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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.* -->
88 lines
2.8 KiB
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88 lines
2.8 KiB
Text
---
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description: |
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[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
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It allows you to store and query vectors directly within a Weaviate 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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Weaviate 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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- Hybrid 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 Weaviate 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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To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart).
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## Documentation
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See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
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sidebar_label: Remote - Weaviate
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title: remote::weaviate
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---
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# remote::weaviate
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## Description
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[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
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It allows you to store and query vectors directly within a Weaviate 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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Weaviate 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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- Hybrid 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 Weaviate 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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To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart).
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## Documentation
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See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `weaviate_api_key` | `str \| None` | No | | The API key for the Weaviate instance |
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| `weaviate_cluster_url` | `str \| None` | No | localhost:8080 | The URL of the Weaviate cluster |
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| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) |
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## Sample Configuration
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```yaml
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weaviate_api_key: null
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weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db
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```
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