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https://github.com/meta-llama/llama-stack.git
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2c43285e22
**This PR changes configurations in a backward incompatible way.**
Run configs today repeat full SQLite/Postgres snippets everywhere a
store is needed, which means duplicated credentials, extra connection
pools, and lots of drift between files. This PR introduces named storage
backends so the stack and providers can share a single catalog and
reference those backends by name.
## Key Changes
- Add `storage.backends` to `StackRunConfig`, register each KV/SQL
backend once at startup, and validate that references point to the right
family.
- Move server stores under `storage.stores` with lightweight references
(backend + namespace/table) instead of full configs.
- Update every provider/config/doc to use the new reference style;
docs/codegen now surface the simplified YAML.
## Migration
Before:
```yaml
metadata_store:
type: sqlite
db_path: ~/.llama/distributions/foo/registry.db
inference_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
conversations_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
```
After:
```yaml
storage:
backends:
kv_default:
type: kv_sqlite
db_path: ~/.llama/distributions/foo/kvstore.db
sql_default:
type: sql_postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
stores:
metadata:
backend: kv_default
namespace: registry
inference:
backend: sql_default
table_name: inference_store
max_write_queue_size: 10000
num_writers: 4
conversations:
backend: sql_default
table_name: openai_conversations
```
Provider configs follow the same pattern—for example, a Chroma vector
adapter switches from:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
kvstore:
type: sqlite
db_path: ~/.llama/distributions/foo/chroma.db
```
to:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
persistence:
backend: kv_default
namespace: vector_io::chroma_remote
```
Once the backends are declared, everything else just points at them, so
rotating credentials or swapping to Postgres happens in one place and
the stack reuses a single connection pool.
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90 lines
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---
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description: |
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[Chroma](https://www.trychroma.com/) is an inline and remote vector
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database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
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That means you're not limited to storing vectors in memory or in a separate service.
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## Features
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Chroma supports:
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- Store embeddings and their metadata
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- Vector search
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- Full-text search
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- Document storage
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- Metadata filtering
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- Multi-modal retrieval
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## Usage
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To use Chrome in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use chroma.
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3. Start storing and querying vectors.
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## Installation
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You can install chroma using pip:
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```bash
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pip install chromadb
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```
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## Documentation
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See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
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sidebar_label: Remote - Chromadb
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title: remote::chromadb
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---
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# remote::chromadb
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## Description
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[Chroma](https://www.trychroma.com/) is an inline and remote vector
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database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
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That means you're not limited to storing vectors in memory or in a separate service.
|
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## Features
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Chroma supports:
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- Store embeddings and their metadata
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- Vector search
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- Full-text search
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- Document storage
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- Metadata filtering
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- Multi-modal retrieval
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## Usage
|
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To use Chrome in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use chroma.
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3. Start storing and querying vectors.
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## Installation
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You can install chroma using pip:
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```bash
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pip install chromadb
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```
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## Documentation
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See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `url` | `str \| None` | No | | |
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| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No | | Config for KV store backend |
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## Sample Configuration
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```yaml
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url: ${env.CHROMADB_URL}
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persistence:
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namespace: vector_io::chroma_remote
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backend: kv_default
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```
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