diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 0d79070..0000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..6734f07 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,114 @@ +# Continuous integration for HAI. +# +# What this proves: the app lints, typechecks, and passes its unit tests, and +# the two Node side-projects (ingestion, evals) typecheck. +# +# What this deliberately does NOT do: run the eval suite. The evals need a local +# Ollama serving a ~9GB target model and a ~5GB judge model, and a single run +# takes hours on a laptop. A hosted runner has neither, so an eval job here +# could only ever be a stub — and a green "evals passing" badge that never +# graded a transcript is precisely the kind of reassuring-but-meaningless number +# this project already published once and had to retract (see research/README.md). +# Eval numbers come from `pnpm eval` on a machine with the models, and land in +# evals/reports/ with the judge's evidence attached so they can be checked. + +name: CI + +on: + push: + branches: [main] + pull_request: + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + NODE_VERSION: '22' + PNPM_VERSION: '10.13.1' + +jobs: + app: + name: app — lint, typecheck, test + runs-on: ubuntu-latest + defaults: + run: + working-directory: app + steps: + - uses: actions/checkout@v4 + + - uses: pnpm/action-setup@v4 + with: + version: ${{ env.PNPM_VERSION }} + + - uses: actions/setup-node@v4 + with: + node-version: ${{ env.NODE_VERSION }} + cache: pnpm + cache-dependency-path: app/pnpm-lock.yaml + + - run: pnpm install --frozen-lockfile + + # next-env.d.ts and the generated route types are gitignored, so they have + # to be produced before tsc can see them. `typegen` does that without + # paying for a full build. + - name: Generate Next.js types + run: pnpm exec next typegen + + - name: Lint + run: pnpm lint + + - name: Typecheck + run: pnpm exec tsc --noEmit + + - name: Unit tests + run: pnpm test + + evals: + name: evals — typecheck + runs-on: ubuntu-latest + defaults: + run: + working-directory: evals + steps: + - uses: actions/checkout@v4 + + - uses: pnpm/action-setup@v4 + with: + version: ${{ env.PNPM_VERSION }} + + - uses: actions/setup-node@v4 + with: + node-version: ${{ env.NODE_VERSION }} + cache: pnpm + cache-dependency-path: evals/pnpm-lock.yaml + + - run: pnpm install --frozen-lockfile + + - name: Typecheck + run: pnpm typecheck + + ingestion: + name: ingestion — typecheck + runs-on: ubuntu-latest + defaults: + run: + working-directory: ingestion + steps: + - uses: actions/checkout@v4 + + - uses: pnpm/action-setup@v4 + with: + version: ${{ env.PNPM_VERSION }} + + - uses: actions/setup-node@v4 + with: + node-version: ${{ env.NODE_VERSION }} + cache: pnpm + cache-dependency-path: ingestion/pnpm-lock.yaml + + - name: Install + run: pnpm install --frozen-lockfile + + - name: Typecheck + run: pnpm typecheck diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e665851 --- /dev/null +++ b/.gitignore @@ -0,0 +1,83 @@ +# --- Python --- +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +dist/ +*.egg-info/ +.eggs/ +.venv/ +venv/ +env/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +*.ipynb_checkpoints + +# --- Node / Next.js --- +node_modules/ +.next/ +out/ +.turbo/ +.vercel/ +*.tsbuildinfo +next-env.d.ts +coverage/ + +# --- macOS --- +.DS_Store +.AppleDouble +.LSOverride + +# --- Env files (never commit secrets) --- +.env +.env.local +.env.*.local +.env.development +.env.production +!.env.example +!*.env.example + +# --- Corpus source documents (licensing) --- +# The Sphere Handbook is all-rights-reserved: local educational/research use is +# permitted, redistribution is not. Other corpus PDFs are looser but are kept out +# of git for consistency. Re-download them with ingestion/fetch-corpus.sh. +# Provenance and per-file licenses stay tracked in ingestion/corpus/SOURCES.md. +ingestion/corpus/*.pdf + +# --- Ingestion pipeline artifacts --- +ingestion/.extract-cache/ + +# --- Deployment seed (licensing) --- +# scripts/deploy/export-corpus.sh writes the extracted chunk text here to seed a +# cloud database. That text *is* the source documents, so committing it would +# redistribute the Sphere Handbook exactly as publishing the PDF would — the +# same restriction that keeps ingestion/corpus/*.pdf out of git. Regenerate it +# from a local ingest instead; it takes seconds. +supabase/seed/*.csv.gz + +# --- Archives / backups --- +*.zip +*.tar.gz +*.json.backup_* + +# --- Logs --- +*.log +npm-debug.log* +pnpm-debug.log* +yarn-debug.log* +yarn-error.log* + +# --- Editor --- +.vscode/ +.idea/ +*.swp + +# --- Vercel --- +# Added by `vercel link`, which also appends a bare `.env*`. That pattern is +# kept narrower here: it sits after the `!.env.example` negation above and would +# otherwise re-ignore the example files the repo deliberately tracks. +.vercel +.env.local diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..ee3fac0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Sam Frons + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 12e8bb3..a95846e 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,176 @@ -# HAI -Humanitarian AI bot +# HAI — Humanitarian AI Assistant + +An agentic, citation-grounded AI assistant for humanitarian work. It answers +questions against primary references — the Sphere Handbook, the Core +Humanitarian Standard, and IASC guidance — and calls live tools for current +crisis data and structured humanitarian datasets, rather than relying on +facts memorized into model weights. + +## What it is + +- **Retrieval-grounded, not fine-tuned.** Answers cite the source passage + they're grounded in. See [`research/README.md`](research/README.md) for + why an earlier fine-tuning approach was abandoned. +- **Tool-using.** The assistant searches humanitarian standards, pulls + current crisis updates, and queries structured humanitarian data — it + doesn't just generate from a static prompt. +- **Runs entirely on a local model by default.** Chat and embeddings both go + through local Ollama; no key and no per-token cost unless you deliberately + point `LLM_BASE_URL` at a hosted endpoint. +- **Safety layer.** Every message is screened for personally identifiable + data before it reaches the model, refused with the IASC principle it + engages named explicitly, and offered a safe rephrasing — not a bare + "I can't help with that." 114 tests cover it. +- **Evaluated by an independent judge.** A held-out set of 26 domain + scenarios (`petri/seeds/humanitarian_test_scenarios.json`) is graded by a + model from a different family than the one being tested, against explicit + expected facts — not a self-graded, keyword-ratio heuristic. See + [`evals/README.md`](evals/README.md). + +![HAI chat, empty state](docs/assets/chat-empty-en.png) + +## Architecture + +```mermaid +flowchart LR + UI["Next.js UI\n(chat, playbooks, guides)"] --> API["/api/chat\nAI SDK v7"] + API --> Safety["Safety layer\nPII interception"] + Safety --> LLM["Local Ollama\nqwen2.5:14b\n(swappable via env)"] + LLM --> T1["search_standards\nSupabase pgvector\nhybrid search"] + LLM --> T2["crisis_updates\nIFRC GO / ReliefWeb"] + LLM --> T3["humanitarian_data\nHDX HAPI"] + LLM --> I18n["i18n: EN / FR / AR / ES\n(RTL for Arabic)"] +``` + +- **UI**: Next.js app (`app/`) — chat, six role playbooks, three guides, and + a coach mode that adds a short prompting lesson to each answer. +- **`/api/chat`**: AI SDK v7 route. The model is local `qwen2.5:14b` by + default (`ollama create hai-qwen2.5 -f app/ollama/Modelfile` bakes in the + 16k context window HAI needs); `LLM_BASE_URL`/`LLM_MODEL`/`LLM_API_KEY` + point it at any OpenAI-compatible endpoint instead, with no code change. +- **Safety layer**: deterministic regex/heuristic screening + (`app/src/lib/safety/pii.ts`) runs on every message before it reaches the + model — phone numbers, emails, case/registration identifiers, GPS + coordinates, dates of birth, pasted rosters. An optional second-pass LLM + screen (`PII_LLM_SCREEN=true`) catches bare names, off by default because + it costs a full model round-trip per message. +- **Tools**: + - `search_standards` — hybrid (vector + full-text, reciprocal rank fusion) + search over 1,631 ingested chunks of humanitarian standards in a local + Supabase pgvector instance. + - `crisis_updates` — live situation reports. Prefers ReliefWeb, which + since November 2025 requires an OCHA-approved `appname`; without one + (the default) it falls back to **IFRC GO** (no registration required) + and tells the model, and the user, which source answered. + - `humanitarian_data` — structured country indicators from **HDX HAPI** + (population, food security, funding, humanitarian needs). No key + required. +- **i18n**: UI chrome in English, French, Arabic, Spanish, with RTL layout + for Arabic. The model answers in whatever language the user writes in, + independent of the UI locale. +- **Eval harness** (`evals/`): drives the 26-scenario suite against the live + `/api/chat` route and grades transcripts with a judge model from a + different family than the target — run outside the serving path, not on + every request. + +## Quickstart + +Everything below runs locally with no paid API calls. Expect **10–15 +minutes** if the models are already pulled, longer for the first `ollama +pull` (multi-GB) and the first corpus ingestion (embedding ~1,600 chunks +takes about 6 minutes once the corpus is fetched). + +**Prerequisites** + +- [Ollama](https://ollama.com), running (`ollama serve`) +- [Docker](https://docs.docker.com/get-docker/) (Supabase's local stack runs in it) +- [pnpm](https://pnpm.io) (`corepack enable` gets you `pnpm@10.13.1`, pinned in `app/package.json`) +- [Supabase CLI](https://supabase.com/docs/guides/local-development/cli/getting-started) + +**1 — Pull the models and build the HAI variant** + +```bash +ollama pull qwen2.5:14b +ollama pull mxbai-embed-large +ollama create hai-qwen2.5 -f app/ollama/Modelfile # bakes in the 16k context HAI needs +``` + +**2 — Fetch the corpus** (Sphere Handbook, CHS, three IASC guidance docs — not committed; see licensing note in `ingestion/README.md`) + +```bash +cd ingestion +./fetch-corpus.sh # downloads and verifies sha256 against known-good copies +``` + +**3 — Start Supabase and apply migrations** + +```bash +cd .. # repo root +supabase start # applies supabase/migrations/ automatically; prints keys +``` + +Note the `API_URL` (`http://127.0.0.1:54421`) and `SERVICE_ROLE_KEY`/`ANON_KEY` +it prints — the default ports are shifted +100 from Supabase's usual range +because another local project already holds `5432x` on this machine. + +**4 — Ingest the corpus** + +```bash +cd ingestion +pnpm install +cp .env.example .env # fill in SUPABASE_SERVICE_ROLE_KEY from step 3 +pnpm ingest # extract -> chunk -> contextualize -> embed -> load +pnpm search "minimum water supply per person per day" # sanity check +``` + +**5 — Configure and run the app** + +```bash +cd ../app +pnpm install +cp .env.example .env.local # fill in SUPABASE_ANON_KEY from step 3 +pnpm dev +``` + +Open `http://localhost:3000`. `RELIEFWEB_APPNAME` and `HDX_APP_IDENTIFIER` +are optional — both tools work without them (see Architecture above). + +Full detail on each ingestion stage, the RPC contract, and known gaps (the +corpus currently loads without contextual-retrieval preambles — retrieval +works, the ranking boost doesn't yet) is in +[`ingestion/README.md`](ingestion/README.md). + +| ![PII interception banner](docs/assets/pii-safety-notice.png) | ![Arabic RTL layout](docs/assets/chat-arabic-rtl.png) | ![Playbooks index](docs/assets/playbooks-index.png) | +|---|---|---| +| Safety layer: a case-detail paste caught before it reaches the model, with the IASC principle named | Arabic locale — full RTL layout, not just translated labels | The six role playbooks | + +## Docs + +| Doc | What's in it | +|---|---| +| [`docs/STRATEGY.md`](docs/STRATEGY.md) | Why this architecture — the case for grounding over fine-tuning | +| [`docs/ENABLEMENT.md`](docs/ENABLEMENT.md) | How an organization adopts AI well; the framework the playbooks/guides implement | +| [`docs/DEMO.md`](docs/DEMO.md) | 5-minute demo script, beat by beat | +| [`research/README.md`](research/README.md) | Honest postmortem: the original fine-tune + audit prototype, and the three bugs that invalidated its results | +| [`content/playbooks/`](content/playbooks/) | Six role-specific playbooks (program, protection, MEAL, comms, grants, logistics) | +| [`content/guides/`](content/guides/) | Effective prompting, responsible use, starting a community of practice | +| [`evals/README.md`](evals/README.md) | How the eval harness works and why the judge is trustworthy | + +## Repo layout + +| Path | Contents | +|---|---| +| `app/` | Next.js application (UI + `/api/chat` route, safety layer, i18n) | +| `ingestion/` | Corpus → chunking → embeddings → Supabase pgvector pipeline | +| `evals/` | Eval harness driving `petri/seeds/` scenarios against the live app | +| `petri/seeds/` | The 26-scenario eval suite (kept as-is from the prototype) | +| `content/` | Playbooks and guides shown in the app and used by the eval scenarios | +| `research/` | Archived fine-tune/audit prototype + honest postmortem — **start here** for context on why the project pivoted: [`research/README.md`](research/README.md) | + +## Prior work + +The original prototype attempted a LoRA fine-tune of a local model, +validated by a Petri-style auditor. Three bugs invalidated its results (a +self-judging auditor, a training-data extractor that scraped source code +instead of prose, and a passing threshold that let empty answers through). +Full writeup, with file:line references: [`research/README.md`](research/README.md). diff --git a/app/.env.example b/app/.env.example new file mode 100644 index 0000000..d3174ad --- /dev/null +++ b/app/.env.example @@ -0,0 +1,122 @@ +# Chat model, via any OpenAI-compatible endpoint. +# Defaults below run entirely on local Ollama — no key, no per-token cost. +# Swap LLM_BASE_URL for a hosted OpenAI-compatible endpoint (Groq, Gemini's +# compatibility layer, OpenRouter) without touching code. Check that +# endpoint's pricing first: hosted inference may bill per token. +# +# The hosted demo uses Groq's free tier, which requires a model that does tool +# calling — HAI cannot answer without calling search_standards: +# +# LLM_BASE_URL=https://api.groq.com/openai/v1 +# LLM_MODEL=openai/gpt-oss-120b +# LLM_API_KEY=gsk_... # https://console.groq.com/keys +# LLM_REASONING_FORMAT=hidden +# +# Groq retired every Llama and Kimi-K2 model on 2026-08-16, so an older guide +# naming llama-3.3-70b-versatile will fail with a 404 model_not_found. Check +# https://console.groq.com/docs/deprecations before pinning a model. +# +# LLM_REASONING_FORMAT is passed straight through to the endpoint. Set it to +# "hidden" with any reasoning model: otherwise the model returns +# reasoning_content, the SDK echoes it back on the next step of the tool loop, +# and Groq rejects its own field — the search succeeds and the answer never +# arrives. Leave it unset for local Ollama and for endpoints that reject +# unknown body parameters. See docs/DEPLOY.md. +LLM_BASE_URL=http://localhost:11434/v1 +LLM_MODEL=qwen2.5:14b +LLM_API_KEY=ollama + +# IMPORTANT for local Ollama: the 4096-token default context is too small for +# HAI, and overflowing it silently drops the system prompt — after which the +# model loses its grounding and language rules mid-answer. Either build the +# provided variant, which bakes in a 16k context: +# +# ollama create hai-qwen2.5 -f app/ollama/Modelfile +# LLM_MODEL=hai-qwen2.5 +# +# ...or raise it server-wide and restart Ollama: +# launchctl setenv OLLAMA_CONTEXT_LENGTH 16384 # macOS app +# OLLAMA_CONTEXT_LENGTH=16384 ollama serve # foreground +# +# Verify with: curl -s localhost:11434/api/ps | grep context_length + +# Query embeddings for the search_standards tool. Must come from the same model +# the corpus was ingested with (see ingestion/embed.ts): a different model at the +# same 1024 dimensions produces vectors of the right shape in the wrong space, +# and every search then ranks by noise while looking perfectly healthy. +# +# ollama (default) local Ollama. Free, no key. +# hf Hugging Face Inference, serving mixedbread-ai/mxbai-embed-large-v1 — +# the upstream of Ollama's mxbai-embed-large, so vectors stay +# compatible with a corpus embedded locally. +EMBEDDINGS_PROVIDER=ollama + +# Used when EMBEDDINGS_PROVIDER=ollama. +OLLAMA_BASE_URL=http://localhost:11434 +EMBEDDING_MODEL=mxbai-embed-large + +# Used when EMBEDDINGS_PROVIDER=hf. Create at +# https://huggingface.co/settings/tokens with the "Inference Providers" +# permission. A free account carries a small monthly inference credit +# (~$0.10); when it runs out, requests fail rather than bill, and HAI degrades +# to full-text-only search with a visible notice. +HF_TOKEN= + +# Supabase (server-side, pgvector search). RLS on standards_chunks grants +# SELECT only, so the anon key is safe to use here — no service role needed +# for search. Local stack: `supabase status` from the repo root. +NEXT_PUBLIC_SUPABASE_URL=http://127.0.0.1:54421 +SUPABASE_ANON_KEY= + +# Optional. ReliefWeb's JSON API requires an appname pre-approved by OCHA +# (https://apidoc.reliefweb.int/parameters#appname). Without it, crisis_updates +# falls back to IFRC GO, which needs no registration. +RELIEFWEB_APPNAME= + +# Optional. HDX HAPI attribution string: base64 of "appname:email". +# Defaults to a demo identifier; no registration required either way. +HDX_APP_IDENTIFIER= + +# Cost / abuse controls +# +# RATE_LIMIT_RPM paces one client: a per-IP sliding window, default 20/min. It +# lives in process memory, so on Vercel each serverless instance keeps its own +# copy and it resets on every deploy — a politeness control, not a spend control. +RATE_LIMIT_RPM= + +# MAX_DAILY_REQUESTS is the spend control: one shared counter in Postgres, +# default 500/day across the whole deployment, enforced atomically so concurrent +# instances cannot overshoot it. Requires the daily_request_cap migration. +# +# Ignored entirely when LLM_BASE_URL points at localhost, where inference is free +# and a ceiling would only cap your own demo — set it explicitly to exercise the +# enforcement path locally. +MAX_DAILY_REQUESTS= + +# Data-responsibility screening. +# +# The deterministic pass (regex + heuristics over phone numbers, emails, case +# and registration identifiers, GPS coordinates, dates of birth, and pasted +# person rosters) always runs and costs microseconds. It cannot see a bare +# personal name. +# +# Setting PII_LLM_SCREEN=true adds a second-pass classification call in front of +# every message the regexes cleared, catching bare names and re-identifying +# detail combinations. It is off by default because it is a full model +# round-trip paid before the first token of the answer. Measured on a loaded +# Apple-silicon laptop with local Ollama: qwen2.5:14b took 3.2s on a trivial +# prompt when quiet and 69s or more on the real 221-token screen prompt under +# contention. It fails open on timeout, so on a busy machine it costs latency +# and buys nothing. Enable it against an endpoint that can classify in about a +# second. +PII_LLM_SCREEN=false + +# Optional. Model for that second pass; defaults to LLM_MODEL. Pick for +# judgement, not size: phi3.5 obeyed the format but missed a personal name, and +# nuextract ignored the yes/no instruction entirely and replied with extracted +# prose. Both were measured; neither is recommended. +PII_SCREEN_MODEL= + +# Optional. Milliseconds before the second pass gives up and lets the request +# through. Default 8000. +PII_SCREEN_TIMEOUT_MS= diff --git a/app/.gitignore b/app/.gitignore new file mode 100644 index 0000000..7b8da95 --- /dev/null +++ b/app/.gitignore @@ -0,0 +1,42 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* +!.env.example + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/app/AGENTS.md b/app/AGENTS.md new file mode 100644 index 0000000..643577d --- /dev/null +++ b/app/AGENTS.md @@ -0,0 +1,9 @@ + + +# This is NOT the Next.js you know + +This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` (resolved from this file's directory; in monorepos the `next` package may not be visible from the repo root) before writing any code. Heed deprecation notices. + +This block is written and re-added by `next dev` — verify at `node_modules/next/dist/server/lib/generate-agent-files.js`. Removing it from a diff only re-creates the uncommitted change; committing it with your work keeps the tree clean. + + diff --git a/app/CLAUDE.md b/app/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/app/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/app/README.md b/app/README.md new file mode 100644 index 0000000..81064f8 --- /dev/null +++ b/app/README.md @@ -0,0 +1,51 @@ +# HAI app + +The Next.js application: chat UI, `/api/chat` route, safety layer, and the +role playbooks / guides content pages. See the [root README](../README.md) +for what HAI is and the full quickstart (models, corpus ingestion, Supabase). +This file covers just this package. + +## Key directories + +| Path | What's in it | +|---|---| +| `src/app/` | Routes — chat (`/`), playbooks, guides, `/api/chat` | +| `src/lib/tools/` | The three tools the model calls: `search-standards.ts`, `crisis-updates.ts` (IFRC GO / ReliefWeb), `humanitarian-data.ts` (HDX HAPI) | +| `src/lib/retrieval/` | `search.ts` — embeds the query via Ollama and calls the `search_standards_hybrid` Supabase RPC | +| `src/lib/safety/` | `pii.ts` (deterministic regex/heuristic screening), `llm-screen.ts` (optional second-pass), `intercept.ts` (turns findings into the banner copy) | +| `src/lib/prompts/` | `system.ts` (base system prompt), `coach.ts` (coach-mode addition) | +| `src/lib/i18n/` | `locales.ts`, `dictionary.ts`, `context.tsx` — EN/FR/AR/ES UI chrome, RTL for Arabic | +| `src/lib/llm/provider.ts` | The one place the chat/embedding model endpoint is chosen | +| `src/components/` | Chat UI, citations/source panel, playbook and guide views | + +## Environment + +Copy `.env.example` to `.env.local` and fill in what's marked required below. +Everything else has a working default. + +| Variable | Required | Default | Purpose | +|---|---|---|---| +| `LLM_BASE_URL` | no | `http://localhost:11434/v1` | OpenAI-compatible chat endpoint | +| `LLM_MODEL` | no | `qwen2.5:14b` | Set to `hai-qwen2.5` after building the 16k-context variant (see root quickstart) | +| `LLM_API_KEY` | no | `ollama` | Ignored by Ollama; required by hosted OpenAI-compatible endpoints | +| `OLLAMA_BASE_URL` | no | `http://localhost:11434` | Used for query embeddings, independent of `LLM_BASE_URL` | +| `EMBEDDING_MODEL` | no | `mxbai-embed-large` | Must match what the corpus was ingested with | +| `NEXT_PUBLIC_SUPABASE_URL` | **yes** | `http://127.0.0.1:54421` | Local Supabase API URL (`supabase status` from repo root) | +| `SUPABASE_ANON_KEY` | **yes** | — | Read-only; RLS grants `search_standards_hybrid` SELECT only | +| `RELIEFWEB_APPNAME` | no | unset | OCHA-approved appname; without it `crisis_updates` falls back to IFRC GO | +| `HDX_APP_IDENTIFIER` | no | demo identifier | Attribution string for HDX HAPI, no registration required | +| `RATE_LIMIT_RPM` | no | unset | Optional abuse control | +| `PII_LLM_SCREEN` | no | `false` | Adds a second-pass LLM classification before every message; off by default because it's a full model round-trip (see `.env.example` for measured latency) | +| `PII_SCREEN_MODEL` | no | `LLM_MODEL` | Model for that second pass | +| `PII_SCREEN_TIMEOUT_MS` | no | `8000` | Fails open on timeout | + +## Commands + +```bash +pnpm dev # next dev +pnpm build # next build +pnpm start # next start (after build) +pnpm lint # eslint +pnpm test # vitest run +pnpm test:watch # vitest +``` diff --git a/app/content/README.md b/app/content/README.md new file mode 100644 index 0000000..d26c77b --- /dev/null +++ b/app/content/README.md @@ -0,0 +1,3 @@ +# Content + +This directory holds HAI's AI-enablement content — playbooks and guides for humanitarian staff adopting the assistant into their daily work. The philosophy behind it: meet staff where they are, at whatever skill level they're starting from, rather than assuming AI fluency; teach through transparency, showing why a prompt works instead of handing over templates to copy blindly; treat adoption as the goal before automation, building trust and good habits with general-purpose prompting before investing engineering effort in custom features; and escalate proven use cases to custom tooling — when a real, repeated need outgrows what a well-crafted prompt can do, that's the signal to build, not a reason to have built it sooner. diff --git a/app/content/guides/effective-prompting.md b/app/content/guides/effective-prompting.md new file mode 100644 index 0000000..6247de4 --- /dev/null +++ b/app/content/guides/effective-prompting.md @@ -0,0 +1,96 @@ +--- +id: effective-prompting +title: Effective Prompting for Humanitarian Work +skill_levels: [beginner, intermediate, advanced] +summary: A skill-progressive guide to writing prompts that get accurate, citable, actionable answers from HAI — with every example drawn from real humanitarian scenarios. +--- + +# Effective Prompting for Humanitarian Work + +A good prompt isn't a magic phrase — it's a clearly scoped question with enough context that the answer can be checked. This guide walks from the basics up to the techniques experienced users rely on daily. Every example uses a real humanitarian scenario, because the habits that work for "summarize this sitrep" are different from the habits that work for "help me plan a response." + +## Beginner: the anatomy of a good prompt + +A prompt that works well usually has three parts: + +1. **What you want** — a specific action (summarize, compare, draft, look up), not a vague one ("help me with"). +2. **The scope** — what's in bounds and what's out. A country, a date range, a document, a sector. +3. **The format** — how you want the answer shaped, so you can use it without reformatting. + +Compare these two prompts: + +> ❌ "Tell me about water standards." +> ✅ "What does Sphere specify as the minimum water quantity per person per day for a general distribution scenario, and which technical chapter is that from?" + +The second version tells HAI exactly what to look up (a specific indicator), where to look (Sphere), and implicitly asks for a citation (the chapter). The first version could produce three paragraphs of generalities that are hard to act on or verify. + +Another comparison: + +> ❌ "Summarize the situation in [country]." +> ✅ "Summarize ReliefWeb updates on [crisis] from the last 7 days into 5 bullet points, each with its source and date." + +Adding a timeframe, a bullet count, and a citation requirement turns an open-ended request into something you can check line by line. + +## Beginner: iteration is normal, not a failure + +The first answer rarely comes back exactly right, and that's fine — treat the first response as a draft, not a verdict. If the scope is too broad, narrow it: "That covered too much — focus only on the WASH-related updates." If the format isn't useful, ask for a different one: "Put that in a table instead of prose." Iterating on a prompt takes seconds and almost always beats trying to write the perfect prompt on the first try. + +## Intermediate: asking for sources + +Every claim HAI makes about a standard, a figure, or a piece of guidance should be traceable to something you can check — either a passage in the standards corpus or a live data pull. Build that into the prompt rather than checking it after the fact: + +> "Using CHS commitment 4 (support that does not cause harm), draft 3 bullet points for our proposal's do-no-harm section for a WASH project in [context]. Cite the specific commitment language you're drawing from." + +> "Pull the latest HDX HAPI displacement figures for [region] and give me the number with its source and date — don't round or estimate." + +If an answer doesn't come with a source, ask for one directly: "Where is that from — can you point me to the specific passage or dataset?" If HAI can't produce one, treat the claim as unverified. + +## Intermediate: structured outputs + +Asking for a specific format does two things: it makes the answer directly usable, and it makes gaps visible. A table with an empty cell is obviously incomplete; a paragraph with a missing fact just reads as vague. + +> "Compare this draft indicator list [paste] against Sphere's WASH key indicators. Output a table with columns: our indicator, matching Sphere indicator (or 'custom'), and the Sphere citation." + +> "List the eligibility criteria and submission deadline from this donor RFA [paste] as a bulleted checklist." + +## Intermediate: multilingual prompting + +HAI can draft in and translate to other languages, but treat the output as a first pass for a fluent-speaker review, not a finished translation — especially for terms with legal, medical, or protection significance where a slightly-off word choice changes meaning. + +> "Translate this program update into French at a reading level appropriate for a general community audience, and flag any phrase you're unsure translates precisely." + +> "Draft this same complaints-mechanism explanation in both English and Swahili, keeping the meaning of 'confidential' and 'safe to report' as close as possible in both — flag any term where a direct translation might lose nuance." + +Asking the model to flag its own uncertainty is more useful than asking it to just produce a confident-sounding translation. + +## Advanced: role framing and bounded tasks + +Telling HAI to act as a specific kind of reviewer narrows what it looks for, which makes the review more useful and easier to check: + +> "Act as a MEAL technical reviewer: read this draft logframe indicator table [paste] and flag every indicator that is not SMART (specific, measurable, achievable, relevant, time-bound), explaining which criterion it fails." + +Notice this prompt does three things advanced users rely on: it names a role, it names a specific framework to check against (SMART), and it bounds the task to *flagging*, not *deciding* — the model surfaces problems, you make the call. + +## Advanced: separating grounded content from inference + +For any task that mixes retrieval (standards, live data) with reasoning (drafting a theory of change, connecting two sectors), ask the model to distinguish what it's citing from what it's inferring: + +> "Draft a one-paragraph theory of change for an integrated nutrition and WASH response in [context], referencing the linkage between Sphere's WASH and Food Security and Nutrition chapters. Flag anything in your draft that's your own inference versus something grounded in a cited source." + +This single instruction — "flag your inference" — is the difference between a draft you can trust at a glance and one you have to re-derive from scratch to check. + +## Advanced: anti-fabrication instructions + +When you're supplying the facts and only want help with structure or language, say so explicitly: + +> "Using only this line-item budget [paste] and this activity description [paste], draft a budget narrative paragraph for the staffing line. Don't invent any figure not in the budget." + +Without that instruction, a model asked to "make this sound more complete" may fill perceived gaps with plausible-sounding numbers. With it, a missing figure becomes a visible gap instead of an invisible guess. + +## A quick checklist before you send a prompt + +- Did I say exactly what I want (summarize / compare / draft / look up)? +- Did I scope it (country, date range, document, sector)? +- Did I specify the format I need back? +- If facts matter, did I ask for sources or restrict the model to data I provided? +- Did I check the response for a citation, not just plausibility? diff --git a/app/content/guides/responsible-use.md b/app/content/guides/responsible-use.md new file mode 100644 index 0000000..db57024 --- /dev/null +++ b/app/content/guides/responsible-use.md @@ -0,0 +1,53 @@ +--- +id: responsible-use +title: Responsible Use of AI in Humanitarian Work +skill_levels: [beginner, intermediate, advanced] +summary: Grounded in the IASC Operational Guidance on Data Responsibility — what never to input, how to verify, and when to escalate to a human. +--- + +# Responsible Use of AI in Humanitarian Work + +This guide is grounded in the IASC's *Operational Guidance on Data Responsibility in Humanitarian Action* (Data Responsibility Working Group, April 2023), which sets out twelve principles humanitarian actors already work under: accountability, confidentiality, coordination and collaboration, data security, defined purpose/necessity/proportionality, fairness and legitimacy, human rights-based approach, people-centered and inclusive, personal data protection, quality, retention and destruction, and transparency — all underpinned by the overarching commitment to do no harm. Using AI doesn't create a new ethics framework. It creates a new place those same principles have to be applied. + +## Never input beneficiary personal data — including near-misses + +The clearest-cut rule: no personally identifiable information (PII) about an affected person goes into an AI prompt. Not a name, not a case number, not a combination of details that could re-identify someone even without a name attached. + +The near-misses are where this actually goes wrong, because they don't feel like a violation in the moment: + +- **"Can you summarize this case list for me?"