mirror of
https://github.com/hwchase17/langchain
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607 lines
19 KiB
Plaintext
607 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "df29b30a-fd27-4e08-8269-870df5631f9e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 1\n",
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"title: Extraction\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b84edb4e",
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"metadata": {},
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"source": [
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/extraction.ipynb)\n",
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"\n",
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"## Use case\n",
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"\n",
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"Getting structured output from raw LLM generations is hard.\n",
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"\n",
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"For example, suppose you need the model output formatted with a specific schema for:\n",
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"\n",
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"- Extracting a structured row to insert into a database \n",
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"- Extracting API parameters\n",
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"- Extracting different parts of a user query (e.g., for semantic vs keyword search)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "178dbc59",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "97f474d4",
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"metadata": {},
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"source": [
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"## Overview \n",
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"\n",
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"There are two primary approaches for this:\n",
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"\n",
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"- `Functions`: Some LLMs can call [functions](https://openai.com/blog/function-calling-and-other-api-updates) to extract arbitrary entities from LLM responses.\n",
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"\n",
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"- `Parsing`: [Output parsers](/docs/modules/model_io/output_parsers/) are classes that structure LLM responses. \n",
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"\n",
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"Only some LLMs support functions (e.g., OpenAI), and they are more general than parsers. \n",
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"\n",
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"Parsers extract precisely what is enumerated in a provided schema (e.g., specific attributes of a person).\n",
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"\n",
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"Functions can infer things beyond of a provided schema (e.g., attributes about a person that you did not ask for)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "25d89f21",
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"metadata": {},
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"source": [
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"## Quickstart\n",
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"\n",
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"OpenAI functions are one way to get started with extraction.\n",
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"\n",
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"Define a schema that specifies the properties we want to extract from the LLM output.\n",
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"\n",
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"Then, we can use `create_extraction_chain` to extract our desired schema using an OpenAI function call."
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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": "3f5ec7a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install langchain openai \n",
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"\n",
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"# Set env var OPENAI_API_KEY or load from a .env file:\n",
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"# import dotenv\n",
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"# dotenv.load_dotenv()"
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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": 12,
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"id": "3e017ba0",
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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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"[{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},\n",
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" {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]"
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]
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},
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"execution_count": 12,
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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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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import create_extraction_chain\n",
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"\n",
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"# Schema\n",
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"schema = {\n",
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" \"properties\": {\n",
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" \"name\": {\"type\": \"string\"},\n",
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" \"height\": {\"type\": \"integer\"},\n",
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" \"hair_color\": {\"type\": \"string\"},\n",
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" },\n",
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" \"required\": [\"name\", \"height\"],\n",
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"}\n",
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"\n",
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"# Input \n",
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"inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\"\"\"\n",
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"\n",
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"# Run chain\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
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"chain = create_extraction_chain(schema, llm)\n",
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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f7eb826",
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"metadata": {},
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"source": [
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"## Option 1: OpenAI functions\n",
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"\n",
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"### Looking under the hood\n",
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"\n",
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"Let's dig into what is happening when we call `create_extraction_chain`.\n",
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"\n",
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"The [LangSmith trace](https://smith.langchain.com/public/72bc3205-7743-4ca6-929a-966a9d4c2a77/r) shows that we call the function `information_extraction` on the input string, `inp`.\n",
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"\n",
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"\n",
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"\n",
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"This `information_extraction` function is defined [here](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/openai_functions/extraction.py) and returns a dict.\n",
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"\n",
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"We can see the `dict` in the model output:\n",
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"```\n",
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" {\n",
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" \"info\": [\n",
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" {\n",
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" \"name\": \"Alex\",\n",
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" \"height\": 5,\n",
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" \"hair_color\": \"blonde\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Claudia\",\n",
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" \"height\": 6,\n",
