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@ -90,24 +90,28 @@ Response to User |
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} |
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} |
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``` |
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``` |
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### Tool Registry (11 tools across 7 files) |
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### Tool Registry |
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**11 Tools Built Across 7 Files:** |
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| Tool | File | Purpose | |
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| Tool | File | Purpose | |
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| ------------------------------------ | ------------------------ | ------------------------------------- | |
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|------|------|---------| |
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| `portfolio_analysis` | portfolio.py | Live Ghostfolio holdings via API | |
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| portfolio_analysis | portfolio.py | Live Ghostfolio holdings, allocation, performance | |
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| `add_property` | property_tracker.py | Add real estate to SQLite DB | |
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| compliance_check | portfolio.py | Concentration risk, regulatory flags | |
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| `get_properties` / `list_properties` | property_tracker.py | List all active properties | |
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| tax_estimate | portfolio.py | Tax liability estimation | |
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| `update_property` | property_tracker.py | Update value/mortgage on a property | |
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| get_market_data | market_data.py | Live stock prices via Yahoo Finance | |
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| `remove_property` | property_tracker.py | Soft-delete property | |
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| add_property | property_tracker.py | CRUD — create property record | |
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| `analyze_equity_options` | property_tracker.py | 3 equity scenarios (keep/refi/rental) | |
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| get_properties | property_tracker.py | CRUD — read all properties | |
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| `get_total_net_worth` | property_tracker.py | Portfolio + real estate combined | |
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| update_property | property_tracker.py | CRUD — update property values | |
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| `calculate_down_payment_power` | wealth_bridge.py | Portfolio → down payment ability | |
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| remove_property | property_tracker.py | CRUD — delete property record | |
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| `calculate_job_offer_affordability` | wealth_bridge.py | COL-adjusted salary comparison | |
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| analyze_equity_options | property_tracker.py | Home equity scenario analysis | |
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| `calculate_relocation_runway` | relocation_runway.py | Financial stability timeline | |
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| get_total_net_worth | property_tracker.py | Portfolio + real estate combined | |
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| `analyze_wealth_position` | wealth_visualizer.py | Fed Reserve wealth benchmarks | |
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| calculate_relocation_runway | relocation_runway.py | Financial stability timeline | |
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| `analyze_life_decision` | life_decision_advisor.py | Multi-tool orchestrator | |
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| analyze_wealth_position | wealth_visualizer.py | Fed Reserve peer comparison | |
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| `plan_family_finances` | family_planner.py | Childcare + family cost modeling | |
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| simulate_real_estate_strategy | realestate_strategy.py | Buy-hold retirement projection | |
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| `simulate_real_estate_strategy` | realestate_strategy.py | Buy-hold-rent projection | |
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| plan_family_finances | family_planner.py | Childcare cost impact | |
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| analyze_life_decision | life_decision_advisor.py | Job offer, relocation decisions | |
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| calculate_down_payment_power | wealth_bridge.py | Portfolio to home purchase | |
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--- |
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--- |
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@ -123,58 +127,45 @@ connection to reduce cold-start latency on the first request. |
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## Verification Strategy |
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## Verification Strategy |
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### 3 Verification Systems Implemented |
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**Three verification systems implemented:** |
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**Verification 1 — Confidence Scoring** (`main.py::calculate_confidence`) |
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Every `/chat` response includes a `confidence` score (0.0–1.0). The score is computed |
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dynamically based on: |
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- Base: 0.85 |
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- Deduction: −0.20 if tool result contains an error |
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- Addition: +0.10 if response uses a verified data source (citations present) |
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- Addition: +0.05 for high-reliability tools (portfolio_analysis, property_tracker) |
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- Clamped: [0.40, 0.99] |
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Example: `{"confidence": 0.95, "verified": true}` |
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**Verification 2 — Source Attribution (Citation Enforcement)** (`graph.py` system prompt) |
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The LLM system prompt enforces a citation rule for every factual claim: |
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**1. Confidence Scoring** |
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Every /chat response includes a confidence score between 0.0 and 1.0. Score is based on tool |
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success, data source reliability, and query type. Responses with confidence below 0.80 have |
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verified=false returned to the client. |
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- Portfolio data → cites `"Ghostfolio live data"` |
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**2. Source Attribution (Citation Enforcement)** |
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- Real estate data → cites `"ACTRIS/Unlock MLS January 2026"` |
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The system prompt enforces a citation rule: every factual claim must name its data source. |
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- Federal Reserve benchmarks → cites `"Federal Reserve SCF 2022"` |
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Portfolio data cites "Ghostfolio live data". Real estate projections cite user-provided |
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- User assumptions → cites `"based on your assumption of X%"` |
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assumptions. Federal Reserve data is cited by name. The LLM cannot return a number |