** — pasting a spreadsheet of household names, ages, and needs to save time on a report. This is a personal data protection breach the moment it's pasted, regardless of what happens to the output. +- **"Help me write up this incident."** — describing a protection incident with the survivor's age, village, and the date, thinking the name is enough to omit. Age plus location plus date is frequently enough to identify someone in a small community. +- **"What's the best way to phrase this feedback from [name]?"** — a single beneficiary's quote, verbatim, dropped into a drafting prompt for a report. Even one identifiable quote is personal data. +- **"Translate this list of GBV survivor testimonies for the annex."** — arguably the highest-risk version of this mistake, combining sensitive personal data with a task (translation) people don't usually flag as a data-handling task at all. + +The test that catches most of these: **if you wouldn't paste it into a public Slack channel, don't paste it into an AI chat.** Aggregate first, anonymize first, or don't use AI for that specific task. + +## Verification is a duty, not a courtesy + +Under the *quality* and *accountability* principles, the people relying on your output — colleagues, donors, affected communities — are trusting that what you tell them is accurate. AI-assisted output doesn't lower that bar; if anything it raises the verification workload, because a fluent, confident-sounding answer is easier to under-check than a rough draft you know needs work. + +Concretely: every number gets checked against its source before it's used in anything that leaves your desk. Every standards citation gets checked against the actual handbook page. Every "the current guidance says" gets checked against the current guidance, because AI can retrieve an outdated or general version of something that's since been revised. + +## Bias awareness in conflict contexts + +Humanitarian information environments are contested by design — parties to a conflict have incentives to shape the narrative, and public data sources reflect whatever got reported, not necessarily what's most severe. An AI system summarizing that environment inherits its skew: if reporting from one area is thinner than another, a summary can understate need there without any signal that it's doing so. + +Two working habits address this directly: + +- Ask explicitly about coverage gaps: "What areas or population groups does this ReliefWeb data likely under-represent?" +- Treat a clean, confident-sounding summary of a contested situation with more suspicion, not less. Reality on the ground is rarely as tidy as a well-written paragraph. + +## When to escalate to a human + +Escalate — meaning, stop and get a person's judgment — whenever a task involves: + +- A decision about an individual's eligibility, risk level, or safety. +- Content that will be published under your organization's name on a politically sensitive topic. +- Any donor compliance question where getting it wrong has financial consequences. +- A situation where AI flags its own uncertainty (a translation it's unsure about, an inference it's separated from a citation) — that flag is a signal to bring in a person, not to accept the best available guess. + +AI producing a well-formatted, confident answer to a question it shouldn't be answering is a known failure mode. The way to catch it is a habit, not a filter: ask "is this a judgment call a trained person needs to make?" before you ask "did AI answer this well?" + +## Environmental and cost awareness: local versus cloud models + +Not every task needs the largest available cloud model. A local, on-device model draws far less power per query and keeps data from leaving your environment at all — which matters both for the retention and destruction principle and for contexts with limited or costly connectivity. As a rough guide: routine, low-stakes drafting and lookups are well suited to smaller or local models; tasks that need the most current live data, the deepest reasoning, or the widest context are where a larger cloud model earns its higher cost and footprint. Match the tool to the task rather than defaulting to the most powerful option out of habit. diff --git a/app/content/guides/starting-a-community-of-practice.md b/app/content/guides/starting-a-community-of-practice.md new file mode 100644 index 0000000..d223d86 --- /dev/null +++ b/app/content/guides/starting-a-community-of-practice.md @@ -0,0 +1,52 @@ +--- +id: starting-a-community-of-practice +title: Starting an AI Community of Practice +skill_levels: [beginner, intermediate] +summary: A practical guide to building adoption through champions, prompt-sharing, and office hours — and feeding real use cases back into engineering priorities. +--- + +# Starting an AI Community of Practice + +Rolling out a tool like HAI doesn't end at the training session. Adoption happens in the weeks after, in the small moments where someone is stuck on a report and doesn't know whether AI could help, or tries it once, gets a mediocre result, and quietly stops. A community of practice is how you catch those moments instead of losing them. + +## The champions model + +Pick 3–6 people across roles — not necessarily the most technical staff, but the ones colleagues already ask for help. A program officer who's genuinely curious and vocal about what worked (and what didn't) does more for adoption than a technical expert who finds the tool obvious and can't articulate why others find it hard. + +Champions' job is small and concrete: use the tool visibly in their own work, answer questions when a colleague gets stuck, and bring back what they're seeing — good and bad — to the group running this community of practice. It's not a training role and shouldn't be treated as extra unpaid work; budget a modest, explicit time allowance (an hour or two a week) so it doesn't quietly compete with their actual job. + +## Prompt-sharing rituals + +The single highest-leverage habit is a shared, low-friction place to post prompts that worked — a channel, a shared doc, whatever your organization already uses for this kind of thing. The habit matters more than the tool. A simple format keeps it useful: + +- The prompt itself +- What it was for +- What made it work (specific to your context, cited a source, restricted to provided data, etc.) + +This does two things at once: it gives newer users a library of starting points instead of a blank page, and it surfaces, over time, which kinds of tasks the tool handles well versus poorly in your organization's actual work — which feeds directly into the feedback loop below. + +## Office hours + +A recurring, short, drop-in session — 30 minutes, same time every week or two — where anyone can bring a real task and work through it live with a champion. Two things make office hours work: + +- **Keep it task-based, not lecture-based.** People show up with an actual report or proposal they're stuck on, not to hear a refresher presentation. +- **Protect it from becoming an escalation queue for tool bugs.** Office hours are for "how do I prompt this well," not "the tool is broken" — route the latter to whoever owns the engineering side, and say so explicitly so people don't lose confidence in the session when a bug shows up. + +## Measuring adoption: leading indicators, not vanity metrics + +Total query volume looks good on a slide and tells you almost nothing about whether adoption is actually taking hold. Watch these instead: + +- **Breadth, not just volume**: how many distinct roles and teams are using it regularly, not just how many queries one enthusiastic person is running. +- **Return usage**: are people coming back to it for a second and third task, or trying it once and stopping? A drop-off after one use is a stronger signal than low volume. +- **Prompt-sharing participation**: are people other than the champions contributing to the shared prompt library? That's a sign the tool has become part of normal workflow, not a champion-only novelty. +- **Unprompted requests for access or training**: someone from a team you haven't onboarded yet asking to join is worth more than a dozen queries from a team that's already in. + +Vanity metrics to be skeptical of: raw query counts, "percentage of staff who attended a training," or any number that goes up just because you told people to use the tool for a week. + +## The feedback loop: onboarding versus custom build + +Not every use case that comes up in office hours or the prompt-sharing channel is done being served by prompting alone. Some of them are the same request showing up over and over, in a form specific enough that it's worth engineering time to build a dedicated feature or tool integration instead of asking every user to reconstruct the same careful prompt from scratch. + +The signal to watch for is repetition with friction: the same task, requested by multiple people, that consistently needs a long or fragile prompt to get right. When you see that pattern, that's the case to escalate to whoever owns HAI's engineering roadmap — not every good prompt, just the ones enough people need often enough that a purpose-built tool would clearly beat teaching everyone the workaround. + +Keep this loop visible: when a use case does get escalated and built, say so back to the community of practice. It closes the loop for the people who raised it, and it reinforces that office hours and prompt-sharing aren't just training exercises — they're a real input into what gets built next. diff --git a/app/content/playbooks/communications.md b/app/content/playbooks/communications.md new file mode 100644 index 0000000..4ef02d7 --- /dev/null +++ b/app/content/playbooks/communications.md @@ -0,0 +1,61 @@ +--- +id: communications +title: Communications Officer Playbook +role: Communications Officer +icon: 📰 +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for sitrep summarization, donor reporting drafts, and multilingual drafting — with every fact traced back to a source before publication. +--- + +# Communications Officer Playbook + +Communications work is where an AI-invented statistic or a mistranslated protection term does the most damage fastest, because it goes out publicly. The rule that makes AI safe to use here is simple: AI can touch the prose, but every fact in that prose has to trace back to a document you gave it or a source it retrieved — never to what the model "remembers." + +## Where AI genuinely helps in this role + +1. **Summarizing lengthy sitreps or reports** into a shorter internal or public-facing brief. +2. **Drafting a first-pass donor report narrative** from bullet-point inputs and indicator data you supply. +3. **Multilingual drafting support** for public communications — a first pass that a native or fluent speaker then reviews. +4. **Pulling current ReliefWeb updates** on a crisis to draft a situation snapshot with dates and sources attached. +5. **Producing multiple framing variants** of the same fact set — outcome-focused, needs-focused, partnership-focused — for different audiences. + +## Where AI should NOT be used + +- **Never publish AI-drafted content about an active crisis without a fact-check pass** against a primary source — a ReliefWeb report, an OCHA sitrep, a cluster update. +- **Never draft messaging that names or identifies specific beneficiaries, survivors, or vulnerable individuals.** +- **Never let AI draft final messaging on a politically sensitive or conflict-related topic without senior sign-off** — nuance errors here carry real safety and reputational risk. +- **Never treat an AI translation as publication-ready.** Route it through a native or fluent speaker first, especially for terms with legal or protection significance. +- **Never let AI invent a statistic or attribute a figure to a source it hasn't actually retrieved.** + +## Example prompts + +**1. (Beginner)** "Summarize this sitrep [paste public document] into 5 bullet points for an internal team update." +*Why it works: the input is already a public document, the output length is bounded, and the task is low-risk since nothing new is being asserted.* + +**2. (Beginner)** "Pull the latest ReliefWeb updates on [crisis] from the past week and list the headlines with dates and sources." +*Why it works: uses the live data tool and requests dates and sources per item, so the summary is independently checkable.* + +**3. (Intermediate)** "Draft a 150-word public update on our [sector] response in [context], using only the figures I provide here [paste figures]. Do not add any statistic I haven't given you." +*Why it works: an explicit instruction against fabricating numbers, plus a word-count constraint that controls format.* + +**4. (Intermediate)** "Translate this English program update into [language] at a reading level appropriate for a general community audience, and flag any phrase you're unsure translates precisely." +*Why it works: asks the model to surface its own translation uncertainty instead of presenting a guess with false confidence.* + +**5. (Intermediate)** "Rewrite this donor report paragraph [paste] in a more concise, results-focused tone, keeping every number and citation exactly as written." +*Why it works: separates what AI can touch (prose, tone) from what it can't (facts, citations) — the instruction makes that boundary explicit.* + +**6. (Advanced)** "Act as an editor: read this draft press statement [paste] and flag any claim that isn't sourced to a document I've provided, any language that could compromise beneficiary anonymity, and any term that doesn't match IASC-standard terminology." +*Why it works: three distinct, checkable review criteria — sourcing, anonymity, terminology — replace an open-ended "improve this" that would be hard to verify.* + +**7. (Advanced)** "Draft 3 alternate versions of this donor report executive summary [paste]: one emphasizing outcomes, one emphasizing needs context (citing the ReliefWeb/HDX data used), and one emphasizing partnership. Keep the underlying facts identical across all three." +*Why it works: fixes the factual content as a constant and isolates framing as the only variable, so you're comparing tone choices, not accidentally comparing different facts.