You will get RAG System Setup & Deployment
Project details
I'll build a production-ready RAG (Retrieval-Augmented Generation) system with hybrid search that combines vector embeddings and keyword matching for maximum accuracy. Unlike basic implementations, my systems are designed for real users with proper error handling, state management, and monitoring.
What makes my RAG systems different:
✓ Hybrid Search Architecture - Combines semantic vector search with keyword matching for 94%+ accuracy
✓ Production-Grade Code - Thread-safe, async workflows, comprehensive logging, and graceful fallbacks
✓ Scalable Design - Handle 10K+ documents and concurrent users without performance degradation
✓ Full Documentation - Clear setup guides, API documentation, and maintenance instructions
I've delivered 250+ hours of AI systems on Upwork with expertise in GPT-4, Azure OpenAI, LangChain, and PostgreSQL with pgvector. Every project includes clean code, deployment support, and knowledge transfer so your team can maintain and extend the system.
Whether you need intelligent document search, AI-powered customer support, or knowledge base automation, I'll deliver a system that works reliably in production—not just a demo.
What makes my RAG systems different:
✓ Hybrid Search Architecture - Combines semantic vector search with keyword matching for 94%+ accuracy
✓ Production-Grade Code - Thread-safe, async workflows, comprehensive logging, and graceful fallbacks
✓ Scalable Design - Handle 10K+ documents and concurrent users without performance degradation
✓ Full Documentation - Clear setup guides, API documentation, and maintenance instructions
I've delivered 250+ hours of AI systems on Upwork with expertise in GPT-4, Azure OpenAI, LangChain, and PostgreSQL with pgvector. Every project includes clean code, deployment support, and knowledge transfer so your team can maintain and extend the system.
Whether you need intelligent document search, AI-powered customer support, or knowledge base automation, I'll deliver a system that works reliably in production—not just a demo.
AI Algorithms
Autoencoder, Convolutional Neural Network, Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, Automatic Speech Recognition, Conversational AI, Image Processing, Machine Translation, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, GPT-3, GPT-4, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$200
|
Standard
$400
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | - | - | |
NLP Tokenization | - | - | |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Document Source Integration
(+ 2 Days)
+$150
1-Hour Training Session
+$100
3 reviews
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LP
Liza P.
Apr 3, 2026
Audio Replay Data Collection Participants Needed (One-Time Task) - O-0250042
Great work!
JC
Jeremy C.
Dec 22, 2025
AI Integration Engineer for ChatGPT with Python knowledge (No Agencies) (Intermediate / Intern)
Yassmen was generally polite to work with and worked hard to solve the assigned tasks. This was a junior role so we expected there would be learning involved. Language (english) was a challenge however this role was a recommendation from another team member so we expected this.
Pros
- Friendly
- Worked hard to get a solution
- Followed task instructions
Cons
- English Skills - Unable to have conversations in English resulting in only text. Relied heavily on teammate who spoke the same language.
- Communication - Lacking in clear communication regarding current work / deadlines. Constantly required us to chase for updates on project items and often to chase the teammate to get an answer due to lack of answer.
- Handover was incomplete
- Flexibility - on a few occasions was upset because the priorities changed due to shifting business priorities. Unfortunately this is the nature of business.
Pros
- Friendly
- Worked hard to get a solution
- Followed task instructions
Cons
- English Skills - Unable to have conversations in English resulting in only text. Relied heavily on teammate who spoke the same language.
- Communication - Lacking in clear communication regarding current work / deadlines. Constantly required us to chase for updates on project items and often to chase the teammate to get an answer due to lack of answer.
- Handover was incomplete
- Flexibility - on a few occasions was upset because the priorities changed due to shifting business priorities. Unfortunately this is the nature of business.
MH
Mohamed H.
Jun 11, 2025
Real time speech to speech pipeline MVP
About Yassmen
AI Engineer | LLM & Agentic Workflows Specialist
55%
Job Success
Cairo, Egypt - 6:11 pm local time
With 3 years of experience, I've built systems that go beyond the demo straight to real users, real load, and real edge cases. My clients come to me when they need agent workflows that route and recover intelligently, knowledge systems that retrieve the right answer fast, and conversation flows that hold context across complex multi-turn interactions.
I don't just integrate an API and call it a system. I think about concurrency, state persistence, error handling, and what happens when things go wrong at 2am because that's what production actually looks like.
My expertise includes:
1. Multi-agent orchestration: LangChain, LangGraph, OpenAI Assistants, custom routing logic
2. RAG systems: hybrid search, pgvector, document ingestion and embedding pipelines
3. Conversational AI: GPT-4, Azure OpenAI, Claude, multi-turn context management
4. Speech AI: real-time speech-to-speech pipelines, speech emotion recognition, audio processing
5. Computer Vision: image processing pipelines, deep learning models, OpenCV
6. OCR & Document AI: text extraction, document parsing, intelligent data capture
7. Backend systems: Python, FastAPI, PostgreSQL, async workflows, Docker
8. Platform integrations: WhatsApp Business API, web chat, messaging platforms
9 Reliability: Langfuse, logging, fallback handling, human-in-the-loop workflows
If you need it built properly, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Yassmen can start the project.
Delivery time starts when Yassmen receives requirements from you.
Yassmen works on your project following the steps below.
Revisions may occur after the delivery date.
step
Review requirements and discuss project scope via message/call
step
Design RAG architecture and document processing pipeline based on your use case