docs: add docs for distributed agents sample

Author: radu-mocanu

docs/interrupt_models.md

@@ -9,7 +9,9 @@ UiPath platform and the Langchain coded agents.
 
 ### 1. InvokeProcess
 
-The `InvokeProcess` model is utilized to invoke a process within UiPath cloud platform. Upon completion of the invoked process, the current agent will automatically resume execution.
+The `InvokeProcess` model is utilized to invoke a process within the UiPath cloud platform.
+This process can be of various types, including API workflows, Agents or RPA automation.
+Upon completion of the invoked process, the current agent will automatically resume execution.
 
 #### Attributes:
 - **name** (str): The name of the process to invoke.
@@ -20,6 +22,8 @@ The `InvokeProcess` model is utilized to invoke a process within UiPath cloud pl
 process_output = interrupt(InvokeProcess(name="MyProcess", input_arguments={"arg1": "value1"}))
 ```
 
+For a practical implementation of the `InvokeProcess` model, refer to the sample usage in the [planner.py](../../samples/multi-agent-planner-researcher-coder-distributed/src/multi-agent-distributed/planner.py#L184) file. This example demonstrates how to invoke a process with dynamic input arguments, showcasing the integration of the interrupt functionality within a multi-agent system or a system where an agent integrates with RPA processes and API workflows.
+
 ---
 
 ### 2. WaitJob

docs/sample_images/coder-agent-package-overview.png

@@ -12,7 +12,7 @@ Each agent functions as an independent entrypoint and can be deployed as a separ
 
 The system uses LangGraph to create a directed graph of agents that can communicate and pass state to each other.
 
-### Planner Graph
+### Planner Agent Graph
 ```mermaid
 ---
 config:
@@ -34,7 +34,7 @@ graph TD;
 	classDef last fill:#bfb6fc
 ```
 
-### Researcher Agent
+### Researcher Agent Graph
 ```mermaid
 ---
 config:
@@ -61,7 +61,7 @@ graph TD;
 	classDef last fill:#bfb6fc
 ```
 
-### Coder Agent
+### Coder Agent Graph
 ```mermaid
 ---
 config:
@@ -146,6 +146,91 @@ uipath run coder '{"messages":[{"content":"Let me help you state the Pythagorean
 6. Coder agent calculates c = √(2² + 3²) = √(4 + 9) = √13 ≈ 3.606
 7. Planner combines the responses and returns the final answer to the user
 
+## Steps to Execute Project on UiPath Cloud Platform
+
+1. **Clone the Repository**
+   ```bash
+   git clone https://github.com/UiPath/uipath-langchain-python.git
+   ```
+
+2. **Navigate to the Sample Directory**
+   - **For Windows:**
+     ```bash
+     cd .\uipath-langchain-python\samples\multi-agent-planner-researcher-coder-distributed
+     ```
+
+   - **For Unix-like systems (Linux, macOS):**
+     ```bash
+     cd ./uipath-langchain-python/samples/multi-agent-planner-researcher-coder-distributed
+     ```
+
+3. **Create and Activate a Virtual Python Environment**
+   ```bash
+   pip install uv
+   uv venv -p 3.11 .venv
+   .venv\Scripts\activate  # For Windows
+   source .venv/bin/activate  # For Unix-like systems
+   uv sync
+   ```
+
+4. **Authenticate to UiPath Cloud Platform**
+   ```bash
+   uipath auth
+   ```
+   > **Note:** After successful authentication in the browser, you will be prompted to select the tenant where you wish to publish the package containing the agents.
+   ```
+   Received log data
+   Received authentication information
+   Available tenants:
+     0: cosmin
+     1: DefaultTenant
+     2: Demo
+     3: lucian
+     4: Solutions
+     5: SolutionsTest
+     6: Test
+     7: TestRoles
+   Select tenant: 2
+   ```
+
+5. **Package and Publish Agents**
+   ```bash
+   uipath pack
+   uipath publish
+   ```
+   > **Note:** At this step, you will be required to select the feed where the package should be published:
+   ```
+   Select feed type:
+     0: Tenant package feed
+     1: Personal workspace
+   Select feed: 0
+   ```
+
+6. **Create 3 Agent Processes in Orchestrator**
+   - **Planner Agent**
+     ![planner-agent-package-overview](../../docs/sample_images/planner-agent-package-overview.png)
+     ![planner-agent-process-configuration](../../docs/sample_images/planner-agent-process-configuration.png)
+
+   - **Researcher Agent**
+     ![researcher-agent-package-overview](../../docs/sample_images/researcher-agent-package-overview.png)
+     ![researcher-agent-process-configuration](../../docs/sample_images/researcher-agent-process-configuration.png)
+
+   - **Coder Agent**
+     ![coder-agent-package-overview](../../docs/sample_images/coder-agent-package-overview.png)
+     ![coder-agent-process-configuration](../../docs/sample_images/coder-agent-process-configuration.png)
+
+   > **Note:** Please ensure that, for coder and researcher entry points, the process display names are: *coder-agent* and *researcher-agent*.
+
+7. **Run the Planner Agent with Any Question as Input Argument**
+   > **Tip:** For an action plan of ~5 steps, you can try feeding the following input to the agent:
+   ```
+   Could you find a Python solution for the N-Queens puzzle for N=8? Please also analyze why this solution works,
+   considering the key programming concepts that it uses.
+   Then, revise the solution to handle a dynamic value of N, where N is any positive integer.
+   After that, research on the time and space complexity of this new solution.
+   Lastly, demonstrate this revised Python solution with N=10.
+   ```
+
 ## Implementation Details
 
 The system is implemented using:

docs/sample_images/coder-agent-process-configuration.png

@@ -3,7 +3,7 @@ name = "multi-agents-distributed"
 version = "0.0.1"
 description = "Supervisor agent that coordinates between a researcher and a coder"
 authors = [
-    { name = "Radu Mocanu" }
+    { name = "Radu Mocanu", email = "radu.mocanu@uipath.com" }
 ]
 requires-python = ">=3.10"
 dependencies = [