* + +**8. (Advanced)** "Given this cluster-level sitrep [paste, public], draft a table comparing reported needs by sector against Sphere's four technical chapters, and note which sectors have the least standards coverage in this update." +*Why it works: a structured cross-reference against a named framework, with the output format specified up front.* + +## Verify before you act + +- Fact-check every number and quote in an AI draft against the primary source before it goes external — treat the draft as unverified until you've checked it line by line. +- Route anything naming individuals, survivors, or specific communities through a protection review before publication. +- Have a native or fluent speaker review any AI-assisted translation before public use, especially for legal, protection, or medical terms. +- Get senior or comms-lead sign-off on anything touching a politically sensitive angle of a crisis, no matter how polished the AI draft reads. diff --git a/app/content/playbooks/field-logistics.md b/app/content/playbooks/field-logistics.md new file mode 100644 index 0000000..f346b6b --- /dev/null +++ b/app/content/playbooks/field-logistics.md @@ -0,0 +1,61 @@ +--- +id: field-logistics +title: Field Logistics Officer Playbook +role: Field Logistics Officer +icon: 🚚 +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for procurement document drafting, distribution planning aids, and supply pipeline queries — never for vendor decisions or real-time safety calls. +--- + +# Field Logistics Officer Playbook + +Logistics work has two things AI should never touch: who gets a contract, and whether a distribution site is safe today. Everything short of those two calls — drafting language, checking a document against a standard, pulling supply data — is fair game. + +## Where AI genuinely helps in this role + +1. **Drafting first-pass procurement document language** — RFQ or tender descriptions — from the technical specs you provide. +2. **Drafting distribution planning checklists** referencing Sphere technical standards for the relevant sector, such as water point spacing or shelter plot sizing. +3. **Summarizing HDX HAPI supply, market, and price data** to inform a procurement planning brief. +4. **Drafting a generic distribution site flow-plan description** — crowd flow, queuing, protection considerations — for review by the field team. +5. **Cross-checking a draft procurement package** against a standard documentation checklist. + +## Where AI should NOT be used + +- **Never let AI select a vendor or make an award decision.** Procurement integrity requires the actual competitive process and a documented human decision. +- **Never paste actual beneficiary lists, headcounts by name, or household-level distribution data into a prompt.** Use aggregated numbers only. +- **Never rely on AI for real-time supply chain or market price data.** Always pull the live HDX HAPI or market monitoring data rather than trusting a remembered figure. +- **Never use AI-drafted content as the final safety or security assessment for a distribution site.** Site risk assessment needs an in-person or currently-informed security review. +- **Never let a procurement document go out with AI-drafted technical specs that haven't been checked by the technical or sector lead who knows the actual equipment needs.** + +## Example prompts + +**1. (Beginner)** "What does Sphere recommend as the minimum water quantity per person per day for a general distribution scenario?" +*Why it works: a direct factual lookup with a citation, and no site-specific data is needed to answer it.* + +**2. (Beginner)** "Draft a generic RFQ template structure — section headers only — for procuring [item category]." +*Why it works: a template-only request keeps vendor and pricing data out of the prompt entirely.* + +**3. (Intermediate)** "Pull the latest HDX HAPI market or price data available for [commodity] in [country] and summarize the trend over the last quarter with sources." +*Why it works: uses the live data tool and requires a sourced trend rather than a guessed or remembered figure.* + +**4. (Intermediate)** "Using these technical specifications [paste specs], draft the technical requirements section of an RFQ for [item], keeping every spec exactly as given." +*Why it works: restricts the drafting to the provided specs, guarding against invented technical details slipping into a procurement document.* + +**5. (Intermediate)** "Draft a distribution site flow-plan checklist for [item] distribution to approximately [aggregate number] households, referencing Sphere guidance on site planning and crowd management." +*Why it works: uses an aggregate household count rather than a household list, and cites a named standard as the basis for the checklist.* + +**6. (Advanced)** "Act as a procurement compliance reviewer: given this draft procurement package [paste, redact vendor-identifying info if sensitive] and our standard procurement checklist [paste], list any required document that appears to be missing." +*Why it works: role-framed as a document-completeness check, explicitly not a vendor evaluation or award recommendation.* + +**7. (Advanced)** "Compare the shelter plot sizing in this site plan draft [paste, aggregate figures only] against Sphere's Shelter and Settlement minimum standards, citing the specific standard and indicator." +*Why it works: uses aggregate, site-level figures rather than household-level data, and requires a citation to the exact chapter and indicator.* + +**8. (Advanced)** "Given this list of recent HDX HAPI supply and market indicators for [region] [tool output], draft a one-page procurement risk brief flagging any commodity showing a price spike or supply constraint, and note where the data is more than 2 weeks old." +*Why it works: asks the model to flag its own data's recency, preventing a stale figure from being presented as current in a risk brief.* + +## Verify before you act + +- Confirm any market, price, or supply figure against the live HDX HAPI dashboard or a current market monitoring report before using it in a procurement decision — pulled data can be dated by the time a plan is finalized. +- Route every procurement document through the actual competitive procurement process and required human sign-offs — AI drafting doesn't replace the paper-trail integrity procurement compliance requires. +- Have the technical or sector lead verify AI-drafted technical specifications against actual equipment and material needs before an RFQ goes out. +- Get a current, field-informed security assessment for any distribution site plan. Never treat an AI-drafted flow-plan as a safety clearance. diff --git a/app/content/playbooks/grants-partnerships.md b/app/content/playbooks/grants-partnerships.md new file mode 100644 index 0000000..4f5a936 --- /dev/null +++ b/app/content/playbooks/grants-partnerships.md @@ -0,0 +1,61 @@ +--- +id: grants-partnerships +title: Grants & Partnerships Officer Playbook +role: Grants & Partnerships Officer +icon: 🤝 +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for donor compliance checks, budget narrative drafting, and due diligence summaries — always grounded in the actual current guidance text, never model memory of donor rules. +--- + +# Grants & Partnerships Officer Playbook + +Donor rules change every funding round, and getting them wrong costs real money and real relationships. The one rule that governs everything else in this playbook: **never ask AI to recall a donor's rules from memory.** Paste the actual current guidance, RFA, or checklist text, and ask AI to work only from what's in front of it. + +## Where AI genuinely helps in this role + +1. **Cross-checking a draft proposal or report against a specific donor's compliance checklist** — when you provide the actual checklist text. +2. **Drafting first-pass budget narrative language** from a line-item budget and activity description you supply. +3. **Summarizing publicly available information** (registration status, published reports) as a starting point for a partner due diligence file. +4. **Drafting partnership agreement clause language** for legal or grants staff to review — not to finalize. +5. **Extracting eligibility criteria and deadlines** from a long donor call or RFA document into a quick-reference bullet list. + +## Where AI should NOT be used + +- **Never rely on AI's memory of donor rules** — USAID, ECHO, BHA, FCDO, or any other donor's requirements shift per call and per year. Always paste the current guidance and constrain AI to that text. +- **Never let AI make the final due diligence risk determination on a partner.** That call requires the actual verification steps — registration checks, reference calls, sanctions screening — done by a person. +- **Never submit AI-drafted compliance language without a grants specialist verifying it against the live donor portal or guidelines.** +- **Never paste a partner's confidential financial or personal data into a due diligence prompt.** Work from redacted summaries only. +- **Never treat an AI-drafted budget narrative number as authoritative.** It should mirror the actual budget file, not recalculate or estimate figures. + +## Example prompts + +**1. (Beginner)** "Summarize this donor RFA document [paste actual text] into a bullet list of eligibility criteria and the submission deadline." +*Why it works: works from pasted primary text rather than model memory of donor rules, and the extraction task is bounded and easy to spot-check.* + +**2. (Beginner)** "What are Sphere's Shelter and Settlement minimum standards I should reference in a shelter proposal's technical approach section?" +*Why it works: a general standards lookup, answerable with a citation, with no donor-specific or partner-specific risk involved.* + +**3. (Intermediate)** "Using only this line-item budget [paste] and this activity description [paste], draft a budget narrative paragraph explaining the cost basis for the staffing line. Don't invent any figure not in the budget." +*Why it works: an explicit anti-fabrication instruction restricts the model to the provided data rather than letting it interpolate plausible-sounding numbers.* + +**4. (Intermediate)** "Compare this draft proposal section [paste] against this donor compliance checklist [paste actual checklist text] and list any checklist item not addressed." +*Why it works: both sides of the comparison are pasted primary text, not recalled from memory, so the resulting gap list is checkable against real documents.* + +**5. (Intermediate)** "Draft a due diligence summary template — headers and prompts only, not content — for assessing a new local partner, covering registration, governance, financial management, and safeguarding." +*Why it works: requests a structural template, not a real judgment about a real partner, so no confidential data needs to enter the prompt.* + +**6. (Advanced)** "Act as a compliance reviewer: given this current [donor] cost principles excerpt [paste] and our draft budget narrative [paste], flag any language that doesn't align with the excerpt, citing the specific clause." +*Why it works: grounds every flag in pasted source text with a citation requirement, closing off the risk of relying on stale donor-rule memory.* + +**7. (Advanced)** "Draft 3 clauses for a sub-award agreement covering data protection responsibilities, referencing the IASC Data Responsibility principles on confidentiality and personal data protection, for our legal team to review." +*Why it works: names the specific principles being drawn on and explicitly routes the output to legal review rather than treating it as final language.* + +**8. (Advanced)** "Given this partner's publicly available annual report and registration record [paste public excerpts], summarize what's verifiable from public sources and separately list what still needs verification through direct outreach or reference checks." +*Why it works: explicitly separates what's confirmed from public sources from what still needs human follow-up, preventing the summary from reading as a completed due diligence file when it isn't one.* + +## Verify before you act + +- Always paste the actual current donor guidance or RFA text rather than asking AI to recall rules from memory — requirements change per funding round. +- Have a grants specialist verify any AI-generated compliance gap list against the live donor portal before submission. +- Complete required due diligence steps — sanctions screening, reference checks, registration verification — through the actual verification channels. An AI summary of public information is a starting point, not a substitute. +- Confirm every number in a budget narrative against the actual budget file, line by line, before submission. diff --git a/app/content/playbooks/index.json b/app/content/playbooks/index.json new file mode 100644 index 0000000..b440621 --- /dev/null +++ b/app/content/playbooks/index.json @@ -0,0 +1,44 @@ +[ + { + "id": "program-officer", + "title": "Program Officer Playbook", + "role": "Program Officer", + "summary": "Using HAI for program design, proposal drafting, and workplan review — grounded in Sphere, CHS, and live crisis data rather than model memory.", + "file": "program-officer.md" + }, + { + "id": "protection-officer", + "title": "Protection Officer Playbook", + "role": "Protection Officer", + "summary": "Using HAI for protection mainstreaming checks, referral pathway lookups, and template drafting — with a hard line against any identifiable case data.", + "file": "protection-officer.md" + }, + { + "id": "meal-officer", + "title": "MEAL Officer Playbook", + "role": "MEAL Officer", + "summary": "Using HAI for indicator design, survey question review, and qualitative coding assistance across Monitoring, Evaluation, Accountability and Learning work.", + "file": "meal-officer.md" + }, + { + "id": "communications", + "title": "Communications Officer Playbook", + "role": "Communications Officer", + "summary": "Using HAI for sitrep summarization, donor reporting drafts, and multilingual drafting — with every fact traced back to a source before publication.", + "file": "communications.md" + }, + { + "id": "grants-partnerships", + "title": "Grants & Partnerships Officer Playbook", + "role": "Grants & Partnerships Officer", + "summary": "Using HAI for donor compliance checks, budget narrative drafting, and due diligence summaries — always grounded in current guidance text, never model memory of donor rules.", + "file": "grants-partnerships.md" + }, + { + "id": "field-logistics", + "title": "Field Logistics Officer Playbook", + "role": "Field Logistics Officer", + "summary": "Using HAI for procurement document drafting, distribution planning aids, and supply pipeline queries — never for vendor decisions or real-time safety calls.", + "file": "field-logistics.md" + } +] diff --git a/app/content/playbooks/meal-officer.md b/app/content/playbooks/meal-officer.md new file mode 100644 index 0000000..9e9d155 --- /dev/null +++ b/app/content/playbooks/meal-officer.md @@ -0,0 +1,61 @@ +--- +id: meal-officer +title: MEAL Officer Playbook +role: MEAL Officer +icon: 📊 +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for indicator design, survey question review, and qualitative coding assistance across Monitoring, Evaluation, Accountability and Learning work. +--- + +# MEAL Officer Playbook + +MEAL work depends on getting the mechanics right — a question that isn't leading, an indicator that's actually measurable, a coding frame that holds up against real transcripts. AI is useful for the mechanical checks and the first-draft structure. It is not a substitute for the evidence itself, and it should never see raw respondent data. + +## Where AI genuinely helps in this role + +1. **Cross-checking draft indicators** against Sphere's key indicators for the relevant sector, flagging which are established and which are custom. +2. **Reviewing survey questions** for leading, double-barreled, or otherwise biased phrasing before piloting. +3. **Drafting a first-pass qualitative coding framework** from a research question, to be validated against real anonymized data before use. +4. **Grounding indicator targets in live baseline data** pulled from HDX HAPI rather than an assumed number. +5. **Drafting the structure of a learning brief or lessons-learned summary** from bullet-point inputs, before you fill in the actual findings. + +## Where AI should NOT be used + +- **Never to code actual qualitative transcripts or open-ended survey responses that could contain identifiable information.