Typed LLM Outputs in PHP

dev-main 2024-06-22 13:34 UTC

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Last update: 2024-06-22 13:34:12 UTC


👩‍🏫 adrienbrault/instructrice

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Typing LLM completions

Best in class LLMs are able to output JSON following a schema you provide, usually JSON-Schema. This significantly expands the ways you can leverage LLMs in your application!

Think of the input as:

  • A context, anything that is or can be converted to text, like emails/pdfs/html/xlsx
  • A schema, "Here is the form you need to fill to complete your task"
  • An optional prompt, giving a specific task, rules, etc

And the output/outcome is whichever structure best matches your use case and domain.

The python instructor cookbook has interesting examples.


Instructrice is a PHP library that simplifies working with structured output from LLMs in a type-safe manner.


  • Flexible schema options:
  • symfony/serializer integration to deserialize LLMs outputs
  • Streaming first:
    • As a developer you can be more productive with faster feedback loops than waiting for outputs to complete. This also makes slower local models more usable.
    • You can provide a much better and snappier UX to your users.
    • The headaches of parsing incomplete JSON are handled for you.
  • A set of pre-configured LLMs with the best available settings. Set your API keys and switch between different providers and models without having to think about the model name, json mode, function calling, etc.

A Symfony Bundle is also available.

Installation and Usage

$ composer require adrienbrault/instructrice:@dev
use AdrienBrault\Instructrice\InstructriceFactory;
use AdrienBrault\Instructrice\LLM\Provider\Ollama;
use AdrienBrault\Instructrice\LLM\Provider\OpenAi;
use AdrienBrault\Instructrice\LLM\Provider\Anthropic;

$instructrice = InstructriceFactory::create(
    defaultLlm: Ollama::HERMES2THETA_LLAMA3_8B,
    apiKeys: [ // Unless you inject keys here, api keys will be fetched from environment variables
        OpenAi::class => $openAiApiKey,
        Anthropic::class => $anthropicApiKey,

List of object

use AdrienBrault\Instructrice\Attribute\Prompt;

class Character
    // The prompt annotation lets you add instructions specific to a property
    #[Prompt('Just the first name.')]
    public string $name;
    public ?string $rank = null;

$characters = $instructrice->getList(
    'Colonel Jack O\'Neil walks into a bar and meets Major Samanta Carter. They call Teal\'c to join them.',

array:3 [
  0 => Character^ {
    +name: "Jack"
    +rank: "Colonel"
  1 => Character^ {
    +name: "Samanta"
    +rank: "Major"
  2 => Character^ {
    +name: "Teal'c"
    +rank: null


$character = $instructrice->get(
    type: Character::class,
    context: 'Colonel Jack O\'Neil.',

Character^ {
  +name: "Jack"
  +rank: "Colonel"

Dynamic Schema

$label = $instructrice->get(
    type: [
        'type' => 'string',
        'enum' => ['positive', 'neutral', 'negative'],
    context: 'Amazing great cool nice',
    prompt: 'Sentiment analysis',


You can also use third party json schema libraries like goldspecdigital/oooas to generate the schema:

Supported providers

Provider Environment Variables Enum API Key Creation URL
Ollama OLLAMA_HOST Ollama
OpenAI OPENAI_API_KEY OpenAi API Key Management
Anthropic ANTHROPIC_API_KEY Anthropic API Key Management
Mistral MISTRAL_API_KEY Mistral API Key Management
Fireworks AI FIREWORKS_API_KEY Fireworks API Key Management
Groq GROQ_API_KEY Groq API Key Management
Together AI TOGETHER_API_KEY Together API Key Management
Deepinfra DEEPINFRA_API_KEY Deepinfra API Key Management
Perplexity PERPLEXITY_API_KEY Perplexity API Key Management
Anyscale ANYSCALE_API_KEY Anyscale API Key Management
OctoAI OCTOAI_API_KEY OctoAI API Key Management

The supported providers are Enums, which you can pass to the llm argument of InstructriceFactory::create:

use AdrienBrault\Instructrice\InstructriceFactory;
use AdrienBrault\Instructrice\LLM\Provider\OpenAi;

    llm: OpenAi::GPT_4T, // API Key will be fetched from the OPENAI_API_KEY environment variable

Supported models

Strategy 📄 Text 🧩 JSON 🚀 Function
Commercial usage 💼 ✅ Yes ⚠️ Yes, but ❌ Nope

Open Weights


💼 ctx Ollama Mistral Fireworks Groq Together DeepInfra Perplexity Anyscale OctoAI
Mistral 7B 32k 🧩 🧩 68/s 📄 98/s 📄 88/s !ctx=16k! 🧩 🧩
Mixtral 8x7B 32k 🧩 🧩 44/s 🧩 237/s 🚀 560/s 🚀 99/s 📄 119/s !ctx=16k! 🧩 🧩
Mixtral 8x22B 65k 🧩 🧩 77/s 🧩 77/s 📄 52/s 🧩 40/s 📄 62/s !ctx=16k! 🧩 🧩
Phi-3-Mini-4K 4k 🧩
Phi-3-Mini-128K 128k 🧩
Phi-3-Medium-4K 4k 🧩
Phi-3-Medium-128K 128k 🧩
Qwen2 0.5B 32k 🧩
Qwen2 1.5B 32k 🧩
Qwen2 7B 128k 🧩
Llama3 8B ⚠️ 8k 📄 🧩 280/s 🚀 800/s 📄 194/s 🧩 133/s 📄 121/s 🧩 🧩
Llama3 70B ⚠️ 8k 🧩 🧩 116/s 🚀 270/s 📄 105/s 🧩 26/s 📄 42/s 🧩 🧩
Gemma 7B ⚠️ 8k 🚀 800/s 📄 118/s 🧩 64/s 🧩
DBRX ⚠️ 32k 🧩 50/s 📄 72/s 🧩
Qwen2 72B ⚠️ 128k 🧩
Qwen1.5 32B ⚠️ 32k 📄 🧩
Command R 128k 📄
Command R+ 128k 📄

Throughputs from .

