Replace llama.cpp with ONNX Runtime for reranking

Dockerfile

@@ -1,23 +1,3 @@
-FROM node:lts AS llama-builder
-
-ARG LLAMA_CPP_RELEASE_TAG="b6604"
-
-RUN apt-get update && apt-get install -y \
-  build-essential \
-  cmake \
-  ccache \
-  git \
-  curl
-
-RUN cd /tmp && \
-  git clone https://github.com/ggerganov/llama.cpp.git && \
-  cd llama.cpp && \
-  git checkout $LLAMA_CPP_RELEASE_TAG && \
-  cmake -B build -DGGML_NATIVE=OFF -DLLAMA_CURL=OFF && \
-  cmake --build build --config Release -j --target llama-server && \
-  mkdir -p /usr/local/lib/llama && \
-  find build -type f \( -name "libllama.so" -o -name "libmtmd.so" -o -name "libggml.so" -o -name "libggml-base.so" -o -name "libggml-cpu.so" \) -exec cp {} /usr/local/lib/llama/ \;
-
 FROM node:lts
 
 ARG SEARXNG_COMMIT_SHA="6da6eee265daeb4a62ab638d6921522bf405de69"
@@ -60,10 +40,6 @@ RUN chmod 644 $SEARXNG_SETTINGS_PATH && \
   /usr/local/searxng/searxng-venv/bin/pip install -r requirements.txt && \
   /usr/local/searxng/searxng-venv/bin/pip install --no-build-isolation -e .
 
-COPY --from=llama-builder /tmp/llama.cpp/build/bin/llama-server /usr/local/bin/
-COPY --from=llama-builder /usr/local/lib/llama/* /usr/local/lib/
-RUN ldconfig /usr/local/lib
-
 USER ${USERNAME}
 
 WORKDIR ${APP_DIR}
@@ -86,7 +62,13 @@ RUN npm ci
 
 COPY --chown=${USERNAME}:${USERNAME} . .
 
-RUN git config --global --add safe.directory ${APP_DIR} && \
+# The commit hash is optional build metadata, so a build context without a
+# usable repository must not fail the build. This happens when building from a
+# git worktree, where `.git` is a file pointing at a gitdir outside the context;
+# git then treats every command as fatal, including `config --global`.
+RUN git config --global --add safe.directory ${APP_DIR} 2>/dev/null || true; \
+  git rev-parse --short HEAD >/dev/null 2>&1 || \
+  echo "WARNING: no usable git repository in the build context, so the app will report an empty commit hash."; \
   npm run build
 
 HEALTHCHECK --interval=5m CMD curl -f http://localhost:7860/status || exit 1

README.md

@@ -85,7 +85,7 @@ flowchart LR
     subgraph container [Docker container]
         Server[App server]
         SearXNG[SearXNG<br/>metasearch]
-        Reranker[Reranker<br/>llama-server]
+        Reranker[Reranker<br/>ONNX Runtime]
     end
     UI <--> Storage
     UI -->|search| Server

agents.md

@@ -30,7 +30,7 @@ This is your navigation hub. Start here, follow the links, and return when you n
 - **`docs/ui-components.md`** - Component architecture and PubSub patterns
 - **`docs/search-history.md`** - History database schema and management
 - **`docs/conversation-memory.md`** - Token budgeting and rolling summaries
-- **`docs/reranking.md`** - Reranker subsystem and llama-server lifecycle
+- **`docs/reranking.md`** - Reranker subsystem and model lifecycle
 - **`docs/glossary.md`** - Codebase-specific terms and domain concepts
 
 ### Development
@@ -128,19 +128,19 @@ Need to:
 - `server/internalApiEndpointServerHook.ts` - `/inference` proxy to self-hosted API
 - `server/validateAccessKeyServerHook.ts` - Access key validation endpoint
 - `server/statusEndpointServerHook.ts` - `/status` health check endpoint
-- `server/rerankerServiceHook.ts` - llama-server lifecycle management for reranking
+- `server/rerankerServiceHook.ts` - Reranker model lifecycle management
 - `server/compressionServerHook.ts` - gzip/brotli compression for responses
 - `server/crossOriginServerHook.ts` - COOP/COEP headers for SharedArrayBuffer
 - `server/cacheServerHook.ts` - Cache-Control headers (preview server only)
 - `server/webSearchService.ts` - SearXNG integration with circuit breaker and retry logic
-- `server/rerankerService.ts` - Reranker service (llama-server process management)
+- `server/rerankerService.ts` - Reranker service (ONNX Runtime inference)
 - `server/rankSearchResults.ts` - Score-based filtering and result reordering
 - `server/searchToken.ts` - CSRF token generation and storage
 - `server/verifiedTokens.ts` - In-memory `Set<string>` of verified session tokens
 - `server/verifyTokenAndRateLimit.ts` - Token verification and rate limiting
 - `server/handleTokenVerification.ts` - Search token validation logic
 - `server/searchesSinceLastRestart.ts` - In-memory search counters for analytics
-- `server/downloadFileFromHuggingFaceRepository.ts` - Downloads GGUF models from HuggingFace
+- `server/downloadFileFromHuggingFaceRepository.ts` - Downloads model files from HuggingFace
 