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- Projections → flagged as `"not financial advice / estimate only"` |
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without its source. |
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The LLM cannot return a number without naming its source. |
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**3. Domain Constraint Check** |
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A pre-return scan runs on every financial response checking for high-risk phrases |
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("guaranteed return", "you should buy", "risk-free"). Responses containing these |
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phrases without appropriate disclaimers are flagged. Every financial projection |
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includes "not financial advice" language. |
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**Verification 3 — Domain Constraint Check** (`main.py::check_financial_response`) |
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**Note on plan vs delivery:** |
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The pre-search described a fact-check node with tool_result_id tagging. The implemented |
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approach achieves the same goal differently: citation enforcement is in the system prompt |
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rather than a separate node, which proved more reliable in practice because it cannot |
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be bypassed by the routing logic. |
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Before every response is returned, it is scanned for high-risk financial advice phrases: |
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### Human-in-the-Loop (Implemented) |
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```python |
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Write operations (buy, sell, add transaction, add cash) use an awaiting_confirmation flow. |
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HIGH_RISK_PHRASES = [ |
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When the user expresses a write intent (e.g. "buy 10 shares of AAPL"), the write_prepare |
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"you should buy", "you should sell", "i recommend buying", |
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node builds a confirmation payload and sets awaiting_confirmation=True. The user sees a |
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"guaranteed return", "will definitely", "certain to", |
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summary and must reply "yes" or "confirm" to proceed. Only then does write_execute run |
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"risk-free", "always profitable", |
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the actual Ghostfolio API call. This prevents accidental trades. |
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] |
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``` |
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If a high-risk phrase is found AND there is no disclaimer present, `verified: false` is |
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returned in the response. Disclaimers that pass the check include: |
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_"not financial advice"_, _"consult an advisor"_, _"projection"_, _"estimate"_. |
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Every `/chat` response includes `verification_details` with `passed`, `flags`, and |
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`has_disclaimer` fields. |
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--- |
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--- |
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## Eval Results |
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## Eval Results |
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**Test Suite:** 182 test cases across 10 test files |
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**Test Suite:** 183 test cases across 10 test files |
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**Pass Rate:** 100% (182/182) |
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**Pass Rate:** 100% (183/183) |
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### Test Categories |
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### Test Categories |
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@ -277,28 +268,23 @@ Every `/chat` response includes: |
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## Open Source Contribution |
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## Open Source Contribution |
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**Contribution Type:** New agent layer + eval dataset as brownfield addition |
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**Contribution Type:** Public Eval Dataset |
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**Repository:** [github.com/lakshmipunukollu-ai/ghostfolio-agent-priya](https://github.com/lakshmipunukollu-ai/ghostfolio-agent-priya) |
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**Branch:** `feature/complete-showcase` |
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**What was contributed:** |
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The complete real estate agent layer (14 tools, 182 tests, full observability setup) is |
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designed as a reusable brownfield addition to any Ghostfolio fork. The `agent/` directory is |
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self-contained with its own FastAPI server, LangGraph graph, SQLite database, and test suite. |
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**Zero changes to Ghostfolio core.** No existing files were modified outside of Angular routing |
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**What was delivered:** |
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and module registration. All additions are in: |
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183 test cases for finance AI agents — released publicly on GitHub as the first eval dataset |
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for agents built on Ghostfolio. |
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- `agent/` — the entire AI agent (new directory) |
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**Note on plan vs delivery:** |
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- `apps/client/src/app/pages/` — new Real Estate page (additive) |
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The pre-search planned an npm package and Hugging Face dataset release. During development, |
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- `apps/client/src/app/components/` — new AI chat component (additive) |
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the eval dataset approach was chosen instead because it provides more direct value to developers |
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forking Ghostfolio — they can run the test suite immediately without installing a package. |
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The dataset is MIT licensed and accepts contributions. |
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**To contribute back upstream:** |
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**Location:** |
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github.com/lakshmipunukollu-ai/ghostfolio/tree/submission/final/agent/evals |
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The `agent/` directory could be submitted as a PR to the main Ghostfolio repo as an optional |
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**Documentation:** |
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AI agent add-on. The eval dataset (`agent/evals/`) is releasable as a public benchmark for |
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agent/evals/EVAL_DATASET_README.md |
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finance AI agents. |
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--- |
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--- |
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Priyanka Punukollu
1 month ago