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Build a Personalized Shopping Assistant with Zep Memory, GPT-4 and Google Sheets
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https://n8nworkflows.xyz/workflows/build-a-personalized-shopping-assistant-with-zep-memory--gpt-4-and-google-sheets-7363
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# Build a Personalized Shopping Assistant with Zep Memory, GPT-4 and Google Sheets
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### 1. Workflow Overview
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This workflow implements a **Personalized Shopping Assistant** for Infystore, designed to interact with customers via chat, providing real-time and context-aware answers about product inventory, order tracking, and return policies. It leverages Zep Memory for conversation context, GPT-4 via OpenAI for natural language understanding and generation, and Google Sheets as a backend database for inventory, orders, and policy data.
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**Use Cases:**
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- Checking product availability and details
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- Tracking order status by phone number
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- Providing return policy information
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- Guiding customers through return processes
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**Logical Blocks:**
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- **1.1 Input Reception:** Captures chat messages from users via a chat trigger.
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- **1.2 AI Processing and Memory:** Processes user input with GPT-4 and LangChain Agent, manages conversation memory with Zep.
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- **1.3 Data Lookup:** Queries Google Sheets for inventory, order, and return policy data as requested.
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- **1.4 Response Generation:** Uses the AI Agent node to generate context-aware, concise, polite responses based on retrieved data and conversation history.
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---
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### 2. Block-by-Block Analysis
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#### 1.1 Input Reception
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- **Overview:** Listens for incoming user chat messages through a webhook and passes them to the AI processing block.
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- **Nodes Involved:**
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- When chat message received
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- **Node Details:**
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| Node Name | Details |
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|-----------------------|---------|
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| When chat message received | - Type: LangChain Chat Trigger<br>- Role: Entry point, webhook listener for incoming chat messages.<br>- Configuration: Default webhook with no additional parameters.<br>- Input: External chat client message.<br>- Output: Passes user message payload (in `$json.chatInput`) to next node.<br>- Edge cases: Webhook failures, message format errors, concurrency issues.<br>- Version: 1.1 |
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#### 1.2 AI Processing and Memory
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- **Overview:** Uses GPT-4 model with LangChain Agent node, enriched by conversation memory stored/retrieved via Zep Memory node, to understand and generate responses to user queries.
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- **Nodes Involved:**
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- AI Agent
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- OpenAI Chat Model
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- Zep
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- **Node Details:**
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| Node Name | Details |
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|----------------|---------|
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| AI Agent | - Type: LangChain Agent Node<br>- Role: Core conversational AI logic processing user input with system prompts and chaining retrieval tools.<br>- Configuration: Uses system messages defining assistant behavior and dialogue flows for inventory, orders, returns, and general rules.<br>- Key expressions: Uses `{{$json.chatInput}}` to feed user text.<br>- Inputs: Receives user message from "When chat message received".<br>- Outputs: Sends response downstream.<br>- Integrations: Calls Google Sheets nodes as AI tools, uses Zep as AI memory.<br>- Edge cases: Misinterpretation of user input, incomplete extraction of entities, memory sync issues.<br>- Version: 2<br>- Sub-workflow: None |
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| OpenAI Chat Model | - Type: LM Chat OpenAI<br>- Role: Language model provider (GPT-4.1-mini).<br>- Configuration: Model set to "gpt-4.1-mini", linked with OpenAI credentials.<br>- Input: Text from AI Agent.<br>- Output: Language generation responses.<br>- Edge cases: API rate limits, authentication errors, latency.<br>- Version: 1.2 |
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| Zep | - Type: LangChain Memory Zep<br>- Role: Persistent conversation memory storage and retrieval.<br>- Configuration: Uses Zep API credentials.<br>- Input: Feeds conversation history to AI Agent.<br>- Output: Updated memory state.<br>- Edge cases: API connectivity, data consistency.<br>- Version: 1.3 |
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#### 1.3 Data Lookup
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- **Overview:** Queries Google Sheets for dynamic data regarding inventory, orders, and return policies as required by the conversation context.
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- **Nodes Involved:**
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- Get_Inventory
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- Get_Orders
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- Get_ReturnPolicy
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- **Node Details:**
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| Node Name | Details |
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|----------------|---------|
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| Get_Inventory | - Type: Google Sheets Tool<br>- Role: Retrieve product information (stock level, restock ETA, description) from "Product Details" sheet.<br>- Configuration: Reads from Sheet with gid=0 in specified Google Sheet document.<br>- Credentials: Google Sheets OAuth2.<br>- Input: Triggered by AI Agent to fetch product details.<br>- Output: Returns product availability data.<br>- Edge cases: Sheet access permissions, empty or missing data, query errors.<br>- Version: 4.6 |
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| Get_Orders | - Type: Google Sheets Tool<br>- Role: Fetch order details using phone number from "Order Tracking" sheet.<br>- Configuration: Reads from Sheet with gid=2103540895.<br>- Credentials: Google Sheets OAuth2.<br>- Input: Triggered by AI Agent to fetch user orders.<br>- Output: Order status, delivery ETA.<br>- Edge cases: Phone number mismatches, no orders found, data format issues.<br>- Version: 4.6 |
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| Get_ReturnPolicy | - Type: Google Sheets Tool<br>- Role: Retrieve return policy details.<br>- Configuration: Reads from Sheet with gid=1762722848.<br>- Credentials: Google Sheets OAuth2.<br>- Input: Triggered by AI Agent upon return policy inquiries.<br>- Output: Return window days, conditions.<br>- Edge cases: Missing policy data, sheet access failures.<br>- Version: 4.6 |
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#### 1.4 Response Generation
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- **Overview:** The AI Agent node composes replies based on the structured system instructions, data fetched from sheets, and conversation memory, ensuring concise, polite, and contextually relevant answers.