** Aggregate or fully anonymize first — and if that's not possible, don't use AI on that data at all. +- **Never as the sole validator of an indicator target.** Target-setting needs your actual baseline data and sector judgment, not a generic benchmark. +- **Never to fabricate a completion or impact percentage.** If the real dataset isn't available, ask for the data to be pulled — don't ask AI to estimate a plausible-sounding number as a stand-in. +- **Never to draft final evaluation conclusions or ratings.** Turning evidence into a judgment is the evaluator's job; AI can organize the evidence, not weigh it. +- **Never to translate or adapt a survey instrument into another language without a bilingual reviewer checking cultural and semantic accuracy** — a mistranslated question can silently bias an entire dataset. + +## Example prompts + +**1. (Beginner)** "What indicators does Sphere recommend for measuring food security and nutrition outcomes, and what's the minimum standard for each?" +*Why it works: a direct standards lookup, answerable via `search_standards` with a citation for each indicator.* + +**2. (Beginner)** "Review this survey question for leading or double-barreled phrasing: '[paste question]'" +*Why it works: a narrow, mechanical linguistic check with no data involved — easy to verify the answer yourself.* + +**3. (Intermediate)** "Compare our draft indicator list [paste, no PII] against Sphere's WASH key indicators and tell me which of ours map to an established Sphere indicator versus are custom, citing the Sphere reference for each match." +*Why it works: requires a citation per matched item, so you can spot-check the mapping rather than trust it wholesale.* + +**4. (Intermediate)** "Draft 5 candidate qualitative codes for analyzing responses to the open-ended question 'What barriers did you face accessing [service]?' based on common barrier categories in humanitarian access literature. I'll validate these against our own anonymized transcripts." +*Why it works: explicitly frames the output as a draft to be validated against real data, not a finished coding scheme.* + +**5. (Intermediate)** "Pull the most recent HDX HAPI indicators available for [sector] in [country] and tell me what baseline data exists that we could use to set a realistic indicator target." +*Why it works: uses the live data tool for grounding instead of guessing at a target number from general knowledge.* + +**6. (Advanced)** "Act as a MEAL technical reviewer: read this draft logframe indicator table [paste] and flag every indicator that is not SMART (specific, measurable, achievable, relevant, time-bound), explaining which criterion it fails." +*Why it works: applies a named, consistent framework and bounds the task to flagging, not rewriting — the decision authority stays with you.* + +**7. (Advanced)** "Given this list of already-anonymized, aggregated theme frequencies from our qualitative analysis [paste counts only, no quotes or identifiers], draft a plain-language summary paragraph suitable for a donor report, and flag any theme where the sample size seems too small to generalize." +*Why it works: the input is explicitly restricted to aggregated counts, and the model is asked to flag its own uncertainty about small-N generalization rather than presenting every theme with equal confidence.* + +**8. (Advanced)** "Draft an accountability-to-affected-people feedback loop description for a cash program, referencing CHS commitments 5 and 7, in a format I can hand to a designer." +*Why it works: names the specific commitments by number and specifies the downstream format, avoiding a vague "make this better" ask.* + +## Verify before you act + +- Never paste raw transcripts, open-ended responses, or interview notes that could contain names or locations — aggregate or anonymize first, and skip AI entirely if you can't. +- Validate any AI-suggested coding framework against a sample of real, anonymized data with a second coder before applying it at scale. +- Confirm indicator target numbers against your own baseline dataset — an AI-suggested target is a starting point, not a commitment. +- Have a MEAL technical lead review any SMART-indicator gap list before changing a logframe already agreed with a donor. diff --git a/app/content/playbooks/program-officer.md b/app/content/playbooks/program-officer.md new file mode 100644 index 0000000..7275fc6 --- /dev/null +++ b/app/content/playbooks/program-officer.md @@ -0,0 +1,61 @@ +--- +id: program-officer +title: Program Officer Playbook +role: Program Officer +icon: 🗂️ +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for program design, proposal drafting, and workplan review — grounded in Sphere, CHS, and live crisis data rather than model memory. +--- + +# Program Officer Playbook + +Program design and proposal writing run on judgment calls AI can't make — what a community actually needs, what's politically feasible, what a donor will fund this cycle. What AI can do is speed up the parts of the job that are mechanical: finding the right standard, summarizing a pile of sitreps, checking a draft against a checklist. Use it there, and keep the judgment calls yours. + +## Where AI genuinely helps in this role + +1. **Drafting first-cut theory-of-change or logframe language** against Sphere and CHS anchors, so you start from a structured draft instead of a blank page. +2. **Summarizing situation reports and crisis updates** (via live ReliefWeb data) into a needs-analysis section, with dates and sources attached so it's checkable. +3. **Cross-checking proposal indicators** against Sphere's key indicators for the relevant sector, flagging which are standard and which are custom. +4. **Turning bullet-point inputs into workplan narrative** — activity descriptions, sequencing rationale — that you then edit for accuracy and tone. +5. **Spotting compliance gaps** between a draft proposal and CHS's nine commitments before a technical reviewer sees it. + +## Where AI should NOT be used + +- **Never for the final go/no-go on program design trade-offs.** Whether to prioritize shelter over WASH in a given context is a contextual and often political judgment — AI can lay out the standards, not make the call. +- **Never with identifiable beneficiary data pasted into a prompt.** Needs data belongs in aggregate; case-level detail doesn't belong in a chat window. +- **Never as the sole word on indicator targets.** Targets need baseline data and sector-specialist sign-off, not a plausible-sounding number. +- **Never for donor-specific compliance rules from memory.** Donor requirements change per call — always work from the actual current guidance document, not what a model "remembers" about a donor. +- **Never to fill a gap in the data.** If a needs or coverage figure isn't available, ask HAI to pull it live or flag it as missing — don't let a plausible-sounding placeholder become the number that ships. + +## Example prompts + +**1. (Beginner)** "Summarize the last 30 days of ReliefWeb situation reports for [country] related to [crisis], and list the top 3 sectoral needs mentioned, with the source and date for each." +*Why it works: specific country and timeframe bound the search; asking for source and date per item makes the summary independently checkable rather than a vague impression.* + +**2. (Beginner)** "What does Sphere specify as the minimum standard for safe drinking water access per person per day, and which technical chapter is it from?" +*Why it works: a direct factual question HAI can answer from `search_standards` with a citation — no ambiguity about what "answer" means.* + +**3. (Intermediate)** "Using CHS commitment 4 (support that does not cause harm) and commitment 1 (participation and voice), draft 3 bullet points for our proposal's do-no-harm section for a WASH project in [context]. Cite the specific commitment language you're drawing from." +*Why it works: names the exact commitments by number, bounds the output to 3 bullets, and requires a citation — so you can verify the language actually traces back to CHS rather than a generic paraphrase.* + +**4. (Intermediate)** "Pull the latest HDX HAPI population and displacement figures for [region] and draft a two-sentence needs-justification paragraph citing the data source and date." +*Why it works: requests a live pull instead of trusting a remembered number, and specifies the exact output length and required citation.* + +**5. (Intermediate)** "Compare this workplan activity list [paste] against Sphere's Shelter and Settlement technical chapter and flag any standard activities we may have missed. Don't assume — cite the section for each flag." +*Why it works: explicit anti-hallucination instruction ("don't assume — cite"), and the task is scoped to flagging gaps, not deciding what to add.* + +**6. (Advanced)** "Act as a reviewer: read this draft proposal narrative [paste, no beneficiary names] and identify every place a numeric claim needs a citation to Sphere, HDX, or ReliefWeb. List them as a table with the paragraph reference." +*Why it works: role framing plus a structured table output narrows the task to exactly one thing — finding uncited numbers — rather than an open-ended edit.* + +**7. (Advanced)** "Draft a one-paragraph theory of change for an integrated nutrition and WASH response in [context], explicitly referencing the linkage between Sphere's WASH and Food Security and Nutrition chapters. Flag anything in your draft that's your inference versus something grounded in a cited source." +*Why it works: asks the model to separate grounded content from its own inference — a check most prompts skip, and one that matters most on cross-sector reasoning.* + +**8. (Advanced)** "Given this indicator table [paste, anonymized], check whether each indicator matches an established Sphere key indicator or is custom, and note which custom indicators would need MEAL sign-off before submission." +*Why it works: bounded classification task that explicitly routes the harder decision — whether a custom indicator is acceptable — back to a human specialist instead of letting the model decide.* + +## Verify before you act + +- Confirm any Sphere or CHS citation against the actual handbook page before it goes into a proposal — retrieval can mis-locate context even when it looks confident. +- Cross-check donor-specific compliance language against the donor's current published guidelines, not a remembered summary — requirements shift by funding round. +- Get sector technical-lead sign-off on any indicator language before submission. +- Re-verify any live data figure (population, displacement, coverage) against the HDX or ReliefWeb source directly before publishing — a pulled number can be stale by the time a proposal is finalized. diff --git a/app/content/playbooks/protection-officer.md b/app/content/playbooks/protection-officer.md new file mode 100644 index 0000000..a8dd952 --- /dev/null +++ b/app/content/playbooks/protection-officer.md @@ -0,0 +1,61 @@ +--- +id: protection-officer +title: Protection Officer Playbook +role: Protection Officer +icon: 🛡️ +skill_levels: [beginner, intermediate, advanced] +summary: Using HAI for protection mainstreaming checks, referral pathway lookups, and template drafting — with a hard line against any identifiable case data. +--- + +# Protection Officer Playbook + +Protection work runs on trust — a survivor's trust that their information stays contained, a community's trust that "confidential" means confidential. That trust is the reason this playbook leads with a boundary, not a use case: **treat any AI chat, including HAI, as a channel that could someday be read by someone other than you.** Never put a real case, a real name, or a real identifying detail into it. Everything below assumes that boundary holds. + +## Where AI genuinely helps in this role + +1. **Looking up referral pathway structure and standards** — what a referral pathway document should contain, what IASC guidance says about timelines — as a starting point, not a live directory. +2. **Checking a program design against protection mainstreaming principles** (Sphere's Protection Principles, CHS commitment 4) using only generic, non-case program details. +3. **Drafting blank case-note or intake templates** — headers and structure only, never filled with real content. +4. **Drafting generic, non-individualized talking points** for explaining complaints and feedback mechanisms to a community. +5. **Summarizing publicly available IASC or cluster guidance** on protection mainstreaming, GBV, or child safeguarding minimum standards. + +## Where AI should NOT be used + +- **Never paste actual beneficiary names, ages, locations, case numbers, or incident details into any prompt** — even a first name plus a village plus an incident type can re-identify someone. If you're unsure whether something counts as identifying, don't paste it. +- **Never for beneficiary eligibility or protection risk-level decisions.** That judgment belongs to a trained case worker who has seen the actual case. +- **Never as the sole source for a live referral pathway.** Pathways change; always confirm the current version with the actual cluster or Area of Responsibility (AoR) focal point before referring someone. +- **Never for safety planning content tied to an individual survivor.** Use AI only for generic protocol or template drafting — a real safety plan is caseworker-only work. +- **Never assume de-identification is complete just because you removed the name.** Age, location, incident type, and date together can still identify a person even with no name attached. + +## Example prompts + +**1. (Beginner)** "What are the minimum actions for protection mainstreaming according to Sphere's Protection Principles?" +*Why it works: a general standards lookup with no case data involved, answerable with a citation.* + +**2. (Beginner)** "Draft a blank case-note template structure — headers only, no example content — covering presenting concern, actions taken, and referral status." +*Why it works: explicitly restricts the request to structure, not content, which keeps identifiable information out by construction.* + +**3. (Intermediate)** "Summarize the IASC guidelines on GBV referral pathway design — what are the core components a referral pathway document should have?" +*Why it works: asks for structural guidance rather than a specific, current pathway with real service names or locations, and is answerable from a citable source.* + +**4. (Intermediate)** "Using CHS commitment 5 (safe reporting of concerns) language, draft 3 generic talking points staff can use to explain our complaints mechanism to a community, without referencing any individual case." +*Why it works: ties the ask to a named commitment and explicitly excludes individual cases from the output.* + +**5. (Intermediate)** "Check this generic distribution plan description [paste, no beneficiary data] against protection mainstreaming principles and flag anything that could create protection risks — crowding, visibility, unsafe access routes." +*Why it works: the input is a general program plan, not case data, and the task is bounded to flagging risks, not adjudicating them.