You will get a RAG knowledge base chatbot using OpenAI, LangChain, and vector database
Rising Talent
Project details
I will build a RAG knowledge base chatbot that answers questions from your own documents, PDFs, FAQs, SOPs, website content, product documentation, or internal knowledge base.
Unlike a basic chatbot, this system retrieves relevant information from your content first, then generates accurate, context-aware answers using OpenAI, LangChain, and a vector database such as Pinecone, Supabase Vector, or ChromaDB.
This is ideal for SaaS support, internal knowledge assistants, PDF chatbots, customer support bots, SOP assistants, policy assistants, product documentation assistants, education platforms, and teams with large business documents.
Depending on your package, I can build a basic document chatbot, a RAG chatbot with vector search, or a production-ready RAG assistant with document ingestion, backend API, chat interface, source-grounded answers, document upload flow, admin panel, authentication if needed, deployment support, documentation, and handoff.
Unlike a basic chatbot, this system retrieves relevant information from your content first, then generates accurate, context-aware answers using OpenAI, LangChain, and a vector database such as Pinecone, Supabase Vector, or ChromaDB.
This is ideal for SaaS support, internal knowledge assistants, PDF chatbots, customer support bots, SOP assistants, policy assistants, product documentation assistants, education platforms, and teams with large business documents.
Depending on your package, I can build a basic document chatbot, a RAG chatbot with vector search, or a production-ready RAG assistant with document ingestion, backend API, chat interface, source-grounded answers, document upload flow, admin panel, authentication if needed, deployment support, documentation, and handoff.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$499
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 28 days |
Number of Revisions | 1 | 2 | 0 |
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 Isaar
AI Automation Engineer | SaaS MVPs, RAG, AI Agents, Python & FastAPI
Bristol, United Kingdom - 10:53 pm local time
Common usecases : AI workflow tools, CRM, B2B platforms, e-commerce, API platforms, document parsing and file management systems, business dashboards, payment integrations.
Core stack : Python, Django, FastAPI, React, Next.js, PostgreSQL, MySQL, Redis, REST APIs,
AI integrations : n8n, Zapier
LLMs : OpenAI, Gemini AI, OCR, Llama, Langchain, CrewAI
DevOps : AWS, Google Cloud, Docker, Git
Payment integrations : Stripe, Cashfree, HyperPay, Tabby, and Tamara.
Production-ready systems with clean backend architecture, secure APIs, well-structured databases, automation logic, AI integrations, and intuitive dashboards designed to support real business operations.
My approach :
- understand the business goal, plan clear milestones,
- Translate business requirements to technical specs with clear scope
- Clear and regular communication.
- Deliver on time, matched to specs.
Steps for completing your project
After purchasing the project, send requirements so Isaar can start the project.
Delivery time starts when Isaar receives requirements from you.
Isaar works on your project following the steps below.
Revisions may occur after the delivery date.
Knowledge Review
I will review your documents, FAQs, SOPs, chatbot goal, target users, and expected answer style before development.
RAG Setup
I will prepare document ingestion, embeddings, vector database, OpenAI/LangChain retrieval, and chatbot logic.