Merge branch 'llamastack:main' into model_unregisteration_error_message

docs/docs/providers/files/index.mdx

@@ -1,4 +1,7 @@
 ---
+description: "Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs."
 sidebar_label: Files
 title: Files
 ---
@@ -7,4 +10,8 @@ title: Files
 
 ## Overview
 
+Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs.
+
 This section contains documentation for all available providers for the **files** API.

docs/docs/providers/inference/index.mdx

@@ -1,5 +1,7 @@
 ---
-description: "Llama Stack Inference API for generating completions, chat completions, and embeddings.
+description: "Inference
+
+    Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
     This API provides the raw interface to the underlying models. Two kinds of models are supported:
     - LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.
@@ -12,7 +14,9 @@ title: Inference
 
 ## Overview
 
-Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Inference
+
+    Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
     This API provides the raw interface to the underlying models. Two kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.

docs/docs/providers/inference/remote_anthropic.mdx

@@ -15,6 +15,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `str \| None` | No |  | API key for Anthropic models |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_azure.mdx

@@ -22,6 +22,7 @@ https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Azure API key for Azure |
 | `api_base` | `<class 'pydantic.networks.HttpUrl'>` | No |  | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) |
 | `api_version` | `str \| None` | No |  | Azure API version for Azure (e.g., 2024-12-01-preview) |

docs/docs/providers/inference/remote_bedrock.mdx

@@ -15,6 +15,7 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
 | `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
 | `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |

docs/docs/providers/inference/remote_cerebras.mdx

@@ -15,6 +15,7 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `base_url` | `<class 'str'>` | No | https://api.cerebras.ai | Base URL for the Cerebras API |
 | `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Cerebras API Key |
 

docs/docs/providers/inference/remote_databricks.mdx

@@ -15,6 +15,7 @@ Databricks inference provider for running models on Databricks' unified analytic
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `str \| None` | No |  | The URL for the Databricks model serving endpoint |
 | `api_token` | `<class 'pydantic.types.SecretStr'>` | No |  | The Databricks API token |
 

docs/docs/providers/inference/remote_fireworks.mdx

@@ -15,6 +15,7 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | https://api.fireworks.ai/inference/v1 | The URL for the Fireworks server |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Fireworks.ai API Key |
 

docs/docs/providers/inference/remote_gemini.mdx

@@ -15,6 +15,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `str \| None` | No |  | API key for Gemini models |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_groq.mdx

@@ -15,6 +15,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `str \| None` | No |  | The Groq API key |
 | `url` | `<class 'str'>` | No | https://api.groq.com | The URL for the Groq AI server |
 

docs/docs/providers/inference/remote_llama-openai-compat.mdx

@@ -15,6 +15,7 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `str \| None` | No |  | The Llama API key |
 | `openai_compat_api_base` | `<class 'str'>` | No | https://api.llama.com/compat/v1/ | The URL for the Llama API server |
 

docs/docs/providers/inference/remote_nvidia.mdx

@@ -15,6 +15,7 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | https://integrate.api.nvidia.com | A base url for accessing the NVIDIA NIM |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The NVIDIA API key, only needed of using the hosted service |
 | `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |

docs/docs/providers/inference/remote_ollama.mdx

@@ -15,8 +15,8 @@ Ollama inference provider for running local models through the Ollama runtime.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | http://localhost:11434 |  |
-| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_openai.mdx

@@ -15,6 +15,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `api_key` | `str \| None` | No |  | API key for OpenAI models |
 | `base_url` | `<class 'str'>` | No | https://api.openai.com/v1 | Base URL for OpenAI API |
 

docs/docs/providers/inference/remote_passthrough.mdx

@@ -15,6 +15,7 @@ Passthrough inference provider for connecting to any external inference service
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No |  | The URL for the passthrough endpoint |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | API Key for the passthrouth endpoint |
 

docs/docs/providers/inference/remote_runpod.mdx

@@ -15,6 +15,7 @@ RunPod inference provider for running models on RunPod's cloud GPU platform.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `str \| None` | No |  | The URL for the Runpod model serving endpoint |
 | `api_token` | `str \| None` | No |  | The API token |
 

docs/docs/providers/inference/remote_sambanova.mdx

@@ -15,6 +15,7 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | https://api.sambanova.ai/v1 | The URL for the SambaNova AI server |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The SambaNova cloud API Key |
 

docs/docs/providers/inference/remote_tgi.mdx

@@ -15,6 +15,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No |  | The URL for the TGI serving endpoint |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_together.mdx

@@ -15,6 +15,7 @@ Together AI inference provider for open-source models and collaborative AI devel
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | https://api.together.xyz/v1 | The URL for the Together AI server |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Together AI API Key |
 

docs/docs/providers/inference/remote_vertexai.mdx

@@ -54,6 +54,7 @@ Available Models:
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `project` | `<class 'str'>` | No |  | Google Cloud project ID for Vertex AI |
 | `location` | `<class 'str'>` | No | us-central1 | Google Cloud location for Vertex AI |
 

docs/docs/providers/inference/remote_vllm.mdx

@@ -15,11 +15,11 @@ Remote vLLM inference provider for connecting to vLLM servers.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `str \| None` | No |  | The URL for the vLLM model serving endpoint |
 | `max_tokens` | `<class 'int'>` | No | 4096 | Maximum number of tokens to generate. |
 | `api_token` | `str \| None` | No | fake | The API token |
 | `tls_verify` | `bool \| str` | No | True | Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file. |
-| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_watsonx.mdx

@@ -15,6 +15,7 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `url` | `<class 'str'>` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The watsonx API key |
 | `project_id` | `str \| None` | No |  | The Project ID key |

docs/docs/providers/safety/index.mdx

@@ -1,4 +1,7 @@
 ---
+description: "Safety
+
+    OpenAI-compatible Moderations API."
 sidebar_label: Safety
 title: Safety
 ---
@@ -7,4 +10,8 @@ title: Safety
 
 ## Overview
 
+Safety
+
+    OpenAI-compatible Moderations API.
+
 This section contains documentation for all available providers for the **safety** API.

docs/docs/providers/safety/remote_bedrock.mdx

@@ -15,6 +15,7 @@ AWS Bedrock safety provider for content moderation using AWS's safety services.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
 | `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
 | `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |

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

@@ -1443,8 +1443,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "List all chat completions.",
-                "description": "List all chat completions.",
+                "summary": "List chat completions.",
+                "description": "List chat completions.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1520,8 +1520,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
-                "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
+                "summary": "Create chat completions.",
+                "description": "Create chat completions.\nGenerate an OpenAI-compatible chat completion for the given messages using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1565,8 +1565,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Describe a chat completion by its ID.",
-                "description": "Describe a chat completion by its ID.",
+                "summary": "Get chat completion.",
+                "description": "Get chat completion.\nDescribe a chat completion by its ID.",
                 "parameters": [
                     {
                         "name": "completion_id",
@@ -1610,8 +1610,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
-                "description": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
+                "summary": "Create completion.",
+                "description": "Create completion.\nGenerate an OpenAI-compatible completion for the given prompt using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1655,8 +1655,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
-                "description": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
+                "summary": "Create embeddings.",
+                "description": "Create embeddings.\nGenerate OpenAI-compatible embeddings for the given input using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1700,8 +1700,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns a list of files that belong to the user's organization.",
-                "description": "Returns a list of files that belong to the user's organization.",
+                "summary": "List files.",
+                "description": "List files.\nReturns a list of files that belong to the user's organization.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1770,8 +1770,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Upload a file that can be used across various endpoints.",
-                "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
+                "summary": "Upload file.",
+                "description": "Upload file.\nUpload a file that can be used across various endpoints.\n\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1831,8 +1831,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns information about a specific file.",
-                "description": "Returns information about a specific file.",
+                "summary": "Retrieve file.",
+                "description": "Retrieve file.\nReturns information about a specific file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1874,8 +1874,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Delete a file.",
-                "description": "Delete a file.",
+                "summary": "Delete file.",
+                "description": "Delete file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1919,8 +1919,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns the contents of the specified file.",
-                "description": "Returns the contents of the specified file.",
+                "summary": "Retrieve file content.",
+                "description": "Retrieve file content.\nReturns the contents of the specified file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1999,8 +1999,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Classifies if text and/or image inputs are potentially harmful.",
-                "description": "Classifies if text and/or image inputs are potentially harmful.",
+                "summary": "Create moderation.",
+                "description": "Create moderation.\nClassifies if text and/or image inputs are potentially harmful.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -2044,8 +2044,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List all OpenAI responses.",
-                "description": "List all OpenAI responses.",
+                "summary": "List all responses.",
+                "description": "List all responses.",
                 "parameters": [
                     {
                         "name": "after",
@@ -2119,8 +2119,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Create a new OpenAI response.",
-                "description": "Create a new OpenAI response.",
+                "summary": "Create a model response.",
+                "description": "Create a model response.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -2184,8 +2184,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Retrieve an OpenAI response by its ID.",
-                "description": "Retrieve an OpenAI response by its ID.",
+                "summary": "Get a model response.",
+                "description": "Get a model response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2227,8 +2227,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Delete an OpenAI response by its ID.",
-                "description": "Delete an OpenAI response by its ID.",
+                "summary": "Delete a response.",
+                "description": "Delete a response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2272,8 +2272,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List input items for a given OpenAI response.",
-                "description": "List input items for a given OpenAI response.",
+                "summary": "List input items.",
+                "description": "List input items.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -13366,12 +13366,13 @@
         },
         {
             "name": "Files",
-            "description": ""
+            "description": "This API is used to upload documents that can be used with other Llama Stack APIs.",
+            "x-displayName": "Files"
         },
         {
             "name": "Inference",
-            "description": "This API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
-            "x-displayName": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "x-displayName": "Inference"
         },
         {
             "name": "Models",
@@ -13383,7 +13384,8 @@
         },
         {
             "name": "Safety",
-            "description": ""
+            "description": "OpenAI-compatible Moderations API.",
+            "x-displayName": "Safety"
         },
         {
             "name": "Telemetry",

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

@@ -1033,8 +1033,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: List all chat completions.
-      description: List all chat completions.
+      summary: List chat completions.
+      description: List chat completions.
       parameters:
         - name: after
           in: query
@@ -1087,10 +1087,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible chat completion for the given messages using
-        the specified model.
+      summary: Create chat completions.
       description: >-
+        Create chat completions.
+
         Generate an OpenAI-compatible chat completion for the given messages using
         the specified model.
       parameters: []
@@ -1122,8 +1122,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: Describe a chat completion by its ID.
-      description: Describe a chat completion by its ID.
+      summary: Get chat completion.
+      description: >-
+        Get chat completion.
+
+        Describe a chat completion by its ID.
       parameters:
         - name: completion_id
           in: path
@@ -1153,10 +1156,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible completion for the given prompt using the specified
-        model.
+      summary: Create completion.
       description: >-
+        Create completion.
+
         Generate an OpenAI-compatible completion for the given prompt using the specified
         model.
       parameters: []
@@ -1189,10 +1192,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate OpenAI-compatible embeddings for the given input using the specified
-        model.
+      summary: Create embeddings.
       description: >-
+        Create embeddings.
+
         Generate OpenAI-compatible embeddings for the given input using the specified
         model.
       parameters: []
@@ -1225,9 +1228,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns a list of files that belong to the user's organization.
+      summary: List files.
       description: >-
+        List files.
+
         Returns a list of files that belong to the user's organization.
       parameters:
         - name: after
@@ -1285,11 +1289,13 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Upload a file that can be used across various endpoints.
+      summary: Upload file.
       description: >-
+        Upload file.
+
         Upload a file that can be used across various endpoints.
 
+
         The file upload should be a multipart form request with:
 
         - file: The File object (not file name) to be uploaded.
@@ -1338,9 +1344,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns information about a specific file.
+      summary: Retrieve file.
       description: >-
+        Retrieve file.
+
         Returns information about a specific file.
       parameters:
         - name: file_id
@@ -1372,8 +1379,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: Delete a file.
-      description: Delete a file.
+      summary: Delete file.
+      description: Delete file.
       parameters:
         - name: file_id
           in: path
@@ -1405,9 +1412,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns the contents of the specified file.
+      summary: Retrieve file content.
       description: >-
+        Retrieve file content.
+
         Returns the contents of the specified file.
       parameters:
         - name: file_id
@@ -1464,9 +1472,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: >-
-        Classifies if text and/or image inputs are potentially harmful.
+      summary: Create moderation.
       description: >-
+        Create moderation.
+
         Classifies if text and/or image inputs are potentially harmful.
       parameters: []
       requestBody:
@@ -1497,8 +1506,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: List all OpenAI responses.
-      description: List all OpenAI responses.
+      summary: List all responses.
+      description: List all responses.
       parameters:
         - name: after
           in: query
@@ -1549,8 +1558,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Create a new OpenAI response.
-      description: Create a new OpenAI response.
+      summary: Create a model response.
+      description: Create a model response.
       parameters: []
       requestBody:
         content:
@@ -1592,8 +1601,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Retrieve an OpenAI response by its ID.
-      description: Retrieve an OpenAI response by its ID.
+      summary: Get a model response.
+      description: Get a model response.
       parameters:
         - name: response_id
           in: path
@@ -1623,8 +1632,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Delete an OpenAI response by its ID.
-      description: Delete an OpenAI response by its ID.
+      summary: Delete a response.
+      description: Delete a response.
       parameters:
         - name: response_id
           in: path
@@ -1654,10 +1663,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: >-
-        List input items for a given OpenAI response.
-      description: >-
-        List input items for a given OpenAI response.
+      summary: List input items.
+      description: List input items.
       parameters:
         - name: response_id
           in: path
@@ -10011,9 +10018,16 @@ tags:
     x-displayName: >-
       Llama Stack Evaluation API for running evaluations on model and agent candidates.
   - name: Files
-    description: ''
+    description: >-
+      This API is used to upload documents that can be used with other Llama Stack
+      APIs.
+    x-displayName: Files
   - name: Inference
     description: >-
+      Llama Stack Inference API for generating completions, chat completions, and
+      embeddings.
+
+
       This API provides the raw interface to the underlying models. Two kinds of models
       are supported:
 
@@ -10021,15 +10035,14 @@ tags:
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
-    x-displayName: >-
-      Llama Stack Inference API for generating completions, chat completions, and
-      embeddings.
+    x-displayName: Inference
   - name: Models
     description: ''
   - name: PostTraining (Coming Soon)
     description: ''
   - name: Safety
-    description: ''
+    description: OpenAI-compatible Moderations API.
+    x-displayName: Safety
   - name: Telemetry
     description: ''
   - name: VectorIO

