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Automated Email Classification & Response System with Groq AI and Pinecone
https://n8nworkflows.xyz/workflows/automated-email-classification---response-system-with-groq-ai-and-pinecone-6202
# Automated Email Classification & Response System with Groq AI and Pinecone
### 1. Workflow Overview
This workflow automates the classification and response process for incoming emails using a combination of rule-based logic, Large Language Models (LLMs) including Groq AI models, and vector search with Pinecone. It integrates email reception, categorization by topic, AI-assisted decision making, sentiment analysis, and dynamic routing of responses or notifications to relevant teams. The workflow also supports processing business documents into a vector database for enriched AI context retrieval.
**Target Use Cases:**
- Automated triage of customer emails by category (HR, billing, complaints, feedback, inquiries, sales, unknown)
- AI-enhanced evaluation of job applications
- Sentiment analysis of feedback emails
- Automated replies to customer inquiries using retrieval-augmented generation (RAG) based on company documents
- Notifications to internal teams depending on category
- Document ingestion and embedding to support AI responses
**Logical Blocks:**
- **1.1 Email Reception and Basic Categorization:** Receiving emails via IMAP, initial rule-based classification
- **1.2 AI Classification Fallback:** Use of Groq AI and LangChain LLMs to classify emails that don’t match simple rules
- **1.3 Category-Based Routing:** Switch node routes emails to different processing paths and email notifications
- **1.4 HR Candidate Evaluation:** AI evaluates candidate suitability and triggers acceptance/rejection workflows
- **1.5 Feedback Sentiment Analysis:** Sentiment analysis on feedback emails with notifications and social media posting
- **1.6 Inquiry Handling with RAG:** Retrieval augmented generation for answering customer inquiries using Pinecone vector search
- **1.7 Document Processing to Vector Store:** Ingest business documents (PDF) into Pinecone for knowledge base enrichment
---
### 2. Block-by-Block Analysis
#### 1.1 Email Reception and Basic Categorization
- **Overview:**
This block listens for new emails via IMAP, downloads attachments, and applies a JavaScript rule-based categorization based on subject keywords.
- **Nodes Involved:**
- Email Trigger (IMAP)
- Code (Rule-based Category Assignment)
- Switch1 (Category Routing)
- Sticky Note (EMAIL CLASSIFIER USING SWITCH)
- **Node Details:**
- **Email Trigger (IMAP)**
- Type: Email Read (IMAP)
- Role: Listens for incoming emails and downloads attachments
- Config: Default IMAP settings, attachments enabled
- Inputs: none (trigger node)
- Outputs: Email JSON with metadata and content
- Edge cases: Connection/authentication failures, attachment download errors
- **Code (Rule-based categorization)**
- Type: Code node (JavaScript)
- Role: Assigns email category by checking subject keywords (e.g., "resume" → hr, "invoice" → billing)
- Key expressions: Regex tests on lowercase subject
- Inputs: Email JSON from IMAP trigger
- Outputs: JSON with added `category` field
- Edge cases: Missing subject, unexpected subject formats
- **Switch1**
- Type: Switch node
- Role: Routes emails based on the `category` field computed in the Code node
- Categories: hr, billing, complaint, feedback, inquiry, sales, unknown
- Inputs: Categorized email JSON
- Outputs: Branches per category for further processing
- Edge cases: Misclassification if category not matched correctly
- **Sticky Note (EMAIL CLASSIFIER USING SWITCH)**
- Annotation describing the purpose of the Switch node for visual clarity
#### 1.2 AI Classification Fallback
- **Overview:**
For emails that cannot be categorized confidently by rules, this block uses Groq AI and LangChain LLM chains to classify emails and normalize AI outputs.
