kb/README.md

# kb

Personal knowledge base with hybrid search (full-text + semantic vector search).

Client-server architecture: a **FastAPI engine** running in Docker (with optional GPU acceleration), a lightweight **Go CLI client**, and an **MCP server** for native agent integration.

## Architecture

```
Go CLI (kb) ──HTTP──▶ FastAPI Engine (Docker) ──▶ SQLite + GPU
                            ▲
MCP Agents  ──MCP/HTTP──▶ MCP Server (Docker) ──┘
```

- **Engine**: Keeps the embedding model warm in memory. Handles search, ingestion, document management, and note mutation via REST API. Runs in Docker with NVIDIA GPU, AMD GPU (ROCm), or CPU-only support.
- **Client**: Single static Go binary. No Python, no ML dependencies, instant startup. Talks to the engine over HTTP.
- **MCP Server**: Exposes kb operations as native MCP tools over Streamable HTTP. Runs as a separate Docker container alongside the engine. Use tags to scope agent data from user documents.
- **Storage**: Single SQLite database with FTS5 (keyword search) and sqlite-vec (vector search). Portable via bind mount — just copy the data directory between hosts.

## Quick start

### 1. Start the engine

**From pre-built images** (recommended):

```bash
# NVIDIA GPU
docker run -d --name kb-engine \
  --gpus all \
  -p 8000:8000 \
  -v ~/kb-data:/data \
  -e KB_MODEL=all-MiniLM-L6-v2 \
  -e KB_DEVICE=auto \
  -e KB_API_KEY=your-secret-key \
  --restart unless-stopped \
  docker.dcglab.co.uk/dcg/kb/engine:latest-nvidia

# AMD GPU (ROCm)
docker run -d --name kb-engine \
  --device /dev/kfd --device /dev/dri \
  --group-add video \
  -p 8000:8000 \
  -v ~/kb-data:/data \
  -e KB_MODEL=all-MiniLM-L6-v2 \
  -e KB_DEVICE=auto \
  -e KB_API_KEY=your-secret-key \
  --restart unless-stopped \
  docker.dcglab.co.uk/dcg/kb/engine:latest-rocm

# CPU only (no GPU required — smaller image)
docker run -d --name kb-engine \
  -p 8000:8000 \
  -v ~/kb-data:/data \
  -e KB_MODEL=all-MiniLM-L6-v2 \
  -e KB_API_KEY=your-secret-key \
  --restart unless-stopped \
  docker.dcglab.co.uk/dcg/kb/engine:latest-cpu
```

Or use a compose file from the repo:

```bash
# NVIDIA GPU
KB_DATA_PATH=~/kb-data docker compose -f engine/compose.nvidia.yaml up -d

# AMD GPU (ROCm)
KB_DATA_PATH=~/kb-data docker compose -f engine/compose.rocm.yaml up -d

# CPU only
KB_DATA_PATH=~/kb-data docker compose -f engine/compose.cpu.yaml up -d
```

See [DEVELOPER.md](DEVELOPER.md) to run the engine from source.

The engine will download the embedding model on first start (~90MB) and load it into memory (GPU or CPU). Check readiness:

```bash
curl http://localhost:8000/api/v1/health
# {"status": "healthy"}
```

### 2. Install the client

**From a release** (recommended):

Check [releases](https://gitea.dcglab.co.uk/steve/kb/releases) for the latest client tag, then:

```bash
# Set the version tag
TAG=client-v3.0.0

# Linux (amd64)
curl -L -o kb https://gitea.dcglab.co.uk/steve/kb/releases/download/${TAG}/kb-linux-amd64

# Linux (arm64)
curl -L -o kb https://gitea.dcglab.co.uk/steve/kb/releases/download/${TAG}/kb-linux-arm64

# macOS (Apple Silicon)
curl -L -o kb https://gitea.dcglab.co.uk/steve/kb/releases/download/${TAG}/kb-darwin-arm64

# macOS (Intel)
curl -L -o kb https://gitea.dcglab.co.uk/steve/kb/releases/download/${TAG}/kb-darwin-amd64

# Then install
chmod +x kb
sudo mv kb /usr/local/bin/
```

See [DEVELOPER.md](DEVELOPER.md) to build the client from source.

