Merge branch 'main' into responses_object

.github/dependabot.yml

@@ -9,6 +9,7 @@ updates:
       day: "saturday"
     commit-message:
       prefix: chore(github-deps)
+
   - package-ecosystem: "uv"
     directory: "/"
     schedule:
@@ -19,3 +20,14 @@ updates:
       - python
     commit-message:
       prefix: chore(python-deps)
+
+  - package-ecosystem: npm
+    directory: "/llama_stack/ui"
+    schedule:
+      interval: "weekly"
+      day: "saturday"
+    labels:
+      - type/dependencies
+      - javascript
+    commit-message:
+      prefix: chore(ui-deps)

.github/workflows/changelog.yml

@@ -17,7 +17,7 @@ jobs:
       pull-requests: write  # for peter-evans/create-pull-request to create a PR
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
         with:
           ref: main
           fetch-depth: 0

.github/workflows/install-script-ci.yml

@@ -16,14 +16,14 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
+      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # 5.0.0
       - name: Run ShellCheck on install.sh
         run: shellcheck scripts/install.sh
   smoke-test-on-dev:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/integration-auth-tests.yml

@@ -18,7 +18,7 @@ on:
       - '.github/workflows/integration-auth-tests.yml' # This workflow
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -31,7 +31,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/integration-sql-store-tests.yml

@@ -16,7 +16,7 @@ on:
       - '.github/workflows/integration-sql-store-tests.yml' # This workflow
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -44,7 +44,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/integration-tests.yml

@@ -65,7 +65,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Setup test environment
         uses: ./.github/actions/setup-test-environment

.github/workflows/integration-vector-io-tests.yml

@@ -33,7 +33,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/pre-commit.yml

@@ -8,7 +8,7 @@ on:
     branches: [main]
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -20,7 +20,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
         with:
           # For dependabot PRs, we need to checkout with a token that can push changes
           token: ${{ github.actor == 'dependabot[bot]' && secrets.GITHUB_TOKEN || github.token }}
@@ -36,20 +36,16 @@ jobs:
             **/requirements*.txt
             .pre-commit-config.yaml
 
-      # npm ci may fail -
+      - name: Set up Node.js
-      #   npm error `npm ci` can only install packages when your package.json and package-lock.json or npm-shrinkwrap.json are in sync. Please update your lock file with `npm install` before continuing.
+        uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
-      #   npm error Invalid: lock file's llama-stack-client@0.2.17 does not satisfy llama-stack-client@0.2.18
+        with:
+          node-version: '20'
+          cache: 'npm'
+          cache-dependency-path: 'llama_stack/ui/'
 
-      # - name: Set up Node.js
+      - name: Install npm dependencies
-      #   uses: actions/setup-node@39370e3970a6d050c480ffad4ff0ed4d3fdee5af # v4.1.0
+        run: npm ci
-      #   with:
+        working-directory: llama_stack/ui
-      #     node-version: '20'
-      #     cache: 'npm'
-      #     cache-dependency-path: 'llama_stack/ui/'
-
-      # - name: Install npm dependencies
-      #   run: npm ci
-      #   working-directory: llama_stack/ui
 
       - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
         continue-on-error: true

.github/workflows/providers-build.yml

@@ -26,7 +26,7 @@ on:
       - 'pyproject.toml'
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -36,7 +36,7 @@ jobs:
       distros: ${{ steps.set-matrix.outputs.distros }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Generate Distribution List
         id: set-matrix
@@ -55,7 +55,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -79,7 +79,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -92,7 +92,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -106,6 +106,10 @@ jobs:
       - name: Inspect the container image entrypoint
         run: |
           IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
+          if [ -z "$IMAGE_ID" ]; then
+            echo "No image found"
+            exit 1
+          fi
           entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
           echo "Entrypoint: $entrypoint"
           if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then
@@ -117,7 +121,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -140,6 +144,10 @@ jobs:
       - name: Inspect UBI9 image
         run: |
           IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
+          if [ -z "$IMAGE_ID" ]; then
+            echo "No image found"
+            exit 1
+          fi
           entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
           echo "Entrypoint: $entrypoint"
           if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then

.github/workflows/python-build-test.yml

@@ -21,10 +21,10 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
     - name: Install uv
-      uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
+      uses: astral-sh/setup-uv@4959332f0f014c5280e7eac8b70c90cb574c9f9b # v6.6.0
       with:
         python-version: ${{ matrix.python-version }}
         activate-environment: true

.github/workflows/record-integration-tests.yml

@@ -46,7 +46,7 @@ jobs:
           echo "::endgroup::"
 
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
         with:
           fetch-depth: 0
 

.github/workflows/semantic-pr.yml

@@ -22,6 +22,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check PR Title's semantic conformance
-        uses: amannn/action-semantic-pull-request@0723387faaf9b38adef4775cd42cfd5155ed6017 # v5.5.3
+        uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6.1.1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test-external-provider-module.yml

@@ -27,7 +27,7 @@ jobs:
         # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/test-external.yml

@@ -27,7 +27,7 @@ jobs:
         # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/ui-unit-tests.yml

@@ -13,7 +13,7 @@ on:
   workflow_dispatch:
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -26,10 +26,10 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Setup Node.js
-        uses: actions/setup-node@39370e3970a6d050c480ffad4ff0ed4d3fdee5af # v4.1.0
+        uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'npm'

.github/workflows/unit-tests.yml

@@ -18,7 +18,7 @@ on:
   workflow_dispatch:
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -32,7 +32,7 @@ jobs:
           - "3.13"
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/update-readthedocs.yml

@@ -27,7 +27,7 @@ on:
       - '.github/workflows/update-readthedocs.yml'
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -37,7 +37,7 @@ jobs:
       TOKEN: ${{ secrets.READTHEDOCS_TOKEN }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.pre-commit-config.yaml

@@ -146,31 +146,13 @@ repos:
         pass_filenames: false
         require_serial: true
         files: ^.github/workflows/.*$
-      # ui-prettier and ui-eslint are disabled until we can avoid `npm ci`, which is slow and may fail -
+      - id: ui-linter
-      #   npm error `npm ci` can only install packages when your package.json and package-lock.json or npm-shrinkwrap.json are in sync. Please update your lock file with `npm install` before continuing.
+        name: Format & Lint UI
-      #   npm error Invalid: lock file's llama-stack-client@0.2.17 does not satisfy llama-stack-client@0.2.18
+        entry: bash ./scripts/run-ui-linter.sh
-      # and until we have infra for installing prettier and next via npm -
+        language: system
-      #   Lint UI code with ESLint.....................................................Failed
+        files: ^llama_stack/ui/.*\.(ts|tsx)$
-      #   - hook id: ui-eslint
+        pass_filenames: false
-      #   - exit code: 127
+        require_serial: true
-      #   > ui@0.1.0 lint
-      #   > next lint --fix --quiet
-      #   sh: line 1: next: command not found
-      #
-      # - id: ui-prettier
-      #   name: Format UI code with Prettier
-      #   entry: bash -c 'cd llama_stack/ui && npm ci && npm run format'
-      #   language: system
-      #   files: ^llama_stack/ui/.*\.(ts|tsx)$
-      #   pass_filenames: false
-      #   require_serial: true
-      # - id: ui-eslint
-      #   name: Lint UI code with ESLint
-      #   entry: bash -c 'cd llama_stack/ui && npm run lint -- --fix --quiet'
-      #   language: system
-      #   files: ^llama_stack/ui/.*\.(ts|tsx)$
-      #   pass_filenames: false
-      #   require_serial: true
 
       - id: check-log-usage
         name: Ensure 'llama_stack.log' usage for logging

docs/_static/llama-stack-spec.html

@@ -4605,6 +4605,49 @@
                 }
             }
         },
+        "/v1/inference/rerank": {
+            "post": {
+                "responses": {
+                    "200": {
+                        "description": "RerankResponse with indices sorted by relevance score (descending).",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "$ref": "#/components/schemas/RerankResponse"
+                                }
+                            }
+                        }
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "Inference"
+                ],
+                "description": "Rerank a list of documents based on their relevance to a query.",
+                "parameters": [],
+                "requestBody": {
+                    "content": {
+                        "application/json": {
+                            "schema": {
+                                "$ref": "#/components/schemas/RerankRequest"
+                            }
+                        }
+                    },
+                    "required": true
+                }
+            }
+        },
         "/v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume": {
             "post": {
                 "responses": {
@@ -16418,12 +16461,16 @@
                     "value": {
                         "type": "number",
                         "description": "The numeric value of the metric at this timestamp"
+                    },
+                    "unit": {
+                        "type": "string"
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "timestamp",
-                    "value"
+                    "value",
+                    "unit"
                 ],
                 "title": "MetricDataPoint",
                 "description": "A single data point in a metric time series."
@@ -16981,6 +17028,95 @@
                 ],
                 "title": "RegisterVectorDbRequest"
             },
+            "RerankRequest": {
+                "type": "object",
+                "properties": {
+                    "model": {
+                        "type": "string",
+                        "description": "The identifier of the reranking model to use."
+                    },
+                    "query": {
+                        "oneOf": [
+                            {
+                                "type": "string"
+                            },
+                            {
+                                "$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam"
+                            },
+                            {
+                                "$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam"
+                            }
+                        ],
+                        "description": "The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length."
+                    },
+                    "items": {
+                        "type": "array",
+                        "items": {
+                            "oneOf": [
+                                {
+                                    "type": "string"
+                                },
+                                {
+                                    "$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam"
+                                },
+                                {
+                                    "$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam"
+                                }
+                            ]
+                        },
+                        "description": "List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length."
+                    },
+                    "max_num_results": {
+                        "type": "integer",
+                        "description": "(Optional) Maximum number of results to return. Default: returns all."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "model",
+                    "query",
+                    "items"
+                ],
+                "title": "RerankRequest"
+            },
+            "RerankData": {
+                "type": "object",
+                "properties": {
+                    "index": {
+                        "type": "integer",
+                        "description": "The original index of the document in the input list"
+                    },
+                    "relevance_score": {
+                        "type": "number",
+                        "description": "The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "index",
+                    "relevance_score"
+                ],
+                "title": "RerankData",
+                "description": "A single rerank result from a reranking response."
+            },
+            "RerankResponse": {
+                "type": "object",
+                "properties": {
+                    "data": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/RerankData"
+                        },
+                        "description": "List of rerank result objects, sorted by relevance score (descending)"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "data"
+                ],
+                "title": "RerankResponse",
+                "description": "Response from a reranking request."
+            },
             "ResumeAgentTurnRequest": {
                 "type": "object",
                 "properties": {

