Laravel AI Assistant for [Niche] Industry

We’re hiring a skilled Laravel developer to implement a production-ready Retrieval-Augmented Generation (RAG) pipeline for an AI-powered assistant.

The system will allow lawyers to:

Ask Niche questions and get intelligent, contextual answers

Upload legal documents (PDF, DOCX) and receive summaries or draft documents.

Reference relevant texts or sections intelligently

Generate GPT-4-based Niche outputs grounded in facts

-What You’ll Build

You will be responsible for building a full Laravel backend that:

Accepts queries or inputs

Generates OpenAI embeddings using text-embedding-3-small

Performs semantic search using a vector database (Supabase pgvector or Qdrant)

Constructs GPT-4 prompts with contextual Niche content

Returns AI-generated responses and logs them in a database

--Key Responsibilities

Core Services

EmbeddingService: Call OpenAI and return vector

VectorSearchService: Fetch top-N relevant Niche Documents using vector search

GPTService: Inject context + input → get GPT-4 response

Controller Logic

Accept user input

Generate embeddings

Retrieve similar docs

Build prompt → get response from GPT-4

Return and save output

File Handling

Handle document uploads (PDF/DOCX)

Extract and store text content

Artisan Command

Batch embedding script to embed content into vector DB


-- Stack Required

Laravel 10+

OpenAI API (Embeddings + GPT-4)

Supabase (pgvector) or Qdrant

PostgreSQL

Orhanerday OpenAI SDK or native Laravel HTTP client

Document parsers (for PDF, DOCX)



Must Handle Edge Cases
Case Solution
Very long input Split into chunks before embedding
No relevant docs GPT fallback: “Based on general [Niche] knowledge…”
OpenAI rate limits Retry with exponential backoff
Duplicate content Uniqueness validation using act + section


--Suggested File Structure
app/
├── Http/Controllers/AIController.php
├── Services/
│ ├── EmbeddingService.php
│ ├── GPTService.php
│ └── VectorSearchService.php
app/Console/Commands/EmbedLegalDocuments.php
database/
├── migrations/
├── seeders/

To Apply

Please share:

A brief intro and relevant Laravel + AI API experience

Any examples of GPT, embeddings, or vector DB work (if any)

Your comfort level with Supabase or Qdrant

Your estimated delivery timeline & rate (fixed/hourly)

-- Project Timeline & Terms

Start: Immediate

Duration: 2–3 weeks (MVP)

Budget: Open to fair proposals

Long-term collaboration possible