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" \"hair_color\": \"brunette\"\n",
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" }\n",
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" ]\n",
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" }\n",
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"```\n",
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"\n",
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"The `create_extraction_chain` then parses the raw LLM output for us using [`JsonKeyOutputFunctionsParser`](https://github.com/langchain-ai/langchain/blob/f81e613086d211327b67b0fb591fd4d5f9a85860/libs/langchain/langchain/chains/openai_functions/extraction.py#L62).\n",
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"\n",
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"This results in the list of JSON objects returned by the chain above:\n",
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"```\n",
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"[{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},\n",
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" {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]\n",
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" ```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dcb03138",
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"metadata": {},
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"source": [
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"### Multiple entity types\n",
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"\n",
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"We can extend this further.\n",
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"\n",
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"Let's say we want to differentiate between dogs and people.\n",
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"\n",
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"We can add `person_` and `dog_` prefixes for each property"
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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": "01eae733",
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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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"[{'person_name': 'Alex',\n",
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" 'person_height': 5,\n",
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" 'person_hair_color': 'blonde',\n",
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" 'dog_name': 'Frosty',\n",
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" 'dog_breed': 'labrador'},\n",
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" {'person_name': 'Claudia',\n",
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" 'person_height': 6,\n",
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" 'person_hair_color': 'brunette'}]"
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]
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},
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"execution_count": 8,
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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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"schema = {\n",
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" \"properties\": {\n",
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" \"person_name\": {\"type\": \"string\"},\n",
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" \"person_height\": {\"type\": \"integer\"},\n",
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" \"person_hair_color\": {\"type\": \"string\"},\n",
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" \"dog_name\": {\"type\": \"string\"},\n",
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" \"dog_breed\": {\"type\": \"string\"},\n",
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" },\n",
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" \"required\": [\"person_name\", \"person_height\"],\n",
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"}\n",
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"\n",
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"chain = create_extraction_chain(schema, llm)\n",
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"\n",
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"inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
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"Alex's dog Frosty is a labrador and likes to play hide and seek.\"\"\"\n",
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"\n",
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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f205905c",
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"metadata": {},
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"source": [
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"### Unrelated entities\n",
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"\n",
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"If we use `required: []`, we allow the model to return **only** person attributes or **only** dog attributes for a single entity (person or dog)."
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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": 14,
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"id": "6ff4ac7e",
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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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"[{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},\n",
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" {'person_name': 'Claudia',\n",
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" 'person_height': 6,\n",
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" 'person_hair_color': 'brunette'},\n",
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" {'dog_name': 'Willow', 'dog_breed': 'German Shepherd'},\n",
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" {'dog_name': 'Milo', 'dog_breed': 'border collie'}]"
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]
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},
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"execution_count": 14,
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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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"schema = {\n",
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" \"properties\": {\n",
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" \"person_name\": {\"type\": \"string\"},\n",
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" \"person_height\": {\"type\": \"integer\"},\n",
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" \"person_hair_color\": {\"type\": \"string\"},\n",
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" \"dog_name\": {\"type\": \"string\"},\n",
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" \"dog_breed\": {\"type\": \"string\"},\n",
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" },\n",
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" \"required\": [],\n",
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"}\n",
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"\n",
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"chain = create_extraction_chain(schema, llm)\n",
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"\n",
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"inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
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"Willow is a German Shepherd that likes to play with other dogs and can always be found playing with Milo, a border collie that lives close by.\"\"\"\n",
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"\n",
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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "34f3b958",
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"metadata": {},
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"source": [
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"### Extra information\n",
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"\n",
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"The power of functions (relative to using parsers alone) lies in the ability to perform sematic extraction.\n",
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"\n",
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"In particular, `we can ask for things that are not explictly enumerated in the schema`.\n",
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"\n",
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"Suppose we want unspecified additional information about dogs. \n",
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"\n",
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"We can use add a placeholder for unstructured extraction, `dog_extra_info`."
