mirror of https://github.com/hwchase17/langchain
mv popular and additional chains to use cases (#8242)
Bagatur
1 year ago
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# Preventing harmful outputs
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One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
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- [Moderation chain](/docs/use_cases/safety/moderation): Explicitly check if any output text is harmful and flag it.
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- [Constitutional chain](/docs/use_cases/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
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---
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sidebar_position: 4
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---
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# Additional
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import DocCardList from "@theme/DocCardList";
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<DocCardList />
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# Dynamically selecting from multiple prompts
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This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.
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import Example from "@snippets/modules/chains/additional/multi_prompt_router.mdx"
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<Example/>
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---
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sidebar_position: 3
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---
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# Popular
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import DocCardList from "@theme/DocCardList";
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<DocCardList />
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label: 'Integrations'
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label: 'How to'
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# Dynamically selecting from multiple retrievers
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# Dynamically select from multiple retrievers
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This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.
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This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.
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# Document QA
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# QA over in-memory documents
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Here we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our [Document chains](/docs/modules/chains/document/).
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Here we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our [Document chains](/docs/modules/chains/document/).
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---
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---
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sidebar_position: 1
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sidebar_position: 1
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---
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---
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# Retrieval QA
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# QA using a Retriever
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This example showcases question answering over an index.
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This example showcases question answering over an index.
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6605e7f7",
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"metadata": {},
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"source": [
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"# Extraction\n",
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"\n",
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"The extraction chain uses the OpenAI `functions` parameter to specify a schema to extract entities from a document. This helps us make sure that the model outputs exactly the schema of entities and properties that we want, with their appropriate types.\n",
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"\n",
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"The extraction chain is to be used when we want to extract several entities with their properties from the same passage (i.e. what people were mentioned in this passage?)"
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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": "34f04daf",
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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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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\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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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import create_extraction_chain, create_extraction_chain_pydantic\n",
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"from langchain.prompts import ChatPromptTemplate"
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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": "a2648974",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ef034ce",
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"metadata": {},
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"source": [
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"## Extracting entities"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78ff9df9",
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"metadata": {},
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"source": [
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"To extract entities, we need to create a schema where we specify all the properties we want to find and the type we expect them to have. We can also specify which of these properties are required and which are optional."
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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": "4ac43eba",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"}"
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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": 5,
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"id": "640bd005",
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"metadata": {},
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"outputs": [],
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"source": [
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"inp = \"\"\"\n",
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"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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" \"\"\""
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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": "64313214",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain(schema, llm)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17c48adb",
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"metadata": {},
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"source": [
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"As we can see, we extracted the required entities and their properties in the required format (it even calculated Claudia's height before returning!)"
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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": 7,
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"id": "cc5436ed",
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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": 7,
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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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"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": "8d51fcdc",
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"metadata": {},
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"source": [
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"## Several entity types"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5813affe",
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"metadata": {},
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"source": [
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"Notice that we are using OpenAI functions under the hood and thus the model can only call one function per request (with one, unique 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": "511b9838",
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"metadata": {},
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"source": [
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"If we want to extract more than one entity type, we need to introduce a little hack - we will define our properties with an included entity type. \n",
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"\n",
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"Following we have an example where we also want to extract dog attributes from the passage. Notice the 'person_' and 'dog_' prefixes we use for each property; this tells the model which entity type the property refers to. In this way, the model can return properties from several entity types in one single 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": 8,
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"id": "cf243a26",
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"metadata": {},
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"outputs": [],
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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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"}"
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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": "52841fb3",
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"metadata": {},
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"outputs": [],
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"source": [
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"inp = \"\"\"\n",
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"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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" \"\"\""
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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": 5,
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"id": "93f904ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain(schema, llm)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb074f7b",
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"metadata": {},
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"source": [
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"People attributes and dog attributes were correctly extracted from the text in the same 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": 6,
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"id": "db3e9e17",
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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": 11,
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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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"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": "0273e0e2",
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"metadata": {},
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"source": [
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"## Unrelated entities"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c07b3480",
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"metadata": {},
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"source": [
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"What if our entities are unrelated? In that case, the model will return the unrelated entities in different dictionaries, allowing us to successfully extract several unrelated entity types in the same call."
