Merge branch 'main' into chroma

.github/actions/run-and-record-tests/action.yml

@@ -66,11 +66,11 @@ runs:
       shell: bash
       run: |
         echo "Checking for recording changes"
-        git status --porcelain tests/integration/recordings/
+        git status --porcelain tests/integration/
 
-        if [[ -n $(git status --porcelain tests/integration/recordings/) ]]; then
+        if [[ -n $(git status --porcelain tests/integration/) ]]; then
           echo "New recordings detected, committing and pushing"
-          git add tests/integration/recordings/
+          git add tests/integration/
 
           git commit -m "Recordings update from CI (suite: ${{ inputs.suite }})"
           git fetch origin ${{ github.ref_name }}

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

@@ -170,7 +170,8 @@ jobs:
           uv run --no-sync \
             pytest -sv --stack-config="files=inline::localfs,inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
             tests/integration/vector_io \
-            --embedding-model inline::sentence-transformers/all-MiniLM-L6-v2
+            --embedding-model nomic-ai/nomic-embed-text-v1.5 \
+            --embedding-dimension 768
 
       - name: Check Storage and Memory Available After Tests
         if: ${{ always() }}

benchmarking/k8s-benchmark/stack-configmap.yaml

@@ -115,8 +115,8 @@ data:
       password: ${env.POSTGRES_PASSWORD:=llamastack}
     models:
     - metadata:
-        embedding_dimension: 384
+        embedding_dimension: 768
-      model_id: all-MiniLM-L6-v2
+      model_id: nomic-embed-text-v1.5
       provider_id: sentence-transformers
       model_type: embedding
     - model_id: ${env.INFERENCE_MODEL}

benchmarking/k8s-benchmark/stack_run_config.yaml

@@ -112,8 +112,8 @@ inference_store:
   password: ${env.POSTGRES_PASSWORD:=llamastack}
 models:
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 - model_id: ${env.INFERENCE_MODEL}

docs/docs/building_applications/rag.mdx

@@ -47,8 +47,8 @@ client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_P
 vector_db_id = "my_documents"
 response = client.vector_dbs.register(
     vector_db_id=vector_db_id,
-    embedding_model="all-MiniLM-L6-v2",
+    embedding_model="nomic-embed-text-v1.5",
-    embedding_dimension=384,
+    embedding_dimension=768,
     provider_id="faiss",
 )
 ```

docs/docs/distributions/k8s/stack_run_config.yaml

@@ -110,8 +110,8 @@ inference_store:
   password: ${env.POSTGRES_PASSWORD:=llamastack}
 models:
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 - metadata: {}

docs/docs/getting_started/detailed_tutorial.mdx

@@ -164,7 +164,7 @@ Available Models
 ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
 ┃ model_type      ┃ identifier                          ┃ provider_resource_id                ┃ metadata                                  ┃ provider_id           ┃
 ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
-│ embedding       │ ollama/all-minilm:l6-v2             │ all-minilm:l6-v2                    │ {'embedding_dimension': 384.0}            │ ollama                │
+│ embedding       │ ollama/nomic-embed-text:v1.5        │ nomic-embed-text:v1.5               │ {'embedding_dimension': 768.0}            │ ollama                │
 ├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤
 │ ...             │ ...                                 │ ...                                 │                                           │ ...                   │
 ├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤

docs/docs/references/llama_stack_client_cli_reference.md

@@ -224,8 +224,8 @@ llama-stack-client vector_dbs list
 ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
 ┃ identifier               ┃ provider_id ┃ provider_resource_id     ┃ vector_db_type ┃ params                            ┃
 ┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ my_demo_vector_db        │ faiss       │ my_demo_vector_db        │                │ embedding_dimension: 384          │
+│ my_demo_vector_db        │ faiss       │ my_demo_vector_db        │                │ embedding_dimension: 768          │
-│                          │             │                          │                │ embedding_model: all-MiniLM-L6-v2 │
+│                          │             │                          │                │ embedding_model: nomic-embed-text-v1.5 │
 │                          │             │                          │                │ type: vector_db                   │
 │                          │             │                          │                │                                   │
 └──────────────────────────┴─────────────┴──────────────────────────┴────────────────┴───────────────────────────────────┘
@@ -244,8 +244,8 @@ Required arguments:
 Optional arguments:
 - `--provider-id`: Provider ID for the vector db
 - `--provider-vector-db-id`: Provider's vector db ID
-- `--embedding-model`: Embedding model to use. Default: `all-MiniLM-L6-v2`
+- `--embedding-model`: Embedding model to use. Default: `nomic-embed-text-v1.5`
-- `--embedding-dimension`: Dimension of embeddings. Default: 384
+- `--embedding-dimension`: Dimension of embeddings. Default: 768
 
 ### `llama-stack-client vector_dbs unregister`
 Delete a vector db

docs/getting_started.ipynb

@@ -1352,8 +1352,8 @@
         "vector_db_id = f\"test-vector-db-{uuid.uuid4().hex}\"\n",
         "client.vector_dbs.register(\n",
         "    vector_db_id=vector_db_id,\n",
-        "    embedding_model=\"all-MiniLM-L6-v2\",\n",
+        "    embedding_model=\"nomic-embed-text-v1.5\",\n",
-        "    embedding_dimension=384,\n",
+        "    embedding_dimension=768,\n",
         ")\n",
         "client.tool_runtime.rag_tool.insert(\n",
         "    documents=documents,\n",

docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb

@@ -831,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -860,8 +860,8 @@
     "vector_db_id = f\"test_vector_db_{uuid.uuid4()}\"\n",
     "client.vector_dbs.register(\n",
     "    vector_db_id=vector_db_id,\n",
-    "    embedding_model=\"all-MiniLM-L6-v2\",\n",
+    "    embedding_model=\"nomic-embed-text-v1.5\",\n",
-    "    embedding_dimension=384,\n",
+    "    embedding_dimension=768,\n",
     "    provider_id=selected_vector_provider.provider_id,\n",
     ")\n",
     "\n",

docs/static/deprecated-llama-stack-spec.html

@@ -1662,7 +1662,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
+                                "$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
                             }
                         }
                     },
@@ -2436,13 +2436,13 @@
                     "VectorIO"
                 ],
                 "summary": "Creates a vector store.",
-                "description": "Creates a vector store.",
+                "description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
                             }
                         }
                     },
@@ -2622,7 +2622,7 @@
                     "VectorIO"
                 ],
                 "summary": "Create a vector store file batch.",
-                "description": "Create a vector store file batch.",
+                "description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
                 "parameters": [
                     {
                         "name": "vector_store_id",
@@ -2638,7 +2638,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
                             }
                         }
                     },
@@ -8174,7 +8174,7 @@
                 "title": "OpenAICompletionChoice",
                 "description": "A choice from an OpenAI-compatible completion response."
             },
-            "OpenaiEmbeddingsRequest": {
+            "OpenAIEmbeddingsRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "model": {
@@ -8197,6 +8197,7 @@
                     },
                     "encoding_format": {
                         "type": "string",
+                        "default": "float",
                         "description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
                     },
                     "dimensions": {
@@ -8213,7 +8214,8 @@
                     "model",
                     "input"
                 ],
-                "title": "OpenaiEmbeddingsRequest"
+                "title": "OpenAIEmbeddingsRequestWithExtraBody",
+                "description": "Request parameters for OpenAI-compatible embeddings endpoint."
             },
             "OpenAIEmbeddingData": {
                 "type": "object",
@@ -12061,19 +12063,19 @@
                 "title": "VectorStoreObject",
                 "description": "OpenAI Vector Store object."
             },
-            "OpenaiCreateVectorStoreRequest": {
+            "OpenAICreateVectorStoreRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "name": {
                         "type": "string",
-                        "description": "A name for the vector store."
+                        "description": "(Optional) A name for the vector store"
                     },
                     "file_ids": {
                         "type": "array",
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
+                        "description": "List of file IDs to include in the vector store"
                     },
                     "expires_after": {
                         "type": "object",
@@ -12099,7 +12101,7 @@
                                 }
                             ]
                         },
-                        "description": "The expiration policy for a vector store."
+                        "description": "(Optional) Expiration policy for the vector store"
                     },
                     "chunking_strategy": {
                         "type": "object",
@@ -12125,7 +12127,7 @@
                                 }
                             ]
                         },
-                        "description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
+                        "description": "(Optional) Strategy for splitting files into chunks"
                     },
                     "metadata": {
                         "type": "object",
@@ -12151,23 +12153,12 @@
                                 }
                             ]
                         },
-                        "description": "Set of 16 key-value pairs that can be attached to an object."
+                        "description": "Set of key-value pairs that can be attached to the vector store"
-                    },
-                    "embedding_model": {
-                        "type": "string",
-                        "description": "The embedding model to use for this vector store."
-                    },
-                    "embedding_dimension": {
-                        "type": "integer",
-                        "description": "The dimension of the embedding vectors (default: 384)."
-                    },
-                    "provider_id": {
-                        "type": "string",
-                        "description": "The ID of the provider to use for this vector store."
                     }
                 },
                 "additionalProperties": false,
-                "title": "OpenaiCreateVectorStoreRequest"
+                "title": "OpenAICreateVectorStoreRequestWithExtraBody",
+                "description": "Request to create a vector store with extra_body support."
             },
             "OpenaiUpdateVectorStoreRequest": {
                 "type": "object",
@@ -12337,7 +12328,7 @@
                 "title": "VectorStoreChunkingStrategyStaticConfig",
                 "description": "Configuration for static chunking strategy."
             },
-            "OpenaiCreateVectorStoreFileBatchRequest": {
+            "OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "file_ids": {
@@ -12345,7 +12336,7 @@
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use."
+                        "description": "A list of File IDs that the vector store should use"
                     },
                     "attributes": {
                         "type": "object",
@@ -12371,18 +12362,19 @@
                                 }
                             ]
                         },
-                        "description": "(Optional) Key-value attributes to store with the files."
+                        "description": "(Optional) Key-value attributes to store with the files"
                     },
                     "chunking_strategy": {
                         "$ref": "#/components/schemas/VectorStoreChunkingStrategy",
-                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
+                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "file_ids"
                 ],
-                "title": "OpenaiCreateVectorStoreFileBatchRequest"
+                "title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
+                "description": "Request to create a vector store file batch with extra_body support."
             },
             "VectorStoreFileBatchObject": {
                 "type": "object",

docs/static/deprecated-llama-stack-spec.yaml

@@ -1203,7 +1203,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiEmbeddingsRequest'
+              $ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
         required: true
       deprecated: true
   /v1/openai/v1/files:
@@ -1792,13 +1792,16 @@ paths:
       tags:
         - VectorIO
       summary: Creates a vector store.
-      description: Creates a vector store.
+      description: >-
+        Creates a vector store.
+
+        Generate an OpenAI-compatible vector store with the given parameters.
       parameters: []
       requestBody:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
         required: true
       deprecated: true
   /v1/openai/v1/vector_stores/{vector_store_id}:
@@ -1924,7 +1927,11 @@ paths:
       tags:
         - VectorIO
       summary: Create a vector store file batch.
-      description: Create a vector store file batch.
+      description: >-
+        Create a vector store file batch.
+
+        Generate an OpenAI-compatible vector store file batch for the given vector
+        store.
       parameters:
         - name: vector_store_id
           in: path
@@ -1937,7 +1944,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
         required: true
       deprecated: true
   /v1/openai/v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
@@ -6035,7 +6042,7 @@ components:
       title: OpenAICompletionChoice
       description: >-
         A choice from an OpenAI-compatible completion response.
-    OpenaiEmbeddingsRequest:
+    OpenAIEmbeddingsRequestWithExtraBody:
       type: object
       properties:
         model:
@@ -6054,6 +6061,7 @@ components:
             multiple inputs in a single request, pass an array of strings.
         encoding_format:
           type: string
+          default: float
           description: >-
             (Optional) The format to return the embeddings in. Can be either "float"
             or "base64". Defaults to "float".
@@ -6071,7 +6079,9 @@ components:
       required:
         - model
         - input
-      title: OpenaiEmbeddingsRequest
+      title: OpenAIEmbeddingsRequestWithExtraBody
+      description: >-
+        Request parameters for OpenAI-compatible embeddings endpoint.
     OpenAIEmbeddingData:
       type: object
       properties:
@@ -9147,19 +9157,18 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
-    OpenaiCreateVectorStoreRequest:
+    "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
         name:
           type: string
-          description: A name for the vector store.
+          description: (Optional) A name for the vector store
         file_ids:
           type: array
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use. Useful for tools
+            List of file IDs to include in the vector store
-            like `file_search` that can access files.
         expires_after:
           type: object
           additionalProperties:
@@ -9171,7 +9180,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The expiration policy for a vector store.
+            (Optional) Expiration policy for the vector store
         chunking_strategy:
           type: object
           additionalProperties:
@@ -9183,8 +9192,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The chunking strategy used to chunk the file(s). If not set, will use
+            (Optional) Strategy for splitting files into chunks
-            the `auto` strategy.
         metadata:
           type: object
           additionalProperties:
@@ -9196,21 +9204,12 @@ components:
               - type: array
               - type: object
           description: >-
-            Set of 16 key-value pairs that can be attached to an object.
+            Set of key-value pairs that can be attached to the vector store
-        embedding_model:
-          type: string
-          description: >-
-            The embedding model to use for this vector store.
-        embedding_dimension:
-          type: integer
-          description: >-
-            The dimension of the embedding vectors (default: 384).
-        provider_id:
-          type: string
-          description: >-
-            The ID of the provider to use for this vector store.
       additionalProperties: false
-      title: OpenaiCreateVectorStoreRequest
+      title: >-
+        OpenAICreateVectorStoreRequestWithExtraBody
+      description: >-
+        Request to create a vector store with extra_body support.
     OpenaiUpdateVectorStoreRequest:
       type: object
       properties:
@@ -9331,7 +9330,7 @@ components:
       title: VectorStoreChunkingStrategyStaticConfig
       description: >-
         Configuration for static chunking strategy.
-    OpenaiCreateVectorStoreFileBatchRequest:
+    "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
         file_ids:
@@ -9339,7 +9338,7 @@ components:
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use.
+            A list of File IDs that the vector store should use
         attributes:
           type: object
           additionalProperties:
@@ -9351,16 +9350,19 @@ components:
               - type: array
               - type: object
           description: >-
-            (Optional) Key-value attributes to store with the files.
+            (Optional) Key-value attributes to store with the files
         chunking_strategy:
           $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) The chunking strategy used to chunk the file(s). Defaults to
-            auto.
+            auto
       additionalProperties: false
       required:
         - file_ids
-      title: OpenaiCreateVectorStoreFileBatchRequest
+      title: >-
+        OpenAICreateVectorStoreFileBatchRequestWithExtraBody
+      description: >-
+        Request to create a vector store file batch with extra_body support.
     VectorStoreFileBatchObject:
       type: object
       properties:

docs/static/llama-stack-spec.html

@@ -765,7 +765,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
+                                "$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
                             }
                         }
                     },
@@ -3170,13 +3170,13 @@
                     "VectorIO"
                 ],
                 "summary": "Creates a vector store.",
-                "description": "Creates a vector store.",
+                "description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
                             }
                         }
                     },
@@ -3356,7 +3356,7 @@
                     "VectorIO"
                 ],
                 "summary": "Create a vector store file batch.",
-                "description": "Create a vector store file batch.",
+                "description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
                 "parameters": [
                     {
                         "name": "vector_store_id",
@@ -3372,7 +3372,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
                             }
                         }
                     },
@@ -6324,7 +6324,7 @@
                 "title": "ConversationItemDeletedResource",
                 "description": "Response for deleted conversation item."
             },
-            "OpenaiEmbeddingsRequest": {
+            "OpenAIEmbeddingsRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "model": {
@@ -6347,6 +6347,7 @@
                     },
                     "encoding_format": {
                         "type": "string",
+                        "default": "float",
                         "description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
                     },
                     "dimensions": {
@@ -6363,7 +6364,8 @@
                     "model",
                     "input"
                 ],
-                "title": "OpenaiEmbeddingsRequest"
+                "title": "OpenAIEmbeddingsRequestWithExtraBody",
+                "description": "Request parameters for OpenAI-compatible embeddings endpoint."
             },
             "OpenAIEmbeddingData": {
                 "type": "object",
@@ -9816,284 +9818,6 @@
                 "title": "ListOpenAIResponseInputItem",
                 "description": "List container for OpenAI response input items."
             },
-            "CompletionMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "assistant",
-                        "default": "assistant",
-                        "description": "Must be \"assistant\" to identify this as the model's response"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the model's response"
-                    },
-                    "stop_reason": {
-                        "type": "string",
-                        "enum": [
-                            "end_of_turn",
-                            "end_of_message",
-                            "out_of_tokens"
-                        ],
-                        "description": "Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`: The model finished generating the entire response. - `StopReason.end_of_message`: The model finished generating but generated a partial response -- usually, a tool call. The user may call the tool and continue the conversation with the tool's response. - `StopReason.out_of_tokens`: The model ran out of token budget."
-                    },
-                    "tool_calls": {
-                        "type": "array",
-                        "items": {
-                            "$ref": "#/components/schemas/ToolCall"
-                        },
-                        "description": "List of tool calls. Each tool call is a ToolCall object."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content",
-                    "stop_reason"
-                ],
-                "title": "CompletionMessage",
-                "description": "A message containing the model's (assistant) response in a chat conversation."
-            },
-            "ImageContentItem": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "image",
-                        "default": "image",
-                        "description": "Discriminator type of the content item. Always \"image\""
-                    },
-                    "image": {
-                        "type": "object",
-                        "properties": {
-                            "url": {
-                                "$ref": "#/components/schemas/URL",
-                                "description": "A URL of the image or data URL in the format of data:image/{type};base64,{data}. Note that URL could have length limits."
-                            },
-                            "data": {
-                                "type": "string",
-                                "contentEncoding": "base64",
-                                "description": "base64 encoded image data as string"
-                            }
-                        },
-                        "additionalProperties": false,
-                        "description": "Image as a base64 encoded string or an URL"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "image"
-                ],
-                "title": "ImageContentItem",
-                "description": "A image content item"
-            },
-            "InterleavedContent": {
-                "oneOf": [
-                    {
-                        "type": "string"
-                    },
-                    {
-                        "$ref": "#/components/schemas/InterleavedContentItem"
-                    },
-                    {
-                        "type": "array",
-                        "items": {
-                            "$ref": "#/components/schemas/InterleavedContentItem"
-                        }
-                    }
-                ]
-            },
-            "InterleavedContentItem": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/ImageContentItem"
-                    },
-                    {
-                        "$ref": "#/components/schemas/TextContentItem"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "type",
-                    "mapping": {
-                        "image": "#/components/schemas/ImageContentItem",
-                        "text": "#/components/schemas/TextContentItem"
-                    }
-                }
-            },
-            "Message": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/UserMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/SystemMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/ToolResponseMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/CompletionMessage"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "role",
-                    "mapping": {
-                        "user": "#/components/schemas/UserMessage",
-                        "system": "#/components/schemas/SystemMessage",
-                        "tool": "#/components/schemas/ToolResponseMessage",
-                        "assistant": "#/components/schemas/CompletionMessage"
-                    }
-                }
-            },
-            "SystemMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "system",
-                        "default": "system",
-                        "description": "Must be \"system\" to identify this as a system message"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content"
-                ],
-                "title": "SystemMessage",
-                "description": "A system message providing instructions or context to the model."
