feat: consolidate most distros into "starter" (#2516)

# What does this PR do?

* Removes a bunch of distros
* Removed distros were added into the "starter" distribution
* Doc for "starter" has been added
* Partially reverts https://github.com/meta-llama/llama-stack/pull/2482
  since inference providers are disabled by default and can be turned on
  manually via env variable.
* Disables safety in starter distro

Closes: https://github.com/meta-llama/llama-stack/issues/2502.

~Needs: https://github.com/meta-llama/llama-stack/pull/2482 for Ollama
to work properly in the CI.~

TODO:

- [ ] We can only update `install.sh` when we get a new release.
- [x] Update providers documentation
- [ ] Update notebooks to reference starter instead of ollama

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/templates/remote-vllm/__init__.py

@@ -1,7 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from .vllm import get_distribution_template  # noqa: F401

llama_stack/templates/remote-vllm/build.yaml

@@ -1,36 +0,0 @@
-version: 2
-distribution_spec:
-  description: Use (an external) vLLM server for running LLM inference
-  providers:
-    inference:
-    - remote::vllm
-    - inline::sentence-transformers
-    vector_io:
-    - inline::faiss
-    - remote::chromadb
-    - remote::pgvector
-    safety:
-    - inline::llama-guard
-    agents:
-    - inline::meta-reference
-    eval:
-    - inline::meta-reference
-    datasetio:
-    - remote::huggingface
-    - inline::localfs
-    scoring:
-    - inline::basic
-    - inline::llm-as-judge
-    - inline::braintrust
-    telemetry:
-    - inline::meta-reference
-    tool_runtime:
-    - remote::brave-search
-    - remote::tavily-search
-    - inline::rag-runtime
-    - remote::model-context-protocol
-    - remote::wolfram-alpha
-image_type: conda
-additional_pip_packages:
-- aiosqlite
-- sqlalchemy[asyncio]