docs/_static/llama-stack-spec.yaml

@@ -3264,6 +3264,37 @@ paths:
             schema:
               $ref: '#/components/schemas/QueryTracesRequest'
         required: true
+  /v1/inference/rerank:
+    post:
+      responses:
+        '200':
+          description: >-
+            RerankResponse with indices sorted by relevance score (descending).
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/RerankResponse'
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - Inference
+      description: >-
+        Rerank a list of documents based on their relevance to a query.
+      parameters: []
+      requestBody:
+        content:
+          application/json:
+            schema:
+              $ref: '#/components/schemas/RerankRequest'
+        required: true
   /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume:
     post:
       responses:
@@ -12242,10 +12273,13 @@ components:
           type: number
           description: >-
             The numeric value of the metric at this timestamp
+        unit:
+          type: string
       additionalProperties: false
       required:
         - timestamp
         - value
+        - unit
       title: MetricDataPoint
       description: >-
         A single data point in a metric time series.
@@ -12656,6 +12690,76 @@ components:
         - vector_db_id
         - embedding_model
       title: RegisterVectorDbRequest
+    RerankRequest:
+      type: object
+      properties:
+        model:
+          type: string
+          description: >-
+            The identifier of the reranking model to use.
+        query:
+          oneOf:
+            - type: string
+            - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam'
+            - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam'
+          description: >-
+            The search query to rank items against. Can be a string, text content
+            part, or image content part. The input must not exceed the model's max
+            input token length.
+        items:
+          type: array
+          items:
+            oneOf:
+              - type: string
+              - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam'
+              - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam'
+          description: >-
+            List of items to rerank. Each item can be a string, text content part,
+            or image content part. Each input must not exceed the model's max input
+            token length.
+        max_num_results:
+          type: integer
+          description: >-
+            (Optional) Maximum number of results to return. Default: returns all.
+      additionalProperties: false
+      required:
+        - model
+        - query
+        - items
+      title: RerankRequest
+    RerankData:
+      type: object
+      properties:
+        index:
+          type: integer
+          description: >-
+            The original index of the document in the input list
+        relevance_score:
+          type: number
+          description: >-
+            The relevance score from the model output. Values are inverted when applicable
+            so that higher scores indicate greater relevance.
+      additionalProperties: false
+      required:
+        - index
+        - relevance_score
+      title: RerankData
+      description: >-
+        A single rerank result from a reranking response.
+    RerankResponse:
+      type: object
+      properties:
+        data:
+          type: array
+          items:
+            $ref: '#/components/schemas/RerankData'
+          description: >-
+            List of rerank result objects, sorted by relevance score (descending)
+      additionalProperties: false
+      required:
+        - data
+      title: RerankResponse
+      description: Response from a reranking request.
     ResumeAgentTurnRequest:
       type: object
       properties:

docs/source/distributions/configuration.md

@@ -225,8 +225,32 @@ server:
   port: 8321  # Port to listen on (default: 8321)
   tls_certfile: "/path/to/cert.pem"  # Optional: Path to TLS certificate for HTTPS
   tls_keyfile: "/path/to/key.pem"    # Optional: Path to TLS key for HTTPS
+  cors: true  # Optional: Enable CORS (dev mode) or full config object
 ```
 
+### CORS Configuration
+
+CORS (Cross-Origin Resource Sharing) can be configured in two ways:
+
+**Local development** (allows localhost origins only):
+```yaml
+server:
+  cors: true
+```
+
+**Explicit configuration** (custom origins and settings):
+```yaml
+server:
+  cors:
+    allow_origins: ["https://myapp.com", "https://app.example.com"]
+    allow_methods: ["GET", "POST", "PUT", "DELETE"]
+    allow_headers: ["Content-Type", "Authorization"]
+    allow_credentials: true
+    max_age: 3600
+```
+
+When `cors: true`, the server enables secure localhost-only access for local development. For production, specify exact origins to maintain security.
+
 ### Authentication Configuration
 
 > **Breaking Change (v0.2.14)**: The authentication configuration structure has changed. The previous format with `provider_type` and `config` fields has been replaced with a unified `provider_config` field that includes the `type` field. Update your configuration files accordingly.
@@ -618,6 +642,54 @@ Content-Type: application/json
 }
 ```
 
+### CORS Configuration
+
+Configure CORS to allow web browsers to make requests from different domains. Disabled by default.
+
+#### Quick Setup
+
+For development, use the simple boolean flag:
+
+```yaml
+server:
+  cors: true  # Auto-enables localhost with any port
+```
+
+This automatically allows `http://localhost:*` and `https://localhost:*` with secure defaults.
+
+#### Custom Configuration
+
+For specific origins and full control:
+
+```yaml
+server:
+  cors:
+    allow_origins: ["https://myapp.com", "https://staging.myapp.com"]
+    allow_credentials: true
+    allow_methods: ["GET", "POST", "PUT", "DELETE"]
+    allow_headers: ["Content-Type", "Authorization"]
+    allow_origin_regex: "https://.*\\.example\\.com"  # Optional regex pattern
+    expose_headers: ["X-Total-Count"]
+    max_age: 86400
+```
+
+#### Configuration Options
+
+| Field                | Description                                    | Default |
+| -------------------- | ---------------------------------------------- | ------- |
+| `allow_origins`      | List of allowed origins. Use `["*"]` for any. | `["*"]` |
+| `allow_origin_regex` | Regex pattern for allowed origins (optional). | `None`  |
+| `allow_methods`      | Allowed HTTP methods.                          | `["*"]` |
+| `allow_headers`      | Allowed headers.                               | `["*"]` |
+| `allow_credentials`  | Allow credentials (cookies, auth headers).    | `false` |
+| `expose_headers`     | Headers exposed to browser.                   | `[]`    |
+| `max_age`            | Preflight cache time (seconds).               | `600`   |
+
+**Security Notes**:
+- `allow_credentials: true` requires explicit origins (no wildcards)
+- `cors: true` enables localhost access only (secure for development)
+- For public APIs, always specify exact allowed origins
+
 ## Extending to handle Safety
 
 Configuring Safety can be a little involved so it is instructive to go through an example.

docs/source/distributions/importing_as_library.md

@@ -17,7 +17,6 @@ client = LlamaStackAsLibraryClient(
     # provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
     provider_data={"tavily_search_api_key": os.environ["TAVILY_SEARCH_API_KEY"]},
 )
-client.initialize()
 ```
 
 This will parse your config and set up any inline implementations and remote clients needed for your implementation.
@@ -32,5 +31,4 @@ If you've created a [custom distribution](https://llama-stack.readthedocs.io/en/
 
 ```python
 client = LlamaStackAsLibraryClient(config_path)
-client.initialize()
 ```

docs/source/providers/batches/index.md

@@ -2,12 +2,15 @@
 
 ## Overview
 
-Protocol for batch processing API operations.
+The Batches API enables efficient processing of multiple requests in a single operation,
-
-    The Batches API enables efficient processing of multiple requests in a single operation,
     particularly useful for processing large datasets, batch evaluation workflows, and
     cost-effective inference at scale.
 
+    The API is designed to allow use of openai client libraries for seamless integration.
+
+    This API provides the following extensions:
+     - idempotent batch creation
+
     Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.

docs/source/providers/files/index.md

@@ -10,4 +10,5 @@ This section contains documentation for all available providers for the **files*
 :maxdepth: 1
 
 inline_localfs
+remote_s3
 ```

docs/source/providers/files/remote_s3.md

@@ -0,0 +1,33 @@
+# remote::s3
+
+## Description
+
+AWS S3-based file storage provider for scalable cloud file management with metadata persistence.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `bucket_name` | `<class 'str'>` | No |  | S3 bucket name to store files |
+| `region` | `<class 'str'>` | No | us-east-1 | AWS region where the bucket is located |
+| `aws_access_key_id` | `str \| None` | No |  | AWS access key ID (optional if using IAM roles) |
+| `aws_secret_access_key` | `str \| None` | No |  | AWS secret access key (optional if using IAM roles) |
+| `endpoint_url` | `str \| None` | No |  | Custom S3 endpoint URL (for MinIO, LocalStack, etc.) |
+| `auto_create_bucket` | `<class 'bool'>` | No | False | Automatically create the S3 bucket if it doesn't exist |
+| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
+
+## Sample Configuration
+
+```yaml
+bucket_name: ${env.S3_BUCKET_NAME}
+region: ${env.AWS_REGION:=us-east-1}
+aws_access_key_id: ${env.AWS_ACCESS_KEY_ID:=}
+aws_secret_access_key: ${env.AWS_SECRET_ACCESS_KEY:=}
+endpoint_url: ${env.S3_ENDPOINT_URL:=}
+auto_create_bucket: ${env.S3_AUTO_CREATE_BUCKET:=false}
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/s3_files_metadata.db
+
+```
+

docs/source/providers/post_training/index.md

@@ -9,7 +9,9 @@ This section contains documentation for all available providers for the **post_t
 ```{toctree}
 :maxdepth: 1
 
-inline_huggingface
+inline_huggingface-cpu
-inline_torchtune
+inline_huggingface-gpu
+inline_torchtune-cpu
+inline_torchtune-gpu
 remote_nvidia
 ```

docs/source/providers/post_training/inline_huggingface-cpu.md

@@ -0,0 +1,41 @@
+# inline::huggingface-cpu
+
+## Description
+
+HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `device` | `<class 'str'>` | No | cuda |  |
+| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No |  |  |
+| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface |  |
+| `chat_template` | `<class 'str'>` | No | <|user|>
+{input}
+<|assistant|>
+{output} |  |
+| `model_specific_config` | `<class 'dict'>` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} |  |
+| `max_seq_length` | `<class 'int'>` | No | 2048 |  |
+| `gradient_checkpointing` | `<class 'bool'>` | No | False |  |
+| `save_total_limit` | `<class 'int'>` | No | 3 |  |
+| `logging_steps` | `<class 'int'>` | No | 10 |  |
+| `warmup_ratio` | `<class 'float'>` | No | 0.1 |  |
+| `weight_decay` | `<class 'float'>` | No | 0.01 |  |
+| `dataloader_num_workers` | `<class 'int'>` | No | 4 |  |
+| `dataloader_pin_memory` | `<class 'bool'>` | No | True |  |
+| `dpo_beta` | `<class 'float'>` | No | 0.1 |  |
+| `use_reference_model` | `<class 'bool'>` | No | True |  |
+| `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid |  |
+| `dpo_output_dir` | `<class 'str'>` | No |  |  |
+
+## Sample Configuration
+
+```yaml
+checkpoint_format: huggingface
+distributed_backend: null
+device: cpu
+dpo_output_dir: ~/.llama/dummy/dpo_output
+
+```
+

docs/source/providers/post_training/inline_huggingface-gpu.md

@@ -0,0 +1,41 @@
+# inline::huggingface-gpu
+
+## Description
+
+HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `device` | `<class 'str'>` | No | cuda |  |
+| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No |  |  |
+| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface |  |
+| `chat_template` | `<class 'str'>` | No | <|user|>
+{input}
+<|assistant|>
+{output} |  |
+| `model_specific_config` | `<class 'dict'>` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} |  |
+| `max_seq_length` | `<class 'int'>` | No | 2048 |  |
+| `gradient_checkpointing` | `<class 'bool'>` | No | False |  |
+| `save_total_limit` | `<class 'int'>` | No | 3 |  |
+| `logging_steps` | `<class 'int'>` | No | 10 |  |
+| `warmup_ratio` | `<class 'float'>` | No | 0.1 |  |
+| `weight_decay` | `<class 'float'>` | No | 0.01 |  |
+| `dataloader_num_workers` | `<class 'int'>` | No | 4 |  |
+| `dataloader_pin_memory` | `<class 'bool'>` | No | True |  |
+| `dpo_beta` | `<class 'float'>` | No | 0.1 |  |
+| `use_reference_model` | `<class 'bool'>` | No | True |  |
+| `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid |  |
+| `dpo_output_dir` | `<class 'str'>` | No |  |  |
+
+## Sample Configuration
+
+```yaml
+checkpoint_format: huggingface
+distributed_backend: null
+device: cpu
+dpo_output_dir: ~/.llama/dummy/dpo_output
+
+```
+

docs/source/providers/post_training/inline_torchtune-cpu.md

@@ -0,0 +1,20 @@
+# inline::torchtune-cpu
+
+## Description
+
+TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `torch_seed` | `int \| None` | No |  |  |
+| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta |  |
+
+## Sample Configuration
+
+```yaml
+checkpoint_format: meta
+
+```
+

docs/source/providers/post_training/inline_torchtune-gpu.md

@@ -0,0 +1,20 @@
+# inline::torchtune-gpu
+
+## Description
+
+TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `torch_seed` | `int \| None` | No |  |  |
+| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta |  |
+
+## Sample Configuration
+
+```yaml
+checkpoint_format: meta
+
+```
+

llama_stack/apis/batches/batches.py

@@ -29,12 +29,16 @@ class ListBatchesResponse(BaseModel):
 
 @runtime_checkable
 class Batches(Protocol):
-    """Protocol for batch processing API operations.
+    """
-
     The Batches API enables efficient processing of multiple requests in a single operation,
     particularly useful for processing large datasets, batch evaluation workflows, and
     cost-effective inference at scale.
 
