From 78ef43d26f82070244fd4f016f07d6f7292f84a2 Mon Sep 17 00:00:00 2001 From: Ashok Karale Date: Fri, 31 Oct 2025 21:31:33 +0530 Subject: [PATCH 1/2] Added customer-care-agent --- samples/README.md | 3 + .../.env.example | 8 + .../AssistmAIgic Context/Fan Warrenty.pdf | Bin 0 -> 104014 bytes .../AssistmAIgic Context/Fans.pdf | Bin 0 -> 157213 bytes .../AssistmAIgic Context/Lights.pdf | Bin 0 -> 145583 bytes .../AssistmAIgic DB/Order Book.csv | 1001 +++++ .../MCP Tools/Categorize_Emails_Solution.uis | Bin 0 -> 13460 bytes .../MCP Tools/EmailOperations.uis | Bin 0 -> 34168 bytes .../MCP Tools/GetOrderDetails.uis | Bin 0 -> 9533 bytes .../MCP Tools/ReviewEmailResponseSolution.uis | Bin 0 -> 72388 bytes .../MCP Tools/SentimentAnalysisSolution.uis | Bin 0 -> 18033 bytes .../README.md | 236 + .../agent.mermaid | 34 + .../input.json | 5 + .../langgraph.json | 7 + .../assistmaigic-customer-care-agent/main.py | 567 +++ .../pyproject.toml | 11 + .../requirements.txt | 3 + .../uipath.json | 119 + .../assistmaigic-customer-care-agent/uv.lock | 3801 +++++++++++++++++ 20 files changed, 5795 insertions(+) create mode 100644 samples/assistmaigic-customer-care-agent/.env.example create mode 100644 samples/assistmaigic-customer-care-agent/AssistmAIgic Context/Fan Warrenty.pdf create mode 100644 samples/assistmaigic-customer-care-agent/AssistmAIgic Context/Fans.pdf create mode 100644 samples/assistmaigic-customer-care-agent/AssistmAIgic Context/Lights.pdf create mode 100644 samples/assistmaigic-customer-care-agent/AssistmAIgic DB/Order Book.csv create mode 100644 samples/assistmaigic-customer-care-agent/MCP Tools/Categorize_Emails_Solution.uis create mode 100644 samples/assistmaigic-customer-care-agent/MCP Tools/EmailOperations.uis create mode 100644 samples/assistmaigic-customer-care-agent/MCP Tools/GetOrderDetails.uis create mode 100644 samples/assistmaigic-customer-care-agent/MCP Tools/ReviewEmailResponseSolution.uis create mode 100644 samples/assistmaigic-customer-care-agent/MCP Tools/SentimentAnalysisSolution.uis create mode 100644 samples/assistmaigic-customer-care-agent/README.md create mode 100644 samples/assistmaigic-customer-care-agent/agent.mermaid create mode 100644 samples/assistmaigic-customer-care-agent/input.json create mode 100644 samples/assistmaigic-customer-care-agent/langgraph.json create mode 100644 samples/assistmaigic-customer-care-agent/main.py create mode 100644 samples/assistmaigic-customer-care-agent/pyproject.toml create mode 100644 samples/assistmaigic-customer-care-agent/requirements.txt create mode 100644 samples/assistmaigic-customer-care-agent/uipath.json create mode 100644 samples/assistmaigic-customer-care-agent/uv.lock diff --git a/samples/README.md b/samples/README.md index fe2bf1c0..a2778f60 100644 --- a/samples/README.md +++ b/samples/README.md @@ -23,3 +23,6 @@ This sample shows how to build a LangGraph agent that connects to a remote UiPat ## [Ticket classification](ticket-classification) This sample demonstrates automatic classification of support tickets into categories. It includes a human approval step via UiPath Action Center. + +## [AssistmAIgic customer care agent](assistmAIgic-customer-care-agent) +This sample demonstrates agent that automates customer support by handling queries received via email. It can understand and process emails, extract relevant information like order IDs, retrieve order details, categorize emails, analyze sentiment, and generate responses using a knowledge base. It also supports multilingual communication and can escalate complex cases to human agents. diff --git a/samples/assistmaigic-customer-care-agent/.env.example b/samples/assistmaigic-customer-care-agent/.env.example new file mode 100644 index 00000000..5b19dbcf --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/.env.example @@ -0,0 +1,8 @@ +UIPATH_URL="https://cloud.uipath.com/ORG/TENANT" +UIPATH_ACCESS_TOKEN="your-token" +UIPATH_TENANT_ID="your-tenant-id" +UIPATH_ORGANIZATION_ID="your-organization-id" +MCP_SERVER_URL=https://your-mcp-server-url-will-go-here + +# Common defaults +UIPATH_FOLDER_PATH="Shared" \ No newline at end of file diff --git a/samples/assistmaigic-customer-care-agent/AssistmAIgic Context/Fan Warrenty.pdf b/samples/assistmaigic-customer-care-agent/AssistmAIgic Context/Fan Warrenty.pdf new file mode 100644 index 0000000000000000000000000000000000000000..06573dfab9a8fa4de9e9f31913e4fa4990451dc5 GIT binary patch literal 104014 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a/samples/assistmaigic-customer-care-agent/README.md b/samples/assistmaigic-customer-care-agent/README.md new file mode 100644 index 00000000..140e04f2 --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/README.md @@ -0,0 +1,236 @@ +### ✨ **AssistmAIgic** – Intelligent Email Agent for Order & Product Support + +**AssistmAIgic** is a specialized automation agent designed to streamline customer email handling for product orders and inquiries. Built on LangGraph’s state machine architecture and integrated with UiPath’s Model Context Portal (MCP), it delivers fast, accurate, and context-aware responses while ensuring human oversight for sensitive cases. + +image + +--- + +## Mermaid diagram +```mermaid +flowchart TD + step_start["Start"] --> step_translate_email_language["Translate Email Language"] + step_translate_email_language --> step_extract_order_id["Extract Order ID"] + step_extract_order_id -. order_id_missing .