llama_stack/templates/remote-vllm/doc_template.md

@@ -1,284 +0,0 @@
----
-orphan: true
----
-# Remote vLLM Distribution
-```{toctree}
-:maxdepth: 2
-:hidden:
-
-self
-```
-
-The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
-
-{{ providers_table }}
-
-You can use this distribution if you want to run an independent vLLM server for inference.
-
-{% if run_config_env_vars %}
-### Environment Variables
-
-The following environment variables can be configured:
-
-{% for var, (default_value, description) in run_config_env_vars.items() %}
-- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
-{% endfor %}
-{% endif %}
-
-
-## Setting up vLLM server
-
-In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
-server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
-[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
-that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
-
-### Setting up vLLM server on AMD GPU
-
-AMD provides two main vLLM container options:
-- rocm/vllm: Production-ready container
-- rocm/vllm-dev: Development container with the latest vLLM features
-
-Please check the [Blog about ROCm vLLM Usage](https://rocm.blogs.amd.com/software-tools-optimization/vllm-container/README.html) to get more details.
-
-Here is a sample script to start a ROCm vLLM server locally via Docker:
-
-```bash
-export INFERENCE_PORT=8000
-export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export CUDA_VISIBLE_DEVICES=0
-export VLLM_DIMG="rocm/vllm-dev:main"
-
-docker run \
-    --pull always \
-    --ipc=host \
-    --privileged \
-    --shm-size 16g \
-    --device=/dev/kfd \
-    --device=/dev/dri \
-    --group-add video \
-    --cap-add=SYS_PTRACE \
-    --cap-add=CAP_SYS_ADMIN \
-    --security-opt seccomp=unconfined \
-    --security-opt apparmor=unconfined \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    --env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-    -p $INFERENCE_PORT:$INFERENCE_PORT \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    $VLLM_DIMG \
-    python -m vllm.entrypoints.openai.api_server \
-    --model $INFERENCE_MODEL \
-    --port $INFERENCE_PORT
-```
-
-Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
-
-If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
-
-```bash
-export SAFETY_PORT=8081
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-export CUDA_VISIBLE_DEVICES=1
-export VLLM_DIMG="rocm/vllm-dev:main"
-
-docker run \
-    --pull always \
-    --ipc=host \
-    --privileged \
-    --shm-size 16g \
-    --device=/dev/kfd \
-    --device=/dev/dri \
-    --group-add video \
-    --cap-add=SYS_PTRACE \
-    --cap-add=CAP_SYS_ADMIN \
-    --security-opt seccomp=unconfined \
-    --security-opt apparmor=unconfined \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    --env "HIP_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" \
-    -p $SAFETY_PORT:$SAFETY_PORT \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    $VLLM_DIMG \
-    python -m vllm.entrypoints.openai.api_server \
-    --model $SAFETY_MODEL \
-    --port $SAFETY_PORT
-```
-
-### Setting up vLLM server on NVIDIA GPU
-
-Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:
-
-```bash
-export INFERENCE_PORT=8000
-export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export CUDA_VISIBLE_DEVICES=0
-
-docker run \
-    --pull always \
-    --runtime nvidia \
-    --gpus $CUDA_VISIBLE_DEVICES \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    -p $INFERENCE_PORT:$INFERENCE_PORT \
-    --ipc=host \
-    vllm/vllm-openai:latest \
-    --gpu-memory-utilization 0.7 \
-    --model $INFERENCE_MODEL \
-    --port $INFERENCE_PORT
-```
-
-Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
-
-If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
-
-```bash
-export SAFETY_PORT=8081
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-export CUDA_VISIBLE_DEVICES=1
-
-docker run \
-    --pull always \
-    --runtime nvidia \
-    --gpus $CUDA_VISIBLE_DEVICES \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    -p $SAFETY_PORT:$SAFETY_PORT \
-    --ipc=host \
-    vllm/vllm-openai:latest \
-    --gpu-memory-utilization 0.7 \
-    --model $SAFETY_MODEL \
-    --port $SAFETY_PORT
-```
-
-### Setting up vLLM server on Intel GPU
-
-Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend:
-- [intel/vllm](https://hub.docker.com/r/intel/vllm)
-
-Here is a sample script to start a vLLM server locally via Docker using Intel provided container:
-
-```bash
-export INFERENCE_PORT=8000
-export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct
-export ZE_AFFINITY_MASK=0
-
-docker run \
-    --pull always \
-    --device /dev/dri \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-    -p $INFERENCE_PORT:$INFERENCE_PORT \
-    --ipc=host \
-    intel/vllm:xpu \
-    --gpu-memory-utilization 0.7 \
-    --model $INFERENCE_MODEL \
-    --port $INFERENCE_PORT
-```
-
-If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
-
-```bash
-export SAFETY_PORT=8081
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-export ZE_AFFINITY_MASK=1
-
-docker run \
-    --pull always \
-    --device /dev/dri \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-    --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
-    -p $SAFETY_PORT:$SAFETY_PORT \
-    --ipc=host \
-    intel/vllm:xpu \
-    --gpu-memory-utilization 0.7 \
-    --model $SAFETY_MODEL \
-    --port $SAFETY_PORT
-```
-
-## Running Llama Stack
-
-Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
-
-### Via Docker
-
-This method allows you to get started quickly without having to build the distribution code.
-
-```bash
-export INFERENCE_PORT=8000
-export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export LLAMA_STACK_PORT=8321
-
-# You need a local checkout of llama-stack to run this, get it using
-# git clone https://github.com/meta-llama/llama-stack.git
-cd /path/to/llama-stack
-
-docker run \
-  --pull always \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \
-  llamastack/distribution-{{ name }} \
-  --config /root/my-run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
-  --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1
-```
-
-If you are using Llama Stack Safety / Shield APIs, use:
-
-```bash
-export SAFETY_PORT=8081
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-
-# You need a local checkout of llama-stack to run this, get it using
-# git clone https://github.com/meta-llama/llama-stack.git
-cd /path/to/llama-stack
-
-docker run \
-  --pull always \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ~/.llama:/root/.llama \
-  -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
-  llamastack/distribution-{{ name }} \
-  --config /root/my-run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
-  --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \
-  --env SAFETY_MODEL=$SAFETY_MODEL \
-  --env SAFETY_VLLM_URL=http://host.docker.internal:$SAFETY_PORT/v1
-```
-
-
-### Via Conda
-
-Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
-
-```bash
-export INFERENCE_PORT=8000
-export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export LLAMA_STACK_PORT=8321
-
-cd distributions/remote-vllm
-llama stack build --template remote-vllm --image-type conda
-
-llama stack run ./run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
-  --env VLLM_URL=http://localhost:$INFERENCE_PORT/v1
-```
-
-If you are using Llama Stack Safety / Shield APIs, use:
-
-```bash
-export SAFETY_PORT=8081
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-
-llama stack run ./run-with-safety.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
-  --env VLLM_URL=http://localhost:$INFERENCE_PORT/v1 \
-  --env SAFETY_MODEL=$SAFETY_MODEL \
-  --env SAFETY_VLLM_URL=http://localhost:$SAFETY_PORT/v1
-```

