Ashik Sali
Retrieval-Augmented Generation (RAG) Pipeline for Financial Data Analysis

Retrieval-Augmented Generation (RAG) Pipeline for Financial Data Analysis


AI RAG LangChain LLM Finance

Overview

Large Language Models are powerful, but on their own they lack access to private, up-to-date, or domain-specific financial data.
This project solves that limitation by building a Retrieval-Augmented Generation (RAG) pipeline that grounds LLM responses in relevant financial documents.

The system allows users to ask natural-language questions about financial concepts, companies, or reports and receive context-aware, explainable answers sourced from real data rather than hallucinated knowledge.

High-Level Architecture

RAG System Architecture

At a high level, the system combines:

  • A frontend that captures user queries
  • A LangChain agent that orchestrates reasoning and tool usage
  • A vector database for semantic document retrieval
  • An LLM that generates grounded responses using retrieved context

Step-by-Step Flow (Simple View)

RAG Flow Diagram

  1. User asks a question (e.g., “What is EBITDA?” or “Explain recent earnings trends”)
  2. The query is sent to a backend Python FastAPI service
  3. The query is embedded and matched against a vector database
  4. Relevant document chunks are retrieved
  5. The LLM receives both the question and retrieved context
  6. A context-aware answer is streamed back to the user

Agent & Tooling Logic

Agent Decision Flow

The LangChain agent decides dynamically:

  • Whether a tool is required (search, retrieval, calculator, etc.)
  • Which documents are relevant
  • How to structure the final response

Key Design Choices

  • Tool Calling: Enables retrieval, calculations, and reasoning
  • Memory: Maintains conversational context across turns
  • Streaming Responses: Improves UX by returning partial answers in real time

Vector Search & Retrieval

Vector Database Flow

  • Financial documents are split into chunks
  • Each chunk is converted into vector embeddings
  • Queries are embedded and matched using semantic similarity
  • Only the most relevant chunks are passed to the LLM

This ensures answers are grounded in real data, not just model intuition.

Why RAG Matters for Finance

Traditional LLMs:

  • Cannot access private financial documents
  • Hallucinate facts
  • Struggle with up-to-date market data

This RAG pipeline:

  • Grounds answers in real financial sources
  • Improves accuracy and trust
  • Scales across reports, filings, and research notes
  • Enables explainable AI for finance use cases

Key Features

  • Context-aware financial Q&A
  • Semantic document retrieval using embeddings
  • Tool-based agent reasoning
  • Streaming responses for better UX
  • Modular and extensible architecture

Tech Stack

  • Language: Python
  • Frameworks: LangChain, FastAPI
  • LLM: OpenAI API
  • Vector Database: FAISS / ChromaDB
  • Concepts: RAG, Tool Calling, Memory, Embeddings