chore: add RAG sample

pyproject.toml

@@ -5,7 +5,7 @@ description = "UiPath Langchain"
 readme = { file = "README.md", content-type = "text/markdown" }
 requires-python = ">=3.10"
 dependencies = [
-    "uipath>=2.0.33, <2.1.0",
+    "uipath>=2.0.45, <2.1.0",
     "langgraph>=0.2.70",
     "langchain-core>=0.3.34",
     "langgraph-checkpoint-sqlite>=2.0.3",

samples/RAG-quiz-generator/README.md

@@ -0,0 +1,244 @@
+# RAG Sample
+
+## Overview
+
+The RAG Sample project demonstrates the implementation of a Retrieval-Augmented Generation (RAG) system using [UiPath Context Grounding](https://docs.uipath.com/automation-cloud/automation-cloud/latest/admin-guide/about-context-grounding).
+The system comprises two langchain agents:
+1. **Quiz-Generator-RAG-Agent**: Attempts to generate a quiz using the given topic, leveraging only the information obtained through RAG.
+    In case it fails to gather sufficient relevant data, the system might opt to invoke the _Researcher-RAG-Agent_.
+2. **Researcher-RAG-Agent**: Retrieves pertinent information from the Internet based on the provided search instructions and uploads
+    it into a UiPath Context Grounding Index.
+
+## System Architecture
+
+The system utilizes LangGraph to create a directed graph of agents that can communicate and share state.
+
+### Quiz Generator Agent Graph
+
+```mermaid
+---
+---
+config:
+  flowchart:
+    curve: linear
+---
+graph TD;
+	__start__([<p>__start__</p>]):::first
+	invoke_researcher(invoke_researcher)
+	create_quiz(create_quiz)
+	return_quiz(return_quiz)
+	prepare_input(prepare_input)
+	__end__([<p>__end__</p>]):::last
+	__start__ --> prepare_input;
+	invoke_researcher --> create_quiz;
+	prepare_input --> create_quiz;
+	return_quiz --> __end__;
+	create_quiz -.-> invoke_researcher;
+	create_quiz -.-> return_quiz;
+	classDef default fill:#f2f0ff,line-height:1.2
+	classDef first fill-opacity:0
+	classDef last fill:#bfb6fc
+```
+
+### Researcher Agent Graph
+
+```mermaid
+---
+---
+config:
+  flowchart:
+    curve: linear
+---
+graph TD;
+	__start__([<p>__start__</p>]):::first
+	research(research)
+	add_data_to_context_grounding_index(add_data_to_context_grounding_index)
+	prepare_input(prepare_input)
+	create_file_name(create_file_name)
+	__end__([<p>__end__</p>]):::last
+	__start__ --> prepare_input;
+	add_data_to_context_grounding_index --> __end__;
+	create_file_name --> research;
+	prepare_input --> create_file_name;
+	research --> add_data_to_context_grounding_index;
+	classDef default fill:#f2f0ff,line-height:1.2
+	classDef first fill-opacity:0
+	classDef last fill:#bfb6fc
+
+```
+## Agent Responsibilities
+
+- **Quiz Generator Agent**:
+  - Receives user topics and executes searches using RAG.
+  - Decides if there is enough relevant information to generate a quiz.
+  - May ask _Researcher Agent_ for additional information.
+  - Returns final quiz to the user, complying to the requested output format _(question, difficulty, expected answer)_.
+
+- **Researcher Agent**:
+  - Retrieves information using a Tavily search tool.
+  - Uploads the data to a storage bucket (that the Context Grounding Index relies on).
+  - Triggers the  index ingestion.
+
+## Steps to Execute Project on UiPath Cloud Platform
+### Prerequisites
+1. **Create an Orchestrator Storage Bucket**
+
+For this demo we'll create it in the _Shared_ folder.
+
+![navigate to Storage Buckets page](../../docs/sample_images/RAG/create-storage-bucket-1.png)
+
+
+![create storage bucket](../../docs/sample_images/RAG/create-storage-bucket-2.png)
+2. **Create a Context Grounding Index**
+
+Next, we'll create a Context Grounding Index
+
+![navigate to Indexes page](../../docs/sample_images/RAG/create-index-1.png)
+
+When configuring _Index General Details_, we'll select the previously created storage bucket as data source.
+
+![create index](../../docs/sample_images/RAG/create-index-2.png)
+
+That's it! Next, we'll deploy the agents.
+
+### Deploy the agents
+1. **Clone the Repository**
+   ```bash
+   git clone https://github.com/UiPath/uipath-langchain-python.git
+   ```
+
+2. **Navigate to the Sample Directory**
+   - **Windows:**
+     ```bash
+     cd .\uipath-langchain-python\samples\RAG-quiz-generator
+     ```
+
+   - **Unix-like Systems (Linux, macOS):**
+     ```bash