+    The API is designed to allow use of openai client libraries for seamless integration.
+
+    This API provides the following extensions:
+     - idempotent batch creation
+
     Note: This API is currently under active development and may undergo changes.
     """
 
@@ -45,6 +49,7 @@ class Batches(Protocol):
         endpoint: str,
         completion_window: Literal["24h"],
         metadata: dict[str, str] | None = None,
+        idempotency_key: str | None = None,
     ) -> BatchObject:
         """Create a new batch for processing multiple API requests.
 
@@ -52,6 +57,7 @@ class Batches(Protocol):
         :param endpoint: The endpoint to be used for all requests in the batch.
         :param completion_window: The time window within which the batch should be processed.
         :param metadata: Optional metadata for the batch.
+        :param idempotency_key: Optional idempotency key. When provided, enables idempotent behavior.
         :returns: The created batch object.
         """
         ...

llama_stack/apis/inference/inference.py

@@ -473,6 +473,28 @@ class EmbeddingsResponse(BaseModel):
     embeddings: list[list[float]]
 
 
+@json_schema_type
+class RerankData(BaseModel):
+    """A single rerank result from a reranking response.
+
+    :param index: The original index of the document in the input list
+    :param relevance_score: The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance.
+    """
+
+    index: int
+    relevance_score: float
+
+
+@json_schema_type
+class RerankResponse(BaseModel):
+    """Response from a reranking request.
+
+    :param data: List of rerank result objects, sorted by relevance score (descending)
+    """
+
+    data: list[RerankData]
+
+
 @json_schema_type
 class OpenAIChatCompletionContentPartTextParam(BaseModel):
     """Text content part for OpenAI-compatible chat completion messages.
@@ -1046,6 +1068,7 @@ class InferenceProvider(Protocol):
         :returns: A BatchCompletionResponse with the full completions.
         """
         raise NotImplementedError("Batch completion is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
 
     @webmethod(route="/inference/chat-completion", method="POST")
     async def chat_completion(
@@ -1110,6 +1133,7 @@ class InferenceProvider(Protocol):
         :returns: A BatchChatCompletionResponse with the full completions.
         """
         raise NotImplementedError("Batch chat completion is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
 
     @webmethod(route="/inference/embeddings", method="POST")
     async def embeddings(
@@ -1131,6 +1155,25 @@ class InferenceProvider(Protocol):
         """
         ...
 
+    @webmethod(route="/inference/rerank", method="POST", experimental=True)
+    async def rerank(
+        self,
+        model: str,
+        query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+        items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
+        max_num_results: int | None = None,
+    ) -> RerankResponse:
+        """Rerank a list of documents based on their relevance to a query.
+
+        :param model: The identifier of the reranking model to use.
+        :param query: The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.
+        :param items: List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.
+        :param max_num_results: (Optional) Maximum number of results to return. Default: returns all.
+        :returns: RerankResponse with indices sorted by relevance score (descending).
+        """
+        raise NotImplementedError("Reranking is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
+
     @webmethod(route="/openai/v1/completions", method="POST")
     async def openai_completion(
         self,

llama_stack/apis/telemetry/telemetry.py

@@ -386,6 +386,7 @@ class MetricDataPoint(BaseModel):
 
     timestamp: int
     value: float
+    unit: str
 
 
 @json_schema_type
@@ -518,7 +519,7 @@ class Telemetry(Protocol):
         metric_name: str,
         start_time: int,
         end_time: int | None = None,
-        granularity: str | None = "1d",
+        granularity: str | None = None,
         query_type: MetricQueryType = MetricQueryType.RANGE,
         label_matchers: list[MetricLabelMatcher] | None = None,
     ) -> QueryMetricsResponse:

llama_stack/cli/stack/run.py

@@ -15,7 +15,7 @@ from llama_stack.log import get_logger
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
 
-logger = get_logger(name=__name__, category="server")
+logger = get_logger(name=__name__, category="cli")
 
 
 class StackRun(Subcommand):

llama_stack/core/datatypes.py

@@ -318,6 +318,41 @@ class QuotaConfig(BaseModel):
     period: QuotaPeriod = Field(default=QuotaPeriod.DAY, description="Quota period to set")
 
 
+class CORSConfig(BaseModel):
+    allow_origins: list[str] = Field(default_factory=list)
+    allow_origin_regex: str | None = Field(default=None)
+    allow_methods: list[str] = Field(default=["OPTIONS"])
+    allow_headers: list[str] = Field(default_factory=list)
+    allow_credentials: bool = Field(default=False)
+    expose_headers: list[str] = Field(default_factory=list)
+    max_age: int = Field(default=600, ge=0)
+
+    @model_validator(mode="after")
+    def validate_credentials_config(self) -> Self:
+        if self.allow_credentials and (self.allow_origins == ["*"] or "*" in self.allow_origins):
+            raise ValueError("Cannot use wildcard origins with credentials enabled")
+        return self
+
+
+def process_cors_config(cors_config: bool | CORSConfig | None) -> CORSConfig | None:
+    if cors_config is False or cors_config is None:
+        return None
+
+    if cors_config is True:
+        # dev mode: allow localhost on any port
+        return CORSConfig(
+            allow_origins=[],
+            allow_origin_regex=r"https?://localhost:\d+",
+            allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+            allow_headers=["Content-Type", "Authorization", "X-Requested-With"],
+        )
+
+    if isinstance(cors_config, CORSConfig):
+        return cors_config
+
+    raise ValueError(f"Expected bool or CORSConfig, got {type(cors_config).__name__}")
+
+
 class ServerConfig(BaseModel):
     port: int = Field(
         default=8321,
@@ -349,6 +384,12 @@ class ServerConfig(BaseModel):
         default=None,
         description="Per client quota request configuration",
     )
+    cors: bool | CORSConfig | None = Field(
+        default=None,
+        description="CORS configuration for cross-origin requests. Can be:\n"
+        "- true: Enable localhost CORS for development\n"
+        "- {allow_origins: [...], allow_methods: [...], ...}: Full configuration",
+    )
 
 
 class StackRunConfig(BaseModel):

llama_stack/core/library_client.py

@@ -146,39 +146,26 @@ class LlamaStackAsLibraryClient(LlamaStackClient):
     ):
         super().__init__()
         self.async_client = AsyncLlamaStackAsLibraryClient(
-            config_path_or_distro_name, custom_provider_registry, provider_data
+            config_path_or_distro_name, custom_provider_registry, provider_data, skip_logger_removal
         )
         self.pool_executor = ThreadPoolExecutor(max_workers=4)
-        self.skip_logger_removal = skip_logger_removal
         self.provider_data = provider_data
 
         self.loop = asyncio.new_event_loop()
 
-    def initialize(self):
-        if in_notebook():
-            import nest_asyncio
-
-            nest_asyncio.apply()
-            if not self.skip_logger_removal:
-                self._remove_root_logger_handlers()
-
         # use a new event loop to avoid interfering with the main event loop
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
         try:
-            return loop.run_until_complete(self.async_client.initialize())
+            loop.run_until_complete(self.async_client.initialize())
         finally:
             asyncio.set_event_loop(None)
 
-    def _remove_root_logger_handlers(self):
+    def initialize(self):
         """
-        Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
+        Deprecated method for backward compatibility.
         """
-        root_logger = logging.getLogger()
+        pass
-
-        for handler in root_logger.handlers[:]:
-            root_logger.removeHandler(handler)
-            logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
 
     def request(self, *args, **kwargs):
         loop = self.loop
@@ -216,6 +203,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         config_path_or_distro_name: str,
         custom_provider_registry: ProviderRegistry | None = None,
         provider_data: dict[str, Any] | None = None,
+        skip_logger_removal: bool = False,
     ):
         super().__init__()
         # when using the library client, we should not log to console since many
@@ -223,6 +211,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
         os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
 
+        if in_notebook():
+            import nest_asyncio
+
+            nest_asyncio.apply()
+            if not skip_logger_removal:
+                self._remove_root_logger_handlers()
+
         if config_path_or_distro_name.endswith(".yaml"):
             config_path = Path(config_path_or_distro_name)
             if not config_path.exists():
@@ -239,7 +234,24 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         self.provider_data = provider_data
         self.route_impls: RouteImpls | None = None  # Initialize to None to prevent AttributeError
 