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]
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},
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{
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"cell_type": "markdown",
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"id": "01d98af0",
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"metadata": {},
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"source": [
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"Notice that we use `required: []`: we need to 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": 48,
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"id": "e584c993",
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"metadata": {},
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"outputs": [],
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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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"}"
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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": 49,
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"id": "ad6b105f",
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"metadata": {},
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"outputs": [],
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"source": [
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"inp = \"\"\"\n",
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"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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"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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"\"\"\""
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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": 50,
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"id": "6bfe5a33",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain(schema, llm)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "24fe09af",
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"metadata": {},
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"source": [
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"We have each entity in its own separate dictionary, with only the appropriate attributes being returned"
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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": 51,
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"id": "f6e1fd89",
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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": 51,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(inp)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "0ac466d1",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Extra info for an entity"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "d240ffc1",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"What if.. _we don't know what we want?_ More specifically, say we know a few properties we want to extract for a given entity but we also want to know if there's any extra information in the passage. Fortunately, we don't need to structure everything - we can have unstructured extraction as well. \n",
|
|
||||||
"\n",
|
|
||||||
"We can do this by introducing another hack, namely the *extra_info* attribute - let's see an example."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 68,
|
|
||||||
"id": "f19685f6",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"schema = {\n",
|
|
||||||
" \"properties\": {\n",
|
|
||||||
" \"person_name\": {\"type\": \"string\"},\n",
|
|
||||||
" \"person_height\": {\"type\": \"integer\"},\n",
|
|
||||||
" \"person_hair_color\": {\"type\": \"string\"},\n",
|
|
||||||
" \"dog_name\": {\"type\": \"string\"},\n",
|
|
||||||
" \"dog_breed\": {\"type\": \"string\"},\n",
|
|
||||||
" \"dog_extra_info\": {\"type\": \"string\"},\n",
|
|
||||||
" },\n",
|
|
||||||
"}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 81,
|
|
||||||
"id": "200c3477",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"inp = \"\"\"\n",
|
|
||||||
"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",
|
|
||||||
"\n",
|
|
||||||
"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",
|
|
||||||
"\"\"\""
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 82,
|
|
||||||
"id": "ddad7dc6",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = create_extraction_chain(schema, llm)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "e5c0dbbc",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"It is nice to know more about Willow and Milo!"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 83,
|
|
||||||
"id": "c22cfd30",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},\n",
|
|
||||||
" {'person_name': 'Claudia',\n",
|
|
||||||
" 'person_height': 6,\n",
|
|
||||||
" 'person_hair_color': 'brunette'},\n",
|
|
||||||
" {'dog_name': 'Willow',\n",
|
|
||||||
" 'dog_breed': 'German Shepherd',\n",
|
|
||||||
" 'dog_extra_information': 'likes to play with other dogs'},\n",
|
|
||||||
" {'dog_name': 'Milo',\n",
|
|
||||||
" 'dog_breed': 'border collie',\n",
|
|
||||||
" 'dog_extra_information': 'lives close by'}]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 83,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(inp)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "698b4c4d",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Pydantic example"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "6504a6d9",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"We can also use a Pydantic schema to choose the required properties and types and we will set as 'Optional' those that are not strictly required.\n",
|
|
||||||
"\n",
|
|
||||||
"By using the `create_extraction_chain_pydantic` function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. \n",
|
|
||||||
"\n",
|
|
||||||
"In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "6792866b",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from typing import Optional, List\n",
|
|
||||||
"from pydantic import BaseModel, Field"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "36a63761",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"class Properties(BaseModel):\n",
|
|
||||||
" person_name: str\n",
|
|
||||||
" person_height: int\n",
|
|
||||||
" person_hair_color: str\n",
|
|
||||||
" dog_breed: Optional[str]\n",
|
|
||||||
" dog_name: Optional[str]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 9,
|
|
||||||
"id": "8ffd1e57",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"id": "24baa954",
|
|
||||||
"metadata": {
|
|
||||||
"scrolled": false
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"inp = \"\"\"\n",
|
|
||||||
"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",
|
|
||||||
"Alex's dog Frosty is a labrador and likes to play hide and seek.\n",
|
|
||||||
" \"\"\""
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "84e0a241",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"As we can see, we extracted the required entities and their properties in the required format:"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 11,
|
|
||||||
"id": "f771df58",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed='labrador', dog_name='Frosty'),\n",
|
|
||||||
" Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 11,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(inp)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "0df61283",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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
|
|
||||||
}
|
|
||||||
@ -0,0 +1,14 @@
|
|||||||
|
# Code writing
|
||||||
|
|
||||||
|
:::warning
|
||||||
|
All program-writing chains should be treated as *VERY* experimental and should not be used in any environment where sensitive/important data is stored, as there is arbitrary code execution involved in using these.
