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": "b722a235",
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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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"{'movies': [{'actor': [{'name': 'Tom Hanks'},\n",
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" {'film': [{'name': 'Splash'}, {'genre': 'Comedy'}]},\n",
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" {'film': [{'name': 'Big'}, {'genre': 'Comedy'}]},\n",
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" {'film': [{'name': 'A League of Their Own'}, {'genre': 'Drama'}]},\n",
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" {'film': [{'name': 'Sleepless in Seattle'}, {'genre': 'Romance'}]},\n",
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" {'film': [{'name': 'Forrest Gump'}, {'genre': 'Drama'}]},\n",
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" {'film': [{'name': 'Toy Story'}, {'genre': 'Animation'}]},\n",
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" {'film': [{'name': 'Apollo 13'}, {'genre': 'Drama'}]},\n",
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" {'film': [{'name': 'Saving Private Ryan'}, {'genre': 'War'}]},\n",
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" {'film': [{'name': 'Cast Away'}, {'genre': 'Adventure'}]},\n",
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" {'film': [{'name': 'The Green Mile'}, {'genre': 'Drama'}]}]}]}"
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]
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},
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"execution_count": 10,
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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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"parser = XMLOutputParser(tags=[\"movies\", \"actor\", \"film\", \"name\", \"genre\"])\n",
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"prompt = PromptTemplate(\n",
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" template=\"\"\"\n",
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" \n",
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" Human:\n",
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" {query}\n",
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" {format_instructions}\n",
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" Assistant:\"\"\",\n",
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" input_variables=[\"query\"],\n",
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" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
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")\n",
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"\n",
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"\n",
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"_input = prompt.format_prompt(query=actor_query)\n",
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"\n",
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"output = model(_input.to_string())\n",
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"\n",
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"parser.parse(output)"
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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": null,
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"id": "808a5df5-b11e-42a0-bd7a-6b95ca0c3eba",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.9.16"
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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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