refactor: replace default all-MiniLM-L6-v2 embedding model by nomic-embed-text-v1.5 in Llama Stack (#3183)

# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
The purpose of this PR is to replace the Llama Stack's default embedding
model by nomic-embed-text-v1.5.

These are the key reasons why Llama Stack community decided to switch
from all-MiniLM-L6-v2 to nomic-embed-text-v1.5:
1. The training data for
[all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2#training-data)
includes a lot of data sets with various licensing terms, so it is
tricky to know when/whether it is appropriate to use this model for
commercial applications.
2. The model is not particularly competitive on major benchmarks. For
example, if you look at the [MTEB
Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and click
on Miscellaneous/BEIR to see English information retrieval accuracy, you
see that the top of the leaderboard is dominated by enormous models but
also that there are many, many models of relatively modest size whith
much higher Retrieval scores. If you want to look closely at the data, I
recommend clicking "Download Table" because it is easier to browse that
way.

More discussion info can be founded
[here](https://github.com/llamastack/llama-stack/issues/2418)

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes #2418 

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
1. Run `./scripts/unit-tests.sh`
2. Integration tests via CI wokrflow

---------

Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
Co-authored-by: Sébastien Han <seb@redhat.com>

tests/integration/README.md

@@ -35,7 +35,7 @@ Model parameters can be influenced by the following options:
 - `--embedding-model`: comma-separated list of embedding models.
 - `--safety-shield`: comma-separated list of safety shields.
 - `--judge-model`: comma-separated list of judge models.
-- `--embedding-dimension`: output dimensionality of the embedding model to use for testing. Default: 384
+- `--embedding-dimension`: output dimensionality of the embedding model to use for testing. Default: 768
 
 Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped
 if no model is specified.
@@ -82,7 +82,7 @@ OLLAMA_URL=http://localhost:11434 \
   pytest -s -v tests/integration/inference/test_text_inference.py \
    --stack-config=server:starter \
    --text-model=ollama/llama3.2:3b-instruct-fp16 \
-   --embedding-model=sentence-transformers/all-MiniLM-L6-v2
+   --embedding-model=nomic-embed-text-v1.5
 ```
 
 Run tests with auto-server startup on a custom port:
@@ -92,7 +92,7 @@ OLLAMA_URL=http://localhost:11434 \
   pytest -s -v tests/integration/inference/ \
    --stack-config=server:starter:8322 \
    --text-model=ollama/llama3.2:3b-instruct-fp16 \
-   --embedding-model=sentence-transformers/all-MiniLM-L6-v2
+   --embedding-model=nomic-embed-text-v1.5
 ```
 
 ### Testing with Library Client
@@ -120,7 +120,7 @@ Another example: Running Vector IO tests for embedding models:
 ```bash
 pytest -s -v tests/integration/vector_io/ \
    --stack-config=inference=inline::sentence-transformers,vector_io=inline::sqlite-vec \
-   --embedding-model=sentence-transformers/all-MiniLM-L6-v2
+   --embedding-model=nomic-embed-text-v1.5
 ```
 
 ## Recording Modes