-            },
-            "TextContentItem": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "text",
-                        "default": "text",
-                        "description": "Discriminator type of the content item. Always \"text\""
-                    },
-                    "text": {
-                        "type": "string",
-                        "description": "Text content"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "text"
-                ],
-                "title": "TextContentItem",
-                "description": "A text content item"
-            },
-            "ToolCall": {
-                "type": "object",
-                "properties": {
-                    "call_id": {
-                        "type": "string"
-                    },
-                    "tool_name": {
-                        "oneOf": [
-                            {
-                                "type": "string",
-                                "enum": [
-                                    "brave_search",
-                                    "wolfram_alpha",
-                                    "photogen",
-                                    "code_interpreter"
-                                ],
-                                "title": "BuiltinTool"
-                            },
-                            {
-                                "type": "string"
-                            }
-                        ]
-                    },
-                    "arguments": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "call_id",
-                    "tool_name",
-                    "arguments"
-                ],
-                "title": "ToolCall"
-            },
-            "ToolResponseMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "tool",
-                        "default": "tool",
-                        "description": "Must be \"tool\" to identify this as a tool response"
-                    },
-                    "call_id": {
-                        "type": "string",
-                        "description": "Unique identifier for the tool call this response is for"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The response content from the tool"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "call_id",
-                    "content"
-                ],
-                "title": "ToolResponseMessage",
-                "description": "A message representing the result of a tool invocation."
-            },
-            "URL": {
-                "type": "object",
-                "properties": {
-                    "uri": {
-                        "type": "string",
-                        "description": "The URL string pointing to the resource"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "uri"
-                ],
-                "title": "URL",
-                "description": "A URL reference to external content."
-            },
-            "UserMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "user",
-                        "default": "user",
-                        "description": "Must be \"user\" to identify this as a user message"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the message, which can include text and other media"
-                    },
-                    "context": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "(Optional) This field is used internally by Llama Stack to pass RAG context. This field may be removed in the API in the future."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content"
-                ],
-                "title": "UserMessage",
-                "description": "A message from the user in a chat conversation."
-            },
             "RunShieldRequest": {
                 "type": "object",
                 "properties": {
@@ -10104,7 +9828,7 @@
                     "messages": {
                         "type": "array",
                         "items": {
-                            "$ref": "#/components/schemas/Message"
+                            "$ref": "#/components/schemas/OpenAIMessageParam"
                         },
                         "description": "The messages to run the shield on."
                     },
@@ -11070,6 +10794,284 @@
                 ],
                 "title": "RegisterShieldRequest"
             },
+            "CompletionMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "assistant",
+                        "default": "assistant",
+                        "description": "Must be \"assistant\" to identify this as the model's response"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the model's response"
+                    },
+                    "stop_reason": {
+                        "type": "string",
+                        "enum": [
+                            "end_of_turn",
+                            "end_of_message",
+                            "out_of_tokens"
+                        ],
+                        "description": "Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`: The model finished generating the entire response. - `StopReason.end_of_message`: The model finished generating but generated a partial response -- usually, a tool call. The user may call the tool and continue the conversation with the tool's response. - `StopReason.out_of_tokens`: The model ran out of token budget."
+                    },
+                    "tool_calls": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/ToolCall"
+                        },
+                        "description": "List of tool calls. Each tool call is a ToolCall object."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content",
+                    "stop_reason"
+                ],
+                "title": "CompletionMessage",
+                "description": "A message containing the model's (assistant) response in a chat conversation."
+            },
+            "ImageContentItem": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "const": "image",
+                        "default": "image",
+                        "description": "Discriminator type of the content item. Always \"image\""
+                    },
+                    "image": {
+                        "type": "object",
+                        "properties": {
+                            "url": {
+                                "$ref": "#/components/schemas/URL",
+                                "description": "A URL of the image or data URL in the format of data:image/{type};base64,{data}. Note that URL could have length limits."
+                            },
+                            "data": {
+                                "type": "string",
+                                "contentEncoding": "base64",
+                                "description": "base64 encoded image data as string"
+                            }
+                        },
+                        "additionalProperties": false,
+                        "description": "Image as a base64 encoded string or an URL"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type",
+                    "image"
+                ],
+                "title": "ImageContentItem",
+                "description": "A image content item"
+            },
+            "InterleavedContent": {
+                "oneOf": [
+                    {
+                        "type": "string"
+                    },
+                    {
+                        "$ref": "#/components/schemas/InterleavedContentItem"
+                    },
+                    {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/InterleavedContentItem"
+                        }
+                    }
+                ]
+            },
+            "InterleavedContentItem": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/ImageContentItem"
+                    },
+                    {
+                        "$ref": "#/components/schemas/TextContentItem"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "type",
+                    "mapping": {
+                        "image": "#/components/schemas/ImageContentItem",
+                        "text": "#/components/schemas/TextContentItem"
+                    }
+                }
+            },
+            "Message": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/UserMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/SystemMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/ToolResponseMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/CompletionMessage"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "role",
+                    "mapping": {
+                        "user": "#/components/schemas/UserMessage",
+                        "system": "#/components/schemas/SystemMessage",
+                        "tool": "#/components/schemas/ToolResponseMessage",
+                        "assistant": "#/components/schemas/CompletionMessage"
+                    }
+                }
+            },
+            "SystemMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "system",
+                        "default": "system",
+                        "description": "Must be \"system\" to identify this as a system message"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content"
+                ],
+                "title": "SystemMessage",
+                "description": "A system message providing instructions or context to the model."
+            },
+            "TextContentItem": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "const": "text",
+                        "default": "text",
+                        "description": "Discriminator type of the content item. Always \"text\""
+                    },
+                    "text": {
+                        "type": "string",
+                        "description": "Text content"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type",
+                    "text"
+                ],
+                "title": "TextContentItem",
+                "description": "A text content item"
+            },
+            "ToolCall": {
+                "type": "object",
+                "properties": {
+                    "call_id": {
+                        "type": "string"
+                    },
+                    "tool_name": {
+                        "oneOf": [
+                            {
+                                "type": "string",
+                                "enum": [
+                                    "brave_search",
+                                    "wolfram_alpha",
+                                    "photogen",
+                                    "code_interpreter"
+                                ],
+                                "title": "BuiltinTool"
+                            },
+                            {
+                                "type": "string"
+                            }
+                        ]
+                    },
+                    "arguments": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "call_id",
+                    "tool_name",
+                    "arguments"
+                ],
+                "title": "ToolCall"
+            },
+            "ToolResponseMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "tool",
+                        "default": "tool",
+                        "description": "Must be \"tool\" to identify this as a tool response"
+                    },
+                    "call_id": {
+                        "type": "string",
+                        "description": "Unique identifier for the tool call this response is for"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The response content from the tool"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "call_id",
+                    "content"
+                ],
+                "title": "ToolResponseMessage",
+                "description": "A message representing the result of a tool invocation."
+            },
+            "URL": {
+                "type": "object",
+                "properties": {
+                    "uri": {
+                        "type": "string",
+                        "description": "The URL string pointing to the resource"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "uri"
+                ],
+                "title": "URL",
+                "description": "A URL reference to external content."
+            },
+            "UserMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "user",
+                        "default": "user",
+                        "description": "Must be \"user\" to identify this as a user message"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the message, which can include text and other media"
+                    },
+                    "context": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "(Optional) This field is used internally by Llama Stack to pass RAG context. This field may be removed in the API in the future."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content"
+                ],
+                "title": "UserMessage",
+                "description": "A message from the user in a chat conversation."
+            },
             "SyntheticDataGenerateRequest": {
                 "type": "object",
                 "properties": {
@@ -12587,19 +12589,19 @@
                 "title": "VectorStoreObject",
                 "description": "OpenAI Vector Store object."
             },
-            "OpenaiCreateVectorStoreRequest": {
+            "OpenAICreateVectorStoreRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "name": {
                         "type": "string",
-                        "description": "A name for the vector store."
+                        "description": "(Optional) A name for the vector store"
                     },
                     "file_ids": {
                         "type": "array",
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
+                        "description": "List of file IDs to include in the vector store"
                     },
                     "expires_after": {
                         "type": "object",
@@ -12625,7 +12627,7 @@
                                 }
                             ]
                         },
-                        "description": "The expiration policy for a vector store."
+                        "description": "(Optional) Expiration policy for the vector store"
                     },
                     "chunking_strategy": {
                         "type": "object",
@@ -12651,7 +12653,7 @@
                                 }
                             ]
                         },
-                        "description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
+                        "description": "(Optional) Strategy for splitting files into chunks"
                     },
                     "metadata": {
                         "type": "object",
@@ -12677,23 +12679,12 @@
                                 }
                             ]
                         },
-                        "description": "Set of 16 key-value pairs that can be attached to an object."
+                        "description": "Set of key-value pairs that can be attached to the vector store"
-                    },
-                    "embedding_model": {
-                        "type": "string",
-                        "description": "The embedding model to use for this vector store."
-                    },
-                    "embedding_dimension": {
-                        "type": "integer",
-                        "description": "The dimension of the embedding vectors (default: 384)."
-                    },
-                    "provider_id": {
-                        "type": "string",
-                        "description": "The ID of the provider to use for this vector store."
                     }
                 },
                 "additionalProperties": false,
-                "title": "OpenaiCreateVectorStoreRequest"
+                "title": "OpenAICreateVectorStoreRequestWithExtraBody",
+                "description": "Request to create a vector store with extra_body support."
             },
             "OpenaiUpdateVectorStoreRequest": {
                 "type": "object",
@@ -12863,7 +12854,7 @@
                 "title": "VectorStoreChunkingStrategyStaticConfig",
                 "description": "Configuration for static chunking strategy."
             },
-            "OpenaiCreateVectorStoreFileBatchRequest": {
+            "OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "file_ids": {
@@ -12871,7 +12862,7 @@
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use."
+                        "description": "A list of File IDs that the vector store should use"
                     },
                     "attributes": {
                         "type": "object",
@@ -12897,18 +12888,19 @@
                                 }
                             ]
                         },
-                        "description": "(Optional) Key-value attributes to store with the files."
+                        "description": "(Optional) Key-value attributes to store with the files"
                     },
                     "chunking_strategy": {
                         "$ref": "#/components/schemas/VectorStoreChunkingStrategy",
-                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
+                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "file_ids"
                 ],
-                "title": "OpenaiCreateVectorStoreFileBatchRequest"
+                "title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
+                "description": "Request to create a vector store file batch with extra_body support."
             },
             "VectorStoreFileBatchObject": {
                 "type": "object",

docs/static/llama-stack-spec.yaml

@@ -617,7 +617,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiEmbeddingsRequest'
+              $ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/files:
@@ -2413,13 +2413,16 @@ paths:
       tags:
         - VectorIO
       summary: Creates a vector store.
-      description: Creates a vector store.
+      description: >-
+        Creates a vector store.
+
+        Generate an OpenAI-compatible vector store with the given parameters.
       parameters: []
       requestBody:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/vector_stores/{vector_store_id}:
@@ -2545,7 +2548,11 @@ paths:
       tags:
         - VectorIO
       summary: Create a vector store file batch.
-      description: Create a vector store file batch.
+      description: >-
+        Create a vector store file batch.
+
+        Generate an OpenAI-compatible vector store file batch for the given vector
+        store.
       parameters:
         - name: vector_store_id
           in: path
@@ -2558,7 +2565,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
@@ -4797,7 +4804,7 @@ components:
         - deleted
       title: ConversationItemDeletedResource
       description: Response for deleted conversation item.
-    OpenaiEmbeddingsRequest:
+    OpenAIEmbeddingsRequestWithExtraBody:
       type: object
       properties:
         model:
@@ -4816,6 +4823,7 @@ components:
             multiple inputs in a single request, pass an array of strings.
         encoding_format:
           type: string
+          default: float
           description: >-
             (Optional) The format to return the embeddings in. Can be either "float"
             or "base64". Defaults to "float".
@@ -4833,7 +4841,9 @@ components:
       required:
         - model
         - input
-      title: OpenaiEmbeddingsRequest
+      title: OpenAIEmbeddingsRequestWithExtraBody
+      description: >-
+        Request parameters for OpenAI-compatible embeddings endpoint.
     OpenAIEmbeddingData:
       type: object
       properties:
@@ -7591,227 +7601,6 @@ components:
       title: ListOpenAIResponseInputItem
       description: >-
         List container for OpenAI response input items.
-    CompletionMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: assistant
-          default: assistant
-          description: >-
-            Must be "assistant" to identify this as the model's response
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: The content of the model's response
-        stop_reason:
-          type: string
-          enum:
-            - end_of_turn
-            - end_of_message
-            - out_of_tokens
-          description: >-
-            Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`:
-            The model finished generating the entire response. - `StopReason.end_of_message`:
-            The model finished generating but generated a partial response -- usually,
-            a tool call. The user may call the tool and continue the conversation
-            with the tool's response. - `StopReason.out_of_tokens`: The model ran
-            out of token budget.
-        tool_calls:
-          type: array
-          items:
-            $ref: '#/components/schemas/ToolCall'
-          description: >-
-            List of tool calls. Each tool call is a ToolCall object.
-      additionalProperties: false
-      required:
-        - role
-        - content
-        - stop_reason
-      title: CompletionMessage
-      description: >-
-        A message containing the model's (assistant) response in a chat conversation.
-    ImageContentItem:
-      type: object
-      properties:
-        type:
-          type: string
-          const: image
-          default: image
-          description: >-
-            Discriminator type of the content item. Always "image"
-        image:
-          type: object
-          properties:
-            url:
-              $ref: '#/components/schemas/URL'
-              description: >-
-                A URL of the image or data URL in the format of data:image/{type};base64,{data}.
-                Note that URL could have length limits.
-            data:
-              type: string
-              contentEncoding: base64
-              description: base64 encoded image data as string
-          additionalProperties: false
-          description: >-
-            Image as a base64 encoded string or an URL
-      additionalProperties: false
-      required:
-        - type
-        - image
-      title: ImageContentItem
-      description: A image content item
-    InterleavedContent:
-      oneOf:
-        - type: string
-        - $ref: '#/components/schemas/InterleavedContentItem'
-        - type: array
-          items:
-            $ref: '#/components/schemas/InterleavedContentItem'
-    InterleavedContentItem:
-      oneOf:
-        - $ref: '#/components/schemas/ImageContentItem'
-        - $ref: '#/components/schemas/TextContentItem'
-      discriminator:
-        propertyName: type
-        mapping:
-          image: '#/components/schemas/ImageContentItem'
-          text: '#/components/schemas/TextContentItem'
-    Message:
-      oneOf:
-        - $ref: '#/components/schemas/UserMessage'
-        - $ref: '#/components/schemas/SystemMessage'
-        - $ref: '#/components/schemas/ToolResponseMessage'
-        - $ref: '#/components/schemas/CompletionMessage'
-      discriminator:
-        propertyName: role
-        mapping:
-          user: '#/components/schemas/UserMessage'
-          system: '#/components/schemas/SystemMessage'
-          tool: '#/components/schemas/ToolResponseMessage'
-          assistant: '#/components/schemas/CompletionMessage'
-    SystemMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: system
-          default: system
-          description: >-
-            Must be "system" to identify this as a system message
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            The content of the "system prompt". If multiple system messages are provided,
-            they are concatenated. The underlying Llama Stack code may also add other
-            system messages (for example, for formatting tool definitions).
-      additionalProperties: false
-      required:
-        - role
-        - content
-      title: SystemMessage
-      description: >-
-        A system message providing instructions or context to the model.
-    TextContentItem:
-      type: object
-      properties:
-        type:
-          type: string
-          const: text
-          default: text
-          description: >-
-            Discriminator type of the content item. Always "text"
-        text:
-          type: string
-          description: Text content
-      additionalProperties: false
-      required:
-        - type
-        - text
-      title: TextContentItem
-      description: A text content item
-    ToolCall:
-      type: object
-      properties:
-        call_id:
-          type: string
-        tool_name:
-          oneOf:
-            - type: string
-              enum:
-                - brave_search
-                - wolfram_alpha
-                - photogen
-                - code_interpreter
-              title: BuiltinTool
-            - type: string
-        arguments:
-          type: string
-      additionalProperties: false
-      required:
-        - call_id
-        - tool_name
-        - arguments
-      title: ToolCall
-    ToolResponseMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: tool
-          default: tool
-          description: >-
-            Must be "tool" to identify this as a tool response
-        call_id:
-          type: string
-          description: >-
-            Unique identifier for the tool call this response is for
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: The response content from the tool
-      additionalProperties: false
-      required:
-        - role
-        - call_id
-        - content
-      title: ToolResponseMessage
-      description: >-
-        A message representing the result of a tool invocation.
-    URL:
-      type: object
-      properties:
-        uri:
-          type: string
-          description: The URL string pointing to the resource
-      additionalProperties: false
-      required:
-        - uri
-      title: URL
-      description: A URL reference to external content.
-    UserMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: user
-          default: user
-          description: >-
-            Must be "user" to identify this as a user message
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            The content of the message, which can include text and other media
-        context:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            (Optional) This field is used internally by Llama Stack to pass RAG context.
-            This field may be removed in the API in the future.
-      additionalProperties: false
-      required:
-        - role
-        - content
-      title: UserMessage
-      description: >-
-        A message from the user in a chat conversation.
     RunShieldRequest:
       type: object
       properties:
@@ -7821,7 +7610,7 @@ components:
         messages:
           type: array
           items:
-            $ref: '#/components/schemas/Message'
+            $ref: '#/components/schemas/OpenAIMessageParam'
           description: The messages to run the shield on.
         params:
           type: object
@@ -8488,6 +8277,227 @@ components:
       required:
         - shield_id
       title: RegisterShieldRequest
+    CompletionMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: assistant
+          default: assistant
+          description: >-
+            Must be "assistant" to identify this as the model's response
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: The content of the model's response
+        stop_reason:
+          type: string
+          enum:
+            - end_of_turn
+            - end_of_message
+            - out_of_tokens
+          description: >-
+            Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`:
+            The model finished generating the entire response. - `StopReason.end_of_message`:
+            The model finished generating but generated a partial response -- usually,
+            a tool call. The user may call the tool and continue the conversation
+            with the tool's response. - `StopReason.out_of_tokens`: The model ran
+            out of token budget.
+        tool_calls:
+          type: array
+          items:
+            $ref: '#/components/schemas/ToolCall'
+          description: >-
+            List of tool calls. Each tool call is a ToolCall object.
+      additionalProperties: false
+      required:
+        - role
+        - content
+        - stop_reason
+      title: CompletionMessage
+      description: >-
+        A message containing the model's (assistant) response in a chat conversation.