+    def _remove_root_logger_handlers(self):
+        """
+        Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
+        """
+        root_logger = logging.getLogger()
+
+        for handler in root_logger.handlers[:]:
+            root_logger.removeHandler(handler)
+            logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
+
     async def initialize(self) -> bool:
+        """
+        Initialize the async client.
+
+        Returns:
+            bool: True if initialization was successful
+        """
+
         try:
             self.route_impls = None
             self.impls = await construct_stack(self.config, self.custom_provider_registry)

llama_stack/core/routers/datasets.py

@@ -12,7 +12,7 @@ from llama_stack.apis.datasets import DatasetPurpose, DataSource
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class DatasetIORouter(DatasetIO):

llama_stack/core/routers/eval_scoring.py

@@ -16,7 +16,7 @@ from llama_stack.apis.scoring import (
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class ScoringRouter(Scoring):

llama_stack/core/routers/inference.py

@@ -65,7 +65,7 @@ from llama_stack.providers.datatypes import HealthResponse, HealthStatus, Routin
 from llama_stack.providers.utils.inference.inference_store import InferenceStore
 from llama_stack.providers.utils.telemetry.tracing import get_current_span
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class InferenceRouter(Inference):

llama_stack/core/routers/safety.py

@@ -13,7 +13,7 @@ from llama_stack.apis.shields import Shield
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class SafetyRouter(Safety):

llama_stack/core/routers/tool_runtime.py

@@ -22,7 +22,7 @@ from llama_stack.log import get_logger
 
 from ..routing_tables.toolgroups import ToolGroupsRoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class ToolRuntimeRouter(ToolRuntime):

llama_stack/core/routers/vector_io.py

@@ -30,7 +30,7 @@ from llama_stack.apis.vector_io import (
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class VectorIORouter(VectorIO):

llama_stack/core/routing_tables/benchmarks.py

@@ -14,7 +14,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):

llama_stack/core/routing_tables/common.py

@@ -23,7 +23,7 @@ from llama_stack.core.store import DistributionRegistry
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 def get_impl_api(p: Any) -> Api:

llama_stack/core/routing_tables/datasets.py

@@ -26,7 +26,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):

llama_stack/core/routing_tables/models.py

@@ -17,7 +17,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl, lookup_model
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ModelsRoutingTable(CommonRoutingTableImpl, Models):

llama_stack/core/routing_tables/scoring_functions.py

@@ -19,7 +19,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):

llama_stack/core/routing_tables/shields.py

@@ -15,7 +15,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):

llama_stack/core/routing_tables/toolgroups.py

@@ -14,7 +14,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 def parse_toolgroup_from_toolgroup_name_pair(toolgroup_name_with_maybe_tool_name: str) -> str | None:

llama_stack/core/routing_tables/vector_dbs.py

@@ -30,7 +30,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl, lookup_model
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):

llama_stack/core/server/auth.py

@@ -15,7 +15,7 @@ from llama_stack.core.server.auth_providers import create_auth_provider
 from llama_stack.core.server.routes import find_matching_route, initialize_route_impls
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="auth")
+logger = get_logger(name=__name__, category="core::auth")
 
 
 class AuthenticationMiddleware:

llama_stack/core/server/auth_providers.py

@@ -23,7 +23,7 @@ from llama_stack.core.datatypes import (
 )
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="auth")
+logger = get_logger(name=__name__, category="core::auth")
 
 
 class AuthResponse(BaseModel):

llama_stack/core/server/quota.py

@@ -15,7 +15,7 @@ from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
 
-logger = get_logger(name=__name__, category="quota")
+logger = get_logger(name=__name__, category="core::server")
 
 
 class QuotaMiddleware:

llama_stack/core/server/server.py

@@ -28,6 +28,7 @@ from aiohttp import hdrs
 from fastapi import Body, FastAPI, HTTPException, Request, Response
 from fastapi import Path as FastapiPath
 from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
 from openai import BadRequestError
 from pydantic import BaseModel, ValidationError
@@ -40,6 +41,7 @@ from llama_stack.core.datatypes import (
     AuthenticationRequiredError,
     LoggingConfig,
     StackRunConfig,
+    process_cors_config,
 )
 from llama_stack.core.distribution import builtin_automatically_routed_apis
 from llama_stack.core.external import ExternalApiSpec, load_external_apis
@@ -82,7 +84,7 @@ from .quota import QuotaMiddleware
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
 
-logger = get_logger(name=__name__, category="server")
+logger = get_logger(name=__name__, category="core::server")
 
 
 def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
@@ -413,7 +415,7 @@ def main(args: argparse.Namespace | None = None):
         config_contents = yaml.safe_load(fp)
         if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
             logger_config = LoggingConfig(**cfg)
-        logger = get_logger(name=__name__, category="server", config=logger_config)
+        logger = get_logger(name=__name__, category="core::server", config=logger_config)
         if args.env:
             for env_pair in args.env:
                 try:
@@ -483,6 +485,12 @@ def main(args: argparse.Namespace | None = None):
             window_seconds=window_seconds,
         )
 
+    if config.server.cors:
+        logger.info("Enabling CORS")
+        cors_config = process_cors_config(config.server.cors)
+        if cors_config:
+            app.add_middleware(CORSMiddleware, **cors_config.model_dump())
+
     if Api.telemetry in impls:
         setup_logger(impls[Api.telemetry])
     else:

llama_stack/core/store/registry.py

@@ -16,7 +16,7 @@ from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 
-logger = get_logger(__name__, category="core")
+logger = get_logger(__name__, category="core::registry")
 
 
 class DistributionRegistry(Protocol):

llama_stack/core/utils/config_resolution.py

@@ -10,7 +10,7 @@ from pathlib import Path
 from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="config_resolution")
+logger = get_logger(name=__name__, category="core")
 
 
 DISTRO_DIR = Path(__file__).parent.parent.parent.parent / "llama_stack" / "distributions"

llama_stack/distributions/ci-tests/build.yaml

@@ -34,7 +34,7 @@ distribution_spec:
     telemetry:
     - provider_type: inline::meta-reference
     post_training:
-    - provider_type: inline::huggingface
+    - provider_type: inline::huggingface-cpu
     eval:
     - provider_type: inline::meta-reference
     datasetio:

llama_stack/distributions/ci-tests/run.yaml

@@ -156,8 +156,8 @@ providers:
       sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   post_training:
-  - provider_id: huggingface
+  - provider_id: huggingface-cpu
-    provider_type: inline::huggingface
+    provider_type: inline::huggingface-cpu
     config:
       checkpoint_format: huggingface
       distributed_backend: null

llama_stack/distributions/starter-gpu/__init__.py

@@ -0,0 +1,7 @@
+# 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 .starter_gpu import get_distribution_template  # noqa: F401

llama_stack/distributions/starter-gpu/build.yaml

@@ -0,0 +1,59 @@
+version: 2
+distribution_spec:
+  description: Quick start template for running Llama Stack with several popular providers.
+    This distribution is intended for GPU-enabled environments.
+  providers:
+    inference:
+    - provider_type: remote::cerebras
+    - provider_type: remote::ollama
+    - provider_type: remote::vllm
+    - provider_type: remote::tgi
+    - provider_type: remote::fireworks
+    - provider_type: remote::together
+    - provider_type: remote::bedrock
+    - provider_type: remote::nvidia
+    - provider_type: remote::openai
+    - provider_type: remote::anthropic
+    - provider_type: remote::gemini
+    - provider_type: remote::vertexai
+    - provider_type: remote::groq
+    - provider_type: remote::sambanova
+    - provider_type: inline::sentence-transformers
+    vector_io:
+    - provider_type: inline::faiss
+    - provider_type: inline::sqlite-vec
+    - provider_type: inline::milvus
+    - provider_type: remote::chromadb
+    - provider_type: remote::pgvector
+    files:
+    - provider_type: inline::localfs
+    safety:
+    - provider_type: inline::llama-guard
+    - provider_type: inline::code-scanner
+    agents:
+    - provider_type: inline::meta-reference
+    telemetry:
+    - provider_type: inline::meta-reference
+    post_training:
+    - provider_type: inline::torchtune-gpu
+    eval:
+    - provider_type: inline::meta-reference
+    datasetio:
+    - provider_type: remote::huggingface
+    - provider_type: inline::localfs
+    scoring:
+    - provider_type: inline::basic
+    - provider_type: inline::llm-as-judge
+    - provider_type: inline::braintrust
+    tool_runtime:
+    - provider_type: remote::brave-search
+    - provider_type: remote::tavily-search
+    - provider_type: inline::rag-runtime
+    - provider_type: remote::model-context-protocol
+    batches:
+    - provider_type: inline::reference
+image_type: venv
+additional_pip_packages:
+- aiosqlite
+- asyncpg
+- sqlalchemy[asyncio]

llama_stack/distributions/starter-gpu/run.yaml

@@ -0,0 +1,238 @@
+version: 2
+image_name: starter-gpu
+apis:
+- agents
+- batches
+- datasetio
+- eval
+- files
+- inference
+- post_training
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: ${env.CEREBRAS_API_KEY:+cerebras}
+    provider_type: remote::cerebras
+    config:
+      base_url: https://api.cerebras.ai
+      api_key: ${env.CEREBRAS_API_KEY:=}
+  - provider_id: ${env.OLLAMA_URL:+ollama}
+    provider_type: remote::ollama
+    config:
+      url: ${env.OLLAMA_URL:=http://localhost:11434}
+  - provider_id: ${env.VLLM_URL:+vllm}
+    provider_type: remote::vllm
+    config:
+      url: ${env.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: ${env.TGI_URL:+tgi}
+    provider_type: remote::tgi
+    config:
+      url: ${env.TGI_URL:=}
+  - provider_id: fireworks
+    provider_type: remote::fireworks
+    config:
+      url: https://api.fireworks.ai/inference/v1
+      api_key: ${env.FIREWORKS_API_KEY:=}
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+      api_key: ${env.TOGETHER_API_KEY:=}
+  - provider_id: bedrock
+    provider_type: remote::bedrock
+  - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
+    provider_type: remote::nvidia
+    config:
+      url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
+      api_key: ${env.NVIDIA_API_KEY:=}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:=}
+      base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:=}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:=}
+  - provider_id: ${env.VERTEX_AI_PROJECT:+vertexai}
+    provider_type: remote::vertexai
+    config:
+      project: ${env.VERTEX_AI_PROJECT:=}
+      location: ${env.VERTEX_AI_LOCATION:=us-central1}
+  - provider_id: groq
+    provider_type: remote::groq
+    config:
+      url: https://api.groq.com
+      api_key: ${env.GROQ_API_KEY:=}
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1
+      api_key: ${env.SAMBANOVA_API_KEY:=}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db
+  - provider_id: ${env.MILVUS_URL:+milvus}
+    provider_type: inline::milvus
+    config:
+      db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db
+  - provider_id: ${env.CHROMADB_URL:+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db
+  - provider_id: ${env.PGVECTOR_DB:+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:=localhost}
+      port: ${env.PGVECTOR_PORT:=5432}
+      db: ${env.PGVECTOR_DB:=}
+      user: ${env.PGVECTOR_USER:=}
+      password: ${env.PGVECTOR_PASSWORD:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
+      metadata_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+  - provider_id: code-scanner
+    provider_type: inline::code-scanner
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/agents_store.db
+      responses_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/responses_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
+      sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
+      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/trace_store.db
+      otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
+  post_training:
+  - provider_id: torchtune-gpu
+    provider_type: inline::torchtune-gpu
+    config:
+      checkpoint_format: meta
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/meta_reference_eval.db
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/huggingface_datasetio.db
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/localfs_datasetio.db
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+  - 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: rag-runtime
+    provider_type: inline::rag-runtime
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+  batches:
+  - provider_id: reference
+    provider_type: inline::reference
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/batches.db
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/registry.db
+inference_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/inference_store.db
+models: []
+shields:
+- shield_id: llama-guard
+  provider_id: ${env.SAFETY_MODEL:+llama-guard}
+  provider_shield_id: ${env.SAFETY_MODEL:=}
+- shield_id: code-scanner
+  provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
+  provider_shield_id: ${env.CODE_SCANNER_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
+server:
+  port: 8321

llama_stack/distributions/starter-gpu/starter_gpu.py

@@ -0,0 +1,22 @@
+# 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 llama_stack.distributions.template import BuildProvider, DistributionTemplate
+
+from ..starter.starter import get_distribution_template as get_starter_distribution_template
+
+
+def get_distribution_template() -> DistributionTemplate:
+    template = get_starter_distribution_template()
+    name = "starter-gpu"
+    template.name = name
+    template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments."
+
+    template.providers["post_training"] = [
+        BuildProvider(provider_type="inline::torchtune-gpu"),
+    ]
+    return template

