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How to deal with high cardinality categorical variables

Prerequisites

This guide assumes familiarity with the following:

High cardinality data refers to columns in a dataset that contain a large number of unique values. This guide demonstrates some techniques for dealing with these inputs.

For example, you may want to do query analysis to create a filter on a categorical column. One of the difficulties here is that you usually need to specify the EXACT categorical value. The issue is you need to make sure the LLM generates that categorical value exactly. This can be done relatively easy with prompting when there are only a few values that are valid. When there are a high number of valid values then it becomes more difficult, as those values may not fit in the LLM context, or (if they do) there may be too many for the LLM to properly attend to.

In this notebook we take a look at how to approach this.

Setup

Install dependencies

yarn add @langchain/community zod @faker-js/faker

Set environment variables

# Optional, use LangSmith for best-in-class observability
LANGSMITH_API_KEY=your-api-key
LANGCHAIN_TRACING_V2=true

Set up data

We will generate a bunch of fake names

import { faker } from "@faker-js/faker";

const names = Array.from({ length: 10000 }, () => faker.person.fullName());

Let’s look at some of the names

names[0];
"Rolando Wilkinson"
names[567];
"Homer Harber"

Query Analysis

We can now set up a baseline query analysis

import { z } from "zod";

const searchSchema = z.object({
query: z.string(),
author: z.string(),
});

Pick your chat model:

Install dependencies

yarn add @langchain/openai 

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
model: "gpt-3.5-turbo",
temperature: 0
});
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnablePassthrough,
RunnableSequence,
} from "@langchain/core/runnables";

const system = `Generate a relevant search query for a library system`;
const prompt = ChatPromptTemplate.fromMessages([
["system", system],
["human", "{question}"],
]);
const llmWithTools = llm.withStructuredOutput(searchSchema, {
name: "Search",
});
const queryAnalyzer = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
llmWithTools,
]);

We can see that if we spell the name exactly correctly, it knows how to handle it

await queryAnalyzer.invoke("what are books about aliens by Jesse Knight");
{ query: "aliens", author: "Jesse Knight" }

The issue is that the values you want to filter on may NOT be spelled exactly correctly

await queryAnalyzer.invoke("what are books about aliens by jess knight");
{ query: "books about aliens", author: "jess knight" }

Add in all values

One way around this is to add ALL possible values to the prompt. That will generally guide the query in the right direction

const system = `Generate a relevant search query for a library system using the 'search' tool.

The 'author' you return to the user MUST be one of the following authors:

{authors}

Do NOT hallucinate author name!`;
const basePrompt = ChatPromptTemplate.fromMessages([
["system", system],
["human", "{question}"],
]);
const prompt = await basePrompt.partial({ authors: names.join(", ") });

const queryAnalyzerAll = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
llmWithTools,
]);

However… if the list of categoricals is long enough, it may error!

try {
const res = await queryAnalyzerAll.invoke(
"what are books about aliens by jess knight"
);
} catch (e) {
console.error(e);
}
Error: 400 This model's maximum context length is 16385 tokens. However, your messages resulted in 50197 tokens (50167 in the messages, 30 in the functions). Please reduce the length of the messages or functions.
at Function.generate (file:///Users/jacoblee/Library/Caches/deno/npm/registry.npmjs.org/openai/4.47.1/error.mjs:41:20)
at OpenAI.makeStatusError (file:///Users/jacoblee/Library/Caches/deno/npm/registry.npmjs.org/openai/4.47.1/core.mjs:256:25)
at OpenAI.makeRequest (file:///Users/jacoblee/Library/Caches/deno/npm/registry.npmjs.org/openai/4.47.1/core.mjs:299:30)
at eventLoopTick (ext:core/01_core.js:63:7)
at async file:///Users/jacoblee/Library/Caches/deno/npm/registry.npmjs.org/@langchain/openai/0.0.31/dist/chat_models.js:756:29
at async RetryOperation._fn (file:///Users/jacoblee/Library/Caches/deno/npm/registry.npmjs.org/p-retry/4.6.2/index.js:50:12) {
status: 400,
headers: {
"alt-svc": 'h3=":443"; ma=86400',
"cf-cache-status": "DYNAMIC",
"cf-ray": "885f794b3df4fa52-SJC",
"content-length": "340",
"content-type": "application/json",
date: "Sat, 18 May 2024 23:02:16 GMT",
"openai-organization": "langchain",
"openai-processing-ms": "230",
"openai-version": "2020-10-01",
server: "cloudflare",
"set-cookie": "_cfuvid=F_c9lnRuQDUhKiUE2eR2PlsxHPldf1OAVMonLlHTjzM-1716073336256-0.0.1.1-604800000; path=/; domain="... 48 more characters,
"strict-transport-security": "max-age=15724800; includeSubDomains",
"x-ratelimit-limit-requests": "10000",
"x-ratelimit-limit-tokens": "2000000",
"x-ratelimit-remaining-requests": "9999",
"x-ratelimit-remaining-tokens": "1958402",
"x-ratelimit-reset-requests": "6ms",
"x-ratelimit-reset-tokens": "1.247s",
"x-request-id": "req_7b88677d6883fac1520e44543f68c839"
},
request_id: "req_7b88677d6883fac1520e44543f68c839",
error: {
message: "This model's maximum context length is 16385 tokens. However, your messages resulted in 50197 tokens"... 101 more characters,
type: "invalid_request_error",
param: "messages",
code: "context_length_exceeded"
},
code: "context_length_exceeded",
param: "messages",
type: "invalid_request_error",
attemptNumber: 1,
retriesLeft: 6
}

