forked from phoenix-oss/llama-stack-mirror

fix: Adding Embedding model to watsonx inference (#2118 )

# What does this PR do?
Issue Link : https://github.com/meta-llama/llama-stack/issues/2117

## Test Plan
Once added, User will be able to use Sentence Transformer model
`all-MiniLM-L6-v2`

2025-05-12 10:58:22 -07:00

2.9 KiB

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watsonx Distribution

:maxdepth: 2
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self

The llamastack/distribution-watsonx distribution consists of the following provider configurations.

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::watsonx`, `inline::sentence-transformers`
safety	`inline::llama-guard`
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`
vector_io	`inline::faiss`

Environment Variables

The following environment variables can be configured:

LLAMASTACK_PORT: Port for the Llama Stack distribution server (default: 5001)
WATSONX_API_KEY: watsonx API Key (default: ``)
WATSONX_PROJECT_ID: watsonx Project ID (default: ``)

Models

The following models are available by default:

meta-llama/llama-3-3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)
meta-llama/llama-2-13b-chat (aliases: meta-llama/Llama-2-13b)
meta-llama/llama-3-1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)
meta-llama/llama-3-1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)
meta-llama/llama-3-2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)
meta-llama/llama-3-2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)
meta-llama/llama-3-2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)
meta-llama/llama-3-2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)
meta-llama/llama-guard-3-11b-vision (aliases: meta-llama/Llama-Guard-3-11B-Vision)

Prerequisite: API Keys

Make sure you have access to a watsonx API Key. You can get one by referring watsonx.ai.

Running Llama Stack with watsonx

You can do this via Conda (build code), venv 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.

LLAMA_STACK_PORT=5001
docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ./run.yaml:/root/my-run.yaml \
  llamastack/distribution-watsonx \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env WATSONX_API_KEY=$WATSONX_API_KEY \
  --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \
  --env WATSONX_BASE_URL=$WATSONX_BASE_URL

Via Conda

llama stack build --template watsonx --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env WATSONX_API_KEY=$WATSONX_API_KEY \
  --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID

2.9 KiB Raw Blame History