diff --git a/.env.example b/.env.example index bd8ed40..616e3f7 100644 --- a/.env.example +++ b/.env.example @@ -8,8 +8,8 @@ # ----------------------------------------------------------------- # 1. Core Application & Database # ----------------------------------------------------------------- -# Port on the host machine to expose the application (default: 3000) -PORT=3000 +# Port on the host machine to expose the application (default: 5050) +PORT=5050 # MongoDB Connection String (pre-configured with replica set for transactions) DATABASE_URL="mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true" @@ -28,7 +28,30 @@ CLOUDINARY_API_KEY= CLOUDINARY_API_SECRET= # ----------------------------------------------------------------- -# 3. Logging & Telemetry (Optional) +# 3. AI Vector Search - Qdrant (Optional, Beta - OFF by default) +# ----------------------------------------------------------------- +# Leave these commented out and search stays disabled: the app shows a +# "not enabled" notice on the Search page and never downloads any models. +# +# To turn it on you need a running Qdrant service. With docker compose: +# docker compose -f docker-compose.yml -f docker-compose.search.yml up -d +# then uncomment QDRANT_URL below. +# +# Setting QDRANT_URL is the switch — nothing else enables or disables search. +# See docs/features/ai-vector-search.md +# +# QDRANT_URL="http://qdrant:6333" +# +# Host port for the Qdrant API & Dashboard (default: 6335, loopback only). +# Dashboard UI: http://localhost:6335/dashboard +# QDRANT_PORT=6335 +# +# Required if you enable Qdrant's own auth. The app refuses to send this over +# plaintext HTTP to a non-local host. +# QDRANT_API_KEY= + +# ----------------------------------------------------------------- +# 4. Logging & Telemetry (Optional) # ----------------------------------------------------------------- # Log level: fatal | error | warn | info | debug | trace (default: info) LOG_LEVEL=info diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a6d4434..0efa0fd 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,13 +14,24 @@ jobs: - name: Checkout code uses: actions/checkout@v4 - - name: Set up Node.js 20 + - name: Set up Node.js 24 uses: actions/setup-node@v7 with: - node-version: 20 - cache: npm + node-version: 24 + + # node_modules is ~1.7 GB across 59k files, dominated by onnxruntime + # (342 MB) and tfjs (277 MB). Caching the tree itself skips npm ci + # outright; setup-node's `cache: npm` only skipped the download, leaving + # the extraction — the actual cost — to run on every single job. + - name: Restore node_modules + id: node-modules + uses: actions/cache@v4 + with: + path: node_modules + key: node-modules-${{ runner.os }}-node24-${{ hashFiles('package-lock.json') }} - name: Install dependencies + if: steps.node-modules.outputs.cache-hit != 'true' run: npm ci --ignore-scripts - name: Generate Prisma Client diff --git a/.github/workflows/pr-beta.yml b/.github/workflows/pr-beta.yml index b4a11e5..a6da63f 100644 --- a/.github/workflows/pr-beta.yml +++ b/.github/workflows/pr-beta.yml @@ -23,11 +23,12 @@ jobs: with: fetch-depth: 0 - - name: Set up Node.js 20 + - name: Set up Node.js 24 uses: actions/setup-node@v7 with: - node-version: 20 - cache: npm + node-version: 24 + # No npm install in this workflow — only the version scripts run here, + # and they use node builtins. - name: Calculate Beta Version id: semver @@ -36,16 +37,9 @@ jobs: GITHUB_PR_NUMBER: ${{ github.event.pull_request.number }} run: node .github/scripts/calculate-version.mjs --mode=beta - - name: Install dependencies - run: npm ci --ignore-scripts - - - name: Generate Prisma Client - run: npx prisma generate - - - name: Verify Lint & TypeScript - run: | - npm run lint - npx tsc --noEmit + # Lint, typecheck and build verification run in ci.yml on this same + # pull_request trigger — repeating them here meant a third full npm ci + # (~1.7 GB) per PR for no extra signal. - name: Set up Docker Buildx uses: docker/setup-buildx-action@v4 @@ -71,8 +65,13 @@ jobs: ${{ env.REGISTRY }}/${{ steps.semver.outputs.image_name }}:${{ steps.semver.outputs.tag }} build-args: | APP_VERSION=${{ steps.semver.outputs.version }} - cache-from: type=gha - cache-to: type=gha,mode=max + # Separate scopes: sharing the default one meant beta and release + # exports kept overwriting each other's manifest. Beta also reads the + # release scope so a PR starts warm from the last master build. + cache-from: | + type=gha,scope=beta + type=gha,scope=release + cache-to: type=gha,mode=max,scope=beta - name: Beta Release Summary run: | @@ -84,7 +83,7 @@ jobs: echo "" >> $GITHUB_STEP_SUMMARY echo "\`\`\`bash" >> $GITHUB_STEP_SUMMARY echo "# Pull and run this beta container locally:" >> $GITHUB_STEP_SUMMARY - echo "docker run -d -p 3000:3000 --name test-beta \\" >> $GITHUB_STEP_SUMMARY + echo "docker run -d -p 5050:3000 --name test-beta \\" >> $GITHUB_STEP_SUMMARY echo " -e DATABASE_URL=\"mongodb://...\" \\" >> $GITHUB_STEP_SUMMARY echo " ${{ env.REGISTRY }}/${{ steps.semver.outputs.image_name }}:beta-pr-${{ github.event.pull_request.number }}" >> $GITHUB_STEP_SUMMARY echo "\`\`\`" >> $GITHUB_STEP_SUMMARY diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index a515cc0..846c536 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -49,11 +49,12 @@ jobs: fetch-depth: 0 token: ${{ secrets.GITHUB_TOKEN }} - - name: Set up Node.js 20 + - name: Set up Node.js 24 uses: actions/setup-node@v7 with: - node-version: 20 - cache: npm + node-version: 24 + # No npm install in this workflow — only the version scripts run here, + # and they use node builtins. - name: Calculate Release Version id: semver @@ -65,19 +66,9 @@ jobs: IS_TAG_PUSH: ${{ startsWith(github.ref, 'refs/tags/v') }} run: node .github/scripts/calculate-version.mjs --mode=release - - name: Install dependencies - if: steps.semver.outputs.should_release == 'true' - run: npm ci --ignore-scripts - - - name: Generate Prisma Client - if: steps.semver.outputs.should_release == 'true' - run: npx prisma generate - - - name: Verify Lint & TypeScript - if: steps.semver.outputs.should_release == 'true' - run: | - npm run lint - npx tsc --noEmit + # Lint and typecheck already ran in ci.yml for this commit, and the Docker + # build below runs `npm run build` — a broken tree cannot publish an image. + # Installing here just to re-run them cost a full ~1.7 GB npm ci. - name: Update package.json Version if: steps.semver.outputs.should_release == 'true' && steps.semver.outputs.is_tag_push == 'false' @@ -122,8 +113,8 @@ jobs: ${{ env.REGISTRY }}/${{ steps.semver.outputs.image_name }}:${{ github.sha }} build-args: | APP_VERSION=${{ steps.semver.outputs.version }} - cache-from: type=gha - cache-to: type=gha,mode=max + cache-from: type=gha,scope=release + cache-to: type=gha,mode=max,scope=release - name: Create GitHub Release with Auto-Changelog if: steps.semver.outputs.should_release == 'true' && steps.semver.outputs.dry_run == 'false' diff --git a/Dockerfile b/Dockerfile index 9063ee3..ab9ac31 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,7 @@ # syntax=docker/dockerfile:1 # ── Builder ─────────────────────────────────────────────────── -FROM node:20-bookworm-slim AS builder +FROM node:24-bookworm-slim AS builder WORKDIR /app # OpenSSL is required by the Prisma query engine @@ -12,9 +12,19 @@ COPY package.json package-lock.json ./ # --ignore-scripts skips native builds and the postinstall prisma generate (run explicitly below). RUN npm ci --ignore-scripts +# onnxruntime-node ships prebuilt binaries for win32/darwin/linux in one tarball. +# Only linux is reachable from this image, and the other two (~159 MB) otherwise +# ride through npm prune, the runner COPY, and the registry layer cache. +RUN rm -rf node_modules/onnxruntime-node/bin/napi-v*/win32 node_modules/onnxruntime-node/bin/napi-v*/darwin + COPY prisma ./prisma RUN npx prisma generate +# CLIP and face-recognition weights are deliberately NOT baked in. Vector search +# is an optional beta add-on, so shipping ~600 MB of weights to every install +# would tax the majority that never enables it. They are downloaded on the first +# index run instead, into the model_cache volume from docker-compose.search.yml. + COPY . . ARG APP_VERSION=1.0.1 @@ -25,7 +35,7 @@ RUN npm run build RUN npm prune --omit=dev # ── Runner ──────────────────────────────────────────────────── -FROM node:20-bookworm-slim AS runner +FROM node:24-bookworm-slim AS runner WORKDIR /app RUN apt-get update && apt-get install -y --no-install-recommends openssl ca-certificates curl \ @@ -51,6 +61,13 @@ COPY --from=builder --chown=nextjs:nodejs /app/public ./public COPY --from=builder --chown=nextjs:nodejs /app/node_modules ./node_modules COPY --from=builder --chown=nextjs:nodejs /app/prisma ./prisma +# Transformers.js writes downloaded weights here. The path must exist and be +# owned by nextjs in the image: Docker seeds a fresh named volume from the image +# directory, so without this the search add-on's model_cache volume would come +# up root-owned and the non-root app could not write to it. +RUN mkdir -p /app/node_modules/@huggingface/transformers/.cache \ + && chown -R nextjs:nodejs /app/node_modules/@huggingface/transformers/.cache + USER nextjs EXPOSE 3000 diff --git a/README.md b/README.md index e7bc19e..1514e82 100644 --- a/README.md +++ b/README.md @@ -52,7 +52,7 @@ services: pull_policy: always restart: unless-stopped ports: - - "3000:3000" + - "5050:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true depends_on: @@ -90,7 +90,18 @@ volumes: docker compose up -d ``` -🎉 Open **`http://localhost:3000`** in your browser and complete the 60-second onboarding wizard! +🎉 Open **`http://localhost:5050`** in your browser and complete the 60-second onboarding wizard! + +> **Optional: AI Vector Search (Beta).