forked from phoenix-oss/llama-stack-mirror
389767010b
# What does this PR do?
Providers that live outside of the llama-stack codebase are now
supported.
A new property `external_providers_dir` has been added to the main
config and can be configured as follow:
```
external_providers_dir: /etc/llama-stack/providers.d/
```
Where the expected structure is:
```
providers.d/
inference/
custom_ollama.yaml
vllm.yaml
vector_io/
qdrant.yaml
```
Where `custom_ollama.yaml` is:
```
adapter:
adapter_type: custom_ollama
pip_packages: ["ollama", "aiohttp"]
config_class: llama_stack_ollama_provider.config.OllamaImplConfig
module: llama_stack_ollama_provider
api_dependencies: []
optional_api_dependencies: []
```
Obviously the package must be installed on the system, here is the
`llama_stack_ollama_provider` example:
```
$ uv pip show llama-stack-ollama-provider
Using Python 3.10.16 environment at: /Users/leseb/Documents/AI/llama-stack/.venv
Name: llama-stack-ollama-provider
Version: 0.1.0
Location: /Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages
Editable project location: /private/var/folders/mq/rnm5w_7s2d3fxmtkx02knvhm0000gn/T/tmp.ZBHU5Ezxg4/ollama/llama-stack-ollama-provider
Requires:
Required-by:
```
Closes: https://github.com/meta-llama/llama-stack/issues/658
Signed-off-by: Sébastien Han <seb@redhat.com>
65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
# Providers Overview
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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:
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- LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
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- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, etc.),
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- Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
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Providers come in two flavors:
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- **Remote**: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
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- **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.
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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.
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## External Providers
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Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently. See the [External Providers Guide](external) for details.
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## Agents
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Run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
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## DatasetIO
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Interfaces with datasets and data loaders.
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## Eval
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Generates outputs (via Inference or Agents) and perform scoring.
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## Inference
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Runs inference with an LLM.
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## Post Training
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Fine-tunes a model.
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## Safety
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Applies safety policies to the output at a Systems (not only model) level.
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## Scoring
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Evaluates the outputs of the system.
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## Telemetry
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Collects telemetry data from the system.
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## Tool Runtime
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Is associated with the ToolGroup resouces.
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## Vector IO
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Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents.
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Vector IO plays a crucial role in [Retreival Augmented Generation (RAG)](../..//building_applications/rag), where the vector
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io and database are used to store and retrieve documents for retrieval.
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#### Vector IO Providers
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The following providers (i.e., databases) are available for Vector IO:
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```{toctree}
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:maxdepth: 1
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external
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vector_io/faiss
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vector_io/sqlite-vec
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vector_io/chromadb
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vector_io/pgvector
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vector_io/qdrant
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vector_io/milvus
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vector_io/weaviate
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```
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