-> step_auto_reject["Auto Reject"] + step_extract_order_id -. order_id_available .-> step_get_order_details["Get Order Details"] + step_get_order_details --> step_categorize_email["Categorize Email"] + step_categorize_email --> step_analyze_email_sentiment["Analyze Email Sentiment"] + step_analyze_email_sentiment --> step_get_answer_to_query["Get Answer to Query"] + step_get_answer_to_query -. very_negative_sentiment .-> step_human_review["Human Review"] + step_get_answer_to_query -. agent_can_proceed_sentiment .-> step_reply_to_email["Reply to Email"] + step_human_review --> step_reply_to_email + step_reply_to_email --> step_end["End"] + step_auto_reject --> step_end + + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc +``` +--- +### Key Capabilities + +1. **Multilingual Email Handling** + - Accepts customer emails in any language. + - Automatically translates incoming emails to a processing language (e.g., English, Marathi, Hindi). + - Replies are generated and translated back into the customer's original language for seamless and personalize communication. + +2. **Order ID Extraction & Validation** + - Parses email content to extract an 8-digit order ID. + - If found, retrieves order details via MCP tools. + - If missing, sends a polite rejection email requesting the order ID. + +3. **Email Categorization & Sentiment Analysis** + - Classifies emails into categories like: + - *Product Inquiry* + - *Order Status* + - *Complaint* + - *etc.* + - Assesses sentiment as *Positive*, *Negative*, or *Very Negative*. + +4. **Contextual Response Generation** + - Uses a grounding retriever to pull relevant product specs, warranty info, and FAQs from the knowledge base. + - Crafts informative, personalized replies based on the customer's query and order context. + +5. **Human-in-the-Loop (HITL) Escalation** + - If sentiment is *Very Negative*, the agent routes the draft response to a human reviewer for approval or refinement ensuring empathy and precision in sensitive situations. + +6. **Automated Response Delivery** + - Sends the final email response (automated or HITL-approved) to the customer in their original language. + +7. **Interaction Logging** + - Logs the outcome of each email interaction for traceability and analytics. + +--- + +### Benefits + +- Reduces manual workload for support teams. +- Ensures fast, multilingual customer service. +- Escalates complex or emotional cases to human agents. +- Maintains high accuracy and contextual relevance in responses. + +--- +## 0) Local Environment Setup (with `uv`) + +This project assumes you’re using [`uv`](https://github.com/astral-sh/uv) for fast Python installs, virtualenvs, and command execution. + +### 0.1 Install Python 3.12+ if not installed + +```bash +# Install a modern Python (adjust the version if you need) +uv python install 3.12 +``` +### 0.2 Clone the repository +```bash +git clone https://github.com/ashokkarale/assistmAIgicCodedAgent.git +cd assistmAIgicCodedAgent +``` +### 0.3 Install dependencies +```bash +uv pip install -r requirements.txt +``` +### 0.4 Configure environment variables + +Create a .env file and add MCP server URL: +``` +# MCP Server +MCP_SERVER_URL=https://your-mcp-server-url-will-go-here +``` + +### 0.5 Run the UiPath CLI via `uv` + +Using `uv run` means you don’t have to activate the venv: +> **Tip:** You don’t need to “activate” the venv if you use `uv run ...`, but if you prefer activation: +> +> - macOS/Linux: `source .venv/bin/activate` +> - Windows PowerShell: `.venv\Scripts\Activate.ps1` + +```bash +# Log in and write credentials to .env +uv run uipath auth + +# Initialize (scans entrypoints and updates uipath.json) +uv run uipath init + +# Interactive dev loop (recommended) +uv run uipath dev + +# Non-interactive run of classic entrypoint +uv run uipath run main.py '{"message_id": "AAMkAGMwYTQyNTZhLTkxN2ItNGFlNi1iOWViLWM1....[Mail MessageId]", "email_subject":"Order Confirmation - Order #10001018", "email_body": "I recently bought Aeroquiet product and it stopped working in couple of weeks!"}' + +# If you exposed a compiled graph entrypoint called "agent" +# (name exposed in langgraph.json) +uv run uipath run agent -f .\input.json +``` +--- +## 1) UiPath Resources setup required +- Setup you will have to do to run the agent +### 1.1 MCP Tools +- Download and deploy the MCP tools from the project folder 'MCP Tools' +- **Tool Details:** + - **TranslateEmailSubjectAndBodyLanguage** - Translate email subject and body language to agent language if the email is not in agent language. + - **GetOrderDetailsByOrderId** - Get Order Details by Order Id. + - **CategorizeEmail** - Categorize email as per the email subject & body content. + - **ReplyToEmail** - Send email reply. + - **HITL_Review** - Action app to review email response generated by the agent for 'Very Negative' sentiment email. + +### 1.2 MCP Server +- Create UiPath MCP Server and add the deployed tools to the server with descriptions. + image + +### 1.3 Storage Buckets +- Create a storage bucket for Order Database. eg. AssistemAIgic DB. Sample CSV db provided in the project