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# What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> - Updates provider and distro codegen to handle the new format - Migrates provider and distro files to the new format ## Test Plan - Manual testing <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
91 lines
2.4 KiB
Text
91 lines
2.4 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 | sqlite | 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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