+    ImageContentItem:
+      type: object
+      properties:
+        type:
+          type: string
+          const: image
+          default: image
+          description: >-
+            Discriminator type of the content item. Always "image"
+        image:
+          type: object
+          properties:
+            url:
+              $ref: '#/components/schemas/URL'
+              description: >-
+                A URL of the image or data URL in the format of data:image/{type};base64,{data}.
+                Note that URL could have length limits.
+            data:
+              type: string
+              contentEncoding: base64
+              description: base64 encoded image data as string
+          additionalProperties: false
+          description: >-
+            Image as a base64 encoded string or an URL
+      additionalProperties: false
+      required:
+        - type
+        - image
+      title: ImageContentItem
+      description: A image content item
+    InterleavedContent:
+      oneOf:
+        - type: string
+        - $ref: '#/components/schemas/InterleavedContentItem'
+        - type: array
+          items:
+            $ref: '#/components/schemas/InterleavedContentItem'
+    InterleavedContentItem:
+      oneOf:
+        - $ref: '#/components/schemas/ImageContentItem'
+        - $ref: '#/components/schemas/TextContentItem'
+      discriminator:
+        propertyName: type
+        mapping:
+          image: '#/components/schemas/ImageContentItem'
+          text: '#/components/schemas/TextContentItem'
+    Message:
+      oneOf:
+        - $ref: '#/components/schemas/UserMessage'
+        - $ref: '#/components/schemas/SystemMessage'
+        - $ref: '#/components/schemas/ToolResponseMessage'
+        - $ref: '#/components/schemas/CompletionMessage'
+      discriminator:
+        propertyName: role
+        mapping:
+          user: '#/components/schemas/UserMessage'
+          system: '#/components/schemas/SystemMessage'
+          tool: '#/components/schemas/ToolResponseMessage'
+          assistant: '#/components/schemas/CompletionMessage'
+    SystemMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: system
+          default: system
+          description: >-
+            Must be "system" to identify this as a system message
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            The content of the "system prompt". If multiple system messages are provided,
+            they are concatenated. The underlying Llama Stack code may also add other
+            system messages (for example, for formatting tool definitions).
+      additionalProperties: false
+      required:
+        - role
+        - content
+      title: SystemMessage
+      description: >-
+        A system message providing instructions or context to the model.
+    TextContentItem:
+      type: object
+      properties:
+        type:
+          type: string
+          const: text
+          default: text
+          description: >-
+            Discriminator type of the content item. Always "text"
+        text:
+          type: string
+          description: Text content
+      additionalProperties: false
+      required:
+        - type
+        - text
+      title: TextContentItem
+      description: A text content item
+    ToolCall:
+      type: object
+      properties:
+        call_id:
+          type: string
+        tool_name:
+          oneOf:
+            - type: string
+              enum:
+                - brave_search
+                - wolfram_alpha
+                - photogen
+                - code_interpreter
+              title: BuiltinTool
+            - type: string
+        arguments:
+          type: string
+      additionalProperties: false
+      required:
+        - call_id
+        - tool_name
+        - arguments
+      title: ToolCall
+    ToolResponseMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: tool
+          default: tool
+          description: >-
+            Must be "tool" to identify this as a tool response
+        call_id:
+          type: string
+          description: >-
+            Unique identifier for the tool call this response is for
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: The response content from the tool
+      additionalProperties: false
+      required:
+        - role
+        - call_id
+        - content
+      title: ToolResponseMessage
+      description: >-
+        A message representing the result of a tool invocation.
+    URL:
+      type: object
+      properties:
+        uri:
+          type: string
+          description: The URL string pointing to the resource
+      additionalProperties: false
+      required:
+        - uri
+      title: URL
+      description: A URL reference to external content.
+    UserMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: user
+          default: user
+          description: >-
+            Must be "user" to identify this as a user message
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            The content of the message, which can include text and other media
+        context:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            (Optional) This field is used internally by Llama Stack to pass RAG context.
+            This field may be removed in the API in the future.
+      additionalProperties: false
+      required:
+        - role
+        - content
+      title: UserMessage
+      description: >-
+        A message from the user in a chat conversation.
     SyntheticDataGenerateRequest:
       type: object
       properties:
@@ -9612,19 +9622,18 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
-    OpenaiCreateVectorStoreRequest:
+    "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
         name:
           type: string
-          description: A name for the vector store.
+          description: (Optional) A name for the vector store
         file_ids:
           type: array
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use. Useful for tools
+            List of file IDs to include in the vector store
-            like `file_search` that can access files.
         expires_after:
           type: object
           additionalProperties:
@@ -9636,7 +9645,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The expiration policy for a vector store.
+            (Optional) Expiration policy for the vector store
         chunking_strategy:
           type: object
           additionalProperties:
@@ -9648,8 +9657,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The chunking strategy used to chunk the file(s). If not set, will use
+            (Optional) Strategy for splitting files into chunks
-            the `auto` strategy.
         metadata:
           type: object
           additionalProperties:
@@ -9661,21 +9669,12 @@ components:
               - type: array
               - type: object
           description: >-
-            Set of 16 key-value pairs that can be attached to an object.
+            Set of key-value pairs that can be attached to the vector store
-        embedding_model:
-          type: string
-          description: >-
-            The embedding model to use for this vector store.
-        embedding_dimension:
-          type: integer
-          description: >-
-            The dimension of the embedding vectors (default: 384).
-        provider_id:
-          type: string
-          description: >-
-            The ID of the provider to use for this vector store.
       additionalProperties: false
-      title: OpenaiCreateVectorStoreRequest
+      title: >-
+        OpenAICreateVectorStoreRequestWithExtraBody
+      description: >-
+        Request to create a vector store with extra_body support.
     OpenaiUpdateVectorStoreRequest:
       type: object
       properties:
@@ -9796,7 +9795,7 @@ components:
       title: VectorStoreChunkingStrategyStaticConfig
       description: >-
         Configuration for static chunking strategy.
-    OpenaiCreateVectorStoreFileBatchRequest:
+    "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
         file_ids:
@@ -9804,7 +9803,7 @@ components:
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use.
+            A list of File IDs that the vector store should use
         attributes:
           type: object
           additionalProperties:
@@ -9816,16 +9815,19 @@ components:
               - type: array
               - type: object
           description: >-
-            (Optional) Key-value attributes to store with the files.
+            (Optional) Key-value attributes to store with the files
         chunking_strategy:
           $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) The chunking strategy used to chunk the file(s). Defaults to
-            auto.
+            auto
       additionalProperties: false
       required:
         - file_ids
-      title: OpenaiCreateVectorStoreFileBatchRequest
+      title: >-
+        OpenAICreateVectorStoreFileBatchRequestWithExtraBody
+      description: >-
+        Request to create a vector store file batch with extra_body support.
     VectorStoreFileBatchObject:
       type: object
       properties:

docs/static/stainless-llama-stack-spec.html

@@ -765,7 +765,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
+                                "$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
                             }
                         }
                     },
@@ -3170,13 +3170,13 @@
                     "VectorIO"
                 ],
                 "summary": "Creates a vector store.",
-                "description": "Creates a vector store.",
+                "description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
                             }
                         }
                     },
@@ -3356,7 +3356,7 @@
                     "VectorIO"
                 ],
                 "summary": "Create a vector store file batch.",
-                "description": "Create a vector store file batch.",
+                "description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
                 "parameters": [
                     {
                         "name": "vector_store_id",
@@ -3372,7 +3372,7 @@
                     "content": {
                         "application/json": {
                             "schema": {
-                                "$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
+                                "$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
                             }
                         }
                     },
@@ -8333,7 +8333,7 @@
                 "title": "ConversationItemDeletedResource",
                 "description": "Response for deleted conversation item."
             },
-            "OpenaiEmbeddingsRequest": {
+            "OpenAIEmbeddingsRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "model": {
@@ -8356,6 +8356,7 @@
                     },
                     "encoding_format": {
                         "type": "string",
+                        "default": "float",
                         "description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
                     },
                     "dimensions": {
@@ -8372,7 +8373,8 @@
                     "model",
                     "input"
                 ],
-                "title": "OpenaiEmbeddingsRequest"
+                "title": "OpenAIEmbeddingsRequestWithExtraBody",
+                "description": "Request parameters for OpenAI-compatible embeddings endpoint."
             },
             "OpenAIEmbeddingData": {
                 "type": "object",
@@ -11825,284 +11827,6 @@
                 "title": "ListOpenAIResponseInputItem",
                 "description": "List container for OpenAI response input items."
             },
-            "CompletionMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "assistant",
-                        "default": "assistant",
-                        "description": "Must be \"assistant\" to identify this as the model's response"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the model's response"
-                    },
-                    "stop_reason": {
-                        "type": "string",
-                        "enum": [
-                            "end_of_turn",
-                            "end_of_message",
-                            "out_of_tokens"
-                        ],
-                        "description": "Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`: The model finished generating the entire response. - `StopReason.end_of_message`: The model finished generating but generated a partial response -- usually, a tool call. The user may call the tool and continue the conversation with the tool's response. - `StopReason.out_of_tokens`: The model ran out of token budget."
-                    },
-                    "tool_calls": {
-                        "type": "array",
-                        "items": {
-                            "$ref": "#/components/schemas/ToolCall"
-                        },
-                        "description": "List of tool calls. Each tool call is a ToolCall object."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content",
-                    "stop_reason"
-                ],
-                "title": "CompletionMessage",
-                "description": "A message containing the model's (assistant) response in a chat conversation."
-            },
-            "ImageContentItem": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "image",
-                        "default": "image",
-                        "description": "Discriminator type of the content item. Always \"image\""
-                    },
-                    "image": {
-                        "type": "object",
-                        "properties": {
-                            "url": {
-                                "$ref": "#/components/schemas/URL",
-                                "description": "A URL of the image or data URL in the format of data:image/{type};base64,{data}. Note that URL could have length limits."
-                            },
-                            "data": {
-                                "type": "string",
-                                "contentEncoding": "base64",
-                                "description": "base64 encoded image data as string"
-                            }
-                        },
-                        "additionalProperties": false,
-                        "description": "Image as a base64 encoded string or an URL"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "image"
-                ],
-                "title": "ImageContentItem",
-                "description": "A image content item"
-            },
-            "InterleavedContent": {
-                "oneOf": [
-                    {
-                        "type": "string"
-                    },
-                    {
-                        "$ref": "#/components/schemas/InterleavedContentItem"
-                    },
-                    {
-                        "type": "array",
-                        "items": {
-                            "$ref": "#/components/schemas/InterleavedContentItem"
-                        }
-                    }
-                ]
-            },
-            "InterleavedContentItem": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/ImageContentItem"
-                    },
-                    {
-                        "$ref": "#/components/schemas/TextContentItem"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "type",
-                    "mapping": {
-                        "image": "#/components/schemas/ImageContentItem",
-                        "text": "#/components/schemas/TextContentItem"
-                    }
-                }
-            },
-            "Message": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/UserMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/SystemMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/ToolResponseMessage"
-                    },
-                    {
-                        "$ref": "#/components/schemas/CompletionMessage"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "role",
-                    "mapping": {
-                        "user": "#/components/schemas/UserMessage",
-                        "system": "#/components/schemas/SystemMessage",
-                        "tool": "#/components/schemas/ToolResponseMessage",
-                        "assistant": "#/components/schemas/CompletionMessage"
-                    }
-                }
-            },
-            "SystemMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "system",
-                        "default": "system",
-                        "description": "Must be \"system\" to identify this as a system message"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content"
-                ],
-                "title": "SystemMessage",
-                "description": "A system message providing instructions or context to the model."
-            },
-            "TextContentItem": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "text",
-                        "default": "text",
-                        "description": "Discriminator type of the content item. Always \"text\""
-                    },
-                    "text": {
-                        "type": "string",
-                        "description": "Text content"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "text"
-                ],
-                "title": "TextContentItem",
-                "description": "A text content item"
-            },
-            "ToolCall": {
-                "type": "object",
-                "properties": {
-                    "call_id": {
-                        "type": "string"
-                    },
-                    "tool_name": {
-                        "oneOf": [
-                            {
-                                "type": "string",
-                                "enum": [
-                                    "brave_search",
-                                    "wolfram_alpha",
-                                    "photogen",
-                                    "code_interpreter"
-                                ],
-                                "title": "BuiltinTool"
-                            },
-                            {
-                                "type": "string"
-                            }
-                        ]
-                    },
-                    "arguments": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "call_id",
-                    "tool_name",
-                    "arguments"
-                ],
-                "title": "ToolCall"
-            },
-            "ToolResponseMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "tool",
-                        "default": "tool",
-                        "description": "Must be \"tool\" to identify this as a tool response"
-                    },
-                    "call_id": {
-                        "type": "string",
-                        "description": "Unique identifier for the tool call this response is for"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The response content from the tool"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "call_id",
-                    "content"
-                ],
-                "title": "ToolResponseMessage",
-                "description": "A message representing the result of a tool invocation."
-            },
-            "URL": {
-                "type": "object",
-                "properties": {
-                    "uri": {
-                        "type": "string",
-                        "description": "The URL string pointing to the resource"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "uri"
-                ],
-                "title": "URL",
-                "description": "A URL reference to external content."
-            },
-            "UserMessage": {
-                "type": "object",
-                "properties": {
-                    "role": {
-                        "type": "string",
-                        "const": "user",
-                        "default": "user",
-                        "description": "Must be \"user\" to identify this as a user message"
-                    },
-                    "content": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "The content of the message, which can include text and other media"
-                    },
-                    "context": {
-                        "$ref": "#/components/schemas/InterleavedContent",
-                        "description": "(Optional) This field is used internally by Llama Stack to pass RAG context. This field may be removed in the API in the future."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "role",
-                    "content"
-                ],
-                "title": "UserMessage",
-                "description": "A message from the user in a chat conversation."
-            },
             "RunShieldRequest": {
                 "type": "object",
                 "properties": {
@@ -12113,7 +11837,7 @@
                     "messages": {
                         "type": "array",
                         "items": {
-                            "$ref": "#/components/schemas/Message"
+                            "$ref": "#/components/schemas/OpenAIMessageParam"
                         },
                         "description": "The messages to run the shield on."
                     },
@@ -13079,6 +12803,284 @@
                 ],
                 "title": "RegisterShieldRequest"
             },
+            "CompletionMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "assistant",
+                        "default": "assistant",
+                        "description": "Must be \"assistant\" to identify this as the model's response"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the model's response"
+                    },
+                    "stop_reason": {
+                        "type": "string",
+                        "enum": [
+                            "end_of_turn",
+                            "end_of_message",
+                            "out_of_tokens"
+                        ],
+                        "description": "Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`: The model finished generating the entire response. - `StopReason.end_of_message`: The model finished generating but generated a partial response -- usually, a tool call. The user may call the tool and continue the conversation with the tool's response. - `StopReason.out_of_tokens`: The model ran out of token budget."
+                    },
+                    "tool_calls": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/ToolCall"
+                        },
+                        "description": "List of tool calls. Each tool call is a ToolCall object."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content",
+                    "stop_reason"
+                ],
+                "title": "CompletionMessage",
+                "description": "A message containing the model's (assistant) response in a chat conversation."
+            },
+            "ImageContentItem": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "const": "image",
+                        "default": "image",
+                        "description": "Discriminator type of the content item. Always \"image\""
+                    },
+                    "image": {
+                        "type": "object",
+                        "properties": {
+                            "url": {
+                                "$ref": "#/components/schemas/URL",
+                                "description": "A URL of the image or data URL in the format of data:image/{type};base64,{data}. Note that URL could have length limits."
+                            },
+                            "data": {
+                                "type": "string",
+                                "contentEncoding": "base64",
+                                "description": "base64 encoded image data as string"
+                            }
+                        },
+                        "additionalProperties": false,
+                        "description": "Image as a base64 encoded string or an URL"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type",
+                    "image"
+                ],
+                "title": "ImageContentItem",
+                "description": "A image content item"
+            },
+            "InterleavedContent": {
+                "oneOf": [
+                    {
+                        "type": "string"
+                    },
+                    {
+                        "$ref": "#/components/schemas/InterleavedContentItem"
+                    },
+                    {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/InterleavedContentItem"
+                        }
+                    }
+                ]
+            },
+            "InterleavedContentItem": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/ImageContentItem"
+                    },
+                    {
+                        "$ref": "#/components/schemas/TextContentItem"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "type",
+                    "mapping": {
+                        "image": "#/components/schemas/ImageContentItem",
+                        "text": "#/components/schemas/TextContentItem"
+                    }
+                }
+            },
+            "Message": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/UserMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/SystemMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/ToolResponseMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/CompletionMessage"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "role",
+                    "mapping": {
+                        "user": "#/components/schemas/UserMessage",
+                        "system": "#/components/schemas/SystemMessage",
+                        "tool": "#/components/schemas/ToolResponseMessage",
+                        "assistant": "#/components/schemas/CompletionMessage"
+                    }
+                }
+            },
+            "SystemMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "system",
+                        "default": "system",
+                        "description": "Must be \"system\" to identify this as a system message"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content"
+                ],
+                "title": "SystemMessage",
+                "description": "A system message providing instructions or context to the model."
+            },
+            "TextContentItem": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "const": "text",
+                        "default": "text",
+                        "description": "Discriminator type of the content item. Always \"text\""
+                    },
+                    "text": {
+                        "type": "string",
+                        "description": "Text content"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type",
+                    "text"
+                ],
+                "title": "TextContentItem",
+                "description": "A text content item"
+            },
+            "ToolCall": {
+                "type": "object",
+                "properties": {
+                    "call_id": {
+                        "type": "string"
+                    },
+                    "tool_name": {
+                        "oneOf": [
+                            {
+                                "type": "string",
+                                "enum": [
+                                    "brave_search",
+                                    "wolfram_alpha",
+                                    "photogen",
+                                    "code_interpreter"
+                                ],
+                                "title": "BuiltinTool"
+                            },
+                            {
+                                "type": "string"
+                            }
+                        ]
+                    },
+                    "arguments": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "call_id",
+                    "tool_name",
+                    "arguments"
+                ],
+                "title": "ToolCall"
+            },
+            "ToolResponseMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "tool",
+                        "default": "tool",
+                        "description": "Must be \"tool\" to identify this as a tool response"
+                    },
+                    "call_id": {
+                        "type": "string",
+                        "description": "Unique identifier for the tool call this response is for"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The response content from the tool"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "call_id",
+                    "content"
+                ],
+                "title": "ToolResponseMessage",
+                "description": "A message representing the result of a tool invocation."
+            },
+            "URL": {
+                "type": "object",
+                "properties": {
+                    "uri": {
+                        "type": "string",
+                        "description": "The URL string pointing to the resource"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "uri"
+                ],
+                "title": "URL",
+                "description": "A URL reference to external content."
