.github/workflows/Dockerfile

@@ -1 +0,0 @@
-FROM localhost:5000/distribution-kvant:dev

.github/workflows/ci-playground.yaml

@@ -1,73 +0,0 @@
-name: Build and Push playground container
-run-name: Build and Push playground container
-on:
-  workflow_dispatch:
-  #schedule:
-  #  - cron: "0 10 * * *"
-  push:
-    branches:
-      - main
-      - kvant
-    tags:
-      - 'v*'
-  pull_request:
-    branches:
-      - main
-      - kvant
-env:
-  IMAGE: git.kvant.cloud/${{github.repository}}-playground
-jobs:
-  build-playground:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set current time
-        uses: https://github.com/gerred/actions/current-time@master
-        id: current_time
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to git.kvant.cloud registry
-        uses: docker/login-action@v3
-        with:
-          registry: git.kvant.cloud
-          username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }}
-          password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }}
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          # list of Docker images to use as base name for tags
-          images: |
-            ${{env.IMAGE}}
-          # generate Docker tags based on the following events/attributes
-          tags: |
-            type=schedule
-            type=ref,event=branch
-            type=ref,event=pr
-            type=ref,event=tag
-            type=semver,pattern={{version}}
-
-      - name: Build and push to gitea registry
-        uses: docker/build-push-action@v6
-        with:
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          context: .
-          file: llama_stack/distribution/ui/Containerfile
-          provenance: mode=max
-          sbom: true
-          build-args: |
-            BUILD_DATE=${{ steps.current_time.outputs.time }}
-          cache-from: |
-            type=registry,ref=${{ env.IMAGE }}:buildcache
-            type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }}
-            type=registry,ref=${{ env.IMAGE }}:main
-          cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true

.github/workflows/ci.yaml

@@ -1,98 +0,0 @@
-name: Build and Push container
-run-name: Build and Push container
-on:
-  workflow_dispatch:
-  #schedule:
-  #  - cron: "0 10 * * *"
-  push:
-    branches:
-      - main
-      - kvant
-    tags:
-      - 'v*'
-  pull_request:
-    branches:
-      - main
-      - kvant
-env:
-  IMAGE: git.kvant.cloud/${{github.repository}}
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    services:
-      registry:
-        image: registry:2
-        ports:
-          - 5000:5000
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set current time
-        uses: https://github.com/gerred/actions/current-time@master
-        id: current_time
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          driver-opts: network=host
-
-      - name: Login to git.kvant.cloud registry
-        uses: docker/login-action@v3
-        with:
-          registry: git.kvant.cloud
-          username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }}
-          password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }}
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          # list of Docker images to use as base name for tags
-          images: |
-            ${{env.IMAGE}}
-          # generate Docker tags based on the following events/attributes
-          tags: |
-            type=schedule
-            type=ref,event=branch
-            type=ref,event=pr
-            type=ref,event=tag
-            type=semver,pattern={{version}}
-
-      - name: Install uv
-        uses: https://github.com/astral-sh/setup-uv@v5
-        with:
-          # Install a specific version of uv.
-          version: "0.7.8"
-            
-      - name: Build
-        env:
-          USE_COPY_NOT_MOUNT: true
-          LLAMA_STACK_DIR: .
-        run: |
-          uvx --from . llama stack build --template kvant --image-type container
-
-          # docker tag distribution-kvant:dev ${{env.IMAGE}}:kvant
-          # docker push ${{env.IMAGE}}:kvant
-
-          docker tag distribution-kvant:dev localhost:5000/distribution-kvant:dev
-          docker push localhost:5000/distribution-kvant:dev
-
-      - name: Build and push to gitea registry
-        uses: docker/build-push-action@v6
-        with:
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          context: .github/workflows
-          provenance: mode=max
-          sbom: true
-          build-args: |
-            BUILD_DATE=${{ steps.current_time.outputs.time }}
-          cache-from: |
-            type=registry,ref=${{ env.IMAGE }}:buildcache
-            type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }}
-            type=registry,ref=${{ env.IMAGE }}:main
-          cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true