- **Nodes Involved:**
- Basic LLM Chain
- Groq Chat Model
- CLEAN AI AGENT OUTPUT (Code)
- Sticky Note1 (USE FOR AI AGENT WHEN SWITCH FAILS)
- **Node Details:**
- **Basic LLM Chain**
- Type: LangChain LLM chain
- Role: Given subject and body, return exact category from fixed list
- Config: Prompt specifying categories and instructions for classification
- Inputs: Email JSON from Switch1’s unknown category branch
- Outputs: LLM response text with predicted category
- Edge cases: LLM response not matching expected categories
- **Groq Chat Model**
- Type: Groq AI chat model node
- Role: Underlying language model powering the Basic LLM Chain
- Config: Model "llama-3.3-70b-versatile" selected
- Inputs: Text prompt from Basic LLM Chain
- Outputs: Model-generated classification text
- Edge cases: API timeout, rate limits, connectivity issues
- **CLEAN AI AGENT OUTPUT (Code)**
- Type: Code node
- Role: Normalizes LLM output text to one of known categories or "unknown"
- Key expressions: Lowercase trim, inclusion check against category list
- Inputs: Raw LLM output
- Outputs: JSON with standardized `category` field
- Edge cases: Unexpected output formats, empty responses
- **Sticky Note1**
- Explains this block is used when the rule-based switch fails to classify emails
#### 1.3 Category-Based Routing and Notifications
- **Overview:**
Routes emails to different teams or internal workflows based on category, triggering email notifications or further AI processing.
- **Nodes Involved:**
- Multiple emailSend nodes: to hr, to sales, send to customer, send to support team, send email to team
- Sentiment Analysis of feedback
- Switch node for HR acceptance/rejection
- Switch1 main branches
- Sticky Note2 (check if feedback is positive or negative)
- **Node Details:**
- **Send to Customer / Support / Teams (emailSend nodes)**
- Type: Email Send nodes
- Role: Send category-specific emails (e.g., complaint acknowledgment, billing notification, sales lead forwarding)
- Config: Dynamic email subjects and bodies based on JSON data, emails sent from configured credentials
- Inputs: JSON from Switch1 or downstream nodes
- Outputs: None (side effect nodes)
- Edge cases: SMTP authentication issues, invalid recipient emails
- **Sentiment Analysis of feedback**
- Type: LangChain Sentiment Analysis node
- Role: Analyze feedback emails to categorize sentiment as Positive, Neutral, or Negative
- Inputs: Plain text of feedback email
- Outputs: Sentiment category for further routing or notification
- Edge cases: Ambiguous sentiment, empty text
- **Switch (HR decision)**
- Routes AI candidate evaluation to acceptance or rejection email workflows
- **Sticky Note2**
- Notes the sentiment analysis step on feedback emails
#### 1.4 HR Candidate Evaluation
- **Overview:**
Evaluates job application emails via AI, deciding acceptance or rejection and notifying HR and the candidate accordingly.
- **Nodes Involved:**
- Basic LLM Chain1 (HR evaluation prompt)
- Groq Chat Model3 (AI engine for HR evaluation)
- Code2 (parse AI decision)
- Switch (decision accept/reject)
- accepted confirm to candidate (emailSend)
- rejection email (emailSend)
- to hr (emailSend)
- **Node Details:**
- **Basic LLM Chain1**
- Role: AI prompt to evaluate candidate email/resume against job description
- Inputs: Candidate email text
- Outputs: AI decision text (ACCEPT/REJECT with explanation)
- **Groq Chat Model3**
- Groq AI model powering the candidate evaluation prompt
- **Code2**
- Parses AI response to decide acceptance or rejection
- Outputs decision flag and response text
- **Switch**
- Routes based on decision field to acceptance or rejection email flows
- **accepted confirm to candidate / rejection email**
- Sends email notifications to candidate about application status
- **to hr**
- Notifies HR team about accepted candidates
#### 1.5 Feedback Sentiment Analysis and Social Media Posting
- **Overview:**
Analyzes feedback sentiment, sends notifications, and posts positive feedback to Twitter.