* + +**6. (Advanced)** "Act as a reviewer: given this de-identified, hypothetical case-type scenario (not a real case) [paste a synthetic example], what referral options exist under the cluster system for this kind of situation, and what does IASC guidance say about referral timelines?" +*Why it works: the explicit "hypothetical, not a real case" framing lets you reason about pathway logic without any real person's data ever entering the prompt.* + +**7. (Advanced)** "Draft a one-page protection mainstreaming checklist for a cash assistance program in [context], citing Sphere and CHS sources for each checklist item." +*Why it works: a per-item citation requirement makes every checklist line independently verifiable, and the task needs no case-level data at all.* + +**8. (Advanced)** "Compare our draft child safeguarding SOP [paste, redacted of names] against the CHS commitments and IASC guidance on Protection from Sexual Exploitation and Abuse (PSEA), and list gaps as a table." +*Why it works: names the correct framework (PSEA, not a generic "safeguarding standard"), requires redaction up front, and asks for a structured, checkable gap list.* + +## Verify before you act + +- Before sending anything to AI, re-read it once specifically hunting for names, ages, locations, case numbers, or photos. If in doubt, don't paste it — rephrase generically instead. +- Confirm any referral pathway or service list with the current cluster or AoR focal point — an AI summary can be outdated or too generic to act on. +- Route anything touching an actual case, safety plan, or risk assessment to a supervisor or caseworker. An AI draft is never the case record. +- Re-check PSEA and GBV minimum-standard references against the current IASC guidance version before finalizing an SOP or training — this guidance is periodically revised. diff --git a/app/eslint.config.mjs b/app/eslint.config.mjs new file mode 100644 index 0000000..05e726d --- /dev/null +++ b/app/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/app/next.config.ts b/app/next.config.ts new file mode 100644 index 0000000..5d4a56a --- /dev/null +++ b/app/next.config.ts @@ -0,0 +1,19 @@ +import path from "node:path"; +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + // The playbooks and guides routes read markdown from `../content`, which + // sits outside this Next.js project root (`app/`). Output file tracing + // include/exclude globs may not navigate outside the project root with + // `../`, so the tracing root is widened to the repo root and the include + // globs are written relative to that instead. + outputFileTracingRoot: path.join(__dirname, ".."), + outputFileTracingIncludes: { + "/playbooks": ["content/**/*"], + "/playbooks/*": ["content/**/*"], + "/guides": ["content/**/*"], + "/guides/*": ["content/**/*"], + }, +}; + +export default nextConfig; diff --git a/app/ollama/Modelfile b/app/ollama/Modelfile new file mode 100644 index 0000000..63b8299 --- /dev/null +++ b/app/ollama/Modelfile @@ -0,0 +1,25 @@ +# Recommended local model for HAI. +# +# ollama create hai-qwen2.5 -f app/ollama/Modelfile +# # then set LLM_MODEL=hai-qwen2.5 +# +# Why a variant instead of using qwen2.5:14b directly: Ollama serves models with +# a 4096-token context by default, and HAI does not fit in it. The system prompt +# plus the three tool schemas are ~1,900 tokens before the user types anything, +# and every step of a tool loop re-sends the whole conversation. On overflow +# Ollama silently drops the oldest tokens — the system prompt goes first, and +# the model then loses its grounding and language rules mid-answer. Measured +# symptom: the same Sudan question answered in Thai with no tool call at 4096, +# and correctly in English with the right tool call at 16384. +# +# Baking num_ctx into a variant keeps this repeatable and leaves the shared +# Ollama server's own configuration alone. Setting OLLAMA_CONTEXT_LENGTH=16384 +# on the server instead works just as well. + +FROM qwen2.5:14b + +PARAMETER num_ctx 16384 + +# HAI reports thresholds and figures; sampling variety buys nothing and costs +# tool-calling reliability. The chat route also sends temperature 0. +PARAMETER temperature 0 diff --git a/app/package.json b/app/package.json new file mode 100644 index 0000000..839e497 --- /dev/null +++ b/app/package.json @@ -0,0 +1,38 @@ +{ + "name": "app", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "eslint", + "test": "vitest run", + "test:watch": "vitest" + }, + "dependencies": { + "@ai-sdk/openai-compatible": "^3.0.37", + "@ai-sdk/react": "^4.0.82", + "@supabase/supabase-js": "^2.58.0", + "ai": "^7.0.79", + "gray-matter": "^4.0.3", + "next": "16.3.2", + "react": "19.2.8", + "react-dom": "19.2.8", + "react-markdown": "^10.1.0", + "remark-gfm": "^4.0.1", + "zod": "^4.4.3" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.3.2", + "tailwindcss": "^4", + "typescript": "^5", + "vitest": 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1.12.2 + '@unrs/resolver-binding-linux-riscv64-gnu': 1.12.2 + '@unrs/resolver-binding-linux-riscv64-musl': 1.12.2 + '@unrs/resolver-binding-linux-s390x-gnu': 1.12.2 + '@unrs/resolver-binding-linux-x64-gnu': 1.12.2 + '@unrs/resolver-binding-linux-x64-musl': 1.12.2 + '@unrs/resolver-binding-openharmony-arm64': 1.12.2 + '@unrs/resolver-binding-wasm32-wasi': 1.12.2 + '@unrs/resolver-binding-win32-arm64-msvc': 1.12.2 + '@unrs/resolver-binding-win32-ia32-msvc': 1.12.2 + '@unrs/resolver-binding-win32-x64-msvc': 1.12.2 + + update-browserslist-db@1.3.1(browserslist@4.28.8): + dependencies: + browserslist: 4.28.8 + escalade: 3.2.0 + picocolors: 1.1.1 + + uri-js@4.4.1: + dependencies: + punycode: 2.3.1 + + use-sync-external-store@1.6.0(react@19.2.8): + dependencies: + react: 19.2.8 + + vfile-message@4.0.3: + dependencies: + '@types/unist': 3.0.3 + unist-util-stringify-position: 4.0.0 + + vfile@6.0.3: + dependencies: + '@types/unist': 3.0.3 + vfile-message: 4.0.3 + + vite@8.2.2(@types/node@20.19.43)(jiti@2.7.0): + dependencies: + lightningcss: 1.33.0 + picomatch: 4.0.7 + postcss: 8.5.26 + rolldown: 1.2.5 + tinyglobby: 0.2.17 + optionalDependencies: + '@types/node': 20.19.43 + fsevents: 2.3.3 + jiti: 2.7.0 + + vitest@4.1.11(@types/node@20.19.43)(vite@8.2.2(@types/node@20.19.43)(jiti@2.7.0)): + dependencies: + '@vitest/expect': 4.1.11 + '@vitest/mocker': 4.1.11(vite@8.2.2(@types/node@20.19.43)(jiti@2.7.0)) + '@vitest/pretty-format': 4.1.11 + '@vitest/runner': 4.1.11 + '@vitest/snapshot': 4.1.11 + '@vitest/spy': 4.1.11 + '@vitest/utils': 4.1.11 + es-module-lexer: 2.3.2 + expect-type: 1.4.0 + magic-string: 0.30.21 + obug: 2.1.4 + pathe: 2.0.3 + picomatch: 4.0.7 + std-env: 4.2.0 + tinybench: 2.9.0 + tinyexec: 1.3.0 + tinyglobby: 0.2.17 + tinyrainbow: 3.1.1 + vite: 8.2.2(@types/node@20.19.43)(jiti@2.7.0) + why-is-node-running: 2.3.0 + optionalDependencies: + '@types/node': 20.19.43 + transitivePeerDependencies: + - msw + + which-boxed-primitive@1.1.1: + dependencies: + is-bigint: 1.1.0 + is-boolean-object: 1.2.2 + is-number-object: 1.1.1 + is-string: 1.1.1 + is-symbol: 1.1.1 + + which-builtin-type@1.2.1: + dependencies: + call-bound: 1.0.4 + function.prototype.name: 1.2.0 + has-tostringtag: 1.0.2 + is-async-function: 2.1.1 + is-date-object: 1.1.0 + is-finalizationregistry: 1.1.1 + is-generator-function: 1.1.2 + is-regex: 1.2.1 + is-weakref: 1.1.1 + isarray: 2.0.5 + which-boxed-primitive: 1.1.1 + which-collection: 1.0.2 + which-typed-array: 1.1.22 + + which-collection@1.0.2: + dependencies: + is-map: 2.0.3 + is-set: 2.0.3 + is-weakmap: 2.0.2 + is-weakset: 2.0.4 + + which-typed-array@1.1.22: + dependencies: + available-typed-arrays: 1.0.7 + call-bind: 1.0.9 + call-bound: 1.0.4 + for-each: 0.3.5 + get-proto: 1.0.1 + gopd: 1.2.0 + has-tostringtag: 1.0.2 + + which@2.0.2: + dependencies: + isexe: 2.0.0 + + why-is-node-running@2.3.0: + dependencies: + siginfo: 2.0.0 + stackback: 0.0.2 + + word-wrap@1.2.5: {} + + yallist@3.1.1: {} + + yocto-queue@0.1.0: {} + + zod-validation-error@4.0.2(zod@4.4.3): + dependencies: + zod: 4.4.3 + + zod@4.4.3: {} + + zwitch@2.0.4: {} diff --git a/app/pnpm-workspace.yaml b/app/pnpm-workspace.yaml new file mode 100644 index 0000000..581a9d5 --- /dev/null +++ b/app/pnpm-workspace.yaml @@ -0,0 +1,3 @@ +ignoredBuiltDependencies: + - sharp + - unrs-resolver diff --git a/app/postcss.config.mjs b/app/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/app/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/app/public/file.svg b/app/public/file.svg new file mode 100644 index 0000000..004145c --- /dev/null +++ b/app/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/app/public/globe.svg b/app/public/globe.svg new file mode 100644 index 0000000..567f17b --- /dev/null +++ b/app/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/app/public/next.svg b/app/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/app/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/app/public/vercel.svg b/app/public/vercel.svg new file mode 100644 index 0000000..7705396 --- /dev/null +++ b/app/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/app/public/window.svg b/app/public/window.svg new file mode 100644 index 0000000..b2b2a44 --- /dev/null +++ b/app/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/app/src/app/about/page.tsx b/app/src/app/about/page.tsx new file mode 100644 index 0000000..52e791a --- /dev/null +++ b/app/src/app/about/page.tsx @@ -0,0 +1,22 @@ +import type { Metadata } from 'next'; + +import { AboutContent } from '@/components/about-content'; +import { Footer } from '@/components/footer'; +import { SiteHeader } from '@/components/site-header'; + +export const metadata: Metadata = { + title: 'About — HAI', + description: 'How HAI grounds its answers, screens for data responsibility, and is evaluated.', +}; + +export default function AboutPage() { + return ( +
+ +
+ +
+
+ ); +} diff --git a/app/src/app/api/chat/route.ts b/app/src/app/api/chat/route.ts new file mode 100644 index 0000000..2865d73 --- /dev/null +++ b/app/src/app/api/chat/route.ts @@ -0,0 +1,268 @@ +import { + convertToModelMessages, + createUIMessageStream, + createUIMessageStreamResponse, + stepCountIs, + streamText, + type InferUITools, + type UIMessage, +} from 'ai'; + +import { claimDailyRequest, dailyCapMessage } from '@/lib/limits/daily-cap'; +import { getChatModel, getProviderOptions } from '@/lib/llm/provider'; +import { COACH_SYSTEM_PROMPT } from '@/lib/prompts/coach'; +import { SYSTEM_PROMPT } from '@/lib/prompts/system'; +import { warmEmbeddingsEndpoint } from '@/lib/retrieval/embeddings'; +import { warmSupabaseConnection } from '@/lib/retrieval/search'; +import { + buildInterceptionMessage, + buildSafetyNotice, + type SafetyNoticeData, +} from '@/lib/safety/intercept'; +import { llmScreen } from '@/lib/safety/llm-screen'; +import { screenForPii, type PiiFinding } from '@/lib/safety/pii'; +import { haiTools } from '@/lib/tools'; + +export const dynamic = 'force-dynamic'; + +/* + * Serverless execution budget, and a Vercel-only concern — `next dev` and + * `next start` ignore it, so the local demo is unaffected by this number. + * + * 60 rather than the 120 a slow local model wants, because Vercel's Hobby plan + * caps functions at 60s without Fluid compute and rejects the deployment + * outright above it. Hosted inference is fast enough that this is not the + * binding constraint: a multi-step tool-calling turn against Groq finishes well + * inside it. Raise it if you deploy on a plan that allows more. + */ +export const maxDuration = 60; + +// cost: $0.00 per message — inference runs locally through Ollama by default. +// Pointing LLM_BASE_URL at a hosted endpoint may introduce per-token billing. + +/** Custom data parts HAI streams alongside text. */ +export type HaiDataParts = { + 'safety-notice': SafetyNoticeData; +}; + +/** Shared with the client so message parts are typed against the real tools. */ +export type HaiUIMessage = UIMessage>; + +const DEFAULT_RATE_LIMIT_PER_MINUTE = 20; +const RATE_LIMIT_WINDOW_MS = 60_000; + +function ratePerMinute(): number { + const parsed = Number.parseInt(process.env.RATE_LIMIT_RPM ?? '', 10); + return Number.isFinite(parsed) && parsed > 0 ? parsed : DEFAULT_RATE_LIMIT_PER_MINUTE; +} + +/** + * Per-IP rate limit held in process memory. This is genuinely not a production + * rate limiter: it resets on deploy, and every serverless instance keeps its + * own counter, so the effective limit is the configured one multiplied by the + * number of live instances. On Vercel that multiplier is whatever the platform + * decides to scale to, which is exactly why it is paired with the shared daily + * cap below rather than trusted on its own — this one paces a single impatient + * browser, and `claimDailyRequest` is what actually bounds a day. + */ +const requestLog = new Map(); + +/** The daily counter rolls over on the database's `current_date`, i.e. UTC. */ +function secondsUntilUtcMidnight(): number { + const now = new Date(); + const midnight = Date.UTC( + now.getUTCFullYear(), + now.getUTCMonth(), + now.getUTCDate() + 1, + ); + return Math.max(1, Math.ceil((midnight - now.getTime()) / 1000)); +} + +function clientKey(request: Request): string { + const forwarded = request.headers.get('x-forwarded-for'); + if (forwarded) return forwarded.split(',')[0].trim(); + return request.headers.get('x-real-ip') ?? 'unknown'; +} + +function isRateLimited(key: string): boolean { + const now = Date.now(); + const cutoff = now - RATE_LIMIT_WINDOW_MS; + const recent = (requestLog.get(key) ?? []).filter((at) => at > cutoff); + + if (recent.length >= ratePerMinute()) { + requestLog.set(key, recent); + return true; + } + + recent.push(now); + requestLog.set(key, recent); + + // Opportunistic sweep so the map does not grow without bound. + if (requestLog.size > 5_000) { + for (const [entryKey, timestamps] of requestLog) { + if (timestamps.every((at) => at <= cutoff)) requestLog.delete(entryKey); + } + } + + return false; +} + +/* ------------------------------------------------------------------ * + * Data-responsibility screening + * ------------------------------------------------------------------ */ + +const WITHHELD_PLACEHOLDER = + '[An earlier message in this conversation was withheld by data-responsibility screening. Its content was not retained.]'; + +function messageText(message: HaiUIMessage): string { + return message.parts + .filter((part) => part.type === 'text') + .map((part) => part.text) + .join('\n'); +} + +/** + * The client keeps every message it has sent, including one that was refused. + * Left alone, a flagged message would be replayed to the model on the very next + * turn — screening would have delayed the exposure by one request rather than + * prevented it. So earlier flagged turns are replaced with a placeholder before + * the history is converted, and only the newest message triggers a refusal. + * That also means one bad paste never strands the conversation: the user can + * carry on, and the model simply never sees it. + */ +function withHistoryRedacted( + messages: HaiUIMessage[], + newestUserMessage: HaiUIMessage | undefined, +): HaiUIMessage[] { + return messages.map((message) => { + if (message.role !