.gitignore

@@ -24,4 +24,3 @@ venv/
 pytest-report.xml
 .coverage
 .python-version
-data

kvant_build_local.sh

@@ -1,6 +0,0 @@
-#!/usr/bin/env bash
-
-export USE_COPY_NOT_MOUNT=true
-export LLAMA_STACK_DIR=.
-
-uvx --from . llama stack build --template kvant --image-type container  --image-name kvant

kvant_start_local.sh

@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-export LLAMA_STACK_PORT=8321
-# VLLM_API_TOKEN= env file
-# KEYCLOAK_CLIENT_SECRET= env file
-
-
-docker run -it \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v $(pwd)/data:/root/.llama \
-  --mount type=bind,source="$(pwd)"/llama_stack/templates/kvant/run.yaml,target=/root/.llama/config.yaml,readonly \
-  --entrypoint python \
-  --env-file ./.env \
-  distribution-kvant:dev \
-  -m llama_stack.distribution.server.server  --config /root/.llama/config.yaml \
-  --port $LLAMA_STACK_PORT \
-

llama_stack/distribution/ui/Containerfile

@@ -5,8 +5,7 @@ FROM python:3.12-slim
 WORKDIR /app
 COPY . /app/
 RUN /usr/local/bin/python -m pip install --upgrade pip && \
-    /usr/local/bin/pip3 install -r requirements.txt && \
+    /usr/local/bin/pip3 install -r requirements.txt
-    /usr/local/bin/pip3 install -r llama_stack/distribution/ui/requirements.txt
 EXPOSE 8501
 
-ENTRYPOINT ["streamlit", "run", "llama_stack/distribution/ui/app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

llama_stack/distribution/ui/README.md

@@ -48,6 +48,3 @@ uv run --with ".[ui]" streamlit run llama_stack/distribution/ui/app.py
 | TOGETHER_API_KEY           | API key for Together provider      | (empty string)            |
 | SAMBANOVA_API_KEY          | API key for SambaNova provider     | (empty string)            |
 | OPENAI_API_KEY             | API key for OpenAI provider        | (empty string)            |
-| KEYCLOAK_URL               | URL for keycloak authentication    | (empty string)            |
-| KEYCLOAK_REALM             | Keycloak realm                     | default                   |
-| KEYCLOAK_CLIENT_ID         | Client ID for keycloak auth        | (empty string)            |

llama_stack/distribution/ui/app.py

@@ -50,42 +50,6 @@ def main():
     )
     pg.run()
 
-def main2():
-    from dataclasses import asdict
-    st.subheader(f"Welcome {keycloak.user_info['preferred_username']}!")
-    st.write(f"Here is your user information:")
-    st.write(asdict(keycloak))
-
-def get_access_token() -> str|None:
-    return st.session_state.get('access_token')
 
 if __name__ == "__main__":
-    
+    main()
-    from streamlit_keycloak import login
-    import os
-    
-    keycloak_url = os.environ.get("KEYCLOAK_URL")
-    keycloak_realm = os.environ.get("KEYCLOAK_REALM", "default")
-    keycloak_client_id = os.environ.get("KEYCLOAK_CLIENT_ID")
-    
-    if keycloak_url and keycloak_client_id:
-        keycloak = login(
-            url=keycloak_url,
-            realm=keycloak_realm,
-            client_id=keycloak_client_id,
-            custom_labels={
-                "labelButton": "Sign in to kvant",
-                "labelLogin": "Please sign in to your kvant account.",
-                "errorNoPopup": "Unable to open the authentication popup. Allow popups and refresh the page to proceed.",
-                "errorPopupClosed": "Authentication popup was closed manually.",
-                "errorFatal": "Unable to connect to Keycloak using the current configuration."   
-            },
-            auto_refresh=True,
-        )
-
-        if keycloak.authenticated:
-            st.session_state['access_token'] = keycloak.access_token
-            main()
-    # TBD - add other authentications
-    else:
-        main()