 ### Hooks
 - `client/hooks/useSearchHistory.ts` - Search history management from IndexedDB

client/modules/appInfo.test.ts

@@ -0,0 +1,15 @@
+import { describe, expect, it } from "vitest";
+import { appVersion } from "@/modules/appInfo";
+
+describe("appVersion", () => {
+  it("appends the commit hash as semver build metadata when the build had one", () => {
+    // vitest.config.ts defines VITE_COMMIT_SHORT_HASH as "test-hash".
+    expect(appVersion).toMatch(/^\d{4}\.\d{1,2}\.\d{1,2}\+test-hash$/);
+  });
+
+  it("never leaves a dangling separator", () => {
+    // A build context without a usable git repository yields an empty hash, and
+    // "1.2.3+" is not a valid version string.
+    expect(appVersion.endsWith("+")).toBe(false);
+  });
+});

client/modules/appInfo.ts

@@ -10,6 +10,12 @@ export const appName = repository.url.split("/").pop();
  */
 export const appRepository = repository.url;
 /**
- * Application version with build timestamp and commit hash
+ * Application version with build timestamp and, when the build had a git
+ * repository available, the commit hash as semver build metadata.
  */
-export const appVersion = `${getSemanticVersion(VITE_BUILD_DATE_TIME)}+${VITE_COMMIT_SHORT_HASH}`;
+export const appVersion = [
+  getSemanticVersion(VITE_BUILD_DATE_TIME),
+  VITE_COMMIT_SHORT_HASH,
+]
+  .filter(Boolean)
+  .join("+");

docs/configuration.md

@@ -197,16 +197,13 @@ Same structure but without volume mounts and with pre-built assets.
 
 ### Dockerfile Environment
 
-The Dockerfile sets up:
-1. **Builder stage**: Compiles `llama-server` from llama.cpp
-2. **Runtime stage**: 
+The Dockerfile sets up a single runtime stage:
    - Node.js LTS
    - Python 3 + SearXNG
-   - llama-server binary
 
 The app runs under the `node` user, with the app directory at `/home/node/app`. The production image starts the app with `npm start -- --host` (i.e. `vite preview`), not `npm run dev`.
 
-**Multi-service container** runs all three concurrently via shell process composition.
+**Multi-service container** runs SearXNG and Node.js concurrently via shell process composition.
 
 ## Vite Environment Injection
 

docs/development-commands.md

@@ -10,7 +10,7 @@
 
 ## Docker
 
-- **`docker compose up`**: Development environment with SearXNG, llama-server, and Node.js
+- **`docker compose up`**: Development environment with SearXNG and Node.js
 - **`docker compose -f docker-compose.production.yml up --build`**: Production deployment
 
 ## Testing
@@ -70,8 +70,7 @@ Used by both `on-push-to-main` and `on-pull-request-to-main` to run the producti
 ### Docker Image Builds
 
 The Docker image uses a multi-stage build:
-1. **Builder stage** (`llama-builder`): Compiles llama-server from llama.cpp source, extracts shared libraries (`libllama.so`, `libmtmd.so`, `libggml.so`, etc.)
-2. **Runtime stage**: Installs Python/SearXNG, copies llama-server binaries, builds the Vite frontend, runs SearXNG and Node.js in a single container via shell process composition
+The image installs Python/SearXNG, builds the Vite frontend, and runs SearXNG and Node.js in a single container via shell process composition. No compilation step is required: the reranker's ONNX Runtime binaries ship prebuilt with the npm dependency.
 