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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": "40c7b26f",
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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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"[{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},\n",
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" {'person_name': 'Claudia',\n",
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" 'person_height': 6,\n",
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" 'person_hair_color': 'brunette'},\n",
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" {'dog_name': 'Willow',\n",
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" 'dog_breed': 'German Shepherd',\n",
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" 'dog_extra_info': 'likes to play with other dogs'},\n",
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" {'dog_name': 'Milo',\n",
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" 'dog_breed': 'border collie',\n",
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" 'dog_extra_info': 'lives close by'}]"
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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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"schema = {\n",
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" \"properties\": {\n",
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" \"person_name\": {\"type\": \"string\"},\n",
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" \"person_height\": {\"type\": \"integer\"},\n",
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" \"person_hair_color\": {\"type\": \"string\"},\n",
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" \"dog_name\": {\"type\": \"string\"},\n",
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" \"dog_breed\": {\"type\": \"string\"},\n",
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" \"dog_extra_info\": {\"type\": \"string\"},\n",
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" },\n",
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"}\n",
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"\n",
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"chain = create_extraction_chain(schema, llm)\n",
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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a949c60",
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"metadata": {},
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"source": [
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"This gives us additional information about the dogs."
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]
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},
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{
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"cell_type": "markdown",
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"id": "bf71ddce",
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"metadata": {},
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"source": [
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"### Pydantic \n",
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"\n",
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"Pydantic is a data validation and settings management library for Python. \n",
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"\n",
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"It allows you to create data classes with attributes that are automatically validated when you instantiate an object.\n",
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"\n",
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"Lets define a class with attributes annotated with types."
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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": 4,
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"id": "d36a743b",
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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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"[Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed=None, dog_name=None),\n",
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" Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)]"
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]
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},
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"execution_count": 4,
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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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"from typing import Optional, List\n",
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"from pydantic import BaseModel, Field\n",
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"from langchain.chains import create_extraction_chain_pydantic\n",
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"\n",
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"# Pydantic data class\n",
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"class Properties(BaseModel):\n",
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" person_name: str\n",
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" person_height: int\n",
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" person_hair_color: str\n",
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" dog_breed: Optional[str]\n",
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" dog_name: Optional[str]\n",
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" \n",
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"# Extraction\n",
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"chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)\n",
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"\n",
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"# Run \n",
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"inp = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\"\"\"\n",
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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07a0351a",
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"metadata": {},
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"source": [
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"As we can see from the [trace](https://smith.langchain.com/public/fed50ae6-26bb-4235-a254-e0b7a229d10f/r), we use the function `information_extraction`, as above, with the Pydantic schema. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "cbd9f121",
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"metadata": {},
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"source": [
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"## Option 2: Parsing\n",
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"\n",
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"[Output parsers](/docs/modules/model_io/output_parsers/) are classes that help structure language model responses. \n",
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"\n",
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"As shown above, they are used to parse the output of the OpenAI function calls in `create_extraction_chain`.\n",
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"\n",
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"But, they can be used independent of functions.\n",
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"\n",
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"### Pydantic\n",
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"\n",
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"Just as a above, let's parse a generation based on a Pydantic data class."
