Custom RAG AI Chatbot for PDFs, Docs & WebsitesShyam Ranasara
Turn your static PDFs, internal company docs, and website pages into an intelligent, citation-backed AI assistant—without paying expensive monthly vector database fees.
I build production-ready Retrieval-Augmented Generation (RAG) chatbots using Python, LangChain, FAISS, and Google Gemini (or OpenAI/local LLMs). Every answer is grounded strictly in your private data with zero hallucination.
WHAT'S INCLUDED:
• Multi-Format Ingestion: PDFs, DOCX, CSVs, TXT, or live Website URL scraping.
• Zero-Cost Vector Search: High-speed local FAISS indexing (saves $70+/mo over managed cloud vector DBs).
• Context-Aware LLM: Powered by Google Gemini Pro/Flash or GPT-4o with multi-turn conversational memory.
• Verified Citations: Every response cites the exact document name and page number.
• Ready-to-Use UI or API: Delivered with a clean Streamlit UI, FastAPI backend, or embeddable website widget.
MY 4-STEP PROCESS:
Discovery & Data Audit: Review your document structure, target queries, and deployment environment.
Ingestion & Indexing: Build the chunking pipeline and generate the FAISS vector store.
RAG & Guardrail Tuning: Configure LangChain retrieval thresholds and anti-hallucination system prompts.
UI/API Wrapping & Handoff: Connect the frontend/API, test edge cases, and hand over full documented source code.
WHAT I NEED FROM YOU TO KICK OFF:
• Sample documents (PDFs/Docs/CSVs) or target Website URLs.
• Your preferred LLM provider (Google Gemini or OpenAI API key).
• Preferred delivery format (Standalone Web UI, FastAPI backend, or Website Widget).
Shyam's other services
Contact for pricing
Duration4 weeks
Tags
FastAPI
Google Gemini
LangChain
Python
React
AI Engineer
Artificial Intelligence
Backend Developer
Machine Learning Engineer
Service provided by
Shyam Ranasara Indore, India
- 1
- Followers
Custom RAG AI Chatbot for PDFs, Docs & WebsitesShyam Ranasara
Contact for pricing
Duration4 weeks
Tags
FastAPI
Google Gemini
LangChain
Python
React
AI Engineer
Artificial Intelligence
Backend Developer
Machine Learning Engineer
Turn your static PDFs, internal company docs, and website pages into an intelligent, citation-backed AI assistant—without paying expensive monthly vector database fees.
I build production-ready Retrieval-Augmented Generation (RAG) chatbots using Python, LangChain, FAISS, and Google Gemini (or OpenAI/local LLMs). Every answer is grounded strictly in your private data with zero hallucination.
WHAT'S INCLUDED:
• Multi-Format Ingestion: PDFs, DOCX, CSVs, TXT, or live Website URL scraping.
• Zero-Cost Vector Search: High-speed local FAISS indexing (saves $70+/mo over managed cloud vector DBs).
• Context-Aware LLM: Powered by Google Gemini Pro/Flash or GPT-4o with multi-turn conversational memory.
• Verified Citations: Every response cites the exact document name and page number.
• Ready-to-Use UI or API: Delivered with a clean Streamlit UI, FastAPI backend, or embeddable website widget.
MY 4-STEP PROCESS:
Discovery & Data Audit: Review your document structure, target queries, and deployment environment.
Ingestion & Indexing: Build the chunking pipeline and generate the FAISS vector store.
RAG & Guardrail Tuning: Configure LangChain retrieval thresholds and anti-hallucination system prompts.
UI/API Wrapping & Handoff: Connect the frontend/API, test edge cases, and hand over full documented source code.
WHAT I NEED FROM YOU TO KICK OFF:
• Sample documents (PDFs/Docs/CSVs) or target Website URLs.
• Your preferred LLM provider (Google Gemini or OpenAI API key).
• Preferred delivery format (Standalone Web UI, FastAPI backend, or Website Widget).
Shyam's other services
Contact for pricing