** Semantic prompt, image and face search is +> **not enabled by default** — it adds a Qdrant service and downloads ~600 MB of +> model weights on first index. Everything else works fine without it. To opt in, +> grab [`docker-compose.search.yml`](docker-compose.search.yml) and run: +> +> ```bash +> docker compose -f docker-compose.yml -f docker-compose.search.yml up -d +> ``` +> +> Full setup and trade-offs: [AI Vector Search](docs/features/ai-vector-search.md). --- @@ -129,6 +140,7 @@ This project follows an automated semantic CI/CD versioning lifecycle directly c | Feature | Description | | :--- | :--- | +| 🔍 **Multimodal Vector Search** *(Beta, optional)* | Natural-language prompt search (CLIP), visual similarity image search, and facial recognition search with Qdrant. **Off by default** — [how to enable](docs/features/ai-vector-search.md). | | 🧙‍♂️ **Interactive Onboarding** | Built-in setup wizard with real-time Instagram cookie testing and avatar preview. | | 👥 **Multi-Profile Support** | Manage multiple Instagram accounts with separate sessions and isolated bookmarks. | | 📈 **Account Timelines** | Automatically records username changes, bio updates, verification changes, and lost accounts. | @@ -161,7 +173,9 @@ When running via Docker Compose, **zero environment variables are required**. Op | Variable | Default | Description | | :--- | :--- | :--- | -| `PORT` | `3000` | Port exposed on host | +| `PORT` | `5050` | Port exposed on host for web app | +| `QDRANT_URL` | _(unset)_ | **Optional.** Setting this switches on the beta vector search. Unset = disabled | +| `QDRANT_PORT` | `6335` | Host port for the Qdrant API & Dashboard, only used with search enabled | | `DATABASE_URL` | `mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true` | MongoDB connection string (replica set enabled) | | `CLOUDINARY_CLOUD_NAME` | `""` | Optional Cloudinary cloud name for permanent media | | `CLOUDINARY_API_KEY` | `""` | Optional Cloudinary API key | diff --git a/coolify-compose.yml b/coolify-compose.yml index b448d54..b1487bb 100644 --- a/coolify-compose.yml +++ b/coolify-compose.yml @@ -10,11 +10,15 @@ services: restart: unless-stopped expose: - "3000" + ports: + - "${PORT:-5050}:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true - NODE_ENV=production - PORT=3000 - HOSTNAME=0.0.0.0 + # AI Vector Search is an optional beta add-on, off by default. + # To enable it, see docs/features/ai-vector-search.md # Optional Cloudinary CDN Configuration - CLOUDINARY_CLOUD_NAME=${CLOUDINARY_CLOUD_NAME:-} - CLOUDINARY_API_KEY=${CLOUDINARY_API_KEY:-} diff --git a/docker-compose.search.yml b/docker-compose.search.yml new file mode 100644 index 0000000..1b0c5f0 --- /dev/null +++ b/docker-compose.search.yml @@ -0,0 +1,55 @@ +# Optional add-on: AI Vector Search (Beta) +# +# Search is NOT part of the default deployment. It adds a Qdrant service and +# makes the app download ~600 MB of CLIP and face-recognition model weights on +# the first index run, which most archives do not need. +# +# Enable it by layering this file over the base compose: +# docker compose -f docker-compose.yml -f docker-compose.search.yml up -d +# +# Disable it again by dropping the second -f and recreating: +# docker compose -f docker-compose.yml up -d --remove-orphans +# +# See docs/features/ai-vector-search.md + +services: + app: + environment: + # Presence of QDRANT_URL is what switches the feature on. + - QDRANT_URL=http://qdrant:6333 + - QDRANT_API_KEY=${QDRANT_API_KEY:-} + - QDRANT_PORT=${QDRANT_PORT:-6335} + volumes: + # Model weights are downloaded once and cached here, so restarts and + # image upgrades do not re-fetch them. + - model_cache:/app/node_modules/@huggingface/transformers/.cache + depends_on: + qdrant: + condition: service_healthy + + qdrant: + image: qdrant/qdrant:v1.13.4 + container_name: instagram_saved_posts_qdrant + restart: unless-stopped + ports: + # Bound to loopback: the API has no authentication unless you set + # QDRANT__SERVICE__API_KEY, so it must never be published on 0.0.0.0. + - "127.0.0.1:${QDRANT_PORT:-6335}:6333" + volumes: + - qdrant_data:/qdrant/storage + ulimits: + nofile: + soft: 65535 + hard: 65535 + healthcheck: + test: ["CMD-SHELL", "bash -c ': >/dev/tcp/127.0.0.1/6333' || exit 1"] + interval: 5s + timeout: 5s + retries: 10 + start_period: 2s + +volumes: + qdrant_data: + name: instagram_saved_posts_qdrant_data + model_cache: + name: instagram_saved_posts_model_cache diff --git a/docker-compose.yml b/docker-compose.yml index 665c99f..74a2562 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -10,12 +10,14 @@ services: container_name: instagram_saved_posts_app restart: unless-stopped ports: - - "${PORT:-3000}:3000" + - "${PORT:-5050}:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true - NODE_ENV=production - PORT=3000 - HOSTNAME=0.0.0.0 + # AI Vector Search is an optional beta add-on and is off by default. + # To enable: docker compose -f docker-compose.yml -f docker-compose.search.yml up -d # Optional: Cloudinary for permanent media CDN - CLOUDINARY_CLOUD_NAME=${CLOUDINARY_CLOUD_NAME:-} - CLOUDINARY_API_KEY=${CLOUDINARY_API_KEY:-} diff --git a/docs/deployment/coolify.md b/docs/deployment/coolify.md index 5cf1825..aac1f75 100644 --- a/docs/deployment/coolify.md +++ b/docs/deployment/coolify.md @@ -29,11 +29,15 @@ services: restart: unless-stopped expose: - "3000" + ports: + - "${PORT:-5050}:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true - NODE_ENV=production - PORT=3000 - HOSTNAME=0.0.0.0 + # AI Vector Search is an optional beta add-on, off by default. + # To enable it, see ../features/ai-vector-search.md - CLOUDINARY_CLOUD_NAME=${CLOUDINARY_CLOUD_NAME:-} - CLOUDINARY_API_KEY=${CLOUDINARY_API_KEY:-} - CLOUDINARY_API_SECRET=${CLOUDINARY_API_SECRET:-} @@ -73,4 +77,8 @@ volumes: 2. Set the destination port to `3000`. ### Step 4: Deploy +> Want semantic image and face search? It is an optional beta add-on that is off +> by default — see [AI Vector Search](../features/ai-vector-search.md) for the +> extra service and environment variable to add here. + Click **Deploy**. Coolify will orchestrate the containers, provision Traefik routing, issue Let's Encrypt certificates, and make your app accessible securely over HTTPS! diff --git a/docs/deployment/docker-compose.md b/docs/deployment/docker-compose.md index 87f32da..d04f087 100644 --- a/docs/deployment/docker-compose.md +++ b/docs/deployment/docker-compose.md @@ -39,14 +39,27 @@ curl -fsSL https://raw.githubusercontent.com/gitnasr/Instagram-Saved-Posts/maste docker compose up -d ``` +> **Optional: AI Vector Search (Beta).** Semantic image and face search is not +> part of the default stack — it needs an extra Qdrant service and downloads +> ~600 MB of model weights on first index. To include it, layer the add-on file: +> +> ```bash +> docker compose -f docker-compose.yml -f docker-compose.search.yml up -d +> ``` +> +> See [AI Vector Search](../features/ai-vector-search.md). + ### 4. Verify Running Containers ```bash docker compose ps ``` You should see: -- `instagram_saved_posts_app`: Next.js frontend and scraper engine (port 3000). +- `instagram_saved_posts_app`: Next.js frontend and scraper engine (port 5050, accessible at `http://localhost:5050`). - `instagram_saved_posts_mongo`: MongoDB database instance (healthy). +Plus, only if you launched with the search add-on: +- `instagram_saved_posts_qdrant`: Qdrant vector database (port 6335, Dashboard at `http://localhost:6335/dashboard`). + > [!NOTE] > The compose configuration uses `pull_policy: always` for the `app` service. This ensures `docker compose up -d` always pulls the latest image update from GitHub Container Registry (GHCR) when tracking `:latest` or `:beta`. @@ -61,6 +74,10 @@ To check volume details: docker volume inspect instagram_saved_posts_mongo_data ``` +With the search add-on enabled there are two more: `instagram_saved_posts_qdrant_data` +holds the vectors and `instagram_saved_posts_model_cache` holds the downloaded model +weights. Both are derived data — deleting them costs a reindex, not your archive. + --- ## 🔄 Automatic Updates with Watchtower diff --git a/docs/deployment/dokploy.md b/docs/deployment/dokploy.md index d2606cf..d5363c4 100644 --- a/docs/deployment/dokploy.md +++ b/docs/deployment/dokploy.md @@ -28,13 +28,14 @@ services: pull_policy: always restart: unless-stopped ports: - - "3000:3000" + - "5050:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true - NODE_ENV=production - PORT=3000 - HOSTNAME=0.0.0.0 - # Optional: Cloudinary credentials + # AI Vector Search is an optional beta add-on, off by default. + # To enable it, see ../features/ai-vector-search.md - CLOUDINARY_CLOUD_NAME=${CLOUDINARY_CLOUD_NAME} - CLOUDINARY_API_KEY=${CLOUDINARY_API_KEY} - CLOUDINARY_API_SECRET=${CLOUDINARY_API_SECRET} @@ -72,12 +73,16 @@ volumes: ### Step 3: Configure Domain & SSL 1. In Dokploy, open the **Domains** tab for the `app` service. 2. Add your custom domain (e.g. `instagram.yourdomain.com`). -3. Select Port `3000`. +3. Select Port `5050`. 