- **Nodes Involved:**
- Sentiment Analysis of feedback
- Groq Chat Model1 (sentiment AI)
- X (Twitter node)
- send email to team (for negative feedback notifications)
- **Node Details:**
- **Sentiment Analysis of feedback & Groq Chat Model1**
- Combined to analyze and confirm sentiment category
- **X (Twitter)**
- Posts positive customer feedback as tweets
- Configured with Twitter credentials
- Inputs: Feedback text
- **send email to team**
- Sends email notifications on negative feedback to internal team
#### 1.6 Inquiry Handling with RAG (Retrieval Augmented Generation)
- **Overview:**
For inquiry category emails, retrieves relevant context from Pinecone vector store and generates AI email replies.
- **Nodes Involved:**
- Pinecone Vector Store (retrieve mode)
- Embeddings Cohere (embeddings generation)
- RAG INQURY REPLY (LangChain agent)
- Groq Chat Model2 (underlying AI model)
- send reply to customer (emailSend)
- **Node Details:**
- **Embeddings Cohere**
- Generates embeddings for incoming inquiry email text for similarity search
- **Pinecone Vector Store**
- Retrieves relevant documents based on embeddings as context for AI reply
- **RAG INQURY REPLY**
- Agent node uses retrieved context and inquiry text to generate professional response email
- Has output parser to structure AI output
- **Groq Chat Model2**
- AI engine for RAG response generation
- **send reply to customer**
- Sends AI-generated response email to the customer
#### 1.7 Document Processing to Vector Store
- **Overview:**
Imports business documents (PDF) from Dropbox, extracts text, cleans it, splits for chunking, generates embeddings, and inserts into Pinecone vector database to enrich company knowledge base.
- **Nodes Involved:**
- When clicking ‘Execute workflow’ (manual trigger)
- HTTP Request (downloads PDF from Dropbox)
- Extract from File (PDF text extraction)
- Code3 (text cleaning)
- Pinecone Vector Store1 (insert mode)
- Embeddings Cohere1 (multilingual embeddings)
- Default Data Loader (prepares document for vector store)
- Recursive Character Text Splitter (splits text into chunks)
- Sticky Note3 (details of business in pdf form to vector db)
- **Node Details:**
- **When clicking ‘Execute workflow’**
- Manual trigger to start document ingestion
- **HTTP Request**
- Downloads PDF document from provided Dropbox URL
- Edge cases: network failures, invalid URL
- **Extract from File**
- Extracts text content from PDF file
- **Code3**
- Cleans extracted text by trimming and removing extra whitespace
- **Recursive Character Text Splitter**
- Splits cleaned text into manageable chunks for vector embedding
- **Default Data Loader**
- Prepares document chunks for embedding and insertion
- **Embeddings Cohere1**
- Generates multilingual embeddings for document chunks
- **Pinecone Vector Store1**
- Inserts embeddings into Pinecone knowledge base index "demokb"
- **Sticky Note3**
- Describes purpose of this document ingestion flow
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|------------------------------|-------------------------------------|---------------------------------------------|-----------------------------|-------------------------------|---------------------------------------------------------------|
| Email Trigger (IMAP) | n8n-nodes-base.emailReadImap | Incoming email reception | - | Code | |
| Code | n8n-nodes-base.code | Rule-based email categorization | Email Trigger (IMAP) | Switch1 | |
| Switch1 | n8n-nodes-base.switch | Routes emails by category | Code | Multiple category branches | EMAIL CLASSIFIER USING SWITCH |
| Basic LLM Chain | @n8n/n8n-nodes-langchain.chainLlm | AI fallback email classification | Switch1 (unknown branch) | CLEAN AI AGENT OUTPUT | USE FOR AI AGENT WHEN SWITCH FAILS |