docs/static/llama-stack-spec.html

@@ -69,8 +69,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "List all chat completions.",
-                "description": "List all chat completions.",
+                "summary": "List chat completions.",
+                "description": "List chat completions.",
                 "parameters": [
                     {
                         "name": "after",
@@ -146,8 +146,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
-                "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
+                "summary": "Create chat completions.",
+                "description": "Create chat completions.\nGenerate an OpenAI-compatible chat completion for the given messages using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -191,8 +191,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Describe a chat completion by its ID.",
-                "description": "Describe a chat completion by its ID.",
+                "summary": "Get chat completion.",
+                "description": "Get chat completion.\nDescribe a chat completion by its ID.",
                 "parameters": [
                     {
                         "name": "completion_id",
@@ -236,8 +236,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
-                "description": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
+                "summary": "Create completion.",
+                "description": "Create completion.\nGenerate an OpenAI-compatible completion for the given prompt using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -758,8 +758,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
-                "description": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
+                "summary": "Create embeddings.",
+                "description": "Create embeddings.\nGenerate OpenAI-compatible embeddings for the given input using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -803,8 +803,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns a list of files that belong to the user's organization.",
-                "description": "Returns a list of files that belong to the user's organization.",
+                "summary": "List files.",
+                "description": "List files.\nReturns a list of files that belong to the user's organization.",
                 "parameters": [
                     {
                         "name": "after",
@@ -873,8 +873,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Upload a file that can be used across various endpoints.",
-                "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
+                "summary": "Upload file.",
+                "description": "Upload file.\nUpload a file that can be used across various endpoints.\n\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -934,8 +934,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns information about a specific file.",
-                "description": "Returns information about a specific file.",
+                "summary": "Retrieve file.",
+                "description": "Retrieve file.\nReturns information about a specific file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -977,8 +977,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Delete a file.",
-                "description": "Delete a file.",
+                "summary": "Delete file.",
+                "description": "Delete file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1022,8 +1022,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns the contents of the specified file.",
-                "description": "Returns the contents of the specified file.",
+                "summary": "Retrieve file content.",
+                "description": "Retrieve file content.\nReturns the contents of the specified file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1067,8 +1067,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "Get the current health status of the service.",
-                "description": "Get the current health status of the service.",
+                "summary": "Get health status.",
+                "description": "Get health status.\nGet the current health status of the service.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1102,8 +1102,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "List all available API routes with their methods and implementing providers.",
-                "description": "List all available API routes with their methods and implementing providers.",
+                "summary": "List routes.",
+                "description": "List routes.\nList all available API routes with their methods and implementing providers.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1170,8 +1170,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Register a model.",
-                "description": "Register a model.",
+                "summary": "Register model.",
+                "description": "Register model.\nRegister a model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1215,8 +1215,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Get a model by its identifier.",
-                "description": "Get a model by its identifier.",
+                "summary": "Get model.",
+                "description": "Get model.\nGet a model by its identifier.",
                 "parameters": [
                     {
                         "name": "model_id",
@@ -1251,8 +1251,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Unregister a model.",
-                "description": "Unregister a model.",
+                "summary": "Unregister model.",
+                "description": "Unregister model.\nUnregister a model.",
                 "parameters": [
                     {
                         "name": "model_id",
@@ -1296,8 +1296,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Classifies if text and/or image inputs are potentially harmful.",
-                "description": "Classifies if text and/or image inputs are potentially harmful.",
+                "summary": "Create moderation.",
+                "description": "Create moderation.\nClassifies if text and/or image inputs are potentially harmful.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1374,8 +1374,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Create a new prompt.",
-                "description": "Create a new prompt.",
+                "summary": "Create prompt.",
+                "description": "Create prompt.\nCreate a new prompt.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1419,8 +1419,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Get a prompt by its identifier and optional version.",
-                "description": "Get a prompt by its identifier and optional version.",
+                "summary": "Get prompt.",
+                "description": "Get prompt.\nGet a prompt by its identifier and optional version.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1471,8 +1471,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Update an existing prompt (increments version).",
-                "description": "Update an existing prompt (increments version).",
+                "summary": "Update prompt.",
+                "description": "Update prompt.\nUpdate an existing prompt (increments version).",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1517,8 +1517,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Delete a prompt.",
-                "description": "Delete a prompt.",
+                "summary": "Delete prompt.",
+                "description": "Delete prompt.\nDelete a prompt.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1562,8 +1562,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Set which version of a prompt should be the default in get_prompt (latest).",
-                "description": "Set which version of a prompt should be the default in get_prompt (latest).",
+                "summary": "Set prompt version.",
+                "description": "Set prompt version.\nSet which version of a prompt should be the default in get_prompt (latest).",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1617,8 +1617,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "List all versions of a specific prompt.",
-                "description": "List all versions of a specific prompt.",
+                "summary": "List prompt versions.",
+                "description": "List prompt versions.\nList all versions of a specific prompt.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1662,8 +1662,8 @@
                 "tags": [
                     "Providers"
                 ],
-                "summary": "List all available providers.",
-                "description": "List all available providers.",
+                "summary": "List providers.",
+                "description": "List providers.\nList all available providers.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1697,8 +1697,8 @@
                 "tags": [
                     "Providers"
                 ],
-                "summary": "Get detailed information about a specific provider.",
-                "description": "Get detailed information about a specific provider.",
+                "summary": "Get provider.",
+                "description": "Get provider.\nGet detailed information about a specific provider.",
                 "parameters": [
                     {
                         "name": "provider_id",
@@ -1742,8 +1742,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List all OpenAI responses.",
-                "description": "List all OpenAI responses.",
+                "summary": "List all responses.",
+                "description": "List all responses.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1817,8 +1817,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Create a new OpenAI response.",
-                "description": "Create a new OpenAI response.",
+                "summary": "Create a model response.",
+                "description": "Create a model response.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1882,8 +1882,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Retrieve an OpenAI response by its ID.",
-                "description": "Retrieve an OpenAI response by its ID.",
+                "summary": "Get a model response.",
+                "description": "Get a model response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -1925,8 +1925,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Delete an OpenAI response by its ID.",
-                "description": "Delete an OpenAI response by its ID.",
+                "summary": "Delete a response.",
+                "description": "Delete a response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -1970,8 +1970,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List input items for a given OpenAI response.",
-                "description": "List input items for a given OpenAI response.",
+                "summary": "List input items.",
+                "description": "List input items.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2063,8 +2063,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Run a shield.",
-                "description": "Run a shield.",
+                "summary": "Run shield.",
+                "description": "Run shield.\nRun a shield.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -4196,8 +4196,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "Get the version of the service.",
-                "description": "Get the version of the service.",
+                "summary": "Get version.",
+                "description": "Get version.\nGet the version of the service.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -12914,16 +12914,18 @@
         },
         {
             "name": "Files",
-            "description": ""
+            "description": "This API is used to upload documents that can be used with other Llama Stack APIs.",
+            "x-displayName": "Files"
         },
         {
             "name": "Inference",
-            "description": "This API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
-            "x-displayName": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "x-displayName": "Inference"
         },
         {
             "name": "Inspect",
-            "description": ""
+            "description": "APIs for inspecting the Llama Stack service, including health status, available API routes with methods and implementing providers.",
+            "x-displayName": "Inspect"
         },
         {
             "name": "Models",
@@ -12931,17 +12933,18 @@
         },
         {
             "name": "Prompts",
-            "description": "",
-            "x-displayName": "Protocol for prompt management operations."
+            "description": "Protocol for prompt management operations.",
+            "x-displayName": "Prompts"
         },
         {
             "name": "Providers",
-            "description": "",
-            "x-displayName": "Providers API for inspecting, listing, and modifying providers and their configurations."
+            "description": "Providers API for inspecting, listing, and modifying providers and their configurations.",
+            "x-displayName": "Providers"
         },
         {
             "name": "Safety",
-            "description": ""
+            "description": "OpenAI-compatible Moderations API.",
+            "x-displayName": "Safety"
         },
         {
             "name": "Scoring",

docs/static/llama-stack-spec.yaml

@@ -33,8 +33,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: List all chat completions.
-      description: List all chat completions.
+      summary: List chat completions.
+      description: List chat completions.
       parameters:
         - name: after
           in: query
@@ -87,10 +87,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible chat completion for the given messages using
-        the specified model.
+      summary: Create chat completions.
       description: >-
+        Create chat completions.
+
         Generate an OpenAI-compatible chat completion for the given messages using
         the specified model.
       parameters: []
@@ -122,8 +122,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: Describe a chat completion by its ID.
-      description: Describe a chat completion by its ID.
+      summary: Get chat completion.
+      description: >-
+        Get chat completion.
+
+        Describe a chat completion by its ID.
       parameters:
         - name: completion_id
           in: path
@@ -153,10 +156,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible completion for the given prompt using the specified
-        model.
+      summary: Create completion.
       description: >-
+        Create completion.
+
         Generate an OpenAI-compatible completion for the given prompt using the specified
         model.
       parameters: []
@@ -603,10 +606,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate OpenAI-compatible embeddings for the given input using the specified
-        model.
+      summary: Create embeddings.
       description: >-
+        Create embeddings.
+
         Generate OpenAI-compatible embeddings for the given input using the specified
         model.
       parameters: []
@@ -639,9 +642,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns a list of files that belong to the user's organization.
+      summary: List files.
       description: >-
+        List files.
+
         Returns a list of files that belong to the user's organization.
       parameters:
         - name: after
@@ -699,11 +703,13 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Upload a file that can be used across various endpoints.
+      summary: Upload file.
       description: >-
+        Upload file.
+
         Upload a file that can be used across various endpoints.
 
+
         The file upload should be a multipart form request with:
 
         - file: The File object (not file name) to be uploaded.
@@ -752,9 +758,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns information about a specific file.
+      summary: Retrieve file.
       description: >-
+        Retrieve file.
+
         Returns information about a specific file.
       parameters:
         - name: file_id
@@ -786,8 +793,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: Delete a file.
-      description: Delete a file.
+      summary: Delete file.
+      description: Delete file.
       parameters:
         - name: file_id
           in: path
@@ -819,9 +826,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns the contents of the specified file.
+      summary: Retrieve file content.
       description: >-
+        Retrieve file content.
+
         Returns the contents of the specified file.
       parameters:
         - name: file_id
@@ -854,9 +862,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: >-
-        Get the current health status of the service.
+      summary: Get health status.
       description: >-
+        Get health status.
+
         Get the current health status of the service.
       parameters: []
       deprecated: false
@@ -882,9 +891,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: >-
-        List all available API routes with their methods and implementing providers.
+      summary: List routes.
       description: >-
+        List routes.
+
         List all available API routes with their methods and implementing providers.
       parameters: []
       deprecated: false
@@ -933,8 +943,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Register a model.
-      description: Register a model.
+      summary: Register model.
+      description: >-
+        Register model.
+
+        Register a model.
       parameters: []
       requestBody:
         content:
@@ -964,8 +977,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Get a model by its identifier.
-      description: Get a model by its identifier.
+      summary: Get model.
+      description: >-
+        Get model.
+
+        Get a model by its identifier.
       parameters:
         - name: model_id
           in: path
@@ -990,8 +1006,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Unregister a model.
-      description: Unregister a model.
+      summary: Unregister model.
+      description: >-
+        Unregister model.
+
+        Unregister a model.
       parameters:
         - name: model_id
           in: path
@@ -1022,9 +1041,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: >-
-        Classifies if text and/or image inputs are potentially harmful.
+      summary: Create moderation.
       description: >-
+        Create moderation.
+
         Classifies if text and/or image inputs are potentially harmful.
       parameters: []
       requestBody:
@@ -1080,8 +1100,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: Create a new prompt.
-      description: Create a new prompt.
+      summary: Create prompt.
+      description: >-
+        Create prompt.
+
+        Create a new prompt.
       parameters: []
       requestBody:
         content:
@@ -1111,9 +1134,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Get a prompt by its identifier and optional version.
+      summary: Get prompt.
       description: >-
+        Get prompt.
+
         Get a prompt by its identifier and optional version.
       parameters:
         - name: prompt_id
@@ -1151,9 +1175,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Update an existing prompt (increments version).
+      summary: Update prompt.
       description: >-
+        Update prompt.
+
         Update an existing prompt (increments version).
       parameters:
         - name: prompt_id
@@ -1185,8 +1210,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: Delete a prompt.
-      description: Delete a prompt.
+      summary: Delete prompt.
+      description: >-
+        Delete prompt.
+
+        Delete a prompt.
       parameters:
         - name: prompt_id
           in: path
@@ -1217,9 +1245,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Set which version of a prompt should be the default in get_prompt (latest).
+      summary: Set prompt version.
       description: >-
+        Set prompt version.
+
         Set which version of a prompt should be the default in get_prompt (latest).
       parameters:
         - name: prompt_id
@@ -1257,8 +1286,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: List all versions of a specific prompt.
-      description: List all versions of a specific prompt.
+      summary: List prompt versions.
+      description: >-
+        List prompt versions.
+
+        List all versions of a specific prompt.
       parameters:
         - name: prompt_id
           in: path
@@ -1290,8 +1322,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Providers
-      summary: List all available providers.
-      description: List all available providers.
+      summary: List providers.
+      description: >-
+        List providers.
+
+        List all available providers.
       parameters: []
       deprecated: false
   /v1/providers/{provider_id}:
@@ -1316,9 +1351,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Providers
-      summary: >-
-        Get detailed information about a specific provider.
+      summary: Get provider.
       description: >-
+        Get provider.
+
         Get detailed information about a specific provider.
       parameters:
         - name: provider_id
@@ -1349,8 +1385,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: List all OpenAI responses.
-      description: List all OpenAI responses.
+      summary: List all responses.
+      description: List all responses.
       parameters:
         - name: after
           in: query
@@ -1401,8 +1437,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Create a new OpenAI response.
-      description: Create a new OpenAI response.
+      summary: Create a model response.
+      description: Create a model response.
       parameters: []
       requestBody:
         content:
@@ -1444,8 +1480,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Retrieve an OpenAI response by its ID.
-      description: Retrieve an OpenAI response by its ID.
+      summary: Get a model response.
+      description: Get a model response.
       parameters:
         - name: response_id
           in: path
@@ -1475,8 +1511,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Delete an OpenAI response by its ID.
-      description: Delete an OpenAI response by its ID.
+      summary: Delete a response.
+      description: Delete a response.
       parameters:
         - name: response_id
           in: path
@@ -1506,10 +1542,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: >-
-        List input items for a given OpenAI response.
-      description: >-
-        List input items for a given OpenAI response.
+      summary: List input items.
+      description: List input items.
       parameters:
         - name: response_id
           in: path
@@ -1578,8 +1612,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: Run a shield.
-      description: Run a shield.
+      summary: Run shield.
+      description: >-
+        Run shield.
+
+        Run a shield.
       parameters: []
       requestBody:
         content:
@@ -3135,8 +3172,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: Get the version of the service.
-      description: Get the version of the service.
+      summary: Get version.
+      description: >-
+        Get version.
+
+        Get the version of the service.
       parameters: []
       deprecated: false
 jsonSchemaDialect: >-
@@ -9749,9 +9789,16 @@ tags:
     x-displayName: >-
       Protocol for conversation management operations.
   - name: Files
-    description: ''
+    description: >-
+      This API is used to upload documents that can be used with other Llama Stack
+      APIs.
+    x-displayName: Files
   - name: Inference
     description: >-
+      Llama Stack Inference API for generating completions, chat completions, and
+      embeddings.
+
+
       This API provides the raw interface to the underlying models. Two kinds of models
       are supported:
 
@@ -9759,23 +9806,25 @@ tags:
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
-    x-displayName: >-
-      Llama Stack Inference API for generating completions, chat completions, and
-      embeddings.
+    x-displayName: Inference
   - name: Inspect
-    description: ''
+    description: >-
+      APIs for inspecting the Llama Stack service, including health status, available
+      API routes with methods and implementing providers.
+    x-displayName: Inspect
   - name: Models
     description: ''
   - name: Prompts
-    description: ''
-    x-displayName: >-
+    description: >-
       Protocol for prompt management operations.
+    x-displayName: Prompts
   - name: Providers
-    description: ''
-    x-displayName: >-
+    description: >-
       Providers API for inspecting, listing, and modifying providers and their configurations.
+    x-displayName: Providers
   - name: Safety
-    description: ''
+    description: OpenAI-compatible Moderations API.
+    x-displayName: Safety
   - name: Scoring
     description: ''
   - name: ScoringFunctions