folder **AssistmAIgic DB** +- Create a storage bucket for storing product, services, support, warrenty and every document required for providing good customer support. e.g. **AssistmAIgic Context**. Sample documents provided in the project. + +### 1.4 Indexes +- Create a RAG Context Grounding Index which will be used by the agent to generate accurate response. e.g. AssistmAIgic_Index. Remember if you choose another name than this example, you will have to update it into `main.py` in the below logic. + +```python +# Initialize Context Grounding for company policy +context_grounding = ContextGroundingRetriever( + index_name="AssistmAIgic_Index", + folder_path="Shared", + number_of_results=1 + ) +``` +--- +## 2) Core Developer Workflow (CLI) + +The **unified CLI** supports both classic automations and LangGraph agents. + +### 2.1 Authenticate + +```bash +uipath auth +``` + +- Opens a browser login and writes credentials to `.env`. +- Required before local runs or publishing. + +### 2.2 Initialize + +```bash +uipath init + +``` + +- Scans the classic entrypoint (`main.py`) and creates/updates `uipath.json` with **input/output schema** and **resource bindings**. +- Re‑run when you change function signatures, add Assets/Queues/Buckets, or new graph entrypoints. + +### 2.3 Local Run & Debug + +```bash +# Interactive development mode (recommended) +uipath dev + +# Non‑interactive quick runs +uipath run main.py '{"message_id": "AAMkAGMwYTQyNTZhLTkxN2ItNGFlNi1iOWViLWM1....[Mail MessageId]", "email_subject":"Order Confirmation - Order #10001018", "email_body": "I recently bought Aeroquiet product and it stopped working in couple of weeks!"}' +# For a compiled graph +uipath run agent -f .\input.json +``` + +- `dev` shows live logs, traces, and chat history. + +### 2.4 Package, Publish, Deploy + +```bash +uipath pack +uipath publish +uipath deploy +``` + +- `deploy` is a wrapper that packs and publishes to your Orchestrator feed. +- Use per‑environment pipelines (Dev → Test → Prod folders/tenants). + +### 2.5 Other Useful Commands + +```bash +uipath invoke # Execute a process remotely (when configured) +uipath eval # Run evaluation scenarios for agents +uipath --help # Discover flags and subcommands +``` +--- +## 3) Environment, Credentials & Configuration + +Both SDKs read their configuration from **environment variables** (directly, or via `.env` loaded by `python-dotenv`). + +### 3.1 Minimal local `.env` + +```bash +UIPATH_URL="https://cloud.uipath.com/ORG/TENANT" +UIPATH_ACCESS_TOKEN="your-token" +UIPATH_TENANT_ID="your-tenant-id" +UIPATH_ORGANIZATION_ID="your-organization-id" +MCP_SERVER_URL=https://your-mcp-server-url-will-go-here + +# Common defaults +UIPATH_FOLDER_PATH="Shared" +``` +--- + +--- +## 4) Links + +- Project README: `./README.md` +- UiPath Python SDK docs & samples: https://uipath.github.io/uipath-python/ +- UiPath LangGraph SDK docs & samples: https://uipath.github.io/uipath-python/langchain/quick_start/ diff --git a/samples/assistmaigic-customer-care-agent/agent.mermaid b/samples/assistmaigic-customer-care-agent/agent.mermaid new file mode 100644 index 00000000..f3c30a62 --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/agent.mermaid @@ -0,0 +1,34 @@ +--- +config: + flowchart: + curve: linear +--- +graph TD; + __start__([

    __start__

    ]):::first + translate_email_language(translate_email_language) + extract_order_id(extract_order_id) + get_order_details(get_order_details) + auto_reject(auto_reject) + categorize_email(categorize_email) + analyze_email_sentiment(analyze_email_sentiment) + get_answer_to_query(get_answer_to_query) + human_review(human_review) + reply_to_email(reply_to_email) + step_end(step_end) + __end__([

    __end__

    ]):::last + __start__ --> translate_email_language; + analyze_email_sentiment --> get_answer_to_query; + auto_reject --> step_end; + categorize_email --> analyze_email_sentiment; + extract_order_id -.  order_id_missing  .-> auto_reject; + extract_order_id -.  order_id_available  .-> get_order_details; + get_answer_to_query -.  very_negative_sentiment  .-> human_review; + get_answer_to_query -.  agent_can_proceed_sentiment  .-> reply_to_email; + get_order_details --> categorize_email; + human_review --> reply_to_email; + reply_to_email --> step_end; + translate_email_language --> extract_order_id; + step_end --> __end__; + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc diff --git a/samples/assistmaigic-customer-care-agent/input.json b/samples/assistmaigic-customer-care-agent/input.json new file mode 100644 index 00000000..7fa3dfcd --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/input.json @@ -0,0 +1,5 @@ +{ + "email_body": "I recently bought Aeroquiet product and I want to check the warrenty details!", + "email_subject": "Order Confirmation - Order #10001018", + "message_id": "AAMkAGMwYTQyNTZhLTkxN2ItNGFlNi1iOWViLWM1MzAzMDgyYTI3YwBGAAAAAAB3XLKtc7IMSIflQ0mGHGr_BwCK6X6hJUx8TY6nVw8TcageAAAAAAEMAACK6X6hJUx8TY6nVw8TcageAAABbwvIAAA=" +} \ No newline at end of file diff --git a/samples/assistmaigic-customer-care-agent/langgraph.json b/samples/assistmaigic-customer-care-agent/langgraph.json new file mode 100644 index 00000000..4d52ccaa --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/langgraph.json @@ -0,0 +1,7 @@ +{ + "dependencies": ["."], + "graphs": { + "agent": "./main.py:graph" + }, + "env": ".env" + } diff --git a/samples/assistmaigic-customer-care-agent/main.py b/samples/assistmaigic-customer-care-agent/main.py new file mode 100644 index 00000000..b1c8d6e2 --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/main.py @@ -0,0 +1,567 @@ +import asyncio +from pydantic import BaseModel +from langgraph.graph import StateGraph, END +from langgraph.types import interrupt, Command +from uipath.models import CreateAction +from uipath_langchain.chat import UiPathChat +from langchain_core.messages import SystemMessage, HumanMessage +from uipath_langchain.retrievers import ContextGroundingRetriever +from uipath import UiPath +from typing import Dict, Any +from dotenv import load_dotenv +from datetime import datetime +from contextlib import asynccontextmanager +from langchain_mcp_adapters.tools import load_mcp_tools +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client +import json, os, logging, ast + +load_dotenv() + +logging.basicConfig(level=logging.INFO) + +# Use UiPathChat for making LLM calls +llm = UiPathChat(model="gpt-4o-2024-08-06") + +uipath_client = UiPath() + +# ---------------- MCP Server Configuration ---------------- +@asynccontextmanager +async def get_mcp_session(): + """MCP session management""" + MCP_SERVER_URL = os.getenv("UIPATH_MCP_SERVER_URL") + if hasattr(uipath_client, 'api_client'): + if hasattr(uipath_client.api_client, 'default_headers'): + auth_header = uipath_client.api_client.default_headers.get('Authorization', '') + if auth_header.startswith('Bearer '): + UIPATH_ACCESS_TOKEN = auth_header.replace('Bearer ', '') + logging.info("Retrieved token from UiPath API client") + + async with streamablehttp_client( + url=MCP_SERVER_URL, + headers={"Authorization": f"Bearer {UIPATH_ACCESS_TOKEN}"} if UIPATH_ACCESS_TOKEN else {}, + timeout=60, + ) as (read, write, session_id_callback): + async with ClientSession(read, write) as session: + await session.initialize() + yield session + +async def get_mcp_tools(): + """Load MCP tools for use with agents""" + logging.info(f"Loading MCP tools...") + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + return tools + +# Initialize Context Grounding for company policy +context_grounding = ContextGroundingRetriever( + index_name="AssistmAIgic_Index", + folder_path="Shared", + number_of_results=1 + ) + +# ---------------- State ---------------- +class GraphState(BaseModel): + """Enhanced state to track the complete assistmAIgic workflow""" + message_id: str | None = None + agent_language: str | None = "English" + email_body: str | None = None + email_subject: str | None = None + translated_email_body: str | None = None + translated_email_subject: str | None = None + mail_communication_language: str | None = None + order_id: str | None = None + order_details: Dict[str, Any] | None = None + email_sentiment: Dict[str, Any] | None = None + isorder_valid: bool | None = None + email_category: str | None = None + email_response: str | None = None + hitl_response: str | None = None + final_status: str | None = None + + +## Translate email body and subject language if required +async def translate_email_language_mcp(email_subject: str, email_body: str, agent_language: str): + """Translate email body and subject language if required by MCP tools""" + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + + # Translate Email details tool + translategetemaildetails_tool = next((tool for tool in tools if "translateemailsubjectandbodylanguage" in tool.name.lower()), None) + if not translategetemaildetails_tool: + logging.error("Translate Email Subject And BodybLanguage tool not found in MCP server") + raise Exception("translateEmailSubjectAndBodyLanguage tool not available") + + logging.info(f"Invoking Translate Email Subject And Body Language MCP tool...") + try: + result = await translategetemaildetails_tool.ainvoke({ + "in_OriginalEmailSubject": email_subject, + "in_OriginalEmailBody": email_body, + "in_Language": agent_language + }) + logging.info(f"Email language translation done via MCP") + # Assuming 'result' is your string variable + email_details_dict = ast.literal_eval(result) if result else None + return email_details_dict if email_details_dict else None + + except Exception as e: + logging.error(f"Error translating email details via MCP: {e}") + raise + +## Get Order Details by order_id via MCP +async def get_order_details_mcp(order_id: str): + """Get Order Details by order_id with MCP tool""" + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + + # Get Order Details tool + order_details_tool = next((tool for tool in tools if "getOrderDetailsByOrderId".lower() in tool.name.lower()), None) + if not order_details_tool: + logging.error("getOrderDetailsByOrderId tool not found in MCP server") + raise Exception("getOrderDetailsByOrderId tool not available") + + logging.info(f"Invoking getOrderDetailsByOrderId MCP tool...") + try: + result = await order_details_tool.ainvoke({ + "in_OrderNumber": order_id + }) + logging.info(f"Fetched order details via MCP tool.") + # Assuming 'result' is your string variable + order_details_dict = ast.literal_eval(result) if result else None + return order_details_dict if