+            },
+            "UserMessage": {
+                "type": "object",
+                "properties": {
+                    "role": {
+                        "type": "string",
+                        "const": "user",
+                        "default": "user",
+                        "description": "Must be \"user\" to identify this as a user message"
+                    },
+                    "content": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "The content of the message, which can include text and other media"
+                    },
+                    "context": {
+                        "$ref": "#/components/schemas/InterleavedContent",
+                        "description": "(Optional) This field is used internally by Llama Stack to pass RAG context. This field may be removed in the API in the future."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "role",
+                    "content"
+                ],
+                "title": "UserMessage",
+                "description": "A message from the user in a chat conversation."
+            },
             "SyntheticDataGenerateRequest": {
                 "type": "object",
                 "properties": {
@@ -14596,19 +14598,19 @@
                 "title": "VectorStoreObject",
                 "description": "OpenAI Vector Store object."
             },
-            "OpenaiCreateVectorStoreRequest": {
+            "OpenAICreateVectorStoreRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "name": {
                         "type": "string",
-                        "description": "A name for the vector store."
+                        "description": "(Optional) A name for the vector store"
                     },
                     "file_ids": {
                         "type": "array",
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
+                        "description": "List of file IDs to include in the vector store"
                     },
                     "expires_after": {
                         "type": "object",
@@ -14634,7 +14636,7 @@
                                 }
                             ]
                         },
-                        "description": "The expiration policy for a vector store."
+                        "description": "(Optional) Expiration policy for the vector store"
                     },
                     "chunking_strategy": {
                         "type": "object",
@@ -14660,7 +14662,7 @@
                                 }
                             ]
                         },
-                        "description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
+                        "description": "(Optional) Strategy for splitting files into chunks"
                     },
                     "metadata": {
                         "type": "object",
@@ -14686,23 +14688,12 @@
                                 }
                             ]
                         },
-                        "description": "Set of 16 key-value pairs that can be attached to an object."
+                        "description": "Set of key-value pairs that can be attached to the vector store"
-                    },
-                    "embedding_model": {
-                        "type": "string",
-                        "description": "The embedding model to use for this vector store."
-                    },
-                    "embedding_dimension": {
-                        "type": "integer",
-                        "description": "The dimension of the embedding vectors (default: 384)."
-                    },
-                    "provider_id": {
-                        "type": "string",
-                        "description": "The ID of the provider to use for this vector store."
                     }
                 },
                 "additionalProperties": false,
-                "title": "OpenaiCreateVectorStoreRequest"
+                "title": "OpenAICreateVectorStoreRequestWithExtraBody",
+                "description": "Request to create a vector store with extra_body support."
             },
             "OpenaiUpdateVectorStoreRequest": {
                 "type": "object",
@@ -14872,7 +14863,7 @@
                 "title": "VectorStoreChunkingStrategyStaticConfig",
                 "description": "Configuration for static chunking strategy."
             },
-            "OpenaiCreateVectorStoreFileBatchRequest": {
+            "OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
                 "type": "object",
                 "properties": {
                     "file_ids": {
@@ -14880,7 +14871,7 @@
                         "items": {
                             "type": "string"
                         },
-                        "description": "A list of File IDs that the vector store should use."
+                        "description": "A list of File IDs that the vector store should use"
                     },
                     "attributes": {
                         "type": "object",
@@ -14906,18 +14897,19 @@
                                 }
                             ]
                         },
-                        "description": "(Optional) Key-value attributes to store with the files."
+                        "description": "(Optional) Key-value attributes to store with the files"
                     },
                     "chunking_strategy": {
                         "$ref": "#/components/schemas/VectorStoreChunkingStrategy",
-                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
+                        "description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "file_ids"
                 ],
-                "title": "OpenaiCreateVectorStoreFileBatchRequest"
+                "title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
+                "description": "Request to create a vector store file batch with extra_body support."
             },
             "VectorStoreFileBatchObject": {
                 "type": "object",

docs/static/stainless-llama-stack-spec.yaml

@@ -620,7 +620,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiEmbeddingsRequest'
+              $ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/files:
@@ -2416,13 +2416,16 @@ paths:
       tags:
         - VectorIO
       summary: Creates a vector store.
-      description: Creates a vector store.
+      description: >-
+        Creates a vector store.
+
+        Generate an OpenAI-compatible vector store with the given parameters.
       parameters: []
       requestBody:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/vector_stores/{vector_store_id}:
@@ -2548,7 +2551,11 @@ paths:
       tags:
         - VectorIO
       summary: Create a vector store file batch.
-      description: Create a vector store file batch.
+      description: >-
+        Create a vector store file batch.
+
+        Generate an OpenAI-compatible vector store file batch for the given vector
+        store.
       parameters:
         - name: vector_store_id
           in: path
@@ -2561,7 +2568,7 @@ paths:
         content:
           application/json:
             schema:
-              $ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
+              $ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
         required: true
       deprecated: false
   /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
@@ -6242,7 +6249,7 @@ components:
         - deleted
       title: ConversationItemDeletedResource
       description: Response for deleted conversation item.
-    OpenaiEmbeddingsRequest:
+    OpenAIEmbeddingsRequestWithExtraBody:
       type: object
       properties:
         model:
@@ -6261,6 +6268,7 @@ components:
             multiple inputs in a single request, pass an array of strings.
         encoding_format:
           type: string
+          default: float
           description: >-
             (Optional) The format to return the embeddings in. Can be either "float"
             or "base64". Defaults to "float".
@@ -6278,7 +6286,9 @@ components:
       required:
         - model
         - input
-      title: OpenaiEmbeddingsRequest
+      title: OpenAIEmbeddingsRequestWithExtraBody
+      description: >-
+        Request parameters for OpenAI-compatible embeddings endpoint.
     OpenAIEmbeddingData:
       type: object
       properties:
@@ -9036,227 +9046,6 @@ components:
       title: ListOpenAIResponseInputItem
       description: >-
         List container for OpenAI response input items.
-    CompletionMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: assistant
-          default: assistant
-          description: >-
-            Must be "assistant" to identify this as the model's response
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: The content of the model's response
-        stop_reason:
-          type: string
-          enum:
-            - end_of_turn
-            - end_of_message
-            - out_of_tokens
-          description: >-
-            Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`:
-            The model finished generating the entire response. - `StopReason.end_of_message`:
-            The model finished generating but generated a partial response -- usually,
-            a tool call. The user may call the tool and continue the conversation
-            with the tool's response. - `StopReason.out_of_tokens`: The model ran
-            out of token budget.
-        tool_calls:
-          type: array
-          items:
-            $ref: '#/components/schemas/ToolCall'
-          description: >-
-            List of tool calls. Each tool call is a ToolCall object.
-      additionalProperties: false
-      required:
-        - role
-        - content
-        - stop_reason
-      title: CompletionMessage
-      description: >-
-        A message containing the model's (assistant) response in a chat conversation.
-    ImageContentItem:
-      type: object
-      properties:
-        type:
-          type: string
-          const: image
-          default: image
-          description: >-
-            Discriminator type of the content item. Always "image"
-        image:
-          type: object
-          properties:
-            url:
-              $ref: '#/components/schemas/URL'
-              description: >-
-                A URL of the image or data URL in the format of data:image/{type};base64,{data}.
-                Note that URL could have length limits.
-            data:
-              type: string
-              contentEncoding: base64
-              description: base64 encoded image data as string
-          additionalProperties: false
-          description: >-
-            Image as a base64 encoded string or an URL
-      additionalProperties: false
-      required:
-        - type
-        - image
-      title: ImageContentItem
-      description: A image content item
-    InterleavedContent:
-      oneOf:
-        - type: string
-        - $ref: '#/components/schemas/InterleavedContentItem'
-        - type: array
-          items:
-            $ref: '#/components/schemas/InterleavedContentItem'
-    InterleavedContentItem:
-      oneOf:
-        - $ref: '#/components/schemas/ImageContentItem'
-        - $ref: '#/components/schemas/TextContentItem'
-      discriminator:
-        propertyName: type
-        mapping:
-          image: '#/components/schemas/ImageContentItem'
-          text: '#/components/schemas/TextContentItem'
-    Message:
-      oneOf:
-        - $ref: '#/components/schemas/UserMessage'
-        - $ref: '#/components/schemas/SystemMessage'
-        - $ref: '#/components/schemas/ToolResponseMessage'
-        - $ref: '#/components/schemas/CompletionMessage'
-      discriminator:
-        propertyName: role
-        mapping:
-          user: '#/components/schemas/UserMessage'
-          system: '#/components/schemas/SystemMessage'
-          tool: '#/components/schemas/ToolResponseMessage'
-          assistant: '#/components/schemas/CompletionMessage'
-    SystemMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: system
-          default: system
-          description: >-
-            Must be "system" to identify this as a system message
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            The content of the "system prompt". If multiple system messages are provided,
-            they are concatenated. The underlying Llama Stack code may also add other
-            system messages (for example, for formatting tool definitions).
-      additionalProperties: false
-      required:
-        - role
-        - content
-      title: SystemMessage
-      description: >-
-        A system message providing instructions or context to the model.
-    TextContentItem:
-      type: object
-      properties:
-        type:
-          type: string
-          const: text
-          default: text
-          description: >-
-            Discriminator type of the content item. Always "text"
-        text:
-          type: string
-          description: Text content
-      additionalProperties: false
-      required:
-        - type
-        - text
-      title: TextContentItem
-      description: A text content item
-    ToolCall:
-      type: object
-      properties:
-        call_id:
-          type: string
-        tool_name:
-          oneOf:
-            - type: string
-              enum:
-                - brave_search
-                - wolfram_alpha
-                - photogen
-                - code_interpreter
-              title: BuiltinTool
-            - type: string
-        arguments:
-          type: string
-      additionalProperties: false
-      required:
-        - call_id
-        - tool_name
-        - arguments
-      title: ToolCall
-    ToolResponseMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: tool
-          default: tool
-          description: >-
-            Must be "tool" to identify this as a tool response
-        call_id:
-          type: string
-          description: >-
-            Unique identifier for the tool call this response is for
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: The response content from the tool
-      additionalProperties: false
-      required:
-        - role
-        - call_id
-        - content
-      title: ToolResponseMessage
-      description: >-
-        A message representing the result of a tool invocation.
-    URL:
-      type: object
-      properties:
-        uri:
-          type: string
-          description: The URL string pointing to the resource
-      additionalProperties: false
-      required:
-        - uri
-      title: URL
-      description: A URL reference to external content.
-    UserMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          const: user
-          default: user
-          description: >-
-            Must be "user" to identify this as a user message
-        content:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            The content of the message, which can include text and other media
-        context:
-          $ref: '#/components/schemas/InterleavedContent'
-          description: >-
-            (Optional) This field is used internally by Llama Stack to pass RAG context.
-            This field may be removed in the API in the future.
-      additionalProperties: false
-      required:
-        - role
-        - content
-      title: UserMessage
-      description: >-
-        A message from the user in a chat conversation.
     RunShieldRequest:
       type: object
       properties:
@@ -9266,7 +9055,7 @@ components:
         messages:
           type: array
           items:
-            $ref: '#/components/schemas/Message'
+            $ref: '#/components/schemas/OpenAIMessageParam'
           description: The messages to run the shield on.
         params:
           type: object
@@ -9933,6 +9722,227 @@ components:
       required:
         - shield_id
       title: RegisterShieldRequest
+    CompletionMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: assistant
+          default: assistant
+          description: >-
+            Must be "assistant" to identify this as the model's response
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: The content of the model's response
+        stop_reason:
+          type: string
+          enum:
+            - end_of_turn
+            - end_of_message
+            - out_of_tokens
+          description: >-
+            Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`:
+            The model finished generating the entire response. - `StopReason.end_of_message`:
+            The model finished generating but generated a partial response -- usually,
+            a tool call. The user may call the tool and continue the conversation
+            with the tool's response. - `StopReason.out_of_tokens`: The model ran
+            out of token budget.
+        tool_calls:
+          type: array
+          items:
+            $ref: '#/components/schemas/ToolCall'
+          description: >-
+            List of tool calls. Each tool call is a ToolCall object.
+      additionalProperties: false
+      required:
+        - role
+        - content
+        - stop_reason
+      title: CompletionMessage
+      description: >-
+        A message containing the model's (assistant) response in a chat conversation.
+    ImageContentItem:
+      type: object
+      properties:
+        type:
+          type: string
+          const: image
+          default: image
+          description: >-
+            Discriminator type of the content item. Always "image"
+        image:
+          type: object
+          properties:
+            url:
+              $ref: '#/components/schemas/URL'
+              description: >-
+                A URL of the image or data URL in the format of data:image/{type};base64,{data}.
+                Note that URL could have length limits.
+            data:
+              type: string
+              contentEncoding: base64
+              description: base64 encoded image data as string
+          additionalProperties: false
+          description: >-
+            Image as a base64 encoded string or an URL
+      additionalProperties: false
+      required:
+        - type
+        - image
+      title: ImageContentItem
+      description: A image content item
+    InterleavedContent:
+      oneOf:
+        - type: string
+        - $ref: '#/components/schemas/InterleavedContentItem'
+        - type: array
+          items:
+            $ref: '#/components/schemas/InterleavedContentItem'
+    InterleavedContentItem:
+      oneOf:
+        - $ref: '#/components/schemas/ImageContentItem'
+        - $ref: '#/components/schemas/TextContentItem'
+      discriminator:
+        propertyName: type
+        mapping:
+          image: '#/components/schemas/ImageContentItem'
+          text: '#/components/schemas/TextContentItem'
+    Message:
+      oneOf:
+        - $ref: '#/components/schemas/UserMessage'
+        - $ref: '#/components/schemas/SystemMessage'
+        - $ref: '#/components/schemas/ToolResponseMessage'
+        - $ref: '#/components/schemas/CompletionMessage'
+      discriminator:
+        propertyName: role
+        mapping:
+          user: '#/components/schemas/UserMessage'
+          system: '#/components/schemas/SystemMessage'
+          tool: '#/components/schemas/ToolResponseMessage'
+          assistant: '#/components/schemas/CompletionMessage'
+    SystemMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: system
+          default: system
+          description: >-
+            Must be "system" to identify this as a system message
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            The content of the "system prompt". If multiple system messages are provided,
+            they are concatenated. The underlying Llama Stack code may also add other
+            system messages (for example, for formatting tool definitions).
+      additionalProperties: false
+      required:
+        - role
+        - content
+      title: SystemMessage
+      description: >-
+        A system message providing instructions or context to the model.
+    TextContentItem:
+      type: object
+      properties:
+        type:
+          type: string
+          const: text
+          default: text
+          description: >-
+            Discriminator type of the content item. Always "text"
+        text:
+          type: string
+          description: Text content
+      additionalProperties: false
+      required:
+        - type
+        - text
+      title: TextContentItem
+      description: A text content item
+    ToolCall:
+      type: object
+      properties:
+        call_id:
+          type: string
+        tool_name:
+          oneOf:
+            - type: string
+              enum:
+                - brave_search
+                - wolfram_alpha
+                - photogen
+                - code_interpreter
+              title: BuiltinTool
+            - type: string
+        arguments:
+          type: string
+      additionalProperties: false
+      required:
+        - call_id
+        - tool_name
+        - arguments
+      title: ToolCall
+    ToolResponseMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: tool
+          default: tool
+          description: >-
+            Must be "tool" to identify this as a tool response
+        call_id:
+          type: string
+          description: >-
+            Unique identifier for the tool call this response is for
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: The response content from the tool
+      additionalProperties: false
+      required:
+        - role
+        - call_id
+        - content
+      title: ToolResponseMessage
+      description: >-
+        A message representing the result of a tool invocation.
+    URL:
+      type: object
+      properties:
+        uri:
+          type: string
+          description: The URL string pointing to the resource
+      additionalProperties: false
+      required:
+        - uri
+      title: URL
+      description: A URL reference to external content.
+    UserMessage:
+      type: object
+      properties:
+        role:
+          type: string
+          const: user
+          default: user
+          description: >-
+            Must be "user" to identify this as a user message
+        content:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            The content of the message, which can include text and other media
+        context:
+          $ref: '#/components/schemas/InterleavedContent'
+          description: >-
+            (Optional) This field is used internally by Llama Stack to pass RAG context.
+            This field may be removed in the API in the future.
+      additionalProperties: false
+      required:
+        - role
+        - content
+      title: UserMessage
+      description: >-
+        A message from the user in a chat conversation.
     SyntheticDataGenerateRequest:
       type: object
       properties:
@@ -11057,19 +11067,18 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
-    OpenaiCreateVectorStoreRequest:
+    "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
         name:
           type: string
-          description: A name for the vector store.
+          description: (Optional) A name for the vector store
         file_ids:
           type: array
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use. Useful for tools
+            List of file IDs to include in the vector store
-            like `file_search` that can access files.
         expires_after:
           type: object
           additionalProperties:
@@ -11081,7 +11090,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The expiration policy for a vector store.
+            (Optional) Expiration policy for the vector store
         chunking_strategy:
           type: object
           additionalProperties:
@@ -11093,8 +11102,7 @@ components:
               - type: array
               - type: object
           description: >-
-            The chunking strategy used to chunk the file(s). If not set, will use
+            (Optional) Strategy for splitting files into chunks
-            the `auto` strategy.
         metadata:
           type: object
           additionalProperties:
@@ -11106,21 +11114,12 @@ components:
               - type: array
               - type: object
           description: >-
-            Set of 16 key-value pairs that can be attached to an object.
+            Set of key-value pairs that can be attached to the vector store
-        embedding_model:
-          type: string
-          description: >-
-            The embedding model to use for this vector store.
-        embedding_dimension:
-          type: integer
-          description: >-
-            The dimension of the embedding vectors (default: 384).
-        provider_id:
-          type: string
-          description: >-
-            The ID of the provider to use for this vector store.
       additionalProperties: false
-      title: OpenaiCreateVectorStoreRequest
+      title: >-
+        OpenAICreateVectorStoreRequestWithExtraBody
+      description: >-
+        Request to create a vector store with extra_body support.
     OpenaiUpdateVectorStoreRequest:
       type: object
       properties:
@@ -11241,7 +11240,7 @@ components:
       title: VectorStoreChunkingStrategyStaticConfig
       description: >-
         Configuration for static chunking strategy.
-    OpenaiCreateVectorStoreFileBatchRequest:
+    "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
         file_ids:
@@ -11249,7 +11248,7 @@ components:
           items:
             type: string
           description: >-
-            A list of File IDs that the vector store should use.
+            A list of File IDs that the vector store should use
         attributes:
           type: object
           additionalProperties:
@@ -11261,16 +11260,19 @@ components:
               - type: array
               - type: object
           description: >-
-            (Optional) Key-value attributes to store with the files.
+            (Optional) Key-value attributes to store with the files
         chunking_strategy:
           $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) The chunking strategy used to chunk the file(s). Defaults to
-            auto.
+            auto
       additionalProperties: false
       required:
         - file_ids
-      title: OpenaiCreateVectorStoreFileBatchRequest
+      title: >-
+        OpenAICreateVectorStoreFileBatchRequestWithExtraBody
+      description: >-
+        Request to create a vector store file batch with extra_body support.
