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I will build a rag knowledge base chatbot with source citations
Asad Rafique
À propos de ce service
I will build a retrieval-augmented generation system that lets users ask questions from your documentation, policies, help centre, PDFs or internal knowledge while receiving source-grounded answers. This service is designed for SaaS products, customer-support portals and internal business systems. I handle more than the chat UI: document ingestion, chunking, metadata, embeddings, retrieval, citations, permissions and production safeguards are implemented according to the agreed scope.
Possible features:
- PDF, document or help-centre ingestion
- PostgreSQL/pgvector or Qdrant retrieval
- Tenant and role-aware knowledge access
- Source citations
- Conversation history
- Admin upload and re-indexing workflow
- Retrieval evaluation and feedback
- OpenAI or Claude integration
I can integrate the assistant directly into an existing Laravel/PHP application or expose it through secure APIs for another platform. RAG quality depends on document quality and the required retrieval rules. Message me before ordering with sample documents, user roles and expected questions.
Découvrez Asad Rafique
Asad Rafique
Senior Laravel and AI Automation Engineer n8n APIs SaaS
- DePakistan
- Membre depuisnov. 2012
- Temps de réponse moy.1 heure
- Dernière commande8 mois
Langues
Ourdou, Punjabi, Anglais, Hindi
Senior Laravel Developer & AI Automation Engineer with 9+ years of experience building SaaS platforms, APIs, dashboards, eCommerce systems, POS solutions, and business automation workflows. I help businesses automate operations using Laravel, n8n, AI agents, APIs, webhooks, queues, and cloud infrastructure. Experienced with WooCommerce, Shopify, Stripe, Cloudflare, VPS, CI/CD, and production-ready systems.
Mon portfolio
FAQ
Is this different from a basic chatbot?
Yes. A RAG assistant retrieves relevant information from your approved knowledge sources before generating an answer. The implementation can include citations, metadata filters and access rules rather than relying only on the model's general knowledge.
Which vector database will you use?
I will recommend PostgreSQL with pgvector or Qdrant based on your current infrastructure, document volume, filtering needs and operational requirements. The database choice is finalized during scoping.
Can different clients or teams have separate documents?
Yes. Tenant-aware or role-aware retrieval can be implemented in the Standard or Premium scope after reviewing your authorization model.
Do you guarantee every answer will be correct?
No responsible RAG system can guarantee perfect answers. I can implement citations, evaluation cases, fallback behaviour and feedback controls to reduce unsupported responses and measure quality.
Are AI API and hosting costs included?
No. Model usage, vector hosting, storage and other third-party charges are paid by the client directly.
Can you ingest any website automatically?
Only content the client owns or is authorized to process. The source must permit access and be technically suitable. Website crawling is scoped separately from document ingestion.
