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| title: Finance RAG Analyst | |
| emoji: 📉 | |
| colorFrom: purple | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.5.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Financial RAG Demo | |
| This demo showcases a **constrained Financial RAG pipeline** designed to reduce hallucinations through **explicit routing and hard constraints**, not prompt tricks. | |
| --- | |
| ## What this demo does | |
| - Routes queries based on detected company entities (Apple / Microsoft) | |
| - Prevents accidental cross-company document mixing | |
| - Processes financial tables as images to preserve structure | |
| - Explicitly rejects unsupported or ambiguous queries | |
| --- | |
| ## How to test it | |
| Try the following queries: | |
| - `What was Apple’s total revenue in 2023?` | |
| - `What is Microsoft’s operating income?` | |
| - `Compare Apple and Microsoft revenues` → rejected or limited | |
| - `What was Google’s revenue in 2023?` → rejected | |
| The UI shows retrieved pages and scores to make the pipeline inspectable. | |
| --- | |
| ## Important limitations | |
| - Explicit multi-company questions may trigger cross-entity reasoning | |
| - Source-constrained prompts are not strictly enforced | |
| - Dataset is intentionally small (demo-only) | |
| For full technical details and design discussion, see the GitHub repository linked on the CV. |