|
||||||
|
:::
|
||||||
|
|
||||||
|
Much like humans, LLMs are great at writing out programs, but not always great at executing them. For example, they can write down complex mathematical equations far better than they can compute the results. In such cases, it is useful to combine an LLM with a program runtime, so that the LLM converts unstructured text to a program and then a simpler tool (like a calculator) actually executes the program.
|
||||||
|
|
||||||
|
In other cases, only a program can be used to access the desired information (e.g., the contents of a directory on your computer). In such cases it is again useful to let an LLM generate the code and a separate tool to execute it.
|
||||||
|
|
||||||
|
import DocCardList from "@theme/DocCardList";
|
||||||
|
|
||||||
|
<DocCardList />
|
||||||
|
|
||||||
@ -0,0 +1,566 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6605e7f7",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Extraction with OpenAI Functions\n",
|
||||||
|
"\n",
|
||||||
|
"The extraction chain uses the OpenAI `functions` parameter to specify a schema to extract entities from a document. This helps us make sure that the model outputs exactly the schema of entities and properties that we want, with their appropriate types.\n",
|
||||||
|
"\n",
|
||||||
|
"The extraction chain is to be used when we want to extract several entities with their properties from the same passage (i.e. what people were mentioned in this passage?)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"id": "34f04daf",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
|
||||||
|
" warnings.warn(\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from langchain.chat_models import ChatOpenAI\n",
|
||||||
|
"from langchain.chains import create_extraction_chain, create_extraction_chain_pydantic\n",
|
||||||
|
"from langchain.prompts import ChatPromptTemplate"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"id": "a2648974",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5ef034ce",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Extracting entities"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "78ff9df9",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"To extract entities, we need to create a schema where we specify all the properties we want to find and the type we expect them to have. We can also specify which of these properties are required and which are optional."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "4ac43eba",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"schema = {\n",
|
||||||
|
" \"properties\": {\n",
|
||||||
|
" \"name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"height\": {\"type\": \"integer\"},\n",
|
||||||
|
" \"hair_color\": {\"type\": \"string\"},\n",
|
||||||
|
" },\n",
|
||||||
|
" \"required\": [\"name\", \"height\"],\n",
|
||||||
|
"}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"id": "640bd005",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"inp = \"\"\"\n",
|
||||||
|
"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",
|
||||||
|
" \"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"id": "64313214",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"chain = create_extraction_chain(schema, llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "17c48adb",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"As we can see, we extracted the required entities and their properties in the required format (it even calculated Claudia's height before returning!)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"id": "cc5436ed",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"[{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},\n",
|
||||||
|
" {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(inp)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "8d51fcdc",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Several entity types"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5813affe",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Notice that we are using OpenAI functions under the hood and thus the model can only call one function per request (with one, unique schema)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "511b9838",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"If we want to extract more than one entity type, we need to introduce a little hack - we will define our properties with an included entity type. \n",
|
||||||
|
"\n",
|
||||||
|
"Following we have an example where we also want to extract dog attributes from the passage. Notice the 'person_' and 'dog_' prefixes we use for each property; this tells the model which entity type the property refers to. In this way, the model can return properties from several entity types in one single call."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"id": "cf243a26",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"schema = {\n",
|
||||||
|
" \"properties\": {\n",
|
||||||
|
" \"person_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"person_height\": {\"type\": \"integer\"},\n",
|
||||||
|
