📊 LangLLaMA-FactQA: Financial Fact Extraction from Amazon 10-K Using RDF, SPARQL, LangChain, and LLaMA 3

🔍 Extract structured financial knowledge from SEC 10-K filings using RDF graphs, query them via SPARQL, and interface naturally using LLaMA 3 LLM through LangChain—all powered by Apache Jena Fuseki.

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📽 Demo Video

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🚀 Project Description

This project allows users to query financial data from Amazon's 10-K filing (XBRL to RDF) through a local LangChain+Ollama-powered chatbot.

• 🧠 Converts XBRL filings to RDF triples
• 🔎 Loads the .ttl (Turtle) file into a local Apache Jena Fuseki triplestore
• 🧾 Uses SPARQL queries dynamically generated by LLaMA 3 LLM
• 💬 Interface built using Streamlit for ease of access

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🧱 Tech Stack Used

Layer	Tools
LLM	Ollama with LLaMA 3
Prompting	LangChain
Graph DB	Apache Jena Fuseki
File Conversion	Arelle (for XBRL to RDF pipeline)
UI	Streamlit
RDF Format	Turtle (.ttl)
Language	Python 3.10+
Environment	Conda + Windows 11

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Demo

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📁 Folder Structure

📂 LangLLaMA-FactQA/
├── amzn-2024.ttl               # Converted RDF Turtle file
├── xbrl_to_rdf.py              # Script to convert XBRL to RDF
├── sparql_query.py             # Basic SPARQL test script
├── main.py                     # LangChain + LLaMA SPARQL interface
├── app.py                      # Streamlit UI
├── requirements.txt            # Python dependencies
└── demo_screenshot.png         # UI screenshot preview

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🛠 Setup Instructions

1. Clone & Setup Environment

git clone https://github.com/noorcs39/langllama-factqa
cd langllama-factqa
conda create -n ldm python=3.10
conda activate ldm
pip install -r requirements.txt

2. Run Apache Jena Fuseki

• Download and unzip Apache Jena Fuseki
• Run fuseki-server batch file
• Open http://localhost:3030
• Add dataset /amzn
• Upload amzn-2024.ttl

3. Pull LLaMA 3 and Test Ollama

ollama pull llama3
ollama run llama3

4. Launch Chatbot UI

streamlit run app.py

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💡 Example Questions

• What is the value of AccountsPayableCurrent?
• How much is AccountsReceivableNetCurrent?
• Give me the value of AccruedLiabilitiesCurrent.

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👤 Author

Noor Uddin
📧 noor.cs2@yahoo.com
🌐 https://noorcs39.github.io/Nooruddin

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📜 License

This project is licensed under the MIT License.

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🙏 Acknowledgements

• SEC EDGAR for public 10-K filings
• Arelle for XBRL parsing
• LangChain & Ollama for powerful LLM pipelines
• Apache Jena for SPARQL and RDF storage