Fine Tune

💼 ctx Base Ollama Fireworks Together DeepInfra OctoAI
Hermes 2 Pro Mistral 7B Mistral 7B 🧩 🧩 🧩
FireFunction V1 Mixtral 8x7B 🚀
WizardLM 2 7B Mistral 7B 🧩
WizardLM 2 8x22B Mixtral 8x7B 📄 🧩 🧩
Capybara 34B 200k Yi 34B 🧩
Hermes 2 Pro Llama3 8B ⚠️ Llama3 8B 📄
Hermes 2 Theta Llama3 8B ⚠️ Llama3 8B 📄
Dolphin 2.9 ⚠️ 8k Llama3 8B 🧩 📄 🧩


Provider Model ctx
Mistral Large 32k ✅ 26/s
OpenAI GPT-4o 128k 🚀
OpenAI GPT-4 Turbo 128k 🚀 24/s
OpenAI GPT-3.5 Turbo 16k 🚀 72/s
Anthropic Claude 3 Haiku 200k 📄 88/s
Anthropic Claude 3 Sonnet 200k 📄 59/s
Anthropic Claude 3 Opus 200k 📄 26/s
Google Gemini 1.5 Flash 1000k 🧩 136/s
Google Gemini 1.5 Pro 1000k 🧩 57/s
Perplexity Sonar Small Chat 16k 📄
Perplexity Sonar Small Online 12k 📄
Perplexity Sonar Medium Chat 16k 📄
Perplexity Sonar Medium Online 12k 📄

Throughputs from .

Automate updating these tables by scraping , along with chatboard arena elo.? Would be a good use case / showcase of this library/cli?

Custom Models


If you want to use an Ollama model that is not available in the enum, you can use the Ollama::create static method:

use AdrienBrault\Instructrice\LLM\LLMConfig;
use AdrienBrault\Instructrice\LLM\Cost;
use AdrienBrault\Instructrice\LLM\OpenAiJsonStrategy;
use AdrienBrault\Instructrice\LLM\Provider\Ollama;

    llm: Ollama::create(
        'codestral:22b-v0.1-q5_K_M', // check its license first!


You can also use any OpenAI compatible api by passing an LLMConfig:

use AdrienBrault\Instructrice\LLM\LLMConfig;
use AdrienBrault\Instructrice\LLM\Cost;
use AdrienBrault\Instructrice\LLM\OpenAiJsonStrategy;

    llm: new LLMConfig(
        uri: '',
        model: 'meta-llama/Llama-3-70b-chat-hf',
        contextWindow: 8000,
        label: 'Llama 3 70B',
        provider: 'Together',
        cost: Cost::create(0.9),
        strategy: OpenAiJsonStrategy::JSON,
        headers: [
            'Authorization' => 'Bearer ' . $apiKey,


You may configure the LLM using a DSN:

  • the scheme is the provider: openai, openai-http, anthropic, google
  • the password is the api key
  • the host, port and path are the api endpoints without the scheme
  • the query string:
    • model is the model name
    • context is the context window
    • strategy is the strategy to use:
      • json for json mode with the schema in the prompt only
      • json_with_schema for json mode with probably the completion perfectly constrained to the schema
      • tool_any
      • tool_auto
      • tool_function


use AdrienBrault\Instructrice\InstructriceFactory;

$instructrice = InstructriceFactory::create(
    defaultLlm: 'openai://'

    llm: 'openai-http://localhost:11434?model=adrienbrault/nous-hermes2theta-llama3-8b&strategy=json&context=8000'

    llm: 'openai://'

    llm: 'google://'

    llm: 'anthropic://'


You may also implement LLMInterface.


Obviously inspired by instructor-php and instructor.

How is it different from instructor php?

Both libraries essentially do the same thing:

  • Automatic schema generation from classes
  • Multiple LLM/Providers abstraction/support
  • Many strategies to extract data: function calling, json mode, etc
  • Automatic deserialization/hydration
  • Maybe validation/retries later for this lib.

However, instructice differs with:

  • Streaming first.
  • Preconfigured provider+llms, to not have to worry about:
    • Json mode, function calling, etc
    • The best prompt format to use
    • Your options for local models
    • Whether streaming works. For example, groq can only do streaming without json-mode/function calling.
  • PSR-3 logging
  • Guzzle+symfony/http-client support
  • No messages. You just pass context, prompt.
    • I am hoping that this choice enables cool things later like supporting few-shots examples, evals, etc
  • More flexible schema options
  • Higher level abstraction. You aren't able to provide a list of messages, while it is possible with instructor-php.


Things to look into:

DSPy is very interesting. There are great ideas to be inspired by.

Ideally this library is good to prototype with, but can support more advanced extraction workflows with few shot examples, some sort of eval system, generating samples/output like DSPy, etc

Would be cool to have a CLI, that accepts a FQCN and a context.

instructrice get "App\Entity\Customer" "$(cat" 

Autosave all input/schema/output in sqlite db. Like llm? Leverage that to test examples, add few shots, evals?