chore: update README for 0.2.1 release (#91)

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>

.github/workflows/pre-commit.yml

@@ -28,7 +28,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 

.github/workflows/pypi.yml

@@ -37,7 +37,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 
@@ -48,7 +48,7 @@ jobs:
           fetch-depth: 0
 
       - name: Build and inspect python package
-        uses: hynek/build-and-inspect-python-package@b5076c307dc91924a82ad150cdd1533b444d3310 # v2.12.0
+        uses: hynek/build-and-inspect-python-package@c52c3a4710070b50470d903818a7b25115dcd076 # v2.13.0
 
       - name: Run 'test-build.sh'
         run: $GITHUB_WORKSPACE/tests/test-build.sh
@@ -71,7 +71,7 @@ jobs:
     needs: build-package
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 
@@ -102,7 +102,7 @@ jobs:
     needs: build-package
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 

.github/workflows/test-container.yml

@@ -28,7 +28,7 @@ jobs:
       INFERENCE_MODEL: ${{ inputs.inference_model || 'llama3.2:3b' }}
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 
@@ -66,6 +66,9 @@ jobs:
       - name: Run 'test-container.sh'
         run: $GITHUB_WORKSPACE/tests/test-container.sh
 
+      - name: Run 'test-ui-linux.sh'
+        run: $GITHUB_WORKSPACE/tests/test-ui-linux.sh
+
       - name: Upload logs
         uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
         if: always()

.github/workflows/test-external-providers.yml

@@ -47,7 +47,7 @@ jobs:
       INFERENCE_MODEL: ${{ matrix.inference_model }}
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 
@@ -92,6 +92,9 @@ jobs:
       - name: Run 'test-external-providers.sh'
         run: $GITHUB_WORKSPACE/tests/test-external-providers.sh
 
+      - name: Run 'test-rag.sh'
+        run: $GITHUB_WORKSPACE/tests/test-rag.sh
+
       - name: Upload logs
         uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
         if: always()

.github/workflows/test-lls-integration.yml

@@ -28,7 +28,7 @@ jobs:
       INFERENCE_MODEL: ${{ inputs.inference_model || 'llama3.2:3b' }}
     steps:
       - name: Harden Runner
-        uses: step-security/harden-runner@0634a2670c59f64b4a01f0f96f84700a4088b9f0 # v2.12.0
+        uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
         with:
           egress-policy: audit
 

README.md

@@ -17,8 +17,8 @@ This will install Llama Stack and RamaLama as well if they are not installed alr
 > [!WARNING]
 > The following workaround is currently needed to run this provider - see https://github.com/containers/ramalama-stack/issues/53 for more details
 > ```bash
-> curl --create-dirs --output ~/.llama/providers.d/remote/inference/ramalama.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.0/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml
+> curl --create-dirs --output ~/.llama/providers.d/remote/inference/ramalama.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.1/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml
-> curl --create-dirs --output ~/.llama/distributions/ramalama/ramalama-run.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.0/src/ramalama_stack/ramalama-run.yaml
+> curl --create-dirs --output ~/.llama/distributions/ramalama/ramalama-run.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.1/src/ramalama_stack/ramalama-run.yaml
 > ```
 
