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I will build a rag based ai search and question answering system
Mobeen R
About this gig
I will build a custom RAG-based AI search and question-answering system that allows users to search documents, articles, knowledge bases, or other data using natural language and receive relevant, context-aware AI answers.
What I can build:
- RAG (Retrieval-Augmented Generation) pipeline
- Semantic search using AI embeddings
- Vector database integration
- AI-powered question answering
- LLM/API integration
- Document and knowledge-base search
- AI-generated summaries
- Python/Flask web application
- LangChain-based document processing
- Custom search and Q&A interface
Your system can be designed for PDFs, documents, articles, websites, FAQs, research content, company knowledge bases, and other structured or unstructured data.
Technologies may include: Python, Flask, LangChain, Sentence Transformers, ChromaDB, Hugging Face, and LLM APIs, depending on your requirements.
You will receive functional source code and a working AI search/Q&A solution based on the selected package.
Please contact me before ordering if you have a complex or custom requirement.
Get to know Mobeen R
Mobeen R
Python Developer
- FromPakistan
- Member sinceSep 2026
- Avg. response time1 hour
Languages
Urdu, English
I am a results-driven Bachelor of Science in Information Technology graduate with hands-on experience in developing scalable web applications and data-driven solutions using Python, Flask, and MongoDB. I am proficient in designing full-stack systems, including REST APIs, semantic search, and Retrieval-Augmented Generation (RAG) workflows.
FAQ
What is a RAG-based AI system?
RAG stands for Retrieval-Augmented Generation. It allows an AI system to retrieve relevant information from your data and use that information to generate context-aware answers.
What type of data can you use with RAG?
I can work with documents, PDFs, articles, FAQs, knowledge bases, website content, research material, and other structured or unstructured data, depending on the project requirements.
Can you integrate an LLM into my existing application?
Yes. I can integrate an LLM or compatible AI API into an existing application and connect it with the retrieval pipeline.
Can you build semantic search?
Yes. I can implement embedding-based semantic search so users can search your data using natural language rather than relying only on exact keyword matching.
Can you integrate a vector database?
Yes. I can integrate a suitable vector database such as ChromaDB or another compatible solution depending on your project requirements.
Can you build the system using Python?
Yes. Python can be used for the backend, document processing, retrieval pipeline, embeddings, and LLM integration.
Can you work with an existing application?
Yes. I can integrate RAG search and Q&A functionality into an existing application after reviewing its current technology stack and structure.
Do you provide the source code?
Yes. Source code is included according to the selected package.
Can you build a custom user interface?
yes. Custom web interfaces can be included depending on the selected package and project requirements.
Should I contact you before placing an order?
For complex projects, custom integrations, or existing applications, yes. Please send your requirements and data details first so the appropriate package or custom offer can be selected.