| Groq Chat Model | @n8n/n8n-nodes-langchain.lmChatGroq | LLM engine for classification | Basic LLM Chain | Basic LLM Chain | |
| CLEAN AI AGENT OUTPUT | n8n-nodes-base.code | Normalize AI classification output | Basic LLM Chain | Switch1 | USE FOR AI AGENT WHEN SWITCH FAILS |
| Basic LLM Chain1 | @n8n/n8n-nodes-langchain.chainLlm | Evaluate job application suitability | Switch1 (hr branch) | Code2 | |
| Groq Chat Model3 | @n8n/n8n-nodes-langchain.lmChatGroq | LLM engine for HR candidate evaluation | Basic LLM Chain1 | Basic LLM Chain1 | |
| Code2 | n8n-nodes-base.code | Parse AI decision (accept/reject) | Basic LLM Chain1 | Switch | |
| Switch | n8n-nodes-base.switch | Routes candidate decision to email flows | Code2 | accepted confirm to candidate, rejection email | |
| accepted confirm to candidate| n8n-nodes-base.emailSend | Send acceptance email to candidate | Switch | to hr | |
| rejection email | n8n-nodes-base.emailSend | Send rejection email to candidate | Switch | - | |
| to hr | n8n-nodes-base.emailSend | Notify HR team of accepted candidate | accepted confirm to candidate| - | |
| Sentiment Analysis of feedback| @n8n/n8n-nodes-langchain.sentimentAnalysis| Analyze sentiment of feedback emails | Switch1 (feedback branch) | X, send email to team | check if feedback is positive or negative |
| Groq Chat Model1 | @n8n/n8n-nodes-langchain.lmChatGroq | LLM engine for sentiment analysis | Sentiment Analysis of feedback| Sentiment Analysis of feedback| |
| X | n8n-nodes-base.twitter | Post positive feedback tweets | Sentiment Analysis of feedback| - | |
| send email to team | n8n-nodes-base.emailSend | Notify team of negative feedback | Sentiment Analysis of feedback| - | |
| Send to customer | n8n-nodes-base.emailSend | Acknowledge complaints to customers | Switch1 (complaint branch) | send to support team | |
| send to support team | n8n-nodes-base.emailSend | Notify support team of complaints | Send to customer | - | |
| TO SALES TEAM | n8n-nodes-base.emailSend | Forward sales emails to sales team | Switch1 (sales branch) | - | |
| bill send to team | n8n-nodes-base.emailSend | Notify billing team of billing emails | Switch1 (billing branch) | - | |
| RAG INQURY REPLY | @n8n/n8n-nodes-langchain.agent | Generate AI replies to inquiries from RAG context | Pinecone Vector Store | send reply to customer | |
| Groq Chat Model2 | @n8n/n8n-nodes-langchain.lmChatGroq | LLM engine for RAG-based inquiry reply | RAG INQURY REPLY | RAG INQURY REPLY | |
| Pinecone Vector Store | @n8n/n8n-nodes-langchain.vectorStorePinecone| Retrieve documents for inquiry context | Embeddings Cohere | RAG INQURY REPLY | |
| Embeddings Cohere | @n8n/n8n-nodes-langchain.embeddingsCohere| Generate embeddings for inquiry text | Switch1 (inquiry branch) | Pinecone Vector Store | |
| send reply to customer | n8n-nodes-base.emailSend | Send AI-generated replies to customers | RAG INQURY REPLY | - | |
| When clicking ‘Execute workflow’| n8n-nodes-base.manualTrigger | Manual trigger to start document ingestion | - | HTTP Request | details of business in pdf form to vector db |
| HTTP Request | n8n-nodes-base.httpRequest | Download PDF business document | When clicking ‘Execute workflow’| Extract from File | |
| Extract from File | n8n-nodes-base.extractFromFile | Extract text from downloaded PDF | HTTP Request | Code3 | |
| Code3 | n8n-nodes-base.code | Clean extracted text | Extract from File | Pinecone Vector Store1 | |
| Recursive Character Text Splitter| @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter| Break text into chunks for embedding | Default Data Loader | Default Data Loader | |
| Default Data Loader | @n8n/n8n-nodes-langchain.documentDefaultDataLoader| Prepare text chunks for embedding | Recursive Character Text Splitter| Pinecone Vector Store1 | |