order_details_dict else None + + except Exception as e: + logging.error(f"Error Fetching order details via MCP: {e}") + raise + +## Get Email Categorization done via MCP +async def categorize_email_mcp(content_to_categorize: str): + """Categorize email body and subject with MCP tool""" + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + + # Get email categorized using Categorize Email tool + categorize_email_tool = next((tool for tool in tools if "categorizeEmail".lower() in tool.name.lower()), None) + if not categorize_email_tool: + logging.error("categorizeEmail tool not found in MCP server") + raise Exception("Categorize Email tool not available") + + logging.info(f"Invoking Categorize Email tool...") + try: + result = await categorize_email_tool.ainvoke({ + "in_Content_To_Categorize": content_to_categorize + }) + logging.info(f"Categorized email via MCP tool.") + # Assuming 'result' is your string variable + email_category_dict = ast.literal_eval(result) if result else None + return email_category_dict if email_category_dict else None + + except Exception as e: + logging.error(f"Error Categorizing email via MCP: {e}") + raise + +## Email Sentiment Analysis via MCP +async def analyze_email_sentiment_mcp(content_to_sentiment_analysis: str): + """Email Sentiment Analysis email body and subject with MCP tool""" + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + + # Email Sentiment Analysis tool + email_sentiment_analysis_tool = next((tool for tool in tools if "emailSentimentAnalysis".lower() in tool.name.lower()), None) + if not email_sentiment_analysis_tool: + logging.error("emailSentimentAnalysis tool not found in MCP server") + raise Exception("Email Sentiment Analysis tool not available") + + logging.info(f"Invoking emailSentimentAnalysis tool...") + try: + result = await email_sentiment_analysis_tool.ainvoke({ + "in_Content_To_Analysis": content_to_sentiment_analysis + }) + logging.info(f"Email Sentiment Analysis via MCP tool.") + # Assuming 'result' is your string variable + sentiment_analysis_dict = ast.literal_eval(result) if result else None + return sentiment_analysis_dict if sentiment_analysis_dict else None + + except Exception as e: + logging.error(f"Error Email Sentiment Analysis via MCP: {e}") + raise + +# ---------------- Graph Nodes ---------------- +class GraphOutput(BaseModel): + report: str + +# ---------------- Nodes ---------------- +# Translate email body and subject language if required via MCP integration +async def translate_email_language_node(state: GraphState) -> GraphOutput: + """Translate email body and subject language if required by MCP integration""" + email_details = await translate_email_language_mcp( + state.email_subject, + state.email_body, + state.agent_language + ) + + return state.model_copy(update={ + "mail_communication_language": email_details['out_CommunicationLanguage'] or None, + "translated_email_body": email_details['out_TranslatedMailBody'] or None, + "translated_email_subject": email_details['out_TranslatedMailSubject'] or None + }) + +# ---------------- Nodes ---------------- +# Extract order id from email body & subject +async def extract_order_id_node(state: GraphState) -> GraphState: + """Extract order id information from the request""" + system_prompt = """You are a data extraction expert tasked with extracting order id information from input text. + + Your goal is to extract the following fields: + 1. order id - the order id will be 8 digit number + + Instructions: + - Only return a JSON object with keys: order_id + - If a field cannot be determined, return null. + - If multiple order ids are present, extract the first one only. + - Only output the JSON. Do not include any explanations, commentary, or extra text. + + Examples: + + User message: "My order number is 12345678 and I need help." + Output: + { + "order_id": "12345678" + } + User message: "My order number is #12345678 and I need help." + Output: + { + "order_id": "12345678" + } + User message: "My order no is #12345678 and I need help." + Output: + { + "order_id": "12345678" + } + User message: "My order #12345678 and I need help." + Output: + { + "order_id": "12345678" + } + User message: "I have purchased a fan with order id 87654321 last week." + Output: + { + "order_id": "87654321", + } + + User message: "My TV isn't working properly." + Output: + { + "order_id": null + } + """ + + output = await llm.ainvoke( + [SystemMessage(system_prompt), + HumanMessage("Extract the order ID from this message: " + state.email_body + "\n" + state.email_subject)] + ) + try: + payload = json.loads(output.content) + except Exception: + payload = None + + raw = (output.content or "").lower() + null_patterns = ['"order_id": null', "'order_id' is null", "order_id is null", "order_id: null"] + + if any(p in raw for p in null_patterns): + + extracted_order_id = None + else: + extracted_order_id = None + if isinstance(payload, dict): + extracted_order_id = payload.get("order_id") + + logging.info(f"Extracted Order ID: {extracted_order_id}") + return state.model_copy(update={ + "order_id": extracted_order_id or None + }) + +# ---------------- Nodes ---------------- +# Get Order Details by order_id via MCP integration +async def get_order_details_node(state: GraphState) -> GraphOutput: + """Get Order Details by order_id with MCP tool""" + order_details_obj = await get_order_details_mcp( + state.order_id + ) + + return state.model_copy(update={ + "order_details": order_details_obj["out_OrderDetails"] or None + }) + +def end_node(state: GraphState) -> GraphState: + """Final node to log the agent run completion""" + logging.info(f"Email processing completed. Status: {state.final_status}") + return state + +# ---------------- Nodes ---------------- +# Auto-reject email via MCP integration +async def auto_reject_node(state: GraphState) -> GraphState: + """Send auto-rejection email via MCP integration""" + logging.info(f"Auto-rejecting email due to missing order id.") + await reply_email_mcp( + message_id=state.message_id, + llmprompt_to_prepare_reply="We regret to inform you that your order Id is missing from your email. Please provide a valid order Id for us to assist you further.", + reply_language=state.mail_communication_language + ) + + return state.model_copy(update={"final_status": "completed"}) + +# ---------------- Nodes ---------------- +# Reply to email via MCP integration +async def reply_to_email_node(state: GraphState) -> GraphState: + """Send email reply via MCP integration""" + logging.info(f"Replying to email via MCP integration.") + await reply_email_mcp( + message_id=state.message_id, + llmprompt_to_prepare_reply = state.hitl_response if state.hitl_response else state.email_response, + reply_language=state.mail_communication_language + ) + + return state.model_copy(update={"final_status": "completed"}) + +# ---------------- MCP Email Reply Function ---------------- +async def reply_email_mcp(message_id: str, llmprompt_to_prepare_reply: str, reply_language: str): + """Send email reply via MCP integration""" + async with get_mcp_session() as session: + tools = await load_mcp_tools(session) + + # Find the email tool + reply_to_email_tool = next((tool for tool in tools if "replyToEmail".lower() in tool.name.lower()), None) + if not reply_to_email_tool: + logging.error("Email reply tool not found in MCP server.") + raise Exception("Email reply tool not available.") + + try: + await reply_to_email_tool.ainvoke({ + "in_Message_Id": message_id, + "in_llmprompt_to_prepare_reply": llmprompt_to_prepare_reply, + "in_Reply_Language": reply_language + }) + logging.info(f"Replied to email via MCP tool.") + + except Exception as e: + logging.error(f"Error replying to email via MCP: {e}") + raise + +# ---------------- Nodes ---------------- +# Categorize email via MCP integration +async def categorize_email_node(state: GraphState) -> GraphOutput: + """Categorize email by body and subject""" + categorize_email_obj = await categorize_email_mcp( + state.translated_email_body + "\n" + state.translated_email_subject + ) + + return state.model_copy(update={ + "email_category": categorize_email_obj["out_Category"] or None + }) + +# ---------------- Helper Functions ---------------- +async def get_answer_with_context(state: GraphState) -> str: + """Get answer to user query with context grounding and LLM analysis""" + + # Default return value + email_response = None + logging.info(f"Generating email response with context grounding and LLM analysis...") + try: + # Your existing policy check logic... + context_query = f"""Get details for the product {state.order_details.get("ProductName", "")}. Provide detailed information about the product features, specifications, warranty policy, and any other relevant details that can help in addressing customer inquiries or issues related to this product. If required, also include information about common troubleshooting steps or usage guidelines for this product and customer care contact details.""" + + # Try to get products context + try: + products_context = context_grounding.invoke(context_query) + logging.debug(f"Retrieved {len(products_context) if products_context else 0} documents") + except Exception as e: + logging.error(f"Context grounding failed: {e}") + return email_response + + if products_context: + # Process documents... + llm_context = [] + for doc in products_context: + product_specification = doc.page_content + source = doc.metadata.get('source', 'Unknown') + page = doc.metadata.get('page_number', '1') + product_specification_docs = f"Source: {source} (Page {page})\n{product_specification}" + llm_context.append(product_specification_docs) + + # # LLM analysis... + try: + system_prompt = f""" + You are an AI product assistant designed to help customers with their product-related queries. Your primary responsibilities are: + 1. Understand and interpret customer queries about products. + 2. Use the provided context grounding index to find relevant information about the products. + 3. Formulate clear, concise, and accurate responses to customer queries based on the information from the context index. + 4. If the exact information is not available, provide the most relevant information you can find and be transparent about any limitations. + 5. Always maintain a professional, helpful, and friendly tone in your responses. + 6. If you cannot find an answer to a query, politely inform the customer and suggest alternative ways they might get the information they need. + + Use this context to answer the customer