Embedders

Embedders convert text (code chunks) into vector representations that enable semantic search.

Available Embedders

Provider	Type	Pros	Cons
Ollama	Local	Privacy, free, no internet	Requires local resources
LM Studio	Local	Privacy, OpenAI-compatible API, GUI	Requires local resources
OpenAI	Cloud	High quality, fast	Costs money, sends code to cloud

Ollama (Local)

Setup

1. Install Ollama:

# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

2. Start the server:

ollama serve

3. Pull an embedding model:

ollama pull nomic-embed-text

Configuration

embedder:
  provider: ollama
  model: nomic-embed-text
  endpoint: http://localhost:11434
  dimensions: 768

Available Models

Model	Dimensions	Speed	Quality
nomic-embed-text	768	Fast	Good
mxbai-embed-large	1024	Medium	Better
all-minilm	384	Very Fast	Basic
qwen3-embedding:0.6b	1024	Medium	Excellent
embeddinggemma	768	Very Fast	Better

Troubleshooting

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Test embedding
curl http://localhost:11434/api/embeddings -d '{
  "model": "nomic-embed-text",
  "prompt": "Hello world"
}'

LM Studio (Local)

LM Studio provides an OpenAI-compatible API for running embedding models locally with a user-friendly GUI.

Setup

1. Download and install LM Studio

2. Start LM Studio and load an embedding model (e.g., nomic-embed-text)

3. Enable the local server (default: http://127.0.0.1:1234)

Configuration

embedder:
  provider: lmstudio
  model: text-embedding-nomic-embed-text-v1.5
  endpoint: http://127.0.0.1:1234

Available Models

Any embedding model supported by LM Studio, including:

Model	Dimensions	Notes
nomic-embed-text-v1.5	768	Good general purpose
bge-small-en-v1.5	384	Fast, smaller
bge-large-en-v1.5	1024	Higher quality

Troubleshooting

# Check if LM Studio server is running
curl http://127.0.0.1:1234/v1/models

# Test embedding
curl http://127.0.0.1:1234/v1/embeddings -d '{
  "model": "text-embedding-nomic-embed-text-v1.5",
  "input": ["Hello world"]
}'

OpenAI (Cloud)

Setup

1. Get an API key from OpenAI Platform

2. Set the environment variable:

export OPENAI_API_KEY=sk-...

Configuration

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}
  dimensions: 1536

Azure OpenAI / Microsoft Foundry

For Azure OpenAI or other OpenAI-compatible providers, use a custom endpoint:

embedder:
  provider: openai
  model: text-embedding-ada-002
  endpoint: https://YOUR-RESOURCE.openai.azure.com/v1
  api_key: ${AZURE_OPENAI_API_KEY}
  dimensions: 1536

Available Models

Model	Dimensions	Price (per 1M tokens)
text-embedding-3-small	1536	$0.02
text-embedding-3-large	3072	$0.13

Cost Estimation

For a typical codebase:

• 10,000 lines of code ≈ 50,000 tokens
• Initial index: ~$0.001 with text-embedding-3-small
• Ongoing updates: negligible

Adding a New Embedder

To add a new embedding provider:

1. Implement the Embedder interface in embedder/:

type Embedder interface {
    Embed(ctx context.Context, text string) ([]float32, error)
    EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)
    Dimensions() int
}

2. Add configuration in config/config.go

3. Wire it up in the CLI commands

See Contributing for more details.