mirror of
https://github.com/meta-llama/llama-stack.git
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c1f7ba3aed
Splits the meta-reference safety implementation into three distinct providers: - inline::llama-guard - inline::prompt-guard - inline::code-scanner Note that this PR is a backward incompatible change to the llama stack server. I have added deprecation_error field to ProviderSpec -- the server reads it and immediately barfs. This is used to direct the user with a specific message on what action to perform. An automagical "config upgrade" is a bit too much work to implement right now :/ (Note that we will be gradually prefixing all inline providers with inline:: -- I am only doing this for this set of new providers because otherwise existing configuration files will break even more badly.)
323 lines
25 KiB
Markdown
323 lines
25 KiB
Markdown
# Developer Guide: Assemble a Llama Stack Distribution
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This guide will walk you through the steps to get started with building a Llama Stack distributiom from scratch with your choice of API providers. Please see the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) if you just want the basic steps to start a Llama Stack distribution.
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## Step 1. Build
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### Llama Stack Build Options
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```
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llama stack build -h
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```
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We will start build our distribution (in the form of a Conda environment, or Docker image). In this step, we will specify:
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- `name`: the name for our distribution (e.g. `my-stack`)
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- `image_type`: our build image type (`conda | docker`)
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- `distribution_spec`: our distribution specs for specifying API providers
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- `description`: a short description of the configurations for the distribution
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- `providers`: specifies the underlying implementation for serving each API endpoint
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- `image_type`: `conda` | `docker` to specify whether to build the distribution in the form of Docker image or Conda environment.
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After this step is complete, a file named `<name>-build.yaml` and template file `<name>-run.yaml` will be generated and saved at the output file path specified at the end of the command.
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::::{tab-set}
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:::{tab-item} Building from Scratch
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- For a new user, we could start off with running `llama stack build` which will allow you to a interactively enter wizard where you will be prompted to enter build configurations.
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```
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llama stack build
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> Enter a name for your Llama Stack (e.g. my-local-stack): my-stack
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> Enter the image type you want your Llama Stack to be built as (docker or conda): conda
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Llama Stack is composed of several APIs working together. Let's select
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the provider types (implementations) you want to use for these APIs.
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Tip: use <TAB> to see options for the providers.
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> Enter provider for API inference: meta-reference
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> Enter provider for API safety: inline::llama-guard
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> Enter provider for API agents: meta-reference
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> Enter provider for API memory: inline::faiss
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> Enter provider for API datasetio: meta-reference
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> Enter provider for API scoring: meta-reference
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> Enter provider for API eval: meta-reference
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> Enter provider for API telemetry: meta-reference
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> (Optional) Enter a short description for your Llama Stack:
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You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml`
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```
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:::
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:::{tab-item} Building from a template
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- To build from alternative API providers, we provide distribution templates for users to get started building a distribution backed by different providers.
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The following command will allow you to see the available templates and their corresponding providers.
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```
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llama stack build --list-templates
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```
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```
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| Template Name | Providers | Description |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| hf-serverless | { | Like local, but use Hugging Face Inference API (serverless) for running LLM |
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| | "inference": "remote::hf::serverless", | inference. |
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| | "memory": "meta-reference", | See https://hf.co/docs/api-inference. |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| together | { | Use Together.ai for running LLM inference |
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| | "inference": "remote::together", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::weaviate" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| fireworks | { | Use Fireworks.ai for running LLM inference |
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| | "inference": "remote::fireworks", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::weaviate", | |
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| | "remote::chromadb", | |
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| | "remote::pgvector" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| databricks | { | Use Databricks for running LLM inference |
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| | "inference": "remote::databricks", | |
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| | "memory": "meta-reference", | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| vllm | { | Like local, but use vLLM for running LLM inference |
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| | "inference": "vllm", | |
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| | "memory": "meta-reference", | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| tgi | { | Use TGI for running LLM inference |
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| | "inference": "remote::tgi", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::chromadb", | |
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| | "remote::pgvector" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| bedrock | { | Use Amazon Bedrock APIs. |
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| | "inference": "remote::bedrock", | |
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| | "memory": "meta-reference", | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| meta-reference-gpu | { | Use code from `llama_stack` itself to serve all llama stack APIs |
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| | "inference": "meta-reference", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::chromadb", | |
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| | "remote::pgvector" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| meta-reference-quantized-gpu | { | Use code from `llama_stack` itself to serve all llama stack APIs |
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| | "inference": "meta-reference-quantized", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::chromadb", | |
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| | "remote::pgvector" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| ollama | { | Use ollama for running LLM inference |
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| | "inference": "remote::ollama", | |
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| | "memory": [ | |
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| | "meta-reference", | |
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| | "remote::chromadb", | |
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| | "remote::pgvector" | |
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| | ], | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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| hf-endpoint | { | Like local, but use Hugging Face Inference Endpoints for running LLM inference. |
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| | "inference": "remote::hf::endpoint", | See https://hf.co/docs/api-endpoints. |
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| | "memory": "meta-reference", | |
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| | "safety": "meta-reference", | |
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| | "agents": "meta-reference", | |
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| | "telemetry": "meta-reference" | |
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| | } | |
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+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
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```
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You may then pick a template to build your distribution with providers fitted to your liking.