     VectorStoreFileBatchObject:
       type: object
       properties:

docs/zero_to_hero_guide/05_Memory101.ipynb

@@ -161,6 +161,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
+      "id": "4ad70258",
       "metadata": {},
       "outputs": [],
       "source": [
@@ -180,8 +181,8 @@
         "# Create a vector database with optimized settings for general use\n",
         "client.vector_dbs.register(\n",
         "    vector_db_id=VECTOR_DB_ID,\n",
-        "    embedding_model=\"all-MiniLM-L6-v2\",\n",
+        "    embedding_model=\"nomic-embed-text-v1.5\",\n",
-        "    embedding_dimension=384,  # This is the dimension for all-MiniLM-L6-v2\n",
+        "    embedding_dimension=768,  # This is the dimension for nomic-embed-text-v1.5\n",
         "    provider_id=provider_id,\n",
         ")"
       ]

llama_stack/apis/inference/inference.py

@@ -1140,6 +1140,25 @@ class OpenAIChatCompletionRequestWithExtraBody(BaseModel, extra="allow"):
     user: str | None = None
 
 
+# extra_body can be accessed via .model_extra
+@json_schema_type
+class OpenAIEmbeddingsRequestWithExtraBody(BaseModel, extra="allow"):
+    """Request parameters for OpenAI-compatible embeddings endpoint.
+
+    :param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
+    :param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings.
+    :param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float".
+    :param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
+    :param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
+    """
+
+    model: str
+    input: str | list[str]
+    encoding_format: str | None = "float"
+    dimensions: int | None = None
+    user: str | None = None
+
+
 @runtime_checkable
 @trace_protocol
 class InferenceProvider(Protocol):
@@ -1200,21 +1219,11 @@ class InferenceProvider(Protocol):
     @webmethod(route="/embeddings", method="POST", level=LLAMA_STACK_API_V1)
     async def openai_embeddings(
         self,
-        model: str,
+        params: Annotated[OpenAIEmbeddingsRequestWithExtraBody, Body(...)],
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         """Create embeddings.
 
         Generate OpenAI-compatible embeddings for the given input using the specified model.
-
-        :param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
-        :param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings.
-        :param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float".
-        :param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
-        :param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
         :returns: An OpenAIEmbeddingsResponse containing the embeddings.
         """
         ...

llama_stack/apis/safety/safety.py

@@ -9,7 +9,7 @@ from typing import Any, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.shields import Shield
 from llama_stack.apis.version import LLAMA_STACK_API_V1
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
@@ -107,7 +107,7 @@ class Safety(Protocol):
     async def run_shield(
         self,
         shield_id: str,
-        messages: list[Message],
+        messages: list[OpenAIMessageParam],
         params: dict[str, Any],
     ) -> RunShieldResponse:
         """Run shield.

llama_stack/apis/vector_io/vector_io.py

@@ -11,6 +11,7 @@
 import uuid
 from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
+from fastapi import Body
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.inference import InterleavedContent
@@ -466,6 +467,40 @@ class VectorStoreFilesListInBatchResponse(BaseModel):
     has_more: bool = False
 
 
+# extra_body can be accessed via .model_extra
+@json_schema_type
+class OpenAICreateVectorStoreRequestWithExtraBody(BaseModel, extra="allow"):
+    """Request to create a vector store with extra_body support.
+
+    :param name: (Optional) A name for the vector store
+    :param file_ids: List of file IDs to include in the vector store
+    :param expires_after: (Optional) Expiration policy for the vector store
+    :param chunking_strategy: (Optional) Strategy for splitting files into chunks
+    :param metadata: Set of key-value pairs that can be attached to the vector store
+    """
+
+    name: str | None = None
+    file_ids: list[str] | None = None
+    expires_after: dict[str, Any] | None = None
+    chunking_strategy: dict[str, Any] | None = None
+    metadata: dict[str, Any] | None = None
+
+
+# extra_body can be accessed via .model_extra
+@json_schema_type
+class OpenAICreateVectorStoreFileBatchRequestWithExtraBody(BaseModel, extra="allow"):
+    """Request to create a vector store file batch with extra_body support.
+
+    :param file_ids: A list of File IDs that the vector store should use
+    :param attributes: (Optional) Key-value attributes to store with the files
+    :param chunking_strategy: (Optional) The chunking strategy used to chunk the file(s). Defaults to auto
+    """
+
+    file_ids: list[str]
+    attributes: dict[str, Any] | None = None
+    chunking_strategy: VectorStoreChunkingStrategy | None = None
+
+
 class VectorDBStore(Protocol):
     def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
 
@@ -516,25 +551,11 @@ class VectorIO(Protocol):
     @webmethod(route="/vector_stores", method="POST", level=LLAMA_STACK_API_V1)
     async def openai_create_vector_store(
         self,
-        name: str | None = None,
+        params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
-        file_ids: list[str] | None = None,
-        expires_after: dict[str, Any] | None = None,
-        chunking_strategy: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-        embedding_model: str | None = None,
-        embedding_dimension: int | None = 384,
-        provider_id: str | None = None,
     ) -> VectorStoreObject:
         """Creates a vector store.
 
-        :param name: A name for the vector store.
+        Generate an OpenAI-compatible vector store with the given parameters.
-        :param file_ids: A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files.
-        :param expires_after: The expiration policy for a vector store.
-        :param chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy.
-        :param metadata: Set of 16 key-value pairs that can be attached to an object.
-        :param embedding_model: The embedding model to use for this vector store.
-        :param embedding_dimension: The dimension of the embedding vectors (default: 384).
-        :param provider_id: The ID of the provider to use for this vector store.
         :returns: A VectorStoreObject representing the created vector store.
         """
         ...
@@ -827,16 +848,12 @@ class VectorIO(Protocol):
     async def openai_create_vector_store_file_batch(
         self,
         vector_store_id: str,
-        file_ids: list[str],
+        params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch.
 
+        Generate an OpenAI-compatible vector store file batch for the given vector store.
         :param vector_store_id: The ID of the vector store to create the file batch for.
-        :param file_ids: A list of File IDs that the vector store should use.
-        :param attributes: (Optional) Key-value attributes to store with the files.
-        :param chunking_strategy: (Optional) The chunking strategy used to chunk the file(s). Defaults to auto.
         :returns: A VectorStoreFileBatchObject representing the created file batch.
         """
         ...

llama_stack/core/library_client.py

@@ -513,6 +513,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         # Strip NOT_GIVENs to use the defaults in signature
         body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
 
+        # Check if there's an unwrapped body parameter among multiple parameters
+        # (e.g., path param + body param like: vector_store_id: str, params: Annotated[Model, Body(...)])
+        unwrapped_body_param = None
+        for param in params_list:
+            if is_unwrapped_body_param(param.annotation):
+                unwrapped_body_param = param
+                break
+
         # Convert parameters to Pydantic models where needed
         converted_body = {}
         for param_name, param in sig.parameters.items():
@@ -522,5 +530,11 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
                     converted_body[param_name] = value
                 else:
                     converted_body[param_name] = convert_to_pydantic(param.annotation, value)
+            elif unwrapped_body_param and param.name == unwrapped_body_param.name:
+                # This is the unwrapped body param - construct it from remaining body keys
+                base_type = get_args(param.annotation)[0]
+                # Extract only the keys that aren't already used by other params
+                remaining_keys = {k: v for k, v in body.items() if k not in converted_body}
+                converted_body[param.name] = base_type(**remaining_keys)
 
         return converted_body

llama_stack/core/routers/inference.py

@@ -40,6 +40,7 @@ from llama_stack.apis.inference import (
     OpenAICompletion,
     OpenAICompletionRequestWithExtraBody,
     OpenAICompletionWithInputMessages,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIMessageParam,
     Order,
@@ -279,26 +280,18 @@ class InferenceRouter(Inference):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: Annotated[OpenAIEmbeddingsRequestWithExtraBody, Body(...)],
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         logger.debug(
-            f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}",
+            f"InferenceRouter.openai_embeddings: model={params.model}, input_type={type(params.input)}, encoding_format={params.encoding_format}, dimensions={params.dimensions}",
-        )
-        model_obj = await self._get_model(model, ModelType.embedding)
-        params = dict(
-            model=model_obj.identifier,
-            input=input,
-            encoding_format=encoding_format,
-            dimensions=dimensions,
-            user=user,
         )
+        model_obj = await self._get_model(params.model, ModelType.embedding)
+
+        # Update model to use resolved identifier
+        params.model = model_obj.identifier
 
         provider = await self.routing_table.get_provider_impl(model_obj.identifier)
-        return await provider.openai_embeddings(**params)
+        return await provider.openai_embeddings(params)
 
     async def list_chat_completions(
         self,

llama_stack/core/routers/vector_io.py

@@ -6,12 +6,16 @@
 
 import asyncio
 import uuid
-from typing import Any
+from typing import Annotated, Any
+
+from fastapi import Body
 
 from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.vector_io import (
     Chunk,
+    OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
+    OpenAICreateVectorStoreRequestWithExtraBody,
     QueryChunksResponse,
     SearchRankingOptions,
     VectorIO,
@@ -51,30 +55,18 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
-    async def _get_first_embedding_model(self) -> tuple[str, int] | None:
+    async def _get_embedding_model_dimension(self, embedding_model_id: str) -> int:
-        """Get the first available embedding model identifier."""
+        """Get the embedding dimension for a specific embedding model."""
-        try:
-            # Get all models from the routing table
         all_models = await self.routing_table.get_all_with_type("model")
 
-            # Filter for embedding models
+        for model in all_models:
-            embedding_models = [
+            if model.identifier == embedding_model_id and model.model_type == ModelType.embedding:
-                model
+                dimension = model.metadata.get("embedding_dimension")
-                for model in all_models
-                if hasattr(model, "model_type") and model.model_type == ModelType.embedding
-            ]
-
-            if embedding_models:
-                dimension = embedding_models[0].metadata.get("embedding_dimension", None)
                 if dimension is None:
-                    raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
+                    raise ValueError(f"Embedding model '{embedding_model_id}' has no embedding_dimension in metadata")
-                return embedding_models[0].identifier, dimension
+                return int(dimension)
-            else:
+
-                logger.warning("No embedding models found in the routing table")
+        raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
-                return None
-        except Exception as e:
-            logger.error(f"Error getting embedding models: {e}")
-            return None
 
     async def register_vector_db(
         self,
@@ -120,24 +112,35 @@ class VectorIORouter(VectorIO):
     # OpenAI Vector Stores API endpoints
     async def openai_create_vector_store(
         self,
-        name: str,
+        params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
-        file_ids: list[str] | None = None,
-        expires_after: dict[str, Any] | None = None,
-        chunking_strategy: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-        embedding_model: str | None = None,
-        embedding_dimension: int | None = None,
-        provider_id: str | None = None,
     ) -> VectorStoreObject:
-        logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
+        # Extract llama-stack-specific parameters from extra_body
+        extra = params.model_extra or {}
+        embedding_model = extra.get("embedding_model")
+        embedding_dimension = extra.get("embedding_dimension")
+        provider_id = extra.get("provider_id")
 
-        # If no embedding model is provided, use the first available one
+        logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={provider_id}")
+
+        # Require explicit embedding model specification
         if embedding_model is None:
-            embedding_model_info = await self._get_first_embedding_model()
+            raise ValueError("embedding_model is required in extra_body when creating a vector store")
-            if embedding_model_info is None:
+
-                raise ValueError("No embedding model provided and no embedding models available in the system")
+        if embedding_dimension is None:
-            embedding_model, embedding_dimension = embedding_model_info
+            embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
-            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+
+        # Auto-select provider if not specified
+        if provider_id is None:
+            num_providers = len(self.routing_table.impls_by_provider_id)
+            if num_providers == 0:
+                raise ValueError("No vector_io providers available")
+            if num_providers > 1:
+                available_providers = list(self.routing_table.impls_by_provider_id.keys())
+                raise ValueError(
+                    f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
+                    f"Available providers: {available_providers}"
+                )
+            provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
 
         vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(
@@ -146,20 +149,19 @@ class VectorIORouter(VectorIO):
             embedding_dimension=embedding_dimension,
             provider_id=provider_id,
             provider_vector_db_id=vector_db_id,
-            vector_db_name=name,
+            vector_db_name=params.name,
         )
         provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
-        return await provider.openai_create_vector_store(
+
-            name=name,
+        # Update model_extra with registered values so provider uses the already-registered vector_db
-            file_ids=file_ids,
+        if params.model_extra is None:
-            expires_after=expires_after,
+            params.model_extra = {}
-            chunking_strategy=chunking_strategy,
+        params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
-            metadata=metadata,
+        params.model_extra["provider_id"] = registered_vector_db.provider_id
-            embedding_model=embedding_model,
+        params.model_extra["embedding_model"] = embedding_model
-            embedding_dimension=embedding_dimension,
+        params.model_extra["embedding_dimension"] = embedding_dimension
-            provider_id=registered_vector_db.provider_id,
+
-            provider_vector_db_id=registered_vector_db.provider_resource_id,
+        return await provider.openai_create_vector_store(params)
-        )
 
     async def openai_list_vector_stores(
         self,
@@ -219,7 +221,8 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_retrieve_vector_store: {vector_store_id}")
-        return await self.routing_table.openai_retrieve_vector_store(vector_store_id)
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store(vector_store_id)
 
     async def openai_update_vector_store(
         self,
@@ -229,7 +232,8 @@ class VectorIORouter(VectorIO):
         metadata: dict[str, Any] | None = None,
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
-        return await self.routing_table.openai_update_vector_store(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store(
             vector_store_id=vector_store_id,
             name=name,
             expires_after=expires_after,
@@ -241,7 +245,8 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreDeleteResponse:
         logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
-        return await self.routing_table.openai_delete_vector_store(vector_store_id)
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_delete_vector_store(vector_store_id)
 
     async def openai_search_vector_store(
         self,
@@ -254,7 +259,8 @@ class VectorIORouter(VectorIO):
         search_mode: str | None = "vector",
     ) -> VectorStoreSearchResponsePage:
         logger.debug(f"VectorIORouter.openai_search_vector_store: {vector_store_id}")
-        return await self.routing_table.openai_search_vector_store(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_search_vector_store(
             vector_store_id=vector_store_id,
             query=query,
             filters=filters,
@@ -272,7 +278,8 @@ class VectorIORouter(VectorIO):
         chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileObject:
         logger.debug(f"VectorIORouter.openai_attach_file_to_vector_store: {vector_store_id}, {file_id}")
-        return await self.routing_table.openai_attach_file_to_vector_store(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_attach_file_to_vector_store(
             vector_store_id=vector_store_id,
             file_id=file_id,
             attributes=attributes,
@@ -289,7 +296,8 @@ class VectorIORouter(VectorIO):
         filter: VectorStoreFileStatus | None = None,
     ) -> list[VectorStoreFileObject]:
         logger.debug(f"VectorIORouter.openai_list_files_in_vector_store: {vector_store_id}")
-        return await self.routing_table.openai_list_files_in_vector_store(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store(
             vector_store_id=vector_store_id,
             limit=limit,
             order=order,
@@ -304,7 +312,8 @@ class VectorIORouter(VectorIO):
         file_id: str,
     ) -> VectorStoreFileObject:
         logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file: {vector_store_id}, {file_id}")
-        return await self.routing_table.openai_retrieve_vector_store_file(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
@@ -315,7 +324,8 @@ class VectorIORouter(VectorIO):
         file_id: str,
     ) -> VectorStoreFileContentsResponse:
         logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
-        return await self.routing_table.openai_retrieve_vector_store_file_contents(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_contents(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
@@ -327,7 +337,8 @@ class VectorIORouter(VectorIO):
         attributes: dict[str, Any],
     ) -> VectorStoreFileObject:
         logger.debug(f"VectorIORouter.openai_update_vector_store_file: {vector_store_id}, {file_id}")
-        return await self.routing_table.openai_update_vector_store_file(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
             attributes=attributes,
@@ -339,7 +350,8 @@ class VectorIORouter(VectorIO):
         file_id: str,
     ) -> VectorStoreFileDeleteResponse:
         logger.debug(f"VectorIORouter.openai_delete_vector_store_file: {vector_store_id}, {file_id}")
-        return await self.routing_table.openai_delete_vector_store_file(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_delete_vector_store_file(
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
@@ -370,17 +382,13 @@ class VectorIORouter(VectorIO):
     async def openai_create_vector_store_file_batch(
         self,
         vector_store_id: str,
-        file_ids: list[str],
+        params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileBatchObject:
-        logger.debug(f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(file_ids)} files")
+        logger.debug(
-        return await self.routing_table.openai_create_vector_store_file_batch(
+            f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(params.file_ids)} files"
-            vector_store_id=vector_store_id,
-            file_ids=file_ids,
-            attributes=attributes,
-            chunking_strategy=chunking_strategy,
         )
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_create_vector_store_file_batch(vector_store_id, params)
 
     async def openai_retrieve_vector_store_file_batch(
         self,
@@ -388,7 +396,8 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreFileBatchObject:
         logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_batch: {batch_id}, {vector_store_id}")
-        return await self.routing_table.openai_retrieve_vector_store_file_batch(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_batch(
             batch_id=batch_id,
             vector_store_id=vector_store_id,
         )
@@ -404,7 +413,8 @@ class VectorIORouter(VectorIO):
         order: str | None = "desc",
     ) -> VectorStoreFilesListInBatchResponse:
         logger.debug(f"VectorIORouter.openai_list_files_in_vector_store_file_batch: {batch_id}, {vector_store_id}")
-        return await self.routing_table.openai_list_files_in_vector_store_file_batch(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store_file_batch(
             batch_id=batch_id,
             vector_store_id=vector_store_id,
             after=after,
@@ -420,7 +430,8 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreFileBatchObject:
         logger.debug(f"VectorIORouter.openai_cancel_vector_store_file_batch: {batch_id}, {vector_store_id}")
-        return await self.routing_table.openai_cancel_vector_store_file_batch(
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_cancel_vector_store_file_batch(
             batch_id=batch_id,
             vector_store_id=vector_store_id,
         )

llama_stack/core/server/auth_providers.py

@@ -5,13 +5,11 @@
 # the root directory of this source tree.
 
 import ssl
-import time
 from abc import ABC, abstractmethod
-from asyncio import Lock
 from urllib.parse import parse_qs, urljoin, urlparse
 
 import httpx
-from jose import jwt
+import jwt
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.common.errors import TokenValidationError
@@ -98,9 +96,7 @@ class OAuth2TokenAuthProvider(AuthProvider):
 
     def __init__(self, config: OAuth2TokenAuthConfig):
         self.config = config
-        self._jwks_at: float = 0.0
+        self._jwks_client: jwt.PyJWKClient | None = None
-        self._jwks: dict[str, str] = {}
-        self._jwks_lock = Lock()
 
     async def validate_token(self, token: str, scope: dict | None = None) -> User:
         if self.config.jwks:
@@ -109,23 +105,60 @@ class OAuth2TokenAuthProvider(AuthProvider):
             return await self.introspect_token(token, scope)
         raise ValueError("One of jwks or introspection must be configured")
 
+    def _get_jwks_client(self) -> jwt.PyJWKClient:
+        if self._jwks_client is None:
+            ssl_context = None
+            if not self.config.verify_tls:
+                # Disable SSL verification if verify_tls is False
+                ssl_context = ssl.create_default_context()
+                ssl_context.check_hostname = False
+                ssl_context.verify_mode = ssl.CERT_NONE
+            elif self.config.tls_cafile:
+                # Use custom CA file if provided
+                ssl_context = ssl.create_default_context(
+                    cafile=self.config.tls_cafile.as_posix(),
+                )
+                # If verify_tls is True and no tls_cafile, ssl_context remains None (use system defaults)
+
+            # Prepare headers for JWKS request - this is needed for Kubernetes to authenticate
+            # to the JWK endpoint, we must use the token in the config to authenticate
+            headers = {}
+            if self.config.jwks and self.config.jwks.token:
+                headers["Authorization"] = f"Bearer {self.config.jwks.token}"
+
+            self._jwks_client = jwt.PyJWKClient(
+                self.config.jwks.uri if self.config.jwks else None,
+                cache_keys=True,
+                max_cached_keys=10,
+                lifespan=self.config.jwks.key_recheck_period if self.config.jwks else None,
+                headers=headers,
+                ssl_context=ssl_context,
+            )
+        return self._jwks_client
+
     async def validate_jwt_token(self, token: str, scope: dict | None = None) -> User:
         """Validate a token using the JWT token."""
