phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-12-28 08:51:59 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'main' into feat/litellm_sambanova_usage

Browse source
This commit is contained in:
Jorge Piedrahita Ortiz 2025-03-19 16:51:59 -05:00 • committed by GitHub
commit 02a4f9ac59
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
69 changed files with 1128 additions and 445 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/source/index.md
View file

@ -15,8 +15,6 @@ Llama Stack defines and standardizes the core building blocks needed to bring ge
- **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android
- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack
We focus on making it easy to build production applications with the Llama model family - from the latest Llama 3.3 to specialized models like Llama Guard for safety.
```{image} ../_static/llama-stack.png
:alt: Llama Stack
:width: 400px

3
docs/source/introduction/index.md
View file

@ -48,7 +48,7 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
**Robust Ecosystem**
- Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
- Ecosystem offers tailored infrastructure, software, and services for deploying Llama models.
- Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models.
### Our Philosophy
@ -57,7 +57,6 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
- **Composability**: Every component is independent but works together seamlessly
- **Production Ready**: Built for real-world applications, not just demos
- **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
- **Llama First**: Explicit focus on Meta's Llama models and partnering ecosystem
With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.

21
docs/source/providers/vector_io/qdrant.md
View file

@ -3,21 +3,36 @@ orphan: true
---
# Qdrant
[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
allows you to store and query vectors directly in memory.
That means you'll get fast and efficient vector retrieval.
> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
>
> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
## Features
- Easy to use
- Lightweight and easy to use
- Fully integrated with Llama Stack
- Apache 2.0 license terms
- Store embeddings and their metadata
- Supports search by
[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
## Usage
To use Qdrant in your Llama Stack project, follow these steps:
1. Install the necessary dependencies.
2. Configure your Llama Stack project to use Faiss.
2. Configure your Llama Stack project to use Qdrant.
3. Start storing and querying vectors.
## Installation