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# What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> - add model_type in example - change "Memory" to "VectorIO" as column name - update index.md and README.md <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> run pre-commit to catch changes. --------- Signed-off-by: Wen Zhou <wenzhou@redhat.com> Co-authored-by: Sébastien Han <seb@redhat.com>
116 lines
2.7 KiB
Markdown
116 lines
2.7 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., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.),
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- Vector databases (e.g., FAISS, SQLite-Vec, ChromaDB, Weaviate, Qdrant, Milvus, PGVector, etc.),
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- Safety providers (e.g., Meta's Llama Guard, Prompt Guard, Code Scanner, AWS Bedrock Guardrails, etc.),
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- Tool Runtime providers (e.g., RAG Runtime, Brave Search, 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.
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```{toctree}
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:maxdepth: 1
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external
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```
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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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```{toctree}
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:maxdepth: 1
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agents/index
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```
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## DatasetIO
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Interfaces with datasets and data loaders.
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```{toctree}
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:maxdepth: 1
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datasetio/index
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```
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## Eval
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Generates outputs (via Inference or Agents) and perform scoring.
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```{toctree}
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:maxdepth: 1
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eval/index
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```
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## Inference
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Runs inference with an LLM.
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```{toctree}
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:maxdepth: 1
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inference/index
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```
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## Post Training
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Fine-tunes a model.
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```{toctree}
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:maxdepth: 1
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post_training/index
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```
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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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```{toctree}
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:maxdepth: 1
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safety/index
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```
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## Scoring
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Evaluates the outputs of the system.
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```{toctree}
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:maxdepth: 1
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scoring/index
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```
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## Telemetry
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Collects telemetry data from the system.
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```{toctree}
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:maxdepth: 1
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telemetry/index
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```
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## Tool Runtime
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Is associated with the ToolGroup resouces.
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```{toctree}
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:maxdepth: 1
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tool_runtime/index
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```
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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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```{toctree}
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:maxdepth: 1
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vector_io/index
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```
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