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I will build a custom rag application with python, qdrant and llms
Rahman
Über diesen Service
Need a RAG application that can answer questions from your own documents?
I will build a custom Retrieval-Augmented Generation (RAG) application in Python that retrieves relevant information from your documents and uses an LLM to generate grounded answers.
What I can build:
- Document loading and text chunking
- Embedding generation with Sentence Transformers
- Vector search with Qdrant
- Reranking of retrieved results
- LLM-based answer generation
- Grounding verification against retrieved evidence
- FastAPI integration where required
- Clean, maintainable Python code
- Source code included with your order
I can work with your existing application or build the RAG component as a standalone service.
The exact features depend on the package you choose. If your requirements are more complex than the listed packages, message me before ordering so we can define the scope clearly.
My focus is on building RAG systems that are structured, testable, and designed around reliable retrieval rather than simply connecting an LLM to a prompt.
Lerne Rahman kennen
Rahman
AI ML GenAI Engineer RAG Agentic AI
- AusNepal
- Mitglied seitAug. 2026
- ⌀ Antwortzeit1 Stunde
Sprachen
Nepali, Englisch
I’m an AI/ML and GenAI engineer focused on building reliable, production-oriented AI systems.
I build RAG applications, agentic AI workflows, LLM-powered services, and Python APIs using technologies such as LangGraph, FastAPI, vector databases, embeddings, reranking, and grounding verification.
My focus goes beyond making an LLM generate an answer. I care about retrieval quality, evidence grounding, structured workflows, validation, testing, and production reliability.
I can help turn an AI idea into a structured, testable system designed to work beyond a simple prototype.
Mein Portfolio
FAQ
What do you need from me to build the RAG application?
I need your requirements, the documents or data you want the system to work with, and details about your existing application if you need integration.
Can you build a RAG application from scratch?
Yes. I can build the RAG component as a standalone application or integrate it into an existing Python application, depending on the selected package.
Can the RAG system use my own documents?
Yes. The system can be designed to retrieve information from your provided documents and use the retrieved evidence for answer generation.
Which technologies do you use?
I primarily work with Python, Sentence Transformers, Qdrant, LangChain, FastAPI, and LLMs. The exact technologies depend on the project requirements and selected package.
Can you integrate RAG into my existing application?
Yes. RAG functionality can be integrated into an existing application when the required interfaces and project structure are available.
Do you provide the source code?
Yes. Source code is included according to the package you select.
Do you fine-tune LLMs?
Fine-tuning is not included in this gig. This service focuses on RAG architecture, retrieval, reranking, grounding, and LLM integration.
Can you build a more complex RAG system than the packages describe?
Yes. If you have requirements involving additional retrieval, API, evaluation, or grounding functionality, contact me before ordering so we can define the scope and pricing.