4. Enable **HTTPS (Let's Encrypt)**. ### Step 4: Deploy Click **Deploy** at the top right. Dokploy will pull the container images, verify MongoDB health, and start the application automatically! +> Want semantic image and face search? It is an optional beta add-on that is off +> by default — see [AI Vector Search](../features/ai-vector-search.md) for the +> extra service and environment variable to add here. + --- ## 🔒 Optional: Dokploy Environment Variables diff --git a/docs/deployment/reverse-proxy-and-sso.md b/docs/deployment/reverse-proxy-and-sso.md index cc1d4ad..e83f8d1 100644 --- a/docs/deployment/reverse-proxy-and-sso.md +++ b/docs/deployment/reverse-proxy-and-sso.md @@ -53,7 +53,7 @@ server { ssl_certificate_key /path/to/privkey.pem; location / { - proxy_pass http://127.0.0.1:3000; + proxy_pass http://127.0.0.1:5050; proxy_http_version 1.1; proxy_set_header Upgrade $http_upgrade; proxy_set_header Connection 'upgrade'; @@ -72,5 +72,5 @@ server { If using Cloudflare Tunnels: 1. In Cloudflare Zero Trust -> **Access** -> **Tunnels**, create a tunnel. -2. Add a Public Hostname pointing to `http://localhost:3000` (or `http://app:3000` if on docker network). +2. Add a Public Hostname pointing to `http://localhost:5050` (or `http://app:3000` if on docker network). 3. Optional: Add Cloudflare Access Applications for email PIN or Google authentication. diff --git a/docs/features/ai-vector-search.md b/docs/features/ai-vector-search.md index 0765ec7..a635733 100644 --- a/docs/features/ai-vector-search.md +++ b/docs/features/ai-vector-search.md @@ -1,38 +1,145 @@ --- -title: "In-Browser & Qdrant AI Vector Search (Coming Soon)" -description: "Roadmap for in-browser Transformer.js, self-hosted Qdrant vector indexing, and facial recognition." +title: "AI Vector Search (Beta)" +description: "Optional CLIP-based semantic image search, facial recognition, and Qdrant vector indexing. Off by default." --- -# In-Browser & Qdrant AI Vector Search +# AI Vector Search (Beta) -> **Status: Coming Soon / In-Browser Active Development** -> -> We are actively developing client-side in-browser inference using Transformer.js (WebGPU) and optional self-hosted Qdrant vector backend integration. +> **This feature is optional and switched off by default.** +> A standard install has no Qdrant service and no `QDRANT_URL`, the Search page +> shows a "not enabled" notice, and no model weights are ever downloaded. -Saved Posts Tracker is designing privacy-first deep learning models for visual understanding and facial identification without requiring expensive third-party AI APIs. +Once enabled, Saved Posts Tracker indexes every saved post's image (and detected +faces) into [Qdrant](https://qdrant.tech) for semantic search — no third-party AI +APIs, self-hosted. -## 1. Multimodal CLIP Embeddings (In-Browser & Qdrant) +## Should you enable it? -- **Model**: HuggingFace CLIP (`Xenova/clip-vit-base-patch32`) executed locally in-browser via `@huggingface/transformers` or on backend. -- **Dimensionality**: 512 floating-point vectors. -- **Query Mechanism**: Both image pixels and text descriptions are projected into the same latent embedding space. -- **Vector Storage**: Client-side vector index with optional [Qdrant](https://qdrant.tech) backend for large-scale self-hosted instances. +It is worth it if you want to find posts by describing them, by uploading a +similar image, or by a person's face. It costs: -### Example Natural Language Queries: +| | | +|---|---| +| **Extra service** | One Qdrant container plus a storage volume | +| **First-run download** | ~600 MB of CLIP and face-recognition model weights | +| **Memory** | Roughly 1–2 GB more while indexing | +| **Indexing time** | Every saved image is embedded once; large archives take a while | + +If you only want the archive, the scraper and the dashboard, skip it. Everything +else works exactly the same without it. + +## Enabling it + +Search turns on when the app can see a `QDRANT_URL`. That is the only switch — +there is no separate feature flag. + +### Docker Compose + +A second compose file layers the Qdrant service and the env var onto the base +stack, so nothing needs editing: + +```bash +docker compose -f docker-compose.yml -f docker-compose.search.yml up -d +``` + +To turn it back off, drop the second file and recreate: + +```bash +docker compose -f docker-compose.yml up -d --remove-orphans +``` + +Your posts are untouched either way — vectors are derived data and are rebuilt +by reindexing. + +### Dokploy / Coolify + +Those templates are pasted as a single file, so add the pieces by hand. In the +`app` service `environment:` block: + +```yaml + - QDRANT_URL=http://qdrant:6333 +``` + +Give the app a volume for the downloaded model weights, so they survive +restarts and image upgrades: + +```yaml + volumes: + - model_cache:/app/node_modules/@huggingface/transformers/.cache +``` + +Add `qdrant` to the app's `depends_on:`: + +```yaml + depends_on: + mongo: + condition: service_healthy + qdrant: + condition: service_healthy +``` + +Then add the service and its volume: + +```yaml + qdrant: + image: qdrant/qdrant:v1.13.4 + restart: unless-stopped + ports: + # Loopback only — see the security note below. + - "127.0.0.1:6335:6333" + volumes: + - qdrant_data:/qdrant/storage + ulimits: + nofile: + soft: 65535 + hard: 65535 + healthcheck: + test: ["CMD-SHELL", "bash -c ': >/dev/tcp/127.0.0.1/6333' || exit 1"] + interval: 5s + timeout: 5s + retries: 10 + start_period: 2s + +volumes: + qdrant_data: + model_cache: +``` + +### After enabling + +Open **Search** and press **Index Archive Now** (or `POST /api/search/reindex`). +The first run downloads the model weights, so it is slower than later ones. + +> **Security:** Qdrant ships with **no authentication**. Publishing its port on +> `0.0.0.0` exposes every vector and payload — and lets anyone delete your +> collections. Keep the `127.0.0.1:` prefix, or set +> `QDRANT__SERVICE__API_KEY` and put it behind your reverse proxy. The app +> refuses to send `QDRANT_API_KEY` over plaintext HTTP to a non-local host. + +## 1. Multimodal CLIP Embeddings + +- **Model**: HuggingFace CLIP (`Xenova/clip-vit-base-patch16`), run server-side via `@huggingface/transformers` at full precision. +- **Dimensionality**: 512-d vectors, cosine distance. +- **Query mechanism**: post images and text queries are both projected into the same latent space, so a plain-language query retrieves visually matching posts (`/search`, "text prompt" tab). Captions and creator usernames are also matched lexically and merged in via Reciprocal Rank Fusion. +- **Storage**: `post_images` Qdrant collection, one point per post thumbnail / carousel slide. + +### Example natural language queries: - `"Moody neon cyberpunk street"` - `"Warm wooden Scandinavian interior"` - `"Minimalist typography posters with Swiss grid"` -## 2. In-Browser Face Detection & Facial Descriptors +## 2. Face Detection & Facial Descriptors -- **Model**: `@vladmandic/face-api` running on `@tensorflow/tfjs` in-browser backend. -- **Dimensionality**: 128-dimensional facial descriptor vectors. -- **Clustering**: Automatically detects faces in saved images, extracts biometric embeddings, and allows filtering all posts containing matching individuals with zero biometric data leakage. +- **Model**: `@vladmandic/face-api` on `@tensorflow/tfjs`, run server-side during indexing. +- **Dimensionality**: 128-d facial descriptor vectors, Euclidean distance. +- **Storage**: `post_faces` Qdrant collection — lets you filter all posts containing a matching face. -## Reindexing Vectors CLI +## Reindexing Vectors -When vector indexing is activated, you will be able to run: +After changing the embedding model, or to index newly saved posts, run: ```bash -npm run reindex:vectors +npm run reindex:vectors -- --all ``` + +or trigger it per-profile from the UI, or via `POST /api/search/reindex`. diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md index 9aa7088..a6e3418 100644 --- a/docs/getting-started/quickstart.md +++ b/docs/getting-started/quickstart.md @@ -57,4 +57,4 @@ npm install npm run dev ``` -Open [http://localhost:3000](http://localhost:3000) in your web browser. +Open [http://localhost:3000](http://localhost:3000) in your web browser (or [http://localhost:5050](http://localhost:5050) if running via Docker Compose). diff --git a/docs/index.md b/docs/index.md index f2d5b21..db70ada 100644 --- a/docs/index.md +++ b/docs/index.md @@ -38,7 +38,7 @@ Saved Posts Tracker addresses every one of these problems with a unified, self-h - **Resumable Checkpoint Scraper**: Uses `checkpointMaxId` to safely pause and resume scraping across rate limits and network interruptions without duplicates. - **Append-Only Event Timelines**: Detects and logs username renames (`from @old to @new`), account deletions (`lost`), recoveries, and privacy flips. - **Cloudinary CDN Mirroring**: Automatically syncs photos, multi-slide carousels, and video clips to permanent cloud storage. -- **In-Browser & Qdrant Vector AI (Coming Soon)**: 512-dimensional CLIP multimodal search and TensorFlow.js in-browser biometric face detection. +- **Qdrant Vector AI (Beta, optional)**: 512-dimensional CLIP multimodal search and face-descriptor matching. Off by default — see [AI Vector Search](features/ai-vector-search.md) to enable it. ## Next Steps @@ -59,7 +59,7 @@ Saved Posts Tracker addresses every one of these problems with a unified, self-h ### ✨ Features & Integrations - [Cloudinary Permanent CDN](features/cloudinary-cdn.md): Permanent media hosting to protect against expiring Instagram CDN links. -- [AI Vector Search & Face Recognition](features/ai-vector-search.md): Multimodal CLIP search and biometric face clustering. +- [AI Vector Search & Face Recognition](features/ai-vector-search.md) *(Beta, optional — off by default)*: Multimodal CLIP search and biometric face clustering. ### 🚢 Deployment Guides - [Docker Compose](deployment/docker-compose.md): Standalone server, home lab, and VPS deployment. diff --git a/dokploy-compose.yml b/dokploy-compose.yml index aba15f1..917d679 100644 --- a/dokploy-compose.yml +++ b/dokploy-compose.yml @@ -9,12 +9,14 @@ services: pull_policy: always restart: unless-stopped ports: - - "3000:3000" + - "5050:3000" environment: - DATABASE_URL=mongodb://mongo:27017/instagram?replicaSet=rs0&directConnection=true - NODE_ENV=production - PORT=3000 - HOSTNAME=0.0.0.0 + # AI Vector Search is an optional beta add-on, off by default. + # To enable it, see docs/features/ai-vector-search.md # Optional: Cloudinary credentials for permanent media CDN - CLOUDINARY_CLOUD_NAME=${CLOUDINARY_CLOUD_NAME} - CLOUDINARY_API_KEY=${CLOUDINARY_API_KEY} diff --git a/install.ps1 b/install.ps1 index 2c5d89a..f0e6603 100644 --- a/install.ps1 +++ b/install.ps1 @@ -54,15 +54,22 @@ Write-Host "==> Pulling container images and starting services..." -ForegroundCo docker compose pull docker compose up -d +# Resolve effective published ports +$resolvedAppPort = (docker compose port app 3000 2>$null) -replace '.