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5fe6098350
# 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>
110 lines
3.6 KiB
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110 lines
3.6 KiB
Text
---
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description: |
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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That means you'll get fast and efficient vector retrieval.
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> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
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> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
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>
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> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
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## Features
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- Apache 2.0 license terms
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- Store embeddings and their metadata
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- Supports search by
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[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
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and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
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- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
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- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
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- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
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## Usage
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To use Qdrant 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 Qdrant.
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3. Start storing and querying vectors.
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## Installation
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You can install Qdrant using docker:
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```bash
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docker pull qdrant/qdrant
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```
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## Documentation
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See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
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sidebar_label: Qdrant
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title: inline::qdrant
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---
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# inline::qdrant
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## Description
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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That means you'll get fast and efficient vector retrieval.
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> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
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> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
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>
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> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
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## Features
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- Apache 2.0 license terms
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- Store embeddings and their metadata
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- Supports search by
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[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
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and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
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- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
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- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
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- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
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## Usage
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To use Qdrant 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 Qdrant.
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3. Start storing and querying vectors.
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## Installation
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You can install Qdrant using docker:
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```bash
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docker pull qdrant/qdrant
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```
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## Documentation
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See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `path` | `str` | No | | |
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| `persistence` | `KVStoreReference` | No | | |
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## Sample Configuration
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```yaml
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path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
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persistence:
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namespace: vector_io::qdrant
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backend: kv_default
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```
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