llama_stack/templates/remote-vllm/run-with-safety.yaml

@@ -1,147 +0,0 @@
-version: 2
-image_name: remote-vllm
-apis:
-- agents
-- datasetio
-- eval
-- inference
-- safety
-- scoring
-- telemetry
-- tool_runtime
-- vector_io
-providers:
-  inference:
-  - provider_id: vllm-inference
-    provider_type: remote::vllm
-    config:
-      url: ${env.VLLM_URL:=http://localhost:8000/v1}
-      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-      api_token: ${env.VLLM_API_TOKEN:=fake}
-      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-  - provider_id: vllm-safety
-    provider_type: remote::vllm
-    config:
-      url: ${env.SAFETY_VLLM_URL}
-      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-      api_token: ${env.VLLM_API_TOKEN:=fake}
-      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-  - provider_id: sentence-transformers
-    provider_type: inline::sentence-transformers
-    config: {}
-  vector_io:
-  - provider_id: faiss
-    provider_type: inline::faiss
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/faiss_store.db
-  safety:
-  - provider_id: llama-guard
-    provider_type: inline::llama-guard
-    config:
-      excluded_categories: []
-  agents:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/responses_store.db
-  eval:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/meta_reference_eval.db
-  datasetio:
-  - provider_id: huggingface
-    provider_type: remote::huggingface
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
-  - provider_id: localfs
-    provider_type: inline::localfs
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/localfs_datasetio.db
-  scoring:
-  - provider_id: basic
-    provider_type: inline::basic
-    config: {}
-  - provider_id: llm-as-judge
-    provider_type: inline::llm-as-judge
-    config: {}
-  - provider_id: braintrust
-    provider_type: inline::braintrust
-    config:
-      openai_api_key: ${env.OPENAI_API_KEY:=}
-  telemetry:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/trace_store.db
-  tool_runtime:
-  - provider_id: brave-search
-    provider_type: remote::brave-search
-    config:
-      api_key: ${env.BRAVE_SEARCH_API_KEY:=}
-      max_results: 3
-  - provider_id: tavily-search
-    provider_type: remote::tavily-search
-    config:
-      api_key: ${env.TAVILY_SEARCH_API_KEY:=}
-      max_results: 3
-  - provider_id: rag-runtime
-    provider_type: inline::rag-runtime
-    config: {}
-  - provider_id: model-context-protocol
-    provider_type: remote::model-context-protocol
-    config: {}
-  - provider_id: wolfram-alpha
-    provider_type: remote::wolfram-alpha
-    config:
-      api_key: ${env.WOLFRAM_ALPHA_API_KEY:=}
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/inference_store.db
-models:
-- metadata: {}
-  model_id: ${env.INFERENCE_MODEL}
-  provider_id: vllm-inference
-  model_type: llm
-- metadata: {}
-  model_id: ${env.SAFETY_MODEL}
-  provider_id: vllm-safety
-  model_type: llm
-- metadata:
-    embedding_dimension: 384
-  model_id: all-MiniLM-L6-v2
-  provider_id: sentence-transformers
-  model_type: embedding
-shields:
-- shield_id: ${env.SAFETY_MODEL}
-vector_dbs: []
-datasets: []
-scoring_fns: []
-benchmarks: []
-tool_groups:
-- toolgroup_id: builtin::websearch
-  provider_id: tavily-search
-- toolgroup_id: builtin::rag
-  provider_id: rag-runtime
-- toolgroup_id: builtin::wolfram_alpha
-  provider_id: wolfram-alpha
-server:
-  port: 8321

