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7605631df7
Remove invalid default parameters from KVStoreConfig and SqlStoreConfig Annotated types which were causing UnsupportedFieldAttributeWarning and producing incorrect string values instead of proper config objects. Add proper default_factory to all KVStoreConfig and SqlStoreConfig fields across core datatypes and provider configs, ensuring they instantiate SqliteKVStoreConfig or SqliteSqlStoreConfig objects with correct defaults. This improves usability by allowing configs to be instantiated without explicitly providing storage configuration while maintaining type safety and discriminated union functionality. Signed-off-by: Charlie Doern <cdoern@redhat.com>
91 lines
2.5 KiB
Text
91 lines
2.5 KiB
Text
---
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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: Chromadb
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title: inline::chromadb
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---
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# inline::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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| `db_path` | `<class 'str'>` | No | | |
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| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | namespace=None type='sqlite' db_path='~/.llama/runtime/kvstore.db' | Config for KV store backend |
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## Sample Configuration
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```yaml
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db_path: ${env.CHROMADB_PATH}
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db
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```
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