 1. First you will need a RamaLama server running - see [the RamaLama project](https://github.com/containers/ramalama) docs for more information.
@@ -61,3 +61,20 @@ and Schrödinger\'s cat?" The librarian replied, "It rings a bell, but I\'m not
     ]
 )
 ```
+
+## Llama Stack User Interface
+
+Llama Stack includes an experimental user-interface, check it out
+[here](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/distribution/ui).
+
+To deploy the UI, run this:
+
+```bash
+podman run -d --rm --network=container:ramalama --name=streamlit quay.io/redhat-et/streamlit_client:0.1.0
+```
+
+> [!NOTE]
+> If running on MacOS (not Linux), `--network=host` doesn't work. You'll need to publish additional ports `8321:8321` and `8501:8501` with the ramalama serve command,
+> then run with `network=container:ramalama`.
+>
+> If running on Linux use `--network=host` or `-p 8501:8501` instead. The streamlit container will be able to access the ramalama endpoint with either.

pyproject.toml

@@ -17,6 +17,7 @@ dependencies = [
     "aiosqlite>=0.21.0",
     "autoevals>=0.0.129",
     "blobfile>=3.0.0",
+    "chardet>=3.0.0",
     "datasets>=3.6.0",
     "fastapi>=0.115.12",
     "httpx>=0.28.1",
@@ -32,6 +33,7 @@ dependencies = [
     "pymilvus>=2.5.10",
     "ramalama==0.9.0",
     "requests>=2.32.3",
+    "sentence-transformers>=3.0.0",
     "six>=1.17.0",
     "sqlalchemy>=2.0.41",
     "torch>=2.7.0",

requirements.txt

@@ -43,6 +43,8 @@ certifi==2025.4.26
     #   httpcore
     #   httpx
     #   requests
+chardet==5.2.0
+    # via ramalama-stack
 charset-normalizer==3.4.2
     # via requests
 chevron==0.14.0
@@ -124,6 +126,7 @@ huggingface-hub==0.32.4
     #   datasets
     #   llama-stack
     #   peft
+    #   sentence-transformers
     #   tokenizers
     #   transformers
 idna==3.10
@@ -140,6 +143,8 @@ jinja2==3.1.6
     #   torch
 jiter==0.10.0
     # via openai
+joblib==1.5.1
+    # via scikit-learn
 jsonschema==4.24.0
     # via
     #   autoevals
@@ -179,6 +184,8 @@ numpy==2.2.6
     #   pandas
     #   peft
     #   ramalama-stack
+    #   scikit-learn
+    #   scipy
     #   transformers
 nvidia-cublas-cu12==12.6.4.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
     # via
@@ -255,7 +262,9 @@ pandas==2.2.3
 peft==0.15.2
     # via ramalama-stack
 pillow==11.2.1
-    # via llama-stack
+    # via
+    #   llama-stack
+    #   sentence-transformers
 polyleven==0.9.0
     # via autoevals
 prompt-toolkit==3.0.51
@@ -359,6 +368,14 @@ safetensors==0.5.3
     #   accelerate
     #   peft
     #   transformers
+scikit-learn==1.7.0
+    # via sentence-transformers
+scipy==1.15.3
+    # via
+    #   scikit-learn
+    #   sentence-transformers
+sentence-transformers==4.1.0
+    # via ramalama-stack
 setuptools==80.9.0
     # via
     #   llama-stack
@@ -391,6 +408,8 @@ termcolor==3.1.0
     #   fire
     #   llama-stack
     #   llama-stack-client
+threadpoolctl==3.6.0
+    # via scikit-learn
 tiktoken==0.9.0
     # via llama-stack
 tokenizers==0.21.1
@@ -400,6 +419,7 @@ torch==2.7.0
     #   accelerate
     #   peft
     #   ramalama-stack
+    #   sentence-transformers
 tqdm==4.67.1
     # via
     #   datasets
@@ -408,10 +428,12 @@ tqdm==4.67.1
     #   milvus-lite
     #   openai
     #   peft
+    #   sentence-transformers
     #   transformers
 transformers==4.52.4
     # via
     #   peft
+    #   sentence-transformers
     #   trl
 triton==3.3.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
     # via torch
@@ -429,6 +451,7 @@ typing-extensions==4.14.0
     #   pydantic
     #   pydantic-core
     #   referencing
+    #   sentence-transformers
     #   sqlalchemy
     #   torch
     #   typing-inspection

src/ramalama_stack/ramalama-run.yaml

@@ -17,6 +17,9 @@ providers:
     provider_type: remote::ramalama
     config:
       url: ${env.RAMALAMA_URL:http://localhost:8080}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
   vector_io:
   - provider_id: milvus
     provider_type: inline::milvus
@@ -42,7 +45,7 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
+      service_name: ${env.OTEL_SERVICE_NAME:llamastack}
       sinks: ${env.TELEMETRY_SINKS:console,sqlite}
       sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ramalama}/trace_store.db
   eval:
@@ -118,6 +121,11 @@ models:
   model_id: ${env.INFERENCE_MODEL}
   provider_id: ramalama
   model_type: llm
+- metadata:
+    embedding_dimension: 384
+  model_id: all-MiniLM-L6-v2
+  provider_id: sentence-transformers
+  model_type: embedding
 shields: []
 vector_dbs: []
 datasets: []

tests/test-rag.py

@@ -0,0 +1,166 @@
+import os
+import uuid
+from llama_stack_client import LlamaStackClient, RAGDocument
+
+
+def setup_client():
+    """Initialize Llama Stack client with configuration"""
