You will get a Generative AI Chatbot using LangChain, FAISS & Hugging Face Models
Project details
You will get a custom Generative AI chatbot built with LangChain, FAISS, and Hugging Face models, designed to provide intelligent, context-aware responses from your own data. With hands-on experience delivering end-to-end AI solutions, I create chatbots that combine accuracy, scalability, and clean design.
I specialize in Retrieval-Augmented Generation (RAG) pipelines, vector database integration, and prompt engineering — enabling your chatbot to learn directly from PDFs, websites, or business documents. The solution includes a Streamlit or FastAPI interface, with deployment options via Docker and AWS EC2 for production-grade performance.
This project is ideal for startups, founders, and enterprises looking to automate FAQs, internal knowledge bases, or data-driven assistance using state-of-the-art Generative AI. Every delivery includes detailed code comments, documentation, and post-delivery support.
Let’s turn your idea into a reliable, future-ready AI assistant that understands your data and delivers real-world value.
I specialize in Retrieval-Augmented Generation (RAG) pipelines, vector database integration, and prompt engineering — enabling your chatbot to learn directly from PDFs, websites, or business documents. The solution includes a Streamlit or FastAPI interface, with deployment options via Docker and AWS EC2 for production-grade performance.
This project is ideal for startups, founders, and enterprises looking to automate FAQs, internal knowledge bases, or data-driven assistance using state-of-the-art Generative AI. Every delivery includes detailed code comments, documentation, and post-delivery support.
Let’s turn your idea into a reliable, future-ready AI assistant that understands your data and delivers real-world value.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AI-Generated Art, AI-Generated Code, Conversational AI, Machine Translation, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Copy.ai, GitHub Copilot, Gradio, Hugging Face, Jasper AI, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, BLOOM, ChatGPT, Dolly, GPT-4, GPT-J, GPT-Neo, LLaMA, Naive Bayes Classifier, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$100
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 2 | 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 |
About Leena
AI Engineer | Machine Learning, Deep Learning & Generative AI Expert
Raipur, India - 3:40 am local time
I specialize in end-to-end AI development, from predictive models and automation pipelines to LLM-powered applications using LangChain, RAG, and vector databases (FAISS/Pinecone).
I’ve led cross-functional AI projects that improved model accuracy, reduced manual workload, and streamlined decision-making through data-driven automation.
With expertise in Python, TensorFlow, FastAPI, Docker, and AWS EC2, I build intelligent, scalable, and production-ready AI systems that create measurable business impact.
Steps for completing your project
After purchasing the project, send requirements so Leena can start the project.
Delivery time starts when Leena receives requirements from you.
Leena works on your project following the steps below.
Revisions may occur after the delivery date.
Project Understanding & Data Review
Review client goals and provided data (PDFs, text, or links) to define chatbot scope. If data collection or web scraping is needed, mention it explicitly. Public data extraction can be added at extra cost based on complexity and volume.
Data Processing & Vectorization
Clean and process provided data, create vector embeddings using FAISS, and prepare the knowledge base for chatbot training and retrieval.