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WhatsApp Recipe Suggestions from Pantry Items with Gemini AI & FatSecret API
https://n8nworkflows.xyz/workflows/whatsapp-recipe-suggestions-from-pantry-items-with-gemini-ai---fatsecret-api-7919
# WhatsApp Recipe Suggestions from Pantry Items with Gemini AI & FatSecret API
### 1. Workflow Overview
This workflow enables a WhatsApp chatbot that suggests recipes based on a user's pantry items and preferences, leveraging Google’s Gemini AI for natural language understanding and the FatSecret API for recipe data retrieval. The main use case is a conversational cooking assistant where users text ingredients or dietary needs via WhatsApp and receive tailored recipe suggestions.
The workflow logic is organized into these blocks:
- **1.1 Input Reception:** Captures incoming WhatsApp messages.
- **1.2 AI Processing & Memory:** Uses Google Gemini AI and a memory buffer to interpret user messages, maintain context, and convert free text into structured recipe search parameters.
- **1.3 Recipe Search Tool Invocation:** Calls the FatSecret Recipes Search API with AI-generated parameters.
- **1.4 Response Composition & Delivery:** Formats AI responses and recipe results, then sends them back to the user on WhatsApp.
---
### 2. Block-by-Block Analysis
#### Block 1.1: Input Reception
**Overview:**
This block captures incoming WhatsApp messages in real time via a webhook, triggering the workflow to process user queries.
**Nodes Involved:**
- WhatsApp Trigger
**Node Details:**
- **WhatsApp Trigger**
- **Type & Role:** Captures WhatsApp message events via webhook.
- **Configuration:** Listens specifically for "messages" updates; uses WhatsApp Trigger API credentials linked to the chatbot.
- **Expressions/Variables:** Extracts sender phone number and message text from incoming JSON at `messages[0].from` and `messages[0].text.body`.
- **Connections:** Outputs to "The Chef Agent" node for AI processing.
- **Edge Cases:** Webhook connectivity issues, invalid or unsupported message formats, or API authentication failures.
- **Version:** 1
---
#### Block 1.2: AI Processing & Memory
**Overview:**
Processes the user’s message using Google Gemini AI to extract structured recipe search parameters, while maintaining conversational context using a memory buffer.
**Nodes Involved:**
- The Chef Agent
- Google Gemini Chat Model1
- Simple Memory
**Node Details:**
- **The Chef Agent**
- **Type & Role:** LangChain agent node acting as the core AI processor and orchestrator.
- **Configuration:**
- Input text is the user's WhatsApp message body.
- System prompt defines roles and instructions for PantryChef, including how to parse user input into FatSecret API parameters (via `$fromAI` keys).
- Handles multiple intents: ingredient extraction, dietary restrictions, calorie limits, cuisine preferences, paging, etc.
- Outputs either AI-generated parameters to call the FatSecret API or a user prompt if clarification is needed.
- **Expressions/Variables:** Uses `$json.messages[0].text.body` for input text; emits `$fromAI` keys for parameters like `search_expression`, `cal_max`, `page_number`.
- **Input Connections:** From WhatsApp Trigger; AI language model and HTTP tool integrations via LangChain AI plugin connections.
- **Output Connections:** Main output to "Send message" (WhatsApp response); AI tool output to "Fatsecret_Recipes" HTTP request; AI memory output to "Simple Memory".
- **Edge Cases:** Misinterpretation of user input, failure to extract parameters, API rate limits, or timeout in AI response.
- **Version:** 2.2
- **Google Gemini Chat Model1**
- **Type & Role:** Google Gemini AI language model node used by the agent for natural language understanding and generation.
- **Configuration:** Uses Google PaLM API credentials. No additional options configured explicitly.
- **Connections:** Provides language model integration to "The Chef Agent".
- **Edge Cases:** API quota exhaustion, authentication failure, or latency causing delays.
- **Version:** 1
- **Simple Memory**
- **Type & Role:** LangChain memory node storing recent conversational context to maintain state across messages.
- **Configuration:** Uses a windowed memory buffer keyed by sender phone number (`sessionKey` from WhatsApp sender); keeps last 5 messages for context.
- **Connections:** Memory input/output linked with "The Chef Agent" for conversational continuity.
- **Edge Cases:** Memory overflow if many users simultaneously; session key extraction failures if incoming message format changes.
- **Version:** 1.3
---
#### Block 1.3: Recipe Search Tool Invocation
**Overview:**
Executes the API call to FatSecret’s recipe search endpoint using parameters extracted by the AI agent, to retrieve matching recipes.
**Nodes Involved:**
- Fatsecret_Recipes
**Node Details:**
- **Fatsecret_Recipes**
- **Type & Role:** HTTP Request node calling FatSecret’s v3 recipe search API.
- **Configuration:**
- Method: GET with query parameters.
- Query parameters include `search_expression`, `format=json`, `max_results=5`, and optional calorie max (`calories.to`).
- OAuth2 credentials configured for FatSecret API authentication.
- **Expressions/Variables:** Values for `search_expression` and `calories.to` are dynamically injected from AI agent outputs via `$fromAI` overrides.
- **Connections:** Invoked as AI tool call from "The Chef Agent".
- **Edge Cases:** OAuth token expiration or failure, network timeouts, invalid parameter values causing API errors, rate limiting by FatSecret.
- **Version:** 4.2
---
#### Block 1.4: Response Composition & Delivery
**Overview:**
Formats the AI-generated recipe suggestions or clarification prompts and sends them back to the user on WhatsApp.
**Nodes Involved:**
- Send message
**Node Details:**
- **Send message**
- **Type & Role:** WhatsApp node sending outbound messages to users.