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- **Nodes Involved:**
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- AI Agent (same as in 1.2)
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- **Node Details:** Already covered in 1.2
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**Sticky Note:**
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- Attached near Google Sheets nodes, contains a link to the sample Google Sheet used for data storage:
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https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing
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---
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### 3. Summary Table
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| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
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|--------------------------|--------------------------------|--------------------------------------------|----------------------------|---------------------------|------------------------------------------------------------------------------------------------|
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| When chat message received | LangChain Chat Trigger | Entry point, receives chat messages | (External webhook) | AI Agent | |
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| AI Agent | LangChain Agent Node | Processes input, manages dialogue & logic | When chat message received | Zep, Get_Orders, Get_Inventory, Get_ReturnPolicy | |
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| OpenAI Chat Model | LM Chat OpenAI | Provides GPT-4 language model backend | AI Agent | AI Agent | |
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| Zep | LangChain Memory Zep | Stores and retrieves conversation memory | AI Agent | AI Agent | |
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| Get_Orders | Google Sheets Tool | Fetches order details from Google Sheets | AI Agent | AI Agent | Sample Google Sheet link: https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing |
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| Get_Inventory | Google Sheets Tool | Fetches product inventory details | AI Agent | AI Agent | Sample Google Sheet link: https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing |
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| Get_ReturnPolicy | Google Sheets Tool | Retrieves return policy information | AI Agent | AI Agent | Sample Google Sheet link: https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing |
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| Sticky Note | Sticky Note | Provides sample Google Sheets link | | | https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing |
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---
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### 4. Reproducing the Workflow from Scratch
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1. **Create the "When chat message received" node:**
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- Type: LangChain Chat Trigger
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- Purpose: Receive incoming chat messages via webhook.
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- Configuration: Default settings; no extra parameters.
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- Position: Leftmost, start of workflow.
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2. **Create the "AI Agent" node:**
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- Type: LangChain Agent
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- Connect input to "When chat message received" node main output.
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- Parameter “text”: Set to `={{ $json.chatInput }}`
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- System message: Insert comprehensive instructions defining assistant role, dialogue flows for inventory, orders, returns, and general rules as per overview.
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- Set prompt type to "define".
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- Connect AI Agent outputs to Zep and the three Google Sheets nodes as AI tools.
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3. **Set up the "OpenAI Chat Model" node:**
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- Type: LM Chat OpenAI
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- Model: Select "gpt-4.1-mini".
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- Credentials: Configure with valid OpenAI API credentials.
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- Connect output to AI Agent’s language model input.
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4. **Create the "Zep" memory node:**
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- Type: LangChain Memory Zep
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- Credentials: Configure with valid Zep API credentials.
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- Connect input and output to AI Agent’s memory interface.
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5. **Create three Google Sheets nodes for data retrieval:**
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a. **"Get_Inventory":**
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- Type: Google Sheets Tool
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- Document ID: Use the Google Sheet ID `17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo`
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- Sheet Name: Use gid `0` (Product Details sheet)
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- Credentials: Configure with Google Sheets OAuth2 credentials.
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- Connect input from AI Agent’s AI tool output.
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b. **"Get_Orders":**
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- Type: Google Sheets Tool
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- Document ID: Same as above.
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- Sheet Name: Use gid `2103540895` (Order Tracking sheet)
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- Credentials: Same Google OAuth2.
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- Connect input from AI Agent’s AI tool output.
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c. **"Get_ReturnPolicy":**
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- Type: Google Sheets Tool
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- Document ID: Same as above.
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- Sheet Name: Use gid `1762722848` (Return Policy sheet)
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- Credentials: Same Google OAuth2.
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- Connect input from AI Agent’s AI tool output.
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6. **Arrange connections:**
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- "When chat message received" → "AI Agent"
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- "AI Agent" → "Zep" (ai_memory)
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- "AI Agent" → "Get_Orders" (ai_tool)
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- "AI Agent" → "Get_Inventory" (ai_tool)
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- "AI Agent" → "Get_ReturnPolicy" (ai_tool)
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- "OpenAI Chat Model" → "AI Agent" (ai_languageModel)
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7. **Credentials setup:**
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- Obtain and configure:
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- OpenAI API credentials (for GPT-4)
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- Zep API credentials (for memory)
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- Google Sheets OAuth2 credentials with access to the specified Google Sheet
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8. **System message configuration:**
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- Copy the detailed system prompt provided in AI Agent node parameters, describing assistant behavior, dialogue flows for inventory, order tracking, return policies, and general response rules.
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9. **Testing and validation:**
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- Send test chat messages via webhook.
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- Verify responses align with inventory data, order tracking info, and return policies.
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- Validate conversation memory persistence across multiple messages.
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---
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### 5. General Notes & Resources
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| Note Content | Context or Link |
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|-----------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
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| Sample Google Sheet URL containing inventory, order tracking, and return policy sheets: | https://docs.google.com/spreadsheets/d/17PsTWr5shCgA2RnHuj1xwqSRX7uBcb-psRYZA2jFOTo/edit?usp=sharing |
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| System prompt includes strict rules: no markdown, no images, no repeated questions, concise and polite replies. | Embedded in AI Agent node’s systemMessage parameter. |
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| Workflow uses LangChain Agent pattern integrating multiple AI tools and memory node for contextual assistance. | n8n LangChain nodes documentation: https://docs.n8n.io/integrations/builtin/core-nodes/langchain-agent/ |
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---
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**Disclaimer:** The provided text and documentation exclusively originate from an automated workflow created with n8n, adhering strictly to content policies, handling only legal and public data.
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