### 3. Configure the client

The client works with zero configuration if the engine is on localhost:8000. To customise, create `~/.kb/client.yaml`:

```yaml
engine_url: http://localhost:8000
api_key: ""
default_format: human
```

Override via environment variables (`KB_ENGINE_URL`, `KB_API_KEY`) or CLI flags (`--engine`, `--api-key`, `--format`).

### 4. Use it

```bash
# Add notes
kb addnote "Always restart nginx after config changes"
kb addnote "Server room is building 3, floor 2" --tags ops

# Add files (async — uploads and exits immediately)
kb addfile ~/docs/manual.pdf --tags admin
kb addfile ~/notes/ --recursive

# Check ingestion progress
kb jobs

# Search
kb search "how to install git"
kb search "deploy process" --tags ops --type pdf

# Update a note in place
kb updatenote 42 "revised note content"

# Manage
kb list
kb info 1
kb tags
kb tag 1 --add important
kb export 1 -o manual.pdf    # download original file
kb remove 3 --yes
kb status

# Bulk operations
kb bulk-remove --tags "draft,old" --type note --yes
kb bulk-tag --type note --add "archived" --yes
kb bulk-set-tags --tags "old-scheme" --set "new-scheme" --yes
```

## How it works

- **Ingestion**: Files are uploaded to the engine and queued for async processing. The engine chunks documents (PDFs via Docling, markdown by headers, code by AST/functions, notes as whole text), generates embeddings on GPU, and stores everything in SQLite.
- **Search**: Hybrid retrieval combining BM25 keyword scoring (FTS5) and vector similarity (sqlite-vec), merged via Reciprocal Rank Fusion. Sub-100ms with a warm model.
- **Output**: JSON (for scripts/LLM tool use) or human-readable terminal format. Use `--format json` on any command.

## Engine configuration

The engine is configured via environment variables (set in the compose file or via `docker compose` CLI):

| Variable | Default | Description |
|---|---|---|
| `KB_DATA_DIR` | `/data` | Data directory inside the container (bind-mounted) |
| `KB_MODEL` | `all-MiniLM-L6-v2` | HuggingFace embedding model name |
| `KB_DEVICE` | `auto` | Embedding/search device: `auto`, `cpu`, or `cuda` |
| `KB_INGEST_DEVICE` | `auto` | Docling layout detection device: `auto`, `cpu`, or `cuda` |
| `KB_API_KEY` | (none) | Optional Bearer token for API authentication |
| `KB_SEARCH_THRESHOLD` | `0.01` | Minimum score for search results (filters noise) |
| `KB_PORT` | `8000` | Port to expose |
| `KB_HOST` | `0.0.0.0` | Host to bind to |
| `HF_HUB_OFFLINE` | (none) | Set to `1` to prevent model downloads (use cached only) |
| `KB_DATA_PATH` | `./data` | Host path for bind mount (compose variable, not used by engine) |

## Data portability

The data directory contains everything: SQLite database, model cache, and staging files. To migrate between hosts:

```bash
# On source host
rsync -a ~/kb-data/ user@target:/home/user/kb-data/

# On target host
KB_DATA_PATH=~/kb-data docker compose -f compose.nvidia.yaml up -d
```

Data is device-agnostic — you can ingest on NVIDIA and serve from AMD or CPU (or any combination) with the same data directory.

## MCP server (agent integration)

The MCP server exposes kb operations as native MCP tools over Streamable HTTP, so agents can search, add notes, upload files, and manage documents without shelling out to the CLI. Includes setup guides for Claude Code, VS Code, Cursor, Windsurf, and JetBrains IDEs.

See **[MCP.md](MCP.md)** for full details — server setup, available tools, tag-based organisation, configuration, and client examples.

## Agent skill

If you are restricted from using MCP server, or you just prefer to utilise Agent SKILLS, please also see `SKILL.md` for the skill definition.