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# What does this PR do? This PR improves type hint cleanup in auto-generated provider documentation by adding regex logic. **Issues Fixed:** - Type hints with missing closing brackets (e.g., `list[str` instead of `list[str]`) - Types showing as `<class 'bool'>`, `<class 'str'>` instead of `bool`, `str` - The multi-line YAML frontmatter in index documentation files wasn't ideal, so we now add the proper `|` character. **Changes:** 1. Replaced string replacement (`.replace`) with regex-based type cleaning to preserve the trailing bracket in case of `list` and `dict`. 2. Adds the `|` character for multi-line YAML descriptions. 3. I have regenerated the docs. However, let me know if that's not needed. ## Test Plan 1. Ran uv run python scripts/provider_codegen.py - successfully regenerated all docs 2. We can see that the updated docs handle correctly type hint cleanup and multi-line yaml descriptions have now the `|` character. ### Note to the reviewer(s) This is my first contribution to your lovely repo! Initially I was going thourgh docs (wanted to use `remote::gemini` as provider) and realized the issue. I've read the [CONTRIBUTING.md](https://github.com/llamastack/llama-stack/blob/main/CONTRIBUTING.md) and decided to open the PR. Let me know if there's anything I did wrong and I'll update my PR! --------- Signed-off-by: thepetk <thepetk@gmail.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
88 lines
2.5 KiB
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88 lines
2.5 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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| `persistence` | `KVStoreReference \| None` | 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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persistence:
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namespace: vector_io::weaviate
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backend: kv_default
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```
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