" \"person_hair_color\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_breed\": {\"type\": \"string\"},\n",
|
||||||
|
" },\n",
|
||||||
|
" \"required\": [\"person_name\", \"person_height\"],\n",
|
||||||
|
"}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "52841fb3",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"inp = \"\"\"\n",
|
||||||
|
"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",
|
||||||
|
"Alex's dog Frosty is a labrador and likes to play hide and seek.\n",
|
||||||
|
" \"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"id": "93f904ab",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"chain = create_extraction_chain(schema, llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "eb074f7b",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"People attributes and dog attributes were correctly extracted from the text in the same call"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"id": "db3e9e17",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"[{'person_name': 'Alex',\n",
|
||||||
|
" 'person_height': 5,\n",
|
||||||
|
" 'person_hair_color': 'blonde',\n",
|
||||||
|
" 'dog_name': 'Frosty',\n",
|
||||||
|
" 'dog_breed': 'labrador'},\n",
|
||||||
|
" {'person_name': 'Claudia',\n",
|
||||||
|
" 'person_height': 6,\n",
|
||||||
|
" 'person_hair_color': 'brunette'}]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 11,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(inp)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "0273e0e2",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Unrelated entities"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "c07b3480",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"What if our entities are unrelated? In that case, the model will return the unrelated entities in different dictionaries, allowing us to successfully extract several unrelated entity types in the same call."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "01d98af0",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Notice that we use `required: []`: we need to allow the model to return **only** person attributes or **only** dog attributes for a single entity (person or dog)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 48,
|
||||||
|
"id": "e584c993",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"schema = {\n",
|
||||||
|
" \"properties\": {\n",
|
||||||
|
" \"person_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"person_height\": {\"type\": \"integer\"},\n",
|
||||||
|
" \"person_hair_color\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_breed\": {\"type\": \"string\"},\n",
|
||||||
|
" },\n",
|
||||||
|
" \"required\": [],\n",
|
||||||
|
"}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 49,
|
||||||
|
"id": "ad6b105f",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"inp = \"\"\"\n",
|
||||||
|
"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",
|
||||||
|
"\n",
|
||||||
|
"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",
|
||||||
|
"\"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 50,
|
||||||
|
"id": "6bfe5a33",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"chain = create_extraction_chain(schema, llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "24fe09af",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"We have each entity in its own separate dictionary, with only the appropriate attributes being returned"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 51,
|
||||||
|
"id": "f6e1fd89",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"[{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},\n",
|
||||||
|
" {'person_name': 'Claudia',\n",
|
||||||
|
" 'person_height': 6,\n",
|
||||||
|
" 'person_hair_color': 'brunette'},\n",
|
||||||
|
" {'dog_name': 'Willow', 'dog_breed': 'German Shepherd'},\n",
|
||||||
|
" {'dog_name': 'Milo', 'dog_breed': 'border collie'}]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 51,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(inp)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "0ac466d1",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Extra info for an entity"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "d240ffc1",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"What if.. _we don't know what we want?_ More specifically, say we know a few properties we want to extract for a given entity but we also want to know if there's any extra information in the passage. Fortunately, we don't need to structure everything - we can have unstructured extraction as well. \n",
|
||||||
|
"\n",
|
||||||
|
"We can do this by introducing another hack, namely the *extra_info* attribute - let's see an example."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 68,
|
||||||
|
"id": "f19685f6",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"schema = {\n",
|
||||||
|
" \"properties\": {\n",
|
||||||
|
" \"person_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"person_height\": {\"type\": \"integer\"},\n",
|
||||||
|