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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": "64650362",
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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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"People(people=[Person(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed=None, dog_name=None), Person(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)])"
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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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"from typing import Sequence\n",
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"from langchain.prompts import (\n",
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" PromptTemplate,\n",
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" ChatPromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.llms import OpenAI\n",
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"from pydantic import BaseModel, Field, validator\n",
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"from langchain.output_parsers import PydanticOutputParser\n",
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"\n",
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"class Person(BaseModel):\n",
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" person_name: str\n",
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" person_height: int\n",
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" person_hair_color: str\n",
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" dog_breed: Optional[str]\n",
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" dog_name: Optional[str]\n",
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"\n",
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"class People(BaseModel):\n",
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" \"\"\"Identifying information about all people in a text.\"\"\"\n",
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" people: Sequence[Person]\n",
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"\n",
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" \n",
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"# Run \n",
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"query = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\"\"\"\n",
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"\n",
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"# Set up a parser + inject instructions into the prompt template.\n",
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"parser = PydanticOutputParser(pydantic_object=People)\n",
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"\n",
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"# Prompt\n",
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"prompt = PromptTemplate(\n",
|
|
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
|
|
" input_variables=[\"query\"],\n",
|
|
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
|
|
")\n",
|
|
"\n",
|
|
"# Run\n",
|
|
"_input = prompt.format_prompt(query=query)\n",
|
|
"model = OpenAI(temperature=0)\n",
|
|
"output = model(_input.to_string())\n",
|
|
"parser.parse(output)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "826899df",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can see from the [LangSmith trace](https://smith.langchain.com/public/8e3aa858-467e-46a5-aa49-5db65f0a2b9a/r) that we get the same output as above.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"We can see that we provide a two-shot prompt in order to instruct the LLM to output in our desired format.\n",
|
|
"\n",
|
|
"And, we need to do a bit more work:\n",
|
|
"\n",
|
|
"* Define a class that holds multiple instances of `Person`\n",
|
|
"* Explicty parse the output of the LLM to the Pydantic class\n",
|
|
"\n",
|
|
"We can see this for other cases, too."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "837c350e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain.prompts import (\n",
|
|
" PromptTemplate,\n",
|
|
" ChatPromptTemplate,\n",
|
|
" HumanMessagePromptTemplate,\n",
|
|
")\n",
|
|
"from langchain.llms import OpenAI\n",
|
|
"from pydantic import BaseModel, Field, validator\n",
|
|
"from langchain.output_parsers import PydanticOutputParser\n",
|
|
"\n",
|
|
"# Define your desired data structure.\n",
|
|
"class Joke(BaseModel):\n",
|
|
" setup: str = Field(description=\"question to set up a joke\")\n",
|
|
" punchline: str = Field(description=\"answer to resolve the joke\")\n",
|
|
"\n",
|
|
" # You can add custom validation logic easily with Pydantic.\n",
|
|
" @validator(\"setup\")\n",
|
|
" def question_ends_with_question_mark(cls, field):\n",
|
|
" if field[-1] != \"?\":\n",
|
|
" raise ValueError(\"Badly formed question!\")\n",
|
|
" return field\n",
|
|
"\n",
|
|
"# And a query intented to prompt a language model to populate the data structure.\n",
|
|
"joke_query = \"Tell me a joke.\"\n",
|
|
"\n",
|
|
"# Set up a parser + inject instructions into the prompt template.\n",
|
|
"parser = PydanticOutputParser(pydantic_object=Joke)\n",
|
|
"\n",
|
|
"# Prompt\n",
|
|
"prompt = PromptTemplate(\n",
|
|
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
|
|
" input_variables=[\"query\"],\n",
|
|
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
|
|
")\n",
|
|
"\n",
|
|
"# Run\n",
|
|
"_input = prompt.format_prompt(query=joke_query)\n",
|
|
"model = OpenAI(temperature=0)\n",
|
|
"output = model(_input.to_string())\n",
|
|
"parser.parse(output)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d3601bde",
|
|
"metadata": {},
|
|
"source": [
|
|
"As we can see, we get an output of the `Joke` class, which respects our originally desired schema: 'setup' and 'punchline'.\n",
|
|
"\n",
|
|
"We can look at the [LangSmith trace](https://smith.langchain.com/public/69f11d41-41be-4319-93b0-6d0eda66e969/r) to see exactly what is going on under the hood.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Going deeper\n",
|
|
"\n",
|
|
"* The [output parser](/docs/modules/model_io/output_parsers/) documentation includes various parser examples for specific types (e.g., lists, datetimne, enum, etc). \n",
|
|
"* [JSONFormer](/docs/integrations/llms/jsonformer_experimental) offers another way for structured decoding of a subset of the JSON Schema.\n",
|
|
"* [Kor](https://eyurtsev.github.io/kor/) is another library for extraction where schema and examples can be provided to the LLM."
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|