Merge branch 'main' into nvidia-e2e-notebook

docs/source/building_applications/tools.md

@@ -43,27 +43,6 @@ The tool requires an API key which can be provided either in the configuration o
 
 > **NOTE:** When using Tavily Search and Bing Search, the inference output will still display "Brave Search." This is because Llama models have been trained with Brave Search as a built-in tool. Tavily and bing is just being used in lieu of Brave search.
 
-#### Code Interpreter
-
-The Code Interpreter allows execution of Python code within a controlled environment.
-
-```python
-# Register Code Interpreter tool group
-client.toolgroups.register(
-    toolgroup_id="builtin::code_interpreter", provider_id="code_interpreter"
-)
-```
-
-Features:
-- Secure execution environment using `bwrap` sandboxing
-- Matplotlib support for generating plots
-- Disabled dangerous system operations
-- Configurable execution timeouts
-
-> ⚠️ Important: The code interpreter tool can operate in a controlled environment locally or on Podman containers. To ensure proper functionality in containerized environments:
-> - The container requires privileged access (e.g., --privileged).
-> - Users without sufficient permissions may encounter permission errors. (`bwrap: Can't mount devpts on /newroot/dev/pts: Permission denied`)
-> - 🔒 Security Warning: Privileged mode grants elevated access and bypasses security restrictions. Use only in local, isolated, or controlled environments.
 
 #### WolframAlpha
 
@@ -102,7 +81,7 @@ Features:
 - Context retrieval with token limits
 
 
-> **Note:** By default, llama stack run.yaml defines toolgroups for web search, code interpreter and rag, that are provided by tavily-search, code-interpreter and rag providers.
+> **Note:** By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers.
 
 ## Model Context Protocol (MCP) Tools
 

docs/source/distributions/remote_hosted_distro/watsonx.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-watsonx` distribution consists of the following pro
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss` |
 
 

docs/source/distributions/self_hosted_distro/bedrock.md

@@ -19,7 +19,7 @@ The `llamastack/distribution-bedrock` distribution consists of the following pro
 | safety | `remote::bedrock` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/cerebras.md

@@ -12,7 +12,7 @@ The `llamastack/distribution-cerebras` distribution consists of the following pr
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/fireworks.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-fireworks` distribution consists of the following p
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/groq.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-groq` distribution consists of the following provid
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
 | vector_io | `inline::faiss` |
 
 

docs/source/distributions/self_hosted_distro/meta-reference-gpu.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/ollama.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/passthrough.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-passthrough` distribution consists of the following
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/remote-vllm.md

@@ -21,7 +21,7 @@ The `llamastack/distribution-remote-vllm` distribution consists of the following
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -19,7 +19,7 @@ The `llamastack/distribution-sambanova` distribution consists of the following p
 | inference | `remote::sambanova` |
 | safety | `inline::llama-guard` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/tgi.md

@@ -23,7 +23,7 @@ The `llamastack/distribution-tgi` distribution consists of the following provide
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/together.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-together` distribution consists of the following pr
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/providers/external.md

@@ -53,7 +53,9 @@ Here's a list of known external providers that you can use with Llama Stack:
 | Name | Description | API | Type | Repository |
 |------|-------------|-----|------|------------|
 | KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
+| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
 | RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) |
+| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) |
 
 ### Remote Provider Specification
 

docs/source/providers/vector_io/milvus.md

@@ -0,0 +1,107 @@
+---
+orphan: true
+---
+# Milvus
+
+[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
+allows you to store and query vectors directly within a Milvus database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use Milvus in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Milvus.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Milvus using pymilvus:
+
+```bash
+pip install pymilvus
+```
+
+## Configuration
+
+In Llama Stack, Milvus can be configured in two ways:
+- **Inline (Local) Configuration** - Uses Milvus-Lite for local storage
+- **Remote Configuration** - Connects to a remote Milvus server
+
+### Inline (Local) Configuration
+
+The simplest method is local configuration, which requires setting `db_path`, a path for locally storing Milvus-Lite files:
+
+```yaml
+vector_io:
+  - provider_id: milvus
+    provider_type: inline::milvus
+    config:
+      db_path: ~/.llama/distributions/together/milvus_store.db
+```
+
+### Remote Configuration
+
+Remote configuration is suitable for larger data storage requirements:
+
+#### Standard Remote Connection
+
+```yaml
+vector_io:
+  - provider_id: milvus
+    provider_type: remote::milvus
+    config:
+      uri: "http://<host>:<port>"
+      token: "<user>:<password>"
+```
+
+#### TLS-Enabled Remote Connection (One-way TLS)
+
+For connections to Milvus instances with one-way TLS enabled:
+
+```yaml
+vector_io:
+  - provider_id: milvus
+    provider_type: remote::milvus
+    config:
+      uri: "https://<host>:<port>"
+      token: "<user>:<password>"
+      secure: True
+      server_pem_path: "/path/to/server.pem"
+```
+
+#### Mutual TLS (mTLS) Remote Connection
+
+For connections to Milvus instances with mutual TLS (mTLS) enabled:
+
+```yaml
+vector_io:
+  - provider_id: milvus
+    provider_type: remote::milvus
+    config:
+      uri: "https://<host>:<port>"
+      token: "<user>:<password>"
+      secure: True
+      ca_pem_path: "/path/to/ca.pem"
+      client_pem_path: "/path/to/client.pem"
+      client_key_path: "/path/to/client.key"
+```
+
+#### Key Parameters for TLS Configuration
+
+- **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`.
+- **`server_pem_path`**: Path to the **server certificate** for verifying the server’s identity (used in one-way TLS).
+- **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS).
+- **`client_pem_path`**: Path to the **client certificate** file (required for mTLS).
+- **`client_key_path`**: Path to the **client private key** file (required for mTLS).
+
+## Documentation
+See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
+
+For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).

docs/source/providers/vector_io/mivus.md

@@ -1,31 +0,0 @@
----
-orphan: true
----
-# Milvus
-
-[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
-allows you to store and query vectors directly within a Milvus database.
-That means you're not limited to storing vectors in memory or in a separate service.
-
-## Features
-
-- Easy to use
-- Fully integrated with Llama Stack
-
-## Usage
-
-To use Milvus in your Llama Stack project, follow these steps:
-
-1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Milvus.
-3. Start storing and querying vectors.
-
-## Installation
-
-You can install Milvus using pymilvus:
-
-```bash
-pip install pymilvus
-```
-## Documentation
-See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.