You will get AI-Powered Document Search Retrieval System | FastAPI + LangChain + OpenAI
Rising Talent
Project details
I developed a production-ready AI-powered document intelligence system that helps businesses quickly access and utilize information from large documents. The system ingests documents, splits them into semantic chunks, and generates vector embeddings using OpenAI, enabling fast and accurate semantic search.
Companies can upload contracts, reports, or manuals and instantly retrieve the most relevant information through AI-powered question answering. The backend uses FastAPI and asynchronous SQLAlchemy for high-performance processing, while a Flutter frontend provides a user-friendly interface for document upload and queries.
Supporting Retrieval-Augmented Generation (RAG), the system allows AI to provide context-aware answers by referencing the most relevant document segments. Additional features include batch embedding processing, secure API endpoints, and cloud deployment on Render.
This project reduces manual research time, improves decision-making, and demonstrates scalable application of LangChain, OpenAI embeddings, vector databases, and async Python — ideal for businesses seeking intelligent knowledge management and AI-driven insights.
Companies can upload contracts, reports, or manuals and instantly retrieve the most relevant information through AI-powered question answering. The backend uses FastAPI and asynchronous SQLAlchemy for high-performance processing, while a Flutter frontend provides a user-friendly interface for document upload and queries.
Supporting Retrieval-Augmented Generation (RAG), the system allows AI to provide context-aware answers by referencing the most relevant document segments. Additional features include batch embedding processing, secure API endpoints, and cloud deployment on Render.
This project reduces manual research time, improves decision-making, and demonstrates scalable application of LangChain, OpenAI embeddings, vector databases, and async Python — ideal for businesses seeking intelligent knowledge management and AI-driven insights.
Purpose
BusinessIndustry
Animals & Pets, Architecture & Interior Design, Beauty & Cosmetics, Business Services & Consulting, Education, Energy, Environmental, Events Planning, Financial Services, Food & Beverage, Manufacturing & Storage, Marketing & Advertising, Media & Entertainment, Medical & Pharmaceutical, Religion & Spirituality, Retail & Wholesale, Software, Sports & Fitness, Telecommunications, Travel & TourismLanguage
EnglishWhat's included
| Service Tiers |
Starter
$200
|
Standard
$700
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 30 days |
Number of Hours of Work | 24 | 59 | 180 |
Scriptwriting | - | - | - |
Summary Report | |||
Social Media Replies | - | ||
Email Support | |||
Live Chat Support | - | - |
About Ali
AI Agent & RAG Developer | LangChain | LangGraph | CrewAI |FastAPI
Gilgit, Pakistan - 5:38 pm local time
Using LangChain, LangGraph, and CrewAI, I help companies automate complex workflows, build document intelligence tools, and deploy multi-agent pipelines connected to live databases, APIs, and business logic. I also build cross-platform mobile apps in Flutter when clients need a polished frontend for their AI backend.
⎯⎯⎯⎯ What I build ⎯⎯⎯⎯
→ RAG Chatbots & Document Q&A — Upload PDFs, internal docs, or connect a database. Your team queries it in plain English. Built with LangChain, FastAPI, and vector stores (Pinecone, FAISS, ChromaDB).
→ Multi-Agent Automation — Complex workflows broken into specialized AI agents using CrewAI or LangGraph. Research agents, writer agents, QA agents, orchestrators — fully automated pipelines that run while you sleep.
→ AI-Powered APIs — FastAPI and Flask backends that wrap LLM logic into clean, documented REST APIs your team can call from anywhere.
→ Flutter Mobile Apps — Cross-platform iOS and Android apps. Especially strong when paired with an AI backend — I handle both sides.
→ Java / Spring Boot Systems — Enterprise-grade backends and microservices for teams that need robust, scalable architecture.
⎯⎯⎯⎯ My stack ⎯⎯⎯⎯
AI/LLM: LangChain · LangGraph · CrewAI · OpenAI API · Claude API · HuggingFace
Backend: Python · FastAPI · Flask · Java · Spring Boot
Mobile: Flutter · Dart
Databases: PostgreSQL · MongoDB · Pinecone · FAISS · ChromaDB
Infra: Docker · REST APIs · Git
⎯⎯⎯⎯ Why clients come back ⎯⎯⎯⎯
I've completed 5+ AI projects . Clients rehire me because I communicate clearly, deliver on time, and build systems I'd be comfortable maintaining myself. I don't disappear after delivery — I make sure the thing actually works in your environment.
If you need an AI system built properly, let's talk. Send me a message describing what you're trying to automate or build, and I'll tell you honestly whether I can help and what it would take.
Steps for completing your project
After purchasing the project, send requirements so Ali can start the project.
Delivery time starts when Ali receives requirements from you.
Ali works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture
Deliver: design doc, PoC endpoint for chunking & embedding
Backend + DB
Deliver: ingest and query endpoints, vector DB schema, tests