+    base_url = "http://localhost:8321"
+
+    client = LlamaStackClient(base_url=base_url, api_key="none", timeout=10.0)
+
+    print(f"Connected to Llama Stack server at {base_url}")
+    return client
+
+
+def setup_inference_params():
+    """Configure inference parameters"""
+    model_id = os.getenv(
+        "INFERENCE_MODEL",
+        "bartowski/Meta-Llama-3-8B-Instruct-GGUF/Meta-Llama-3-8B-Instruct-Q5_K_M.gguf",
+    )
+
+    temperature = float(os.getenv("TEMPERATURE", 0.0))
+    if temperature > 0.0:
+        top_p = float(os.getenv("TOP_P", 0.95))
+        strategy = {"type": "top_p", "temperature": temperature, "top_p": top_p}
+    else:
+        strategy = {"type": "greedy"}
+
+    max_tokens = int(os.getenv("MAX_TOKENS", 4096))
+
+    sampling_params = {
+        "strategy": strategy,
+        "max_tokens": max_tokens,
+    }
+
+    stream_env = os.getenv("STREAM", "False")
+    stream = stream_env == "True"
+
+    print("Inference Parameters:")
+    print(f"\tModel: {model_id}")
+    print(f"\tSampling Parameters: {sampling_params}")
+    print(f"\tStream: {stream}")
+
+    return model_id, sampling_params, stream
+
+
+def setup_vector_db(client):
+    """Setup vector database for RAG"""
+    vector_db_id = f"test_vector_db_{uuid.uuid4().hex[:8]}"
+
+    # Find embedding model from available models
+    models = client.models.list()
+    embedding_model = None
+    for model in models:
+        if hasattr(model, "model_type") and model.model_type == "embedding":
+            embedding_model = model.identifier
+            break
+
+    if not embedding_model:
+        raise Exception("No embedding model found")
+
+    print(f"Using embedding model: {embedding_model}")
+
+    # Register vector database
+    client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model=embedding_model,
+        embedding_dimension=int(os.getenv("VDB_EMBEDDING_DIMENSION", 384)),
+        provider_id=os.getenv("VDB_PROVIDER", "milvus"),
+    )
+
+    # Ingest simple test documents instead of external URLs
+    test_content = [
+        "RamaLama Stack is an external provider for Llama Stack that allows for the use of RamaLama for inference.",
+        "Podman is a container management tool that provides a Docker-compatible command line interface without requiring a daemon.",
+        "Podman can run containers rootlessly and provides robust security isolation.",
+    ]
+
+    documents = [
+        RAGDocument(
+            document_id=f"test_doc_{i}",
+            content=content,
+            mime_type="text/plain",
+            metadata={"source": f"test_document_{i}"},
+        )
+        for i, content in enumerate(test_content)
+    ]
+
+    print(f"Ingesting {len(documents)} test documents into vector database...")
+    client.tool_runtime.rag_tool.insert(
+        documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=int(os.getenv("VECTOR_DB_CHUNK_SIZE", 128)),
+    )
+
+    print(f"Vector database '{vector_db_id}' setup complete")
+    return vector_db_id
+
+
+def run_rag_query(client, model_id, sampling_params, stream, vector_db_id, query):
+    """Execute RAG query and return response"""
+    print(f"\nUser> {query}")
+
+    rag_response = client.tool_runtime.rag_tool.query(
+        content=query, vector_db_ids=[vector_db_id]
+    )
+
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+
+    prompt_context = rag_response.content
+    extended_prompt = f"Please answer the given query using the context below.\n\nCONTEXT:\n{prompt_context}\n\nQUERY:\n{query}"
+    messages.append({"role": "user", "content": extended_prompt})
+
+    response = client.inference.chat_completion(
+        messages=messages,
+        model_id=model_id,
+        sampling_params=sampling_params,
+        stream=stream,
+    )
+
+    print("inference> ", end="")
+    if stream:
+        for chunk in response:
+            if hasattr(chunk, "event") and hasattr(chunk.event, "delta"):
+                if hasattr(chunk.event.delta, "text"):
+                    print(chunk.event.delta.text, end="")
+        print()
+    else:
+        print(response.completion_message.content)
+
+
+def main():
+    """Main function to run RAG test"""
+    print("=== Llama Stack RAG Test ===")
+
+    try:
+        client = setup_client()
+        model_id, sampling_params, stream = setup_inference_params()
+
+        vector_db_id = setup_vector_db(client)
+
+        queries = [
+            "What is RamaLama Stack?",
+            "What is Podman?",
+            "Can Podman run in rootless mode?",
+        ]
+
+        print("\n=== Running RAG Queries ===")
+        for query in queries:
+            run_rag_query(
+                client, model_id, sampling_params, stream, vector_db_id, query
+            )