+     cd ./uipath-langchain-python/samples/RAG-quiz-generator
+     ```
+
+3. **Create and Activate a Virtual Python Environment**
+   ```bash
+   pip install uv
+   uv venv -p 3.11 .venv
+   .venv\Scripts\activate  # Windows
+   source .venv/bin/activate  # Unix-like Systems
+   uv sync
+   ```
+
+4. **Authenticate with UiPath Cloud Platform**
+   ```bash
+   uipath auth
+   ```
+   > **Note:** After successful authentication in the browser, select the tenant for publishing the agent package.
+```
+👇 Select tenant:
+  0: DefaultTenant
+  1: Tenant2
+  2: Tenant3
+...
+Select tenant: 2
+```
+
+5. **Package and Publish Agents**
+   ```bash
+   uipath pack
+   uipath publish --my-workspace
+   ```
+```
+⠋ Publishing most recent package: RAG-agents.1.0.0.nupkg ...
+✓  Package published successfully!
+⠇ Getting process information ...
+🔗 Process configuration link: [LINK]
+💡 Use the link above to configure any environment variables
+```
+> Note: when publishing to _my-workspace_ a process will be auto-provisioned for you.
+> However, our package contains 2 agents (i.e. 2 entrypoints) thus we'll need to create an additional process.
+
+6. **Configure Agent Processes in Orchestrator**
+
+Follow the returned URL from the publish command:
+
+1. **Researcher Agent**
+
+Make sure _researcher-RAG-agent_ entrypoint is selected, set TAVILY and ANTHROPIC api keys and click on **Next**.
+
+![env-vars-researcher](../../docs/sample_images/RAG/researcher-1.png)
+
+> Note: We can leave everything empty in the _input_ section.
+
+On the _package requirements_ page click **Next**.
+
+![create-researcher](../../docs/sample_images/RAG/researcher-2.png)
+
+> Make sure the process name is _researcher-RAG-agent_.
+
+2. **Quiz Generator Agent**
+
+Lastly, we need to create the quiz generator agent.
+Create a new process in _my-workspace_.
+
+![process-page](../../docs/sample_images/RAG/process-page.png)
+
+![create-process](../../docs/sample_images/RAG/create-process.png)
+
+![create quiz generator agent](../../docs/sample_images/RAG/create-quiz-generator.png)
+
+7. **Run the agents on UiPath Cloud Platform**
+
+We can run the agent from _Processes_ page
+
+![run-process-button](../../docs/sample_images/RAG/run-process-1.png)
+
+![run-process](../../docs/sample_images/RAG/run-process-2.png)
+
+> ℹ️ Here is how we can easily copy any folder path.
+
+![folder-path](../../docs/sample_images/RAG/folder-path.png)
+
+8. **Conclusions**
+
+We can monitor our agent execution from the job info panel.
+
+![job-info](../../docs/sample_images/RAG/job-info.png)
+
+_Quiz-generator-RAG-agent_ may choose to invoke researcher agent
+multiple times before creating the quiz. The output should look like this:
+
+![agent-output](../../docs/sample_images/RAG/output.png)
+
+We can also check the storage bucket content.
+
+![click-storage-bucket](../../docs/sample_images/RAG/click-storage-bucket.png)
+
+This is the information fetched by our _researcher agent_ that the _quiz generator_ used to create the quiz using RAG.
+We can download this file/files to analyze them.
+
+![storage-bucket-content](../../docs/sample_images/RAG/storage-bucket-content.png)
+
+## Local Debugging
+You can locally debug individual agents by invoking them directly:
+
+#### Researcher Agent
+Run the researcher agent with:
+
+```bash
+uipath run researcher-RAG-agent '{"search_instructions":"Need data about spanish cuisine", "index_name":"sample-RAG-index", "index_folder_path":"<index_folder_path>"}'
+# or use .json file input
+uipath run researcher-RAG-agent -f input.example/researcher-debug-input.json
+```
+> **_NOTE:_**  This assumes that an index named _sample-RAG-index_ is created in the folder identified by the index_folder_path parameter.
+
+
+#### Quiz Generator Agent
+Run the quiz generator agent with:
+
+```bash
+uipath run quiz-generator-RAG-agent '{"quiz_topic":"spanish cuisine", "index_name":"sample-RAG-index", "index_folder_path":"<index_folder_path>"}'
+# or use .json file input
+uipath run quiz-generator-RAG-agent -f input.example/quiz-generator-debug-input.json
+```
+> **_NOTE:_**  This assumes that an agent named _researcher-RAG-agent_ is created in the folder identified by the folder_path parameter passed to _InvokeProcess_ method.
+> <br> ℹ️ check [researcher-RAG-agent.py](src/agents/quiz-generator-RAG-agent.py) file.
+

