add Llama Stack quickstart guide and notebook demo

docs/en/solutions/How_to_Create_an_AI_Agent_with_LlamaStack.md

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+---
+products:
+   - Alauda AI
+kind:
+   - Solution
+ProductsVersion:
+   - 4.x
+---
+
+# How To Create AI Agent with Llama Stack
+
+## Overview
+
+Llama Stack is a framework for building and running AI agents with tools. It provides a server-based architecture that enables developers to create agents that can interact with users, access external tools, and perform complex reasoning tasks. This guide provides a quickstart example for creating an AI Agent using Llama Stack.
+
+## Prerequisites
+
+- Llama Stack Server installed and running (see notebook for installation and startup instructions)
+  - For deploying Llama Stack Server on Kubernetes, refer to the [Kubernetes Deployment Guide](https://llamastack.github.io/docs/deploying/kubernetes_deployment)
+- Access to a Notebook environment (e.g., Jupyter Notebook, JupyterLab, or similar)
+- Python environment with `llama-stack-client` and required dependencies installed
+- API key for the LLM provider (e.g., DeepSeek API key)
+
+## Quickstart
+
+A simple example of creating an AI Agent with Llama Stack is available here: [llama_stack_quickstart.ipynb](/llama-stack/llama_stack_quickstart.ipynb). The configuration file [llama_stack_config.yaml](/llama-stack/llama_stack_config.yaml) is also required. Download both files and upload them to a Notebook environment to run.
+
+The notebook demonstrates:
+- Llama Stack Server installation and configuration
+- Server startup and connection setup
+- Tool definition using the `@client_tool` decorator (weather query tool example)
+- Client connection to Llama Stack Server
+- Model selection and Agent creation with tools and instructions
+- Agent execution with session management and streaming responses
+- Result handling and display
+
+## Additional Resources
+
+For more resources on developing AI Agents with Llama Stack, see:
+
+- [Llama Stack Documentation](https://llamastack.github.io/docs) - The official Llama Stack documentation covering all usage-related topics, API providers, and core concepts.
+- [Llama Stack Core Concepts](https://llamastack.github.io/docs/concepts) - Deep dive into Llama Stack architecture, API stability, and resource management.
+- [Llama Stack GitHub Repository](https://github.com/llamastack/llama-stack) - Source code, example applications, distribution configurations, and how to add new API providers.
+- [Llama Stack Example Apps](https://github.com/llamastack/llama-stack-apps/) - Official examples demonstrating how to use Llama Stack in various scenarios.

docs/public/llama-stack/llama_stack_config.yaml

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+version: "2"
+image_name: llama-stack-demo
+apis:
+  - inference
+  - agents
+  - safety
+  - tool_runtime
+  - vector_io
+  - files
+
+providers:
+  inference:
+    - provider_id: openai
+      provider_type: remote::openai
+      config:
+        api_key: ${env.API_KEY}
+        base_url: https://api.deepseek.com/v1
+  agents:
+    - provider_id: meta-reference
+      provider_type: inline::meta-reference
+      config:
+        persistence:
+          agent_state:
+            backend: kv_default
+            namespace: agents
+          responses:
+            backend: sql_default
+            table_name: responses
+  safety:
+    - provider_id: llama-guard
+      provider_type: inline::llama-guard
+      config:
+        excluded_categories: []
+  tool_runtime: []
+  vector_io:
+    - provider_id: sqlite-vec
+      provider_type: inline::sqlite-vec
+      config:
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama-stack-demo}/sqlite_vec.db
+        persistence:
+          backend: kv_default
+          namespace: vector_io::sqlite_vec
+  files:
+    - provider_id: localfs
+      provider_type: inline::localfs
+      config:
+        storage_dir: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama-stack-demo}/files
+        metadata_store:
+          backend: sql_default
+          table_name: files_metadata
+
+metadata_store:
+  type: sqlite
+
+models:
+  - metadata: {}
+    model_id: deepseek/deepseek-chat
+    provider_id: openai
+    provider_model_id: deepseek-chat
+    model_type: llm