" \"person_hair_color\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_name\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_breed\": {\"type\": \"string\"},\n",
|
||||||
|
" \"dog_extra_info\": {\"type\": \"string\"},\n",
|
||||||
|
" },\n",
|
||||||
|
"}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 81,
|
||||||
|
"id": "200c3477",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"inp = \"\"\"\n",
|
||||||
|
"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",
|
||||||
|
"\n",
|
||||||
|
"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",
|
||||||
|
"\"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 82,
|
||||||
|
"id": "ddad7dc6",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"chain = create_extraction_chain(schema, llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "e5c0dbbc",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"It is nice to know more about Willow and Milo!"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 83,
|
||||||
|
"id": "c22cfd30",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"[{'person_name': 'Alex', 'person_height': 5, 'person_hair_color': 'blonde'},\n",
|
||||||
|
" {'person_name': 'Claudia',\n",
|
||||||
|
" 'person_height': 6,\n",
|
||||||
|
" 'person_hair_color': 'brunette'},\n",
|
||||||
|
" {'dog_name': 'Willow',\n",
|
||||||
|
" 'dog_breed': 'German Shepherd',\n",
|
||||||
|
" 'dog_extra_information': 'likes to play with other dogs'},\n",
|
||||||
|
" {'dog_name': 'Milo',\n",
|
||||||
|
" 'dog_breed': 'border collie',\n",
|
||||||
|
" 'dog_extra_information': 'lives close by'}]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 83,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(inp)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "698b4c4d",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Pydantic example"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6504a6d9",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"We can also use a Pydantic schema to choose the required properties and types and we will set as 'Optional' those that are not strictly required.\n",
|
||||||
|
"\n",
|
||||||
|
"By using the `create_extraction_chain_pydantic` function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. \n",
|
||||||
|
"\n",
|
||||||
|
"In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"id": "6792866b",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from typing import Optional, List\n",
|
||||||
|
"from pydantic import BaseModel, Field"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"id": "36a63761",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"class Properties(BaseModel):\n",
|
||||||
|
" person_name: str\n",
|
||||||
|
" person_height: int\n",
|
||||||
|
" person_hair_color: str\n",
|
||||||
|
" dog_breed: Optional[str]\n",
|
||||||
|
" dog_name: Optional[str]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"id": "8ffd1e57",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"id": "24baa954",
|
||||||
|
"metadata": {
|
||||||
|
"scrolled": false
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"inp = \"\"\"\n",
|
||||||
|
"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",
|
||||||
|
"Alex's dog Frosty is a labrador and likes to play hide and seek.\n",
|
||||||
|
" \"\"\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "84e0a241",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"As we can see, we extracted the required entities and their properties in the required format:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 11,
|
||||||
|
"id": "f771df58",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"[Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed='labrador', dog_name='Frosty'),\n",
|
||||||
|
" Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 11,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain.run(inp)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
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"id": "0df61283",
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"metadata": {},
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|
"outputs": [],
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"source": []
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}
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|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
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|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -0,0 +1,7 @@
|
|||||||
|
# Analyzing graph data
|
||||||
|
|
||||||
|
Graph databases give us a powerful way to represent and query real-world relationships. There are a number of chains that make it easy to use LLMs to interact with various graph DBs.
|
||||||
|
|
||||||
|
import DocCardList from "@theme/DocCardList";
|
||||||
|
|
||||||
|
<DocCardList />
|
||||||
@ -0,0 +1 @@
|
|||||||
|
label: 'Integration-specific'
|
||||||
@ -0,0 +1,8 @@
|
|||||||
|
# Self-checking
|
||||||
|
|
||||||
|
One of the main issues with using LLMs is that they can often hallucinate and make false claims. One of the surprisingly effective ways to remediate this is to use the LLM itself to check its own answers.