*:' +$appPort = if ($resolvedAppPort) { $resolvedAppPort.Trim() } else { "5050" } +$resolvedQdrantPort = (docker compose port qdrant 6333 2>$null) -replace '.*:' +$qdrantPort = if ($resolvedQdrantPort) { $resolvedQdrantPort.Trim() } else { "6335" } + Write-Host "`n======================================================" -ForegroundColor Green Write-Host " 🎉 InstaSave Tracker is successfully installed!" -ForegroundColor Green Write-Host "======================================================`n" -ForegroundColor Green Write-Host "Access your instance at:" -Write-Host " 👉 Local: http://localhost:3000" -ForegroundColor Cyan +Write-Host " 👉 Web App: http://localhost:$appPort" -ForegroundColor Cyan +Write-Host " 👉 Qdrant Dashboard: http://localhost:$qdrantPort/dashboard" -ForegroundColor Cyan Write-Host "`nNext steps:" -Write-Host " 1. Open http://localhost:3000 in your browser." +Write-Host " 1. Open http://localhost:$appPort in your browser." Write-Host " 2. Complete the quick onboarding wizard." Write-Host " 3. Start archiving and exploring your saved posts!`n" diff --git a/install.sh b/install.sh index 97707f6..b8a9573 100644 --- a/install.sh +++ b/install.sh @@ -78,16 +78,20 @@ ${DOCKER_COMPOSE} up -d # Completion Banner HOST_IP=$(hostname -I 2>/dev/null | awk '{print $1}' || echo "localhost") -PORT="3000" +RESOLVED_APP_PORT=$(${DOCKER_COMPOSE} port app 3000 2>/dev/null | awk -F: '{print $NF}') +PORT="${RESOLVED_APP_PORT:-5050}" +RESOLVED_QDRANT_PORT=$(${DOCKER_COMPOSE} port qdrant 6333 2>/dev/null | awk -F: '{print $NF}') +QDRANT_PORT="${RESOLVED_QDRANT_PORT:-6335}" echo -e "\n${GREEN}${BOLD}======================================================${NC}" echo -e "${GREEN}${BOLD} 🎉 InstaSave Tracker is successfully installed!${NC}" echo -e "${GREEN}${BOLD}======================================================${NC}\n" echo -e "Access your instance at:" -echo -e " 👉 Local: ${CYAN}${BOLD}http://localhost:${PORT}${NC}" +echo -e " 👉 Web App: ${CYAN}${BOLD}http://localhost:${PORT}${NC}" if [ "$HOST_IP" != "localhost" ]; then -echo -e " 👉 Network: ${CYAN}${BOLD}http://${HOST_IP}:${PORT}${NC}" +echo -e " 👉 Network Web App: ${CYAN}${BOLD}http://${HOST_IP}:${PORT}${NC}" fi +echo -e " 👉 Qdrant Dashboard: ${CYAN}${BOLD}http://localhost:${QDRANT_PORT}/dashboard${NC}" echo -e "\nNext steps:" echo -e " 1. Open ${CYAN}http://localhost:${PORT}${NC} in your browser." echo -e " 2. Complete the quick 60-second onboarding wizard." diff --git a/knip.json b/knip.json index b3d9ed4..1cea984 100644 --- a/knip.json +++ b/knip.json @@ -3,5 +3,5 @@ "entry": ["src/app/**/*.{ts,tsx}", "src/instrumentation*.ts"], "project": ["src/**/*.{ts,tsx}"], "ignore": ["src/components/ui/**"], - "ignoreDependencies": ["tw-animate-css", "shadcn", "tailwindcss", "postcss"] + "ignoreDependencies": ["tw-animate-css", "shadcn", "tailwindcss", "postcss", "@tensorflow/tfjs-backend-wasm"] } diff --git a/next.config.ts b/next.config.ts index 4758186..51debb7 100644 --- a/next.config.ts +++ b/next.config.ts @@ -6,6 +6,12 @@ const nextConfig: NextConfig = { "@prisma/client", ".prisma/client", "pino", + "onnxruntime-node", + "@huggingface/transformers", + "sharp", + "@vladmandic/face-api", + "@tensorflow/tfjs", + "@tensorflow/tfjs-backend-wasm", ], }; diff --git a/package-lock.json b/package-lock.json index ecf1795..1e4e764 100644 --- a/package-lock.json +++ b/package-lock.json @@ -10,9 +10,14 @@ "hasInstallScript": true, "license": "MIT", "dependencies": { + "@huggingface/transformers": "^4.2.0", "@prisma/client": "^6.19.3", + "@qdrant/js-client-rest": "^1.19.0", "@tanstack/react-query": "^5.102.8", "@tanstack/react-query-devtools": "^5.102.8", + "@tensorflow/tfjs": "^4.22.0", + "@tensorflow/tfjs-backend-wasm": "^4.22.0", + "@vladmandic/face-api": "^1.7.15", "axios": "^1.13.6", "class-variance-authority": "^0.7.1", "cloudinary": "^2.11.0", @@ -26,6 +31,7 @@ "react": "19.2.8", "react-dom": "19.2.8", "recharts": "^2.15.4", + "sharp": "^0.35.4", "sonner": "^2.0.8", "tailwind-merge": "^3.6.0" }, @@ -40,6 +46,7 @@ "prisma": "^6.12.0", "shadcn": "^4.19.0", "tailwindcss": "^4", + "tsx": "^4.23.13", "tw-animate-css": "^1.4.0", "typescript": "^5" } @@ -744,261 +751,1291 @@ "tslib": "^2.4.0" } }, - "node_modules/@eslint-community/eslint-utils": { - "version": "4.9.1", - "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.9.1.tgz", - "integrity": 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"node": ">=8" + } + }, "node_modules/yocto-queue": { "version": "0.1.0", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", diff --git a/package.json b/package.json index b431da2..66bb367 100644 --- a/package.json +++ b/package.json @@ -33,12 +33,22 @@ "lint": "eslint", "prisma:generate": "prisma generate", "postinstall": "prisma generate", - "sync:docs": "node scripts/sync-docs.mjs" + "sync:docs": "node scripts/sync-docs.mjs", + "reindex:vectors": "tsx --env-file-if-exists=.env scripts/reindex-vectors.ts", + "test:vectors": "tsx scripts/test-vector-search.ts" + }, + "engines": { + "node": ">=22" }, "dependencies": { + "@huggingface/transformers": "^4.2.0", "@prisma/client": "^6.19.3", + "@qdrant/js-client-rest": "^1.19.0", "@tanstack/react-query": "^5.102.8", "@tanstack/react-query-devtools": "^5.102.8", + "@tensorflow/tfjs": "^4.22.0", + "@tensorflow/tfjs-backend-wasm": "^4.22.0", + "@vladmandic/face-api": "^1.7.15", "axios": "^1.13.6", "class-variance-authority": "^0.7.1", "cloudinary": "^2.11.0", @@ -52,6 +62,7 @@ "react": "19.2.8", "react-dom": "19.2.8", "recharts": "^2.15.4", + "sharp": "^0.35.4", "sonner": "^2.0.8", "tailwind-merge": "^3.6.0" }, @@ -66,6 +77,7 @@ "prisma": "^6.12.0", "shadcn": "^4.19.0", "tailwindcss": "^4", + "tsx": "^4.23.13", "tw-animate-css": "^1.4.0", "typescript": "^5" } diff --git a/scripts/reindex-vectors.ts b/scripts/reindex-vectors.ts new file mode 100644 index 0000000..699bb47 --- /dev/null +++ b/scripts/reindex-vectors.ts @@ -0,0 +1,38 @@ +// Env comes from `tsx --env-file-if-exists=.env` (see the reindex:vectors script). +import { PrismaClient } from "@prisma/client"; +import { runVectorIndex, getCurrentIndexState } from "../src/lib/vector/index-posts"; + +function getArg(name: string): string | undefined { + const idx = process.argv.indexOf(`--${name}`); + return idx !== -1 ? process.argv[idx + 1] : undefined; +} + +async function main() { + const prisma = new PrismaClient(); + const profileArg = getArg("profile"); + const all = process.argv.includes("--all"); + + if (!profileArg && !all) { + console.error("Usage: npm run reindex:vectors -- --profile OR --all"); + process.exit(1); + } + + const profiles = all + ? await prisma.profile.findMany({ select: { id: true, name: true } }) + : [{ id: profileArg!, name: profileArg! }]; + + for (const profile of profiles) { + console.log(`\n[reindex-vectors] Indexing profile ${profile.name} (${profile.id})...`); + await runVectorIndex(profile.id); + const state = getCurrentIndexState(profile.id); + console.log("[reindex-vectors] Done:", state); + if (state?.status === "failed" || (state?.failedItems ?? 0) > 0) process.exitCode = 1; + } + + await prisma.$disconnect(); +} + +main().catch((err) => { + console.error("[reindex-vectors] Failed:", err); + process.exit(1); +}); diff --git a/scripts/test-vector-search.ts b/scripts/test-vector-search.ts new file mode 100644 index 0000000..c098a9d --- /dev/null +++ b/scripts/test-vector-search.ts @@ -0,0 +1,71 @@ +/** + * Self-check for the pure vector-search logic. Run: npm run test:vectors + * Covers the parts that silently return wrong results if they break — + * point ids (re-index would duplicate instead of upsert), score calibration, + * and the higher-is-better normalisation shared by all three search modes. + */ +import assert from "assert"; +import { describeVectorParamsMismatch, pointId } from "../src/lib/vector/qdrant-client"; +import { bestHitPerPost, calibrate } from "../src/lib/vector/search-api"; + +// pointId must be a stable UUIDv5 — these vectors were verified against the +// `uuid` package's v5() before that dependency was dropped. +assert.equal(pointId("p", "1", "thumbnail", 0), "bdf94c6f-29ce-5542-870a-9eda13efc726"); +assert.equal(pointId("p", "1", "carousel", 3, 2), "37c303b0-0e07-5438-a850-27c6443f5fce"); +assert.equal(pointId("p", "1", "carousel", 3, 0), pointId("p", "1", "carousel", 3, 0)); +assert.notEqual(pointId("p", "1", "carousel", 3, 0), pointId("p", "1", "carousel", 3, 1)); +assert.notEqual(pointId("p", "1", "carousel", 3), pointId("p", "1", "carousel", 3, 3)); + +// calibrate clamps at both ends and is monotonically increasing in between. +assert.equal(calibrate(0.1, 0.2, 0.4, 0.45, 0.95), 0.45); +assert.equal(calibrate(0.9, 0.2, 0.4, 0.45, 0.95), 0.95); +assert.equal(calibrate(0.3, 0.2, 0.4, 0.45, 0.95), 0.7); +assert.ok(calibrate(0.35, 0.2, 0.4, 0.45, 0.95) > calibrate(0.25, 0.2, 0.4, 0.45, 0.95)); + +const hit = (postPk: string, score: number, carouselPosition?: number) => ({ + score, + payload: { postPk, carouselPosition, imageUrl: `${postPk}-${carouselPosition ?? 