Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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crates/ruvector-cli/docs/README.md
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crates/ruvector-cli/docs/README.md
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# Ruvector CLI and MCP Server
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High-performance command-line interface and Model Context Protocol (MCP) server for Ruvector vector database.
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## Table of Contents
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- [Installation](#installation)
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- [CLI Usage](#cli-usage)
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- [MCP Server](#mcp-server)
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- [Configuration](#configuration)
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- [Examples](#examples)
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- [Shell Completions](#shell-completions)
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## Installation
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```bash
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# Build from source
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cargo build --release -p ruvector-cli
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# Install binaries
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cargo install --path crates/ruvector-cli
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# The following binaries will be available:
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# - ruvector (CLI tool)
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# - ruvector-mcp (MCP server)
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```
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## CLI Usage
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### Create a Database
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```bash
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# Create with specific dimensions
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ruvector create --path ./my-vectors.db --dimensions 384
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# Use default location (./ruvector.db)
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ruvector create --dimensions 1536
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```
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### Insert Vectors
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```bash
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# From JSON file
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ruvector insert --db ./my-vectors.db --input vectors.json --format json
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# From CSV file
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ruvector insert --db ./my-vectors.db --input vectors.csv --format csv
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# From NumPy file
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ruvector insert --db ./my-vectors.db --input embeddings.npy --format npy
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# Hide progress bar
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ruvector insert --db ./my-vectors.db --input vectors.json --no-progress
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```
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#### Input Format Examples
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**JSON format:**
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```json
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[
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{
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"id": "doc1",
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"vector": [0.1, 0.2, 0.3, ...],
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"metadata": {
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"title": "Document 1",
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"category": "science"
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}
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},
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{
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"id": "doc2",
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"vector": [0.4, 0.5, 0.6, ...],
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"metadata": {
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"title": "Document 2",
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"category": "tech"
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}
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}
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]
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```
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**CSV format:**
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```csv
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id,vector,metadata
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doc1,"[0.1, 0.2, 0.3]","{\"title\": \"Document 1\"}"
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doc2,"[0.4, 0.5, 0.6]","{\"title\": \"Document 2\"}"
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```
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### Search Vectors
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```bash
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# Search with JSON array
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ruvector search --db ./my-vectors.db --query "[0.1, 0.2, 0.3]" --top-k 10
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# Search with comma-separated values
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ruvector search --db ./my-vectors.db --query "0.1, 0.2, 0.3" -k 5
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# Show full vectors in results
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ruvector search --db ./my-vectors.db --query "[0.1, 0.2, 0.3]" --show-vectors
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```
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### Database Info
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```bash
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# Show database statistics
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ruvector info --db ./my-vectors.db
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```
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Output example:
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```
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Database Statistics
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Vectors: 10000
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Dimensions: 384
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Distance Metric: Cosine
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HNSW Configuration:
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M: 32
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ef_construction: 200
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ef_search: 100
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```
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### Benchmark Performance
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```bash
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# Run 1000 queries
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ruvector benchmark --db ./my-vectors.db --queries 1000
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# Custom number of queries
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ruvector benchmark --db ./my-vectors.db -n 5000
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```
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Output example:
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```
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Running benchmark...
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Queries: 1000
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Dimensions: 384
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Benchmark Results:
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Total time: 2.45s
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Queries per second: 408
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Average latency: 2.45ms
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```
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### Export Database
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```bash
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# Export to JSON
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ruvector export --db ./my-vectors.db --output backup.json --format json
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# Export to CSV
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ruvector export --db ./my-vectors.db --output backup.csv --format csv
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```
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### Import from Other Databases
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```bash
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# Import from FAISS (coming soon)
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ruvector import --db ./my-vectors.db --source faiss --source-path index.faiss
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# Import from Pinecone (coming soon)
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ruvector import --db ./my-vectors.db --source pinecone --source-path config.json
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```
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### Global Options
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```bash
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# Use custom config file
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ruvector --config ./custom-config.toml info --db ./my-vectors.db
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# Enable debug mode
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ruvector --debug search --db ./my-vectors.db --query "[0.1, 0.2, 0.3]"
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# Disable colors
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ruvector --no-color info --db ./my-vectors.db
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```
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## MCP Server
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The Ruvector MCP server provides programmatic access via the Model Context Protocol.
