Financial RAG Engine

Cloud-deployed Retrieval-Augmented Generation system for financial concept queries, built with Airflow, FastAPI, and Streamlit on GCP.

🌐 Live Deployment

Service	URL	Purpose
Streamlit Frontend	https://financial-rag-frontend-387661610307.us-central1.run.app	User interface
FastAPI Backend	https://financial-rag-api-387661610307.us-central1.run.app/docs	REST API
Cloud Storage	gs://financial-rag-artifacts/	Data artifacts
Airflow	Cloud Composer	Workflow orchestration

🎥 Demo Video

Watch the complete pipeline in action: Financial RAG Engine Demo

📘 Interactive Codelab

View the full tutorial here:
👉 Open Google Codelab

🏗️ Architecture

User → Streamlit → FastAPI → ChromaDB (Vector Store)
                       ↓
                  PostgreSQL (Cache)
                       ↓
                  Wikipedia (Fallback)
       
Airflow DAGs → Automated Updates

📦 Components

PDF Processing Pipeline

Automated 6-step pipeline (pipeline_parse_chunck_vectorstore.py):

1. Parse PDF with Docling
2. Convert to chunks (31 pages)
3. Experiment with 4 chunking strategies
4. Build vectorstore with optimal strategy
5. Evaluate retrieval performance
6. Upload to GCS

Best Strategy: Medium Chunks (1000 chars, 200 overlap)

• Precision@3: 0.933
• MRR: 1.0
• Total: 122 chunks

Airflow DAGs (Cloud Composer)

• fintbx_ingest_dag: Weekly PDF processing
• concept_seed_dag: On-demand concept seeding

FastAPI Backend (Cloud Run)

Endpoints:

• POST /query: Query concepts with automatic caching
• POST /seed: Batch pre-generate notes

Features:

• ChromaDB vector search
• Wikipedia fallback
• PostgreSQL caching
• Structured JSON responses

Streamlit Frontend (Cloud Run)

Features:

• Concept query interface
• Cache status indicators
• Source tracking (textbook vs Wikipedia)
• Search history
• PDF section references

Evaluation & Benchmarking

Quality Metrics:

• Accuracy: 100%
• Completeness: 100%
• Citation Fidelity: 100%

Performance:

• Embedding Cost: $0.0222 (342 chunks)
• Retrieval Success: 100%
• Model: text-embedding-3-large

📊 Key Results

🎯 Chosen Strategy: Medium Chunks

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📊 Experimental Results

Strategy Comparison

Strategy	Chunks	Precision@3	Recall@3	MRR	Avg Time
Small (500)	225	0.867	1.30	1.00	0.76s
Medium (1000)	118	0.933	1.40	1.00	0.56s
Large (2000)	65	0.933	1.40	0.90	0.95s
Paragraph	115	0.933	1.40	1.00	0.32s

Testing: 5 queries evaluated across 4 strategies using text-embedding-3-large

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💡 Justification

Quantitative Evidence

1. Highest Precision (93.3%)

• 6.6% better than Small Chunks (86.7%)
• 93 out of 100 retrieved results are relevant
• Minimizes noise in search results

2. Best Recall (1.40)

• Retrieves all relevant documents plus additional context
• Tied for highest with Large and Paragraph strategies
• Ensures comprehensive information coverage

3. Perfect MRR (1.0)

• Most relevant document always ranks first
• Outperforms Large Chunks (MRR = 0.9)
• Critical for user experience

4. Optimal Speed (0.56s)

• 26% faster than Small (0.76s)
• 41% faster than Large (0.95s)
• Acceptable for real-time applications

5. Balanced Chunk Count (118)

• Not over-fragmented (vs 225 for Small)
• Not too coarse (vs 65 for Large)
• Optimal for processing efficiency

---

📖 Concrete Examples

Example 1: Product Overview Query

Query: "What is Financial Toolbox?"

Medium Chunks Result:

[1] Similarity: 0.572 | Page 28 | Product Description
    "Financial Toolbox Product Description
     Analyze financial data and develop financial models
     Financial Toolbox provides functions for mathematical modeling
     and statistical analysis of financial data..."

