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https://github.com/jasoncobra3/finops-copilot

An end-to-end AI-powered FinOps platform that ingests cloud billing data, analyzes cost trends, answers natural-language questions using a RAG pipeline (LangChain + FAISS + sentence-transformers + Groq), and provides actionable cost optimization recommendations. Includes a FastAPI backend and Streamlit dashboard UI - fully containerized with Docker
https://github.com/jasoncobra3/finops-copilot

ai-assistant cloud-cost-optimization cloud-enginee cost-analytics data-analysis devops docker faiss faiss-vector-database fastapi finops groq langchain llm pandas rag rag-pipeline sentence-transformers sqlite3 streamlit

Last synced: 5 days ago
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An end-to-end AI-powered FinOps platform that ingests cloud billing data, analyzes cost trends, answers natural-language questions using a RAG pipeline (LangChain + FAISS + sentence-transformers + Groq), and provides actionable cost optimization recommendations. Includes a FastAPI backend and Streamlit dashboard UI - fully containerized with Docker

Awesome Lists containing this project

README 

          
# 🌩️ AI-driven Cloud Cost Copilot (FinOps Assistant)



---



## 📌 Overview

**The FinOps Assistant is an AI-driven cloud cost analytics platform designed to help organizations monitor, analyze, and optimize their cloud spending.**

It provides:

- End-to-end pipeline: ETL → KPIs → RAG → Recommendations → API → UI

- KPI dashboards to analyze cost trends

- Natural-language Q&A over your cost data and FinOps documentation

- Actionable recommendations to reduce costs



[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/jasoncobra3/FinOps-Copilot)

  

---



## 🧩 Features



- Cost Analysis — Monthly spend trends, top cost drivers, cost anomalies

- RAG Q&A — Ask natural questions like “Why did compute cost increase in May?”

- Recommendations — Detect idle resources, tagging gaps, sudden spikes

- UI + API — Interactive Streamlit dashboard + FastAPI backend

- Evaluation Suite — Measure retrieval quality (Recall@k) + answer quality



---



## 🧠 Architecture

 ![](docs/Assets/updated_architecture.png) 



 ---



 ## 🗂️ Directory Structure

 

 ```

├── app/

│   ├── main.py             # FastAPI backend entrypoint

│   ├── models.py            # SQLAlchemy schema (billing, resources)

│   ├── analytics.py         # KPI & trend calculations

│   ├── rag.py               # RAG pipeline setup (retriever + LLM)

│   ├── etl.py               # ETL piepline to validate data

|   ├── reccomendations.py   # Suggestions Generation

|   ├── rag_qa.py            # LangChain QA chain logic

│   └── validators.py        # input validation, prompt-injection guard 

|                        

├── docs/                     # Project documentation

│   ├── finops.md             # Refernce Docs for LLM

│   ├── PRD.pdf               # Product requirement Document

│   ├── Technical Design doc.pdf

│   ├── Assets/              

|

├── UI/

│   └── app.py                # Streamlit frontend dashboard

├── scripts/

│   ├── ingest_billing.py

│   ├── seed _resources.py

│   ├── build_faiss_index.py

│   ├── update_assignments.py

|   ├── manage_data.py

│   └── ...

|

├── tests/                    # Testing Files for evaluation

│   ├── conftest.py

│   ├── evaluate_rag.py

│   ├── test_analytics.py

│   ├── test_reccomendations.py

|   ├── test_reccomendations_api.py

│   └── rag_eval_results.json

|

|

├── data/                     # SQLite DB (billing.db) & Data Files

├── requirements.txt

├── Dockerfile

├── docker-compose.yml

├── .dockerignore

├── .env.example

└── README.md



```

---



## ⚙️ Tech Stack



| Layer         | Technology                                                    | Purpose                       |

| ------------- | ------------------------------------------------------------- | ----------------------------- |

| ETL & DB      |Python, SQLAlchemy, SQLite                                     | Data ingestion + schema       |

| Analytics     |     Pandas, SQL                                               | KPI & cost trend calculations |

| RAG Pipeline  | sentence-transformers, FAISS, LangChain, Groq LLM             | Natural-language Q\&A         |

| API Layer     |    FastAPI                                                    | Serve KPIs and Q\&A endpoints |

| Frontend      |             Streamlit                                         | Interactive dashboard UI      |

| Deployment    | Docker, Docker Compose                                        | Containerization              |

| Observability | LangSmith                                                     | Logging + traces              |

| Security      | Prompt-injection guards, keyword filters                      | Secure LLM input              |



---



## 🧪 Setup & Running Locally



1. **Clone repo**

   ```bash

   git clone https://github.com/jasoncobra3/FinOps-Copilot.git

   cd FinOps-Copilot



2. **Create environment**

   ```bash

   python -m venv .venv

   source venv/bin/activate  # or venv\\Scripts\\activate (Windows)



3. **Install dependencies:**

   ```bash

   pip install -r requirements.tx



4. **Initialize DB**

   ```bash

     python -m app.models



5. **Generate Sammple Data**

   ```bash

   python scripts/generate_sample_data.py



7. **Ingest sample data**

   ```bash

   python scripts/ingest.py  --input data/sample_billing.csv

   python scripts/seed_resources.py

   python scripts/update_assignments.py ##generate randomness in data



9. **Build RAG index**

   ```bash

   python scripts/build_faiss_index.py



10. **Run FAstAPI Backend**

    ```bash

    uvicorn app.main:app --reload

    

- FastAPI - http://localhost:8000

    

12. **Run Streamlit Frontend**

    ```bash

    streamlit run UI/app.py

     

- Streamlit - http://localhost:8501

    

---



## 📊 Evaluation



**Run RAG retrieval evaluation**

  ```bash

     python tests/eval_rag.py

  ```

- Outputs Recall@1/3/5 scores in CSV

- Also includes answer quality scores (1–5 rubric)



---

## 🧠 Design Decisions & Trade-offs



- SQLite chosen for local simplicity, can migrate to PostgreSQL later

- FAISS is fast and lightweight for demo scale (vs hosted vector DBs)

- LangChain simplifies orchestration but adds abstraction → documented clearly

- Streamlit is quick to build for MVP, though not production-grade

- Groq LLM chosen to avoid dependency on paid APIs



---



## 🚀 Future Work



- Migrate to PostgreSQL for multi-user deployments

- Add real billing data ingestion from cloud APIs

- Implement cost optimization engine (idle resource detection, right-sizing)

- Add user auth + role-based access control

- Enhance Streamlit UI (filters, charts, reports)

- Deploy on Railway or Render with CI/CD



---



## 📸 Screenshots

| Dashboard KPI View| Month DropDown | Reccommendations |

|----------------|------------|---------------|

| ![](docs/Assets/Screenshot_1.png) | ![](docs/Assets/Screenshot_2.png) | ![](docs/Assets/Screenshot_3.png)|



| Underutilized resources| AI RAG Chatbot | Response With Resource group |

|----------------|------------|----------------|

|![](docs/Assets/Screenshot_5.png)| ![](docs/Assets/Screenshot_7.png) | ![](docs/Assets/Screenshot_8.png) |



---

## 🌟 Contributing

**Feel free to fork, star, or submit a pull request to contribute improvements!**