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102 lines
2.5 KiB
Markdown
102 lines
2.5 KiB
Markdown
# API 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.md
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```
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```{include} openai.md
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:start-after: ## OpenAI API Compatibility
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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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## 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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## 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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## 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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## 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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## Tool Runtime
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Is associated with the ToolGroup resources.
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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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## Files Providers
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Is associated with the **files** APIs
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```{toctree}
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:maxdepth: 1
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files/index
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```
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