query: + {llm_context} + + Remember, your goal is to provide the best possible assistance to customers regarding their product inquiries. + + Return the output in plain string. No JSON format is required. + """ + user_prompt = f""" + Please process the following customer query about our product: + Query: {state.translated_email_subject} + "\n" +{state.translated_email_body} + + To answer this query: + 1. Analyze the query to understand what information the customer is seeking. + 2. Search the context grounding index for relevant product information. + 3. Formulate a clear and concise answer based on the information found. + 4. If additional product details are available and relevant, include them in your response. + 5. Ensure your response is helpful and directly addresses the customer's query. + + Provide your response in the following format: + - Your response to the customer's query + + """ + + response = await llm.ainvoke([ + SystemMessage(system_prompt), + HumanMessage(user_prompt) + ]) + + result = response.content + return result + + except Exception as e: + logging.error(f"LLM get_answer_with_context failed: {e}") + return email_response or None + else: + logging.error("No context found") + return email_response or None + + except Exception as e: + logging.error(f"Get_answer_with_context completely failed: {e}") + return email_response or None + +async def get_answer_to_query_node(state: GraphState) -> GraphState: + """Get answer to user query with context grounding and LLM analysis""" + answer_of_query = await get_answer_with_context(state) + + return state.model_copy(update={ + "email_response": answer_of_query or None + }) + +# ---------------- Nodes ---------------- +# Analyze email sentiment via MCP integration +async def analyze_email_sentiment_node(state: GraphState) -> GraphOutput: + """Analyze email sentiment by body and subject""" + logging.info(f"Analyzing email sentiment via MCP integration...") + email_sentiment_obj = await analyze_email_sentiment_mcp( + state.translated_email_body + "\n" + state.translated_email_subject + ) + # Normalize email sentiment to a dict (MCP may return a stringified dict) + + sentiment_raw = None + if isinstance(email_sentiment_obj, dict): + sentiment_raw = email_sentiment_obj.get("out_Email_Sentiment") + else: + sentiment_raw = email_sentiment_obj + + sentiment_dict = None + if isinstance(sentiment_raw, dict): + sentiment_dict = sentiment_raw + elif isinstance(sentiment_raw, str): + try: + sentiment_dict = json.loads(sentiment_raw) + except Exception: + try: + sentiment_dict = ast.literal_eval(sentiment_raw) + except Exception: + sentiment_dict = {"raw": sentiment_raw} + else: + sentiment_dict = None + + return state.model_copy(update={ + "email_sentiment": sentiment_dict or None + }) + +# ---------------- Nodes ---------------- +# Human-in-the-loop review in case of negative sentiment node +async def human_review_node(state: GraphState) -> Command: + """Send to human review""" + logging.info(f"Sending email response to HITL review due to very negative sentiment.") + action_data = interrupt( + CreateAction( + app_name="HITL_Review", + title="Very Negative sentiment email response review", + data={ + "EmailSubject": state.translated_email_subject, + "EmailBody": state.translated_email_body, + "InitialResponse": state.email_response + }, + app_version=1, + app_folder_path="Shared/ReviewEmailResponseSolution" + ) + ) + + email_response = action_data.get("InitialResponse") + + return Command(update={ + "hitl_response": email_response + }) + +# ---------------- Condition Functions ---------------- +def should_go_to_order_id_auto_reject(state: GraphState): + """Check if order_id is missing and autorejection is required""" + return "order_id_missing" if state.order_id is None else "order_id_available" + +# ---------------- Condition Functions ---------------- +def should_go_to_HITL_review(state: GraphState): + """Check if email sentiment is very negative and HITL review is required""" + return "very_negative_sentiment" if state.email_sentiment.get("label") == "Very Negative" else "agent_can_proceed_sentiment" + +# ---------------- Build Graph ---------------- +graph = StateGraph(GraphState) + +# Add all nodes +graph.add_node("translate_email_language", translate_email_language_node) #first node +graph.add_node("extract_order_id", extract_order_id_node) +graph.add_node("get_order_details", get_order_details_node) +graph.add_node("auto_reject", auto_reject_node) +graph.add_node("categorize_email", categorize_email_node) +graph.add_node("analyze_email_sentiment", analyze_email_sentiment_node) +graph.add_node("get_answer_to_query", get_answer_to_query_node) +graph.add_node("human_review", human_review_node) +graph.add_node("reply_to_email", reply_to_email_node) +graph.add_node("step_end", end_node) + +# Set entry point +graph.set_entry_point("translate_email_language") +# Add edges +graph.add_edge("translate_email_language", "extract_order_id") +#graph.add_edge("extract_order_id", "get_order_details") +graph.add_conditional_edges( + "extract_order_id", + should_go_to_order_id_auto_reject, + { + "order_id_missing": "auto_reject", + "order_id_available": "get_order_details" + } + ) +graph.add_edge("auto_reject", "step_end") +graph.add_edge("get_order_details", "categorize_email") +graph.add_edge("categorize_email", "analyze_email_sentiment") +graph.add_edge("analyze_email_sentiment", "get_answer_to_query") +graph.add_conditional_edges( + "get_answer_to_query", + should_go_to_HITL_review, + { + "very_negative_sentiment": "human_review", + "agent_can_proceed_sentiment": "reply_to_email" + } + ) +graph.add_edge("human_review", "reply_to_email") +graph.add_edge("reply_to_email", "step_end") +graph.add_edge("step_end", END) + +# Compile the graph +agent = graph.compile() \ No newline at end of file diff --git a/samples/assistmaigic-customer-care-agent/pyproject.toml b/samples/assistmaigic-customer-care-agent/pyproject.toml new file mode 100644 index 00000000..0a5b403c --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "AssistmAIgic" +version = "0.0.5" +description = "AssistmAIgic is an AI-powered agent that automates customer support by handling queries received via email. It can understand and process emails, extract relevant information like order IDs, retrieve order details, categorize emails, analyze sentiment, and generate responses using a knowledge base. It also supports multilingual communication and can escalate complex cases to human agents." +authors = [{ name = "Ashok Karale", email = "karaleashoks@gmail.com" }] +dependencies = [ + "uipath-langchain>=0.0.106", + "langchain-anthropic>=0.3.8", + "langchain-mcp-adapters>=0.1.11", +] +requires-python = ">=3.10" diff --git a/samples/assistmaigic-customer-care-agent/requirements.txt b/samples/assistmaigic-customer-care-agent/requirements.txt new file mode 100644 index 00000000..eae7347f --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/requirements.txt @@ -0,0 +1,3 @@ +langchain-anthropic +langchain-mcp-adapters +langgraph \ No newline at end of file diff --git a/samples/assistmaigic-customer-care-agent/uipath.json b/samples/assistmaigic-customer-care-agent/uipath.json new file mode 100644 index 00000000..4a0cc689 --- /dev/null +++ b/samples/assistmaigic-customer-care-agent/uipath.json @@ -0,0 +1,119 @@ +{ + "entryPoints": [ + { + "filePath": "agent", + "uniqueId": "a62d5004-9e2f-4134-beff-1f2f34f0eba0", + "type": "agent", + "input": { + "type": "object", + "properties": { + "message_id": { + "type": "string" + }, + "agent_language": { + "type": "string" + }, + "email_body": { + "type": "string" + }, + "email_subject": { + "type": "string" + }, + "translated_email_body": { + "type": "string" + }, + "translated_email_subject": { + "type": "string" + }, + "mail_communication_language": { + "type": "string" + }, + "order_id": { + "type": "string" + }, + "order_details": { + "additionalProperties": true, + "type": "object" + }, + "email_sentiment": { + "additionalProperties": true, + "type": "object" + }, + "isorder_valid": { + "type": "boolean" + }, + "email_category": { + "type": "string" + }, + "email_response": { + "type": "string" + }, + "hitl_response": { + "type": "string" + }, + "final_status": { + "type": "string" + } + }, + "required": [] + }, + "output": { + "type": "object", + "properties": { + "message_id": { + "type": "string" + }, + "agent_language": { + "type": "string" + }, + "email_body": { + "type": "string" + }, + "email_subject": { + "type": "string" + }, + "translated_email_body": { + "type": "string" + }, + "translated_email_subject": { + "type": "string" + }, + "mail_communication_language": { + "type": "string" + }, + "order_id": { + "type": "string" + }, + "order_details": { + "additionalProperties": true, + "type": "object" + }, + "email_sentiment": { + "additionalProperties": true, + "type": "object" + }, + "isorder_valid": { + "type": "boolean" + }, + "email_category": { + "type": "string" + }, + "email_response": { + "type": "string" + }, + "hitl_response": { + "type": "string" + }, + "final_status": { + "type": "string" + } + }, + "required": [] + } + } + ], + "bindings": { + "version": "2.0", + "resources": [] + } +} \ No newline at end of file diff --git a/samples/assistmaigic-customer-care-agent/uv.lock b/samples/assistmaigic-customer-care-agent/uv.lock new file mode 100644 index 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a/samples/assistmaigic-customer-care-agent/README.md b/samples/assistmaigic-customer-care-agent/README.md index 140e04f2..defa744a 100644 --- a/samples/assistmaigic-customer-care-agent/README.md +++ b/samples/assistmaigic-customer-care-agent/README.md @@ -1,6 +1,6 @@ ### ✨ **AssistmAIgic** – Intelligent Email Agent for Order & Product Support -**AssistmAIgic** is a specialized automation agent designed to streamline customer email handling for product orders and inquiries. Built on LangGraph’s state machine architecture and integrated with UiPath’s Model Context Portal (MCP), it delivers fast, accurate, and context-aware responses while ensuring human oversight for sensitive cases. +**AssistmAIgic** is a specialized automation agent designed to streamline customer email handling for product orders and inquiries. Built on LangGraph’s state machine architecture and integrated with UiPath’s Model Context Protocol (MCP), it delivers fast, accurate, and context-aware responses while ensuring human oversight for sensitive cases. image

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