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### Start Server
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```bash
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# STDIO transport (for local communication)
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ruvector-mcp --transport stdio
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# SSE transport (for HTTP streaming)
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ruvector-mcp --transport sse --host 127.0.0.1 --port 3000
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# With custom config
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ruvector-mcp --config ./mcp-config.toml --transport sse
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# Debug mode
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ruvector-mcp --debug --transport stdio
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```
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### MCP Tools
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The server exposes the following tools:
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#### 1. vector_db_create
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Create a new vector database.
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**Parameters:**
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- `path` (string, required): Database file path
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- `dimensions` (integer, required): Vector dimensions
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- `distance_metric` (string, optional): Distance metric (euclidean, cosine, dotproduct, manhattan)
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**Example:**
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```json
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{
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"name": "vector_db_create",
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"arguments": {
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"path": "./my-db.db",
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"dimensions": 384,
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"distance_metric": "cosine"
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}
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}
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```
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#### 2. vector_db_insert
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Insert vectors into database.
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**Parameters:**
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- `db_path` (string, required): Database path
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- `vectors` (array, required): Array of vector objects
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**Example:**
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```json
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{
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"name": "vector_db_insert",
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"arguments": {
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"db_path": "./my-db.db",
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"vectors": [
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{
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"id": "vec1",
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"vector": [0.1, 0.2, 0.3],
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"metadata": {"label": "test"}
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}
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]
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}
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}
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```
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#### 3. vector_db_search
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Search for similar vectors.
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**Parameters:**
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- `db_path` (string, required): Database path
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- `query` (array, required): Query vector
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- `k` (integer, optional, default: 10): Number of results
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- `filter` (object, optional): Metadata filters
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**Example:**
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```json
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{
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"name": "vector_db_search",
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"arguments": {
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"db_path": "./my-db.db",
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"query": [0.1, 0.2, 0.3],
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"k": 5
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}
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}
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```
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#### 4. vector_db_stats
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Get database statistics.
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**Parameters:**
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- `db_path` (string, required): Database path
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**Example:**
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```json
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{
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"name": "vector_db_stats",
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"arguments": {
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"db_path": "./my-db.db"
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}
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}
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```
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#### 5. vector_db_backup
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Backup database to file.
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**Parameters:**
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- `db_path` (string, required): Database path
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- `backup_path` (string, required): Backup file path
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**Example:**
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```json
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{
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"name": "vector_db_backup",
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"arguments": {
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"db_path": "./my-db.db",
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"backup_path": "./backup.db"
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}
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}
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```
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### MCP Resources
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The server provides access to database resources via URIs:
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- `database://local/default`: Default database resource
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### MCP Prompts
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Available prompt templates:
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- `semantic-search`: Generate semantic search queries
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## Configuration
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Ruvector can be configured via TOML files, environment variables, or CLI arguments.
