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# What does this PR do? See https://github.com/meta-llama/llama-stack/pull/1171 which is the original PR. Author: @zc277584121 feat: add [Milvus](https://milvus.io/) vectorDB note: I use the MilvusClient to implement it instead of AsyncMilvusClient, because when I tested AsyncMilvusClient, it would raise issues about evenloop, which I think AsyncMilvusClient SDK is not robust enough to be compatible with llama_stack framework. ## Test Plan have passed the unit test and ene2end test Here is my end2end test logs, including the client code, client log, server logs from inline and remote settings [test_end2end_logs.zip](https://github.com/user-attachments/files/18964391/test_end2end_logs.zip) --------- Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Cheney Zhang <chen.zhang@zilliz.com>
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Providers Overview
The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, etc.),
- Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
Providers come in two flavors:
- Remote: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
- Inline: the provider is fully specified and implemented within the Llama Stack codebase. It may be a simple wrapper around an existing library, or a full fledged implementation within Llama Stack.
Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally.
Agents
Run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
DatasetIO
Interfaces with datasets and data loaders.
Eval
Generates outputs (via Inference or Agents) and perform scoring.
Inference
Runs inference with an LLM.
Post Training
Fine-tunes a model.
Safety
Applies safety policies to the output at a Systems (not only model) level.
Scoring
Evaluates the outputs of the system.
Telemetry
Collects telemetry data from the system.
Tool Runtime
Is associated with the ToolGroup resouces.
Vector IO
Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents. Vector IO plays a crucial role in Retreival Augmented Generation (RAG), where the vector io and database are used to store and retrieve documents for retrieval.
Vector IO Providers
The following providers (i.e., databases) are available for Vector IO:
:maxdepth: 1
vector_io/faiss
vector_io/sqlite-vec
vector_io/chromadb
vector_io/pgvector
vector_io/qdrant
vector_io/milvus
vector_io/weaviate