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For example, to build a distribution with TGI as the inference provider, you can run:
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```
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llama stack build --template tgi
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```
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```
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$ llama stack build --template tgi
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...
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You can now edit ~/.llama/distributions/llamastack-tgi/tgi-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-tgi/tgi-run.yaml`
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```
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:::
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:::{tab-item} Building from a pre-existing build config file
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- In addition to templates, you may customize the build to your liking through editing config files and build from config files with the following command.
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- The config file will be of contents like the ones in `llama_stack/templates/*build.yaml`.
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```
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$ cat llama_stack/templates/ollama/build.yaml
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name: ollama
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distribution_spec:
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description: Like local, but use ollama for running LLM inference
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providers:
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inference: remote::ollama
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memory: inline::faiss
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safety: inline::llama-guard
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agents: meta-reference
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telemetry: meta-reference
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image_type: conda
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```
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```
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llama stack build --config llama_stack/templates/ollama/build.yaml
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```
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:::
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:::{tab-item} Building Docker
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> [!TIP]
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> Podman is supported as an alternative to Docker. Set `DOCKER_BINARY` to `podman` in your environment to use Podman.
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To build a docker image, you may start off from a template and use the `--image-type docker` flag to specify `docker` as the build image type.
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```
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llama stack build --template ollama --image-type docker
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```
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```
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$ llama stack build --template ollama --image-type docker
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...
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Dockerfile created successfully in /tmp/tmp.viA3a3Rdsg/DockerfileFROM python:3.10-slim
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...
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You can now edit ~/meta-llama/llama-stack/tmp/configs/ollama-run.yaml and run `llama stack run ~/meta-llama/llama-stack/tmp/configs/ollama-run.yaml`
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```
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After this step is successful, you should be able to find the built docker image and test it with `llama stack run <path/to/run.yaml>`.
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:::
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::::
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## Step 2. Run
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Now, let's start the Llama Stack Distribution Server. You will need the YAML configuration file which was written out at the end by the `llama stack build` step.
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```
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llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml
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```
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```
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$ llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml
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Loaded model...
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Serving API datasets
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GET /datasets/get
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GET /datasets/list
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POST /datasets/register
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Serving API inspect
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GET /health
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GET /providers/list
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GET /routes/list
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Serving API inference
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POST /inference/chat_completion
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POST /inference/completion
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POST /inference/embeddings
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Serving API scoring_functions
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GET /scoring_functions/get
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GET /scoring_functions/list
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POST /scoring_functions/register
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Serving API scoring
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POST /scoring/score
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POST /scoring/score_batch
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Serving API memory_banks
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GET /memory_banks/get
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GET /memory_banks/list
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POST /memory_banks/register
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Serving API memory
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POST /memory/insert
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POST /memory/query
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Serving API safety
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POST /safety/run_shield
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Serving API eval
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POST /eval/evaluate
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POST /eval/evaluate_batch
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POST /eval/job/cancel
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GET /eval/job/result
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GET /eval/job/status
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Serving API shields
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GET /shields/get
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GET /shields/list
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POST /shields/register
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Serving API datasetio
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GET /datasetio/get_rows_paginated
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Serving API telemetry
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GET /telemetry/get_trace
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POST /telemetry/log_event
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Serving API models
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GET /models/get
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GET /models/list
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POST /models/register
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Serving API agents
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POST /agents/create
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POST /agents/session/create
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POST /agents/turn/create
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POST /agents/delete
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POST /agents/session/delete
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POST /agents/session/get
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POST /agents/step/get
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POST /agents/turn/get
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Listening on ['::', '0.0.0.0']:5000
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INFO: Started server process [2935911]
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INFO: Waiting for application startup.
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INFO: Application startup complete.
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INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit)
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INFO: 2401:db00:35c:2d2b:face:0:c9:0:54678 - "GET /models/list HTTP/1.1" 200 OK
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```
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> [!IMPORTANT]
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> The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines.
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> [!TIP]
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> You might need to use the flag `--disable-ipv6` to Disable IPv6 support
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