llama_stack/distributions/starter/build.yaml

@@ -1,6 +1,7 @@
 version: 2
 distribution_spec:
-  description: Quick start template for running Llama Stack with several popular providers
+  description: Quick start template for running Llama Stack with several popular providers.
+    This distribution is intended for CPU-only environments.
   providers:
     inference:
     - provider_type: remote::cerebras
@@ -34,7 +35,7 @@ distribution_spec:
     telemetry:
     - provider_type: inline::meta-reference
     post_training:
-    - provider_type: inline::huggingface
+    - provider_type: inline::huggingface-cpu
     eval:
     - provider_type: inline::meta-reference
     datasetio:

llama_stack/distributions/starter/run.yaml

@@ -156,8 +156,8 @@ providers:
       sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/trace_store.db
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   post_training:
-  - provider_id: huggingface
+  - provider_id: huggingface-cpu
-    provider_type: inline::huggingface
+    provider_type: inline::huggingface-cpu
     config:
       checkpoint_format: huggingface
       distributed_backend: null

llama_stack/distributions/starter/starter.py

@@ -120,7 +120,7 @@ def get_distribution_template() -> DistributionTemplate:
         ],
         "agents": [BuildProvider(provider_type="inline::meta-reference")],
         "telemetry": [BuildProvider(provider_type="inline::meta-reference")],
-        "post_training": [BuildProvider(provider_type="inline::huggingface")],
+        "post_training": [BuildProvider(provider_type="inline::huggingface-cpu")],
         "eval": [BuildProvider(provider_type="inline::meta-reference")],
         "datasetio": [
             BuildProvider(provider_type="remote::huggingface"),
@@ -178,7 +178,7 @@ def get_distribution_template() -> DistributionTemplate:
     return DistributionTemplate(
         name=name,
         distro_type="self_hosted",
-        description="Quick start template for running Llama Stack with several popular providers",
+        description="Quick start template for running Llama Stack with several popular providers. This distribution is intended for CPU-only environments.",
         container_image=None,
         template_path=None,
         providers=providers,

llama_stack/models/llama/llama3/multimodal/model.py

@@ -36,7 +36,7 @@ from .utils import get_negative_inf_value, to_2tuple
 
 MP_SCALE = 8
 
-logger = get_logger(name=__name__, category="models")
+logger = get_logger(name=__name__, category="models::llama")
 
 
 def reduce_from_tensor_model_parallel_region(input_):

llama_stack/models/llama/llama3/tool_utils.py

@@ -11,7 +11,7 @@ from llama_stack.log import get_logger
 
 from ..datatypes import BuiltinTool, RecursiveType, ToolCall, ToolPromptFormat
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="models::llama")
 
 BUILTIN_TOOL_PATTERN = r'\b(?P<tool_name>\w+)\.call\(query="(?P<query>[^"]*)"\)'
 CUSTOM_TOOL_CALL_PATTERN = re.compile(r"<function=(?P<function_name>[^}]+)>(?P<args>{.*?})")

llama_stack/models/llama/llama4/quantization/loader.py

@@ -18,7 +18,7 @@ from ...datatypes import QuantizationMode
 from ..model import Transformer, TransformerBlock
 from ..moe import MoE
 
-log = get_logger(name=__name__, category="models")
+log = get_logger(name=__name__, category="models::llama")
 
 
 def swiglu_wrapper_no_reduce(

llama_stack/models/llama/quantize_impls.py

@@ -9,7 +9,7 @@ import collections
 
 from llama_stack.log import get_logger
 
-log = get_logger(name=__name__, category="llama")
+log = get_logger(name=__name__, category="models::llama")
 
 try:
     import fbgemm_gpu.experimental.gen_ai  # noqa: F401

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -84,7 +84,7 @@ MEMORY_QUERY_TOOL = "knowledge_search"
 WEB_SEARCH_TOOL = "web_search"
 RAG_TOOL_GROUP = "builtin::rag"
 
-logger = get_logger(name=__name__, category="agents")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class ChatAgent(ShieldRunnerMixin):

llama_stack/providers/inline/agents/meta_reference/agents.py

@@ -51,7 +51,7 @@ from .config import MetaReferenceAgentsImplConfig
 from .persistence import AgentInfo
 from .responses.openai_responses import OpenAIResponsesImpl
 
-logger = get_logger(name=__name__, category="agents")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class MetaReferenceAgentsImpl(Agents):

llama_stack/providers/inline/agents/meta_reference/persistence.py

@@ -17,7 +17,7 @@ from llama_stack.core.request_headers import get_authenticated_user
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import KVStore
 
-log = get_logger(name=__name__, category="agents")
+log = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class AgentSessionInfo(Session):

llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -41,7 +41,7 @@ from .utils import (
     convert_response_text_to_chat_response_format,
 )
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="openai::responses")
 
 
 class OpenAIResponsePreviousResponseWithInputItems(BaseModel):

llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -47,7 +47,7 @@ from llama_stack.log import get_logger
 from .types import ChatCompletionContext, ChatCompletionResult
 from .utils import convert_chat_choice_to_response_message, is_function_tool_call
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class StreamingResponseOrchestrator:

llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py

@@ -38,7 +38,7 @@ from llama_stack.log import get_logger
 
 from .types import ChatCompletionContext, ToolExecutionResult
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class ToolExecutor:

llama_stack/providers/inline/agents/meta_reference/responses/utils.py

@@ -17,6 +17,8 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseOutputMessageContent,
     OpenAIResponseOutputMessageContentOutputText,
     OpenAIResponseOutputMessageFunctionToolCall,
+    OpenAIResponseOutputMessageMCPCall,
+    OpenAIResponseOutputMessageMCPListTools,
     OpenAIResponseText,
 )
 from llama_stack.apis.inference import (
@@ -99,14 +101,22 @@ async def convert_response_input_to_chat_messages(
     """
     messages: list[OpenAIMessageParam] = []
     if isinstance(input, list):
+        # extract all OpenAIResponseInputFunctionToolCallOutput items
+        # so their corresponding OpenAIToolMessageParam instances can
+        # be added immediately following the corresponding
+        # OpenAIAssistantMessageParam
+        tool_call_results = {}
         for input_item in input:
             if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
-                messages.append(
+                tool_call_results[input_item.call_id] = OpenAIToolMessageParam(
-                    OpenAIToolMessageParam(
+                    content=input_item.output,
-                        content=input_item.output,
+                    tool_call_id=input_item.call_id,
-                        tool_call_id=input_item.call_id,
-                    )
                 )
+
+        for input_item in input:
+            if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
+                # skip as these have been extracted and inserted in order
+                pass
             elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
                 tool_call = OpenAIChatCompletionToolCall(
                     index=0,
@@ -117,6 +127,28 @@ async def convert_response_input_to_chat_messages(
                     ),
                 )
                 messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                if input_item.call_id in tool_call_results:
+                    messages.append(tool_call_results[input_item.call_id])
+                    del tool_call_results[input_item.call_id]
+            elif isinstance(input_item, OpenAIResponseOutputMessageMCPCall):
+                tool_call = OpenAIChatCompletionToolCall(
+                    index=0,
+                    id=input_item.id,
+                    function=OpenAIChatCompletionToolCallFunction(
+                        name=input_item.name,
+                        arguments=input_item.arguments,
+                    ),
+                )
+                messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                messages.append(
+                    OpenAIToolMessageParam(
+                        content=input_item.output,
+                        tool_call_id=input_item.id,
+                    )
+                )
+            elif isinstance(input_item, OpenAIResponseOutputMessageMCPListTools):
+                # the tool list will be handled separately
+                pass
             else:
                 content = await convert_response_content_to_chat_content(input_item.content)
                 message_type = await get_message_type_by_role(input_item.role)
@@ -125,6 +157,10 @@ async def convert_response_input_to_chat_messages(
                         f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
                     )
                 messages.append(message_type(content=content))
+        if len(tool_call_results):
+            raise ValueError(
+                f"Received function_call_output(s) with call_id(s) {tool_call_results.keys()}, but no corresponding function_call"
+            )
     else:
         messages.append(OpenAIUserMessageParam(content=input))
     return messages

llama_stack/providers/inline/agents/meta_reference/safety.py

@@ -11,7 +11,7 @@ from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.telemetry import tracing
 
-log = get_logger(name=__name__, category="agents")
+log = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class SafetyException(Exception):  # noqa: N818

llama_stack/providers/inline/batches/reference/batches.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import asyncio
+import hashlib
 import itertools
 import json
 import time
@@ -136,28 +137,45 @@ class ReferenceBatchesImpl(Batches):
         endpoint: str,
         completion_window: Literal["24h"],
         metadata: dict[str, str] | None = None,
+        idempotency_key: str | None = None,
     ) -> BatchObject:
         """
         Create a new batch for processing multiple API requests.
 
-        Error handling by levels -
+        This implementation provides optional idempotency: when an idempotency key
-         0. Input param handling, results in 40x errors before processing, e.g.
+        (idempotency_key) is provided, a deterministic ID is generated based on the input
-           - Wrong completion_window
+        parameters. If a batch with the same parameters already exists, it will be
-           - Invalid metadata types
+        returned instead of creating a duplicate. Without an idempotency key,
-           - Unknown endpoint
+        each request creates a new batch with a unique ID.
-          -> no batch created
+
-         1. Errors preventing processing, result in BatchErrors aggregated in process_batch, e.g.
+        Args:
-           - input_file_id missing
+            input_file_id: The ID of an uploaded file containing requests for the batch.
-           - invalid json in file
+            endpoint: The endpoint to be used for all requests in the batch.
-           - missing custom_id, method, url, body
+            completion_window: The time window within which the batch should be processed.
-           - invalid model
+            metadata: Optional metadata for the batch.
-           - streaming
+            idempotency_key: Optional idempotency key for enabling idempotent behavior.
-          -> batch created, validation sends to failed status
+
-         2. Processing errors, result in error_file_id entries, e.g.
+        Returns:
-           - Any error returned from inference endpoint
+            The created or existing batch object.
-          -> batch created, goes to completed status
         """
 