0}.jpg` }, +}); + +// Cosine (higher is better): keeps the best slide per post, drops sub-floor noise. +const cosine = bestHitPerPost( + [hit("a", 0.31, 0), hit("a", 0.45, 2), hit("b", 0.1, 0), hit("c", 0.28, 1)], + (s) => (s >= 0.26 ? s : null) +); +assert.deepEqual([...cosine.keys()].sort(), ["a", "c"]); +assert.equal(cosine.get("a")!.quality, 0.45); +assert.equal(cosine.get("a")!.carouselPosition, 2); +assert.equal(cosine.get("a")!.imageUrl, "a-2.jpg"); + +// Euclid (lower is better): the closest face must win, and anything past the +// identity threshold must be dropped — an inverted comparison here would rank +// strangers above the actual person. +const THRESHOLD = 0.62; +const faces = bestHitPerPost( + [hit("a", 0.55), hit("a", 0.2), hit("b", 0.71)], + (d) => (d <= THRESHOLD ? THRESHOLD - d : null) +); +assert.deepEqual([...faces.keys()], ["a"]); +assert.equal(Number(faces.get("a")!.quality.toFixed(2)), 0.42); // from distance 0.2, the closer match + +// Hits without a usable postPk payload are ignored rather than crashing. +assert.equal(bestHitPerPost([{ score: 0.9, payload: null }], (s) => s).size, 0); + +// A collection left over from a different embedding model must be reported, not +// silently written to — 768 is SigLIP2-base, 512 is the CLIP ViT-B/16 we use. +const imageSpec = { name: "post_images", size: 512, distance: "Cosine" as const }; +assert.equal(describeVectorParamsMismatch(imageSpec, { size: 512, distance: "Cosine" }), null); +assert.match( + describeVectorParamsMismatch(imageSpec, { size: 768, distance: "Cosine" }) ?? "", + /size 768 .*writes size 512/ +); +// Right dimension, wrong metric still scores everything wrong. +assert.match( + describeVectorParamsMismatch(imageSpec, { size: 512, distance: "Euclid" }) ?? "", + /delete the collection/ +); +// Named-vector or unreadable configs are a mismatch, not a pass. +assert.ok(describeVectorParamsMismatch(imageSpec, undefined)); +assert.ok(describeVectorParamsMismatch(imageSpec, {})); + +console.log("vector search self-check: all assertions passed"); diff --git a/src/app/(dashboard)/search/page.tsx b/src/app/(dashboard)/search/page.tsx new file mode 100644 index 0000000..0771173 --- /dev/null +++ b/src/app/(dashboard)/search/page.tsx @@ -0,0 +1,792 @@ +"use client"; + +import { useEffect, useRef, useState } from "react"; +import { toast } from "sonner"; +import { Header } from "@/components/layout/header"; +import { Card } from "@/components/ui/card"; +import { Button } from "@/components/ui/button"; +import { Input } from "@/components/ui/input"; +import { Tabs, TabsList, TabsTrigger, TabsContent } from "@/components/ui/tabs"; +import { Skeleton } from "@/components/ui/skeleton"; +import { Progress } from "@/components/ui/progress"; +import { + Dialog, + DialogContent, + DialogHeader, + DialogTitle, + DialogDescription, + DialogFooter, +} from "@/components/ui/dialog"; +import { PostCard } from "@/components/posts/post-card"; +import { PostDetailDialog } from "@/components/posts/post-detail-dialog"; +import { + useSearchByText, + useSearchByImage, + useSearchByFace, + useVectorIndexStatus, + useReindexVectors, + type VectorSearchError, +} from "@/hooks/use-vector-search"; +import { + Search as SearchIcon, + ImageIcon, + ScanFace, + Upload, + Sparkles, + RefreshCw, + Database, + X, + AlertCircle, + ExternalLink, + BarChart3, + Clock, + Calendar, + Layers, + Activity, + CheckCircle2, +} from "lucide-react"; +import { VECTOR_SEARCH_DOCS_URL } from "@/lib/constants"; +import type { Post, VectorSearchHit } from "@/types"; + +type SearchMode = "text" | "image" | "face"; + +const EXAMPLE_PROMPTS = [ + "Minimalist architecture", + "Golden hour sunset", + "Vintage car aesthetics", + "Coffee and workspace", + "Dark moody portrait", + "Tokyo street photography", +]; + +export default function SearchPage() { + const [mode, setMode] = useState("text"); + const modeRef = useRef(mode); + const searchTokenRef = useRef(0); + + useEffect(() => { + modeRef.current = mode; + }, [mode]); + + const [textQuery, setTextQuery] = useState(""); + const [previewUrl, setPreviewUrl] = useState(null); + + // Revoke object URLs on preview change and component unmount + useEffect(() => { + return () => { + if (previewUrl && previewUrl.startsWith("blob:")) { + URL.revokeObjectURL(previewUrl); + } + }; + }, [previewUrl]); + const [results, setResults] = useState(null); + const [activeQueryLabel, setActiveQueryLabel] = useState(null); + const [selectedPost, setSelectedPost] = useState(null); + const [showIndexModal, setShowIndexModal] = useState(false); + const [showStatsModal, setShowStatsModal] = useState(false); + const [searchErrorMessage, setSearchErrorMessage] = useState(null); + const fileInputRef = useRef(null); + + const searchByText = useSearchByText(); + const searchByImage = useSearchByImage(); + const searchByFace = useSearchByFace(); + const { data: indexStatusData, isLoading: isLoadingStatus } = useVectorIndexStatus(); + const reindexMutation = useReindexVectors(); + + const isPending = + mode === "text" + ? searchByText.isPending + : mode === "image" + ? searchByImage.isPending + : searchByFace.isPending; + + const handleSearchError = (err: VectorSearchError | Error) => { + const isIndexNeeded = "needsIndexing" in err ? err.needsIndexing : false; + if (isIndexNeeded) { + setShowIndexModal(true); + setSearchErrorMessage("Vector index has not been built yet. Please run the indexer first."); + } else { + setSearchErrorMessage(err.message); + toast.error(err.message); + } + }; + + const handleModeChange = (newMode: SearchMode) => { + modeRef.current = newMode; + searchTokenRef.current += 1; + searchByText.reset(); + searchByImage.reset(); + searchByFace.reset(); + setMode(newMode); + setResults(null); + setPreviewUrl(null); + setActiveQueryLabel(null); + setSearchErrorMessage(null); + }; + + const triggerTextSearch = (query: string) => { + const trimmed = query.trim(); + if (!trimmed) return; + + const token = ++searchTokenRef.current; + const requestMode: SearchMode = "text"; + + setResults(null); + setSearchErrorMessage(null); + setActiveQueryLabel(`"${trimmed}"`); + + searchByText.mutate(trimmed, { + onSuccess: (hits) => { + if (token !== searchTokenRef.current || modeRef.current !== requestMode) return; + setResults(hits); + }, + onError: (err) => { + if (token !== searchTokenRef.current || modeRef.current !== requestMode) return; + handleSearchError(err); + }, + }); + }; + + const handleTextSubmit = (e?: React.FormEvent) => { + if (e) e.preventDefault(); + triggerTextSearch(textQuery); + }; + + const handleFile = (file: File) => { + const token = ++searchTokenRef.current; + const requestMode = modeRef.current; + const objectUrl = URL.createObjectURL(file); + + setPreviewUrl(objectUrl); + setResults(null); + setSearchErrorMessage(null); + setActiveQueryLabel(file.name); + + // Ignore a response whose search was superseded by a newer one or a mode switch. + const isStale = () => token !== searchTokenRef.current || modeRef.current !== requestMode; + const mutation = requestMode === "image" ? searchByImage : searchByFace; + mutation.mutate(file, { + onSuccess: (hits) => { + if (!isStale()) setResults(hits); + }, + onError: (err) => { + if (!isStale()) handleSearchError(err); + }, + }); + }; + + const handleReindex = () => { + setShowIndexModal(false); + reindexMutation.mutate(undefined, { + onSuccess: () => toast.success("Vector indexing initiated in background."), + onError: (err) => toast.error(err.message), + }); + }; + + const currentIndex = indexStatusData?.current; + const isIndexRunning = currentIndex?.status === "running"; + const indexProgressPct = + currentIndex && currentIndex.totalItems > 0 + ? Math.round((currentIndex.indexedItems / currentIndex.totalItems) * 100) + : 0; + + const liveness = indexStatusData?.liveness; + const stats = indexStatusData?.stats; + const dashboardUrl = liveness?.dashboardUrl || null; + // Search is opt-in: no QDRANT_URL means the feature was never switched on, + // which is a normal deployment rather than a fault. + const searchEnabled = indexStatusData?.configured ?? false; + const lastRunFailed = + searchEnabled && !isLoadingStatus && !isIndexRunning && stats?.status === "failed"; + const hasNeverIndexed = + searchEnabled && + !isLoadingStatus && + !isIndexRunning && + !lastRunFailed && + (!stats || stats.indexedItems === 0); + + return ( +
+
+
+ {/* Qdrant Liveness Badge */} + {liveness && ( +
+
+ + {liveness.status === "healthy" + ? `Qdrant (${liveness.latencyMs}ms)` + : liveness.status === "degraded" + ? "Qdrant (Empty)" + : "Qdrant Offline"} + +
+ )} + + {/* Statistics Button */} + + + {/* Qdrant Dashboard Link */} + {dashboardUrl && ( + + )} + + {/* Reindex Button */} + +
+
+ + {/* Feature switched off — nothing below this point can do anything useful. */} + {!isLoadingStatus && !searchEnabled && ( + +
+ +
+
+