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### Configuration File
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Create a `ruvector.toml` file:
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```toml
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[database]
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storage_path = "./ruvector.db"
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dimensions = 384
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distance_metric = "Cosine"
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[database.hnsw]
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m = 32
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ef_construction = 200
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ef_search = 100
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max_elements = 10000000
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[cli]
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progress = true
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colors = true
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batch_size = 1000
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[mcp]
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host = "127.0.0.1"
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port = 3000
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cors = true
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```
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### Environment Variables
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```bash
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export RUVECTOR_STORAGE_PATH="./my-db.db"
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export RUVECTOR_DIMENSIONS=384
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export RUVECTOR_DISTANCE_METRIC="cosine"
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export RUVECTOR_MCP_HOST="0.0.0.0"
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export RUVECTOR_MCP_PORT=8080
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```
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### Configuration Precedence
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1. CLI arguments (highest priority)
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2. Environment variables
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3. Configuration file
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4. Default values (lowest priority)
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### Default Config Locations
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Ruvector looks for config files in these locations:
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1. `./ruvector.toml`
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2. `./.ruvector.toml`
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3. `~/.config/ruvector/config.toml`
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4. `/etc/ruvector/config.toml`
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## Examples
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### Building a Semantic Search Engine
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```bash
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# 1. Create database
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ruvector create --path ./search.db --dimensions 384
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# 2. Generate embeddings (external script)
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python generate_embeddings.py --input documents/ --output embeddings.json
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# 3. Insert embeddings
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ruvector insert --db ./search.db --input embeddings.json
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# 4. Search
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ruvector search --db ./search.db --query "[0.1, 0.2, ...]" -k 10
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```
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### Batch Processing Pipeline
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```bash
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#!/bin/bash
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DB="./vectors.db"
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DIMS=768
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# Create database
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ruvector create --path $DB --dimensions $DIMS
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# Process batches
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for file in data/batch_*.json; do
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echo "Processing $file..."
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ruvector insert --db $DB --input $file --no-progress
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done
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# Verify
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ruvector info --db $DB
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# Benchmark
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ruvector benchmark --db $DB --queries 1000
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```
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### Using with Claude Code
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```bash
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# Start MCP server
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ruvector-mcp --transport stdio
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# Claude Code can now use vector database tools
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# Example prompt: "Create a vector database and insert embeddings from my documents"
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```
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## Shell Completions
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Generate shell completions for better CLI experience:
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```bash
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# Bash
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ruvector --generate-completions bash > ~/.local/share/bash-completion/completions/ruvector
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# Zsh
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ruvector --generate-completions zsh > ~/.zsh/completions/_ruvector
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# Fish
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ruvector --generate-completions fish > ~/.config/fish/completions/ruvector.fish
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```
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## Error Handling
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Ruvector provides helpful error messages:
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```bash
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# Missing required argument
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$ ruvector create
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Error: Missing required argument: --dimensions
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# Invalid vector dimensions
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$ ruvector insert --db test.db --input vectors.json
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Error: Vector dimension mismatch. Expected: 384, Got: 768
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Suggestion: Ensure all vectors have the correct dimensionality
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# Database not found
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$ ruvector info --db nonexistent.db
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Error: Failed to open database: No such file or directory
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Suggestion: Create the database first with: ruvector create --path nonexistent.db --dimensions <dims>
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# Use --debug for full stack traces
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$ ruvector --debug info --db nonexistent.db
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```
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## Performance Tips
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1. **Batch Inserts**: Insert vectors in batches for better performance
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2. **HNSW Tuning**: Adjust `ef_construction` and `ef_search` based on your accuracy/speed requirements
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3. **Quantization**: Enable quantization for memory-constrained environments
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4. **Dimensions**: Use appropriate dimensions for your use case (384 for smaller models, 1536 for larger)
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5. **Distance Metric**: Choose based on your embeddings:
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- Cosine: Normalized embeddings (most common)
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- Euclidean: Absolute distances
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- Dot Product: When magnitude matters
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## Troubleshooting
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### Build Issues
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```bash
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# Ensure Rust is up to date
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rustup update
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# Clean build
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cargo clean && cargo build --release -p ruvector-cli
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```
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### Runtime Issues
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```bash
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# Enable debug logging
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RUST_LOG=debug ruvector info --db test.db
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# Check database integrity
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ruvector info --db test.db
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# Backup before operations
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cp test.db test.db.backup
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```
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## Contributing
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See the main Ruvector repository for contribution guidelines.
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## License
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MIT License - see LICENSE file for details.
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Reference in New Issue
Block a user