// Flagship AI Project

Fin
RAG

A production-grade Financial Retrieval-Augmented Generation platform for intelligent analysis of SEC filings, earnings reports, and financial documents — using hybrid retrieval and conversational AI workflows.

// Retrieval Method

BM25 + Vector

Dense + Sparse Hybrid

// Reranking

Cross-Encoder

Precision Reranking Engine

// Vector DB

pgvector

PostgreSQL Semantic Store

// LLM Engine

Groq Llama 3

Financial Intelligence Core

// Overview

Overview

Engineered a production-grade RAG platform for intelligent analysis of financial documents. The system combines hybrid BM25 + vector retrieval with a cross-encoder reranking pipeline, enabling precise, context-aware responses over large corpora of SEC filings and earnings reports. Backed by pgvector for semantic search and Groq Llama 3 for financial reasoning, it delivers a fully conversational AI workflow over structured financial data.

// Specs

Specifications

Domain

Financial Document Intelligence

Retrieval

Hybrid BM25 + Dense Vector Search

Reranker

Cross-Encoder (Sentence Transformers)

Vector Store

PostgreSQL + pgvector extension

LLM

Groq Llama 3 (Financial Reasoning)

Backend

FastAPI (Python)

Frontend

Next.js (TypeScript)

// Features

Features

Hybrid BM25 + vector retrieval combining dense semantic search with sparse keyword matching

Cross-encoder reranking pipeline for precision ordering of retrieved financial passages

pgvector-backed semantic store enabling fast similarity search over large document corpora

Conversational AI interface built on Groq Llama 3 for multi-turn financial Q&A

Ingestion pipeline for SEC filings, earnings transcripts, and structured financial reports

Full-stack deployment with FastAPI backend and Next.js frontend interface

// Tech Stack

Tech Stack

FastAPI Next.js PostgreSQL pgvector BM25 Hybrid RAG Sentence Transformers Cross-Encoder Groq API Llama 3 Python TypeScript

Interested
in FinRAG?

Want to discuss the architecture or explore collaboration? Let's talk about how RAG systems can power financial intelligence at scale.

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