Vector Search is not enabled

+

+ Search is an optional beta feature. It needs a Qdrant service and the{" "} + QDRANT_URL{" "} + environment variable — neither ships in the default deployment, because + the embedding models it downloads are a heavy addition most archives + do not need. +

+
+ + + )} + + {/* Failed Index Run Callout */} + {lastRunFailed && ( + +
+ +
+

Last index run failed

+

+ {stats?.lastError || "Unknown error"} +

+
+
+ + + )} + + {/* Unindexed Archive Warning Callout */} + {hasNeverIndexed && ( + +
+ +
+

Archive Vector Index Not Found

+

+ Your saved posts must be embedded into Qdrant before search queries can find matches. +

+
+
+ + + )} + + {/* Search Error Callout Banner */} + {searchErrorMessage && ( + +
+ + {searchErrorMessage} +
+ + + )} + + {/* Index progress banner */} + {isIndexRunning && currentIndex && ( + +
+ + + Indexing archive in progress... + + + {currentIndex.indexedItems} / {currentIndex.totalItems} items ({indexProgressPct}%) + +
+ +
+ Faces detected: {currentIndex.facesIndexed} + {currentIndex.failedItems > 0 && ( + Failures: {currentIndex.failedItems} + )} +
+ + )} + + {/* Search Input Controls */} + {searchEnabled && ( + + handleModeChange(v as SearchMode)} + > + {/* Full labels overflow a phone viewport, so the qualifier drops below sm. */} + + + + Prompt Search + + + + Visual Similarity + + + + Face Recognition + + + + {/* Tab 1: Free-text prompt search */} + +
+
+ + setTextQuery(e.target.value)} + className="pl-9 pr-8 h-10 rounded-[6px] bg-surface-2 border-hairline text-sm" + /> + {textQuery && ( + + )} +
+ +
+ +
+ Suggestions: + {EXAMPLE_PROMPTS.map((prompt) => ( + + ))} +
+ + + {/* Tab 2 & 3: Text-free search (image upload & face recognition) */} + {(mode === "image" || mode === "face") && ( + + { + const file = e.target.files?.[0]; + if (file) handleFile(file); + e.target.value = ""; + }} + /> + + + + )} + + + )} + + {/* Loading Skeleton */} + {isPending && ( +
+
+
+ {Array.from({ length: 12 }).map((_, i) => ( + + ))} +
+
+ )} + + {/* Search Results */} + {!isPending && results && ( +
+
+

+ {results.length === 0 ? ( + No matching posts found {activeQueryLabel && `for ${activeQueryLabel}`}. + ) : ( + + Found {results.length} matching post + {results.length === 1 ? "" : "s"} {activeQueryLabel && `for ${activeQueryLabel}`}. + + )} +

+
+ + {results.length > 0 && ( +
+ {results.map((hit) => { + const pct = Math.round(hit.score * 100); + return ( +
+ setSelectedPost(hit.post)} + thumbnailOverride={hit.matchedImageUrl} + matchedSlideIndex={hit.matchedSlideIndex} + /> +
+ = 85 + ? "text-amber-400" + : pct >= 70 + ? "text-emerald-400" + : "text-zinc-300" + } + > + {pct}% + + {hit.matchType && ( + + {hit.matchType === "hybrid" + ? "Hybrid" + : hit.matchType === "caption" + ? "Caption" + : hit.matchType === "account" + ? "Author" + : hit.matchType === "face" + ? "Face" + : "Visual"} + + )} +
+
+ ); + })} +
+ )} +
+ )} + + {/* Vector Index Required Popup / Modal */} + + + +
+ + Vector Index Required +
+ + Your saved posts have not been indexed into the vector database yet. Vector search + requires generating CLIP and face embeddings for your saved posts before searches can be run. + + + +
+
+ Required Collections: + post_images, post_faces +
+
+ Current Status: + Not Indexed +
+
+ + + + + + + + + {/* Vector Search Statistics Modal */} + + + +
+ + Vector Search Statistics +
+ + Telemetry, index coverage, and Qdrant cluster statistics across your profiles. + + + + {isLoadingStatus ? ( +
+ + + +
+ ) : ( +
+ {/* Profile Stats Card */} +
+
+ + + Active Profile Index + + + {stats?.status === "failed" ? ( + Failed + ) : stats?.status === "completed" ? ( + + Completed + + ) : stats?.status === "running" ? ( + Running + ) : ( + Not Indexed + )} + +
+ +
+
+ + Last Run: + + + {stats?.lastRunAt + ? new Date(stats.lastRunAt).toLocaleString() + : "Never"} + +
+ +
+ + Post Cutoff: + + + {stats?.cutoffPostDate + ? new Date(stats.cutoffPostDate).toLocaleDateString() + : "None"} + +
+ +
+ + Indexed Items: + + + {stats?.indexedItems ?? 0} / {stats?.totalItems ?? 0} + +
+ +
+ + Faces Found: + + {stats?.facesIndexed ?? 0} +
+
+ + {stats?.lastError || (stats?.failedItems ?? 0) > 0 ? ( +
+ {(stats?.failedItems ?? 0) > 0 && `Failed Items: ${stats?.failedItems} — `} + {stats?.lastError || "Unknown error"} +
+ ) : null} +
+ + {/* Cluster & Global Stats Card */} +
+
+ + + Qdrant Database & Cluster + + + {liveness?.status ?? "unknown"} + +
+ +
+
+ Image Vectors: +