+            print()
+
+        print("=== RAG Test Complete ===")
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    exit(main())

tests/test-rag.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+function test_rag_functionality {
+  echo "===> test_rag_functionality: start"
+
+  if uv run python tests/test-rag.py; then
+    echo "===> test_rag_functionality: pass"
+    return 0
+  else
+    echo "===> test_rag_functionality: fail"
+    echo "RAG test script output above shows the failure details"
+    return 1
+  fi
+}
+
+main() {
+  echo "===> starting 'test-rag'..."
+
+  # Check if services are already running (from previous tests)
+  if curl -s http://localhost:8321/v1/health >/dev/null 2>&1 && curl -s http://localhost:8080/health >/dev/null 2>&1; then
+    echo "Using existing RamaLama and Llama Stack servers"
+  else
+    echo "Starting fresh servers for RAG test"
+    start_and_wait_for_ramalama_server
+    start_and_wait_for_llama_stack_server
+  fi
+
+  if test_rag_functionality; then
+    echo "===> 'test-rag' completed successfully!"
+  else
+    echo "===> 'test-rag' failed!"
+    exit 1
+  fi
+}
+
+TEST_UTILS=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)
+# shellcheck disable=SC1091
+source "$TEST_UTILS/utils.sh"
+main "$@"
+exit 0

tests/test-ui-linux.sh

@@ -0,0 +1,77 @@
+#!/bin/bash
+
+function start_and_wait_for_streamlit_ui_linux {
+  echo "Starting Streamlit UI for Linux..."
+
+  podman run -d --rm --network=host --name=streamlit-ui quay.io/redhat-et/streamlit_client:0.1.0
+
+  echo "Waiting for Streamlit UI to be ready..."
+  for i in {1..30}; do
+    echo "Attempt $i to connect to Streamlit UI..."
+    if curl -s http://localhost:8501 >/dev/null 2>&1; then
+      echo "Streamlit UI is up and responding on port 8501!"
+      return 0
+    fi
+    if [ "$i" -eq 30 ]; then
+      echo "Streamlit UI failed to start or respond"
+      echo "Container logs:"
+      podman logs streamlit-ui
+      return 1
+    fi
+    sleep 2
+  done
+}
+
+function test_streamlit_ui_linux {
+  echo "===> test_streamlit_ui_linux: start"
+
+  if start_and_wait_for_streamlit_ui_linux; then
+    # Test that the UI is accessible and returns HTML content
+    resp=$(curl -sS http://localhost:8501)
+    if echo "$resp" | grep -q -i "streamlit\|html"; then
+      echo "===> test_streamlit_ui_linux: pass"
+      return 0
+    else
+      echo "===> test_streamlit_ui_linux: fail - UI not serving expected content"
+      echo "Response: $resp"
+      return 1
+    fi
+  else
+    echo "===> test_streamlit_ui_linux: fail - UI failed to start"
+    return 1
+  fi
+}
+
+function cleanup_streamlit_ui {
+  echo "Cleaning up Streamlit UI container..."
+  podman rm -f streamlit-ui >/dev/null 2>&1 || true
+}
+
+main() {
+  echo "===> starting 'test-ui-linux'..."
+
+  # Only run on Linux
+  # Need a fix to published ports in ramalama to run on MacOS
+  if [[ "$OSTYPE" != "linux-gnu"* ]]; then
+    echo "This test is only for Linux systems. Current OS: $OSTYPE"
+    echo "===> 'test-ui-linux' skipped!"
+    exit 0
+  fi
+
+  trap cleanup_streamlit_ui EXIT
+
+  start_and_wait_for_ramalama_server
+  start_and_wait_for_llama_stack_server
+
+  test_streamlit_ui_linux
+
+  cleanup_streamlit_ui
+
+  echo "===> 'test-ui-linux' completed successfully!"
+}
+
+TEST_UTILS=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)
+# shellcheck disable=SC1091
+source "$TEST_UTILS/utils.sh"
+main "$@"
+exit 0

uv.lock

@@ -224,6 +224,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c5/55/51844dd50c4fc7a33b653bfaba4c2456f06955289ca770a5dbd5fd267374/cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9", size = 7249, upload-time = "2023-08-12T20:38:16.269Z" },
 ]
 
+[[package]]
+name = "chardet"
+version = "5.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/f7b6ab21ec75897ed80c17d79b15951a719226b9fababf1e40ea74d69079/chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7", size = 2069618, upload-time = "2023-08-01T19:23:02.662Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/38/6f/f5fbc992a329ee4e0f288c1fe0e2ad9485ed064cac731ed2fe47dcc38cbf/chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970", size = 199385, upload-time = "2023-08-01T19:23:00.661Z" },
+]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.4.2"
@@ -772,6 +781,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/4a/4175a563579e884192ba6e81725fc0448b042024419be8d83aa8a80a3f44/jiter-0.10.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3aa96f2abba33dc77f79b4cf791840230375f9534e5fac927ccceb58c5e604a5", size = 354213, upload-time = "2025-05-18T19:04:41.894Z" },
 ]
 
+[[package]]
+name = "joblib"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
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