Chatbot Financial Q&A with Grounded RAG

A question-answering chatbot that answers financial questions about U.S. public companies using structured SQL data and 10-K filing text from a vector store. Every answer is grounded in the provided data — the assistant never hallucinates.

Tech Stack

Layer	Technology
Frontend	Next.js 16, assistant-ui, shadcn/ui, Tailwind CSS 4, Zustand
Backend	FastAPI, LangChain, LangGraph, OpenAI GPT-4o
SQL Database	PostgreSQL 16 (192 rows, ~48 companies, 2022–2025)
Vector Database	Pinecone (FY2025 10-K filings: Alphabet, Amazon, Apple, Meta)
Auth	JWT (pyjwt) + Argon2
Local Infra	Docker Compose (Postgres + pinecone-local)

Getting Started

1. Prerequisites

• Docker & Docker Compose
• Python 3.11+
• Node.js 22+

2. Environment

Copy .env.example to .env and fill in your values:

cp .env.example .env

Required values: OPENAI_API_KEY, SECRET_KEY, DB credentials, Pinecone credentials.

Local Pinecone runs at localhost:5080 by default. The pinecone-local image is pulled from ghcr.io/pinecone-io/pinecone-local:latest.

3. Launch the full stack

docker compose up -d

This starts:

• PostgreSQL — port 5432 (with auto-seeded financial data)
• Pinecone — port 5080–5081
• Ingestion job — loads SQL data + upserts vector embeddings (runs once, then exits)
• Backend API — port 8000 (FastAPI)
• Frontend — port 3000 (Next.js)

4. Open the app

Navigate to http://localhost:3000, register an account, and start chatting.

Services

Service	Port	Description
frontend	3000	Next.js UI
backend	8000	FastAPI REST API
db	5432	PostgreSQL 16
pinecone	5080–5081	Local Pinecone vector DB
ingestion	—	One-shot data loader