+ {(liveness?.collections.post_images.pointsCount ?? 0).toLocaleString()} +

+
+
+ Face Vectors: +

+ {(liveness?.collections.post_faces.pointsCount ?? 0).toLocaleString()} +

+
+
+ Cluster Latency: +

{liveness?.latencyMs ?? 0} ms

+
+
+
+
+ )} + + + {dashboardUrl ? ( + + ) :
} + + + +
+ + { + if (!open) setSelectedPost(null); + }} + /> +
+ ); +} diff --git a/src/app/api/health/route.ts b/src/app/api/health/route.ts index f7e2ea3..b65e52f 100644 --- a/src/app/api/health/route.ts +++ b/src/app/api/health/route.ts @@ -1,5 +1,10 @@ import { NextResponse } from "next/server"; import { prisma } from "@/lib/prisma"; +import { + checkQdrantLiveness, + getQdrantConfig, + isQdrantConfigured, +} from "@/lib/vector/qdrant-client"; export const dynamic = "force-dynamic"; @@ -12,15 +17,31 @@ export async function GET() { mongoConnected = false; } - const status = mongoConnected ? "healthy" : "degraded"; + // Vector search is opt-in. With QDRANT_URL unset the feature is simply off, + // which is a valid deployment — not a degraded one. Reporting it as degraded + // would fail the container HEALTHCHECK for every install that skipped search. + const searchEnabled = isQdrantConfigured(getQdrantConfig()); + const qdrantLiveness = searchEnabled + ? await checkQdrantLiveness().catch(() => ({ + status: "disconnected" as const, + latencyMs: 0, + })) + : null; + + const isHealthy = + mongoConnected && (!searchEnabled || qdrantLiveness?.status === "healthy"); + const status = isHealthy ? "healthy" : mongoConnected ? "degraded" : "unhealthy"; return NextResponse.json( { status, mongo: mongoConnected ? "connected" : "disconnected", + vectorService: qdrantLiveness + ? { status: qdrantLiveness.status, latencyMs: qdrantLiveness.latencyMs } + : { status: "disabled" }, uptime: process.uptime(), timestamp: new Date().toISOString(), }, - { status: mongoConnected ? 200 : 503 } + { status: isHealthy ? 200 : 503 } ); } diff --git a/src/app/api/search/by-face/route.ts b/src/app/api/search/by-face/route.ts new file mode 100644 index 0000000..2162a41 --- /dev/null +++ b/src/app/api/search/by-face/route.ts @@ -0,0 +1,88 @@ +import { NextRequest, NextResponse } from "next/server"; +import { prisma } from "@/lib/prisma"; +import { getActiveProfile, noActiveProfileResponse } from "@/lib/active-profile"; +import { detectFacesFromBuffer } from "@/lib/vector/face-embedding"; +import { COLLECTIONS, searchByVector } from "@/lib/vector/qdrant-client"; +import { + RESULT_LIMIT, + bestHitPerPost, + calibrate, + qdrantNotConfiguredResponse, + readUploadedImage, + searchErrorResponse, +} from "@/lib/vector/search-api"; +import type { VectorSearchHit } from "@/types"; + +// Standard FaceNet same-person identity boundary. +const FACE_DISTANCE_THRESHOLD = 0.62; +const MAX_SEARCH_FACES = 5; + +export async function POST(request: NextRequest) { + const profile = await getActiveProfile(); + if (!profile) return noActiveProfileResponse(); + + const notConfigured = qdrantNotConfiguredResponse(); + if (notConfigured) return notConfigured; + + const upload = await readUploadedImage(request); + if ("error" in upload) return upload.error; + + try { + const queryFaces = await detectFacesFromBuffer(upload.buffer); + if (queryFaces.length === 0) { + return NextResponse.json( + { error: "No face detected in the uploaded image.", results: [] }, + { status: 400 } + ); + } + if (queryFaces.length > MAX_SEARCH_FACES) { + return NextResponse.json( + { + error: `Too many faces detected (${queryFaces.length}). Please upload an image with at most ${MAX_SEARCH_FACES} faces.`, + results: [], + }, + { status: 400 } + ); + } + + // Every face in the query photo is searched; hits merge by post below, + // regardless of which query face matched. + const hitLists = await Promise.all( + queryFaces.map((descriptor) => + searchByVector(COLLECTIONS.POST_FACES, descriptor, profile.id, RESULT_LIMIT) + ) + ); + + // The faces collection uses Euclid, so Qdrant's score IS the distance — + // lower is better. Convert to closeness so higher is better everywhere else. + const best = bestHitPerPost(hitLists.flat(), (distance) => + distance <= FACE_DISTANCE_THRESHOLD ? FACE_DISTANCE_THRESHOLD - distance : null + ); + + const sorted = [...best.entries()] + .sort((a, b) => b[1].quality - a[1].quality) + .slice(0, RESULT_LIMIT); + + const posts = await prisma.post.findMany({ + where: { profileId: profile.id, pk: { in: sorted.map(([pk]) => pk) } }, + }); + const postByPk = new Map(posts.map((p) => [p.pk, p])); + + const results: VectorSearchHit[] = []; + for (const [pk, hit] of sorted) { + const post = postByPk.get(pk); + if (!post) continue; + results.push({ + post, + score: calibrate(hit.quality, 0, FACE_DISTANCE_THRESHOLD - 0.15, 0.5, 0.99), + matchType: "face", + matchedSlideIndex: hit.carouselPosition, + matchedImageUrl: hit.imageUrl, + }); + } + + return NextResponse.json({ results }); + } catch (err: unknown) { + return searchErrorResponse(err); + } +} diff --git a/src/app/api/search/by-image/route.ts b/src/app/api/search/by-image/route.ts new file mode 100644 index 0000000..af03a60 --- /dev/null +++ b/src/app/api/search/by-image/route.ts @@ -0,0 +1,63 @@ +import { NextRequest, NextResponse } from "next/server"; +import { prisma } from "@/lib/prisma"; +import { getActiveProfile, noActiveProfileResponse } from "@/lib/active-profile"; +import { embedImageFromBuffer } from "@/lib/vector/image-embedding"; +import { COLLECTIONS, searchByVector } from "@/lib/vector/qdrant-client"; +import { + RESULT_LIMIT, + bestHitPerPost, + calibrate, + qdrantNotConfiguredResponse, + readUploadedImage, + searchErrorResponse, +} from "@/lib/vector/search-api"; +import type { VectorSearchHit } from "@/types"; + +const NOISE_FLOOR = 0.26; +// Drop anything far below the best match for this query — image-to-image +// similarity has a sharp elbow, and everything past it is unrelated. +const ELBOW_RATIO = 0.65; + +export async function POST(request: NextRequest) { + const profile = await getActiveProfile(); + if (!profile) return noActiveProfileResponse(); + + const notConfigured = qdrantNotConfiguredResponse(); + if (notConfigured) return notConfigured; + + const upload = await readUploadedImage(request); + if ("error" in upload) return upload.error; + + try { + const vector = await embedImageFromBuffer(upload.buffer); + const hits = await searchByVector(COLLECTIONS.POST_IMAGES, vector, profile.id, RESULT_LIMIT); + + const sorted = [...bestHitPerPost(hits, (s) => (s >= NOISE_FLOOR ? s : null)).entries()].sort( + (a, b) => b[1].quality - a[1].quality + ); + const topScore = sorted[0]?.[1].quality ?? 0; + const kept = sorted.filter(([, h]) => h.quality >= topScore * ELBOW_RATIO); + + const posts = await prisma.post.findMany({ + where: { profileId: profile.id, pk: { in: kept.map(([pk]) => pk) } }, + }); + const postByPk = new Map(posts.map((p) => [p.pk, p])); + + const results: VectorSearchHit[] = []; + for (const [pk, hit] of kept) { + const post = postByPk.get(pk); + if (!post) continue; + results.push({ + post, + score: calibrate(hit.quality, NOISE_FLOOR, 0.81, 0.45, 0.99), + matchType: "visual", + matchedSlideIndex: hit.carouselPosition, + matchedImageUrl: hit.imageUrl, + }); + } + + return NextResponse.json({ results }); + } catch (err: unknown) { + return searchErrorResponse(err); + } +} diff --git a/src/app/api/search/by-text/route.ts b/src/app/api/search/by-text/route.ts new file mode 100644 index 0000000..a81eefa --- /dev/null +++ b/src/app/api/search/by-text/route.ts @@ -0,0 +1,214 @@ +import { NextRequest, NextResponse } from "next/server"; +import { prisma } from "@/lib/prisma"; +import { getActiveProfile, noActiveProfileResponse } from "@/lib/active-profile"; +import { embedText } from "@/lib/vector/text-embedding"; +import { COLLECTIONS, searchByVector } from "@/lib/vector/qdrant-client"; +import { + RESULT_LIMIT, + bestHitPerPost, + calibrate, + qdrantNotConfiguredResponse, + searchErrorResponse, +} from "@/lib/vector/search-api"; +import type { VectorSearchHit } from "@/types"; + +const MAX_QUERY_LENGTH = 1000; + +// Reciprocal Rank Fusion tuning. Text matches outweigh visual ones because an +// exact caption/author hit is a stronger signal than a CLIP similarity. +const RRF_K = 60; +const WEIGHT_VECTOR = 1.0; +const WEIGHT_TEXT = 1.3; + +// Below this raw cosine score a vector hit is noise. +const VECTOR_NOISE_FLOOR = 0.2; +// A visual-only candidate (no text