-        await self._refresh_jwks()
-
         try:
-            header = jwt.get_unverified_header(token)
+            jwks_client: jwt.PyJWKClient = self._get_jwks_client()
-            kid = header["kid"]
+            signing_key = jwks_client.get_signing_key_from_jwt(token)
-            if kid not in self._jwks:
+            algorithm = jwt.get_unverified_header(token)["alg"]
-                raise ValueError(f"Unknown key ID: {kid}")
-            key_data = self._jwks[kid]
-            algorithm = header.get("alg", "RS256")
             claims = jwt.decode(
                 token,
-                key_data,
+                signing_key.key,
                 algorithms=[algorithm],
                 audience=self.config.audience,
                 issuer=self.config.issuer,
+                options={"verify_exp": True, "verify_aud": True, "verify_iss": True},
+            )
+
+            # Decode and verify the JWT
+            claims = jwt.decode(
+                token,
+                signing_key.key,
+                algorithms=[algorithm],
+                audience=self.config.audience,
+                issuer=self.config.issuer,
+                options={"verify_exp": True, "verify_aud": True, "verify_iss": True},
             )
         except Exception as exc:
             raise ValueError("Invalid JWT token") from exc
@@ -201,37 +234,6 @@ class OAuth2TokenAuthProvider(AuthProvider):
         else:
             return "Authentication required. Please provide a valid OAuth2 Bearer token in the Authorization header"
 
-    async def _refresh_jwks(self) -> None:
-        """
-        Refresh the JWKS cache.
-
-        This is a simple cache that expires after a certain amount of time (defined by `key_recheck_period`).
-        If the cache is expired, we refresh the JWKS from the JWKS URI.
-
-        Notes: for Kubernetes which doesn't fully implement the OIDC protocol:
-            * It doesn't have user authentication flows
-            * It doesn't have refresh tokens
-        """
-        async with self._jwks_lock:
-            if self.config.jwks is None:
-                raise ValueError("JWKS is not configured")
-            if time.time() - self._jwks_at > self.config.jwks.key_recheck_period:
-                headers = {}
-                if self.config.jwks.token:
-                    headers["Authorization"] = f"Bearer {self.config.jwks.token}"
-                verify = self.config.tls_cafile.as_posix() if self.config.tls_cafile else self.config.verify_tls
-                async with httpx.AsyncClient(verify=verify) as client:
-                    res = await client.get(self.config.jwks.uri, timeout=5, headers=headers)
-                    res.raise_for_status()
-                    jwks_data = res.json()["keys"]
-                    updated = {}
-                    for k in jwks_data:
-                        kid = k["kid"]
-                        # Store the entire key object as it may be needed for different algorithms
-                        updated[kid] = k
-                    self._jwks = updated
-                    self._jwks_at = time.time()
-
 
 class CustomAuthProvider(AuthProvider):
     """Custom authentication provider that uses an external endpoint."""

llama_stack/distributions/dell/dell.py

@@ -87,11 +87,11 @@ def get_distribution_template() -> DistributionTemplate:
         provider_id="tgi1",
     )
     embedding_model = ModelInput(
-        model_id="all-MiniLM-L6-v2",
+        model_id="nomic-embed-text-v1.5",
         provider_id="sentence-transformers",
         model_type=ModelType.embedding,
         metadata={
-            "embedding_dimension": 384,
+            "embedding_dimension": 768,
         },
     )
     default_tool_groups = [

llama_stack/distributions/dell/run-with-safety.yaml

@@ -114,8 +114,8 @@ models:
   provider_id: tgi1
   model_type: llm
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 shields:

llama_stack/distributions/dell/run.yaml

@@ -106,8 +106,8 @@ models:
   provider_id: tgi0
   model_type: llm
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 shields: []

llama_stack/distributions/meta-reference-gpu/meta_reference.py

@@ -77,11 +77,11 @@ def get_distribution_template() -> DistributionTemplate:
         provider_id="meta-reference-inference",
     )
     embedding_model = ModelInput(
-        model_id="all-MiniLM-L6-v2",
+        model_id="nomic-embed-text-v1.5",
         provider_id="sentence-transformers",
         model_type=ModelType.embedding,
         metadata={
-            "embedding_dimension": 384,
+            "embedding_dimension": 768,
         },
     )
     safety_model = ModelInput(

llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml

@@ -127,8 +127,8 @@ models:
   provider_id: meta-reference-safety
   model_type: llm
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 shields:

llama_stack/distributions/meta-reference-gpu/run.yaml

@@ -113,8 +113,8 @@ models:
   provider_id: meta-reference-inference
   model_type: llm
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 shields: []

llama_stack/distributions/postgres-demo/postgres_demo.py

@@ -85,11 +85,11 @@ def get_distribution_template() -> DistributionTemplate:
         config=SentenceTransformersInferenceConfig.sample_run_config(),
     )
     embedding_model = ModelInput(
-        model_id="all-MiniLM-L6-v2",
+        model_id="nomic-embed-text-v1.5",
         provider_id=embedding_provider.provider_id,
         model_type=ModelType.embedding,
         metadata={
-            "embedding_dimension": 384,
+            "embedding_dimension": 768,
         },
     )
     postgres_config = PostgresSqlStoreConfig.sample_run_config()

llama_stack/distributions/postgres-demo/run.yaml

@@ -95,8 +95,8 @@ models:
   provider_id: vllm-inference
   model_type: llm
 - metadata:
-    embedding_dimension: 384
+    embedding_dimension: 768
-  model_id: all-MiniLM-L6-v2
+  model_id: nomic-embed-text-v1.5
   provider_id: sentence-transformers
   model_type: embedding
 shields:

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

@@ -25,6 +25,7 @@ from llama_stack.apis.inference import (
     OpenAIChatCompletionRequestWithExtraBody,
     OpenAICompletionRequestWithExtraBody,
     OpenAIDeveloperMessageParam,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIMessageParam,
     OpenAISystemMessageParam,
     OpenAIToolMessageParam,
@@ -640,7 +641,9 @@ class ReferenceBatchesImpl(Batches):
                     },
                 }
             else:  # /v1/embeddings
-                embeddings_response = await self.inference_api.openai_embeddings(**request.body)
+                embeddings_response = await self.inference_api.openai_embeddings(
+                    OpenAIEmbeddingsRequestWithExtraBody(**request.body)
+                )
                 assert hasattr(embeddings_response, "model_dump_json"), (
                     "Embeddings response must have model_dump_json method"
                 )

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

@@ -54,11 +54,11 @@ class SentenceTransformersInferenceImpl(
     async def list_models(self) -> list[Model] | None:
         return [
             Model(
-                identifier="all-MiniLM-L6-v2",
+                identifier="nomic-ai/nomic-embed-text-v1.5",
-                provider_resource_id="all-MiniLM-L6-v2",
+                provider_resource_id="nomic-ai/nomic-embed-text-v1.5",
                 provider_id=self.__provider_id__,
                 metadata={
-                    "embedding_dimension": 384,
+                    "embedding_dimension": 768,
                 },
                 model_type=ModelType.embedding,
             ),

llama_stack/providers/inline/safety/code_scanner/code_scanner.py

@@ -10,7 +10,7 @@ from typing import TYPE_CHECKING, Any
 if TYPE_CHECKING:
     from codeshield.cs import CodeShieldScanResult
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
@@ -53,7 +53,7 @@ class MetaReferenceCodeScannerSafetyImpl(Safety):
     async def run_shield(
         self,
         shield_id: str,
-        messages: list[Message],
+        messages: list[OpenAIMessageParam],
         params: dict[str, Any] = None,
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -12,10 +12,9 @@ from typing import Any
 from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem
 from llama_stack.apis.inference import (
     Inference,
-    Message,
     OpenAIChatCompletionRequestWithExtraBody,
+    OpenAIMessageParam,
     OpenAIUserMessageParam,
-    UserMessage,
 )
 from llama_stack.apis.safety import (
     RunShieldResponse,
@@ -165,7 +164,7 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
     async def run_shield(
         self,
         shield_id: str,
-        messages: list[Message],
+        messages: list[OpenAIMessageParam],
         params: dict[str, Any] = None,
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
@@ -175,8 +174,8 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
         messages = messages.copy()
         # some shields like llama-guard require the first message to be a user message
         # since this might be a tool call, first role might not be user
-        if len(messages) > 0 and messages[0].role != Role.user.value:
+        if len(messages) > 0 and messages[0].role != "user":
-            messages[0] = UserMessage(content=messages[0].content)
+            messages[0] = OpenAIUserMessageParam(content=messages[0].content)
 
         # Use the inference API's model resolution instead of hardcoded mappings
         # This allows the shield to work with any registered model
@@ -208,7 +207,7 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
             messages = [input]
 
         # convert to user messages format with role
-        messages = [UserMessage(content=m) for m in messages]
+        messages = [OpenAIUserMessageParam(content=m) for m in messages]
 
         # Determine safety categories based on the model type
         # For known Llama Guard models, use specific categories
@@ -277,7 +276,7 @@ class LlamaGuardShield:
 
         return final_categories
 
-    def validate_messages(self, messages: list[Message]) -> None:
+    def validate_messages(self, messages: list[OpenAIMessageParam]) -> list[OpenAIMessageParam]:
         if len(messages) == 0:
             raise ValueError("Messages must not be empty")
         if messages[0].role != Role.user.value:
@@ -288,7 +287,7 @@ class LlamaGuardShield:
 
         return messages
 
-    async def run(self, messages: list[Message]) -> RunShieldResponse:
+    async def run(self, messages: list[OpenAIMessageParam]) -> RunShieldResponse:
         messages = self.validate_messages(messages)
 
         if self.model == CoreModelId.llama_guard_3_11b_vision.value:
@@ -307,10 +306,10 @@ class LlamaGuardShield:
         content = content.strip()
         return self.get_shield_response(content)
 
-    def build_text_shield_input(self, messages: list[Message]) -> OpenAIUserMessageParam:
+    def build_text_shield_input(self, messages: list[OpenAIMessageParam]) -> OpenAIUserMessageParam:
-        return OpenAIUserMessageParam(role="user", content=self.build_prompt(messages))
+        return OpenAIUserMessageParam(content=self.build_prompt(messages))
 
-    def build_vision_shield_input(self, messages: list[Message]) -> OpenAIUserMessageParam:
+    def build_vision_shield_input(self, messages: list[OpenAIMessageParam]) -> OpenAIUserMessageParam:
         conversation = []
         most_recent_img = None
 
@@ -333,7 +332,7 @@ class LlamaGuardShield:
                     else:
                         raise ValueError(f"Unknown content type: {c}")
 
-                conversation.append(UserMessage(content=content))
+                conversation.append(OpenAIUserMessageParam(content=content))
             else:
                 raise ValueError(f"Unknown content type: {m.content}")
 
@@ -342,9 +341,9 @@ class LlamaGuardShield:
             prompt.append(most_recent_img)
         prompt.append(self.build_prompt(conversation[::-1]))
 
-        return OpenAIUserMessageParam(role="user", content=prompt)
+        return OpenAIUserMessageParam(content=prompt)
 
-    def build_prompt(self, messages: list[Message]) -> str:
+    def build_prompt(self, messages: list[OpenAIMessageParam]) -> str:
         categories = self.get_safety_categories()
         categories_str = "\n".join(categories)
         conversations_str = "\n\n".join(
@@ -377,7 +376,7 @@ class LlamaGuardShield:
 
         raise ValueError(f"Unexpected response: {response}")
 
-    async def run_moderation(self, messages: list[Message]) -> ModerationObject:
+    async def run_moderation(self, messages: list[OpenAIMessageParam]) -> ModerationObject:
         if not messages:
             return self.create_moderation_object(self.model)
 
@@ -388,6 +387,7 @@ class LlamaGuardShield:
             model=self.model,
             messages=[shield_input_message],
             stream=False,
+            temperature=0.0,  # default is 1, which is too high for safety
         )
         response = await self.inference_api.openai_chat_completion(params)
         content = response.choices[0].message.content

llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py

@@ -9,7 +9,7 @@ from typing import Any
 import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
@@ -22,9 +22,7 @@ from llama_stack.apis.shields import Shield
 from llama_stack.core.utils.model_utils import model_local_dir
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
-from llama_stack.providers.utils.inference.prompt_adapter import (
+from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
-    interleaved_content_as_str,
-)
 
 from .config import PromptGuardConfig, PromptGuardType
 
@@ -56,7 +54,7 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
     async def run_shield(
         self,
         shield_id: str,
-        messages: list[Message],
+        messages: list[OpenAIMessageParam],
         params: dict[str, Any],
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
@@ -93,7 +91,7 @@ class PromptGuardShield:
         self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
         self.model = AutoModelForSequenceClassification.from_pretrained(model_dir, device_map=self.device)
 
-    async def run(self, messages: list[Message]) -> RunShieldResponse:
+    async def run(self, messages: list[OpenAIMessageParam]) -> RunShieldResponse:
         message = messages[-1]
         text = interleaved_content_as_str(message.content)
 

llama_stack/providers/registry/inference.py

@@ -43,6 +43,12 @@ def available_providers() -> list[ProviderSpec]:
             pip_packages=[
                 "torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu",
                 "sentence-transformers --no-deps",
+                # required by some SentenceTransformers architectures for tensor rearrange/merge ops
+                "einops",
+                # fast HF tokenization backend used by SentenceTransformers models
+                "tokenizers",
+                # safe and fast file format for storing and loading tensors
+                "safetensors",
             ],
             module="llama_stack.providers.inline.inference.sentence_transformers",
             config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",
@@ -271,7 +277,7 @@ Available Models:
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.watsonx",
             config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig",
-            provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator",
+            provider_data_validator="llama_stack.providers.remote.inference.watsonx.config.WatsonXProviderDataValidator",
             description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.",
         ),
         RemoteProviderSpec(

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -14,6 +14,7 @@ from llama_stack.apis.inference import (
     Inference,
     OpenAIChatCompletionRequestWithExtraBody,
     OpenAICompletionRequestWithExtraBody,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.apis.inference.inference import (
@@ -124,11 +125,7 @@ class BedrockInferenceAdapter(
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()
 

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -6,7 +6,10 @@
 
 from urllib.parse import urljoin
 
-from llama_stack.apis.inference import OpenAIEmbeddingsResponse
+from llama_stack.apis.inference import (
+    OpenAIEmbeddingsRequestWithExtraBody,
+    OpenAIEmbeddingsResponse,
+)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import CerebrasImplConfig
@@ -20,10 +23,6 @@ class CerebrasInferenceAdapter(OpenAIMixin):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()

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

@@ -7,6 +7,7 @@
 from llama_stack.apis.inference.inference import (
     OpenAICompletion,
     OpenAICompletionRequestWithExtraBody,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.log import get_logger
@@ -40,10 +41,6 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()

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

@@ -9,6 +9,7 @@ from openai import NOT_GIVEN
 
 from llama_stack.apis.inference import (
     OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
 )
@@ -78,11 +79,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         """
         OpenAI-compatible embeddings for NVIDIA NIM.
@@ -99,11 +96,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
         )
 
         response = await self.client.embeddings.create(
-            model=await self._get_provider_model_id(model),
+            model=await self._get_provider_model_id(params.model),
-            input=input,
+            input=params.input,
-            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
+            encoding_format=params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
-            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
+            dimensions=params.dimensions if params.dimensions is not None else NOT_GIVEN,
-            user=user if user is not None else NOT_GIVEN,
+            user=params.user if params.user is not None else NOT_GIVEN,
             extra_body=extra_body,
         )
 

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -16,6 +16,7 @@ from llama_stack.apis.inference import (
     OpenAIChatCompletionRequestWithExtraBody,
     OpenAICompletion,
     OpenAICompletionRequestWithExtraBody,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.apis.models import Model
@@ -69,11 +70,7 @@ class PassthroughInferenceAdapter(Inference):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()
 

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

@@ -10,7 +10,10 @@ from collections.abc import Iterable
 from huggingface_hub import AsyncInferenceClient, HfApi
 from pydantic import SecretStr
 
-from llama_stack.apis.inference import OpenAIEmbeddingsResponse
+from llama_stack.apis.inference import (
+    OpenAIEmbeddingsRequestWithExtraBody,
+    OpenAIEmbeddingsResponse,
+)
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -40,11 +43,7 @@ class _HfAdapter(OpenAIMixin):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()
 

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

@@ -11,6 +11,7 @@ from together import AsyncTogether
 from together.constants import BASE_URL
 
 from llama_stack.apis.inference import (
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
@@ -62,11 +63,7 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         """
         Together's OpenAI-compatible embeddings endpoint is not compatible with
@@ -78,25 +75,27 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
          - does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
         """
         # Together support ticket #13332 -> will not fix
-        if user is not None:
+        if params.user is not None:
             raise ValueError("Together's embeddings endpoint does not support user param.")
         # Together support ticket #13333 -> escalated
-        if dimensions is not None:
+        if params.dimensions is not None:
             raise ValueError("Together's embeddings endpoint does not support dimensions param.")
 
         response = await self.client.embeddings.create(
-            model=await self._get_provider_model_id(model),
+            model=await self._get_provider_model_id(params.model),
-            input=input,
+            input=params.input,
-            encoding_format=encoding_format,
+            encoding_format=params.encoding_format,
         )
 
-        response.model = model  # return the user the same model id they provided, avoid exposing the provider model id
+        response.model = (
+            params.model
+        )  # return the user the same model id they provided, avoid exposing the provider model id
 
         # Together support ticket #13330 -> escalated
         #  - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
         if not hasattr(response, "usage") or response.usage is None:
             logger.warning(
-                f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
+                f"Together's embedding endpoint for {params.model} did not return usage information, substituting -1s."