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

@@ -69,8 +69,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "List all chat completions.",
-                "description": "List all chat completions.",
+                "summary": "List chat completions.",
+                "description": "List chat completions.",
                 "parameters": [
                     {
                         "name": "after",
@@ -146,8 +146,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
-                "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
+                "summary": "Create chat completions.",
+                "description": "Create chat completions.\nGenerate an OpenAI-compatible chat completion for the given messages using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -191,8 +191,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Describe a chat completion by its ID.",
-                "description": "Describe a chat completion by its ID.",
+                "summary": "Get chat completion.",
+                "description": "Get chat completion.\nDescribe a chat completion by its ID.",
                 "parameters": [
                     {
                         "name": "completion_id",
@@ -236,8 +236,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
-                "description": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
+                "summary": "Create completion.",
+                "description": "Create completion.\nGenerate an OpenAI-compatible completion for the given prompt using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -758,8 +758,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
-                "description": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
+                "summary": "Create embeddings.",
+                "description": "Create embeddings.\nGenerate OpenAI-compatible embeddings for the given input using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -803,8 +803,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns a list of files that belong to the user's organization.",
-                "description": "Returns a list of files that belong to the user's organization.",
+                "summary": "List files.",
+                "description": "List files.\nReturns a list of files that belong to the user's organization.",
                 "parameters": [
                     {
                         "name": "after",
@@ -873,8 +873,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Upload a file that can be used across various endpoints.",
-                "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
+                "summary": "Upload file.",
+                "description": "Upload file.\nUpload a file that can be used across various endpoints.\n\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -934,8 +934,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns information about a specific file.",
-                "description": "Returns information about a specific file.",
+                "summary": "Retrieve file.",
+                "description": "Retrieve file.\nReturns information about a specific file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -977,8 +977,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Delete a file.",
-                "description": "Delete a file.",
+                "summary": "Delete file.",
+                "description": "Delete file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1022,8 +1022,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns the contents of the specified file.",
-                "description": "Returns the contents of the specified file.",
+                "summary": "Retrieve file content.",
+                "description": "Retrieve file content.\nReturns the contents of the specified file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1067,8 +1067,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "Get the current health status of the service.",
-                "description": "Get the current health status of the service.",
+                "summary": "Get health status.",
+                "description": "Get health status.\nGet the current health status of the service.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1102,8 +1102,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "List all available API routes with their methods and implementing providers.",
-                "description": "List all available API routes with their methods and implementing providers.",
+                "summary": "List routes.",
+                "description": "List routes.\nList all available API routes with their methods and implementing providers.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1170,8 +1170,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Register a model.",
-                "description": "Register a model.",
+                "summary": "Register model.",
+                "description": "Register model.\nRegister a model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1215,8 +1215,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Get a model by its identifier.",
-                "description": "Get a model by its identifier.",
+                "summary": "Get model.",
+                "description": "Get model.\nGet a model by its identifier.",
                 "parameters": [
                     {
                         "name": "model_id",
@@ -1251,8 +1251,8 @@
                 "tags": [
                     "Models"
                 ],
-                "summary": "Unregister a model.",
-                "description": "Unregister a model.",
+                "summary": "Unregister model.",
+                "description": "Unregister model.\nUnregister a model.",
                 "parameters": [
                     {
                         "name": "model_id",
@@ -1296,8 +1296,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Classifies if text and/or image inputs are potentially harmful.",
-                "description": "Classifies if text and/or image inputs are potentially harmful.",
+                "summary": "Create moderation.",
+                "description": "Create moderation.\nClassifies if text and/or image inputs are potentially harmful.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1374,8 +1374,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Create a new prompt.",
-                "description": "Create a new prompt.",
+                "summary": "Create prompt.",
+                "description": "Create prompt.\nCreate a new prompt.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1419,8 +1419,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Get a prompt by its identifier and optional version.",
-                "description": "Get a prompt by its identifier and optional version.",
+                "summary": "Get prompt.",
+                "description": "Get prompt.\nGet a prompt by its identifier and optional version.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1471,8 +1471,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Update an existing prompt (increments version).",
-                "description": "Update an existing prompt (increments version).",
+                "summary": "Update prompt.",
+                "description": "Update prompt.\nUpdate an existing prompt (increments version).",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1517,8 +1517,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Delete a prompt.",
-                "description": "Delete a prompt.",
+                "summary": "Delete prompt.",
+                "description": "Delete prompt.\nDelete a prompt.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1562,8 +1562,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "Set which version of a prompt should be the default in get_prompt (latest).",
-                "description": "Set which version of a prompt should be the default in get_prompt (latest).",
+                "summary": "Set prompt version.",
+                "description": "Set prompt version.\nSet which version of a prompt should be the default in get_prompt (latest).",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1617,8 +1617,8 @@
                 "tags": [
                     "Prompts"
                 ],
-                "summary": "List all versions of a specific prompt.",
-                "description": "List all versions of a specific prompt.",
+                "summary": "List prompt versions.",
+                "description": "List prompt versions.\nList all versions of a specific prompt.",
                 "parameters": [
                     {
                         "name": "prompt_id",
@@ -1662,8 +1662,8 @@
                 "tags": [
                     "Providers"
                 ],
-                "summary": "List all available providers.",
-                "description": "List all available providers.",
+                "summary": "List providers.",
+                "description": "List providers.\nList all available providers.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -1697,8 +1697,8 @@
                 "tags": [
                     "Providers"
                 ],
-                "summary": "Get detailed information about a specific provider.",
-                "description": "Get detailed information about a specific provider.",
+                "summary": "Get provider.",
+                "description": "Get provider.\nGet detailed information about a specific provider.",
                 "parameters": [
                     {
                         "name": "provider_id",
@@ -1742,8 +1742,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List all OpenAI responses.",
-                "description": "List all OpenAI responses.",
+                "summary": "List all responses.",
+                "description": "List all responses.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1817,8 +1817,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Create a new OpenAI response.",
-                "description": "Create a new OpenAI response.",
+                "summary": "Create a model response.",
+                "description": "Create a model response.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1882,8 +1882,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Retrieve an OpenAI response by its ID.",
-                "description": "Retrieve an OpenAI response by its ID.",
+                "summary": "Get a model response.",
+                "description": "Get a model response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -1925,8 +1925,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Delete an OpenAI response by its ID.",
-                "description": "Delete an OpenAI response by its ID.",
+                "summary": "Delete a response.",
+                "description": "Delete a response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -1970,8 +1970,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List input items for a given OpenAI response.",
-                "description": "List input items for a given OpenAI response.",
+                "summary": "List input items.",
+                "description": "List input items.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2063,8 +2063,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Run a shield.",
-                "description": "Run a shield.",
+                "summary": "Run shield.",
+                "description": "Run shield.\nRun a shield.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -4196,8 +4196,8 @@
                 "tags": [
                     "Inspect"
                 ],
-                "summary": "Get the version of the service.",
-                "description": "Get the version of the service.",
+                "summary": "Get version.",
+                "description": "Get version.\nGet the version of the service.",
                 "parameters": [],
                 "deprecated": false
             }
@@ -18487,16 +18487,18 @@
         },
         {
             "name": "Files",
-            "description": ""
+            "description": "This API is used to upload documents that can be used with other Llama Stack APIs.",
+            "x-displayName": "Files"
         },
         {
             "name": "Inference",
-            "description": "This API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
-            "x-displayName": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "x-displayName": "Inference"
         },
         {
             "name": "Inspect",
-            "description": ""
+            "description": "APIs for inspecting the Llama Stack service, including health status, available API routes with methods and implementing providers.",
+            "x-displayName": "Inspect"
         },
         {
             "name": "Models",
@@ -18508,17 +18510,18 @@
         },
         {
             "name": "Prompts",
-            "description": "",
-            "x-displayName": "Protocol for prompt management operations."
+            "description": "Protocol for prompt management operations.",
+            "x-displayName": "Prompts"
         },
         {
             "name": "Providers",
-            "description": "",
-            "x-displayName": "Providers API for inspecting, listing, and modifying providers and their configurations."
+            "description": "Providers API for inspecting, listing, and modifying providers and their configurations.",
+            "x-displayName": "Providers"
         },
         {
             "name": "Safety",
-            "description": ""
+            "description": "OpenAI-compatible Moderations API.",
+            "x-displayName": "Safety"
         },
         {
             "name": "Scoring",

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

@@ -36,8 +36,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: List all chat completions.
-      description: List all chat completions.
+      summary: List chat completions.
+      description: List chat completions.
       parameters:
         - name: after
           in: query
@@ -90,10 +90,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible chat completion for the given messages using
-        the specified model.
+      summary: Create chat completions.
       description: >-
+        Create chat completions.
+
         Generate an OpenAI-compatible chat completion for the given messages using
         the specified model.
       parameters: []
@@ -125,8 +125,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: Describe a chat completion by its ID.
-      description: Describe a chat completion by its ID.
+      summary: Get chat completion.
+      description: >-
+        Get chat completion.
+
+        Describe a chat completion by its ID.
       parameters:
         - name: completion_id
           in: path
@@ -156,10 +159,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible completion for the given prompt using the specified
-        model.
+      summary: Create completion.
       description: >-
+        Create completion.
+
         Generate an OpenAI-compatible completion for the given prompt using the specified
         model.
       parameters: []
@@ -606,10 +609,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate OpenAI-compatible embeddings for the given input using the specified
-        model.
+      summary: Create embeddings.
       description: >-
+        Create embeddings.
+
         Generate OpenAI-compatible embeddings for the given input using the specified
         model.
       parameters: []
@@ -642,9 +645,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns a list of files that belong to the user's organization.
+      summary: List files.
       description: >-
+        List files.
+
         Returns a list of files that belong to the user's organization.
       parameters:
         - name: after
@@ -702,11 +706,13 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Upload a file that can be used across various endpoints.
+      summary: Upload file.
       description: >-
+        Upload file.
+
         Upload a file that can be used across various endpoints.
 
+
         The file upload should be a multipart form request with:
 
         - file: The File object (not file name) to be uploaded.
@@ -755,9 +761,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns information about a specific file.
+      summary: Retrieve file.
       description: >-
+        Retrieve file.
+
         Returns information about a specific file.
       parameters:
         - name: file_id
@@ -789,8 +796,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: Delete a file.
-      description: Delete a file.
+      summary: Delete file.
+      description: Delete file.
       parameters:
         - name: file_id
           in: path
@@ -822,9 +829,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns the contents of the specified file.
+      summary: Retrieve file content.
       description: >-
+        Retrieve file content.
+
         Returns the contents of the specified file.
       parameters:
         - name: file_id
@@ -857,9 +865,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: >-
-        Get the current health status of the service.
+      summary: Get health status.
       description: >-
+        Get health status.
+
         Get the current health status of the service.
       parameters: []
       deprecated: false
@@ -885,9 +894,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: >-
-        List all available API routes with their methods and implementing providers.
+      summary: List routes.
       description: >-
+        List routes.
+
         List all available API routes with their methods and implementing providers.
       parameters: []
       deprecated: false
@@ -936,8 +946,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Register a model.
-      description: Register a model.
+      summary: Register model.
+      description: >-
+        Register model.
+
+        Register a model.
       parameters: []
       requestBody:
         content:
@@ -967,8 +980,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Get a model by its identifier.
-      description: Get a model by its identifier.
+      summary: Get model.
+      description: >-
+        Get model.
+
+        Get a model by its identifier.
       parameters:
         - name: model_id
           in: path
@@ -993,8 +1009,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Models
-      summary: Unregister a model.
-      description: Unregister a model.
+      summary: Unregister model.
+      description: >-
+        Unregister model.
+
+        Unregister a model.
       parameters:
         - name: model_id
           in: path
@@ -1025,9 +1044,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: >-
-        Classifies if text and/or image inputs are potentially harmful.
+      summary: Create moderation.
       description: >-
+        Create moderation.
+
         Classifies if text and/or image inputs are potentially harmful.
       parameters: []
       requestBody:
@@ -1083,8 +1103,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: Create a new prompt.
-      description: Create a new prompt.
+      summary: Create prompt.
+      description: >-
+        Create prompt.
+
+        Create a new prompt.
       parameters: []
       requestBody:
         content:
@@ -1114,9 +1137,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Get a prompt by its identifier and optional version.
+      summary: Get prompt.
       description: >-
+        Get prompt.
+
         Get a prompt by its identifier and optional version.
       parameters:
         - name: prompt_id
@@ -1154,9 +1178,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Update an existing prompt (increments version).
+      summary: Update prompt.
       description: >-
+        Update prompt.
+
         Update an existing prompt (increments version).
       parameters:
         - name: prompt_id
@@ -1188,8 +1213,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: Delete a prompt.
-      description: Delete a prompt.
+      summary: Delete prompt.
+      description: >-
+        Delete prompt.
+
+        Delete a prompt.
       parameters:
         - name: prompt_id
           in: path
@@ -1220,9 +1248,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: >-
-        Set which version of a prompt should be the default in get_prompt (latest).
+      summary: Set prompt version.
       description: >-
+        Set prompt version.
+
         Set which version of a prompt should be the default in get_prompt (latest).
       parameters:
         - name: prompt_id
@@ -1260,8 +1289,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Prompts
-      summary: List all versions of a specific prompt.
-      description: List all versions of a specific prompt.
+      summary: List prompt versions.
+      description: >-
+        List prompt versions.
+
+        List all versions of a specific prompt.
       parameters:
         - name: prompt_id
           in: path
@@ -1293,8 +1325,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Providers
-      summary: List all available providers.
-      description: List all available providers.
+      summary: List providers.
+      description: >-
+        List providers.
+
+        List all available providers.
       parameters: []
       deprecated: false
   /v1/providers/{provider_id}:
@@ -1319,9 +1354,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Providers
-      summary: >-
-        Get detailed information about a specific provider.
+      summary: Get provider.
       description: >-
+        Get provider.
+
         Get detailed information about a specific provider.
       parameters:
         - name: provider_id
@@ -1352,8 +1388,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: List all OpenAI responses.
-      description: List all OpenAI responses.
+      summary: List all responses.
+      description: List all responses.
       parameters:
         - name: after
           in: query
@@ -1404,8 +1440,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Create a new OpenAI response.
-      description: Create a new OpenAI response.
+      summary: Create a model response.
+      description: Create a model response.
       parameters: []
       requestBody:
         content:
@@ -1447,8 +1483,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Retrieve an OpenAI response by its ID.
-      description: Retrieve an OpenAI response by its ID.
+      summary: Get a model response.
+      description: Get a model response.
       parameters:
         - name: response_id
           in: path
@@ -1478,8 +1514,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Delete an OpenAI response by its ID.
-      description: Delete an OpenAI response by its ID.
+      summary: Delete a response.
+      description: Delete a response.
       parameters:
         - name: response_id
           in: path
@@ -1509,10 +1545,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: >-
-        List input items for a given OpenAI response.
-      description: >-
-        List input items for a given OpenAI response.
+      summary: List input items.
+      description: List input items.
       parameters:
         - name: response_id
           in: path
@@ -1581,8 +1615,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: Run a shield.
-      description: Run a shield.
+      summary: Run shield.
+      description: >-
+        Run shield.
+
+        Run a shield.
       parameters: []
       requestBody:
         content:
@@ -3138,8 +3175,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inspect
-      summary: Get the version of the service.
-      description: Get the version of the service.
+      summary: Get version.
+      description: >-
+        Get version.
+
+        Get the version of the service.
       parameters: []
       deprecated: false
   /v1beta/datasetio/append-rows/{dataset_id}:
@@ -13795,9 +13835,16 @@ tags:
     x-displayName: >-
       Llama Stack Evaluation API for running evaluations on model and agent candidates.
   - name: Files
-    description: ''
+    description: >-
+      This API is used to upload documents that can be used with other Llama Stack
+      APIs.
+    x-displayName: Files
   - name: Inference
     description: >-
+      Llama Stack Inference API for generating completions, chat completions, and
+      embeddings.
+
+
       This API provides the raw interface to the underlying models. Two kinds of models
       are supported:
 
@@ -13805,25 +13852,27 @@ tags:
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
-    x-displayName: >-
-      Llama Stack Inference API for generating completions, chat completions, and
-      embeddings.
+    x-displayName: Inference
   - name: Inspect
-    description: ''
+    description: >-
+      APIs for inspecting the Llama Stack service, including health status, available
+      API routes with methods and implementing providers.
+    x-displayName: Inspect
   - name: Models
     description: ''
   - name: PostTraining (Coming Soon)
     description: ''
   - name: Prompts
-    description: ''
-    x-displayName: >-
+    description: >-
       Protocol for prompt management operations.
+    x-displayName: Prompts
   - name: Providers
-    description: ''
-    x-displayName: >-
+    description: >-
       Providers API for inspecting, listing, and modifying providers and their configurations.
+    x-displayName: Providers
   - name: Safety
-    description: ''
+    description: OpenAI-compatible Moderations API.
+    x-displayName: Safety
   - name: Scoring
     description: ''
   - name: ScoringFunctions

llama_stack/apis/agents/agents.py

@@ -797,7 +797,7 @@ class Agents(Protocol):
         self,
         response_id: str,
     ) -> OpenAIResponseObject:
-        """Retrieve an OpenAI response by its ID.
+        """Get a model response.
 
         :param response_id: The ID of the OpenAI response to retrieve.
         :returns: An OpenAIResponseObject.
@@ -826,7 +826,7 @@ class Agents(Protocol):
             ),
         ] = None,
     ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
-        """Create a new OpenAI response.
+        """Create a model response.
 
         :param input: Input message(s) to create the response.
         :param model: The underlying LLM used for completions.
@@ -846,7 +846,7 @@ class Agents(Protocol):
         model: str | None = None,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseObject:
-        """List all OpenAI responses.
+        """List all responses.
 
         :param after: The ID of the last response to return.
         :param limit: The number of responses to return.
@@ -869,7 +869,7 @@ class Agents(Protocol):
         limit: int | None = 20,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseInputItem:
-        """List input items for a given OpenAI response.
+        """List input items.
 
         :param response_id: The ID of the response to retrieve input items for.
         :param after: An item ID to list items after, used for pagination.
@@ -884,7 +884,7 @@ class Agents(Protocol):
     @webmethod(route="/openai/v1/responses/{response_id}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/responses/{response_id}", method="DELETE", level=LLAMA_STACK_API_V1)
     async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
-        """Delete an OpenAI response by its ID.
+        """Delete a response.
 
         :param response_id: The ID of the OpenAI response to delete.
         :returns: An OpenAIDeleteResponseObject

llama_stack/apis/files/files.py

@@ -104,6 +104,11 @@ class OpenAIFileDeleteResponse(BaseModel):
 @runtime_checkable
 @trace_protocol
 class Files(Protocol):
+    """Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs.
+    """
+
     # OpenAI Files API Endpoints
     @webmethod(route="/openai/v1/files", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/files", method="POST", level=LLAMA_STACK_API_V1)
@@ -113,7 +118,8 @@ class Files(Protocol):
         purpose: Annotated[OpenAIFilePurpose, Form()],
         expires_after: Annotated[ExpiresAfter | None, Form()] = None,
     ) -> OpenAIFileObject:
-        """
+        """Upload file.
+
         Upload a file that can be used across various endpoints.
 
