You will get an AI agent with RAG using LangGraph,Langchain , llamaindex and Python
Project details
Build a production-ready AI agent powered by LangGraph and Retrieval-Augmented Generation (RAG).
I develop intelligent AI applications that retrieve information from your knowledge base, orchestrate multi-step workflows using LangGraph, and generate accurate, context-aware responses with modern Large Language Models (LLMs).
Whether you need an internal knowledge assistant, customer support bot, research assistant, or document Q&A system, I'll build a scalable solution tailored to your requirements.
What you'll receive:
✅ LangGraph-powered AI agent workflows
✅ Retrieval-Augmented Generation (RAG)
✅ Vector database integration (ChromaDB, FAISS, or Qdrant)
✅ LLM integration (OpenAI, Gemini, Claude, or local models)
✅ FastAPI backend
✅ Clean, modular, and documented code
✅ Prompt engineering and workflow optimization
✅ Deployment support (Standard & Premium)
✅ Source code and setup instructions
I focus on building reliable, maintainable AI systems that are easy to extend and ready for production.
I develop intelligent AI applications that retrieve information from your knowledge base, orchestrate multi-step workflows using LangGraph, and generate accurate, context-aware responses with modern Large Language Models (LLMs).
Whether you need an internal knowledge assistant, customer support bot, research assistant, or document Q&A system, I'll build a scalable solution tailored to your requirements.
What you'll receive:
✅ LangGraph-powered AI agent workflows
✅ Retrieval-Augmented Generation (RAG)
✅ Vector database integration (ChromaDB, FAISS, or Qdrant)
✅ LLM integration (OpenAI, Gemini, Claude, or local models)
✅ FastAPI backend
✅ Clean, modular, and documented code
✅ Prompt engineering and workflow optimization
✅ Deployment support (Standard & Premium)
✅ Source code and setup instructions
I focus on building reliable, maintainable AI systems that are easy to extend and ready for production.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging Face, PyTorch, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$50
|
Standard
$125
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 2 | 4 | 10 |
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 |
Frequently asked questions
About Chinmai
AI Engineer | FastAPI | Python | LLM Applications | React | Automation
Bengaluru, India - 11:18 am local time
I specialize in developing AI-powered applications, automation tools, and scalable backend systems using Python, FastAPI, React, and modern LLM technologies.
My expertise includes:
✅ AI Chatbots (OpenAI, Gemini)
✅ FastAPI REST APIs
✅ Machine Learning Solutions
✅ AI Automation & Workflow Integration
✅ Data Extraction & Processing
✅ React Web Applications
✅ API Integrations
✅ Python Scripting & Automation
I focus on writing clean, maintainable code while delivering solutions that are reliable, scalable, and easy to use.
Why work with me?
• Clear communication
• Fast turnaround
• Well-documented code
• High-quality solutions
• Long-term support
If you're looking for someone who can turn your ideas into practical AI solutions, I'd love to help.
Let's build something impactful together.
Steps for completing your project
After purchasing the project, send requirements so Chinmai can start the project.
Delivery time starts when Chinmai receives requirements from you.
Chinmai works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Planning
Understand your use case, review your documents or data sources, and define the AI workflow.
Design & Development
Build the LangGraph,llamindex or langchain workflow, integrate the LLM, configure the vector database, and implement the RAG pipeline.