mirror of https://github.com/ghostfolio/ghostfolio
Max P
1 month ago
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# Tasks |
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Last updated: 2026-02-23 |
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## Active Tickets |
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| ID | Feature | Status | Tests | PR / Commit | |
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| --- | --- | --- | --- | --- | |
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| T-001 | Presearch package and architecture direction | Complete | Doc review checklist | Local docs update | |
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| T-002 | ADR foundation in `docs/adr/` | Complete | ADR template and first ADR review | Local docs update | |
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| T-003 | Agent MVP tool 1: `portfolio_analysis` | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-004 | Agent memory and response formatter | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-005 | Eval dataset baseline (MVP 5-10) | Complete | `apps/api/src/app/endpoints/ai/evals/mvp-eval.runner.spec.ts` | Planned | |
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| T-006 | Full eval dataset (50+) | Planned | Dataset validation and regression run | Planned | |
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| T-007 | Observability wiring (LangSmith traces and metrics) | Planned | Trace assertions and latency checks | Planned | |
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| T-008 | Deployment and submission bundle | Complete | `npm run test:ai` + Railway healthcheck + submission docs checklist | Pending push | |
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|
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## Notes |
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- Canonical project requirements: `docs/requirements.md` |
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- ADR location: `docs/adr/` |
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- Detailed execution tracker: `tasks/tasks.md` |
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# AI Cost Analysis |
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Date: 2026-02-23 |
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Project: Ghostfolio Finance Agent MVP |
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Scope: development, testing, and monthly production projections |
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## Pricing Inputs |
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Primary model routing in MVP: |
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- 80% `glm-5` (Z.AI) |
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- 20% `MiniMax-M2.5` (MiniMax) |
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Reference pricing: |
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- Z.AI pricing page (`glm-5`): input `$1.00 / 1M tokens`, output `$3.20 / 1M tokens` |
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Source: https://docs.z.ai/guides/getting-started/pricing |
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- MiniMax pricing page (`MiniMax-M2.5`): input `$0.3 / 1M tokens`, output `$1.2 / 1M tokens` |
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Source: https://www.minimax.io/platform/pricing |
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Blended effective rates used for projections: |
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- Input: `$0.86 / 1M` (`0.8 * 1.00 + 0.2 * 0.30`) |
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- Output: `$2.80 / 1M` (`0.8 * 3.20 + 0.2 * 1.20`) |
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|
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## Development and Testing Costs |
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Measured local verification for this MVP used mocked model calls in Jest suites: |
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- `npm run test:ai` uses mocked `generateText` |
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- `npm run test:mvp-eval` uses mocked `generateText` |
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Direct external LLM calls from automated tests: |
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- API calls: `0` |
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- Input tokens: `0` |
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- Output tokens: `0` |
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- Cost: `$0.00` |
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Manual smoke estimate for development sessions: |
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- Assumed live calls: `40` |
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- Average tokens per call: `2400 input`, `700 output` |
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- Total estimated tokens: `96,000 input`, `28,000 output` |
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- Estimated model cost: |
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`96,000/1,000,000 * 0.86 + 28,000/1,000,000 * 2.80 = $0.16` |
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Observability cost: |
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- LangSmith tracing integration: planned, current spend in this repository phase: `$0.00` |
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## Production Cost Projections |
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Assumptions: |
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- Average AI queries per user per month: `30` (about `1/day`) |
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- Average tokens per query: `2400 input`, `700 output` |
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- Tool call frequency: `1.5 tool calls/query` (portfolio + risk/market mix) |
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- Verification overhead and retry buffer: `25%` |
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- Effective cost/query before buffer: `$0.004024` |
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Monthly projection: |
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| Users | Queries / Month | Model Cost / Month | With 25% Buffer | |
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| --- | ---: | ---: | ---: | |
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| 100 | 3,000 | $12.07 | $15.09 | |
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| 1,000 | 30,000 | $120.72 | $150.90 | |
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| 10,000 | 300,000 | $1,207.20 | $1,509.00 | |
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| 100,000 | 3,000,000 | $12,072.00 | $15,090.00 | |
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## Sensitivity Range (Model Mix) |
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Same token assumptions, model-only monthly cost (without 25% buffer): |
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- 100 users: |
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- all `MiniMax-M2.5`: `$4.68` |
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- all `glm-5`: `$13.92` |
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- 100,000 users: |
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- all `MiniMax-M2.5`: `$4,680` |
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- all `glm-5`: `$13,920` |
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## Instrumentation Plan for Exact Tracking |