         The file upload should be a multipart form request with:
@@ -137,7 +143,8 @@ class Files(Protocol):
         order: Order | None = Order.desc,
         purpose: OpenAIFilePurpose | None = None,
     ) -> ListOpenAIFileResponse:
-        """
+        """List files.
+
         Returns a list of files that belong to the user's organization.
 
         :param after: A cursor for use in pagination. `after` is an object ID that defines your place in the list. For instance, if you make a list request and receive 100 objects, ending with obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the list.
@@ -154,7 +161,8 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> OpenAIFileObject:
-        """
+        """Retrieve file.
+
         Returns information about a specific file.
 
         :param file_id: The ID of the file to use for this request.
@@ -168,8 +176,7 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> OpenAIFileDeleteResponse:
-        """
-        Delete a file.
+        """Delete file.
 
         :param file_id: The ID of the file to use for this request.
         :returns: An OpenAIFileDeleteResponse indicating successful deletion.
@@ -182,7 +189,8 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> Response:
-        """
+        """Retrieve file content.
+
         Returns the contents of the specified file.
 
         :param file_id: The ID of the file to use for this request.

llama_stack/apis/inference/inference.py

@@ -1053,7 +1053,9 @@ class InferenceProvider(Protocol):
         # for fill-in-the-middle type completion
         suffix: str | None = None,
     ) -> OpenAICompletion:
-        """Generate an OpenAI-compatible completion for the given prompt using the specified model.
+        """Create completion.
+
+        Generate an OpenAI-compatible completion for the given prompt using the specified model.
 
         :param model: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
         :param prompt: The prompt to generate a completion for.
@@ -1105,7 +1107,9 @@ class InferenceProvider(Protocol):
         top_p: float | None = None,
         user: str | None = None,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        """Generate an OpenAI-compatible chat completion for the given messages using the specified model.
+        """Create chat completions.
+
+        Generate an OpenAI-compatible chat completion for the given messages using the specified model.
 
         :param model: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
         :param messages: List of messages in the conversation.
@@ -1144,7 +1148,9 @@ class InferenceProvider(Protocol):
         dimensions: int | None = None,
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        """Generate OpenAI-compatible embeddings for the given input using the specified model.
+        """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.
@@ -1157,7 +1163,9 @@ class InferenceProvider(Protocol):
 
 
 class Inference(InferenceProvider):
-    """Llama Stack Inference API for generating completions, chat completions, and embeddings.
+    """Inference
+
+    Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
     This API provides the raw interface to the underlying models. Two kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.
@@ -1173,7 +1181,7 @@ class Inference(InferenceProvider):
         model: str | None = None,
         order: Order | None = Order.desc,
     ) -> ListOpenAIChatCompletionResponse:
-        """List all chat completions.
+        """List chat completions.
 
         :param after: The ID of the last chat completion to return.
         :param limit: The maximum number of chat completions to return.
@@ -1188,7 +1196,9 @@ class Inference(InferenceProvider):
     )
     @webmethod(route="/chat/completions/{completion_id}", method="GET", level=LLAMA_STACK_API_V1)
     async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
-        """Describe a chat completion by its ID.
+        """Get chat completion.
+
+        Describe a chat completion by its ID.
 
         :param completion_id: ID of the chat completion.
         :returns: A OpenAICompletionWithInputMessages.

llama_stack/apis/inspect/inspect.py

@@ -58,9 +58,16 @@ class ListRoutesResponse(BaseModel):
 
 @runtime_checkable
 class Inspect(Protocol):
+    """Inspect
+
+    APIs for inspecting the Llama Stack service, including health status, available API routes with methods and implementing providers.
+    """
+
     @webmethod(route="/inspect/routes", method="GET", level=LLAMA_STACK_API_V1)
     async def list_routes(self) -> ListRoutesResponse:
-        """List all available API routes with their methods and implementing providers.
+        """List routes.
+
+        List all available API routes with their methods and implementing providers.
 
         :returns: Response containing information about all available routes.
         """
@@ -68,7 +75,9 @@ class Inspect(Protocol):
 
     @webmethod(route="/health", method="GET", level=LLAMA_STACK_API_V1)
     async def health(self) -> HealthInfo:
-        """Get the current health status of the service.
+        """Get health status.
+
+        Get the current health status of the service.
 
         :returns: Health information indicating if the service is operational.
         """
@@ -76,7 +85,9 @@ class Inspect(Protocol):
 
     @webmethod(route="/version", method="GET", level=LLAMA_STACK_API_V1)
     async def version(self) -> VersionInfo:
-        """Get the version of the service.
+        """Get version.
+
+        Get the version of the service.
 
         :returns: Version information containing the service version number.
         """

llama_stack/apis/models/models.py

@@ -124,7 +124,9 @@ class Models(Protocol):
         self,
         model_id: str,
     ) -> Model:
-        """Get a model by its identifier.
+        """Get model.
+
+        Get a model by its identifier.
 
         :param model_id: The identifier of the model to get.
         :returns: A Model.
@@ -140,7 +142,9 @@ class Models(Protocol):
         metadata: dict[str, Any] | None = None,
         model_type: ModelType | None = None,
     ) -> Model:
-        """Register a model.
+        """Register model.
+
+        Register a model.
 
         :param model_id: The identifier of the model to register.
         :param provider_model_id: The identifier of the model in the provider.
@@ -156,7 +160,9 @@ class Models(Protocol):
         self,
         model_id: str,
     ) -> None:
-        """Unregister a model.
+        """Unregister model.
+
+        Unregister a model.
 
         :param model_id: The identifier of the model to unregister.
         """

llama_stack/apis/prompts/prompts.py

@@ -94,7 +94,9 @@ class ListPromptsResponse(BaseModel):
 @runtime_checkable
 @trace_protocol
 class Prompts(Protocol):
-    """Protocol for prompt management operations."""
+    """Prompts
+
+    Protocol for prompt management operations."""
 
     @webmethod(route="/prompts", method="GET", level=LLAMA_STACK_API_V1)
     async def list_prompts(self) -> ListPromptsResponse:
@@ -109,7 +111,9 @@ class Prompts(Protocol):
         self,
         prompt_id: str,
     ) -> ListPromptsResponse:
-        """List all versions of a specific prompt.
+        """List prompt versions.
+
+        List all versions of a specific prompt.
 
         :param prompt_id: The identifier of the prompt to list versions for.
         :returns: A ListPromptsResponse containing all versions of the prompt.
@@ -122,7 +126,9 @@ class Prompts(Protocol):
         prompt_id: str,
         version: int | None = None,
     ) -> Prompt:
-        """Get a prompt by its identifier and optional version.
+        """Get prompt.
+
+        Get a prompt by its identifier and optional version.
 
         :param prompt_id: The identifier of the prompt to get.
         :param version: The version of the prompt to get (defaults to latest).
@@ -136,7 +142,9 @@ class Prompts(Protocol):
         prompt: str,
         variables: list[str] | None = None,
     ) -> Prompt:
-        """Create a new prompt.
+        """Create prompt.
+
+        Create a new prompt.
 
         :param prompt: The prompt text content with variable placeholders.
         :param variables: List of variable names that can be used in the prompt template.
@@ -153,7 +161,9 @@ class Prompts(Protocol):
         variables: list[str] | None = None,
         set_as_default: bool = True,
     ) -> Prompt:
-        """Update an existing prompt (increments version).
+        """Update prompt.
+
+        Update an existing prompt (increments version).
 
         :param prompt_id: The identifier of the prompt to update.
         :param prompt: The updated prompt text content.
@@ -169,7 +179,9 @@ class Prompts(Protocol):
         self,
         prompt_id: str,
     ) -> None:
-        """Delete a prompt.
+        """Delete prompt.
+
+        Delete a prompt.
 
         :param prompt_id: The identifier of the prompt to delete.
         """
@@ -181,7 +193,9 @@ class Prompts(Protocol):
         prompt_id: str,
         version: int,
     ) -> Prompt:
-        """Set which version of a prompt should be the default in get_prompt (latest).
+        """Set prompt version.
+
+        Set which version of a prompt should be the default in get_prompt (latest).
 
         :param prompt_id: The identifier of the prompt.
         :param version: The version to set as default.

llama_stack/apis/providers/providers.py

@@ -42,13 +42,16 @@ class ListProvidersResponse(BaseModel):
 
 @runtime_checkable
 class Providers(Protocol):
-    """
+    """Providers
+
     Providers API for inspecting, listing, and modifying providers and their configurations.
     """
 
     @webmethod(route="/providers", method="GET", level=LLAMA_STACK_API_V1)
     async def list_providers(self) -> ListProvidersResponse:
-        """List all available providers.
+        """List providers.
+
+        List all available providers.
 
         :returns: A ListProvidersResponse containing information about all providers.
         """
@@ -56,7 +59,9 @@ class Providers(Protocol):
 
     @webmethod(route="/providers/{provider_id}", method="GET", level=LLAMA_STACK_API_V1)
     async def inspect_provider(self, provider_id: str) -> ProviderInfo:
-        """Get detailed information about a specific provider.
+        """Get provider.
+
+        Get detailed information about a specific provider.
 
         :param provider_id: The ID of the provider to inspect.
         :returns: A ProviderInfo object containing the provider's details.

llama_stack/apis/safety/safety.py

@@ -96,6 +96,11 @@ class ShieldStore(Protocol):
 @runtime_checkable
 @trace_protocol
 class Safety(Protocol):
+    """Safety
+
+    OpenAI-compatible Moderations API.
+    """
+
     shield_store: ShieldStore
 
     @webmethod(route="/safety/run-shield", method="POST", level=LLAMA_STACK_API_V1)
@@ -105,7 +110,9 @@ class Safety(Protocol):
         messages: list[Message],
         params: dict[str, Any],
     ) -> RunShieldResponse:
-        """Run a shield.
+        """Run shield.
+
+        Run a shield.
 
         :param shield_id: The identifier of the shield to run.
         :param messages: The messages to run the shield on.
@@ -117,7 +124,9 @@ class Safety(Protocol):
     @webmethod(route="/openai/v1/moderations", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/moderations", method="POST", level=LLAMA_STACK_API_V1)
     async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
-        """Classifies if text and/or image inputs are potentially harmful.
+        """Create moderation.
+
+        Classifies if text and/or image inputs are potentially harmful.
         :param input: Input (or inputs) to classify.
         Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
         :param model: The content moderation model you would like to use.

llama_stack/cli/stack/run.py

@@ -75,39 +75,6 @@ class StackRun(Subcommand):
             help="Start the UI server",
         )
 
-    def _resolve_config_and_distro(self, args: argparse.Namespace) -> tuple[Path | None, str | None]:
-        """Resolve config file path and distribution name from args.config"""
-        from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
-
-        if not args.config:
-            return None, None
-
-        config_file = Path(args.config)
-        has_yaml_suffix = args.config.endswith(".yaml")
-        distro_name = None
-
-        if not config_file.exists() and not has_yaml_suffix:
-            # check if this is a distribution
-            config_file = Path(REPO_ROOT) / "llama_stack" / "distributions" / args.config / "run.yaml"
-            if config_file.exists():
-                distro_name = args.config
-
-        if not config_file.exists() and not has_yaml_suffix:
-            # check if it's a build config saved to ~/.llama dir
-            config_file = Path(DISTRIBS_BASE_DIR / f"llamastack-{args.config}" / f"{args.config}-run.yaml")
-
-        if not config_file.exists():
-            self.parser.error(
-                f"File {str(config_file)} does not exist.\n\nPlease run `llama stack build` to generate (and optionally edit) a run.yaml file"
-            )
-
-        if not config_file.is_file():
-            self.parser.error(
-                f"Config file must be a valid file path, '{config_file}' is not a file: type={type(config_file)}"
-            )
-
-        return config_file, distro_name
-
     def _run_stack_run_cmd(self, args: argparse.Namespace) -> None:
         import yaml
 

llama_stack/core/routing_tables/models.py

@@ -33,7 +33,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
             try:
                 models = await provider.list_models()
             except Exception as e:
-                logger.warning(f"Model refresh failed for provider {provider_id}: {e}")
+                logger.debug(f"Model refresh failed for provider {provider_id}: {e}")
                 continue
 
             self.listed_providers.add(provider_id)

llama_stack/core/routing_tables/vector_dbs.py

@@ -245,3 +245,65 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
             vector_store_id=vector_store_id,
             file_id=file_id,
         )
+
+    async def openai_create_vector_store_file_batch(
+        self,
+        vector_store_id: str,
+        file_ids: list[str],
+        attributes: dict[str, Any] | None = None,
+        chunking_strategy: Any | None = None,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_create_vector_store_file_batch(
+            vector_store_id=vector_store_id,
+            file_ids=file_ids,
+            attributes=attributes,
+            chunking_strategy=chunking_strategy,
+        )
+
+    async def openai_retrieve_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.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,
+        )
+
+    async def openai_list_files_in_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+        after: str | None = None,
+        before: str | None = None,
+        filter: str | None = None,
+        limit: int | None = 20,
+        order: str | None = "desc",
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.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,
+            before=before,
+            filter=filter,
+            limit=limit,
+            order=order,
+        )
+
+    async def openai_cancel_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.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/models/llama/tokenizer_utils.py

@@ -9,7 +9,7 @@ from pathlib import Path
 
 from llama_stack.log import get_logger
 
-logger = get_logger(__name__, "tokenizer_utils")
+logger = get_logger(__name__, "models")
 
 
 def load_bpe_file(model_path: Path) -> dict[bytes, int]:

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

@@ -22,6 +22,7 @@ async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: dict[Ap
         deps[Api.tool_runtime],
         deps[Api.tool_groups],
         policy,
+        Api.telemetry in deps,
     )
     await impl.initialize()
     return impl

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

@@ -110,6 +110,7 @@ class ChatAgent(ShieldRunnerMixin):
         persistence_store: KVStore,
         created_at: str,
         policy: list[AccessRule],
+        telemetry_enabled: bool = False,
     ):
         self.agent_id = agent_id
         self.agent_config = agent_config
@@ -120,6 +121,7 @@ class ChatAgent(ShieldRunnerMixin):
         self.tool_runtime_api = tool_runtime_api
         self.tool_groups_api = tool_groups_api
         self.created_at = created_at
+        self.telemetry_enabled = telemetry_enabled
 
         ShieldRunnerMixin.__init__(
             self,
@@ -188,28 +190,30 @@ class ChatAgent(ShieldRunnerMixin):
 
     async def create_and_execute_turn(self, request: AgentTurnCreateRequest) -> AsyncGenerator:
         turn_id = str(uuid.uuid4())
-        span = tracing.get_current_span()
-        if span:
-            span.set_attribute("session_id", request.session_id)
-            span.set_attribute("agent_id", self.agent_id)
-            span.set_attribute("request", request.model_dump_json())
-            span.set_attribute("turn_id", turn_id)
-            if self.agent_config.name:
-                span.set_attribute("agent_name", self.agent_config.name)
+        if self.telemetry_enabled:
+            span = tracing.get_current_span()
+            if span is not None:
+                span.set_attribute("session_id", request.session_id)
+                span.set_attribute("agent_id", self.agent_id)
+                span.set_attribute("request", request.model_dump_json())
+                span.set_attribute("turn_id", turn_id)
+                if self.agent_config.name:
+                    span.set_attribute("agent_name", self.agent_config.name)
 
         await self._initialize_tools(request.toolgroups)
         async for chunk in self._run_turn(request, turn_id):
             yield chunk
 
     async def resume_turn(self, request: AgentTurnResumeRequest) -> AsyncGenerator:
-        span = tracing.get_current_span()
-        if span:
-            span.set_attribute("agent_id", self.agent_id)
-            span.set_attribute("session_id", request.session_id)
-            span.set_attribute("request", request.model_dump_json())
-            span.set_attribute("turn_id", request.turn_id)
-            if self.agent_config.name:
-                span.set_attribute("agent_name", self.agent_config.name)
+        if self.telemetry_enabled:
+            span = tracing.get_current_span()
+            if span is not None:
+                span.set_attribute("agent_id", self.agent_id)
+                span.set_attribute("session_id", request.session_id)
+                span.set_attribute("request", request.model_dump_json())
+                span.set_attribute("turn_id", request.turn_id)
+                if self.agent_config.name:
+                    span.set_attribute("agent_name", self.agent_config.name)
 
         await self._initialize_tools()
         async for chunk in self._run_turn(request):
@@ -395,9 +399,12 @@ class ChatAgent(ShieldRunnerMixin):
         touchpoint: str,
     ) -> AsyncGenerator:
         async with tracing.span("run_shields") as span:
-            span.set_attribute("input", [m.model_dump_json() for m in messages])
+            if self.telemetry_enabled and span is not None:
+                span.set_attribute("input", [m.model_dump_json() for m in messages])
+                if len(shields) == 0:
+                    span.set_attribute("output", "no shields")
+
             if len(shields) == 0:
-                span.set_attribute("output", "no shields")
                 return
 
             step_id = str(uuid.uuid4())
@@ -430,7 +437,8 @@ class ChatAgent(ShieldRunnerMixin):
                         )
                     )
                 )
-                span.set_attribute("output", e.violation.model_dump_json())
+                if self.telemetry_enabled and span is not None:
+                    span.set_attribute("output", e.violation.model_dump_json())
 
                 yield CompletionMessage(
                     content=str(e),
@@ -453,7 +461,8 @@ class ChatAgent(ShieldRunnerMixin):
                     )
                 )
             )
-            span.set_attribute("output", "no violations")
+            if self.telemetry_enabled and span is not None:
+                span.set_attribute("output", "no violations")
 
     async def _run(
         self,
@@ -518,8 +527,9 @@ class ChatAgent(ShieldRunnerMixin):
             stop_reason: StopReason | None = None
 
             async with tracing.span("inference") as span:
-                if self.agent_config.name:
-                    span.set_attribute("agent_name", self.agent_config.name)
+                if self.telemetry_enabled and span is not None:
+                    if self.agent_config.name:
+                        span.set_attribute("agent_name", self.agent_config.name)
 
                 def _serialize_nested(value):
                     """Recursively serialize nested Pydantic models to dicts."""
@@ -637,18 +647,19 @@ class ChatAgent(ShieldRunnerMixin):
                     else:
                         raise ValueError(f"Unexpected delta type {type(delta)}")
 