samples/RAG-quiz-generator/env.example

@@ -0,0 +1,2 @@
+TAVILY_API_KEY=xxx
+ANTHROPIC_API_KEY=xxx

samples/RAG-quiz-generator/input.example/quiz-generator-debug-input.json

@@ -0,0 +1,5 @@
+{
+  "quiz_topic":"spanish cuisine",
+  "index_name":"sample-RAG-index",
+  "index_folder_path":"<index_folder_path>"
+}

samples/RAG-quiz-generator/input.example/researcher-debug-input.json

@@ -0,0 +1,5 @@
+{
+  "search_instructions":"Need data about spanish cuisine",
+  "index_name":"sample-RAG-index",
+  "index_folder_path":"<index_folder_path>"
+}

samples/RAG-quiz-generator/langgraph.json

@@ -0,0 +1,8 @@
+{
+    "dependencies": ["."],
+    "graphs": {
+      "researcher-RAG-agent": "./src/agents/researcher-RAG-agent.py:graph",
+      "quiz-generator-RAG-agent": "./src/agents/quiz-generator-RAG-agent.py:graph"
+    },
+    "env": ".env"
+  }

samples/RAG-quiz-generator/pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "RAG-agents"
+version = "0.0.1"
+description = "Project containing two agents: quiz-generator-RAG-agent and researcher-RAG-agent."
+authors = [{ name = "John Doe", email = "john.doe@myemail.com" }]
+dependencies = [
+    "langgraph>=0.2.55",
+    "langchain-community>=0.3.9",
+    "langchain-anthropic>=0.3.8",
+    "langchain-experimental>=0.3.4",
+    "tavily-python>=0.5.0",
+    "uipath-langchain==0.0.105",
+]
+requires-python = ">=3.10"

samples/RAG-quiz-generator/quiz-generator-RAG-agent.mermaid

@@ -0,0 +1,21 @@
+---
+config:
+  flowchart:
+    curve: linear
+---
+graph TD;
+	__start__([<p>__start__</p>]):::first
+	invoke_researcher(invoke_researcher)
+	create_quiz(create_quiz)
+	return_quiz(return_quiz)
+	prepare_input(prepare_input)
+	__end__([<p>__end__</p>]):::last
+	__start__ --> prepare_input;
+	invoke_researcher --> create_quiz;
+	prepare_input --> create_quiz;
+	return_quiz --> __end__;
+	create_quiz -.-> invoke_researcher;
+	create_quiz -.-> return_quiz;
+	classDef default fill:#f2f0ff,line-height:1.2
+	classDef first fill-opacity:0
+	classDef last fill:#bfb6fc

samples/RAG-quiz-generator/researcher-RAG-agent.mermaid

@@ -0,0 +1,20 @@
+---
+config:
+  flowchart:
+    curve: linear
+---
+graph TD;
+	__start__([<p>__start__</p>]):::first
+	research(research)
+	add_data_to_context_grounding_index(add_data_to_context_grounding_index)
+	prepare_input(prepare_input)
+	create_file_name(create_file_name)
+	__end__([<p>__end__</p>]):::last
+	__start__ --> prepare_input;
+	add_data_to_context_grounding_index --> __end__;
+	create_file_name --> research;
+	prepare_input --> create_file_name;
+	research --> add_data_to_context_grounding_index;
+	classDef default fill:#f2f0ff,line-height:1.2
+	classDef first fill-opacity:0
+	classDef last fill:#bfb6fc