---
id: "chroma"
type: "component"
kind: "vector-database"
provider: "default"
category: "data"
docs: "https://docs.trychroma.com/docs/collections/configure"
url: "https://localhost:3000/catalogue/components/chroma"
---

# Chroma

Embedded open-source vector database using HNSW with squared L2 distance on a single node.

| | |
|---|---|
| Provider | [default](https://localhost:3000/catalogue?provider=default) |
| Kind | [Vector database](https://localhost:3000/catalogue/kinds/vector-database) |
| Category | Data |
| Official docs | [https://docs.trychroma.com/docs/collections/configure](https://docs.trychroma.com/docs/collections/configure) |

## Properties

| property | type | values | required | kind default | this component |
|---|---|---|---|---|---|
| `deployment` | enum | `single-node`, `distributed`, `cloud` | no | "single-node" | inherited |
| `dimensions` | integer | — | no | — | inherited |
| `distanceMetric` | enum | `cosine`, `euclidean`, `dot-product` | no | — | **"euclidean"** |
| `efConstruction` | integer | — | no | 100 | inherited |
| `hybridSearch` | boolean | — | no | false | **true** |
| `indexType` | enum | `flat`, `hnsw`, `ivf`, `disk-ann`, `lsh` | no | — | **"hnsw"** |
| `managed` | boolean | — | no | false | **false** |
| `maxNeighbors` | integer | — | no | 16 | inherited |
| `metadataFiltering` | boolean | — | no | false | **true** |
| `namespaces` | boolean | — | no | false | inherited |
| `quantization` | enum | `none`, `scalar`, `product`, `binary` | no | "none" | **"none"** |
| `replicas` | integer | — | no | — | inherited |
| `space` | enum | `l2`, `cosine`, `ip` | no | "l2" | inherited |

Bold values are the ones this component pins.

## Rules that apply · 0

No rule selects this kind or its category.

## Other products of the same kind · 23

- [Alibaba Cloud Milvus](https://localhost:3000/catalogue/components/alibaba-milvus.md) `alibaba-milvus` — alibabacloud
- [Amazon S3 Vectors](https://localhost:3000/catalogue/components/aws-s3-vectors.md) `aws-s3-vectors` — aws
- [Agent Memory](https://localhost:3000/catalogue/components/cloudflare-agent-memory.md) `cloudflare-agent-memory` — cloudflare
- [Cloudflare Vectorize](https://localhost:3000/catalogue/components/cloudflare-vectorize.md) `cloudflare-vectorize` — cloudflare
- [Deep Lake](https://localhost:3000/catalogue/components/deeplake.md) `deeplake` — default
- [Epsilla](https://localhost:3000/catalogue/components/epsilla.md) `epsilla` — default
- [KDB.AI](https://localhost:3000/catalogue/components/kdb-ai.md) `kdb-ai` — default
- [LanceDB](https://localhost:3000/catalogue/components/lancedb.md) `lancedb` — default
- [Marqo](https://localhost:3000/catalogue/components/marqo.md) `marqo` — default
- [Milvus](https://localhost:3000/catalogue/components/milvus.md) `milvus` — default
- [MyScale](https://localhost:3000/catalogue/components/myscale.md) `myscale` — default
- [pgvector](https://localhost:3000/catalogue/components/pgvector.md) `pgvector` — default
- [pgvectorscale](https://localhost:3000/catalogue/components/pgvectorscale.md) `pgvectorscale` — default
- [Pinecone](https://localhost:3000/catalogue/components/pinecone.md) `pinecone` — default
- [Qdrant](https://localhost:3000/catalogue/components/qdrant.md) `qdrant` — default
- [turbopuffer](https://localhost:3000/catalogue/components/turbopuffer.md) `turbopuffer` — default
- [Vald](https://localhost:3000/catalogue/components/vald.md) `vald` — default
- [Generic vector database](https://localhost:3000/catalogue/components/vector-database.md) `vector-database` — default
- [Vespa Cloud](https://localhost:3000/catalogue/components/vespa-cloud.md) `vespa-cloud` — default
- [Weaviate](https://localhost:3000/catalogue/components/weaviate.md) `weaviate` — default
- [Zilliz Cloud](https://localhost:3000/catalogue/components/zilliz-cloud.md) `zilliz-cloud` — default
- [DigitalOcean Vector Databases](https://localhost:3000/catalogue/components/digitalocean-vector-database.md) `digitalocean-vector-database` — digitalocean
- [Vertex AI Vector Search](https://localhost:3000/catalogue/components/google-vertex-ai-vector-search.md) `google-vertex-ai-vector-search` — gcp

## Use it in a design

```yaml
nodes:
  - { id: "chroma", component: "chroma" }
```

## Source (`component` YAML, verbatim)

```yaml
# yaml-language-server: $schema=../../schema/component.schema.json
# Chroma: Apache-2.0 embedded vector database. "Configuring Chroma collections": the default space is
# squared L2, single-node uses an HNSW index with ef_construction 100 and max_neighbors 16 while the
# distributed and Cloud deployments use SPANN. "Introduction": metadata filtering and full-text search.
schemaVersion: "1"
type: "component"
id: "chroma"
kind: "vector-database"
provider: "default"
docs: "https://docs.trychroma.com/docs/collections/configure"
defaults:
  indexType: "hnsw"
  distanceMetric: "euclidean"
  quantization: "none"
  metadataFiltering: true
  hybridSearch: true
  managed: false
properties:
  deployment:
    type: "enum"
    values: ["single-node", "distributed", "cloud"]
    default: "single-node"
  space:
    type: "enum"
    values: ["l2", "cosine", "ip"]
    default: "l2"
  efConstruction:
    type: "integer"
    default: 100
  maxNeighbors:
    type: "integer"
    default: 16
```

Human page: https://localhost:3000/catalogue/components/chroma