llama_stack/templates/remote-vllm/run.yaml

@@ -1,135 +0,0 @@
-version: 2
-image_name: remote-vllm
-apis:
-- agents
-- datasetio
-- eval
-- inference
-- safety
-- scoring
-- telemetry
-- tool_runtime
-- vector_io
-providers:
-  inference:
-  - provider_id: vllm-inference
-    provider_type: remote::vllm
-    config:
-      url: ${env.VLLM_URL:=http://localhost:8000/v1}
-      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-      api_token: ${env.VLLM_API_TOKEN:=fake}
-      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-  - provider_id: sentence-transformers
-    provider_type: inline::sentence-transformers
-    config: {}
-  vector_io:
-  - provider_id: faiss
-    provider_type: inline::faiss
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/faiss_store.db
-  safety:
-  - provider_id: llama-guard
-    provider_type: inline::llama-guard
-    config:
-      excluded_categories: []
-  agents:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/responses_store.db
-  eval:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/meta_reference_eval.db
-  datasetio:
-  - provider_id: huggingface
-    provider_type: remote::huggingface
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
-  - provider_id: localfs
-    provider_type: inline::localfs
-    config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/localfs_datasetio.db
-  scoring:
-  - provider_id: basic
-    provider_type: inline::basic
-    config: {}
-  - provider_id: llm-as-judge
-    provider_type: inline::llm-as-judge
-    config: {}
-  - provider_id: braintrust
-    provider_type: inline::braintrust
-    config:
-      openai_api_key: ${env.OPENAI_API_KEY:=}
-  telemetry:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/trace_store.db
-  tool_runtime:
-  - provider_id: brave-search
-    provider_type: remote::brave-search
-    config:
-      api_key: ${env.BRAVE_SEARCH_API_KEY:=}
-      max_results: 3
-  - provider_id: tavily-search
-    provider_type: remote::tavily-search
-    config:
-      api_key: ${env.TAVILY_SEARCH_API_KEY:=}
-      max_results: 3
-  - provider_id: rag-runtime
-    provider_type: inline::rag-runtime
-    config: {}
-  - provider_id: model-context-protocol
-    provider_type: remote::model-context-protocol
-    config: {}
-  - provider_id: wolfram-alpha
-    provider_type: remote::wolfram-alpha
-    config:
-      api_key: ${env.WOLFRAM_ALPHA_API_KEY:=}
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/inference_store.db
-models:
-- metadata: {}
-  model_id: ${env.INFERENCE_MODEL}
-  provider_id: vllm-inference
-  model_type: llm
-- metadata:
-    embedding_dimension: 384
-  model_id: all-MiniLM-L6-v2
-  provider_id: sentence-transformers
-  model_type: embedding
-shields: []
-vector_dbs: []
-datasets: []
-scoring_fns: []
-benchmarks: []
-tool_groups:
-- toolgroup_id: builtin::websearch
-  provider_id: tavily-search
-- toolgroup_id: builtin::rag
-  provider_id: rag-runtime
-- toolgroup_id: builtin::wolfram_alpha
-  provider_id: wolfram-alpha
-server:
-  port: 8321