llama_stack/distribution/ui/modules/api.py

@@ -7,13 +7,11 @@
 import os
 
 from llama_stack_client import LlamaStackClient
-from llama_stack.distribution.ui.app import get_access_token
 
 
 class LlamaStackApi:
     def __init__(self):
         self.client = LlamaStackClient(
-            api_key=get_access_token(),
             base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:8321"),
             provider_data={
                 "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""),
@@ -30,3 +28,5 @@ class LlamaStackApi:
             scoring_params = {fn_id: None for fn_id in scoring_function_ids}
         return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
 
+
+llama_stack_api = LlamaStackApi()

llama_stack/distribution/ui/page/distribution/datasets.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def datasets():
     st.header("Datasets")
 
-    datasets_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.datasets.list()}
+    datasets_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()}
     if len(datasets_info) > 0:
         selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
         st.json(datasets_info[selected_dataset], expanded=True)

llama_stack/distribution/ui/page/distribution/eval_tasks.py

@@ -6,14 +6,14 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def benchmarks():
     # Benchmarks Section
     st.header("Benchmarks")
 
-    benchmarks_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.benchmarks.list()}
+    benchmarks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.benchmarks.list()}
 
     if len(benchmarks_info) > 0:
         selected_benchmark = st.selectbox("Select an eval task", list(benchmarks_info.keys()), key="benchmark_inspect")

llama_stack/distribution/ui/page/distribution/models.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def models():
     # Models Section
     st.header("Models")
-    models_info = {m.identifier: m.to_dict() for m in LlamaStackApi().client.models.list()}
+    models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()}
 
     selected_model = st.selectbox("Select a model", list(models_info.keys()))
     st.json(models_info[selected_model])

llama_stack/distribution/ui/page/distribution/providers.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def providers():
     st.header("🔍 API Providers")
-    apis_providers_lst = LlamaStackApi().client.providers.list()
+    apis_providers_lst = llama_stack_api.client.providers.list()
     api_to_providers = {}
     for api_provider in apis_providers_lst:
         if api_provider.api in api_to_providers:

llama_stack/distribution/ui/page/distribution/scoring_functions.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def scoring_functions():
     st.header("Scoring Functions")
 
-    scoring_functions_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.scoring_functions.list()}
+    scoring_functions_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.scoring_functions.list()}
 
     selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys()))
     st.json(scoring_functions_info[selected_scoring_function], expanded=True)

llama_stack/distribution/ui/page/distribution/shields.py

@@ -6,14 +6,14 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def shields():
     # Shields Section
     st.header("Shields")
 
-    shields_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.shields.list()}
+    shields_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()}
 
     selected_shield = st.selectbox("Select a shield", list(shields_info.keys()))
     st.json(shields_info[selected_shield])

llama_stack/distribution/ui/page/distribution/vector_dbs.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def vector_dbs():
     st.header("Vector Databases")
-    vector_dbs_info = {v.identifier: v.to_dict() for v in LlamaStackApi().client.vector_dbs.list()}
+    vector_dbs_info = {v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()}
 
     if len(vector_dbs_info) > 0:
         selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys()))

llama_stack/distribution/ui/page/evaluations/app_eval.py

@@ -9,7 +9,7 @@ import json
 import pandas as pd
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 from llama_stack.distribution.ui.modules.utils import process_dataset
 
 
@@ -39,7 +39,7 @@ def application_evaluation_page():
 
     # Select Scoring Functions to Run Evaluation On
     st.subheader("Select Scoring Functions")
-    scoring_functions = LlamaStackApi().client.scoring_functions.list()
+    scoring_functions = llama_stack_api.client.scoring_functions.list()
     scoring_functions = {sf.identifier: sf for sf in scoring_functions}
     scoring_functions_names = list(scoring_functions.keys())
     selected_scoring_functions = st.multiselect(
@@ -48,7 +48,7 @@ def application_evaluation_page():
         help="Choose one or more scoring functions.",
     )
 
