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lc component

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Usage

lc component <command> [arguments] [flags]

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Commands

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lc component list

lc component list

# Aliases
lc component ls
lc comp list

• Component ID and name
• Installation status ( Installed, � Available)
• Description
• Dependencies

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lc component add

lc component add <component-id>

# Examples
lc component add llm
lc component add embedding
lc component add vector
lc component add mongodb
lc component add cache
lc component add storage

• Model selection for AI components
• Dependency validation and auto-installation
• Configuration prompts
• Progress indicators

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lc component remove

lc component remove <component-id>

# Aliases
lc component rm
lc comp remove llm

• Dependency check (warns if other components depend on this one)
• Confirmation prompt
• Data preservation options

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lc component info

lc component info <component-id>

# Example
lc component info llm

• Component description
• Current status
• Dependencies
• Configuration
• Resource usage

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lc component update

lc component update <component-id>

# Example
lc component update llm

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Available Components

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AI Components

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llm - Language Model

• Purpose: Run large language models locally
• Dependencies: None
• Models: Llama, Mistral, Qwen, Gemma, etc.
• Port: 11434 (Ollama)

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embedding - Embedding Model

• Purpose: Generate text embeddings for RAG
• Dependencies: llm (shares Ollama instance)
• Models: all-minilm, nomic-embed-text, etc.

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stt - Speech-to-Text

• Purpose: Audio transcription
• Dependencies: None
• Model: OpenAI Whisper

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Database Components

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database - PostgreSQL

• Purpose: Relational database
• Dependencies: None
• Features: Full PostgreSQL 16 with extensions
• Port: 5432

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mongodb - MongoDB

• Purpose: NoSQL document database
• Dependencies: None
• Features: MongoDB 7.0 with auth
• Port: 27017

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vector - Vector Database

• Purpose: Vector similarity search
• Dependencies: database (uses pgvector extension)
• Features: Hybrid search, RAG support

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Infrastructure Components

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cache - Redis Cache

• Purpose: High-performance caching
• Dependencies: None
• Features: Redis 7 with persistence
• Port: 6379

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queue - Message Queue

• Purpose: Background job processing
• Dependencies: cache (uses Redis)
• Features: Job scheduling, pub/sub

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storage - Object Storage

• Purpose: S3-compatible file storage
• Dependencies: None
• Features: MinIO with web console
• Port: 9000/9001

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Examples

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Basic Component Management

# See what's available
lc component list

# Add database to project
lc component add database

# Add AI model
lc component add llm
# (Interactive model selection follows)

# Check what's installed
lc component list

# Get detailed info
lc component info llm

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Building an AI Chat App

# Add core components
lc component add llm
lc component add database
lc component add cache

# Add vector search for RAG
lc component add vector

# Check dependencies are satisfied
lc component list

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E-commerce Backend

# Add databases
lc component add database
lc component add mongodb

# Add infrastructure
lc component add cache
lc component add queue
lc component add storage

# Add AI for recommendations
lc component add llm
lc component add embedding

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Updating Components

# Change LLM model
lc component update llm
# Select new model from list

# Update database configuration
lc component update database
# Modify settings like memory allocation

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Component Dependencies

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Component Status

• Available: Can be added to project
• Installed: Added to project configuration
• Running: Active and serving requests
• Stopped: Installed but not running
• Error: Failed to start or configure

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Best Practices

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Planning Your Stack

1. Start Simple: Begin with core components needed
2. Add Incrementally: Add components as requirements grow
3. Check Dependencies: Understand what each component needs
4. Consider Resources: Each component uses system resources

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Resource Management

# Check resource usage
lc info

# Monitor component performance
lc logs <component-name>

# Remove unused components
lc component remove <unused-component>

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Model Selection Guidelines

• Development: Use smaller models (3B parameters)
• Production: Use larger models (7B+ parameters)
• Specialized: Use task-specific models (code, math, etc.)

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Troubleshooting

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Component Won’t Start

Error: failed to start component 'llm'

1. Check dependencies: lc component info llm
2. Verify resources: lc doctor
3. Check logs: lc logs llm
4. Restart service: lc restart llm

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Dependency Conflicts

Error: component 'queue' requires 'cache'

1. Add dependency first: lc component add cache
2. Add both together: lc component add cache queue

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Model Download Issues

Error: failed to download model 'llama2'

1. Check internet connection
2. Verify disk space: lc info
3. Try different model: lc models list
4. Check Ollama service: lc status

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Related Commands

• lc setup - Interactive component selection
• lc start - Start component services
• lc status - Check component status
• lc models - Manage AI models
• lc info - System resource information