+        # Error handling by levels -
+        #  0. Input param handling, results in 40x errors before processing, e.g.
+        #    - Wrong completion_window
+        #    - Invalid metadata types
+        #    - Unknown endpoint
+        #   -> no batch created
+        #  1. Errors preventing processing, result in BatchErrors aggregated in process_batch, e.g.
+        #    - input_file_id missing
+        #    - invalid json in file
+        #    - missing custom_id, method, url, body
+        #    - invalid model
+        #    - streaming
+        #   -> batch created, validation sends to failed status
+        #  2. Processing errors, result in error_file_id entries, e.g.
+        #    - Any error returned from inference endpoint
+        #   -> batch created, goes to completed status
+
         # TODO: set expiration time for garbage collection
 
         if endpoint not in ["/v1/chat/completions"]:
@@ -171,6 +189,35 @@ class ReferenceBatchesImpl(Batches):
             )
 
         batch_id = f"batch_{uuid.uuid4().hex[:16]}"
+
+        # For idempotent requests, use the idempotency key for the batch ID
+        # This ensures the same key always maps to the same batch ID,
+        # allowing us to detect parameter conflicts
+        if idempotency_key is not None:
+            hash_input = idempotency_key.encode("utf-8")
+            hash_digest = hashlib.sha256(hash_input).hexdigest()[:24]
+            batch_id = f"batch_{hash_digest}"
+
+            try:
+                existing_batch = await self.retrieve_batch(batch_id)
+
+                if (
+                    existing_batch.input_file_id != input_file_id
+                    or existing_batch.endpoint != endpoint
+                    or existing_batch.completion_window != completion_window
+                    or existing_batch.metadata != metadata
+                ):
+                    raise ConflictError(
+                        f"Idempotency key '{idempotency_key}' was previously used with different parameters. "
+                        "Either use a new idempotency key or ensure all parameters match the original request."
+                    )
+
+                logger.info(f"Returning existing batch with ID: {batch_id}")
+                return existing_batch
+            except ResourceNotFoundError:
+                # Batch doesn't exist, continue with creation
+                pass
+
         current_time = int(time.time())
 
         batch = BatchObject(
@@ -185,6 +232,7 @@ class ReferenceBatchesImpl(Batches):
         )
 
         await self.kvstore.set(f"batch:{batch_id}", batch.to_json())
+        logger.info(f"Created new batch with ID: {batch_id}")
 
         if self.process_batches:
             task = asyncio.create_task(self._process_batch(batch_id))

llama_stack/providers/inline/files/localfs/files.py

@@ -11,6 +11,7 @@ from typing import Annotated
 
 from fastapi import File, Form, Response, UploadFile
 
+from llama_stack.apis.common.errors import ResourceNotFoundError
 from llama_stack.apis.common.responses import Order
 from llama_stack.apis.files import (
     Files,
@@ -20,12 +21,15 @@ from llama_stack.apis.files import (
     OpenAIFilePurpose,
 )
 from llama_stack.core.datatypes import AccessRule
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
 from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
 from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl
 
 from .config import LocalfsFilesImplConfig
 
+logger = get_logger(name=__name__, category="files")
+
 
 class LocalfsFilesImpl(Files):
     def __init__(self, config: LocalfsFilesImplConfig, policy: list[AccessRule]) -> None:
@@ -65,6 +69,18 @@ class LocalfsFilesImpl(Files):
         """Get the filesystem path for a file ID."""
         return Path(self.config.storage_dir) / file_id
 
+    async def _lookup_file_id(self, file_id: str) -> tuple[OpenAIFileObject, Path]:
+        """Look up a OpenAIFileObject and filesystem path from its ID."""
+        if not self.sql_store:
+            raise RuntimeError("Files provider not initialized")
+
+        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "client.files.list()")
+
+        file_path = Path(row.pop("file_path"))
+        return OpenAIFileObject(**row), file_path
+
     # OpenAI Files API Implementation
     async def openai_upload_file(
         self,
@@ -157,37 +173,19 @@ class LocalfsFilesImpl(Files):
 
     async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject:
         """Returns information about a specific file."""
-        if not self.sql_store:
+        file_obj, _ = await self._lookup_file_id(file_id)
-            raise RuntimeError("Files provider not initialized")
 
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
+        return file_obj
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
-        return OpenAIFileObject(
-            id=row["id"],
-            filename=row["filename"],
-            purpose=OpenAIFilePurpose(row["purpose"]),
-            bytes=row["bytes"],
-            created_at=row["created_at"],
-            expires_at=row["expires_at"],
-        )
 
     async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse:
         """Delete a file."""
-        if not self.sql_store:
-            raise RuntimeError("Files provider not initialized")
-
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
         # Delete physical file
-        file_path = Path(row["file_path"])
+        _, file_path = await self._lookup_file_id(file_id)
         if file_path.exists():
             file_path.unlink()
 
         # Delete metadata from database
+        assert self.sql_store is not None, "Files provider not initialized"
         await self.sql_store.delete("openai_files", where={"id": file_id})
 
         return OpenAIFileDeleteResponse(
@@ -197,25 +195,17 @@ class LocalfsFilesImpl(Files):
 
     async def openai_retrieve_file_content(self, file_id: str) -> Response:
         """Returns the contents of the specified file."""
-        if not self.sql_store:
-            raise RuntimeError("Files provider not initialized")
-
-        # Get file metadata
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
         # Read file content
-        file_path = Path(row["file_path"])
+        file_obj, file_path = await self._lookup_file_id(file_id)
-        if not file_path.exists():
-            raise ValueError(f"File content not found on disk: {file_path}")
 
-        with open(file_path, "rb") as f:
+        if not file_path.exists():
-            content = f.read()
+            logger.warning(f"File '{file_id}'s underlying '{file_path}' is missing, deleting metadata.")
+            await self.openai_delete_file(file_id)
+            raise ResourceNotFoundError(file_id, "File", "client.files.list()")
 
         # Return as binary response with appropriate content type
         return Response(
-            content=content,
+            content=file_path.read_bytes(),
             media_type="application/octet-stream",
-            headers={"Content-Disposition": f'attachment; filename="{row["filename"]}"'},
+            headers={"Content-Disposition": f'attachment; filename="{file_obj.filename}"'},
         )

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -9,7 +9,6 @@ from collections.abc import AsyncGenerator
 from llama_stack.apis.inference import (
     CompletionResponse,
     InferenceProvider,
-    InterleavedContent,
     LogProbConfig,
     Message,
     ResponseFormat,
@@ -100,25 +99,3 @@ class SentenceTransformersInferenceImpl(
         tool_config: ToolConfig | None = None,
     ) -> AsyncGenerator:
         raise ValueError("Sentence transformers don't support chat completion")
-
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Sentence Transformers")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Sentence Transformers")

llama_stack/providers/inline/telemetry/meta_reference/telemetry.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import datetime
 import threading
 from typing import Any
 
@@ -145,11 +146,41 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
         metric_name: str,
         start_time: int,
         end_time: int | None = None,
-        granularity: str | None = "1d",
+        granularity: str | None = None,
         query_type: MetricQueryType = MetricQueryType.RANGE,
         label_matchers: list[MetricLabelMatcher] | None = None,
     ) -> QueryMetricsResponse:
-        raise NotImplementedError("Querying metrics is not implemented")
+        """Query metrics from the telemetry store.
+
+        Args:
+            metric_name: The name of the metric to query (e.g., "prompt_tokens")
+            start_time: Start time as Unix timestamp
+            end_time: End time as Unix timestamp (defaults to now if None)
+            granularity: Time granularity for aggregation
+            query_type: Type of query (RANGE or INSTANT)
+            label_matchers: Label filters to apply
+
+        Returns:
+            QueryMetricsResponse with metric time series data
+        """
+        # Convert timestamps to datetime objects
+        start_dt = datetime.datetime.fromtimestamp(start_time, datetime.UTC)
+        end_dt = datetime.datetime.fromtimestamp(end_time, datetime.UTC) if end_time else None
+
+        # Use SQLite trace store if available
+        if hasattr(self, "trace_store") and self.trace_store:
+            return await self.trace_store.query_metrics(
+                metric_name=metric_name,
+                start_time=start_dt,
+                end_time=end_dt,
+                granularity=granularity,
+                query_type=query_type,
+                label_matchers=label_matchers,
+            )
+        else:
+            raise ValueError(
+                f"In order to query_metrics, you must have {TelemetrySink.SQLITE} set in your telemetry sinks"
+            )
 
     def _log_unstructured(self, event: UnstructuredLogEvent, ttl_seconds: int) -> None:
         with self._lock:

llama_stack/providers/registry/files.py

@@ -5,9 +5,11 @@
 # the root directory of this source tree.
 
 from llama_stack.providers.datatypes import (
+    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
+    remote_provider_spec,
 )
 from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages
 
@@ -23,4 +25,14 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig",
             description="Local filesystem-based file storage provider for managing files and documents locally.",
         ),
+        remote_provider_spec(
+            api=Api.files,
+            adapter=AdapterSpec(
+                adapter_type="s3",
+                pip_packages=["boto3"] + sql_store_pip_packages,
+                module="llama_stack.providers.remote.files.s3",
+                config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig",
+                description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.",
+            ),
+        ),
     ]

llama_stack/providers/registry/post_training.py

@@ -5,34 +5,74 @@
 # the root directory of this source tree.
 
 
+from typing import cast
+
 from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
 
+# We provide two versions of these providers so that distributions can package the appropriate version of torch.
+# The CPU version is used for distributions that don't have GPU support -- they result in smaller container images.
+torchtune_def = dict(
+    api=Api.post_training,
+    pip_packages=["torchtune==0.5.0", "torchao==0.8.0", "numpy"],
+    module="llama_stack.providers.inline.post_training.torchtune",
+    config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
+    api_dependencies=[
+        Api.datasetio,
+        Api.datasets,
+    ],
+    description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
+)
+
+huggingface_def = dict(
+    api=Api.post_training,
+    pip_packages=["trl", "transformers", "peft", "datasets"],
+    module="llama_stack.providers.inline.post_training.huggingface",
+    config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
+    api_dependencies=[
+        Api.datasetio,
+        Api.datasets,
+    ],
+    description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
+)
+
 
 def available_providers() -> list[ProviderSpec]:
     return [
         InlineProviderSpec(
-            api=Api.post_training,
+            **{
-            provider_type="inline::torchtune",
+                **torchtune_def,
-            pip_packages=["torch", "torchtune==0.5.0", "torchao==0.8.0", "numpy"],
+                "provider_type": "inline::torchtune-cpu",
-            module="llama_stack.providers.inline.post_training.torchtune",
+                "pip_packages": (
-            config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
+                    cast(list[str], torchtune_def["pip_packages"])
-            api_dependencies=[
+                    + ["torch torchtune==0.5.0 torchao==0.8.0 --index-url https://download.pytorch.org/whl/cpu"]
-                Api.datasetio,
+                ),
-                Api.datasets,
+            },
-            ],
-            description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
         ),
         InlineProviderSpec(
-            api=Api.post_training,
+            **{
-            provider_type="inline::huggingface",
+                **huggingface_def,
-            pip_packages=["torch", "trl", "transformers", "peft", "datasets"],
+                "provider_type": "inline::huggingface-cpu",
-            module="llama_stack.providers.inline.post_training.huggingface",
+                "pip_packages": (
-            config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
+                    cast(list[str], huggingface_def["pip_packages"])
-            api_dependencies=[
+                    + ["torch --index-url https://download.pytorch.org/whl/cpu"]
-                Api.datasetio,
+                ),
-                Api.datasets,
+            },
-            ],
+        ),
-            description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
+        InlineProviderSpec(
+            **{
+                **torchtune_def,
+                "provider_type": "inline::torchtune-gpu",
+                "pip_packages": (
+                    cast(list[str], torchtune_def["pip_packages"]) + ["torch torchtune==0.5.0 torchao==0.8.0"]
+                ),
+            },
+        ),
+        InlineProviderSpec(
+            **{
+                **huggingface_def,
+                "provider_type": "inline::huggingface-gpu",
+                "pip_packages": (cast(list[str], huggingface_def["pip_packages"]) + ["torch"]),
+            },
         ),
         remote_provider_spec(
             api=Api.post_training,

