Improve documentation structure and content

docs/components/chat.rst

@@ -26,7 +26,7 @@ with a ``Symfony\AI\Agent\AgentInterface`` and a ``Symfony\AI\Chat\MessageStoreI
 
     $platform = PlatformFactory::create($apiKey);
 
-    $agent = new Agent($platform, 'gpt-40-mini');
+    $agent = new Agent($platform, 'gpt-4o-mini');
     $chat = new Chat($agent, new InMemoryStore());
 
     $chat->submit(Message::ofUser('Hello'));

docs/components/platform.rst

@@ -283,10 +283,10 @@ Server Tools
 
 Some platforms provide built-in server-side tools for enhanced capabilities without custom implementations:
 
-1. **[Gemini](platform/gemini-server-tools.rst)** - URL Context, Google Search, Code Execution
-1. **[VertexAI](platform/vertexai-server-tools.rst)** - URL Context, Google Search, Code Execution
+* :doc:`platform/gemini-server-tools` - URL Context, Google Search, Code Execution
+* :doc:`platform/vertexai-server-tools` - URL Context, Google Search, Code Execution
 
-For complete Vertex AI setup and usage guide, see :doc:`vertexai`.
+For complete Vertex AI setup and usage guide, see :doc:`platform/vertexai`.
 
 Parallel Platform Calls
 -----------------------

docs/components/store.rst

@@ -124,7 +124,7 @@ This leads to a store implementing two methods::
         }
     }
 
-.. _`Retrieval Augmented Generation`: https://de.wikipedia.org/wiki/Retrieval-Augmented_Generation
+.. _`Retrieval Augmented Generation`: https://en.wikipedia.org/wiki/Retrieval-augmented_generation
 .. _`Similarity Search with Cloudflare (RAG)`: https://github.com/symfony/ai/blob/main/examples/rag/cloudflare.php
 .. _`Similarity Search with MariaDB (RAG)`: https://github.com/symfony/ai/blob/main/examples/rag/mariadb-gemini.php
 .. _`Similarity Search with Meilisearch (RAG)`: https://github.com/symfony/ai/blob/main/examples/rag/meilisearch.php