|
||||||
|
|
||||||
|
import DocCardList from "@theme/DocCardList";
|
||||||
|
|
||||||
|
<DocCardList />
|
||||||
|
|
||||||
@ -1,107 +0,0 @@
|
|||||||
```python
|
|
||||||
from langchain.chains.router import MultiPromptChain
|
|
||||||
from langchain.llms import OpenAI
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
physics_template = """You are a very smart physics professor. \
|
|
||||||
You are great at answering questions about physics in a concise and easy to understand manner. \
|
|
||||||
When you don't know the answer to a question you admit that you don't know.
|
|
||||||
|
|
||||||
Here is a question:
|
|
||||||
{input}"""
|
|
||||||
|
|
||||||
|
|
||||||
math_template = """You are a very good mathematician. You are great at answering math questions. \
|
|
||||||
You are so good because you are able to break down hard problems into their component parts, \
|
|
||||||
answer the component parts, and then put them together to answer the broader question.
|
|
||||||
|
|
||||||
Here is a question:
|
|
||||||
{input}"""
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
prompt_infos = [
|
|
||||||
{
|
|
||||||
"name": "physics",
|
|
||||||
"description": "Good for answering questions about physics",
|
|
||||||
"prompt_template": physics_template
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "math",
|
|
||||||
"description": "Good for answering math questions",
|
|
||||||
"prompt_template": math_template
|
|
||||||
}
|
|
||||||
]
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
chain = MultiPromptChain.from_prompts(OpenAI(), prompt_infos, verbose=True)
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
print(chain.run("What is black body radiation?"))
|
|
||||||
```
|
|
||||||
|
|
||||||
<CodeOutputBlock lang="python">
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
> Entering new MultiPromptChain chain...
|
|
||||||
physics: {'input': 'What is black body radiation?'}
|
|
||||||
> Finished chain.
|
|
||||||
|
|
||||||
|
|
||||||
Black body radiation is the emission of electromagnetic radiation from a body due to its temperature. It is a type of thermal radiation that is emitted from the surface of all objects that are at a temperature above absolute zero. It is a spectrum of radiation that is influenced by the temperature of the body and is independent of the composition of the emitting material.
|
|
||||||
```
|
|
||||||
|
|
||||||
</CodeOutputBlock>
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
print(chain.run("What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"))
|
|
||||||
```
|
|
||||||
|
|
||||||
<CodeOutputBlock lang="python">
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
> Entering new MultiPromptChain chain...
|
|
||||||
math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
|
|
||||||
> Finished chain.
|
|
||||||
?
|
|
||||||
|
|
||||||
The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. To solve this problem, we can break down the question into two parts: finding the first prime number greater than 40, and then finding a number that is divisible by 3.
|
|
||||||
|
|
||||||
The first step is to find the first prime number greater than 40. A prime number is a number that is only divisible by 1 and itself. The next prime number after 40 is 41.
|
|
||||||
|
|
||||||
The second step is to find a number that is divisible by 3. To do this, we can add 1 to 41, which gives us 42. Now, we can check if 42 is divisible by 3. 42 divided by 3 is 14, so 42 is divisible by 3.
|
|
||||||
|
|
||||||
Therefore, the answer to the question is 43.
|
|
||||||
```
|
|
||||||
|
|
||||||
</CodeOutputBlock>
|
|
||||||
|
|
||||||
|
|
||||||
```python
|
|
||||||
print(chain.run("What is the name of the type of cloud that rins"))
|
|
||||||
```
|
|
||||||
|
|
||||||
<CodeOutputBlock lang="python">
|
|
||||||
|
|
||||||
```
|
|
||||||
|
|
||||||
|
|
||||||
> Entering new MultiPromptChain chain...
|
|
||||||
None: {'input': 'What is the name of the type of cloud that rains?'}
|
|
||||||
> Finished chain.
|
|
||||||
The type of cloud that typically produces rain is called a cumulonimbus cloud. This type of cloud is characterized by its large vertical extent and can produce thunderstorms and heavy precipitation. Is there anything else you'd like to know?
|
|
||||||
```
|
|
||||||
|
|
||||||
</CodeOutputBlock>
|
|
||||||
Loading…
Reference in New Issue