Document RAG Chatbot for a Fortune-1 US Retailer (LangChain + Pinecone)
A Fortune-1 US retailer
Overview
A document-grounded RAG chatbot built for a Fortune-1 US retailer, answering questions over the client’s documents (including medical-insurance/contract material). Built on the platform’s gpt4-langchain-pdf-chatbot foundation with a LangChain + Pinecone + GPT-4 pipeline.
The Challenge
A retailer of this scale holds large volumes of internal documentation that staff need quick, conversational access to. The engagement aimed to deliver a chatbot that could answer accurately from those documents rather than from a general model.
What We Built
A Next.js application with an ingestion step that parses and embeds PDFs (pdf-parse) into a Pinecone index (@pinecone-database/pinecone) and a chat path that streams GPT-4 answers grounded in retrieved context (@microsoft/fetch-event-source, react-markdown, remark-gfm). The app exposes server-side pages/api routes for ingestion and chat, with a Tailwind + Radix UI and a docs/medical-insurance-contracts source set. The build dates to spring 2023, in the platform’s early per-client deployment phase.
Technologies & Approach
The platform’s standard LangChain + Pinecone + GPT-4 RAG-over-PDF pattern on Next.js, with API routes handling ingestion and streaming chat. This shared base let each enterprise engagement start from a proven retrieval pipeline and focus on the client’s documents and branding.
Outcome / Impact
Delivered a working document-grounded chatbot for a Fortune-1 retailer and reinforced the reusable RAG pipeline that the platform deployed across multiple enterprise verticals.
Capabilities Demonstrated
- RAG over enterprise document sets
- LangChain + Pinecone ingestion and retrieval
- Streaming GPT-4 chat with Markdown rendering
- Reusable per-client chatbot delivery