llama_stack/templates/remote-vllm/vllm.py

@@ -1,157 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from pathlib import Path
-
-from llama_stack.apis.models import ModelType
-from llama_stack.distribution.datatypes import (
-    ModelInput,
-    Provider,
-    ShieldInput,
-    ToolGroupInput,
-)
-from llama_stack.providers.inline.inference.sentence_transformers import (
-    SentenceTransformersInferenceConfig,
-)
-from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
-from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
-from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
-
-
-def get_distribution_template() -> DistributionTemplate:
-    providers = {
-        "inference": ["remote::vllm", "inline::sentence-transformers"],
-        "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
-        "safety": ["inline::llama-guard"],
-        "agents": ["inline::meta-reference"],
-        "eval": ["inline::meta-reference"],
-        "datasetio": ["remote::huggingface", "inline::localfs"],
-        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
-        "telemetry": ["inline::meta-reference"],
-        "tool_runtime": [
-            "remote::brave-search",
-            "remote::tavily-search",
-            "inline::rag-runtime",
-            "remote::model-context-protocol",
-            "remote::wolfram-alpha",
-        ],
-    }
-    name = "remote-vllm"
-    inference_provider = Provider(
-        provider_id="vllm-inference",
-        provider_type="remote::vllm",
-        config=VLLMInferenceAdapterConfig.sample_run_config(
-            url="${env.VLLM_URL:=http://localhost:8000/v1}",
-        ),
-    )
-    embedding_provider = Provider(
-        provider_id="sentence-transformers",
-        provider_type="inline::sentence-transformers",
-        config=SentenceTransformersInferenceConfig.sample_run_config(),
-    )
-    vector_io_provider = Provider(
-        provider_id="faiss",
-        provider_type="inline::faiss",
-        config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-    )
-
-    inference_model = ModelInput(
-        model_id="${env.INFERENCE_MODEL}",
-        provider_id="vllm-inference",
-    )
-    safety_model = ModelInput(
-        model_id="${env.SAFETY_MODEL}",
-        provider_id="vllm-safety",
-    )
-    embedding_model = ModelInput(
-        model_id="all-MiniLM-L6-v2",
-        provider_id="sentence-transformers",
-        model_type=ModelType.embedding,
-        metadata={
-            "embedding_dimension": 384,
-        },
-    )
-    default_tool_groups = [
-        ToolGroupInput(
-            toolgroup_id="builtin::websearch",
-            provider_id="tavily-search",
-        ),
-        ToolGroupInput(
-            toolgroup_id="builtin::rag",
-            provider_id="rag-runtime",
-        ),
-        ToolGroupInput(
-            toolgroup_id="builtin::wolfram_alpha",
-            provider_id="wolfram-alpha",
-        ),
-    ]
-
-    return DistributionTemplate(
-        name=name,
-        distro_type="self_hosted",
-        description="Use (an external) vLLM server for running LLM inference",
-        template_path=Path(__file__).parent / "doc_template.md",
-        providers=providers,
-        run_configs={
-            "run.yaml": RunConfigSettings(
-                provider_overrides={
-                    "inference": [inference_provider, embedding_provider],
-                    "vector_io": [vector_io_provider],
-                },
-                default_models=[inference_model, embedding_model],
-                default_tool_groups=default_tool_groups,
-            ),
-            "run-with-safety.yaml": RunConfigSettings(
-                provider_overrides={
-                    "inference": [
-                        inference_provider,
-                        Provider(
-                            provider_id="vllm-safety",
-                            provider_type="remote::vllm",
-                            config=VLLMInferenceAdapterConfig.sample_run_config(
-                                url="${env.SAFETY_VLLM_URL}",
-                            ),
-                        ),
-                        embedding_provider,
-                    ],
-                    "vector_io": [vector_io_provider],
-                },
-                default_models=[
-                    inference_model,
-                    safety_model,
-                    embedding_model,
-                ],
-                default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
-                default_tool_groups=default_tool_groups,
-            ),
-        },
-        run_config_env_vars={
-            "LLAMA_STACK_PORT": (
-                "8321",
-                "Port for the Llama Stack distribution server",
-            ),
-            "INFERENCE_MODEL": (
-                "meta-llama/Llama-3.2-3B-Instruct",
-                "Inference model loaded into the vLLM server",
-            ),
-            "VLLM_URL": (
-                "http://host.docker.internal:5100/v1",
-                "URL of the vLLM server with the main inference model",
-            ),
-            "MAX_TOKENS": (
-                "4096",
-                "Maximum number of tokens for generation",
-            ),
-            "SAFETY_VLLM_URL": (
-                "http://host.docker.internal:5101/v1",
-                "URL of the vLLM server with the safety model",
-            ),
-            "SAFETY_MODEL": (
-                "meta-llama/Llama-Guard-3-1B",
-                "Name of the safety (Llama-Guard) model to use",
-            ),
-        },
-    )