-                span.set_attribute("stop_reason", stop_reason or StopReason.end_of_turn)
-                span.set_attribute(
-                    "input",
-                    json.dumps([json.loads(m.model_dump_json()) for m in input_messages]),
-                )
-                output_attr = json.dumps(
-                    {
-                        "content": content,
-                        "tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls],
-                    }
-                )
-                span.set_attribute("output", output_attr)
+                if self.telemetry_enabled and span is not None:
+                    span.set_attribute("stop_reason", stop_reason or StopReason.end_of_turn)
+                    span.set_attribute(
+                        "input",
+                        json.dumps([json.loads(m.model_dump_json()) for m in input_messages]),
+                    )
+                    output_attr = json.dumps(
+                        {
+                            "content": content,
+                            "tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls],
+                        }
+                    )
+                    span.set_attribute("output", output_attr)
 
             n_iter += 1
             await self.storage.set_num_infer_iters_in_turn(session_id, turn_id, n_iter)
@@ -756,7 +767,9 @@ class ChatAgent(ShieldRunnerMixin):
                         {
                             "tool_name": tool_call.tool_name,
                             "input": message.model_dump_json(),
-                        },
+                        }
+                        if self.telemetry_enabled
+                        else {},
                     ) as span:
                         tool_execution_start_time = datetime.now(UTC).isoformat()
                         tool_result = await self.execute_tool_call_maybe(
@@ -771,7 +784,8 @@ class ChatAgent(ShieldRunnerMixin):
                             call_id=tool_call.call_id,
                             content=tool_result.content,
                         )
-                        span.set_attribute("output", result_message.model_dump_json())
+                        if self.telemetry_enabled and span is not None:
+                            span.set_attribute("output", result_message.model_dump_json())
 
                         # Store tool execution step
                         tool_execution_step = ToolExecutionStep(

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

@@ -64,6 +64,7 @@ class MetaReferenceAgentsImpl(Agents):
         tool_runtime_api: ToolRuntime,
         tool_groups_api: ToolGroups,
         policy: list[AccessRule],
+        telemetry_enabled: bool = False,
     ):
         self.config = config
         self.inference_api = inference_api
@@ -71,6 +72,7 @@ class MetaReferenceAgentsImpl(Agents):
         self.safety_api = safety_api
         self.tool_runtime_api = tool_runtime_api
         self.tool_groups_api = tool_groups_api
+        self.telemetry_enabled = telemetry_enabled
 
         self.in_memory_store = InmemoryKVStoreImpl()
         self.openai_responses_impl: OpenAIResponsesImpl | None = None
@@ -135,6 +137,7 @@ class MetaReferenceAgentsImpl(Agents):
             ),
             created_at=agent_info.created_at,
             policy=self.policy,
+            telemetry_enabled=self.telemetry_enabled,
         )
 
     async def create_agent_session(

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

@@ -97,6 +97,8 @@ class StreamingResponseOrchestrator:
         self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = {}
         # Track final messages after all tool executions
         self.final_messages: list[OpenAIMessageParam] = []
+        # mapping for annotations
+        self.citation_files: dict[str, str] = {}
 
     async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
         # Initialize output messages
@@ -126,6 +128,7 @@ class StreamingResponseOrchestrator:
             # Text is the default response format for chat completion so don't need to pass it
             # (some providers don't support non-empty response_format when tools are present)
             response_format = None if self.ctx.response_format.type == "text" else self.ctx.response_format
+            logger.debug(f"calling openai_chat_completion with tools: {self.ctx.chat_tools}")
             completion_result = await self.inference_api.openai_chat_completion(
                 model=self.ctx.model,
                 messages=messages,
@@ -160,7 +163,7 @@ class StreamingResponseOrchestrator:
             # Handle choices with no tool calls
             for choice in current_response.choices:
                 if not (choice.message.tool_calls and self.ctx.response_tools):
-                    output_messages.append(await convert_chat_choice_to_response_message(choice))
+                    output_messages.append(await convert_chat_choice_to_response_message(choice, self.citation_files))
 
             # Execute tool calls and coordinate results
             async for stream_event in self._coordinate_tool_execution(
@@ -211,6 +214,8 @@ class StreamingResponseOrchestrator:
 
         for choice in current_response.choices:
             next_turn_messages.append(choice.message)
+            logger.debug(f"Choice message content: {choice.message.content}")
+            logger.debug(f"Choice message tool_calls: {choice.message.tool_calls}")
 
             if choice.message.tool_calls and self.ctx.response_tools:
                 for tool_call in choice.message.tool_calls:
@@ -470,6 +475,8 @@ class StreamingResponseOrchestrator:
                     tool_call_log = result.final_output_message
                     tool_response_message = result.final_input_message
                     self.sequence_number = result.sequence_number
+                    if result.citation_files:
+                        self.citation_files.update(result.citation_files)
 
             if tool_call_log:
                 output_messages.append(tool_call_log)

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

@@ -94,7 +94,10 @@ class ToolExecutor:
 
         # Yield the final result
         yield ToolExecutionResult(
-            sequence_number=sequence_number, final_output_message=output_message, final_input_message=input_message
+            sequence_number=sequence_number,
+            final_output_message=output_message,
+            final_input_message=input_message,
+            citation_files=result.metadata.get("citation_files") if result and result.metadata else None,
         )
 
     async def _execute_knowledge_search_via_vector_store(
@@ -129,8 +132,6 @@ class ToolExecutor:
         for results in all_results:
             search_results.extend(results)
 
-        # Convert search results to tool result format matching memory.py
-        # Format the results as interleaved content similar to memory.py
         content_items = []
         content_items.append(
             TextContentItem(
@@ -138,27 +139,58 @@ class ToolExecutor:
             )
         )
 
+        unique_files = set()
         for i, result_item in enumerate(search_results):
             chunk_text = result_item.content[0].text if result_item.content else ""
-            metadata_text = f"document_id: {result_item.file_id}, score: {result_item.score}"
+            # Get file_id from attributes if result_item.file_id is empty
+            file_id = result_item.file_id or (
+                result_item.attributes.get("document_id") if result_item.attributes else None
+            )
+            metadata_text = f"document_id: {file_id}, score: {result_item.score}"
             if result_item.attributes:
                 metadata_text += f", attributes: {result_item.attributes}"
-            text_content = f"[{i + 1}] {metadata_text}\n{chunk_text}\n"
+
+            text_content = f"[{i + 1}] {metadata_text} (cite as <|{file_id}|>)\n{chunk_text}\n"
             content_items.append(TextContentItem(text=text_content))
+            unique_files.add(file_id)
 
         content_items.append(TextContentItem(text="END of knowledge_search tool results.\n"))
+
+        citation_instruction = ""
+        if unique_files:
+            citation_instruction = (
+                " Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). "
+                "Do not add extra punctuation. Use only the file IDs provided (do not invent new ones)."
+            )
+
         content_items.append(
             TextContentItem(
-                text=f'The above results were retrieved to help answer the user\'s query: "{query}". Use them as supporting information only in answering this query.\n',
+                text=f'The above results were retrieved to help answer the user\'s query: "{query}". Use them as supporting information only in answering this query.{citation_instruction}\n',
             )
         )
 
+        # handling missing attributes for old versions
+        citation_files = {}
+        for result in search_results:
+            file_id = result.file_id
+            if not file_id and result.attributes:
+                file_id = result.attributes.get("document_id")
+
+            filename = result.filename
+            if not filename and result.attributes:
+                filename = result.attributes.get("filename")
+            if not filename:
+                filename = "unknown"
+
+            citation_files[file_id] = filename
+
         return ToolInvocationResult(
             content=content_items,
             metadata={
                 "document_ids": [r.file_id for r in search_results],
                 "chunks": [r.content[0].text if r.content else "" for r in search_results],
                 "scores": [r.score for r in search_results],
+                "citation_files": citation_files,
             },
         )
 

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

@@ -27,6 +27,7 @@ class ToolExecutionResult(BaseModel):
     sequence_number: int
     final_output_message: OpenAIResponseOutput | None = None
     final_input_message: OpenAIMessageParam | None = None
+    citation_files: dict[str, str] | None = None
 
 
 @dataclass

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

@@ -4,9 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import re
 import uuid
 
 from llama_stack.apis.agents.openai_responses import (
+    OpenAIResponseAnnotationFileCitation,
     OpenAIResponseInput,
     OpenAIResponseInputFunctionToolCallOutput,
     OpenAIResponseInputMessageContent,
@@ -45,7 +47,9 @@ from llama_stack.apis.inference import (
 )
 
 
-async def convert_chat_choice_to_response_message(choice: OpenAIChoice) -> OpenAIResponseMessage:
+async def convert_chat_choice_to_response_message(
+    choice: OpenAIChoice, citation_files: dict[str, str] | None = None
+) -> OpenAIResponseMessage:
     """Convert an OpenAI Chat Completion choice into an OpenAI Response output message."""
     output_content = ""
     if isinstance(choice.message.content, str):
@@ -57,9 +61,11 @@ async def convert_chat_choice_to_response_message(choice: OpenAIChoice) -> OpenA
             f"Llama Stack OpenAI Responses does not yet support output content type: {type(choice.message.content)}"
         )
 
+    annotations, clean_text = _extract_citations_from_text(output_content, citation_files or {})
+
     return OpenAIResponseMessage(
         id=f"msg_{uuid.uuid4()}",
-        content=[OpenAIResponseOutputMessageContentOutputText(text=output_content)],
+        content=[OpenAIResponseOutputMessageContentOutputText(text=clean_text, annotations=annotations)],
         status="completed",
         role="assistant",
     )
@@ -200,6 +206,53 @@ async def get_message_type_by_role(role: str):
     return role_to_type.get(role)
 
 
+def _extract_citations_from_text(
+    text: str, citation_files: dict[str, str]
+) -> tuple[list[OpenAIResponseAnnotationFileCitation], str]:
+    """Extract citation markers from text and create annotations
+
+    Args:
+        text: The text containing citation markers like [file-Cn3MSNn72ENTiiq11Qda4A]
+        citation_files: Dictionary mapping file_id to filename
+
+    Returns:
+        Tuple of (annotations_list, clean_text_without_markers)
+    """
+    file_id_regex = re.compile(r"<\|(?P<file_id>file-[A-Za-z0-9_-]+)\|>")
+
+    annotations = []
+    parts = []
+    total_len = 0
+    last_end = 0
+
+    for m in file_id_regex.finditer(text):
+        # segment before the marker
+        prefix = text[last_end : m.start()]
+
+        # drop one space if it exists (since marker is at sentence end)
+        if prefix.endswith(" "):
+            prefix = prefix[:-1]
+
+        parts.append(prefix)
+        total_len += len(prefix)
+
+        fid = m.group(1)
+        if fid in citation_files:
+            annotations.append(
+                OpenAIResponseAnnotationFileCitation(
+                    file_id=fid,
+                    filename=citation_files[fid],
+                    index=total_len,  # index points to punctuation
+                )
+            )
+
+        last_end = m.end()
+
+    parts.append(text[last_end:])
+    cleaned_text = "".join(parts)
+    return annotations, cleaned_text
+
+
 def is_function_tool_call(
     tool_call: OpenAIChatCompletionToolCall,
     tools: list[OpenAIResponseInputTool],

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -331,5 +331,8 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
 
         return ToolInvocationResult(
             content=result.content or [],
-            metadata=result.metadata,
+            metadata={
+                **(result.metadata or {}),
+                "citation_files": getattr(result, "citation_files", None),
+            },
         )

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -200,12 +200,10 @@ class FaissIndex(EmbeddingIndex):
 
 class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
     def __init__(self, config: FaissVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.files_api = files_api
         self.cache: dict[str, VectorDBWithIndex] = {}
-        self.kvstore: KVStore | None = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -410,12 +410,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
     """
 
     def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.files_api = files_api
         self.cache: dict[str, VectorDBWithIndex] = {}
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
-        self.kvstore: KVStore | None = None
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)

llama_stack/providers/registry/agents.py

@@ -36,6 +36,9 @@ def available_providers() -> list[ProviderSpec]:
                 Api.tool_runtime,
                 Api.tool_groups,
             ],
+            optional_api_dependencies=[
+                Api.telemetry,
+            ],
             description="Meta's reference implementation of an agent system that can use tools, access vector databases, and perform complex reasoning tasks.",
         ),
     ]

llama_stack/providers/registry/tool_runtime.py

@@ -11,6 +11,7 @@ from llama_stack.providers.datatypes import (
     ProviderSpec,
     RemoteProviderSpec,
 )
+from llama_stack.providers.registry.vector_io import DEFAULT_VECTOR_IO_DEPS
 
 
 def available_providers() -> list[ProviderSpec]:
@@ -18,9 +19,8 @@ def available_providers() -> list[ProviderSpec]:
         InlineProviderSpec(
             api=Api.tool_runtime,
             provider_type="inline::rag-runtime",
-            pip_packages=[
-                "chardet",
-                "pypdf",
+            pip_packages=DEFAULT_VECTOR_IO_DEPS
+            + [
                 "tqdm",
                 "numpy",
                 "scikit-learn",

llama_stack/providers/registry/vector_io.py

@@ -12,13 +12,16 @@ from llama_stack.providers.datatypes import (
     RemoteProviderSpec,
 )
 
+# Common dependencies for all vector IO providers that support document processing
+DEFAULT_VECTOR_IO_DEPS = ["chardet", "pypdf"]
+
 
 def available_providers() -> list[ProviderSpec]:
     return [
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::meta-reference",
-            pip_packages=["faiss-cpu"],
+            pip_packages=["faiss-cpu"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.faiss",
             config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
             deprecation_warning="Please use the `inline::faiss` provider instead.",
@@ -29,7 +32,7 @@ def available_providers() -> list[ProviderSpec]:
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::faiss",
-            pip_packages=["faiss-cpu"],
+            pip_packages=["faiss-cpu"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.faiss",
             config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -82,7 +85,7 @@ more details about Faiss in general.
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::sqlite-vec",
-            pip_packages=["sqlite-vec"],
+            pip_packages=["sqlite-vec"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.sqlite_vec",
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -289,7 +292,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::sqlite_vec",
-            pip_packages=["sqlite-vec"],
+            pip_packages=["sqlite-vec"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.sqlite_vec",
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
@@ -303,7 +306,7 @@ Please refer to the sqlite-vec provider documentation.
             api=Api.vector_io,
             adapter_type="chromadb",
             provider_type="remote::chromadb",
-            pip_packages=["chromadb-client"],
+            pip_packages=["chromadb-client"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.remote.vector_io.chroma",
             config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -345,7 +348,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::chromadb",
-            pip_packages=["chromadb"],
+            pip_packages=["chromadb"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.chroma",
             config_class="llama_stack.providers.inline.vector_io.chroma.ChromaVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -389,7 +392,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
             api=Api.vector_io,
             adapter_type="pgvector",
             provider_type="remote::pgvector",
-            pip_packages=["psycopg2-binary"],
+            pip_packages=["psycopg2-binary"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.remote.vector_io.pgvector",
             config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -500,7 +503,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
             api=Api.vector_io,
             adapter_type="weaviate",
             provider_type="remote::weaviate",
-            pip_packages=["weaviate-client>=4.16.5"],
+            pip_packages=["weaviate-client>=4.16.5"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.remote.vector_io.weaviate",
             config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
             provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
@@ -541,7 +544,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::qdrant",
-            pip_packages=["qdrant-client"],
+            pip_packages=["qdrant-client"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.qdrant",
             config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -594,7 +597,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
             api=Api.vector_io,
             adapter_type="qdrant",
             provider_type="remote::qdrant",
-            pip_packages=["qdrant-client"],
+            pip_packages=["qdrant-client"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.remote.vector_io.qdrant",
             config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -607,7 +610,7 @@ Please refer to the inline provider documentation.
             api=Api.vector_io,
             adapter_type="milvus",
             provider_type="remote::milvus",
-            pip_packages=["pymilvus>=2.4.10"],
+            pip_packages=["pymilvus>=2.4.10"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.remote.vector_io.milvus",
             config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
             api_dependencies=[Api.inference],
@@ -813,7 +816,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::milvus",
-            pip_packages=["pymilvus[milvus-lite]>=2.4.10"],
+            pip_packages=["pymilvus[milvus-lite]>=2.4.10"] + DEFAULT_VECTOR_IO_DEPS,
             module="llama_stack.providers.inline.vector_io.milvus",
             config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
             api_dependencies=[Api.inference],