-    available_models = LlamaStackApi().client.models.list()
+    available_models = llama_stack_api.client.models.list()
     available_models = [m.identifier for m in available_models]
 
     scoring_params = {}
@@ -108,7 +108,7 @@ def application_evaluation_page():
                 progress_bar.progress(progress, text=progress_text)
 
                 # Run evaluation for current row
-                score_res = LlamaStackApi().run_scoring(
+                score_res = llama_stack_api.run_scoring(
                     r,
                     scoring_function_ids=selected_scoring_functions,
                     scoring_params=scoring_params,

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -9,13 +9,13 @@ import json
 import pandas as pd
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 def select_benchmark_1():
     # Select Benchmarks
     st.subheader("1. Choose An Eval Task")
-    benchmarks = LlamaStackApi().client.benchmarks.list()
+    benchmarks = llama_stack_api.client.benchmarks.list()
     benchmarks = {et.identifier: et for et in benchmarks}
     benchmarks_names = list(benchmarks.keys())
     selected_benchmark = st.selectbox(
@@ -47,7 +47,7 @@ def define_eval_candidate_2():
         # Define Eval Candidate
         candidate_type = st.radio("Candidate Type", ["model", "agent"])
 
-        available_models = LlamaStackApi().client.models.list()
+        available_models = llama_stack_api.client.models.list()
         available_models = [model.identifier for model in available_models]
         selected_model = st.selectbox(
             "Choose a model",
@@ -167,7 +167,7 @@ def run_evaluation_3():
     eval_candidate = st.session_state["eval_candidate"]
 
     dataset_id = benchmarks[selected_benchmark].dataset_id
-    rows = LlamaStackApi().client.datasets.iterrows(
+    rows = llama_stack_api.client.datasets.iterrows(
         dataset_id=dataset_id,
     )
     total_rows = len(rows.data)
@@ -208,7 +208,7 @@ def run_evaluation_3():
             progress = i / len(rows)
             progress_bar.progress(progress, text=progress_text)
             # Run evaluation for current row
-            eval_res = LlamaStackApi().client.eval.evaluate_rows(
+            eval_res = llama_stack_api.client.eval.evaluate_rows(
                 benchmark_id=selected_benchmark,
                 input_rows=[r],
                 scoring_functions=benchmarks[selected_benchmark].scoring_functions,

llama_stack/distribution/ui/page/playground/chat.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 # Sidebar configurations
 with st.sidebar:
     st.header("Configuration")
-    available_models = LlamaStackApi().client.models.list()
+    available_models = llama_stack_api.client.models.list()
     available_models = [model.identifier for model in available_models if model.model_type == "llm"]
     selected_model = st.selectbox(
         "Choose a model",
@@ -103,7 +103,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
         else:
             strategy = {"type": "greedy"}
 
-        response = LlamaStackApi().client.inference.chat_completion(
+        response = llama_stack_api.client.inference.chat_completion(
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": prompt},

llama_stack/distribution/ui/page/playground/rag.py

@@ -10,7 +10,7 @@ import streamlit as st
 from llama_stack_client import Agent, AgentEventLogger, RAGDocument
 
 from llama_stack.apis.common.content_types import ToolCallDelta
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 from llama_stack.distribution.ui.modules.utils import data_url_from_file
 
 
@@ -57,14 +57,14 @@ def rag_chat_page():
                     for i, uploaded_file in enumerate(uploaded_files)
                 ]
 
-                providers = LlamaStackApi().client.providers.list()
+                providers = llama_stack_api.client.providers.list()
                 vector_io_provider = None
 
                 for x in providers:
                     if x.api == "vector_io":
                         vector_io_provider = x.provider_id
 
-                LlamaStackApi().client.vector_dbs.register(
+                llama_stack_api.client.vector_dbs.register(
                     vector_db_id=vector_db_name,  # Use the user-provided name
                     embedding_dimension=384,
                     embedding_model="all-MiniLM-L6-v2",
@@ -72,7 +72,7 @@ def rag_chat_page():
                 )
 
