Customer support app & event driven workflows docs (#92)

docs/source/examples.rst

@@ -9,6 +9,7 @@ Examples
    examples/research_agent
    examples/validating_agent_output
    examples/advanced_output_handling
+   examples/customer_support
    examples/streaming_agent_output
    examples/event_driven
    autogen

docs/source/examples/customer_support.rst

@@ -0,0 +1,73 @@
+Customer support chatbot with Ray Serve
+=======================================
+
+This example demonstrates how to build a customer support chatbot using MotleyCrew and Ray Serve.
+The chatbot is designed to answer customer queries based on a database of past issues and their resolutions.
+
+The code for this example can be found `here <https://github.com/ShoggothAI/motleycrew/tree/main/motleycrew/applications/customer_support>`_.
+Also, see the `blog post <https://blog.motleycrew.ai/blog/building-a-customer-support-chatbot-using-motleycrew-and-ray>`_ about this app.
+
+Key Components
+--------------
+
+1. Issue Database
+
+   - Stores information about past issues and their solutions in a tree structure
+   - Intermediate nodes represent issue categories
+   - Leaf nodes represent individual issues
+   - Uses Kuzu to store and query the issue tree through our OGM (see :doc:`../knowledge_graph` for more details)
+
+2. AI Support Agent
+
+   - Attempts to resolve customer issues based on past solutions
+   - Navigates the issue tree to find relevant information
+   - Can ask clarifying questions to the customer
+   - Proposes solutions or escalates to a human agent if necessary
+
+3. Agent Tools
+
+   - IssueTreeViewTool: Allows the agent to navigate the issue tree
+   - CustomerChatTool: Enables the agent to ask additional questions to the customer
+   - ResolveIssueTool: Used to submit a solution or escalate to a human agent
+
+4. Ray Serve Deployment
+
+   - Exposes the chatbot as an API
+   - Allows multiple customers to connect simultaneously
+   - Uses WebSockets over FastAPI for communication
+
+Implementation Details
+----------------------
+
+The support agent is implemented using the "every response is a tool call" design.
+The agent loop can only end with a ResolveIssueTool call or when a constraint (e.g., number of iterations) is reached.
+This is achieved by making the ResolveIssueTool an output handler.
+
+The Ray Serve deployment is configured using a simple decorator:
+
+.. code-block:: python
+
+    @serve.deployment(num_replicas=3, ray_actor_options={"num_cpus": 1, "num_gpus": 0})
+    class SupportAgentDeployment:
+        ...
+
+This setup allows for easy scaling and supports multiple simultaneous sessions balanced between replicas.
+
+Running the Example
+-------------------
+
+The project includes sample issue data that can be used to populate the issue tree.
+
+To run this example:
+
+.. code-block:: bash
+
+    git clone https://github.com/ShoggothAI/motleycrew.git
+    cd motleycrew
+    pip install -r requirements.txt
+
+    python -m motleycrew.applications.customer_support.issue_tree  # populate the issue tree
+    ray start --head
+    python -m motleycrew.applications.customer_support.ray_serve_app
+
+This example showcases the flexibility of MotleyCrew for building agent-based applications, allowing you to choose your preferred agent framework, orchestration model, and deployment solution.

docs/source/examples/event_driven.nblink

@@ -0,0 +1,3 @@
+{
+    "path": "../../../examples/Event-driven orchestration for AI systems.ipynb"
+}