---
id: embed
title: Embed Command
sidebar_position: 8
---
# Embed Command
Generate embeddings for text using various embedding models. The embed command converts text into high-dimensional vectors that can be stored in vector databases for semantic search and RAG applications.
## Overview
Text embeddings are essential for building semantic search systems, recommendation engines, and RAG (Retrieval-Augmented Generation) workflows. The embed command supports multiple embedding models and can process both direct text input and files.
## Usage
```bash
# Generate embeddings for text
lc embed "Your text here"
# Specify model and provider
lc embed -m text-embedding-3-small --provider openai "Important information"
# Store in vector database
lc embed -v knowledge-base "Document content"
# Process files
lc embed -f document.txt,data.pdf
# Using aliases
lc e "Sample text"
```
## Subcommands
The `embed` command is a standalone command without subcommands. All functionality is controlled through options and arguments.
## Options
| `-m` | `--model` | Embedding model to use | None |
| `-p` | `--provider` | Provider for the embedding model | None |
| `-v` | `--vectordb` | Vector database to store embeddings | None |
| `-f` | `--files` | Files to process (comma-separated) | None |
| `-d` | `--debug` | Enable debug output | False |
| `-h` | `--help` | Print help | False |
## Examples
### Basic Text Embedding
```bash
# Simple text embedding
lc embed "Machine learning is transforming software development"
# With specific model
lc embed -m text-embedding-3-large "Complex technical documentation"
```
### Store in Vector Database
```bash
# Create embeddings and store in database
lc embed -v docs "Important company policy information"
# Add multiple pieces of content
lc embed -v knowledge "User manual section 1"
lc embed -v knowledge "User manual section 2"
lc embed -v knowledge "FAQ answers"
```
### Process Files
```bash
# Single file
lc embed -f documentation.md -v docs
# Multiple files
lc embed -f "manual.pdf,guide.txt,readme.md" -v knowledge
# With specific model
lc embed -m text-embedding-ada-002 -f data.txt -v research
```
### RAG Workflow
```bash
# Step 1: Create embeddings from documents
lc embed -f "docs/*.md" --vectordb project-docs
# Step 2: Use in chat with vector context
lc -v project-docs "How do I deploy the application?"
# Step 3: Find similar content
lc similar -v project-docs "deployment process"
```
## See Also
- [Vectors Command](vectors.md)
- [Similar Command](similar.md)
## Troubleshooting
### Common Issues
#### "Embedding model not found"
- **Error**: Specified embedding model doesn't exist
- **Solution**: Use `lc models embed` to list available embedding models
- **Solution**: Check provider supports the model
#### "File not found"
- **Error**: Specified file doesn't exist
- **Solution**: Check file paths and permissions
- **Solution**: Use absolute paths for clarity
#### "Vector database error"
- **Error**: Cannot connect to or create vector database
- **Solution**: Ensure database name is valid
- **Solution**: Check disk space and permissions
#### "Rate limiting"
- **Error**: Too many embedding requests
- **Solution**: Add delays between requests
- **Solution**: Use batch processing for large files
### Best Practices
1. **Choose appropriate models**: Larger models provide better quality but cost more
2. **Chunk large texts**: Break long documents into smaller sections
3. **Consistent models**: Use the same embedding model for search and storage
4. **Batch processing**: Process multiple texts together for efficiency
### Embedding Models
```bash
# List available embedding models
lc models embed
# Common embedding models:
# - text-embedding-3-small (OpenAI)
# - text-embedding-3-large (OpenAI)
# - text-embedding-ada-002 (OpenAI)
# - embed-english-v3.0 (Cohere)
```
### Vector Database Storage
Vector databases are stored in platform-appropriate locations:
| **Linux** | `~/.config/lc/embeddings/` |
| **macOS** | `~/Library/Application Support/lc/embeddings/` |
| **Windows** | `%APPDATA%\lc\embeddings\` |
Each database is a SQLite file containing:
- **vectors** table - Text content, embeddings, and metadata
- **model_info** table - Embedding model and provider information
- **Indexes** - Optimized for fast similarity search