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Overview

Instructor offers a simplified way to work with LLM providers’ APIs supporting caching (currently only Anthropic API), so you can focus on your business logic while still being able to take advantage of lower latency and costs.

Note 1: Instructor supports context caching for Anthropic API and OpenAI API.

Note 2: Context caching is automatic for all OpenAI API calls. Read more in the OpenAI API documentation.

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Example

When you need to process multiple requests with the same context, you can use context caching to improve performance and reduce costs.

In our example we will be analyzing the README.md file of this Github project and generating its summary for 2 target audiences.

<?php
require 'examples/boot.php';

use Cognesy\Polyglot\LLM\Inference;
use Cognesy\Utils\Str;

$data = file_get_contents(__DIR__ . '/../../../README.md');

$inference = (new Inference)->withConnection('anthropic')->withCachedContext(
    messages: [
        ['role' => 'user', 'content' => 'Here is content of README.md file'],
        ['role' => 'user', 'content' => $data],
        ['role' => 'user', 'content' => 'Generate short, very domain specific pitch of the project described in README.md'],
        ['role' => 'assistant', 'content' => 'For whom do you want to generate the pitch?'],
    ],
);

$response = $inference->create(
    messages: [['role' => 'user', 'content' => 'CTO of lead gen software vendor']],
    options: ['max_tokens' => 256],
)->response();

print("----------------------------------------\n");
print("\n# Summary for CTO of lead gen vendor\n");
print("  ({$response->usage()->cacheReadTokens} tokens read from cache)\n\n");
print("----------------------------------------\n");
print($response->content() . "\n");

assert(!empty($response->content()));
assert(Str::contains($response->content(), 'Instructor'));
assert(Str::contains($response->content(), 'lead', false));

$response2 = $inference->create(
    messages: [['role' => 'user', 'content' => 'CIO of insurance company']],
    options: ['max_tokens' => 256],
)->response();

print("----------------------------------------\n");
print("\n# Summary for CIO of insurance company\n");
print("  ({$response2->usage()->cacheReadTokens} tokens read from cache)\n\n");
print("----------------------------------------\n");
print($response2->content() . "\n");

assert(!empty($response2->content()));
assert(Str::contains($response2->content(), 'Instructor'));
assert(Str::contains($response2->content(), 'insurance', false));
//assert($response2->cacheReadTokens > 0);
?>
Working directly with LLMs and JSON - Tools modeCustomize parameters of LLM driver