== 'user') return message; + if (message === newestUserMessage) return message; + if (!screenForPii(messageText(message)).flagged) return message; + + return { + ...message, + parts: [{ type: 'text' as const, text: WITHHELD_PLACEHOLDER }], + }; + }); +} + +/** + * The refusal is streamed as an ordinary assistant message rather than returned + * as an HTTP error. A 4xx renders in the UI as a red "something went wrong" + * banner, which frames a correct data-responsibility decision as a broken app — + * the reading most likely to send someone to paste the same thing into a tool + * with no screening at all. + */ +function interceptionResponse(findings: PiiFinding[]): Response { + const notice = buildSafetyNotice(findings); + const body = buildInterceptionMessage(findings); + + const stream = createUIMessageStream({ + execute: ({ writer }) => { + writer.write({ type: 'start' }); + writer.write({ type: 'start-step' }); + writer.write({ type: 'data-safety-notice', data: notice }); + writer.write({ type: 'text-start', id: 'safety-refusal' }); + writer.write({ type: 'text-delta', id: 'safety-refusal', delta: body }); + writer.write({ type: 'text-end', id: 'safety-refusal' }); + writer.write({ type: 'finish-step' }); + writer.write({ type: 'finish' }); + }, + }); + + return createUIMessageStreamResponse({ stream }); +} + +export async function POST(request: Request) { + // Fire-and-forget, started before anything else: the model has not decided + // whether it needs search_standards yet, but if it does, that decision is + // still a Groq round trip away. Warming the embedding endpoint and the + // Supabase connection now means both are already hot by the time the tool + // actually runs — see the module docs on embedQuery/warmSupabaseConnection + // for the cold-start numbers (measured: ~5.7s cold vs ~0.6–1.5s warm on HF). + warmEmbeddingsEndpoint(); + warmSupabaseConnection(); + + if (isRateLimited(clientKey(request))) { + return Response.json( + { + error: `Rate limit exceeded — ${ratePerMinute()} messages per minute. Wait a moment and try again.`, + }, + { status: 429, headers: { 'Retry-After': '60' } }, + ); + } + + // Claimed before the body is even parsed: a request that cannot be served + // should not cost a model call, a retrieval round-trip, or a screening pass. + // No-ops entirely in local mode, where inference is free. + const daily = await claimDailyRequest(); + if (!daily.allowed) { + return Response.json( + { error: dailyCapMessage(daily) }, + // Until 00:00 UTC, so a client that honours Retry-After waits for the + // actual reset instead of hammering the endpoint for the rest of the day. + { status: 429, headers: { 'Retry-After': String(secondsUntilUtcMidnight()) } }, + ); + } + + let messages: HaiUIMessage[]; + let mode: 'default' | 'coach'; + try { + ({ messages, mode = 'default' } = (await request.json()) as { + messages: HaiUIMessage[]; + mode?: 'default' | 'coach'; + }); + } catch { + return Response.json({ error: 'Malformed request body.' }, { status: 400 }); + } + + if (!Array.isArray(messages) || messages.length === 0) { + return Response.json({ error: 'No messages provided.' }, { status: 400 }); + } + + // Screen before anything else touches the text: no logging, no retrieval, no + // model call. What comes back is masked, so the refusal, the stream, and the + // UI all handle shapes rather than values. + const newestUserMessage = [...messages].reverse().find((m) => m.role === 'user'); + const newestUserText = newestUserMessage ? messageText(newestUserMessage) : ''; + + const deterministic = screenForPii(newestUserText); + if (deterministic.flagged) { + return interceptionResponse(deterministic.findings); + } + + // Only reached when the regex pass found nothing, so the extra round-trip is + // paid on clean messages rather than obvious ones. Off unless PII_LLM_SCREEN=true. + const semantic = await llmScreen(newestUserText); + if (semantic) { + return interceptionResponse([semantic]); + } + + const result = streamText({ + model: getChatModel(), + providerOptions: getProviderOptions(), + system: mode === 'coach' ? COACH_SYSTEM_PROMPT : SYSTEM_PROMPT, + messages: await convertToModelMessages( + withHistoryRedacted(messages, newestUserMessage), + ), + tools: haiTools, + // Near-deterministic on purpose. This assistant reports thresholds and + // figures; sampling variety buys nothing here and measurably costs + // tool-calling reliability on smaller local models, which at higher + // temperatures drift out of the user's language mid-answer. + temperature: 0, + // Multi-step: the model searches the standards, reads what came back, and + // then answers — each of those is a step, and a grounded answer that + // consults several sources needs several. Measured eval transcripts never + // exceed two tool calls in a turn (three steps); four leaves headroom + // above that without letting a stuck loop re-send the full system prompt + // and tool schemas six times against Groq's free-tier tokens-per-minute + // budget — see docs/DEPLOY.md on why that budget is tight. + stopWhen: stepCountIs(4), + }); + + return result.toUIMessageStreamResponse({ + onError: (error) => + error instanceof Error ? error.message : 'The assistant hit an unexpected error.', + }); +} diff --git a/app/src/app/favicon.ico b/app/src/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/app/src/app/favicon.ico differ diff --git a/app/src/app/globals.css b/app/src/app/globals.css new file mode 100644 index 0000000..a257c04 --- /dev/null +++ b/app/src/app/globals.css @@ -0,0 +1,216 @@ +@import "tailwindcss"; + +/* + * HAI's palette — Swiss/International Typographic Style: near-black ink on + * paper white, functional greys carrying structure, and exactly one signal + * colour (a Swiss red) reserved for the things a reader must act on — a + * send button, a live indicator, the wordmark's mark, a focus ring. It does + * not tint hover states or fill cards; those move by border and weight, not + * colour, so the red keeps pointing at what matters. + * + * Amber stays as the separate, functional "advisory" colour it already was + * (PII interception, the hosted-demo notice) — a second colour would dilute + * the red's signal value, so warnings stay amber rather than becoming red. + */ +:root { + --background: #fbfbf9; + --surface: #ffffff; + --surface-muted: #f1f1ec; + --border: #d9d9d2; + --border-strong: #b6b6aa; + + --foreground: #111111; + --muted: #55564e; + --subtle: #83837a; + + --accent: #e30613; + --accent-hover: #b8050f; + --accent-soft: #fdecec; + --accent-border: #f2b9be; + + --notice: #8a5a10; + --notice-soft: #fdf5e6; + --notice-border: #ecd9ae; +} + +@media (prefers-color-scheme: dark) { + :root { + --background: #111110; + --surface: #191917; + --surface-muted: #201f1c; + --border: #38372f; + --border-strong: #4c4a3f; + + --foreground: #f2f1ea; + --muted: #a8a79c; + --subtle: #7d7c72; + + --accent: #ff4045; + --accent-hover: #ff6a6e; + --accent-soft: #341417; + --accent-border: #5c2126; + + --notice: #e0b45f; + --notice-soft: #241d10; + --notice-border: #4a3c1c; + } +} + +@theme inline { + --color-background: var(--background); + --color-surface: var(--surface); + --color-surface-muted: var(--surface-muted); + --color-border-subtle: var(--border); + --color-border-strong: var(--border-strong); + + --color-foreground: var(--foreground); + --color-muted: var(--muted); + --color-subtle: var(--subtle); + + --color-accent: var(--accent); + --color-accent-hover: var(--accent-hover); + --color-accent-soft: var(--accent-soft); + --color-accent-border: var(--accent-border); + + --color-notice: var(--notice); + --color-notice-soft: var(--notice-soft); + --color-notice-border: var(--notice-border); + + --font-sans: var(--font-inter); + --font-mono: var(--font-plex-mono); +} + +body { + background: var(--background); + color: var(--foreground); + font-family: var(--font-inter), ui-sans-serif, system-ui, sans-serif; + -webkit-font-smoothing: antialiased; +} + +/* Swiss type carries hierarchy through weight and tracking, not size alone — + every heading in the app tightens as it grows. */ +h1, h2, h3, .hai-heading { + font-weight: 700; + letter-spacing: -0.02em; +} + +/* A small uppercase label set wide — the Swiss "eyebrow": role tags, source + labels, section markers. Never used for anything longer than a few words. */ +.hai-eyebrow { + font-size: 0.7rem; + font-weight: 600; + letter-spacing: 0.09em; + text-transform: uppercase; +} + +/* Tabular data voice — citations' source · section · page, tool detail + strings, counts. Set in the mono face so numbers align and read as data. */ +.hai-data { + font-family: var(--font-plex-mono), ui-monospace, monospace; + font-feature-settings: "tnum" 1; +} + +/* Tool activity indicator: a slow pulse, not a spinner — the work takes + seconds, and a fast animation reads as urgency the content doesn't have. */ +@keyframes hai-pulse { + 0%, 100% { opacity: 0.35; } + 50% { opacity: 1; } +} + +.hai-pulse { + animation: hai-pulse 1.6s ease-in-out infinite; +} + +@media (prefers-reduced-motion: reduce) { + .hai-pulse { + animation: none; + opacity: 0.7; + } +} + +/* Prose defaults for streamed markdown. Scoped tight so the assistant's + answers stay dense enough to scan under time pressure. */ +.hai-prose { + line-height: 1.65; +} + +.hai-prose > * + * { + margin-top: 0.85rem; +} + +.hai-prose h1, +.hai-prose h2, +.hai-prose h3 { + font-weight: 700; + letter-spacing: -0.01em; + margin-top: 1.5rem; + line-height: 1.3; +} + +.hai-prose h1 { font-size: 1.15rem; } +.hai-prose h2 { font-size: 1.05rem; } +.hai-prose h3 { font-size: 0.975rem; } + +.hai-prose ul, +.hai-prose ol { + padding-left: 1.35rem; +} + +.hai-prose ul { list-style: square; } +.hai-prose ol { list-style: decimal; } + +.hai-prose li + li { margin-top: 0.3rem; } + +.hai-prose a { + color: var(--accent); + text-decoration: underline; + text-underline-offset: 2px; +} + +.hai-prose code { + font-family: var(--font-plex-mono), ui-monospace, monospace; + font-size: 0.875em; + background: var(--surface-muted); + border: 1px solid var(--border); + padding: 0.1em 0.35em; +} + +.hai-prose pre { + background: var(--surface-muted); + border: 1px solid var(--border); + padding: 0.85rem 1rem; + overflow-x: auto; +} + +.hai-prose pre code { + background: none; + border: none; + padding: 0; +} + +.hai-prose blockquote { + border-inline-start: 2px solid var(--accent-border); + padding-inline-start: 0.9rem; + color: var(--muted); +} + +.hai-prose table { + width: 100%; + border-collapse: collapse; + font-size: 0.9em; + display: block; + overflow-x: auto; +} + +.hai-prose th, +.hai-prose td { + border: 1px solid var(--border); + padding: 0.45rem 0.7rem; + text-align: start; + vertical-align: top; +} + +.hai-prose th { + background: var(--surface-muted); + font-weight: 600; +} diff --git a/app/src/app/guides/[id]/page.tsx b/app/src/app/guides/[id]/page.tsx new file mode 100644 index 0000000..2168267 --- /dev/null +++ b/app/src/app/guides/[id]/page.tsx @@ -0,0 +1,27 @@ +import type { Metadata } from 'next'; +import { notFound } from 'next/navigation'; + +import { GuideDetail } from '@/components/guide-detail'; +import { getAllGuideIds, getGuide } from '@/lib/content'; + +export function generateStaticParams() { + return getAllGuideIds().map((id) => ({ id })); +} + +export async function generateMetadata(props: PageProps<'/guides/[id]'>): Promise { + const { id } = await props.params; + const guide = getGuide(id); + if (!guide) return {}; + return { + title: `${guide.title} — HAI`, + description: guide.summary, + }; +} + +export default async function GuideDetailPage(props: PageProps<'/guides/[id]'>) { + const { id } = await props.params; + const guide = getGuide(id); + if (!guide) notFound(); + + return ; +} diff --git a/app/src/app/guides/layout.tsx b/app/src/app/guides/layout.tsx new file mode 100644 index 0000000..fa73651 --- /dev/null +++ b/app/src/app/guides/layout.tsx @@ -0,0 +1,12 @@ +import { Footer } from '@/components/footer'; +import { SiteHeader } from '@/components/site-header'; + +export default function GuidesLayout({ children }: LayoutProps<'/guides'>) { + return ( +
+ +
{children}
+
+
+ ); +} diff --git a/app/src/app/guides/page.tsx b/app/src/app/guides/page.tsx new file mode 100644 index 0000000..f27258c --- /dev/null +++ b/app/src/app/guides/page.tsx @@ -0,0 +1,15 @@ +import type { Metadata } from 'next'; + +import { GuidesIndex } from '@/components/guides-index'; +import { getGuideIndex } from '@/lib/content'; + +export const metadata: Metadata = { + title: 'Guides — HAI', + description: 'General-purpose guides for prompting, responsible use, and adoption.', +}; + +export default function GuidesIndexPage() { + const guides = getGuideIndex(); + + return ; +} diff --git a/app/src/app/layout.tsx b/app/src/app/layout.tsx new file mode 100644 index 0000000..6f31e89 --- /dev/null +++ b/app/src/app/layout.tsx @@ -0,0 +1,35 @@ +import type { Metadata } from "next"; +import { Inter, IBM_Plex_Mono } from "next/font/google"; +import "./globals.css"; + +import { LocaleProvider } from "@/lib/i18n/context"; + +const inter = Inter({ + variable: "--font-inter", + subsets: ["latin"], +}); + +const plexMono = IBM_Plex_Mono({ + variable: "--font-plex-mono", + subsets: ["latin"], + weight: ["400", "500", "600"], +}); + +export const metadata: Metadata = { + title: "HAI — Humanitarian operations assistant", + description: + "Guidance grounded in the Sphere Handbook, the Core Humanitarian Standard, and IASC guidance, with live crisis data and cited sources.", +}; + +export default function RootLayout({ children }: LayoutProps<"/">) { + return ( + + + {children} + + + ); +} diff --git a/app/src/app/page.tsx b/app/src/app/page.tsx new file mode 100644 index 0000000..951d46f --- /dev/null +++ b/app/src/app/page.tsx @@ -0,0 +1,18 @@ +import { Suspense } from 'react'; + +import { Chat } from '@/components/chat'; +import { isLocalInference } from '@/lib/llm/provider'; + +/* + * Whether this instance is the hosted demo is derived from the same LLM_BASE_URL + * the chat route actually uses, resolved here on the server. Deriving it beats a + * separate NEXT_PUBLIC_ flag, which is a second source of truth that can be left + * saying "local" on a deployment that is talking to Groq. + */ +export default function Home() { + return ( + + + + ); +} diff --git a/app/src/app/playbooks/[id]/page.tsx b/app/src/app/playbooks/[id]/page.tsx new file mode 100644 index 0000000..04bf13c --- /dev/null +++ b/app/src/app/playbooks/[id]/page.tsx @@ -0,0 +1,29 @@ +import type { Metadata } from 'next'; +import { notFound } from 'next/navigation'; + +import { PlaybookDetail } from '@/components/playbook-detail'; +import { getAllPlaybookIds, getPlaybook } from '@/lib/content'; + +export function generateStaticParams() { + return getAllPlaybookIds().map((id) => ({ id })); +} + +export async function generateMetadata( + props: PageProps<'/playbooks/[id]'>, +): Promise { + const { id } = await props.params; + const playbook = getPlaybook(id); + if (!playbook) return {}; + return { + title: `${playbook.title} — HAI`, + description: playbook.summary, + }; +} + +export default async function PlaybookDetailPage(props: PageProps<'/playbooks/[id]'>) { + const { id } = await props.params; + const playbook = getPlaybook(id); + if (!playbook) notFound(); + + return ; +} diff --git a/app/src/app/playbooks/layout.tsx b/app/src/app/playbooks/layout.tsx new file mode 100644 index 0000000..0b9004a --- /dev/null +++ b/app/src/app/playbooks/layout.tsx @@ -0,0 +1,12 @@ +import { Footer } from '@/components/footer'; +import { SiteHeader } from '@/components/site-header'; + +export default function PlaybooksLayout({ children }: LayoutProps<'/playbooks'>) { + return ( +
+ +
{children}
+
+
+ ); +} diff --git a/app/src/app/playbooks/page.tsx b/app/src/app/playbooks/page.tsx new file mode 100644 index 0000000..cc5d8ee --- /dev/null +++ b/app/src/app/playbooks/page.tsx @@ -0,0 +1,15 @@ +import type { Metadata } from 'next'; + +import { PlaybooksIndex } from '@/components/playbooks-index'; +import { getPlaybookIndex } from '@/lib/content'; + +export const metadata: Metadata = { + title: 'Playbooks — HAI', + description: 'Role-specific guidance for using HAI well, with example prompts you can try.', +}; + +export default function PlaybooksIndexPage() { + const playbooks = getPlaybookIndex(); + + return ; +} diff --git a/app/src/components/about-content.tsx b/app/src/components/about-content.tsx new file mode 100644 index 0000000..52ea07d --- /dev/null +++ b/app/src/components/about-content.tsx @@ -0,0 +1,43 @@ +'use client'; + +import { useLocale } from '@/lib/i18n/context'; +import { IconDocument, IconSearch, IconShield, IconWarning } from './icons'; + +const SECTION_ICONS = [IconSearch, IconShield, IconDocument, IconWarning]; + +export function AboutContent() { + const { t } = useLocale(); + const sections = [t.about.grounded, t.about.safety, t.about.evals, t.about.limits]; + + return ( +
+