                 # insert documents using the custom vector db name
-                LlamaStackApi().client.tool_runtime.rag_tool.insert(
+                llama_stack_api.client.tool_runtime.rag_tool.insert(
                     vector_db_id=vector_db_name,  # Use the user-provided name
                     documents=documents,
                     chunk_size_in_tokens=512,
@@ -93,7 +93,7 @@ def rag_chat_page():
         )
 
         # select memory banks
-        vector_dbs = LlamaStackApi().client.vector_dbs.list()
+        vector_dbs = llama_stack_api.client.vector_dbs.list()
         vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
         selected_vector_dbs = st.multiselect(
             label="Select Document Collections to use in RAG queries",
@@ -103,7 +103,7 @@ def rag_chat_page():
         )
 
         st.subheader("Inference Parameters", divider=True)
-        available_models = LlamaStackApi().client.models.list()
+        available_models = llama_stack_api.client.models.list()
         available_models = [model.identifier for model in available_models if model.model_type == "llm"]
         selected_model = st.selectbox(
             label="Choose a model",
@@ -167,7 +167,7 @@ def rag_chat_page():
     @st.cache_resource
     def create_agent():
         return Agent(
-            LlamaStackApi().client,
+            llama_stack_api.client,
             model=selected_model,
             instructions=system_prompt,
             sampling_params={
@@ -232,7 +232,7 @@ def rag_chat_page():
             st.session_state.messages.append({"role": "system", "content": system_prompt})
 
         # Query the vector DB
-        rag_response = LlamaStackApi().client.tool_runtime.rag_tool.query(
+        rag_response = llama_stack_api.client.tool_runtime.rag_tool.query(
             content=prompt, vector_db_ids=list(selected_vector_dbs)
         )
         prompt_context = rag_response.content
@@ -251,7 +251,7 @@ def rag_chat_page():
 
             # Run inference directly
             st.session_state.messages.append({"role": "user", "content": extended_prompt})
-            response = LlamaStackApi().client.inference.chat_completion(
+            response = llama_stack_api.client.inference.chat_completion(
                 messages=st.session_state.messages,
                 model_id=selected_model,
                 sampling_params={

llama_stack/distribution/ui/page/playground/tools.py

@@ -13,7 +13,7 @@ from llama_stack_client import Agent
 from llama_stack_client.lib.agents.react.agent import ReActAgent
 from llama_stack_client.lib.agents.react.tool_parser import ReActOutput
 
-from llama_stack.distribution.ui.modules.api import LlamaStackApi
+from llama_stack.distribution.ui.modules.api import llama_stack_api
 
 
 class AgentType(enum.Enum):
@@ -24,7 +24,7 @@ class AgentType(enum.Enum):
 def tool_chat_page():
     st.title("🛠 Tools")
 
-    client = LlamaStackApi().client
+    client = llama_stack_api.client
     models = client.models.list()
     model_list = [model.identifier for model in models if model.api_model_type == "llm"]
 
@@ -55,7 +55,7 @@ def tool_chat_page():
         )
 
         if "builtin::rag" in toolgroup_selection:
-            vector_dbs = LlamaStackApi().client.vector_dbs.list() or []
+            vector_dbs = llama_stack_api.client.vector_dbs.list() or []
             if not vector_dbs:
                 st.info("No vector databases available for selection.")
             vector_dbs = [vector_db.identifier for vector_db in vector_dbs]

llama_stack/distribution/ui/requirements.txt

@@ -1,5 +1,5 @@
-llama-stack-client>=0.2.9
+llama-stack>=0.2.1
+llama-stack-client>=0.2.1
 pandas
 streamlit
 streamlit-option-menu
-streamlit-keycloak

llama_stack/templates/dependencies.json

@@ -1,904 +0,0 @@
-{
-  "bedrock": [
-    "aiosqlite",
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-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "vllm",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "watsonx": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "ibm_watson_machine_learning",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ]
-}

