You will get Build a GPT-4o + LangChain RAG Pipeline for AI-Powered Document Search
Project details
You will get a fully functional Retrieval-Augmented Generation (RAG) pipeline built with GPT-4o and LangChain — ready for production use. With 10M+ legal documents processed and several microservices deployed on Kubernetes, I deliver scalable and fast AI systems that turn raw text into actionable insight. I offer integration with hosted vector stores (Upstash, Pinecone, Supabase) or self-managed Elasticsearch setups. Every line of code is written by me — from data ingestion to semantic search and API delivery.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$250
|
Standard
$500
|
Advanced
$950
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 10 days |
Number of Revisions | 1 | 3 | 5 |
Number of Pages | 4 | 7 | 10 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Additional Page
(+ 2 Days)
+$100
Design Customization
(+ 2 Days)
+$100Frequently asked questions
About Hasan
Python & OpenAI Integration Specialist FastAPI Elasticsearch LangChain
Bursa, Turkey - 8:39 pm local time
I build production-ready, measurably faster back-end systems that turn raw text into actionable insight.
▸ 10 M+ legal documents processed end-to-end – web scraping → OCR (Tesseract) → Turkish NLP normalisation → metadata tagging.
▸ Hybrid vector + keyword search on Elasticsearch cut retrieval time by 40 % for Soorgle.com’s users.
▸ 5-stage RAG pipeline with LangChain + GPT-4o delivers instant case-law summaries and citations.
▸ Deployed on AWS (S3, ECS, OpenSearch) with Docker-based CI/CD; releases dropped from 2 days to 3 hours.
▸ Added Playwright test automation, slashing critical regressions by 70 %.
What I deliver best
Python, Flask / FastAPI API design with >99 % uptime
OpenAI, LangChain, Hugging Face integrations – chatbots, agents, function-calling workflows
Semantic & vector search (Elasticsearch, pgvector, Pinecone)
End-to-end data pipelines: scrape → clean → embed → serve
Cloud-native deployments on AWS (or GCP/Azure) with Docker & Terraform
Robust QA: Playwright, Pytest, GitHub Actions
Why work with me?
Outcome-driven – every feature ships with an explicit metric (speed, accuracy, cost).
Transparent communication – daily updates, clear road-maps, documented code.
Scalability in mind – architectures that survive traffic spikes and future AI add-ons.
Need a back-end that speaks AI fluently? Let’s discuss how we can cut your time-to-insight and boost ROI within weeks, not months.
Steps for completing your project
After purchasing the project, send requirements so Hasan can start the project.
Delivery time starts when Hasan receives requirements from you.
Hasan works on your project following the steps below.
Revisions may occur after the delivery date.
Use Case Review & Dataset Preparation
I will analyze your goal and clean/preprocess the dataset for vector indexing and retrieval.
Vector Store Setup + Embedding
I will embed your documents using OpenAI or a custom model and store them in a vector DB (e.g., Elasticsearch or FAISS).