llama_stack/providers/remote/files/s3/README.md

@@ -0,0 +1,237 @@
+# S3 Files Provider
+
+A remote S3-based implementation of the Llama Stack Files API that provides scalable cloud file storage with metadata persistence.
+
+## Features
+
+- **AWS S3 Storage**: Store files in AWS S3 buckets for scalable, durable storage
+- **Metadata Management**: Uses SQL database for efficient file metadata queries
+- **OpenAI API Compatibility**: Full compatibility with OpenAI Files API endpoints
+- **Flexible Authentication**: Support for IAM roles and access keys
+- **Custom S3 Endpoints**: Support for MinIO and other S3-compatible services
+
+## Configuration
+
+### Basic Configuration
+
+```yaml
+api: files
+provider_type: remote::s3
+config:
+  bucket_name: my-llama-stack-files
+  region: us-east-1
+  metadata_store:
+    type: sqlite
+    db_path: ./s3_files_metadata.db
+```
+
+### Advanced Configuration
+
+```yaml
+api: files
+provider_type: remote::s3
+config:
+  bucket_name: my-llama-stack-files
+  region: us-east-1
+  aws_access_key_id: YOUR_ACCESS_KEY
+  aws_secret_access_key: YOUR_SECRET_KEY
+  endpoint_url: https://s3.amazonaws.com  # Optional for custom endpoints
+  metadata_store:
+    type: sqlite
+    db_path: ./s3_files_metadata.db
+```
+
+### Environment Variables
+
+The configuration supports environment variable substitution:
+
+```yaml
+config:
+  bucket_name: "${env.S3_BUCKET_NAME}"
+  region: "${env.AWS_REGION:=us-east-1}"
+  aws_access_key_id: "${env.AWS_ACCESS_KEY_ID:=}"
+  aws_secret_access_key: "${env.AWS_SECRET_ACCESS_KEY:=}"
+  endpoint_url: "${env.S3_ENDPOINT_URL:=}"
+```
+
+Note: `S3_BUCKET_NAME` has no default value since S3 bucket names must be globally unique.
+
+## Authentication
+
+### IAM Roles (Recommended)
+
+For production deployments, use IAM roles:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  region: us-east-1
+  # No credentials needed - will use IAM role
+```
+
+### Access Keys
+
+For development or specific use cases:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  region: us-east-1
+  aws_access_key_id: AKIAIOSFODNN7EXAMPLE
+  aws_secret_access_key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+## S3 Bucket Setup
+
+### Required Permissions
+
+The S3 provider requires the following permissions:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Effect": "Allow",
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject",
+        "s3:ListBucket"
+      ],
+      "Resource": [
+        "arn:aws:s3:::your-bucket-name",
+        "arn:aws:s3:::your-bucket-name/*"
+      ]
+    }
+  ]
+}
+```
+
+### Automatic Bucket Creation
+
+By default, the S3 provider expects the bucket to already exist. If you want the provider to automatically create the bucket when it doesn't exist, set `auto_create_bucket: true` in your configuration:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  auto_create_bucket: true  # Will create bucket if it doesn't exist
+  region: us-east-1
+```
+
+**Note**: When `auto_create_bucket` is enabled, the provider will need additional permissions:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Effect": "Allow",
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject",
+        "s3:ListBucket",
+        "s3:CreateBucket"
+      ],
+      "Resource": [
+        "arn:aws:s3:::your-bucket-name",
+        "arn:aws:s3:::your-bucket-name/*"
+      ]
+    }
+  ]
+}
+```
+
+### Bucket Policy (Optional)
+
+For additional security, you can add a bucket policy:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Sid": "LlamaStackAccess",
+      "Effect": "Allow",
+      "Principal": {
+        "AWS": "arn:aws:iam::YOUR-ACCOUNT:role/LlamaStackRole"
+      },
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject"
+      ],
+      "Resource": "arn:aws:s3:::your-bucket-name/*"
+    },
+    {
+      "Sid": "LlamaStackBucketAccess",
+      "Effect": "Allow",
+      "Principal": {
+        "AWS": "arn:aws:iam::YOUR-ACCOUNT:role/LlamaStackRole"
+      },
+      "Action": [
+        "s3:ListBucket"
+      ],
+      "Resource": "arn:aws:s3:::your-bucket-name"
+    }
+  ]
+}
+```
+
+## Features
+
+### Metadata Persistence
+
+File metadata is stored in a SQL database for fast queries and OpenAI API compatibility. The metadata includes:
+
+- File ID
+- Original filename
+- Purpose (assistants, batch, etc.)
+- File size in bytes
+- Created and expiration timestamps
+
+### TTL and Cleanup
+
+Files currently have a fixed long expiration time (100 years).
+
+## Development and Testing
+
+### Using MinIO
+
+For self-hosted S3-compatible storage:
+
+```yaml
+config:
+  bucket_name: test-bucket
+  region: us-east-1
+  endpoint_url: http://localhost:9000
+  aws_access_key_id: minioadmin
+  aws_secret_access_key: minioadmin
+```
+
+## Monitoring and Logging
+
+The provider logs important operations and errors. For production deployments, consider:
+
+- CloudWatch monitoring for S3 operations
+- Custom metrics for file upload/download rates
+- Error rate monitoring
+- Performance metrics tracking
+
+## Error Handling
+
+The provider handles various error scenarios:
+
+- S3 connectivity issues
+- Bucket access permissions
+- File not found errors
+- Metadata consistency checks
+
+## Known Limitations
+
+- Fixed long TTL (100 years) instead of configurable expiration
+- No server-side encryption enabled by default
+- No support for AWS session tokens
+- No S3 key prefix organization support
+- No multipart upload support (all files uploaded as single objects)

llama_stack/providers/remote/files/s3/__init__.py

@@ -0,0 +1,20 @@
+# 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 typing import Any
+
+from llama_stack.core.datatypes import Api
+
+from .config import S3FilesImplConfig
+
+
+async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any]):
+    from .files import S3FilesImpl
+
+    # TODO: authorization policies and user separation
+    impl = S3FilesImpl(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/files/s3/config.py

@@ -0,0 +1,42 @@
+# 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 typing import Any
+
+from pydantic import BaseModel, Field
+
+from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
+
+
+class S3FilesImplConfig(BaseModel):
+    """Configuration for S3-based files provider."""
+
+    bucket_name: str = Field(description="S3 bucket name to store files")
+    region: str = Field(default="us-east-1", description="AWS region where the bucket is located")
+    aws_access_key_id: str | None = Field(default=None, description="AWS access key ID (optional if using IAM roles)")
+    aws_secret_access_key: str | None = Field(
+        default=None, description="AWS secret access key (optional if using IAM roles)"
+    )
+    endpoint_url: str | None = Field(default=None, description="Custom S3 endpoint URL (for MinIO, LocalStack, etc.)")
+    auto_create_bucket: bool = Field(
+        default=False, description="Automatically create the S3 bucket if it doesn't exist"
+    )
+    metadata_store: SqlStoreConfig = Field(description="SQL store configuration for file metadata")
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
+        return {
+            "bucket_name": "${env.S3_BUCKET_NAME}",  # no default, buckets must be globally unique
+            "region": "${env.AWS_REGION:=us-east-1}",
+            "aws_access_key_id": "${env.AWS_ACCESS_KEY_ID:=}",
+            "aws_secret_access_key": "${env.AWS_SECRET_ACCESS_KEY:=}",
+            "endpoint_url": "${env.S3_ENDPOINT_URL:=}",
+            "auto_create_bucket": "${env.S3_AUTO_CREATE_BUCKET:=false}",
+            "metadata_store": SqliteSqlStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="s3_files_metadata.db",
+            ),
+        }