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -41,9 +41,6 @@ class DatabricksInferenceAdapter(OpenAIMixin):
             ).serving_endpoints.list()  # TODO: this is not async
         ]
 
-    async def should_refresh_models(self) -> bool:
-        return False
-
     async def openai_completion(
         self,
         model: str,

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

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 from typing import Any
 
-from llama_stack.apis.inference.inference import OpenAICompletion
+from llama_stack.apis.inference.inference import OpenAICompletion, OpenAIEmbeddingsResponse
 from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
@@ -56,3 +56,13 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
         suffix: str | None = None,
     ) -> OpenAICompletion:
         raise NotImplementedError()
+
+    async def openai_embeddings(
+        self,
+        model: str,
+        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/ollama/config.py

@@ -6,8 +6,6 @@
 
 from typing import Any
 
-from pydantic import Field
-
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 
 DEFAULT_OLLAMA_URL = "http://localhost:11434"
@@ -15,10 +13,6 @@ DEFAULT_OLLAMA_URL = "http://localhost:11434"
 
 class OllamaImplConfig(RemoteInferenceProviderConfig):
     url: str = DEFAULT_OLLAMA_URL
-    refresh_models: bool = Field(
-        default=False,
-        description="Whether to refresh models periodically",
-    )
 
     @classmethod
     def sample_run_config(cls, url: str = "${env.OLLAMA_URL:=http://localhost:11434}", **kwargs) -> dict[str, Any]:

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

@@ -72,9 +72,6 @@ class OllamaInferenceAdapter(OpenAIMixin):
                 f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal"
             )
 
-    async def should_refresh_models(self) -> bool:
-        return self.config.refresh_models
-
     async def health(self) -> HealthResponse:
         """
         Performs a health check by verifying connectivity to the Ollama server.

llama_stack/providers/remote/inference/runpod/__init__.py

@@ -11,6 +11,6 @@ async def get_adapter_impl(config: RunpodImplConfig, _deps):
     from .runpod import RunpodInferenceAdapter
 
     assert isinstance(config, RunpodImplConfig), f"Unexpected config type: {type(config)}"
-    impl = RunpodInferenceAdapter(config)
+    impl = RunpodInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -4,69 +4,86 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Any
 
-from llama_stack.apis.inference import *  # noqa: F403
-from llama_stack.apis.inference import OpenAIEmbeddingsResponse
-
-# from llama_stack.providers.datatypes import ModelsProtocolPrivate
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, build_hf_repo_model_entry
-from llama_stack.providers.utils.inference.openai_compat import (
-    get_sampling_options,
-)
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
+from llama_stack.apis.inference import (
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import RunpodImplConfig
 
-# https://docs.runpod.io/serverless/vllm/overview#compatible-models
-# https://github.com/runpod-workers/worker-vllm/blob/main/README.md#compatible-model-architectures
-RUNPOD_SUPPORTED_MODELS = {
-    "Llama3.1-8B": "meta-llama/Llama-3.1-8B",
-    "Llama3.1-70B": "meta-llama/Llama-3.1-70B",
-    "Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
-    "Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
-    "Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
-    "Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
-    "Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
-    "Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
-    "Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
-    "Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
-    "Llama3.2-1B": "meta-llama/Llama-3.2-1B",
-    "Llama3.2-3B": "meta-llama/Llama-3.2-3B",
-}
 
-SAFETY_MODELS_ENTRIES = []
+class RunpodInferenceAdapter(OpenAIMixin):
+    """
+    Adapter for RunPod's OpenAI-compatible API endpoints.
+    Supports VLLM for serverless endpoint self-hosted or public endpoints.
+    Can work with any runpod endpoints that support OpenAI-compatible API
+    """
 
-# Create MODEL_ENTRIES from RUNPOD_SUPPORTED_MODELS for compatibility with starter template
-MODEL_ENTRIES = [
-    build_hf_repo_model_entry(provider_model_id, model_descriptor)
-    for provider_model_id, model_descriptor in RUNPOD_SUPPORTED_MODELS.items()
-] + SAFETY_MODELS_ENTRIES
+    config: RunpodImplConfig
 
+    def get_api_key(self) -> str:
+        """Get API key for OpenAI client."""
+        return self.config.api_token
 
-class RunpodInferenceAdapter(
-    ModelRegistryHelper,
-    Inference,
-):
-    def __init__(self, config: RunpodImplConfig) -> None:
-        ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)
-        self.config = config
+    def get_base_url(self) -> str:
+        """Get base URL for OpenAI client."""
+        return self.config.url
 
-    def _get_params(self, request: ChatCompletionRequest) -> dict:
-        return {
-            "model": self.map_to_provider_model(request.model),
-            "prompt": chat_completion_request_to_prompt(request),
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
-
-    async def openai_embeddings(
+    async def openai_chat_completion(
         self,
         model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
+        messages: list[OpenAIMessageParam],
+        frequency_penalty: float | None = None,
+        function_call: str | dict[str, Any] | None = None,
+        functions: list[dict[str, Any]] | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_completion_tokens: int | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        parallel_tool_calls: bool | None = None,
+        presence_penalty: float | None = None,
+        response_format: OpenAIResponseFormatParam | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[dict[str, Any]] | None = None,
+        top_logprobs: int | None = None,
+        top_p: float | None = None,
         user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
+    ):
+        """Override to add RunPod-specific stream_options requirement."""
+        if stream and not stream_options:
+            stream_options = {"include_usage": True}
+
+        return await super().openai_chat_completion(
+            model=model,
+            messages=messages,
+            frequency_penalty=frequency_penalty,
+            function_call=function_call,
+            functions=functions,
+            logit_bias=logit_bias,
+            logprobs=logprobs,
+            max_completion_tokens=max_completion_tokens,
+            max_tokens=max_tokens,
+            n=n,
+            parallel_tool_calls=parallel_tool_calls,
+            presence_penalty=presence_penalty,
+            response_format=response_format,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            tool_choice=tool_choice,
+            tools=tools,
+            top_logprobs=top_logprobs,
+            top_p=top_p,
+            user=user,
+        )

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

@@ -63,12 +63,6 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
         # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client
         return [m.id for m in await self._get_client().models.list()]
 
-    async def should_refresh_models(self) -> bool:
-        return True
-
-    async def check_model_availability(self, model):
-        return model in self._model_cache
-
     async def openai_embeddings(
         self,
         model: str,

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

@@ -30,10 +30,6 @@ class VLLMInferenceAdapterConfig(RemoteInferenceProviderConfig):
         default=True,
         description="Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file.",
     )
-    refresh_models: bool = Field(
-        default=False,
-        description="Whether to refresh models periodically",
-    )
 
     @field_validator("tls_verify")
     @classmethod

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

@@ -19,7 +19,6 @@ from llama_stack.apis.inference import (
     OpenAIResponseFormatParam,
     ToolChoice,
 )
-from llama_stack.apis.models import Model, ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
     HealthResponse,
@@ -54,25 +53,6 @@ class VLLMInferenceAdapter(OpenAIMixin):
                 "You must provide a URL in run.yaml (or via the VLLM_URL environment variable) to use vLLM."
             )
 
-    async def should_refresh_models(self) -> bool:
-        # Strictly respecting the refresh_models directive
-        return self.config.refresh_models
-
-    async def list_models(self) -> list[Model] | None:
-        models = []
-        async for m in self.client.models.list():
-            model_type = ModelType.llm  # unclear how to determine embedding vs. llm models
-            models.append(
-                Model(
-                    identifier=m.id,
-                    provider_resource_id=m.id,
-                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                    metadata={},
-                    model_type=model_type,
-                )
-            )
-        return models
-
     async def health(self) -> HealthResponse:
         """
         Performs a health check by verifying connectivity to the remote vLLM server.

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -140,14 +140,13 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Api.inference,
         files_api: Files | None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         log.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
         self.config = config
         self.inference_api = inference_api
         self.client = None
         self.cache = {}
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.files_api = files_api
 
     async def initialize(self) -> None:
         self.kvstore = await kvstore_impl(self.config.kvstore)

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -309,14 +309,12 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Inference,
         files_api: Files | None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.cache = {}
         self.client = None
         self.inference_api = inference_api
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     async def initialize(self) -> None:

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -345,14 +345,12 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
         inference_api: Api.inference,
         files_api: Files | None = None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
         self.conn = None
         self.cache = {}
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     async def initialize(self) -> None:

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -27,7 +27,7 @@ from llama_stack.apis.vector_io import (
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
-from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
+from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
     ChunkForDeletion,
@@ -162,14 +162,12 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         inference_api: Api.inference,
         files_api: Files | None = None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
-        self.files_api = files_api
         self.vector_db_store = None
-        self.kvstore: KVStore | None = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self._qdrant_lock = asyncio.Lock()
 
     async def initialize(self) -> None:

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -284,14 +284,12 @@ class WeaviateVectorIOAdapter(
         inference_api: Api.inference,
         files_api: Files | None,
     ) -> None:
+        super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
         self.client_cache = {}
         self.cache = {}
-        self.files_api = files_api
-        self.kvstore: KVStore | None = None
         self.vector_db_store = None
-        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
         self.metadata_collection_name = "openai_vector_stores_metadata"
 
     def _get_client(self) -> weaviate.WeaviateClient:

llama_stack/providers/utils/inference/model_registry.py

@@ -24,6 +24,10 @@ class RemoteInferenceProviderConfig(BaseModel):
         default=None,
         description="List of models that should be registered with the model registry. If None, all models are allowed.",
     )
+    refresh_models: bool = Field(
+        default=False,
+        description="Whether to refresh models periodically from the provider",
+    )
 
 
 # TODO: this class is more confusing than useful right now. We need to make it

llama_stack/providers/utils/inference/openai_mixin.py

@@ -132,7 +132,10 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
 
         :return: An iterable of model IDs or None if not implemented
         """
-        return [m.id async for m in self.client.models.list()]
+        client = self.client
+        async with client:
+            model_ids = [m.id async for m in client.models.list()]
+        return model_ids
 
     async def initialize(self) -> None:
         """
@@ -481,7 +484,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
         return model in self._model_cache
 
     async def should_refresh_models(self) -> bool:
-        return False
+        return self.config.refresh_models
 
     #
     # The model_dump implementations are to avoid serializing the extra fields,

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -12,6 +12,8 @@ import uuid
 from abc import ABC, abstractmethod
 from typing import Any
 
+from pydantic import TypeAdapter
+
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files, OpenAIFileObject
 from llama_stack.apis.vector_dbs import VectorDB
@@ -50,12 +52,16 @@ logger = get_logger(name=__name__, category="providers::utils")
 
 # Constants for OpenAI vector stores
 CHUNK_MULTIPLIER = 5
+FILE_BATCH_CLEANUP_INTERVAL_SECONDS = 24 * 60 * 60  # 1 day in seconds
+MAX_CONCURRENT_FILES_PER_BATCH = 3  # Maximum concurrent file processing within a batch
+FILE_BATCH_CHUNK_SIZE = 10  # Process files in chunks of this size
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
 OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
 OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:{VERSION}::"
 OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
+OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX = f"openai_vector_stores_file_batches:{VERSION}::"
 
 
 class OpenAIVectorStoreMixin(ABC):
@@ -65,11 +71,15 @@ class OpenAIVectorStoreMixin(ABC):
     an openai_vector_stores in-memory cache.
     """
 
-    # These should be provided by the implementing class
-    openai_vector_stores: dict[str, dict[str, Any]]
-    files_api: Files | None
-    # KV store for persisting OpenAI vector store metadata
-    kvstore: KVStore | None
+    # Implementing classes should call super().__init__() in their __init__ method
+    # to properly initialize the mixin attributes.
+    def __init__(self, files_api: Files | None = None, kvstore: KVStore | None = None):
+        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
+        self.openai_file_batches: dict[str, dict[str, Any]] = {}
+        self.files_api = files_api
+        self.kvstore = kvstore
+        self._last_file_batch_cleanup_time = 0
+        self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
 
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
         """Save vector store metadata to persistent storage."""
@@ -159,9 +169,129 @@ class OpenAIVectorStoreMixin(ABC):
         for idx in range(len(raw_items)):
             await self.kvstore.delete(f"{contents_prefix}{idx}")
 
+    async def _save_openai_vector_store_file_batch(self, batch_id: str, batch_info: dict[str, Any]) -> None:
+        """Save file batch metadata to persistent storage."""
+        assert self.kvstore
+        key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{batch_id}"
+        await self.kvstore.set(key=key, value=json.dumps(batch_info))
+        # update in-memory cache
+        self.openai_file_batches[batch_id] = batch_info
+
+    async def _load_openai_vector_store_file_batches(self) -> dict[str, dict[str, Any]]:
+        """Load all file batch metadata from persistent storage."""
+        assert self.kvstore
+        start_key = OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}\xff"
+        stored_data = await self.kvstore.values_in_range(start_key, end_key)
+
+        batches: dict[str, dict[str, Any]] = {}
+        for item in stored_data:
+            info = json.loads(item)
+            batches[info["id"]] = info
+        return batches
+
+    async def _delete_openai_vector_store_file_batch(self, batch_id: str) -> None:
+        """Delete file batch metadata from persistent storage and in-memory cache."""
+        assert self.kvstore
+        key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{batch_id}"
+        await self.kvstore.delete(key)
+        # remove from in-memory cache
+        self.openai_file_batches.pop(batch_id, None)
+
+    async def _cleanup_expired_file_batches(self) -> None:
+        """Clean up expired file batches from persistent storage."""
+        assert self.kvstore
+        start_key = OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}\xff"
+        stored_data = await self.kvstore.values_in_range(start_key, end_key)
+
+        current_time = int(time.time())
+        expired_count = 0
+
+        for item in stored_data:
+            info = json.loads(item)
+            expires_at = info.get("expires_at")
+            if expires_at and current_time > expires_at:
+                logger.info(f"Cleaning up expired file batch: {info['id']}")
+                await self.kvstore.delete(f"{OPENAI_VECTOR_STORES_FILE_BATCHES_PREFIX}{info['id']}")
+                # Remove from in-memory cache if present
+                self.openai_file_batches.pop(info["id"], None)
+                expired_count += 1
+
+        if expired_count > 0:
+            logger.info(f"Cleaned up {expired_count} expired file batches")
+
+    async def _get_completed_files_in_batch(self, vector_store_id: str, file_ids: list[str]) -> set[str]:
+        """Determine which files in a batch are actually completed by checking vector store file_ids."""
+        if vector_store_id not in self.openai_vector_stores:
+            return set()
+
+        store_info = self.openai_vector_stores[vector_store_id]
+        completed_files = set(file_ids) & set(store_info["file_ids"])
+        return completed_files
+
+    async def _analyze_batch_completion_on_resume(self, batch_id: str, batch_info: dict[str, Any]) -> list[str]:
+        """Analyze batch completion status and return remaining files to process.
+
+        Returns:
+            List of file IDs that still need processing. Empty list if batch is complete.
+        """
+        vector_store_id = batch_info["vector_store_id"]
+        all_file_ids = batch_info["file_ids"]
+
+        # Find files that are actually completed
+        completed_files = await self._get_completed_files_in_batch(vector_store_id, all_file_ids)
+        remaining_files = [file_id for file_id in all_file_ids if file_id not in completed_files]
+
+        completed_count = len(completed_files)
+        total_count = len(all_file_ids)
+        remaining_count = len(remaining_files)
+
+        # Update file counts to reflect actual state
+        batch_info["file_counts"] = {
+            "completed": completed_count,
+            "failed": 0,  # We don't track failed files during resume - they'll be retried
+            "in_progress": remaining_count,
+            "cancelled": 0,
+            "total": total_count,
+        }
+
+        # If all files are already completed, mark batch as completed
+        if remaining_count == 0:
+            batch_info["status"] = "completed"
+            logger.info(f"Batch {batch_id} is already fully completed, updating status")
+
+        # Save updated batch info
+        await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+        return remaining_files
+
+    async def _resume_incomplete_batches(self) -> None:
+        """Resume processing of incomplete file batches after server restart."""
+        for batch_id, batch_info in self.openai_file_batches.items():
+            if batch_info["status"] == "in_progress":
+                logger.info(f"Analyzing incomplete file batch: {batch_id}")
+
+                remaining_files = await self._analyze_batch_completion_on_resume(batch_id, batch_info)
+
+                # Check if batch is now completed after analysis
+                if batch_info["status"] == "completed":
+                    continue
+
+                if remaining_files:
+                    logger.info(f"Resuming batch {batch_id} with {len(remaining_files)} remaining files")
+                    # Restart the background processing task with only remaining files
+                    task = asyncio.create_task(self._process_file_batch_async(batch_id, batch_info, remaining_files))
+                    self._file_batch_tasks[batch_id] = task
+
     async def initialize_openai_vector_stores(self) -> None:
-        """Load existing OpenAI vector stores into the in-memory cache."""
+        """Load existing OpenAI vector stores and file batches into the in-memory cache."""
         self.openai_vector_stores = await self._load_openai_vector_stores()
+        self.openai_file_batches = await self._load_openai_vector_store_file_batches()
+        self._file_batch_tasks = {}
+        # TODO: Resume only works for single worker deployment. Jobs with multiple workers will need to be handled differently.
+        await self._resume_incomplete_batches()
+        self._last_file_batch_cleanup_time = 0
 
