ngandu-dev/lugha

Lugha is a PHP Generative AI framework to build chatbot, RAG systems and AI-powered applications

1.0.1 2026-08-19 11:28 UTC

Requires

• php: >=8.3
• symfony/filesystem: ^7.2
• symfony/http-client: ^7.2
• webmozart/assert: ^1.11

Requires (Dev)

• doctrine/dbal: ^3.9
• doctrine/orm: ^3.3
• league/commonmark: ^2.6
• phpstan/phpstan: ^1.11
• phpunit/phpunit: ^11.1
• rector/rector: ^1.2
• smalot/pdfparser: ^2.12
• symfony/var-dumper: ^7.2
• symplify/easy-coding-standard: ^12.2

Suggests

• doctrine/orm: This is require for ORM Vector Stores support. (^3.2)
• league/commonmark: This is required for Markdown parsing. (^2.6)
• smalot/pdfparser: This is required for PDF parsing. (^2.12)

MIT

• bernard-ng

• nlp
• ai
• chatbot
• openai
• llm
• embeddings
• generative
• rag

Published on Packagist.org on 2026-08-19 11:34 UTC

README

Important

Lugha is abandoned in favor of Symfony AI - github.com/symfony/ai. Please use the new repository for all future development, issues, and contributions.

Lugha from Swahili meaning "Language" is a PHP Generative AI Framework that provides a simple and easy way to interact with various AI providers. The main idea is to provide a unified provider-agnostic API for AI models, making it easier to switch between providers.

This project is highly inspired by LangChain and LLPhant, designed for Chatbot, RAG (Retrieval-Augmented Generation) based applications with integration of Embeddings, Completion and Reranking models.

Features

• Provider-agnostic completion, embeddings, and reranking APIs
• Chat history and tool calling
• Document loaders, retrieval, and vector stores

Requirements

• PHP 8.3 or later
• Composer 2

supported providers:

Provider	Link	Features
OpenAI	openai.com	Completion, Embeddings
Mistral	mistral.ai	Completion, Embeddings
Google	ai.google	Completion, Embeddings
GitHub	github.com	Completion, Embeddings
Anthropic	anthropic.com	Completion
Voyager.ai	voyageai.com	Embeddings, Reranking
Ollama	ollama.com	Completion, Embeddings
Deepseek	deepseek.com	completion

Installation

composer require ngandu-dev/lugha

Usage

Embeddings

Embeddings are a type of word representation that allows words with similar meaning to have a similar representation. they can be used to find the similarity between words, phrases, or sentences. useful for document classification, clustering, and information retrieval.

$client = ClientFactory::create(Provider::GOOGLE);
$embeddings = $client->embeddings(
    prompt: 'Hello, world!', 
    config: new EmbeddingsConfig(
        model: 'text-embedding-004',
        dimensions: 512
    )
)->embeddings;

Completion

Completion models are designed to generate human-like text based on the input prompt.

$client = ClientFactory::create(Provider::OPENAI);

• completion from a single prompt

$completion = $client->completion(
    input: 'Hello, world!', 
    config: new CompletionConfig(
        model: 'gpt-3.5-turbo',
        temperature: 0.5,
        maxTokens: 100,
        frequencyPenalty: 0.5,
        presencePenalty: 0.5
    )
)->completion;

• completion from a chat history (conversation)

$completion = $client->chat(
    input: History::fromMessages([
        new Message('You are a chatbot, expert in philosophy', Role::SYSTEM),
        new Message('what is the meaning of life ?', Role::USER)
    ]),
    config: new ChatConfig(model: 'gpt-4-turbo')
)->completion;

• completion with tool calling

#[ToolDefinition(
    name: 'get_weather',
    description: 'Get the weather for a location on a specific date.',
    parameters: [
        new ToolParameter('location', 'string', 'The location to get the weather for.', required: true),
        new ToolParameter('date', 'string', 'The date to get the weather for.', required: true),
    ],
    strict: true
)]
class WeatherProvider
{
    public function __invoke(string $location, string $date): string
    {
        return "The weather in $location on $date is sunny.";
    }
}

$completion = $client->completion(
    input: 'What is the weather in Lubumbashi on January 16th ?',
    config: new CompletionConfig(model: 'gpt-4-turbo'),
    tools: [new WeatherProvider()] 
)->completion;

Reranking

Reranking models are designed to re-rank a list of documents based on the input prompt. useful for search engines, recommendation systems, and information retrieval.

$client = ClientFactory::create(Provider::VOYAGER);

$reRankedDocuments = $client->reranking(
    prompt: 'What is the meaning of life ?',
    documents: [
        new Document('The best way to predict the future is to create it.'),
        new Document('The only way to do great work is to love what you do.'),
        new Document('Life is short, smile while you still have teeth.'),
        new Document('The best time to plant a tree was 20 years ago. The second best time is now.')
    ],
    config: new RerankingConfig(model: 'voyager-1.0', topK: 3)
)->documents;

Development

composer install
composer format
composer quality

Contributing

See CONTRIBUTING.md before opening an issue or pull request.

Security

Report vulnerabilities privately as described in SECURITY.md.

License

Released under the MIT License.

Contributors