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1. Add per-request token usage logging at provider response level. |
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2. Add LangSmith traces for request, tool-call, and verification spans. |
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3. Export weekly token and cost aggregates into a versioned cost ledger. |
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4. Set alert thresholds for cost/query drift and high retry rates. |
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# AI Development Log |
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Date: 2026-02-23 |
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Project: Ghostfolio Finance Agent MVP |
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Domain: Finance |
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## Tools and Workflow |
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The workflow for this sprint followed a strict loop: |
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1. Presearch and architecture alignment in `docs/PRESEARCH.md`. |
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2. Ticket and execution tracking in `tasks/tasks.md` and `Tasks.md`. |
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3. Implementation in the existing Ghostfolio backend and client surfaces. |
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4. Focused verification through AI unit tests and MVP eval tests. |
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5. Deployment through Railway with public health checks. |
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Technical stack used in this MVP: |
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- Backend: NestJS (existing Ghostfolio architecture) |
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- Agent design: custom orchestrator in `ai.service.ts` with helper modules for tool execution |
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- Memory: Redis with 24-hour TTL and max 10 turns |
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- Tools: `portfolio_analysis`, `risk_assessment`, `market_data_lookup` |
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- Models: `glm-5` via Z.AI primary path, `MiniMax-M2.5` fallback path, OpenRouter backup path |
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- Deployment: Railway (moved to GHCR image source for faster deploy cycles) |
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## MCP Usage |
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- Railway CLI and Railway GraphQL API: |
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- linked project/service |
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- switched service image source to `ghcr.io/maxpetrusenko/ghostfolio:main` |
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- redeployed and verified production health |
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- Local shell tooling: |
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- targeted test/eval runs |
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- health checks and deployment diagnostics |
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- GitHub Actions: |
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- GHCR publish workflow on `main` pushes |
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## Effective Prompts |
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The following user prompts drove the highest-impact delivery steps: |
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1. `use z_ai_glm_api_key glm-5 and minimax_api_key minimax m2.5 for mvp` |
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2. `ok 1 and 2 and add data to the app so we can test it` |
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3. `i dotn see activities and how to test and i dont see ai bot windows. where should i see it?` |
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4. `publish you have cli here` |
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5. `ok do 1 and 2 and then 3. AI development log (1 page) 4. AI cost analysis (100/1K/10K/100K users) 5. Submit to GitHub` |
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## Code Analysis |
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Rough authorship estimate for the MVP slice: |
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- AI-generated implementation and docs: ~70% |
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- Human-guided edits, review, and final acceptance decisions: ~30% |
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The largest human contribution focused on: |
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- model/provider routing decisions |
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- deploy-source migration on Railway |
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- quality gates and scope control |
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## Strengths and Limitations |
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Strengths observed: |
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- High velocity on brownfield integration with existing architecture |
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- Fast refactor support for file-size control and helper extraction |
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- Reliable generation of deterministic test scaffolding and eval cases |
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- Strong support for deployment automation and incident-style debugging |
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Limitations observed: |
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- CLI/API edge cases required manual schema introspection |
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- Runtime state and environment drift required explicit verification loops |
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- Exact token-cost accounting still needs production telemetry wiring |
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## Key Learnings |
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1. Clear, constraint-rich prompts produce fast and stable implementation output. |
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2. Deterministic eval cases are essential for regression control during rapid iteration. |
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3. Deploy speed improves materially when runtime builds move from source builds to prebuilt images. |
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4. Production readiness depends on traceability: citations, confidence scores, verification checks, and explicit assumptions in cost reporting. |
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# Tasks |
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Last updated: 2026-02-23 |
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## Active Tickets |
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| ID | Feature | Status | Tests | PR / Commit | |
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| --- | --- | --- | --- | --- | |
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| T-001 | Presearch package and architecture direction | Complete | Doc review checklist | Local docs update | |
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| T-002 | ADR foundation in `docs/adr/` | Complete | ADR template and first ADR review | Local docs update | |