     @abstractmethod
     async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
@@ -457,7 +587,7 @@ class OpenAIVectorStoreMixin(ABC):
                 content = self._chunk_to_vector_store_content(chunk)
 
                 response_data_item = VectorStoreSearchResponse(
-                    file_id=chunk.metadata.get("file_id", ""),
+                    file_id=chunk.metadata.get("document_id", ""),
                     filename=chunk.metadata.get("filename", ""),
                     score=score,
                     attributes=chunk.metadata,
@@ -570,6 +700,14 @@ class OpenAIVectorStoreMixin(ABC):
         if vector_store_id not in self.openai_vector_stores:
             raise VectorStoreNotFoundError(vector_store_id)
 
+        # Check if file is already attached to this vector store
+        store_info = self.openai_vector_stores[vector_store_id]
+        if file_id in store_info["file_ids"]:
+            logger.warning(f"File {file_id} is already attached to vector store {vector_store_id}, skipping")
+            # Return existing file object
+            file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
+            return VectorStoreFileObject(**file_info)
+
         attributes = attributes or {}
         chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
         created_at = int(time.time())
@@ -608,14 +746,16 @@ class OpenAIVectorStoreMixin(ABC):
 
             content = content_from_data_and_mime_type(content_response.body, mime_type)
 
+            chunk_attributes = attributes.copy()
+            chunk_attributes["filename"] = file_response.filename
+
             chunks = make_overlapped_chunks(
                 file_id,
                 content,
                 max_chunk_size_tokens,
                 chunk_overlap_tokens,
-                attributes,
+                chunk_attributes,
             )
-
             if not chunks:
                 vector_store_file_object.status = "failed"
                 vector_store_file_object.last_error = VectorStoreFileLastError(
@@ -828,7 +968,230 @@ class OpenAIVectorStoreMixin(ABC):
         chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch."""
-        raise NotImplementedError("openai_create_vector_store_file_batch is not implemented yet")
+        if vector_store_id not in self.openai_vector_stores:
+            raise VectorStoreNotFoundError(vector_store_id)
+
+        chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
+
+        created_at = int(time.time())
+        batch_id = f"batch_{uuid.uuid4()}"
+        # File batches expire after 7 days
+        expires_at = created_at + (7 * 24 * 60 * 60)
+
+        # Initialize batch file counts - all files start as in_progress
+        file_counts = VectorStoreFileCounts(
+            completed=0,
+            cancelled=0,
+            failed=0,
+            in_progress=len(file_ids),
+            total=len(file_ids),
+        )
+
+        # Create batch object immediately with in_progress status
+        batch_object = VectorStoreFileBatchObject(
+            id=batch_id,
+            created_at=created_at,
+            vector_store_id=vector_store_id,
+            status="in_progress",
+            file_counts=file_counts,
+        )
+
+        batch_info = {
+            **batch_object.model_dump(),
+            "file_ids": file_ids,
+            "attributes": attributes,
+            "chunking_strategy": chunking_strategy.model_dump(),
+            "expires_at": expires_at,
+        }
+        await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+        # Start background processing of files
+        task = asyncio.create_task(self._process_file_batch_async(batch_id, batch_info))
+        self._file_batch_tasks[batch_id] = task
+
+        # Run cleanup if needed (throttled to once every 1 day)
+        current_time = int(time.time())
+        if current_time - self._last_file_batch_cleanup_time >= FILE_BATCH_CLEANUP_INTERVAL_SECONDS:
+            logger.info("Running throttled cleanup of expired file batches")
+            asyncio.create_task(self._cleanup_expired_file_batches())
+            self._last_file_batch_cleanup_time = current_time
+
+        return batch_object
+
+    async def _process_files_with_concurrency(
+        self,
+        file_ids: list[str],
+        vector_store_id: str,
+        attributes: dict[str, Any],
+        chunking_strategy_obj: Any,
+        batch_id: str,
+        batch_info: dict[str, Any],
+    ) -> None:
+        """Process files with controlled concurrency and chunking."""
+        semaphore = asyncio.Semaphore(MAX_CONCURRENT_FILES_PER_BATCH)
+
+        async def process_single_file(file_id: str) -> tuple[str, bool]:
+            """Process a single file with concurrency control."""
+            async with semaphore:
+                try:
+                    vector_store_file_object = await self.openai_attach_file_to_vector_store(
+                        vector_store_id=vector_store_id,
+                        file_id=file_id,
+                        attributes=attributes,
+                        chunking_strategy=chunking_strategy_obj,
+                    )
+                    return file_id, vector_store_file_object.status == "completed"
+                except Exception as e:
+                    logger.error(f"Failed to process file {file_id} in batch {batch_id}: {e}")
+                    return file_id, False
+
+        # Process files in chunks to avoid creating too many tasks at once
+        total_files = len(file_ids)
+        for chunk_start in range(0, total_files, FILE_BATCH_CHUNK_SIZE):
+            chunk_end = min(chunk_start + FILE_BATCH_CHUNK_SIZE, total_files)
+            chunk = file_ids[chunk_start:chunk_end]
+
+            chunk_num = chunk_start // FILE_BATCH_CHUNK_SIZE + 1
+            total_chunks = (total_files + FILE_BATCH_CHUNK_SIZE - 1) // FILE_BATCH_CHUNK_SIZE
+            logger.info(
+                f"Processing chunk {chunk_num} of {total_chunks} ({len(chunk)} files, {chunk_start + 1}-{chunk_end} of {total_files} total files)"
+            )
+
+            async with asyncio.TaskGroup() as tg:
+                chunk_tasks = [tg.create_task(process_single_file(file_id)) for file_id in chunk]
+
+            chunk_results = [task.result() for task in chunk_tasks]
+
+            # Update counts after each chunk for progressive feedback
+            for _, success in chunk_results:
+                self._update_file_counts(batch_info, success=success)
+
+            # Save progress after each chunk
+            await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+    def _update_file_counts(self, batch_info: dict[str, Any], success: bool) -> None:
+        """Update file counts based on processing result."""
+        if success:
+            batch_info["file_counts"]["completed"] += 1
+        else:
+            batch_info["file_counts"]["failed"] += 1
+        batch_info["file_counts"]["in_progress"] -= 1
+
+    def _update_batch_status(self, batch_info: dict[str, Any]) -> None:
+        """Update final batch status based on file processing results."""
+        if batch_info["file_counts"]["failed"] == 0:
+            batch_info["status"] = "completed"
+        elif batch_info["file_counts"]["completed"] == 0:
+            batch_info["status"] = "failed"
+        else:
+            batch_info["status"] = "completed"  # Partial success counts as completed
+
+    async def _process_file_batch_async(
+        self,
+        batch_id: str,
+        batch_info: dict[str, Any],
+        override_file_ids: list[str] | None = None,
+    ) -> None:
+        """Process files in a batch asynchronously in the background."""
+        file_ids = override_file_ids if override_file_ids is not None else batch_info["file_ids"]
+        attributes = batch_info["attributes"]
+        chunking_strategy = batch_info["chunking_strategy"]
+        vector_store_id = batch_info["vector_store_id"]
+        chunking_strategy_adapter: TypeAdapter[VectorStoreChunkingStrategy] = TypeAdapter(VectorStoreChunkingStrategy)
+        chunking_strategy_obj = chunking_strategy_adapter.validate_python(chunking_strategy)
+
+        try:
+            # Process all files with controlled concurrency
+            await self._process_files_with_concurrency(
+                file_ids=file_ids,
+                vector_store_id=vector_store_id,
+                attributes=attributes,
+                chunking_strategy_obj=chunking_strategy_obj,
+                batch_id=batch_id,
+                batch_info=batch_info,
+            )
+
+            # Update final batch status
+            self._update_batch_status(batch_info)
+            await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+            logger.info(f"File batch {batch_id} processing completed with status: {batch_info['status']}")
+
+        except asyncio.CancelledError:
+            logger.info(f"File batch {batch_id} processing was cancelled")
+            # Clean up task reference if it still exists
+            self._file_batch_tasks.pop(batch_id, None)
+            raise  # Re-raise to ensure proper cancellation propagation
+        finally:
+            # Always clean up task reference when processing ends
+            self._file_batch_tasks.pop(batch_id, None)
+
+    def _get_and_validate_batch(self, batch_id: str, vector_store_id: str) -> dict[str, Any]:
+        """Get and validate batch exists and belongs to vector store."""
+        if vector_store_id not in self.openai_vector_stores:
+            raise VectorStoreNotFoundError(vector_store_id)
+
+        if batch_id not in self.openai_file_batches:
+            raise ValueError(f"File batch {batch_id} not found")
+
+        batch_info = self.openai_file_batches[batch_id]
+
+        # Check if batch has expired (read-only check)
+        expires_at = batch_info.get("expires_at")
+        if expires_at:
+            current_time = int(time.time())
+            if current_time > expires_at:
+                raise ValueError(f"File batch {batch_id} has expired after 7 days from creation")
+
+        if batch_info["vector_store_id"] != vector_store_id:
+            raise ValueError(f"File batch {batch_id} does not belong to vector store {vector_store_id}")
+
+        return batch_info
+
+    def _paginate_objects(
+        self,
+        objects: list[Any],
+        limit: int | None = 20,
+        after: str | None = None,
+        before: str | None = None,
+    ) -> tuple[list[Any], bool, str | None, str | None]:
+        """Apply pagination to a list of objects with id fields."""
+        limit = min(limit or 20, 100)  # Cap at 100 as per OpenAI
+
+        # Find start index
+        start_idx = 0
+        if after:
+            for i, obj in enumerate(objects):
+                if obj.id == after:
+                    start_idx = i + 1
+                    break
+
+        # Find end index
+        end_idx = start_idx + limit
+        if before:
+            for i, obj in enumerate(objects[start_idx:], start_idx):
+                if obj.id == before:
+                    end_idx = i
+                    break
+
+        # Apply pagination
+        paginated_objects = objects[start_idx:end_idx]
+
+        # Determine pagination info
+        has_more = end_idx < len(objects)
+        first_id = paginated_objects[0].id if paginated_objects else None
+        last_id = paginated_objects[-1].id if paginated_objects else None
+
+        return paginated_objects, has_more, first_id, last_id
+
+    async def openai_retrieve_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ) -> VectorStoreFileBatchObject:
+        """Retrieve a vector store file batch."""
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+        return VectorStoreFileBatchObject(**batch_info)
 
     async def openai_list_files_in_vector_store_file_batch(
         self,
@@ -841,15 +1204,39 @@ class OpenAIVectorStoreMixin(ABC):
         order: str | None = "desc",
     ) -> VectorStoreFilesListInBatchResponse:
         """Returns a list of vector store files in a batch."""
-        raise NotImplementedError("openai_list_files_in_vector_store_file_batch is not implemented yet")
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+        batch_file_ids = batch_info["file_ids"]
 
-    async def openai_retrieve_vector_store_file_batch(
-        self,
-        batch_id: str,
-        vector_store_id: str,
-    ) -> VectorStoreFileBatchObject:
-        """Retrieve a vector store file batch."""
-        raise NotImplementedError("openai_retrieve_vector_store_file_batch is not implemented yet")
+        # Load file objects for files in this batch
+        batch_file_objects = []
+
+        for file_id in batch_file_ids:
+            try:
+                file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
+                file_object = VectorStoreFileObject(**file_info)
+
+                # Apply status filter if provided
+                if filter and file_object.status != filter:
+                    continue
+
+                batch_file_objects.append(file_object)
+            except Exception as e:
+                logger.warning(f"Could not load file {file_id} from batch {batch_id}: {e}")
+                continue
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        batch_file_objects.sort(key=lambda x: x.created_at, reverse=reverse_order)
+
+        # Apply pagination using helper
+        paginated_files, has_more, first_id, last_id = self._paginate_objects(batch_file_objects, limit, after, before)
+
+        return VectorStoreFilesListInBatchResponse(
+            data=paginated_files,
+            first_id=first_id,
+            last_id=last_id,
+            has_more=has_more,
+        )
 
     async def openai_cancel_vector_store_file_batch(
         self,
@@ -857,4 +1244,24 @@ class OpenAIVectorStoreMixin(ABC):
         vector_store_id: str,
     ) -> VectorStoreFileBatchObject:
         """Cancel a vector store file batch."""
-        raise NotImplementedError("openai_cancel_vector_store_file_batch is not implemented yet")
+        batch_info = self._get_and_validate_batch(batch_id, vector_store_id)
+
+        if batch_info["status"] not in ["in_progress"]:
+            raise ValueError(f"Cannot cancel batch {batch_id} with status {batch_info['status']}")
+
+        # Cancel the actual processing task if it exists
+        if batch_id in self._file_batch_tasks:
+            task = self._file_batch_tasks[batch_id]
+            if not task.done():
+                task.cancel()
+                logger.info(f"Cancelled processing task for file batch: {batch_id}")
+            # Remove from task tracking
+            del self._file_batch_tasks[batch_id]
+
+        batch_info["status"] = "cancelled"
+
+        await self._save_openai_vector_store_file_batch(batch_id, batch_info)
+
+        updated_batch = VectorStoreFileBatchObject(**batch_info)
+
+        return updated_batch

tests/integration/common/recordings/02c93bb3c314427bae2b7a7a6f054792b9f22d2cb4522eab802810be8672d3dc.json

@@ -14,23 +14,22 @@
       "__data__": {
         "models": [
           {
-            "model": "llama3.2:3b",
-            "name": "llama3.2:3b",
-            "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
-            "expires_at": "2025-10-04T12:20:09.202126-07:00",
-            "size": 3367856128,
-            "size_vram": 3367856128,
+            "model": "all-minilm:l6-v2",
+            "name": "all-minilm:l6-v2",
+            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
+            "expires_at": "2025-10-06T16:41:45.231544-07:00",
+            "size": 590204928,
+            "size_vram": 590204928,
             "details": {
               "parent_model": "",
               "format": "gguf",
-              "family": "llama",
+              "family": "bert",
               "families": [
-                "llama"
+                "bert"
               ],
-              "parameter_size": "3.2B",
-              "quantization_level": "Q4_K_M"
-            },
-            "context_length": 4096
+              "parameter_size": "23M",
+              "quantization_level": "F16"
+            }
           }
         ]
       }

tests/integration/common/recordings/8b6244e7be7e4d03874b13df9cb5af4cccef848e79440e4298528185efe45ed4.json