- **Configuration:**
- Sends text taken from `$json.output` generated by "The Chef Agent".
- Uses WhatsApp API credentials for message delivery.
- Recipient phone number dynamically set to the sender's number extracted from the incoming message.
- **Connections:** Connected from "The Chef Agent" main output node.
- **Edge Cases:** Failures in WhatsApp API connectivity, invalid recipient number, message formatting issues, or rate limiting by WhatsApp.
- **Version:** 1
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|----------------------|---------------------------------------------|------------------------------------|---------------------|----------------------|----------------------------------|
| WhatsApp Trigger | WhatsApp Trigger | Receive user messages | | The Chef Agent | |
| The Chef Agent | LangChain Agent | AI processing, parameter extraction | WhatsApp Trigger | Send message, Fatsecret_Recipes, Simple Memory | |
| Google Gemini Chat Model1 | LangChain Google Gemini AI Model | Language model for AI processing | | The Chef Agent | |
| Simple Memory | LangChain Memory Buffer (Window) | Maintain context memory | The Chef Agent (ai_memory) | The Chef Agent (ai_memory) | |
| Fatsecret_Recipes | HTTP Request Tool | Call FatSecret Recipe Search API | The Chef Agent (ai_tool) | The Chef Agent (ai_tool) | |
| Send message | WhatsApp Node | Send response messages to users | The Chef Agent | | |
---
### 4. Reproducing the Workflow from Scratch
1. **Create WhatsApp Trigger Node:**
- Type: WhatsApp Trigger
- Configure webhook with your WhatsApp Business API credentials.
- Enable listening for "messages" updates.
- No special parameters.
- Connect output to The Chef Agent node.
2. **Create The Chef Agent Node:**
- Type: LangChain Agent
- Set input text to `{{$json.messages[0].text.body}}` (extract WhatsApp user message).
- Paste the provided system prompt defining PantryChef’s role, input parsing rules, output contract, and examples.
- Under "AI Language Model," link to "Google Gemini Chat Model1".
- Under "AI Tool," link to "Fatsecret_Recipes" HTTP request node.
- Under "AI Memory," link to "Simple Memory" node.
- Version 2.2 or latest recommended.
- Connect main output to "Send message".
3. **Create Google Gemini Chat Model1 Node:**
- Type: LangChain Google Gemini AI Model
- Provide Google PaLM API credentials with required scopes and billing enabled.
- No additional options required.
- Connect output to "The Chef Agent" AI language model input.
4. **Create Simple Memory Node:**
- Type: LangChain Memory Buffer Window
- Set `sessionKey` to `={{ $('WhatsApp Trigger').item.json.messages[0].from }}` to key memory by sender.
- Set `contextWindowLength` to 5 messages.
- Connect memory input and output to "The Chef Agent" AI memory interface.
5. **Create Fatsecret_Recipes HTTP Request Node:**
- Type: HTTP Request
- Set HTTP method to GET.
- URL: `https://platform.fatsecret.com/rest/recipes/search/v3`
- Add Query Parameters:
- `search_expression` from AI output `$fromAI('parameters0_Value')`
- `format` = `json` (fixed)
- `max_results` = `5` (fixed)
- `calories.to` from AI output `$fromAI('parameters3_Value')` (optional)
- Authentication: OAuth2 using FatSecret API credentials.
- Connect as AI tool input to "The Chef Agent".
6. **Create Send message Node:**
- Type: WhatsApp Node
- Configure WhatsApp API credentials for sending messages.
- Set `textBody` to `={{ $json.output }}` (AI-generated reply).
- Set `recipientPhoneNumber` to `={{ $('WhatsApp Trigger').item.json.messages[0].from }}`.
- Connect main input from "The Chef Agent".
7. **Workflow Connections:**
- WhatsApp Trigger → The Chef Agent
- Google Gemini Chat Model1 → The Chef Agent (AI language model)
- Simple Memory → The Chef Agent (AI memory)
- Fatsecret_Recipes → The Chef Agent (AI tool)
- The Chef Agent → Send message (main output)
8. **Credentials Setup:**
- Configure WhatsApp Trigger and Send message nodes with WhatsApp Business API OAuth2 credentials.
- Configure Google Gemini Chat Model1 with Google PaLM API credentials.
- Configure Fatsecret_Recipes with OAuth2 credentials for FatSecret API.
- Ensure all tokens are valid and permissions enabled.
9. **Test Workflow:**
- Send test WhatsApp messages with ingredient lists or recipe requests to trigger the flow.
- Verify AI extracts parameters, calls FatSecret API, and replies appropriately.
- Adjust system prompt or query parameters as needed.
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------|
| The system prompt in "The Chef Agent" node is central to parsing user messages into precise API calls; tweaking it enables refinement of AI behavior and response style. | N/A |
| Google Gemini (PaLM) API requires setup in Google Cloud Console with appropriate billing and API access enabled for n8n integration. | https://cloud.google.com/palm |
| FatSecret API v3 requires OAuth2 credentials and proper scopes for recipe search; refer to FatSecret developer docs for client registration and token management. | https://platform.fatsecret.com/ |
| WhatsApp Business API credentials must be configured with valid webhook URLs and phone numbers; this workflow assumes a single phone number ID configured for sending messages. | https://developers.facebook.com/docs/whatsapp |
| For best conversational experience, keep memory window short (here 5 messages) to balance context and performance. | N/A |
| The workflow is designed for English language input; adapting for other languages requires modifying the system prompt and potentially AI model settings. | N/A |
---
**Disclaimer:**
The provided text originates exclusively from an automated workflow created with n8n, a tool for integration and automation. This processing strictly adheres to content policies and contains no illegal, offensive, or protected material. All data handled is legal and public.