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| T-003 | Agent MVP tool 1: `portfolio_analysis` | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-004 | Agent memory and response formatter | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-005 | Eval dataset baseline (MVP 5-10) | Complete | `apps/api/src/app/endpoints/ai/evals/mvp-eval.runner.spec.ts` | Planned | |
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| T-006 | Full eval dataset (50+) | Planned | Dataset validation and regression run | Planned | |
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| T-007 | Observability wiring (LangSmith traces and metrics) | Planned | Trace assertions and latency checks | Planned | |
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| T-008 | Deployment and submission bundle | Complete | `npm run test:ai` + Railway healthcheck + submission docs checklist | Pending push | |
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## Notes |
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- Canonical project requirements live in `docs/requirements.md`. |
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- Architecture decisions live in `docs/adr/`. |
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- Root tracker mirror lives in `Tasks.md`. |
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## MVP Local Runbook |
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1. Install dependencies and infra: |
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- `npm install` |
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- `cp .env.dev .env` |
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- `docker compose -f docker/docker-compose.dev.yml up -d` |
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- `npm run database:setup` |
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2. Start API: |
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- `npm run start:server` |
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3. Authenticate and call AI chat endpoint: |
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- Obtain Bearer token using the existing Ghostfolio auth flow. |
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- Call `POST http://localhost:3333/api/v1/ai/chat` with JSON body: |
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- `{"query":"Analyze my portfolio concentration risk","sessionId":"mvp-session-1"}` |
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4. Optional LLM output: |
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- Preferred for MVP: set `z_ai_glm_api_key` (`glm-5`) and `minimax_api_key` (`MiniMax-M2.5`) in `.env`. |
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- Fallback path: `API_KEY_OPENROUTER` and `OPENROUTER_MODEL` in properties store. |
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- Without provider keys, endpoint returns deterministic fallback summaries and still keeps tool and verification metadata. |
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5. Hostinger infra check: |
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- `npm run hostinger:check` |
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## Verification Snapshot (2026-02-23) |
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- `nx run api:lint` passed. |
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- Full `nx test api` fails in existing portfolio calculator tests unrelated to AI endpoint changes. |
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- Focused AI endpoint test command passed: |
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- `npm run test:ai` |
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- `npm run test:mvp-eval` |
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# Todo |
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Updated: 2026-02-23 |
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- [x] Verify current repository state and missing required files |
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- [x] Create `docs/adr/` for architecture decisions |
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- [x] Save `Tasks.md` at repository root |
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- [x] Populate `docs/tasks/tasks.md` |
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- [x] Create `tasks/improvements.md` |
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- [x] Create `tasks/lessons.md` |
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- [x] Confirm files exist on disk |
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- [x] Kick off MVP slice after Presearch refresh (this session) |
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# Tasks |
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Last updated: 2026-02-23 |
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## Active Tickets |
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| ID | Feature | Status | Tests | PR / Commit | |
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| --- | --- | --- | --- | --- | |
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| T-001 | Presearch package and architecture direction | Complete | Doc review checklist | Local docs update | |
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| T-002 | ADR foundation in `docs/adr/` | Complete | ADR template and first ADR review | Local docs update | |
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| T-003 | Agent MVP tool 1: `portfolio_analysis` | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-004 | Agent memory and response formatter | Complete | `apps/api/src/app/endpoints/ai/ai.service.spec.ts` | Planned | |
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| T-005 | Eval dataset baseline (MVP 5-10) | Complete | `apps/api/src/app/endpoints/ai/evals/mvp-eval.runner.spec.ts` | Planned | |
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| T-006 | Full eval dataset (50+) | Planned | Dataset validation and regression run | Planned | |
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| T-007 | Observability wiring (LangSmith traces and metrics) | Planned | Trace assertions and latency checks | Planned | |
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| T-008 | Deployment and submission bundle | Complete | `npm run test:ai` + Railway healthcheck + submission docs checklist | Pending push | |
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## Notes |
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- Canonical project requirements live in `docs/requirements.md`. |
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- Architecture decisions live in `docs/adr/`. |
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- Detailed task board mirror lives in `docs/tasks/tasks.md`. |
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## MVP Start (Finance Agent on Ghostfolio) |
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- [x] Inspect existing AI endpoint and integration points (`ai.controller.ts`, `ai.service.ts`, portfolio and data-provider services) |
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- [x] Add `POST /api/v1/ai/chat` endpoint with request validation |
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- [x] Implement 3 MVP tools in AI service |
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- [x] Add Redis-backed session memory for conversation continuity |
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- [x] Add verification checks and structured output formatter (citations, confidence, verification details) |