 The production image is published to `ghcr.io` with multi-platform support (linux/amd64, linux/arm64). Tags and labels are auto-generated from Git metadata via `docker/metadata-action`.
 

docs/glossary.md

@@ -34,9 +34,9 @@ Instead of a heavy state management library like Redux, MiniSearch uses a minima
 
 ### Reranker
 
-A secondary search stage that takes initial results from SearXNG and re-orders them based on relevance to the query using a cross-encoder model (`jina-reranker-v1-tiny-en`) running on a local `llama-server` instance.
+A secondary search stage that takes initial results from SearXNG and re-orders them based on relevance to the query using a cross-encoder model (`jina-reranker-v1-tiny-en`) running in-process via ONNX Runtime.
 
-- **Implementation**: Spawns `llama-server` child process with `--reranking` and `--pooling rank` flags
+- **Implementation**: Loads the model's ONNX export with `onnxruntime-node`; no child process
 - **Health Check**: Polls `/health` endpoint via `getRerankerStatus`
 - **Scoring**: Results filtered using standard deviation thresholds (`kStandardDeviationFactor = 0.3`)
 - **Fallback**: If reranker is unhealthy, returns unranked SearXNG results
@@ -95,7 +95,7 @@ Middleware registered via Vite plugin hooks (`configureServer`, `configurePrevie
 | `cacheServerHook` | Cache-Control headers |
 | `validateAccessKeyServerHook` | Access key validation |
 | `internalApiEndpointServerHook` | `/inference` proxy |
-| `rerankerServiceHook` | llama-server lifecycle management |
+| `rerankerServiceHook` | Reranker model lifecycle management |
 
 ### Circuit Breaker
 

docs/overview.md

@@ -41,10 +41,10 @@ The application has three primary entry points:
 
 The Docker container runs three services concurrently:
 - **SearXNG** - Privacy-focused metasearch engine
-- **llama-server** - Local AI inference server
+- **ONNX Runtime** - In-process inference for result reranking
 - **Node.js application** - Main application server
 
-The multi-stage build process first compiles llama-server from source, then creates the final runtime image with Node.js and Python environments. The container entrypoint starts SearXNG in the background and then launches the Node.js application.
+The build creates a runtime image with Node.js and Python environments. The container entrypoint starts SearXNG in the background and then launches the Node.js application.
 
 ## State Management Architecture
 
@@ -76,7 +76,7 @@ The system supports two operational modes:
 - Vite preview server (no HMR)
 - Optimized bundle with minification
 
-Both modes run the same underlying services (SearXNG, llama-server) but differ in how the frontend is served and rebuilt.
+Both modes run the same underlying services (SearXNG, the reranker) but differ in how the frontend is served and rebuilt.
 
 ## Search and AI Integration Flow
 
@@ -102,7 +102,7 @@ The system executes two parallel flows when a user submits a query:
 5. Response updates throttled to ~12 updates/sec via `throttleit` to prevent React render overload
 6. Response saved to history database via `saveLlmResponseForQuery`
 
-The `textGeneration` module orchestrates the entire search-to-response flow, managing search requests, LLM context preparation, and response streaming. Search results are optionally reranked using a local llama-server instance before being passed to the LLM for response generation.
+The `textGeneration` module orchestrates the entire search-to-response flow, managing search requests, LLM context preparation, and response streaming. Search results are optionally reranked in-process via ONNX Runtime before being passed to the LLM for response generation.
 
 ### Web Search Service Reliability
 
@@ -192,7 +192,7 @@ MiniSearch implements all server-side logic as Vite plugin hooks. Each hook regi
 | `cacheServerHook` | `server/cacheServerHook.ts` | Cache-Control headers (preview only) |
 | `validateAccessKeyServerHook` | `server/validateAccessKeyServerHook.ts` | Access key validation endpoint |
 | `internalApiEndpointServerHook` | `server/internalApiEndpointServerHook.ts` | `/inference` proxy to self-hosted API |
-| `rerankerServiceHook` | `server/rerankerServiceHook.ts` | llama-server lifecycle management for result reranking |
+| `rerankerServiceHook` | `server/rerankerServiceHook.ts` | Reranker model lifecycle management for result reranking |
 
 Key server-side modules:
 

docs/quick-start.md

@@ -47,7 +47,7 @@ Production mode:
 
 The Docker container runs three services concurrently:
 - **SearXNG** - Privacy-focused metasearch engine (starts in background)
-- **llama-server** - Local AI inference server (for result reranking)
+- **ONNX Runtime** - In-process inference for result reranking
 - **Node.js (Vite)** - Main application server
 
 ### Startup Sequence