docs/cookbook/chatbot-with-memory.rst

@@ -0,0 +1,167 @@
+Building a Chatbot with Memory
+==============================
+
+This guide demonstrates how to build a chatbot that remembers context across conversations
+using Symfony AI's memory management features.
+
+Overview
+--------
+
+Memory providers allow your agents to access conversation history and user-specific information,
+enabling more personalized and context-aware responses. In this example, we'll build a personal
+trainer chatbot that remembers facts about the user.
+
+Prerequisites
+-------------
+
+* Symfony AI Agent component installed
+* OpenAI API key (or any other supported platform)
+* Basic understanding of Symfony AI concepts
+
+Implementation
+--------------
+
+The complete implementation consists of three main parts:
+
+1. Creating a memory provider with user facts
+2. Configuring the agent with memory support
+3. Processing user messages with context awareness
+
+Complete Example
+~~~~~~~~~~~~~~~~
+
+.. literalinclude:: ../../examples/memory/static.php
+   :language: php
+   :linenos:
+
+Key Components
+--------------
+
+Memory Provider
+~~~~~~~~~~~~~~~
+
+The :class:`Symfony\\AI\\Agent\\Memory\\StaticMemoryProvider` stores fixed information that should be consistently available
+to the agent::
+
+    use Symfony\AI\Agent\Memory\StaticMemoryProvider;
+
+    $personalFacts = new StaticMemoryProvider(
+        'My name is Wilhelm Tell',
+        'I wish to be a swiss national hero',
+        'I am struggling with hitting apples but want to be professional with the bow and arrow',
+    );
+
+This information is automatically injected into the system prompt, providing the agent with
+context about the user without cluttering the conversation messages.
+
+Memory Input Processor
+~~~~~~~~~~~~~~~~~~~~~~
+
+The :class:`Symfony\\AI\\Agent\\Memory\\MemoryInputProcessor` handles the injection of memory content into the agent's context::
+
+    use Symfony\AI\Agent\Memory\MemoryInputProcessor;
+
+    $memoryProcessor = new MemoryInputProcessor($personalFacts);
+
+This processor works alongside other input processors like :class:`Symfony\\AI\\Agent\\InputProcessor\\SystemPromptInputProcessor`
+to build a complete context for the agent.
+
+Agent Configuration
+~~~~~~~~~~~~~~~~~~~
+
+The agent is configured with both system prompt and memory processors::
+
+    use Symfony\AI\Agent\Agent;
+
+    $agent = new Agent(
+        $platform,
+        'gpt-4o-mini',
+        [$systemPromptProcessor, $memoryProcessor]
+    );
+
+Processors are applied in order, allowing you to build up the context progressively.
+
+How It Works
+------------
+
+1. **Memory Loading**: When a user message is submitted, the ``MemoryInputProcessor`` loads
+   relevant facts from the memory provider.
+
+2. **Context Injection**: The memory content is prepended to the system prompt, giving the
+   agent access to user-specific information.
+
+3. **Response Generation**: The agent generates a personalized response based on both the
+   current message and the remembered context.
+
+4. **Conversation Flow**: The memory persists across multiple calls, enabling continuous
+   personalized interactions.
+
+Use Dynamic Memory with Embeddings
+----------------------------------
+
+For more sophisticated scenarios, use :class:`Symfony\\AI\\Agent\\Memory\\EmbeddingProvider` to retrieve relevant context
+based on semantic similarity::
+
+    use Symfony\AI\Agent\Memory\EmbeddingProvider;
+
+    $embeddingsMemory = new EmbeddingProvider(
+        $platform,
+        $embeddings,  // Your embeddings model
+        $store        // Your vector store
+    );
+
+This approach allows the agent to recall specific pieces of information from a large
+knowledge base based on the current conversation context.
+
+Bundle Configuration
+--------------------
+
+When using the AI Bundle, you can configure memory directly in your configuration:
+
+.. code-block:: yaml
+
+    # config/packages/ai.yaml
+    ai:
+        agent:
+            trainer:
+                model: 'gpt-4o-mini'
+                prompt:
+                    text: 'Provide short, motivating claims'
+                memory: 'You are a professional trainer with personalized advice'
+
+For dynamic memory using a custom service:
+
+.. code-block:: yaml
+
+    ai:
+        agent:
+            trainer:
+                model: 'gpt-4o-mini'
+                prompt:
+                    text: 'Provide short, motivating claims'
+                memory:
+                    service: 'app.user_memory_provider'
+
+Best Practices
+--------------
+
+* **Keep Static Memory Concise**: Only include essential facts to avoid overwhelming the agent
+* **Separate Concerns**: Use system prompt for behavior, memory for context
+* **Update Dynamically**: For user-specific applications, update memory as you learn more about the user
+* **Test Without Memory**: Verify your agent works correctly both with and without memory enabled
+* **Monitor Token Usage**: Memory content consumes input tokens, so balance comprehensiveness with cost
+
+Use Cases
+---------
+
+* **Personal Assistants**: Remember user preferences, habits, and history
+* **Customer Support**: Recall previous interactions and customer details
+* **Educational Tools**: Track student progress and learning style
+* **Healthcare Applications**: Maintain patient history and treatment context
+
+Related Documentation
+---------------------
+
+* :doc:`../components/agent` - Agent component documentation
+* :doc:`../bundles/ai-bundle` - AI Bundle configuration reference
+* :doc:`rag-implementation` - Retrieval Augmented Generation guide

docs/cookbook/index.rst

@@ -0,0 +1,24 @@
+Cookbook
+========
+
+The cookbook contains practical guides and complete examples for common use cases
+with Symfony AI. Each guide includes working code, explanations, and best practices.
+
+Getting Started Guides
+----------------------
+
+.. toctree::
+    :maxdepth: 1
+
+    chatbot-with-memory
+    rag-implementation
+
+Memory & Context Management
+---------------------------
+
+* :doc:`chatbot-with-memory` - Build chatbots that remember user preferences and conversation history
+
+Retrieval Augmented Generation
+------------------------------
+
+* :doc:`rag-implementation` - Implement complete RAG systems with vector stores and semantic search