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- [x] Add targeted API unit tests for tool selection and response contract |
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- [x] Run lint and API tests |
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- [x] Share required and optional `.env` keys for local MVP run |
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## Session Plan (2026-02-23) |
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- [x] Refresh `docs/PRESEARCH.md` with source-backed framework and eval notes |
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- [x] Add root `Tasks.md` mirror for submission checklist compliance |
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- [x] Add AI chat service tests (tool execution, memory, verification, confidence) |
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- [x] Add MVP runbook snippet for local execution and API invocation |
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- [x] Execute focused verification (lint/test on touched surface) |
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- [x] Update ticket status and evidence links |
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## Session Plan (2026-02-23, UI + Deploy + Test Data) |
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- [x] Add client chat integration method for `POST /api/v1/ai/chat` |
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- [x] Build MVP chat interface in portfolio analysis page |
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- [x] Add focused frontend tests for chat request and response rendering |
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- [x] Verify AI test suite + eval suite after UI changes |
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- [x] Validate Railway API key and project visibility |
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- [x] Install/use Railway CLI and initialize or link project configuration |
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- [x] Add local data setup path and seed script command for MVP testing |
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- [x] Run final verification commands and capture evidence |
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## Session Plan (2026-02-23, Visibility + Test URL) |
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- [x] Diagnose why AI panel and activities are not visible in local UI |
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- [x] Remove analysis page visibility gate that hides AI panel for non-experimental users |
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- [x] Seed MVP activities for all local users to make testing deterministic |
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- [x] Update `docs/CODE-REVIEW.md` with exact local test URLs and validation steps |
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- [x] Run focused tests for touched files and record verification evidence |
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- [x] Capture lesson for UI discoverability and test-path communication |
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## Session Plan (2026-02-23, Publish via CLI) |
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- [x] Validate Railway CLI availability and auth path |
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- [x] Switch to supported Railway CLI version (`@railway/cli`) |
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- [x] Link current repository to configured Railway project/service |
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- [x] Trigger production deployment from local repository |
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- [x] Return deployed URL and post-deploy health check evidence |
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## Session Plan (2026-02-23, Seed Expansion) |
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- [x] Expand AI MVP seed dataset with more symbols and transactions |
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- [x] Add a second account per user for diversification scenarios |
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- [x] Run seeding command and verify row counts and sample orders |
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- [x] Share exact seeded coverage for local and deploy testing |
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## Session Plan (2026-02-23, Submission Bundle Completion) |
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- [x] Switch Railway service from source-build deploys to GHCR image deploys |
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- [x] Trigger redeploy and verify production health endpoint on image-based deploy |
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- [x] Create 1-page AI development log document for submission |
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- [x] Create AI cost analysis document with 100/1K/10K/100K projections |
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- [ ] Push submission documents and deployment updates to `origin/main` |
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## Session Plan (2026-02-23, Railway Crash Recovery) |
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- [x] Reproduce Railway start-command failure locally |
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- [ ] Correct Railway start command to built API entrypoint |
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- [ ] Verify fixed command resolves module-not-found crash |
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- [ ] Update task tracker evidence for deploy follow-up |
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## Verification Notes |
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- `nx run api:lint` completed successfully (existing workspace warnings only). |
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- Full `nx test api` currently fails in pre-existing portfolio calculator suites unrelated to AI endpoint changes. |
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- Focused MVP verification passed: |
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- `npm run test:ai` |
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- `npm run test:mvp-eval` |
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- `npm run hostinger:check` |
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- `npx dotenv-cli -e .env.example -- npx jest apps/client/src/app/pages/portfolio/analysis/ai-chat-panel/ai-chat-panel.component.spec.ts --config apps/client/jest.config.ts` |
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- `npm run railway:check` |
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- `npm run railway:setup` |
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- `npm run database:seed:ai-mvp` |
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- `npx nx run client:build:development-en` |
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- `npx nx run client:lint` |
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- `npx dotenv-cli -e .env -- npx -y @railway/cli@latest up --detach` |
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- `npx dotenv-cli -e .env -- npx -y @railway/cli@latest service status` |
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- `curl -i https://ghostfolio-api-production.up.railway.app/api/v1/health` |
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