llama_stack/providers/remote/files/s3/files.py

@@ -0,0 +1,272 @@
+# 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.
+
+import time
+import uuid
+from typing import Annotated
+
+import boto3
+from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError
+from fastapi import File, Form, Response, UploadFile
+
+from llama_stack.apis.common.errors import ResourceNotFoundError
+from llama_stack.apis.common.responses import Order
+from llama_stack.apis.files import (
+    Files,
+    ListOpenAIFileResponse,
+    OpenAIFileDeleteResponse,
+    OpenAIFileObject,
+    OpenAIFilePurpose,
+)
+from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
+from llama_stack.providers.utils.sqlstore.sqlstore import SqlStore, sqlstore_impl
+
+from .config import S3FilesImplConfig
+
+# TODO: provider data for S3 credentials
+
+
+def _create_s3_client(config: S3FilesImplConfig) -> boto3.client:
+    try:
+        s3_config = {
+            "region_name": config.region,
+        }
+
+        # endpoint URL if specified (for MinIO, LocalStack, etc.)
+        if config.endpoint_url:
+            s3_config["endpoint_url"] = config.endpoint_url
+
+        if config.aws_access_key_id and config.aws_secret_access_key:
+            s3_config.update(
+                {
+                    "aws_access_key_id": config.aws_access_key_id,
+                    "aws_secret_access_key": config.aws_secret_access_key,
+                }
+            )
+
+        return boto3.client("s3", **s3_config)
+
+    except (BotoCoreError, NoCredentialsError) as e:
+        raise RuntimeError(f"Failed to initialize S3 client: {e}") from e
+
+
+async def _create_bucket_if_not_exists(client: boto3.client, config: S3FilesImplConfig) -> None:
+    try:
+        client.head_bucket(Bucket=config.bucket_name)
+    except ClientError as e:
+        error_code = e.response["Error"]["Code"]
+        if error_code == "404":
+            if not config.auto_create_bucket:
+                raise RuntimeError(
+                    f"S3 bucket '{config.bucket_name}' does not exist. "
+                    f"Either create the bucket manually or set 'auto_create_bucket: true' in your configuration."
+                ) from e
+            try:
+                # For us-east-1, we can't specify LocationConstraint
+                if config.region == "us-east-1":
+                    client.create_bucket(Bucket=config.bucket_name)
+                else:
+                    client.create_bucket(
+                        Bucket=config.bucket_name,
+                        CreateBucketConfiguration={"LocationConstraint": config.region},
+                    )
+            except ClientError as create_error:
+                raise RuntimeError(
+                    f"Failed to create S3 bucket '{config.bucket_name}': {create_error}"
+                ) from create_error
+        elif error_code == "403":
+            raise RuntimeError(f"Access denied to S3 bucket '{config.bucket_name}'") from e
+        else:
+            raise RuntimeError(f"Failed to access S3 bucket '{config.bucket_name}': {e}") from e
+
+
+class S3FilesImpl(Files):
+    """S3-based implementation of the Files API."""
+
+    # TODO: implement expiration, for now a silly offset
+    _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60
+
+    def __init__(self, config: S3FilesImplConfig) -> None:
+        self._config = config
+        self._client: boto3.client | None = None
+        self._sql_store: SqlStore | None = None
+
+    async def initialize(self) -> None:
+        self._client = _create_s3_client(self._config)
+        await _create_bucket_if_not_exists(self._client, self._config)
+
+        self._sql_store = sqlstore_impl(self._config.metadata_store)
+        await self._sql_store.create_table(
+            "openai_files",
+            {
+                "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
+                "filename": ColumnType.STRING,
+                "purpose": ColumnType.STRING,
+                "bytes": ColumnType.INTEGER,
+                "created_at": ColumnType.INTEGER,
+                "expires_at": ColumnType.INTEGER,
+                # TODO: add s3_etag field for integrity checking
+            },
+        )
+
+    async def shutdown(self) -> None:
+        pass
+
+    @property
+    def client(self) -> boto3.client:
+        assert self._client is not None, "Provider not initialized"
+        return self._client
+
+    @property
+    def sql_store(self) -> SqlStore:
+        assert self._sql_store is not None, "Provider not initialized"
+        return self._sql_store
+
+    async def openai_upload_file(
+        self,
+        file: Annotated[UploadFile, File()],
+        purpose: Annotated[OpenAIFilePurpose, Form()],
+    ) -> OpenAIFileObject:
+        file_id = f"file-{uuid.uuid4().hex}"
+
+        filename = getattr(file, "filename", None) or "uploaded_file"
+
+        created_at = int(time.time())
+        expires_at = created_at + self._SILLY_EXPIRATION_OFFSET
+        content = await file.read()
+        file_size = len(content)
+
+        await self.sql_store.insert(
+            "openai_files",
+            {
+                "id": file_id,
+                "filename": filename,
+                "purpose": purpose.value,
+                "bytes": file_size,
+                "created_at": created_at,
+                "expires_at": expires_at,
+            },
+        )
+
+        try:
+            self.client.put_object(
+                Bucket=self._config.bucket_name,
+                Key=file_id,
+                Body=content,
+                # TODO: enable server-side encryption
+            )
+        except ClientError as e:
+            await self.sql_store.delete("openai_files", where={"id": file_id})
+
+            raise RuntimeError(f"Failed to upload file to S3: {e}") from e
+
+        return OpenAIFileObject(
+            id=file_id,
+            filename=filename,
+            purpose=purpose,
+            bytes=file_size,
+            created_at=created_at,
+            expires_at=expires_at,
+        )
+
+    async def openai_list_files(
+        self,
+        after: str | None = None,
+        limit: int | None = 10000,
+        order: Order | None = Order.desc,
+        purpose: OpenAIFilePurpose | None = None,
+    ) -> ListOpenAIFileResponse:
+        # this purely defensive. it should not happen because the router also default to Order.desc.
+        if not order:
+            order = Order.desc
+
+        where_conditions = {}
+        if purpose:
+            where_conditions["purpose"] = purpose.value
+
+        paginated_result = await self.sql_store.fetch_all(
+            table="openai_files",
+            where=where_conditions if where_conditions else None,
+            order_by=[("created_at", order.value)],
+            cursor=("id", after) if after else None,
+            limit=limit,
+        )
+
+        files = [
+            OpenAIFileObject(
+                id=row["id"],
+                filename=row["filename"],
+                purpose=OpenAIFilePurpose(row["purpose"]),
+                bytes=row["bytes"],
+                created_at=row["created_at"],
+                expires_at=row["expires_at"],
+            )
+            for row in paginated_result.data
+        ]
+
+        return ListOpenAIFileResponse(
+            data=files,
+            has_more=paginated_result.has_more,
+            # empty string or None? spec says str, ref impl returns str | None, we go with spec
+            first_id=files[0].id if files else "",
+            last_id=files[-1].id if files else "",
+        )
+
+    async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        return OpenAIFileObject(
+            id=row["id"],
+            filename=row["filename"],
+            purpose=OpenAIFilePurpose(row["purpose"]),
+            bytes=row["bytes"],
+            created_at=row["created_at"],
+            expires_at=row["expires_at"],
+        )
+
+    async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        try:
+            self.client.delete_object(
+                Bucket=self._config.bucket_name,
+                Key=row["id"],
+            )
+        except ClientError as e:
+            if e.response["Error"]["Code"] != "NoSuchKey":
+                raise RuntimeError(f"Failed to delete file from S3: {e}") from e
+
+        await self.sql_store.delete("openai_files", where={"id": file_id})
+
+        return OpenAIFileDeleteResponse(id=file_id, deleted=True)
+
+    async def openai_retrieve_file_content(self, file_id: str) -> Response:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        try:
+            response = self.client.get_object(
+                Bucket=self._config.bucket_name,
+                Key=row["id"],
+            )
+            # TODO: can we stream this instead of loading it into memory
+            content = response["Body"].read()
+        except ClientError as e:
+            if e.response["Error"]["Code"] == "NoSuchKey":
+                await self.sql_store.delete("openai_files", where={"id": file_id})
+                raise ResourceNotFoundError(file_id, "File", "files.list()") from e
+            raise RuntimeError(f"Failed to download file from S3: {e}") from e
+
+        return Response(
+            content=content,
+            media_type="application/octet-stream",
+            headers={"Content-Disposition": f'attachment; filename="{row["filename"]}"'},
+        )

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -65,7 +65,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import FireworksImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::fireworks")
 
 
 class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -10,7 +10,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::llama_openai_compat")
 
 
 class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):

llama_stack/providers/remote/inference/nvidia/NVIDIA.md

@@ -41,6 +41,11 @@ client.initialize()
 
 ### Create Completion
 
+> Note on Completion API
+>
+> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does.
+
+
 ```python
 response = client.inference.completion(
     model_id="meta-llama/Llama-3.1-8B-Instruct",
@@ -76,6 +81,73 @@ response = client.inference.chat_completion(
 print(f"Response: {response.completion_message.content}")
 ```
 
+### Tool Calling Example ###
+```python
+from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
+
+tool_definition = ToolDefinition(
+    tool_name="get_weather",
+    description="Get current weather information for a location",
+    parameters={
+        "location": ToolParamDefinition(
+            param_type="string",
+            description="The city and state, e.g. San Francisco, CA",
+            required=True,
+        ),
+        "unit": ToolParamDefinition(
+            param_type="string",
+            description="Temperature unit (celsius or fahrenheit)",
+            required=False,
+            default="celsius",
+        ),
+    },
+)
+
+tool_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
+    tools=[tool_definition],
+)
+
+print(f"Tool Response: {tool_response.completion_message.content}")
+if tool_response.completion_message.tool_calls:
+    for tool_call in tool_response.completion_message.tool_calls:
+        print(f"Tool Called: {tool_call.tool_name}")
+        print(f"Arguments: {tool_call.arguments}")
+```
+
+### Structured Output Example
+```python
+from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType
+
+person_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"},
+        "occupation": {"type": "string"},
+    },
+    "required": ["name", "age", "occupation"],
+}
+
+response_format = JsonSchemaResponseFormat(
+    type=ResponseFormatType.json_schema, json_schema=person_schema
+)
+
+structured_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[
+        {
+            "role": "user",
+            "content": "Create a profile for a fictional person named Alice who is 30 years old and is a software engineer. ",
+        }
+    ],
+    response_format=response_format,
+)
+
+print(f"Structured Response: {structured_response.completion_message.content}")
+```
+
 ### Create Embeddings
 > Note on OpenAI embeddings compatibility
 >

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -7,7 +7,7 @@
 import warnings
 from collections.abc import AsyncIterator
 
-from openai import NOT_GIVEN, APIConnectionError, BadRequestError
+from openai import NOT_GIVEN, APIConnectionError
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -57,7 +57,7 @@ from .openai_utils import (
 )
 from .utils import _is_nvidia_hosted
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
@@ -197,15 +197,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
             }
             extra_body["input_type"] = task_type_options[task_type]
 
-        try:
+        response = await self.client.embeddings.create(
-            response = await self.client.embeddings.create(
+            model=provider_model_id,
-                model=provider_model_id,
+            input=input,
-                input=input,
+            extra_body=extra_body,
-                extra_body=extra_body,
+        )
-            )
-        except BadRequestError as e:
-            raise ValueError(f"Failed to get embeddings: {e}") from e
-
         #
         # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
         #  ->

llama_stack/providers/remote/inference/nvidia/utils.py

@@ -10,7 +10,7 @@ from llama_stack.log import get_logger
 
 from . import NVIDIAConfig
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 def _is_nvidia_hosted(config: NVIDIAConfig) -> bool:

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -85,7 +85,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::ollama")
 
 
 class OllamaInferenceAdapter(
@@ -619,28 +619,6 @@ class OllamaInferenceAdapter(
             response.id = id
             return response
 
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Ollama")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Ollama")
-
 
 async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
     async def _convert_content(content) -> dict:

llama_stack/providers/remote/inference/openai/openai.py

@@ -11,7 +11,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from .config import OpenAIConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::openai")
 
 
 #

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
 
-log = get_logger(name=__name__, category="inference")
+log = get_logger(name=__name__, category="inference::tgi")
 
 
 def build_hf_repo_model_entries():

llama_stack/providers/remote/inference/together/together.py

@@ -61,7 +61,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import TogetherImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::together")
 
 
 class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -85,7 +85,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import VLLMInferenceAdapterConfig
 
-log = get_logger(name=__name__, category="inference")
+log = get_logger(name=__name__, category="inference::vllm")
 
 
 def build_hf_repo_model_entries():
@@ -711,25 +711,3 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             user=user,
         )
         return await self.client.chat.completions.create(**params)  # type: ignore
-
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Ollama")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Ollama")

llama_stack/providers/remote/post_training/nvidia/utils.py

@@ -15,7 +15,7 @@ from llama_stack.providers.remote.post_training.nvidia.config import SFTLoRADefa
 
 from .config import NvidiaPostTrainingConfig
 
-logger = get_logger(name=__name__, category="integration")
+logger = get_logger(name=__name__, category="post_training::nvidia")
 
 
 def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None:

llama_stack/providers/remote/safety/bedrock/bedrock.py

@@ -21,7 +21,7 @@ from llama_stack.providers.utils.bedrock.client import create_bedrock_client
 
 from .config import BedrockSafetyConfig
 
-logger = get_logger(name=__name__, category="safety")
+logger = get_logger(name=__name__, category="safety::bedrock")
 
 
 class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):

llama_stack/providers/remote/safety/nvidia/nvidia.py

@@ -9,7 +9,7 @@ from typing import Any
 import requests
 
 from llama_stack.apis.inference import Message
-from llama_stack.apis.safety import RunShieldResponse, Safety, SafetyViolation, ViolationLevel
+from llama_stack.apis.safety import ModerationObject, RunShieldResponse, Safety, SafetyViolation, ViolationLevel
 from llama_stack.apis.shields import Shield
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
@@ -17,7 +17,7 @@ from llama_stack.providers.utils.inference.openai_compat import convert_message_
 
 from .config import NVIDIASafetyConfig
 
-logger = get_logger(name=__name__, category="safety")
+logger = get_logger(name=__name__, category="safety::nvidia")
 
 
 class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
@@ -67,6 +67,9 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
         self.shield = NeMoGuardrails(self.config, shield.shield_id)
         return await self.shield.run(messages)
 
+    async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
+        raise NotImplementedError("NVIDIA safety provider currently does not implement run_moderation")
+
 
 class NeMoGuardrails:
     """