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# What does this PR do? - Migrates the remaining documentation sections to the new documentation format <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan - Partial migration <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
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28 lines
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---
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title: APIs
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description: Available REST APIs and planned capabilities in Llama Stack
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sidebar_label: APIs
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sidebar_position: 1
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---
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# APIs
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A Llama Stack API is described as a collection of REST endpoints. We currently support the following APIs:
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- **Inference**: run inference with a LLM
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- **Safety**: apply safety policies to the output at a Systems (not only model) level
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- **Agents**: run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
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- **DatasetIO**: interface with datasets and data loaders
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- **Scoring**: evaluate outputs of the system
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- **Eval**: generate outputs (via Inference or Agents) and perform scoring
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- **VectorIO**: perform operations on vector stores, such as adding documents, searching, and deleting documents
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- **Telemetry**: collect telemetry data from the system
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- **Post Training**: fine-tune a model
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- **Tool Runtime**: interact with various tools and protocols
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- **Responses**: generate responses from an LLM using this OpenAI compatible API.
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We are working on adding a few more APIs to complete the application lifecycle. These will include:
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- **Batch Inference**: run inference on a dataset of inputs
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- **Batch Agents**: run agents on a dataset of inputs
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- **Synthetic Data Generation**: generate synthetic data for model development
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- **Batches**: OpenAI-compatible batch management for inference
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