             )
             response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
 

llama_stack/providers/remote/inference/watsonx/config.py

@@ -7,18 +7,18 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
 class WatsonXProviderDataValidator(BaseModel):
-    model_config = ConfigDict(
+    watsonx_project_id: str | None = Field(
-        from_attributes=True,
+        default=None,
-        extra="forbid",
+        description="IBM WatsonX project ID",
     )
-    watsonx_api_key: str | None
+    watsonx_api_key: str | None = None
 
 
 @json_schema_type

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -4,42 +4,259 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from collections.abc import AsyncIterator
 from typing import Any
 
+import litellm
 import requests
 
-from llama_stack.apis.inference import ChatCompletionRequest
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAIChatCompletionRequestWithExtraBody,
+    OpenAIChatCompletionUsage,
+    OpenAICompletion,
+    OpenAICompletionRequestWithExtraBody,
+    OpenAIEmbeddingsRequestWithExtraBody,
+    OpenAIEmbeddingsResponse,
+)
 from llama_stack.apis.models import Model
 from llama_stack.apis.models.models import ModelType
+from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.watsonx.config import WatsonXConfig
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+from llama_stack.providers.utils.telemetry.tracing import get_current_span
+
+logger = get_logger(name=__name__, category="providers::remote::watsonx")
 
 
 class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
     _model_cache: dict[str, Model] = {}
 
+    provider_data_api_key_field: str = "watsonx_api_key"
+
     def __init__(self, config: WatsonXConfig):
+        self.available_models = None
+        self.config = config
+        api_key = config.auth_credential.get_secret_value() if config.auth_credential else None
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="watsonx",
-            api_key_from_config=config.auth_credential.get_secret_value() if config.auth_credential else None,
+            api_key_from_config=api_key,
             provider_data_api_key_field="watsonx_api_key",
+            openai_compat_api_base=self.get_base_url(),
+        )
+
+    async def openai_chat_completion(
+        self,
+        params: OpenAIChatCompletionRequestWithExtraBody,
+    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
+        """
+        Override parent method to add timeout and inject usage object when missing.
+        This works around a LiteLLM defect where usage block is sometimes dropped.
+        """
+
+        # Add usage tracking for streaming when telemetry is active
+        stream_options = params.stream_options
+        if params.stream and get_current_span() is not None:
+            if stream_options is None:
+                stream_options = {"include_usage": True}
+            elif "include_usage" not in stream_options:
+                stream_options = {**stream_options, "include_usage": True}
+
+        model_obj = await self.model_store.get_model(params.model)
+
+        request_params = await prepare_openai_completion_params(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            messages=params.messages,
+            frequency_penalty=params.frequency_penalty,
+            function_call=params.function_call,
+            functions=params.functions,
+            logit_bias=params.logit_bias,
+            logprobs=params.logprobs,
+            max_completion_tokens=params.max_completion_tokens,
+            max_tokens=params.max_tokens,
+            n=params.n,
+            parallel_tool_calls=params.parallel_tool_calls,
+            presence_penalty=params.presence_penalty,
+            response_format=params.response_format,
+            seed=params.seed,
+            stop=params.stop,
+            stream=params.stream,
+            stream_options=stream_options,
+            temperature=params.temperature,
+            tool_choice=params.tool_choice,
+            tools=params.tools,
+            top_logprobs=params.top_logprobs,
+            top_p=params.top_p,
+            user=params.user,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+
+        result = await litellm.acompletion(**request_params)
+
+        # If not streaming, check and inject usage if missing
+        if not params.stream:
+            # Use getattr to safely handle cases where usage attribute might not exist
+            if getattr(result, "usage", None) is None:
+                # Create usage object with zeros
+                usage_obj = OpenAIChatCompletionUsage(
+                    prompt_tokens=0,
+                    completion_tokens=0,
+                    total_tokens=0,
+                )
+                # Use model_copy to create a new response with the usage injected
+                result = result.model_copy(update={"usage": usage_obj})
+            return result
+
+        # For streaming, wrap the iterator to normalize chunks
+        return self._normalize_stream(result)
+
+    def _normalize_chunk(self, chunk: OpenAIChatCompletionChunk) -> OpenAIChatCompletionChunk:
+        """
+        Normalize a chunk to ensure it has all expected attributes.
+        This works around LiteLLM not always including all expected attributes.
+        """
+        # Ensure chunk has usage attribute with zeros if missing
+        if not hasattr(chunk, "usage") or chunk.usage is None:
+            usage_obj = OpenAIChatCompletionUsage(
+                prompt_tokens=0,
+                completion_tokens=0,
+                total_tokens=0,
+            )
+            chunk = chunk.model_copy(update={"usage": usage_obj})
+
+        # Ensure all delta objects in choices have expected attributes
+        if hasattr(chunk, "choices") and chunk.choices:
+            normalized_choices = []
+            for choice in chunk.choices:
+                if hasattr(choice, "delta") and choice.delta:
+                    delta = choice.delta
+                    # Build update dict for missing attributes
+                    delta_updates = {}
+                    if not hasattr(delta, "refusal"):
+                        delta_updates["refusal"] = None
+                    if not hasattr(delta, "reasoning_content"):
+                        delta_updates["reasoning_content"] = None
+
+                    # If we need to update delta, create a new choice with updated delta
+                    if delta_updates:
+                        new_delta = delta.model_copy(update=delta_updates)
+                        new_choice = choice.model_copy(update={"delta": new_delta})
+                        normalized_choices.append(new_choice)
+                    else:
+                        normalized_choices.append(choice)
+                else:
+                    normalized_choices.append(choice)
+
+            # If we modified any choices, create a new chunk with updated choices
+            if any(normalized_choices[i] is not chunk.choices[i] for i in range(len(chunk.choices))):
+                chunk = chunk.model_copy(update={"choices": normalized_choices})
+
+        return chunk
+
+    async def _normalize_stream(
+        self, stream: AsyncIterator[OpenAIChatCompletionChunk]
+    ) -> AsyncIterator[OpenAIChatCompletionChunk]:
+        """
+        Normalize all chunks in the stream to ensure they have expected attributes.
+        This works around LiteLLM sometimes not including expected attributes.
+        """
+        try:
+            async for chunk in stream:
+                # Normalize and yield each chunk immediately
+                yield self._normalize_chunk(chunk)
+        except Exception as e:
+            logger.error(f"Error normalizing stream: {e}", exc_info=True)
+            raise
+
+    async def openai_completion(
+        self,
+        params: OpenAICompletionRequestWithExtraBody,
+    ) -> OpenAICompletion:
+        """
+        Override parent method to add watsonx-specific parameters.
+        """
+        from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+
+        model_obj = await self.model_store.get_model(params.model)
+
+        request_params = await prepare_openai_completion_params(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            prompt=params.prompt,
+            best_of=params.best_of,
+            echo=params.echo,
+            frequency_penalty=params.frequency_penalty,
+            logit_bias=params.logit_bias,
+            logprobs=params.logprobs,
+            max_tokens=params.max_tokens,
+            n=params.n,
+            presence_penalty=params.presence_penalty,
+            seed=params.seed,
+            stop=params.stop,
+            stream=params.stream,
+            stream_options=params.stream_options,
+            temperature=params.temperature,
+            top_p=params.top_p,
+            user=params.user,
+            suffix=params.suffix,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+        return await litellm.atext_completion(**request_params)
+
+    async def openai_embeddings(
+        self,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        Override parent method to add watsonx-specific parameters.
+        """
+        model_obj = await self.model_store.get_model(params.model)
+
+        # Convert input to list if it's a string
+        input_list = [params.input] if isinstance(params.input, str) else params.input
+
+        # Call litellm embedding function with watsonx-specific parameters
+        response = litellm.embedding(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            input=input_list,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            dimensions=params.dimensions,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+
+        # Convert response to OpenAI format
+        from llama_stack.apis.inference import OpenAIEmbeddingUsage
+        from llama_stack.providers.utils.inference.litellm_openai_mixin import b64_encode_openai_embeddings_response
+
+        data = b64_encode_openai_embeddings_response(response.data, params.encoding_format)
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response["usage"]["prompt_tokens"],
+            total_tokens=response["usage"]["total_tokens"],
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=model_obj.provider_resource_id,
+            usage=usage,
         )
-        self.available_models = None
-        self.config = config
 
     def get_base_url(self) -> str:
         return self.config.url
 
-    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
-        # Get base parameters from parent
-        params = await super()._get_params(request)
-
-        # Add watsonx.ai specific parameters
-        params["project_id"] = self.config.project_id
-        params["time_limit"] = self.config.timeout
-        return params
-
     # Copied from OpenAIMixin
     async def check_model_availability(self, model: str) -> bool:
         """

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

@@ -7,7 +7,7 @@
 import json
 from typing import Any
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
@@ -56,7 +56,7 @@ class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
         pass
 
     async def run_shield(
-        self, shield_id: str, messages: list[Message], params: dict[str, Any] = None
+        self, shield_id: str, messages: list[OpenAIMessageParam], params: dict[str, Any] = None
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
         if not shield:

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

@@ -8,12 +8,11 @@ from typing import Any
 
 import requests
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 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
-from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
 
 from .config import NVIDIASafetyConfig
 
@@ -44,7 +43,7 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
         pass
 
     async def run_shield(
-        self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None
+        self, shield_id: str, messages: list[OpenAIMessageParam], params: dict[str, Any] | None = None
     ) -> RunShieldResponse:
         """
         Run a safety shield check against the provided messages.
@@ -118,7 +117,7 @@ class NeMoGuardrails:
         response.raise_for_status()
         return response.json()
 
-    async def run(self, messages: list[Message]) -> RunShieldResponse:
+    async def run(self, messages: list[OpenAIMessageParam]) -> RunShieldResponse:
         """
         Queries the /v1/guardrails/checks endpoint of the NeMo guardrails deployed API.
 
@@ -132,10 +131,9 @@ class NeMoGuardrails:
         Raises:
             requests.HTTPError: If the POST request fails.
         """
-        request_messages = [await convert_message_to_openai_dict_new(message) for message in messages]
         request_data = {
             "model": self.model,
-            "messages": request_messages,
+            "messages": [{"role": message.role, "content": message.content} for message in messages],
             "temperature": self.temperature,
             "top_p": 1,
             "frequency_penalty": 0,

llama_stack/providers/remote/safety/sambanova/sambanova.py

@@ -4,13 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import json
 from typing import Any
 
 import litellm
 import requests
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import OpenAIMessageParam
 from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
@@ -21,7 +20,6 @@ from llama_stack.apis.shields import Shield
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
-from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
 
 from .config import SambaNovaSafetyConfig
 
@@ -72,7 +70,7 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide
         pass
 
     async def run_shield(
-        self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None
+        self, shield_id: str, messages: list[OpenAIMessageParam], params: dict[str, Any] | None = None
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
         if not shield:
@@ -80,12 +78,8 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide
 
         shield_params = shield.params
         logger.debug(f"run_shield::{shield_params}::messages={messages}")
-        content_messages = [await convert_message_to_openai_dict_new(m) for m in messages]
-        logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
 
-        response = litellm.completion(
+        response = litellm.completion(model=shield.provider_resource_id, messages=messages, api_key=self._get_api_key())
-            model=shield.provider_resource_id, messages=content_messages, api_key=self._get_api_key()
-        )
         shield_message = response.choices[0].message.content
 
         if "unsafe" in shield_message.lower():

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -17,6 +17,7 @@ if TYPE_CHECKING:
 from llama_stack.apis.inference import (
     ModelStore,
     OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
 )
@@ -32,26 +33,22 @@ class SentenceTransformerEmbeddingMixin:
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         # Convert input to list format if it's a single string
-        input_list = [input] if isinstance(input, str) else input
+        input_list = [params.input] if isinstance(params.input, str) else params.input
         if not input_list:
             raise ValueError("Empty list not supported")
 
         # Get the model and generate embeddings
-        model_obj = await self.model_store.get_model(model)
+        model_obj = await self.model_store.get_model(params.model)
         embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
         embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
 
         # Convert embeddings to the requested format
         data = []
         for i, embedding in enumerate(embeddings):
-            if encoding_format == "base64":
+            if params.encoding_format == "base64":
                 # Convert float array to base64 string
                 float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
                 embedding_value = base64.b64encode(float_bytes).decode("ascii")
@@ -70,7 +67,7 @@ class SentenceTransformerEmbeddingMixin:
         usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
         return OpenAIEmbeddingsResponse(
             data=data,
-            model=model,
+            model=params.model,
             usage=usage,
         )
 
@@ -86,7 +83,7 @@ class SentenceTransformerEmbeddingMixin:
         def _load_model():
             from sentence_transformers import SentenceTransformer
 
-            return SentenceTransformer(model)
+            return SentenceTransformer(model, trust_remote_code=True)
 
         loaded_model = await asyncio.to_thread(_load_model)
         EMBEDDING_MODELS[model] = loaded_model

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -20,6 +20,7 @@ from llama_stack.apis.inference import (
     OpenAICompletion,
     OpenAICompletionRequestWithExtraBody,
     OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
     ToolChoice,
@@ -189,16 +190,12 @@ class LiteLLMOpenAIMixin(
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        model_obj = await self.model_store.get_model(model)
+        model_obj = await self.model_store.get_model(params.model)
 
         # Convert input to list if it's a string
-        input_list = [input] if isinstance(input, str) else input
+        input_list = [params.input] if isinstance(params.input, str) else params.input
 
         # Call litellm embedding function
         # litellm.drop_params = True
@@ -207,11 +204,11 @@ class LiteLLMOpenAIMixin(
             input=input_list,
             api_key=self.get_api_key(),
             api_base=self.api_base,
-            dimensions=dimensions,
+            dimensions=params.dimensions,
         )
 
         # Convert response to OpenAI format
-        data = b64_encode_openai_embeddings_response(response.data, encoding_format)
+        data = b64_encode_openai_embeddings_response(response.data, params.encoding_format)
 
         usage = OpenAIEmbeddingUsage(
             prompt_tokens=response["usage"]["prompt_tokens"],

llama_stack/providers/utils/inference/openai_mixin.py

@@ -21,6 +21,7 @@ from llama_stack.apis.inference import (
     OpenAICompletion,
     OpenAICompletionRequestWithExtraBody,
     OpenAIEmbeddingData,
+    OpenAIEmbeddingsRequestWithExtraBody,
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
     OpenAIMessageParam,
@@ -316,23 +317,27 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
 
     async def openai_embeddings(
         self,
-        model: str,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         """
         Direct OpenAI embeddings API call.
         """
+        # Prepare request parameters
+        request_params = {
+            "model": await self._get_provider_model_id(params.model),
+            "input": params.input,
+            "encoding_format": params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
+            "dimensions": params.dimensions if params.dimensions is not None else NOT_GIVEN,
+            "user": params.user if params.user is not None else NOT_GIVEN,
+        }
+
+        # Add extra_body if present
+        extra_body = params.model_extra
+        if extra_body:
+            request_params["extra_body"] = extra_body
+
         # Call OpenAI embeddings API with properly typed parameters
-        response = await self.client.embeddings.create(
+        response = await self.client.embeddings.create(**request_params)
-            model=await self._get_provider_model_id(model),
-            input=input,
-            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
-            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
-            user=user if user is not None else NOT_GIVEN,
-        )
 
         data = []
         for i, embedding_data in enumerate(response.data):
@@ -350,7 +355,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
 
         return OpenAIEmbeddingsResponse(
             data=data,
-            model=model,
+            model=params.model,
             usage=usage,
         )
 

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -9,6 +9,7 @@ import base64
 import io
 import json
 import re
+from typing import Any
 
 import httpx
 from PIL import Image as PIL_Image
@@ -23,6 +24,9 @@ from llama_stack.apis.inference import (
     ChatCompletionRequest,
     CompletionRequest,
     Message,
+    OpenAIChatCompletionContentPartImageParam,
+    OpenAIChatCompletionContentPartTextParam,
+    OpenAIFile,
     ResponseFormat,
     ResponseFormatType,
     SystemMessage,
@@ -74,14 +78,22 @@ def decode_assistant_message(content: str, stop_reason: StopReason) -> RawMessag
     return formatter.decode_assistant_message_from_content(content, stop_reason)
 
 
-def interleaved_content_as_str(content: InterleavedContent, sep: str = " ") -> str:
+def interleaved_content_as_str(
+    content: Any,
+    sep: str = " ",
+) -> str:
+    if content is None:
+        return ""
+
     def _process(c) -> str:
         if isinstance(c, str):
             return c
-        elif isinstance(c, ImageContentItem):
+        elif isinstance(c, TextContentItem) or isinstance(c, OpenAIChatCompletionContentPartTextParam):
-            return "<image>"
-        elif isinstance(c, TextContentItem):
             return c.text
+        elif isinstance(c, ImageContentItem) or isinstance(c, OpenAIChatCompletionContentPartImageParam):
+            return "<image>"
+        elif isinstance(c, OpenAIFile):
+            return "<file>"
         else:
             raise ValueError(f"Unsupported content type: {type(c)}")
 

llama_stack/providers/utils/kvstore/sqlite/sqlite.py

@@ -21,12 +21,38 @@ class SqliteKVStoreImpl(KVStore):
     def __init__(self, config: SqliteKVStoreConfig):
         self.db_path = config.db_path
         self.table_name = "kvstore"
+        self._conn: aiosqlite.Connection | None = None
 
     def __str__(self):
         return f"SqliteKVStoreImpl(db_path={self.db_path}, table_name={self.table_name})"
 
+    def _is_memory_db(self) -> bool:
+        """Check if this is an in-memory database."""
+        return self.db_path == ":memory:" or "mode=memory" in self.db_path
+
     async def initialize(self):
-        os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
+        # Skip directory creation for in-memory databases and file: URIs
+        if not self._is_memory_db() and not self.db_path.startswith("file:"):
+            db_dir = os.path.dirname(self.db_path)
+            if db_dir:  # Only create if there's a directory component
+                os.makedirs(db_dir, exist_ok=True)
+
+        # Only use persistent connection for in-memory databases
+        # File-based databases use connection-per-operation to avoid hangs
+        if self._is_memory_db():
+            self._conn = await aiosqlite.connect(self.db_path)
+            await self._conn.execute(
+                f"""
+                CREATE TABLE IF NOT EXISTS {self.table_name} (
+                    key TEXT PRIMARY KEY,
+                    value TEXT,
+                    expiration TIMESTAMP
+                )
+            """
+            )
+            await self._conn.commit()
+        else:
+            # For file-based databases, just create the table
             async with aiosqlite.connect(self.db_path) as db:
                 await db.execute(
                     f"""
@@ -39,7 +65,22 @@ class SqliteKVStoreImpl(KVStore):
                 )
                 await db.commit()
 
+    async def shutdown(self):
+        """Close the persistent connection (only for in-memory databases)."""