{t.about.title}

+

{t.about.intro}

+ + {/* The one genuinely sequential thing on this page — worth showing as a + pipeline rather than folding it into prose. */} +
+ search + → + cite + → + verify +
+ +
+ {sections.map((section, index) => { + const Icon = SECTION_ICONS[index]; + return ( +
+ +
+

{section.heading}

+

{section.body}

+
+
+ ); + })} +
+
+ ); +} diff --git a/app/src/components/chat.tsx b/app/src/components/chat.tsx new file mode 100644 index 0000000..a6ffbd5 --- /dev/null +++ b/app/src/components/chat.tsx @@ -0,0 +1,276 @@ +'use client'; + +import { useChat } from '@ai-sdk/react'; +import { DefaultChatTransport } from 'ai'; +import { useCallback, useEffect, useRef, useState } from 'react'; +import { useRouter, useSearchParams } from 'next/navigation'; + +import type { HaiUIMessage } from '@/app/api/chat/route'; +import { useLocale } from '@/lib/i18n/context'; +import { Citations } from './citations'; +import { Composer } from './composer'; +import { EmptyState } from './empty-state'; +import { HostedModeNotice } from './hosted-mode-notice'; +import { IconCoach, IconMark } from './icons'; +import { LocaleSwitcher } from './locale-switcher'; +import { Markdown } from './markdown'; +import { NavLinks } from './nav-links'; +import { PendingStatus } from './pending-status'; +import { SafetyNotice } from './safety-notice'; +import { SourcePanel } from './source-panel'; +import { collectRetrievalNotice, collectSources, type Source } from './sources'; +import { ToolActivity } from './tool-activity'; + +/** Seconds must pass before the elapsed counter appears — see PendingStatus. */ +const ELAPSED_THRESHOLD_S = 3; + +type MessagePart = HaiUIMessage['parts'][number]; + +function isToolPart(part: MessagePart): boolean { + return part.type.startsWith('tool-'); +} + +function isToolPartDone(part: MessagePart): boolean { + const state = (part as { state?: string }).state; + return state === 'output-available' || state === 'output-error'; +} + +/** + * What the still-forming assistant turn is doing right now, derived from real + * stream state rather than a timer — 'contacting' before anything has + * happened yet, 'writing' once retrieval has finished but no answer text has + * arrived, and `null` whenever a running tool (rendered by `ToolActivity`) or + * actual answer text is already the visible feedback. + */ +function pendingPhase( + status: string, + lastMessage: HaiUIMessage | undefined, +): 'contacting' | 'writing' | null { + if (status === 'submitted') return 'contacting'; + if (status !== 'streaming') return null; + if (!lastMessage || lastMessage.role !== 'assistant') return null; + + const parts = lastMessage.parts; + if (parts.length === 0) return 'contacting'; + if (parts.some((part) => isToolPart(part) && !isToolPartDone(part))) return null; + if (parts.some((part) => part.type === 'text' && part.text.length > 0)) return null; + return 'writing'; +} + +export function Chat({ hosted = false }: { hosted?: boolean }) { + const router = useRouter(); + const searchParams = useSearchParams(); + const { t } = useLocale(); + + // "Try in chat" links land here as `/?q=...`. Prefill the composer from it + // on first render — without sending; the user presses send — via lazy + // initial state rather than an effect, since this only needs to run once. + const [input, setInput] = useState(() => searchParams.get('q') ?? ''); + const [activeSource, setActiveSource] = useState(null); + const [coachMode, setCoachMode] = useState(false); + const scrollAnchor = useRef(null); + + const { messages, sendMessage, status, stop, error } = useChat({ + transport: new DefaultChatTransport({ api: '/api/chat' }), + }); + + const busy = status === 'submitted' || status === 'streaming'; + + // Runs for the whole busy span, not per-phase, so a turn that moves from + // "contacting" to a tool call to "writing" carries one continuous count — + // the wait is one wait to the person reading it, however many phases the + // stream actually goes through. + const [elapsedSeconds, setElapsedSeconds] = useState(null); + useEffect(() => { + // Nothing to tear down when the turn finishes: `elapsedLabel` below is + // gated on `busy` too, so a stale count from the last turn simply never + // renders until the next one overwrites it from a fresh start time. + if (!busy) return; + const startedAt = Date.now(); + const tick = () => { + const seconds = Math.floor((Date.now() - startedAt) / 1000); + setElapsedSeconds(seconds >= ELAPSED_THRESHOLD_S ? seconds : null); + }; + tick(); + const id = setInterval(tick, 1000); + return () => clearInterval(id); + }, [busy]); + + const lastMessage = messages[messages.length - 1]; + const phase = pendingPhase(status, lastMessage); + const elapsedLabel = busy && elapsedSeconds !== null ? t.pending.elapsed(elapsedSeconds) : null; + + useEffect(() => { + scrollAnchor.current?.scrollIntoView({ behavior: 'smooth', block: 'end' }); + }, [messages, status]); + + // Drop `q` from the URL once it's been read, so a refresh doesn't + // re-prefill over whatever the user has typed since. + useEffect(() => { + if (searchParams.get('q')) router.replace('/', { scroll: false }); + }, [searchParams, router]); + + const send = useCallback( + (text: string) => { + const trimmed = text.trim(); + if (!trimmed) return; + setInput(''); + void sendMessage({ text: trimmed }, { body: { mode: coachMode ? 'coach' : 'default' } }); + }, + [sendMessage, coachMode], + ); + + return ( +
+
+
+
+ + {t.appName} + {t.tagline} +
+
+ + + +
+
+ {hosted ? : null} +
+ + {/* Generous asymmetric margin on wide viewports — a wide empty gutter on + the start side, Swiss-poster style, with the content column sitting + right of it rather than centered on the page. */} +
+
+ {messages.length === 0 ? ( + + ) : ( +
+ {messages.map((message, index) => { + const isLast = index === messages.length - 1; + return ( + + ); + })} + {/* The turn hasn't produced an assistant message yet at all — status + 'submitted', or 'start' has fired but no parts exist yet. Once the + assistant message appears, MessageBlock above takes over. */} + {phase && lastMessage?.role !== 'assistant' ? ( + + ) : null} +
+ )} + + {error ? ( +

+ {error.message || t.errorFallback} +

+ ) : null} + +
+
+
+ +
+
+
+ send(input)} onStop={stop} busy={busy} /> +

{t.disclaimer}

+
+
+
+ + setActiveSource(null)} /> +
+ ); +} + +function MessageBlock({ + message, + onSelectSource, + isLast, + pendingPhase, + elapsedLabel, +}: { + message: HaiUIMessage; + onSelectSource: (source: Source) => void; + isLast: boolean; + /** Meaningful only when `isLast` — see `pendingPhase()` in the parent. */ + pendingPhase: 'contacting' | 'writing' | null; + elapsedLabel: string | null; +}) { + const { t } = useLocale(); + + if (message.role === 'user') { + const text = message.parts + .filter((part) => part.type === 'text') + .map((part) => part.text) + .join(''); + + return ( +
+
+

{text}

+
+
+ ); + } + + const sources = collectSources(message); + const notice = collectRetrievalNotice(message); + const lastPartIndex = message.parts.length - 1; + + return ( +
+ {message.parts.map((part, index) => { + if (part.type === 'text') { + return ( +
+ {part.text} +
+ ); + } + if (part.type === 'data-safety-notice') { + return ; + } + if (part.type.startsWith('tool-')) { + // Only the turn's currently-running call — the last part of the + // last message, still in flight — carries the elapsed counter. A + // finished call, or one from an earlier turn, never shows one. Note + // this is independent of `pendingPhase`, which is null precisely + // while a tool is running (see `pendingPhase()` in the parent). + const isLive = isLast && index === lastPartIndex && !isToolPartDone(part); + return ; + } + return null; + })} + + {pendingPhase === 'writing' ? ( + + ) : null} + + +
+ ); +} diff --git a/app/src/components/citations.tsx b/app/src/components/citations.tsx new file mode 100644 index 0000000..ed7945d --- /dev/null +++ b/app/src/components/citations.tsx @@ -0,0 +1,44 @@ +'use client'; + +import { useLocale } from '@/lib/i18n/context'; +import { sourceLabel, type Source } from './sources'; + +export function Citations({ + sources, + notice, + onSelect, +}: { + sources: Source[]; + notice?: string; + onSelect: (source: Source) => void; +}) { + const { t } = useLocale(); + + if (sources.length === 0) { + // A notice with no passages is the honest case worth surfacing: the model + // tried to ground the answer and the corpus had nothing to give it. + return notice ? ( +

+ {notice} +

+ ) : null; + } + + return ( +
+ {t.citations.sources} + {sources.map((source, index) => ( + + ))} +
+ ); +} diff --git a/app/src/components/composer.tsx b/app/src/components/composer.tsx new file mode 100644 index 0000000..fa59350 --- /dev/null +++ b/app/src/components/composer.tsx @@ -0,0 +1,78 @@ +'use client'; + +import { useEffect, useRef, type FormEvent, type KeyboardEvent } from 'react'; + +import { useLocale } from '@/lib/i18n/context'; + +export function Composer({ + value, + onChange, + onSubmit, + onStop, + busy, +}: { + value: string; + onChange: (value: string) => void; + onSubmit: () => void; + onStop: () => void; + busy: boolean; +}) { + const { t } = useLocale(); + const textareaRef = useRef(null); + + // Grow with the content instead of scrolling inside a fixed box — field + // questions are often several lines long. + useEffect(() => { + const el = textareaRef.current; + if (!el) return; + el.style.height = 'auto'; + el.style.height = `${Math.min(el.scrollHeight, 200)}px`; + }, [value]); + + const submit = (event: FormEvent) => { + event.preventDefault(); + if (busy || !value.trim()) return; + onSubmit(); + }; + + const onKeyDown = (event: KeyboardEvent) => { + if (event.key === 'Enter' && !event.shiftKey) { + event.preventDefault(); + if (!busy && value.trim()) onSubmit(); + } + }; + + return ( +
+
+