Oracle RAG Chatbot — Product One-Pager

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The Challenge

Oracle Procurement & Expense modules have extensive documentation spread across 11+ disjointed PDFs totaling ~37.7 MB. Consultants struggle to synthesize this efficiently.

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The Solution

A Retrieval Augmented Generation (RAG) chatbot that ingests Oracle documentation into a vector database, providing accurate, context-aware answers with source citations.

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Pain Points

Google & LLM responses lack verified Oracle sources
Navigation paths often deviate from Oracle standards
Finding verified docs requires manual website crawling

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Solution Architecture

01

Knowledge Base 11 curated docs: Procurement (R12, 25C), P2P, Expenses

02

Ingestion Pipeline PyPDF2 + LangChain for parsing and chunking

03

Vector Storage OpenAI embeddings → Pinecone / Chroma DB

04

Generation Layer Query → Retrieve → LLM → Cited answers

oracle-rag-chatbot.rashigupta.cloud

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Current Status

Phase Implementation

Data layer fully prepared with domain-specific corpus

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Knowledge Base

📄 Oracle Procuremen...

19 MB

📄 Procurement R12 G...

8.4 MB

📄 P2P Process Flows

5.2 MB

📄 Expense Managem...

5.1 MB

📊 Total Corpus

~37.7 MB

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Tech Stack

PyPDF2 OpenAI Pinecone GPT-4 Gemini