Your go-to tool belt to make it easier to work with LLMs.
- 💰 Estimate cost
- ❓ Get token information (token count, characters etc)
- 🔎 NLP: Extract entities
- 📄 NLP: Process text (chunking etc)
Supported LLMs information (for token information):
- OpenAI
- Anthropic
- More coming soon
-
Install the package
npm install @common-web/ai-tools -
Import the tool and use it
import { estimateCost } from '@common-web/ai-tools';
async function main() {
const cost = await estimateCost({
prompt: 'this is my prompt',
});
console.log(cost)
}
main();Estimate costs by filtering by specific model types.
import { estimateCost, ModelTypes } from '@common-web/ai-tools';
async function main() {
const cost = await estimateCost({
prompt: 'this is my prompt',
filters: [
ModelTypes.OpenAI.GPT_4_TURBO_2024_04_09,
ModelTypes.OpenAI.TEXT_EMBEDDING_ADA_002,
ModelTypes.Anthropic.CLAUDE_3_OPUS_20240229,
]
});
console.log(cost)
}
main();Extract entities from your text prompt.
Code:
import { nlp } from '@common-web/ai-tools';
async function main() {
const entities = await nlp.extractEntities({
prompt: `
John Doe.
some random text.
I weigh 60 kg.
random text.
Apple. Nike. Google. notion.com
Japan. Korea. Vietnam.
American.
12PM. noon.`,
});
console.log(entities)
}
main();Response:
show response
[
{
"end": 8,
"start": 0,
"text": "John Doe",
"type": "PERSON"
},
{
"end": 41,
"start": 36,
"text": "60 kg",
"type": "QUANTITY"
},
{
"end": 61,
"start": 55,
"text": "Google",
"type": "ORG"
},
{
"end": 73,
"start": 63,
"text": "notion.com",
"type": "ORG"
},
{
"end": 80,
"start": 75,
"text": "Japan",
"type": "GPE"
},
{
"end": 90,
"start": 82,
"text": "American",
"type": "NORP"
},
{
"end": 96,
"start": 92,
"text": "12PM",
"type": "CARDINAL"
},
{
"end": 102,
"start": 98,
"text": "noon",
"type": "TIME"
}
]Chunk Html into distinct sections by providing sections to split on (ie "h1", "h2", "h3").
Code:
import { nlp } from '@common-web/ai-tools';
async function main() {
const htmlChunks = await nlp.chunk.html({
text: `
<!DOCTYPE html>
<html>
<body>
<div>
<h1>Foo</h1>
<p>Some intro text about Foo.</p>
<div>
<h2>this is header 2 main section</h2>
<p>Lorem ipsum goes here</p>
<h3>this is header 3 #1</h3>
<p>this is header 3 #1 description</p>
<h3>this is header 3 #2</h3>
<p>this is header 3 #2 description.</p>
</div>
<div>
<h2>Baz</h2>
<p>Some text about Baz</p>
</div>
<br>
<p>more text goes here</p>
</div>
</body>
</html>
`,
splitOn: [
['h1', 'header-1'],
['h2', 'header-2'],
['h3', 'header-3'],
],
})
console.log(htmlChunks);
}Response:
show response
[
{
"page_content": "Foo",
"metadata": {},
"type": "Document"
},
{
"page_content": "Some intro text about Foo. \nthis is header 2 main section this is header 3 #1 this is header 3 #2",
"metadata": {
"Header 1": "Foo"
},
"type": "Document"
},
{
"page_content": "Lorem ipsum goes here",
"metadata": {
"Header 1": "Foo",
"Header 2": "this is header 2 main section"
},
"type": "Document"
},
{
"page_content": "this is header 3 #1 description",
"metadata": {
"Header 1": "Foo",
"Header 2": "this is header 2 main section",
"Header 3": "this is header 3 #1"
},
"type": "Document"
},
{
"page_content": "this is header 3 #2 description.",
"metadata": {
"Header 1": "Foo",
"Header 2": "this is header 2 main section",
"Header 3": "this is header 3 #2"
},
"type": "Document"
},
{
"page_content": "Baz",
"metadata": {
"Header 1": "Foo"
},
"type": "Document"
},
{
"page_content": "Some text about Baz",
"metadata": {
"Header 1": "Foo",
"Header 2": "Baz"
},
"type": "Document"
},
{
"page_content": "more text goes here",
"metadata": {
"Header 1": "Foo"
},
"type": "Document"
}
]Chunk Markdown into distinct sections by providing sections to split on (ie "#", "##", "###").
Code:
import { nlp } from '@common-web/ai-tools';
async function main() {
const htmlChunks = await nlp.chunk.markdown({
text: `
# Foo
Some intro text about Foo.
## this is header 2 main section
Lorem ipsum goes here
### this is header 3 #1
this is header 3 #1 description
### this is header 3 #2
this is header 3 #2 description.
## Baz
Some text about Baz
more text goes here
`,
splitOn: [
['#', 'header-1'],
['##', 'header-2'],
['###', 'header-3'],
],
})
console.log(htmlChunks);
}Response:
See HTML response example (it’s the same)