We can try to use a longer context window… but with so much information in there, it is not guaranteed to pick it up reliably

Pick your chat model:

Install dependencies

yarn add @langchain/openai 

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llmLong = new ChatOpenAI({ model: "gpt-4-turbo-preview" });
const structuredLlmLong = llmLong.withStructuredOutput(searchSchema, {
name: "Search",
});
const queryAnalyzerAll = RunnableSequence.from([
{
question: new RunnablePassthrough(),
},
prompt,
structuredLlmLong,
]);
await queryAnalyzerAll.invoke("what are books about aliens by jess knight");
{ query: "aliens", author: "jess knight" }

Find and all relevant values

Instead, what we can do is create a vector store index over the relevant values and then query that for the N most relevant values,

import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";

const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const vectorstore = await MemoryVectorStore.fromTexts(names, {}, embeddings);

const selectNames = async (question: string) => {
const _docs = await vectorstore.similaritySearch(question, 10);
const _names = _docs.map((d) => d.pageContent);
return _names.join(", ");
};

const createPrompt = RunnableSequence.from([
{
question: new RunnablePassthrough(),
authors: selectNames,
},
basePrompt,
]);

await createPrompt.invoke("what are books by jess knight");
ChatPromptValue {
lc_serializable: true,
lc_kwargs: {
messages: [
SystemMessage {
lc_serializable: true,
lc_kwargs: {
content: "Generate a relevant search query for a library system using the 'search' tool.\n" +
"\n" +
"The 'author' you ret"... 243 more characters,
additional_kwargs: {},
response_metadata: {}
},
lc_namespace: [ "langchain_core", "messages" ],
content: "Generate a relevant search query for a library system using the 'search' tool.\n" +
"\n" +
"The 'author' you ret"... 243 more characters,
name: undefined,
additional_kwargs: {},
response_metadata: {}
},
HumanMessage {
lc_serializable: true,
lc_kwargs: {
content: "what are books by jess knight",
additional_kwargs: {},
response_metadata: {}
},
lc_namespace: [ "langchain_core", "messages" ],
content: "what are books by jess knight",
name: undefined,
additional_kwargs: {},
response_metadata: {}
}
]
},
lc_namespace: [ "langchain_core", "prompt_values" ],
messages: [
SystemMessage {
lc_serializable: true,
lc_kwargs: {
content: "Generate a relevant search query for a library system using the 'search' tool.\n" +
"\n" +
"The 'author' you ret"... 243 more characters,
additional_kwargs: {},
response_metadata: {}
},
lc_namespace: [ "langchain_core", "messages" ],
content: "Generate a relevant search query for a library system using the 'search' tool.\n" +
"\n" +
"The 'author' you ret"... 243 more characters,
name: undefined,
additional_kwargs: {},
response_metadata: {}
},
HumanMessage {
lc_serializable: true,
lc_kwargs: {
content: "what are books by jess knight",
additional_kwargs: {},
response_metadata: {}
},
lc_namespace: [ "langchain_core", "messages" ],
content: "what are books by jess knight",
name: undefined,
additional_kwargs: {},
response_metadata: {}
}
]
}
const queryAnalyzerSelect = createPrompt.pipe(llmWithTools);

await queryAnalyzerSelect.invoke("what are books about aliens by jess knight");
{ query: "aliens", author: "Jess Knight" }

Next steps

You’ve now learned how to deal with high cardinality data when constructing queries.

Next, check out some of the other query analysis guides in this section, like how to use few-shotting to improve performance.


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