@@ -0,0 +1,807 @@
+{
+  "test_id": null,
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "This is the content of test file 2"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
+          {
+            "embedding": [
+              0.036525182,
+              -0.0072787926,
+              -0.15320797,
+              -0.028591355,
+              0.028115708,
+              -0.0033384967,
+              0.021494914,
+              -0.023697548,
+              -0.059428893,
+              -0.04412936,
+              -0.014445912,
+              0.06520278,
+              0.013848802,
+              -0.029918822,
+              -0.022794332,
+              -0.012578859,
+              0.060358867,
+              -0.031223036,
+              -0.012306958,
+              -0.028883344,
+              -0.014677056,
+              -0.024171423,
+              -0.047258105,
+              -0.019668069,
+              0.10096786,
+              0.042677063,
+              -0.012945782,
+              0.05772575,
+              -0.09481949,
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+}

tests/integration/common/recordings/9ca52f6470a742d637b9da12ff00b4ab85adbbe4903193947ce19260447e8619.json

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+{
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+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "This is batch test file 1"
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+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:latest"
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tests/integration/common/recordings/aa45f61f2d277765422722394dbeb0f2d1dbd7e7f55f4783caf3e7f768b007e9.json

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tests/integration/common/recordings/d48ba62fab4e243d368ec42e5497b932ab697ffaa1cc79a7caf46b404677fb31.json

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tests/integration/common/recordings/e297006956fc1fb184d0bbaa79f7beaa67a3824a6cd5d7a0e21c8e587ea03980.json

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+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:latest",
+      "input": [
+        "This is the content of test file 1"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:latest"
+  },
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tests/integration/common/recordings/f4b0cf7f241feb7ff68414545a42d5759b33d997f7b1305fc20ae7f7c50faa26.json

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tests/integration/common/recordings/f7a80fae588892aa7031ac972c12030f2bd2ee609d672e9f44275c601800b144.json

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tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-44869b1b.json

@@ -0,0 +1,34 @@
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tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-79be7c70.json

@@ -0,0 +1,25 @@
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tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-7becc84f.json

@@ -0,0 +1,70 @@
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+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-e3b0c442.json

@@ -0,0 +1,15 @@
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+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-e8733dec.json

@@ -0,0 +1,25 @@
+{
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+}

tests/integration/common/recordings/models-bd3df37825f32706c88677a327960bfa47dcf93f2ea6ed882f1186cf4fdda5bb-d5d684a3.json

@@ -0,0 +1,528 @@
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tests/integration/vector_io/recordings/943a7db9bab0934c95417e8befe710b364496c1ee21a75258205830e1df7221b.json

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tests/integration/vector_io/recordings/995712d2e4441339fdd8ca21d87747c9983b0d40cc83fcfd90c5e733ecfb5a35.json

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+              0.009997155,
+              -0.053269707,
+              0.030278698,
+              0.034753144,
+              -0.0069992156,
+              -0.0018294669,
+              0.052869115,
+              -0.047554925,
+              -0.07009094,
+              -0.12028551,
+              -0.016411684,
+              -0.0558196,
+              -0.026485136,
+              -0.07406597,
+              0.052336086,
+              -0.07966716,
+              -0.009600498,
+              -0.016012779,
+              -0.04670444,
+              0.0040856744,
+              0.13087922,
+              -1.9130171e-33,
+              0.04951988,
+              0.04144521,
+              -0.030660233,
+              0.02966906,
+              -0.0019053655,
+              0.038034633,
+              0.053598672,
+              -0.03873592,
+              -0.050682254,
+              0.0163216,
+              -0.018117629,
+              0.02705123,
+              0.014957701,
+              -0.029251544,
+              0.010732444,
+              0.01150037,
+              0.08527361,
+              0.000666767,
+              -0.09031944,
+              0.007236525,
+              -0.0394124,
+              0.032647807,
+              0.029387591,
+              0.0696317,
+              -0.028400488,
+              -0.019728381,
+              -0.08580391,
+              0.050916594,
+              0.07555233,
+              0.0013333871,
+              0.036405865,
+              0.03485496,
+              -0.035891958,
+              -0.03518406,
+              0.08422707,
+              -0.07100648,
+              0.066512264,
+              0.0566844,
+              0.005254722,
+              0.026210023,
+              0.06271422,
+              0.07715752,
+              -0.042685844,
+              0.029498853,
+              0.048694577,
+              0.06829996,
+              0.05471948,
+              -0.014717811,
+              -0.0084376065,
+              -0.007800526,
+              -0.033968475,
+              -0.035792083,
+              -0.01680357,
+              0.056615632,
+              -0.008940466,
+              -0.044396702,
+              0.033141203,
+              -0.020710811,
+              -0.052891865,
+              -0.012946567,
+              0.013425288,
+              0.045469046,
+              0.02655372,
+              -7.159544e-06,
+              0.033383444,
+              0.012771919,
+              0.0050781234,
+              0.05739414,
+              -0.05292731,
+              -0.009027621,
+              0.019719183,
+              -0.0046205786,
+              -0.012921344,
+              0.021115582,
+              0.063510135,
+              0.006540324,
+              0.008657973,
+              -0.044172782,
+              -0.0010352373,
+              0.025917202,
+              0.07357742,
+              0.012915724,
+              -0.010159995,
+              0.05862044,
+              0.0032137444,
+              0.08368076,
+              -0.06552963,
+              0.06294139,
+              0.004963379,
+              0.08497223,
+              -0.030302247,
+              0.028541481,
+              0.103464715,
+              0.03432187,
+              0.039947473,
+              -1.757192e-08,
+              -0.020163277,
+              -0.12507844,
+              0.015846072,
+              0.038265407,
+              -0.0031526515,
+              0.01804952,
+              -0.0817553,
+              0.030486222,
+              -0.02073271,
+              -0.069118954,
+              0.0252006,
+              -0.016496325,
+              -0.018695008,
+              -0.0063493066,
+              -0.08448383,
+              -0.05474651,
+              0.008191211,
+              -0.04699509,
+              0.03820692,
+              0.019186925,
+              -0.006977571,
+              -0.0002934883,
+              0.030278133,
+              -0.009153849,
+              0.030300315,
+              0.04737054,
+              0.06026962,
+              0.09765302,
+              -0.05529498,
+              -0.09553832,
+              0.06008278,
+              -0.025960611,
+              0.034287665,
+              -0.012333093,
+              -0.07106284,
+              0.05141244,
+              0.14179605,
+              0.04709826,
+              -0.049292527,
+              0.014455253,
+              -0.047851674,
+              0.011403938,
+              0.014072481,
+              0.010494679,
+              -0.0009859774,
+              -0.06089218,
+              -0.07293921,
+              -0.07961594,
+              -0.03404924,
+              -0.10086713,
+              -0.031331882,
+              0.0042822976,
+              -0.0045380252,
+              0.09583955,
+              -0.044172354,
+              -0.034359995,
+              0.023726532,
+              0.02167657,
+              -0.06509328,
+              0.09268318,
+              0.055370033,
+              0.003980954,
+              -0.0053826002,
+              -0.07774321
+            ],
+            "index": 0,
+            "object": "embedding"
+          }
+        ],
+        "model": "all-minilm:l6-v2",
+        "object": "list",
+        "usage": {
+          "prompt_tokens": 10,
+          "total_tokens": 10
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/vector_io/recordings/ffea5475c2625b87e302ec419cc536f34da3ce7e80eba86bec16d231aa347d00.json

@@ -0,0 +1,20 @@
+{
+  "test_id": "tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_file_batch_create_and_retrieve[client_with_models-ollama/llama3.2:3b-instruct-fp16-None-ollama/all-minilm:l6-v2-None-384]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/ps",
+    "headers": {},
+    "body": {},
+    "endpoint": "/api/ps",
+    "model": ""
+  },
+  "response": {
+    "body": {
+      "__type__": "ollama._types.ProcessResponse",
+      "__data__": {
+        "models": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/vector_io/test_openai_vector_stores.py

@@ -902,3 +902,290 @@ def test_openai_vector_store_search_modes(llama_stack_client, client_with_models
         search_mode=search_mode,
     )
     assert search_response is not None
+
+
+def test_openai_vector_store_file_batch_create_and_retrieve(compat_client_with_empty_stores, client_with_models):
+    """Test creating and retrieving a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_test_store")
+
+    # Create multiple files
+    file_ids = []
+    for i in range(2):
+        with BytesIO(f"This is batch test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"batch_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    assert batch is not None
+    assert batch.object == "vector_store.file_batch"
+    assert batch.vector_store_id == vector_store.id
+    assert batch.status in ["in_progress", "completed"]
+    assert batch.file_counts.total == len(file_ids)
+    assert hasattr(batch, "id")
+    assert hasattr(batch, "created_at")
+
+    # Wait for batch processing to complete
+    max_retries = 60  # 60 seconds max wait (increased for file processing delays)
+    retries = 0
+    retrieved_batch = None
+    while retries < max_retries:
+        retrieved_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        if retrieved_batch.status in ["completed", "failed"]:
+            break
+        time.sleep(1)
+        retries += 1
+
+    assert retrieved_batch is not None
+    assert retrieved_batch.id == batch.id
+    assert retrieved_batch.vector_store_id == vector_store.id
+    assert retrieved_batch.object == "vector_store.file_batch"
+    assert retrieved_batch.file_counts.total == len(file_ids)
+    assert retrieved_batch.status == "completed"  # Should be completed after processing
+
+
+def test_openai_vector_store_file_batch_list_files(compat_client_with_empty_stores, client_with_models):
+    """Test listing files in a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_list_test_store")
+
+    # Create multiple files
+    file_ids = []
+    for i in range(2):
+        with BytesIO(f"This is batch list test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"batch_list_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    # Wait for batch processing to complete
+    max_retries = 60  # 60 seconds max wait (increased for file processing delays)
+    retries = 0
+    while retries < max_retries:
+        retrieved_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        if retrieved_batch.status in ["completed", "failed"]:
+            break
+        time.sleep(1)
+        retries += 1
+
+    # List all files in the batch
+    files_response = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+    )
+
+    assert files_response is not None
+    assert files_response.object == "list"
+    assert hasattr(files_response, "data")
+    assert len(files_response.data) == len(file_ids)
+
+    # Verify all files are in the response
+    response_file_ids = {file.id for file in files_response.data}
+    assert response_file_ids == set(file_ids)
+
+    # Test pagination with limit
+    limited_response = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=3,
+    )
+
+    assert len(limited_response.data) == 2
+    assert limited_response.has_more is False
+
+    # Test pagination with after cursor
+    first_page = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=2,
+    )
+
+    second_page = compat_client.vector_stores.file_batches.list_files(
+        vector_store_id=vector_store.id,
+        batch_id=batch.id,
+        limit=2,
+        after=first_page.data[-1].id,
+    )
+
+    assert len(first_page.data) == 2
+    assert len(second_page.data) <= 3  # Should be <= remaining files
+    # Ensure no overlap between pages
+    first_page_ids = {file.id for file in first_page.data}
+    second_page_ids = {file.id for file in second_page.data}
+    assert first_page_ids.isdisjoint(second_page_ids)
+
+
+def test_openai_vector_store_file_batch_cancel(compat_client_with_empty_stores, client_with_models):
+    """Test cancelling a vector store file batch."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_cancel_test_store")
+
+    # Create a batch to test cancellation
+    file_ids = []
+    for i in range(2):  # Batch size that allows time for cancellation
+        with BytesIO(f"This is batch cancel test file {i} with substantial content".encode()) as file_buffer:
+            file_buffer.name = f"batch_cancel_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    try:
+        # Cancel the batch immediately after creation
+        cancelled_batch = compat_client.vector_stores.file_batches.cancel(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+
+        assert cancelled_batch is not None
+        assert cancelled_batch.id == batch.id
+        assert cancelled_batch.vector_store_id == vector_store.id
+        assert cancelled_batch.status == "cancelled"
+        assert cancelled_batch.object == "vector_store.file_batch"
+    except Exception:
+        # If cancellation fails (e.g., batch completed too quickly),
+        # verify the batch reached completion instead
+        final_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        assert final_batch.status in ["completed", "cancelled"]
+
+
+def test_openai_vector_store_file_batch_retrieve_contents(compat_client_with_empty_stores, client_with_models):
+    """Test retrieving file contents after file batch processing."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_contents_test_store")
+
+    # Create multiple files with known content
+    file_data = [
+        ("test_file_1.txt", b"This is the content of test file 1"),
+        ("test_file_2.txt", b"This is the content of test file 2"),
+    ]
+
+    file_ids = []
+    for filename, content in file_data:
+        with BytesIO(content) as file_buffer:
+            file_buffer.name = filename
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    # Create a file batch
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    # Wait for batch processing to complete
+    max_retries = 60  # 60 seconds max wait (increased for file processing delays)
+    retries = 0
+    while retries < max_retries:
+        retrieved_batch = compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id=batch.id,
+        )
+        if retrieved_batch.status in ["completed", "failed"]:
+            break
+        time.sleep(1)
+        retries += 1
+
+    assert retrieved_batch.status == "completed"
+
+    # Retrieve file contents for each file in the batch
+    for i, file_id in enumerate(file_ids):
+        file_contents = compat_client.vector_stores.files.content(
+            vector_store_id=vector_store.id,
+            file_id=file_id,
+        )
+
+        assert file_contents is not None
+        assert file_contents.filename == file_data[i][0]
+        assert len(file_contents.content) > 0
+
+        # Verify the content matches what we uploaded
+        content_text = (
+            file_contents.content[0].text
+            if hasattr(file_contents.content[0], "text")
+            else file_contents.content[0]["text"]
+        )
+        assert file_data[i][1].decode("utf-8") in content_text
+
+
+def test_openai_vector_store_file_batch_error_handling(compat_client_with_empty_stores, client_with_models):
+    """Test error handling for file batch operations."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="batch_error_test_store")
+
+    # Test with invalid file IDs (should handle gracefully)
+    file_ids = ["invalid_file_id_1", "invalid_file_id_2"]
+
+    batch = compat_client.vector_stores.file_batches.create(
+        vector_store_id=vector_store.id,
+        file_ids=file_ids,
+    )
+
+    assert batch is not None
+    assert batch.file_counts.total == len(file_ids)
+    # Invalid files should be marked as failed
+    assert batch.file_counts.failed >= 0  # Implementation may vary
+
+    # Determine expected errors based on client type
+    if isinstance(compat_client, LlamaStackAsLibraryClient):
+        errors = ValueError
+    else:
+        errors = (BadRequestError, OpenAIBadRequestError)
+
+    # Test retrieving non-existent batch
+    with pytest.raises(errors):  # Should raise an error for non-existent batch
+        compat_client.vector_stores.file_batches.retrieve(
+            vector_store_id=vector_store.id,
+            batch_id="non_existent_batch_id",
+        )
+
+    # Test operations on non-existent vector store
+    with pytest.raises(errors):  # Should raise an error for non-existent vector store
+        compat_client.vector_stores.file_batches.create(
+            vector_store_id="non_existent_vector_store",
+            file_ids=["any_file_id"],
+        )

tests/unit/providers/agents/meta_reference/test_response_conversion_utils.py

@@ -8,6 +8,7 @@
 import pytest
 
 from llama_stack.apis.agents.openai_responses import (
+    OpenAIResponseAnnotationFileCitation,
     OpenAIResponseInputFunctionToolCallOutput,
     OpenAIResponseInputMessageContentImage,
     OpenAIResponseInputMessageContentText,
@@ -35,6 +36,7 @@ from llama_stack.apis.inference import (
     OpenAIUserMessageParam,
 )
 from llama_stack.providers.inline.agents.meta_reference.responses.utils import (
+    _extract_citations_from_text,
     convert_chat_choice_to_response_message,
     convert_response_content_to_chat_content,
     convert_response_input_to_chat_messages,
@@ -340,3 +342,26 @@ class TestIsFunctionToolCall:
 
         result = is_function_tool_call(tool_call, tools)
         assert result is False
+
+
+class TestExtractCitationsFromText:
+    def test_extract_citations_and_annotations(self):
+        text = "Start [not-a-file]. New source <|file-abc123|>. "
+        text += "Other source <|file-def456|>? Repeat source <|file-abc123|>! No citation."
+        file_mapping = {"file-abc123": "doc1.pdf", "file-def456": "doc2.txt"}
+
+        annotations, cleaned_text = _extract_citations_from_text(text, file_mapping)
+
+        expected_annotations = [
+            OpenAIResponseAnnotationFileCitation(file_id="file-abc123", filename="doc1.pdf", index=30),
+            OpenAIResponseAnnotationFileCitation(file_id="file-def456", filename="doc2.txt", index=44),
+            OpenAIResponseAnnotationFileCitation(file_id="file-abc123", filename="doc1.pdf", index=59),
+        ]
+        expected_clean_text = "Start [not-a-file]. New source. Other source? Repeat source! No citation."
+
+        assert cleaned_text == expected_clean_text
+        assert annotations == expected_annotations
+        # OpenAI cites at the end of the sentence
+        assert cleaned_text[expected_annotations[0].index] == "."
+        assert cleaned_text[expected_annotations[1].index] == "?"
+        assert cleaned_text[expected_annotations[2].index] == "!"