+        if self._conn:
+            await self._conn.close()
+            self._conn = None
+
     async def set(self, key: str, value: str, expiration: datetime | None = None) -> None:
+        if self._conn:
+            # In-memory database with persistent connection
+            await self._conn.execute(
+                f"INSERT OR REPLACE INTO {self.table_name} (key, value, expiration) VALUES (?, ?, ?)",
+                (key, value, expiration),
+            )
+            await self._conn.commit()
+        else:
+            # File-based database with connection per operation
             async with aiosqlite.connect(self.db_path) as db:
                 await db.execute(
                     f"INSERT OR REPLACE INTO {self.table_name} (key, value, expiration) VALUES (?, ?, ?)",
@@ -48,8 +89,25 @@ class SqliteKVStoreImpl(KVStore):
                 await db.commit()
 
     async def get(self, key: str) -> str | None:
+        if self._conn:
+            # In-memory database with persistent connection
+            async with self._conn.execute(
+                f"SELECT value, expiration FROM {self.table_name} WHERE key = ?", (key,)
+            ) as cursor:
+                row = await cursor.fetchone()
+                if row is None:
+                    return None
+                value, expiration = row
+                if not isinstance(value, str):
+                    logger.warning(f"Expected string value for key {key}, got {type(value)}, returning None")
+                    return None
+                return value
+        else:
+            # File-based database with connection per operation
             async with aiosqlite.connect(self.db_path) as db:
-            async with db.execute(f"SELECT value, expiration FROM {self.table_name} WHERE key = ?", (key,)) as cursor:
+                async with db.execute(
+                    f"SELECT value, expiration FROM {self.table_name} WHERE key = ?", (key,)
+                ) as cursor:
                     row = await cursor.fetchone()
                     if row is None:
                         return None
@@ -60,11 +118,30 @@ class SqliteKVStoreImpl(KVStore):
                     return value
 
     async def delete(self, key: str) -> None:
+        if self._conn:
+            # In-memory database with persistent connection
+            await self._conn.execute(f"DELETE FROM {self.table_name} WHERE key = ?", (key,))
+            await self._conn.commit()
+        else:
+            # File-based database with connection per operation
             async with aiosqlite.connect(self.db_path) as db:
                 await db.execute(f"DELETE FROM {self.table_name} WHERE key = ?", (key,))
                 await db.commit()
 
     async def values_in_range(self, start_key: str, end_key: str) -> list[str]:
+        if self._conn:
+            # In-memory database with persistent connection
+            async with self._conn.execute(
+                f"SELECT key, value, expiration FROM {self.table_name} WHERE key >= ? AND key <= ?",
+                (start_key, end_key),
+            ) as cursor:
+                result = []
+                async for row in cursor:
+                    _, value, _ = row
+                    result.append(value)
+                return result
+        else:
+            # File-based database with connection per operation
             async with aiosqlite.connect(self.db_path) as db:
                 async with db.execute(
                     f"SELECT key, value, expiration FROM {self.table_name} WHERE key >= ? AND key <= ?",
@@ -78,6 +155,16 @@ class SqliteKVStoreImpl(KVStore):
 
     async def keys_in_range(self, start_key: str, end_key: str) -> list[str]:
         """Get all keys in the given range."""
+        if self._conn:
+            # In-memory database with persistent connection
+            cursor = await self._conn.execute(
+                f"SELECT key FROM {self.table_name} WHERE key >= ? AND key <= ?",
+                (start_key, end_key),
+            )
+            rows = await cursor.fetchall()
+            return [row[0] for row in rows]
+        else:
+            # File-based database with connection per operation
             async with aiosqlite.connect(self.db_path) as db:
                 cursor = await db.execute(
                     f"SELECT key FROM {self.table_name} WHERE key >= ? AND key <= ?",

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -10,8 +10,9 @@ import mimetypes
 import time
 import uuid
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Annotated, Any
 
+from fastapi import Body
 from pydantic import TypeAdapter
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
@@ -19,6 +20,8 @@ from llama_stack.apis.files import Files, OpenAIFileObject
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
+    OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
+    OpenAICreateVectorStoreRequestWithExtraBody,
     QueryChunksResponse,
     SearchRankingOptions,
     VectorStoreChunkingStrategy,
@@ -340,39 +343,39 @@ class OpenAIVectorStoreMixin(ABC):
 
     async def openai_create_vector_store(
         self,
-        name: str | None = None,
+        params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
-        file_ids: list[str] | None = None,
-        expires_after: dict[str, Any] | None = None,
-        chunking_strategy: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-        embedding_model: str | None = None,
-        embedding_dimension: int | None = 384,
-        provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         """Creates a vector store."""
         created_at = int(time.time())
+
+        # Extract llama-stack-specific parameters from extra_body
+        extra = params.model_extra or {}
+        provider_vector_db_id = extra.get("provider_vector_db_id")
+        embedding_model = extra.get("embedding_model")
+        embedding_dimension = extra.get("embedding_dimension", 768)
+        # use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
+        provider_id = extra.get("provider_id") or getattr(self, "__provider_id__", None)
         # Derive the canonical vector_db_id (allow override, else generate)
         vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
 
-        if provider_id is None:
-            raise ValueError("Provider ID is required")
-
         if embedding_model is None:
             raise ValueError("Embedding model is required")
 
-        # Embedding dimension is required (defaulted to 384 if not provided)
+        # Embedding dimension is required (defaulted to 768 if not provided)
         if embedding_dimension is None:
             raise ValueError("Embedding dimension is required")
 
         # Register the VectorDB backing this vector store
+        if provider_id is None:
+            raise ValueError("Provider ID is required but was not provided")
+
         vector_db = VectorDB(
             identifier=vector_db_id,
             embedding_dimension=embedding_dimension,
             embedding_model=embedding_model,
             provider_id=provider_id,
             provider_resource_id=vector_db_id,
-            vector_db_name=name,
+            vector_db_name=params.name,
         )
         await self.register_vector_db(vector_db)
 
@@ -391,19 +394,19 @@ class OpenAIVectorStoreMixin(ABC):
             "id": vector_db_id,
             "object": "vector_store",
             "created_at": created_at,
-            "name": name,
+            "name": params.name,
             "usage_bytes": 0,
             "file_counts": file_counts.model_dump(),
             "status": status,
-            "expires_after": expires_after,
+            "expires_after": params.expires_after,
             "expires_at": None,
             "last_active_at": created_at,
             "file_ids": [],
-            "chunking_strategy": chunking_strategy,
+            "chunking_strategy": params.chunking_strategy,
         }
 
         # Add provider information to metadata if provided
-        metadata = metadata or {}
+        metadata = params.metadata or {}
         if provider_id:
             metadata["provider_id"] = provider_id
         if provider_vector_db_id:
@@ -417,7 +420,7 @@ class OpenAIVectorStoreMixin(ABC):
         self.openai_vector_stores[vector_db_id] = store_info
 
         # Now that our vector store is created, attach any files that were provided
-        file_ids = file_ids or []
+        file_ids = params.file_ids or []
         tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids]
         await asyncio.gather(*tasks)
 
@@ -976,15 +979,13 @@ class OpenAIVectorStoreMixin(ABC):
     async def openai_create_vector_store_file_batch(
         self,
         vector_store_id: str,
-        file_ids: list[str],
+        params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch."""
         if vector_store_id not in self.openai_vector_stores:
             raise VectorStoreNotFoundError(vector_store_id)
 
-        chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
+        chunking_strategy = params.chunking_strategy or VectorStoreChunkingStrategyAuto()
 
         created_at = int(time.time())
         batch_id = generate_object_id("vector_store_file_batch", lambda: f"batch_{uuid.uuid4()}")
@@ -996,8 +997,8 @@ class OpenAIVectorStoreMixin(ABC):
             completed=0,
             cancelled=0,
             failed=0,
-            in_progress=len(file_ids),
+            in_progress=len(params.file_ids),
-            total=len(file_ids),
+            total=len(params.file_ids),
         )
 
         # Create batch object immediately with in_progress status
@@ -1011,8 +1012,8 @@ class OpenAIVectorStoreMixin(ABC):
 
         batch_info = {
             **batch_object.model_dump(),
-            "file_ids": file_ids,
+            "file_ids": params.file_ids,
-            "attributes": attributes,
+            "attributes": params.attributes,
             "chunking_strategy": chunking_strategy.model_dump(),
             "expires_at": expires_at,
         }

llama_stack/providers/utils/memory/vector_store.py

@@ -21,6 +21,7 @@ from llama_stack.apis.common.content_types import (
     URL,
     InterleavedContent,
 )
+from llama_stack.apis.inference import OpenAIEmbeddingsRequestWithExtraBody
 from llama_stack.apis.tools import RAGDocument
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
@@ -274,10 +275,11 @@ class VectorDBWithIndex:
                 _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
 
         if chunks_to_embed:
-            resp = await self.inference_api.openai_embeddings(
+            params = OpenAIEmbeddingsRequestWithExtraBody(
-                self.vector_db.embedding_model,
+                model=self.vector_db.embedding_model,
-                [c.content for c in chunks_to_embed],
+                input=[c.content for c in chunks_to_embed],
             )
+            resp = await self.inference_api.openai_embeddings(params)
             for c, data in zip(chunks_to_embed, resp.data, strict=False):
                 c.embedding = data.embedding
 
@@ -316,7 +318,11 @@ class VectorDBWithIndex:
         if mode == "keyword":
             return await self.index.query_keyword(query_string, k, score_threshold)
 
-        embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string])
+        params = OpenAIEmbeddingsRequestWithExtraBody(
+            model=self.vector_db.embedding_model,
+            input=[query_string],
+        )
+        embeddings_response = await self.inference_api.openai_embeddings(params)
         query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32)
         if mode == "hybrid":
             return await self.index.query_hybrid(

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
     "openai>=1.107",                                  # for expires_after support
     "prompt-toolkit",
     "python-dotenv",
-    "python-jose[cryptography]",
+    "pyjwt[crypto]>=2.10.0",                          # Pull crypto to support RS256 for jwt. Requires 2.10.0+ for ssl_context support.
     "pydantic>=2.11.9",
     "rich",
     "starlette",

scripts/integration-tests.sh

@@ -290,6 +290,8 @@ pytest -s -v $PYTEST_TARGET \
     -k "$PYTEST_PATTERN" \
     $EXTRA_PARAMS \
     --color=yes \
+    --embedding-model=nomic-ai/nomic-embed-text-v1.5 \
+    --color=yes $EXTRA_PARAMS \
     --capture=tee-sys
 exit_code=$?
 set +x

tests/containers/ollama-with-models.containerfile

@@ -8,7 +8,7 @@ FROM --platform=linux/amd64 ollama/ollama:latest
 RUN ollama serve & \
     sleep 5 && \
     ollama pull llama3.2:3b-instruct-fp16 && \
-    ollama pull all-minilm:l6-v2 && \
+    ollama pull nomic-embed-text:v1.5 && \
     ollama pull llama-guard3:1b
 
 # Set the entrypoint to start ollama serve

tests/containers/ollama-with-vision-model.containerfile

@@ -8,7 +8,7 @@ FROM --platform=linux/amd64 ollama/ollama:latest
 RUN ollama serve & \
     sleep 5 && \
     ollama pull llama3.2-vision:11b && \
-    ollama pull all-minilm:l6-v2
+    ollama pull nomic-embed-text:v1.5
 
 # Set the entrypoint to start ollama serve
 ENTRYPOINT ["ollama", "serve"]

tests/integration/README.md

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

tests/integration/conftest.py

@@ -167,8 +167,8 @@ def pytest_addoption(parser):
     parser.addoption(
         "--embedding-dimension",
         type=int,
-        default=None,
+        default=768,
-        help="Output dimensionality of the embedding model to use for testing. Default: 384 (or setup-specific)",
+        help="Output dimensionality of the embedding model to use for testing. Default: 768",
     )
 
     parser.addoption(
@@ -207,7 +207,7 @@ MODEL_SHORT_IDS = {
     "meta-llama/Llama-3.3-70B-Instruct": "70B",
     "meta-llama/Llama-Guard-3-1B": "Guard1B",
     "meta-llama/Llama-Guard-3-8B": "Guard8B",
-    "all-MiniLM-L6-v2": "MiniLM",
+    "nomic-ai/nomic-embed-text-v1.5": "Nomic-v1.5",
 }
 
 

tests/integration/fixtures/common.py

@@ -151,7 +151,7 @@ def client_with_models(
             model_id=embedding_model_id,
             provider_id=selected_provider.provider_id,
             model_type="embedding",
-            metadata={"embedding_dimension": embedding_dimension or 384},
+            metadata={"embedding_dimension": embedding_dimension or 768},
         )
     return client
 

tests/integration/inference/test_openai_completion.py

@@ -58,7 +58,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         #  does not work with the specified model, gpt-5-mini. Please choose different model and try
         #  again. You can learn more about which models can be used with each operation here:
         #  https://go.microsoft.com/fwlink/?linkid=2197993.'}}"}
-        "remote::watsonx",  # return 404 when hitting the /openai/v1 endpoint
         "remote::llama-openai-compat",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")
@@ -68,6 +67,7 @@ def skip_if_doesnt_support_completions_logprobs(client_with_models, model_id):
     provider_type = provider_from_model(client_with_models, model_id).provider_type
     if provider_type in (
         "remote::ollama",  # logprobs is ignored
+        "remote::watsonx",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider_type} doesn't support /v1/completions logprobs.")
 
@@ -110,6 +110,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
         # Error code 400 - {'message': '"n" > 1 is not currently supported', 'type': 'invalid_request_error', 'param': 'n', 'code': 'wrong_api_format'}
         "remote::cerebras",
         "remote::databricks",  # Bad request: parameter "n" must be equal to 1 for streaming mode
+        "remote::watsonx",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
 
@@ -124,7 +125,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
         "remote::databricks",
         "remote::cerebras",
         "remote::runpod",
-        "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.")
 
@@ -508,6 +508,12 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
     assert "hello world" in normalized_content
 
 
+def skip_if_doesnt_support_completions_stop_sequence(client_with_models, model_id):
+    provider_type = provider_from_model(client_with_models, model_id).provider_type
+    if provider_type in ("remote::watsonx",):  # openai.BadRequestError: Error code: 400
+        pytest.skip(f"Model {model_id} hosted by {provider_type} doesn't support /v1/completions stop sequence.")
+
+
 @pytest.mark.parametrize(
     "test_case",
     [
@@ -516,6 +522,7 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
 )
 def test_openai_completion_stop_sequence(client_with_models, openai_client, text_model_id, test_case):
     skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id)
+    skip_if_doesnt_support_completions_stop_sequence(client_with_models, text_model_id)
 
     tc = TestCase(test_case)
 

tests/integration/inference/test_openai_embeddings.py

@@ -50,11 +50,15 @@ def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
 
 def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id):
     provider = provider_from_model(client_with_models, model_id)
-    if provider.provider_type in (
+    if (
+        provider.provider_type
+        in (
             "remote::together",  # returns 400
             "inline::sentence-transformers",
             # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
             "remote::databricks",
+            "remote::watsonx",  # openai.BadRequestError: Error code: 400 - {'detail': "litellm.UnsupportedParamsError: watsonx does not support parameters: {'dimensions': 384}
+        )
     ):
         pytest.skip(
             f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
@@ -104,7 +108,9 @@ def test_openai_embeddings_single_string(compat_client, client_with_models, embe
     )
 
     assert response.object == "list"
-    assert response.model == embedding_model_id
+
+    # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5)
+    assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}")
     assert len(response.data) == 1
     assert response.data[0].object == "embedding"
     assert response.data[0].index == 0
@@ -126,7 +132,9 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e
     )
 
     assert response.object == "list"
-    assert response.model == embedding_model_id
+
+    # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5)
+    assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}")
     assert len(response.data) == len(input_texts)
 
     for i, embedding_data in enumerate(response.data):
@@ -293,7 +301,9 @@ def test_openai_embeddings_base64_batch_processing(compat_client, client_with_mo
     )
     # Validate response structure
     assert response.object == "list"
-    assert response.model == embedding_model_id
+
+    # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5)
+    assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}")
     assert len(response.data) == len(input_texts)
 
     # Validate each embedding in the batch

tests/integration/recordings/responses/0d61455b3cdc.json

@@ -0,0 +1,805 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "machine learning and artificial intelligence"
+      ]
+    },
+    "endpoint": "/api/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
+  "response": {
+    "body": {
+      "__type__": "ollama._types.EmbedResponse",
+      "__data__": {
+        "model": "nomic-embed-text:latest",
+        "created_at": null,
+        "done": null,
+        "done_reason": null,
+        "total_duration": 52811770,
+        "load_duration": 6194013,
+        "prompt_eval_count": 5,
+        "prompt_eval_duration": null,
+        "eval_count": null,
+        "eval_duration": null,
+        "embeddings": [
+          [
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+            0.03758779,
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+            0.07147327,
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tests/integration/recordings/responses/173419f52486.json

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tests/integration/recordings/responses/1cb154e4c11f.json

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+}

tests/integration/recordings/responses/244113b1bd88.json

@@ -0,0 +1,805 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "What is the secret string?"
+      ]
+    },
+    "endpoint": "/api/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
+  "response": {
+    "body": {
+      "__type__": "ollama._types.EmbedResponse",
+      "__data__": {
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+        "created_at": null,
+        "done": null,
+        "done_reason": null,
+        "total_duration": 64640559,
+        "load_duration": 8275696,
+        "prompt_eval_count": 6,
+        "prompt_eval_duration": null,
+        "eval_count": null,
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tests/integration/recordings/responses/6420c56e645b.json

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tests/integration/recordings/responses/6ced1d8cdaec.json

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+}

tests/integration/recordings/responses/6dffa78a2986.json

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+    "model": "nomic-embed-text:latest"
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+  "response": {
+    "body": {
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+}

tests/integration/recordings/responses/6f7a633cda93.json

@@ -0,0 +1,805 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "The secret string is foobazbar."
+      ]
+    },
+    "endpoint": "/api/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
+  "response": {
+    "body": {
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+}

tests/integration/recordings/responses/964d5655de24.json

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+    "endpoint": "/v1/embeddings",
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+}

tests/integration/recordings/responses/9fe64b1ac325.json

@@ -0,0 +1,805 @@
+{
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+    "url": "http://localhost:11434/api/embeddings",
+    "headers": {},
+    "body": {
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+      "input": [
+        "Python is a high-level programming language with code readability and fewer lines than C++ or Java"
+      ]
+    },
+    "endpoint": "/api/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
+  "response": {
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tests/integration/recordings/responses/a74473841ad4.json

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+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/afc90cb75d1c.json

@@ -0,0 +1,806 @@
+{
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
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+}

tests/integration/recordings/responses/b0a9e4205cad.json

@@ -0,0 +1,806 @@
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