match) must also clear an absolute floor and +// a fraction of this query's best visual score. +const VISUAL_ELBOW_FLOOR = 0.22; +const VISUAL_ELBOW_RATIO = 0.65; + +type MatchType = NonNullable; + +/** Lexical search over captions and creator accounts, best matches first. */ +async function textSearch(profileId: string, rawQuery: string) { + const terms = rawQuery + .split(/\s+/) + .map((t) => t.replace(/^[#@]/, "").trim()) + .filter((t) => t.length >= 2); + + const matchingAccounts = await prisma.account.findMany({ + where: { + profileId, + OR: [ + { username: { contains: rawQuery, mode: "insensitive" } }, + { fullName: { contains: rawQuery, mode: "insensitive" } }, + ], + }, + select: { pk: true }, + take: 30, + }); + const accountPks = matchingAccounts.map((a) => a.pk); + + const conditions: import("@prisma/client").Prisma.PostWhereInput[] = [ + { captionText: { contains: rawQuery, mode: "insensitive" } }, + ]; + if (accountPks.length > 0) conditions.push({ accountPk: { in: accountPks } }); + if (terms.length > 1) { + conditions.push({ + AND: terms.map((term) => ({ + captionText: { contains: term, mode: "insensitive" as const }, + })), + }); + } + + const posts = await prisma.post.findMany({ + where: { profileId, OR: conditions }, + select: { pk: true, captionText: true, accountPk: true }, + take: RESULT_LIMIT, + }); + + const lowerQuery = rawQuery.toLowerCase(); + return posts + .map((p) => { + const isAccount = accountPks.includes(p.accountPk); + // Whole-query caption hit beats an author hit, which beats a term-only hit. + const priority = p.captionText?.toLowerCase().includes(lowerQuery) ? 1 : isAccount ? 2 : 3; + return { pk: p.pk, priority, isAccount }; + }) + .sort((a, b) => a.priority - b.priority); +} + +export async function POST(request: NextRequest) { + const profile = await getActiveProfile(); + if (!profile) return noActiveProfileResponse(); + + const notConfigured = qdrantNotConfiguredResponse(); + if (notConfigured) return notConfigured; + + let query: unknown; + try { + query = (await request.json())?.query; + } catch { + return NextResponse.json( + { error: "Invalid JSON body. Expected { query: string }." }, + { status: 400 } + ); + } + + if (typeof query !== "string" || !query.trim()) { + return NextResponse.json({ error: "Query cannot be empty." }, { status: 400 }); + } + if (query.length > MAX_QUERY_LENGTH) { + return NextResponse.json( + { error: `Query exceeds maximum length of ${MAX_QUERY_LENGTH} characters.` }, + { status: 413 } + ); + } + + const rawQuery = query.trim(); + + try { + // Vector failure is tolerated as long as the lexical side found something. + const vectorPromise = embedText(rawQuery) + .then((v) => searchByVector(COLLECTIONS.POST_IMAGES, v, profile.id, RESULT_LIMIT)) + .catch((e: unknown) => e); + + const [vectorResult, textMatches] = await Promise.all([ + vectorPromise, + textSearch(profile.id, rawQuery), + ]); + + if (!Array.isArray(vectorResult) && textMatches.length === 0) { + throw vectorResult; + } + const vectorHits = Array.isArray(vectorResult) ? vectorResult : []; + + const bestVisual = bestHitPerPost(vectorHits, (s) => (s >= VECTOR_NOISE_FLOOR ? s : null)); + const rankedVisual = [...bestVisual.entries()].sort((a, b) => b[1].quality - a[1].quality); + const visualRank = new Map(rankedVisual.map(([pk], i) => [pk, i + 1])); + const topVisualScore = rankedVisual[0]?.[1].quality ?? 0; + + const textRank = new Map( + textMatches.map((m, i) => [ + m.pk, + { rank: i + 1, matchType: (m.isAccount ? "account" : "caption") as MatchType }, + ]) + ); + + interface Candidate { + pk: string; + rrfScore: number; + score: number; + matchType: MatchType; + matchedSlideIndex?: number; + matchedImageUrl?: string; + } + const candidates: Candidate[] = []; + + for (const pk of new Set([...visualRank.keys(), ...textRank.keys()])) { + const vRank = visualRank.get(pk); + const tInfo = textRank.get(pk); + const vMatch = bestVisual.get(pk); + + // Visual-only candidates below the elbow are noise, not results. + if (!tInfo && vMatch) { + if ( + vMatch.quality < VISUAL_ELBOW_FLOOR || + vMatch.quality < topVisualScore * VISUAL_ELBOW_RATIO + ) { + continue; + } + } + + const rrfScore = + (vRank !== undefined ? WEIGHT_VECTOR / (RRF_K + vRank) : 0) + + (tInfo !== undefined ? WEIGHT_TEXT / (RRF_K + tInfo.rank) : 0); + + let matchType: MatchType = "visual"; + let score = 0.5; + if (vRank !== undefined && tInfo !== undefined) { + // Matching on both signals is the strongest evidence we have. + matchType = "hybrid"; + score = calibrate(vMatch?.quality ?? 0.25, VECTOR_NOISE_FLOOR, 0.42, 0.88, 0.99); + } else if (tInfo !== undefined) { + matchType = tInfo.matchType; + score = tInfo.rank <= 3 ? 0.92 : 0.85; + } else if (vMatch !== undefined) { + score = calibrate(vMatch.quality, VECTOR_NOISE_FLOOR, 0.4, 0.45, 0.95); + } + + candidates.push({ + pk, + rrfScore, + score, + matchType, + matchedSlideIndex: vMatch?.carouselPosition, + matchedImageUrl: vMatch?.imageUrl, + }); + } + + const top = candidates.sort((a, b) => b.rrfScore - a.rrfScore).slice(0, RESULT_LIMIT); + + const posts = await prisma.post.findMany({ + where: { profileId: profile.id, pk: { in: top.map((c) => c.pk) } }, + }); + const postByPk = new Map(posts.map((p) => [p.pk, p])); + + const results: VectorSearchHit[] = []; + for (const c of top) { + const post = postByPk.get(c.pk); + if (!post) continue; + results.push({ + post, + score: c.score, + matchType: c.matchType, + matchedSlideIndex: c.matchedSlideIndex, + matchedImageUrl: c.matchedImageUrl, + }); + } + + return NextResponse.json({ results }); + } catch (err: unknown) { + return searchErrorResponse(err); + } +} diff --git a/src/app/api/search/reindex/route.ts b/src/app/api/search/reindex/route.ts new file mode 100644 index 0000000..fe97d99 --- /dev/null +++ b/src/app/api/search/reindex/route.ts @@ -0,0 +1,58 @@ +import { NextResponse } from "next/server"; +import { runVectorIndex, getCurrentIndexState } from "@/lib/vector/index-posts"; +import { + getQdrantConfig, + isQdrantConfigured, + checkQdrantLiveness, +} from "@/lib/vector/qdrant-client"; +import { getProfileVectorStats } from "@/lib/vector/stats"; +import { getActiveProfile, noActiveProfileResponse } from "@/lib/active-profile"; + +export async function POST() { + const profile = await getActiveProfile(); + if (!profile) return noActiveProfileResponse(); + + if (!isQdrantConfigured(getQdrantConfig())) { + return NextResponse.json( + { error: "Vector search is not configured. Set QDRANT_URL env var." }, + { status: 400 } + ); + } + if (getCurrentIndexState(profile.id)?.status === "running") { + return NextResponse.json( + { error: "A vector index run is already in progress for this profile." }, + { status: 409 } + ); + } + + try { + // Fire and forget — index run happens in background + runVectorIndex(profile.id).catch((err) => { + // Error is captured in the profile's index state and persisted stats + console.error(`[reindex] Background index failed for profile ${profile.id}:`, err); + }); + return NextResponse.json({ status: "started" }); + } catch (error) { + const message = error instanceof Error ? error.message : "Unknown error"; + return NextResponse.json({ error: message }, { status: 400 }); + } +} + +export async function GET() { + const profile = await getActiveProfile(); + if (!profile) return noActiveProfileResponse(); + + const current = getCurrentIndexState(profile.id); + const configured = isQdrantConfigured(getQdrantConfig()); + const [stats, liveness] = await Promise.all([ + getProfileVectorStats(profile.id), + checkQdrantLiveness(profile.id), + ]); + + return NextResponse.json({ + current, + configured, + stats, + liveness, + }); +} diff --git a/src/components/layout/sidebar.tsx b/src/components/layout/sidebar.tsx index ff82b78..67d234f 100644 --- a/src/components/layout/sidebar.tsx +++ b/src/components/layout/sidebar.tsx @@ -6,6 +6,7 @@ import { LayoutDashboard, Users, Play, + Search, Settings, } from "lucide-react"; import { ThemeToggle } from "@/components/layout/theme-toggle"; @@ -16,6 +17,7 @@ const navItems = [ { href: "/", label: "Overview", icon: LayoutDashboard }, { href: "/accounts", label: "Accounts", icon: Users }, { href: "/scrape", label: "Scrape", icon: Play }, + { href: "/search", label: "Search", icon: Search }, { href: "/settings", label: "Settings", icon: Settings }, ]; @@ -75,7 +77,7 @@ export function Sidebar() { {/* Mobile Bottom Bar */} -