llama_stack/templates/kvant/__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 .kvant import get_distribution_template  # noqa: F401

llama_stack/templates/kvant/build.yaml

@@ -1,35 +0,0 @@
-version: '2'
-distribution_spec:
-  description: distribution for kvant cloud
-  providers:
-    inference:
-    - remote::vllm
-    - inline::sentence-transformers
-    vector_io:
-    - inline::faiss
-    - remote::chromadb
-    - remote::pgvector
-    safety:
-    - inline::llama-guard
-    agents:
-    - inline::meta-reference
-    telemetry:
-    - inline::meta-reference
-    eval:
-    - inline::meta-reference
-    datasetio:
-    - remote::huggingface
-    - inline::localfs
-    scoring:
-    - inline::basic
-    - inline::llm-as-judge
-    - inline::braintrust
-    tool_runtime:
-    - remote::brave-search
-    - remote::tavily-search
-    - remote::wolfram-alpha
-    - inline::rag-runtime
-    - remote::model-context-protocol
-image_type: conda
-additional_pip_packages:
-- sqlalchemy[asyncio]

llama_stack/templates/kvant/kvant.py

@@ -1,136 +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.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.passthrough.config import (
-    PassthroughImplConfig,
-)
-from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
-from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
-
-
-def get_distribution_template() -> DistributionTemplate:
-    providers = {
-        "inference": ["remote::openai", "inline::sentence-transformers"],
-        "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
-        "safety": ["inline::llama-guard"],
-        "agents": ["inline::meta-reference"],
-        "telemetry": ["inline::meta-reference"],
-        "eval": ["inline::meta-reference"],
-        "datasetio": ["remote::huggingface", "inline::localfs"],
-        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
-        "tool_runtime": [
-            "remote::brave-search",
-            "remote::tavily-search",
-            "remote::wolfram-alpha",
-            "inline::rag-runtime",
-            "remote::model-context-protocol",
-        ],
-    }
-
-    name = "kvant"
-
-    inference_provider = Provider(
-        provider_id="openai",
-        provider_type="remote::openai",
-        config=PassthroughImplConfig.sample_run_config(),
-    )
-    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}"),
-    )
-
-    default_models = [
-        ModelInput(
-            metadata={},
-            model_id="inference-llama4-maverick",
-            provider_id="openai",
-            provider_model_id="inference-llama4-maverick",
-            model_type=ModelType.llm,
-        ),
-    ]
-
-    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::wolfram_alpha",
-            provider_id="wolfram-alpha",
-        ),
-        ToolGroupInput(
-            toolgroup_id="builtin::rag",
-            provider_id="rag-runtime",
-        ),
-    ]
-
-    return DistributionTemplate(
-        name=name,
-        distro_type="self_hosted",
-        description="Use Passthrough hosted llama-stack endpoint for LLM inference",
-        container_image=None,
-        providers=providers,
-        available_models_by_provider={
-            "openai": [
-                ProviderModelEntry(
-                    provider_model_id="inference-llama4-maverick",
-                    model_type=ModelType.llm,
-                ),
-            ],
-        },
-        run_configs={
-            "run.yaml": RunConfigSettings(
-                provider_overrides={
-                    "inference": [inference_provider, embedding_provider],
-                    "vector_io": [vector_io_provider],
-                },
-                default_models=default_models + [embedding_model],
-                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
-                default_tool_groups=default_tool_groups,
-            ),
-        },
-        run_config_env_vars={
-            "LLAMA_STACK_PORT": (
-                "8321",
-                "Port for the Llama Stack distribution server",
-            ),
-            "OPENAI_API_KEY": (
-                "",
-                "kvant maas API Key",
-            ),
-            "OPENAI_BASE_URL": (
-                "https://maas.kvant.cloud",
-                "kvant maas URL",
-            ),
-        },
-    )