Why Medium Works:

• Complete product description in one chunk
• 791 chars average provides sufficient context
• Not fragmented like Small (would split across 2-3 chunks)
• Not diluted like Large (would mix with installation info)

---

Example 2: Technical Implementation

Query: "Matrix operations"

Medium Chunks Result:

[1] Similarity: 0.051 | Page 33 | Matrix Algebra 💻
    "Matrix Algebra Refresher
     
     Introduction
     The explanations use MATLAB matrix operations.
     
     Adding and Subtracting Matrices
     Matrix addition operates element-by-element:
     A = [1 2; 3 4];
     B = [5 6; 7 8];
     C = A + B..."

Why Medium Works:

• Preserves complete code examples (intro + code + explanation)
• 1000 chars captures full instructional flow
• Code blocks remain intact and executable
• Not split mid-example like Small Chunks

---

Example 3: Domain Concept

Query: "Portfolio optimization"

Medium Chunks Result:

[1] Similarity: 0.163 | Page 7 | Analyzing Portfolios 📊
    "Analyzing Portfolios
     - Portfolio Optimization Against a Benchmark
     - Obtaining Efficient Portfolios for Target Risks
     - Mean-Variance Portfolio Optimization
     [Table: Related functions and page references]"

Why Medium Works:

• Groups related concepts together
• Table of contents stays with section header
• Maintains logical document structure
• Links subsections appropriately

---

🔬 Comparison with Alternatives

vs. Small Chunks (500)

+ Precision: 93.3% vs 86.7% (+6.6%)
+ Recall: 1.40 vs 1.30 (+7.7%)
+ Speed: 0.56s vs 0.76s (26% faster)
+ Fewer chunks: 118 vs 225 (48% reduction)
- MRR: 1.00 (tied)

Verdict: Medium significantly outperforms Small

vs. Large Chunks (2000)

+ MRR: 1.00 vs 0.90 (better ranking)
+ Speed: 0.56s vs 0.95s (41% faster)
+ More granular: 118 vs 65 chunks
- Precision: 93.3% (tied)
- Recall: 1.40 (tied)

Verdict: Medium provides better ranking and speed

vs. Paragraph-focused

- Speed: 0.56s vs 0.32s (Paragraph faster)
+ More predictable chunk sizes
+ Better for production consistency
- Precision: 93.3% (tied)
- Recall: 1.40 (tied)
- MRR: 1.00 (tied)

Verdict: Medium chosen for predictability; Paragraph is valid alternative

Chunking Strategy Comparison

Strategy	Size	Chunks	Precision@3	MRR	Selected
Small	500	225	0.867	1.0	❌
Medium	1000	118	0.933	1.0	✅
Large	2000	65	0.933	0.9	❌
Paragraph	1000	116	0.867	1.0	❌

Selection Rationale: Strategy 2 achieves highest precision (0.933) with perfect MRR (1.0), providing optimal balance between accuracy and context preservation.

📁 Project Structure

financial-rag-engine/
├── pipeline_parse_chunck_vectorstore.py  # Automated pipeline
├── src/
│   ├── parser/              # PDF parsing
│   ├── embeddings/          # Vectorstore building
│   └── experiments/         # Chunking & evaluation
├── dags/                    # Airflow DAGs
├── fastapi_rag_service/     # Backend API
├── streamlit-app/           # Frontend UI
├── data/
│   ├── parsed/              # Processing results
│   ├── experiments/         # Experiment results
│   ├── evaluation/          # Benchmark results
│   └── vectorstore/         # ChromaDB database
└── upload_to_gcs.py         # GCS upload utility

🔧 Prerequisites

# Environment variables (.env)
OPENAI_API_KEY=your-key
GOOGLE_APPLICATION_CREDENTIALS=.credentials/gcp-key.json

📈 Technologies

• PDF Parsing: Docling
• Chunking: LangChain RecursiveCharacterTextSplitter
• Embeddings: OpenAI text-embedding-3-large
• Vector Store: ChromaDB
• Orchestration: Apache Airflow (Cloud Composer)
• Backend: FastAPI
• Frontend: Streamlit
• Cloud: GCP (Cloud Run, Cloud Composer, Cloud Storage)

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