Embedding Providers

vecgrep defaults to local Ollama embeddings and supports managed cloud providers when you choose them.

Provider Matrix

Provider	Default Model	Dimensions	Notes
Ollama	nomic-embed-text	768	Local-first default
OpenAI	text-embedding-3-small	1536	Supports configurable dimensions for text-embedding-3-* models
Cohere	embed-v4.0	1536	Uses retrieval-specific document/query input types
Voyage AI	voyage-code-3	1024	Uses retrieval-specific document/query input types

Local Embedding Presets

vecgrep keeps nomic-embed-text as the built-in default and provides two explicit Ollama presets:

Preset	Model	Dimensions	Context	Best for
fast-local	nomic-embed-text	768	2,048	Lower memory, faster indexing, strong broad recall
quality-code	qwen3-embedding:0.6b	1,024	1,024	Better first-page code retrieval when extra latency and memory are acceptable

List or apply them without manually coordinating model, dimensions, context, options, and templates:

vecgrep config preset
vecgrep config preset quality-code
ollama pull qwen3-embedding:0.6b
vecgrep index --full

Use --global to apply a preset to global defaults. Applying a preset does not download a model or rebuild an index; the command prints both required next steps. It preserves provider endpoints, credentials, throttle/cache settings, and unrelated indexing/search configuration.

To compare both profiles on the bundled labeled Go/polyglot corpus without mutating project configuration or index data:

task bench:embeddings
# Or:
vecgrep benchmark embeddings --profiles fast-local,quality-code

The report includes Top-1, Recall@5, Recall@10, MRR, embedding throughput, and corpus/query latency. Results are machine- and corpus-specific; use --dataset path/to/dataset.json to supply your own labels.

Ollama

ollama pull nomic-embed-text
vecgrep config set embedding.provider ollama
vecgrep config set embedding.model nomic-embed-text
vecgrep config set embedding.dimensions 768

Qwen3 Embedding

Use an explicit Qwen size tag so Ollama does not resolve the bare model name to the much larger 8B variant:

ollama pull qwen3-embedding:0.6b
vecgrep config set embedding.provider ollama
vecgrep config set embedding.model qwen3-embedding:0.6b
vecgrep config set embedding.dimensions 1024
vecgrep index --full

qwen3-embedding:0.6b is a balanced local code-search option with native 1,024-dimensional vectors. Model and dimension changes always require a full rebuild.

Ollama request options

Ollama query, document, and warmup requests use /api/embed. Configure its context and options in vecgrep.yaml. For Qwen3 0.6B, start with a 1,024-token context for vecgrep's default 512-token chunks; the model's 32K maximum consumes substantially more unified memory and is unnecessary for the default chunk size.

embedding:
  ollama_context: 1024
  ollama_options: {}

Optional query and document templates use as the input placeholder:

embedding:
  query_template: "search_query: {{text}}"
  document_template: "search_document: {{text}}"

Templates are part of the persisted embedding profile. Changing either template requires vecgrep index --full.

If your Ollama server uses a non-default URL:

vecgrep config set embedding.ollama_url http://localhost:11434

OpenAI

export OPENAI_API_KEY=sk-your-key
vecgrep config set embedding.provider openai
vecgrep config set embedding.model text-embedding-3-small
vecgrep config set embedding.dimensions 1536
vecgrep index --full

For custom or compatible endpoints:

vecgrep config set embedding.openai_base_url https://example.test/v1

Cohere

export COHERE_API_KEY=your-key
vecgrep config set embedding.provider cohere
vecgrep config set embedding.model embed-v4.0
vecgrep config set embedding.dimensions 1536
vecgrep index --full

Cohere indexing uses search_document; search uses search_query.

Voyage AI

export VOYAGE_API_KEY=your-key
vecgrep config set embedding.provider voyage
vecgrep config set embedding.model voyage-code-3
vecgrep config set embedding.dimensions 1024
vecgrep index --full

Voyage indexing uses document; search uses query.

Re-indexing Rules

Changing provider, model, dimensions, distance metric, or chunking profile changes vector meaning. Run a full rebuild after changing any of those settings:

vecgrep index --full

You can also clear the index:

vecgrep reset --force