llama_stack/templates/kvant/run.yaml

@@ -1,170 +0,0 @@
-version: '2'
-image_name: kvant
-apis:
-- agents
-- datasetio
-- eval
-- inference
-- safety
-- scoring
-- telemetry
-- tool_runtime
-- vector_io
-providers:
-  inference:
-  - provider_id: kvant
-    provider_type: remote::vllm
-    config:
-      url: ${env.VLLM_URL:https://maas.ai-2.kvant.cloud/v1}
-      max_tokens: ${env.VLLM_MAX_TOKENS:400000}
-      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
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/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
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/responses_store.db
-  telemetry:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      service_name: ${env.OTEL_SERVICE_NAME:}
-      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/trace_store.db
-  eval:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      kvstore:
-        type: sqlite
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/meta_reference_eval.db
-  datasetio:
-  - provider_id: huggingface
-    provider_type: remote::huggingface
-    config:
-      kvstore:
-        type: sqlite
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/huggingface_datasetio.db
-  - provider_id: localfs
-    provider_type: inline::localfs
-    config:
-      kvstore:
-        type: sqlite
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/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:}
-  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: wolfram-alpha
-    provider_type: remote::wolfram-alpha
-    config:
-      api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
-  - provider_id: rag-runtime
-    provider_type: inline::rag-runtime
-    config: {}
-  - provider_id: model-context-protocol
-    provider_type: remote::model-context-protocol
-    config: {}
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/inference_store.db
-models:
-- metadata: {}
-  model_id: Llama-4-Maverick-17B-128E-Instruct-FP8
-  provider_id: kvant
-  provider_model_id: inference-llama4-maverick
-  model_type: llm
-- metadata:
-    embedding_dimension: 1024
-    context_length: 8192
-  model_id: inference-bge-m3
-  provider_id: kvant
-  model_type: embedding
-- metadata:
-    embedding_dimension: 384
-  model_id: all-MiniLM-L6-v2
-  provider_id: sentence-transformers
-  model_type: embedding
-shields:
-- shield_id: meta-llama/Llama-Guard-3-8B
-vector_dbs: []
-# - vector_db_id: test-bge
-#   embedding_model: inference-bge-m3
-#   embedding_dimension: 1024
-#   provider_id: faiss
-# - vector_db_id: test-MiniLM-L6-v2
-#   embedding_model: all-MiniLM-L6-v2
-#   embedding_dimension: 384
-#   provider_id: faiss
-datasets: []
-scoring_fns: []
-benchmarks: []
-tool_groups:
-- toolgroup_id: builtin::websearch
-  provider_id: tavily-search
-- toolgroup_id: builtin::wolfram_alpha
-  provider_id: wolfram-alpha
-- toolgroup_id: builtin::rag
-  provider_id: rag-runtime
-server:
-  port: 8321
-  auth:
-    provider_type: "oauth2_token"
-    config:
-      jwks:
-      introspection:
-        url: ${env.KEYCLOAK_INSTROSPECT:https://iam.phoenix-systems.ch/realms/kvant/protocol/openid-connect/token/introspect} 
-        client_id: ${env.KEYCLOAK_CLIENT_ID:llama-stack}
-        client_secret: ${env.KEYCLOAK_CLIENT_SECRET}
-      claims_mapping:
-        sub: projects
-        scope: roles
-        #groups: teams
-        customer/id: teams
-        aud: namespaces

playground_start_local.sh

@@ -1,7 +0,0 @@
-#!/usr/bin/env bash
-
-export KEYCLOAK_URL="https://iam.phoenix-systems.ch"
-export KEYCLOAK_REALM="kvant"
-export KEYCLOAK_CLIENT_ID="llama-stack-playground"
-
-uv run --with ".[ui]" streamlit run llama_stack/distribution/ui/app.py

pyproject.toml

@@ -49,7 +49,6 @@ ui = [
     "pandas",
     "llama-stack-client>=0.2.9",
     "streamlit-option-menu",
-    "streamlit-keycloak",
 ]
 
 [dependency-groups]