| Embeddings Cohere1 | @n8n/n8n-nodes-langchain.embeddingsCohere| Generate multilingual embeddings for document chunks| Default Data Loader | Pinecone Vector Store1 | |
| Pinecone Vector Store1 | @n8n/n8n-nodes-langchain.vectorStorePinecone| Insert embeddings into Pinecone vector DB | Embeddings Cohere1 | - | |
| Sticky Note | n8n-nodes-base.stickyNote | Workflow annotation | - | - | EMAIL CLASSIFIER USING SWITCH |
| Sticky Note1 | n8n-nodes-base.stickyNote | Workflow annotation | - | - | USE FOR AI AGENT WHEN SWITCH FAILS |
| Sticky Note2 | n8n-nodes-base.stickyNote | Workflow annotation | - | - | check if feedback is positive or negative |
| Sticky Note3 | n8n-nodes-base.stickyNote | Workflow annotation | - | - | details of business in pdf form to vector db |
---
### 4. Reproducing the Workflow from Scratch
1. **Create Email Trigger (IMAP):**
- Type: Email Read (IMAP)
- Settings: Enable download attachments, configure IMAP credentials and folder
2. **Add Code node (Rule-based Categorization):**
- Run once for each item
- JS code tests email subject for keywords to assign a category field (hr, billing, complaint, feedback, inquiry, sales, unknown)
3. **Add Switch node (Switch1):**
- Input: category field
- Add output branches for categories: hr, billing, complaint, feedback, inquiry, sales, unknown
4. **For unknown category branch:**
- Add Basic LLM Chain node:
- Prompt: Provide subject and body, request one of known categories
- Add Groq Chat Model node:
- Select model "llama-3.3-70b-versatile"
- Connect Groq Chat Model as AI model for Basic LLM Chain
- Add Code node to normalize AI output to known categories or unknown
- Connect output to Switch1 again for rerouting
5. **For hr category branch:**
- Add Basic LLM Chain1 node with prompt to evaluate candidate email suitability
- Add Groq Chat Model3 node as AI model
- Connect Groq Chat Model3 to Basic LLM Chain1
- Add Code2 node to parse AI decision (accept/reject)
- Add Switch node to route accept or reject outputs
- For accept branch: add emailSend node "accepted confirm to candidate" to notify candidate
- Configure fromEmail, to candidate’s email (from original email trigger)
- Add another emailSend node "to hr" to notify HR team of acceptance
- For reject branch: add emailSend node "rejection email" to notify candidate
6. **For billing category branch:**
- Add emailSend node "bill send to team" to notify billing team
7. **For complaint category branch:**
- Add emailSend node "Send to customer" to acknowledge complaint to customer
- Connect to emailSend node "send to support team" to notify internal support
8. **For feedback category branch:**
- Add Sentiment Analysis node: configure categories Positive, Neutral, Negative
- Add Groq Chat Model1 node as AI model for sentiment analysis
- Add Twitter node "X" configured with Twitter credentials to post positive feedback tweets
- Add emailSend node "send email to team" to notify internal team on negative feedback
9. **For inquiry category branch:**
- Add Embeddings Cohere node to generate embeddings for inquiry text
- Add Pinecone Vector Store node in retrieve mode, linked to "demokb" index
- Add RAG INQURY REPLY LangChain agent node with prompt to reply using retrieved context
- Add Groq Chat Model2 as AI model
- Add emailSend node "send reply to customer" to send generated reply
10. **For sales category branch:**
- Add emailSend node "TO SALES TEAM" to forward sales emails
11. **Document ingestion workflow:**
- Add Manual Trigger node "When clicking ‘Execute workflow’"
- Add HTTP Request node to download PDF from Dropbox URL
- Add Extract from File node to extract text from PDF
- Add Code node (Code3) to clean extracted text
- Add Recursive Character Text Splitter node to chunk text
- Add Default Data Loader node to prepare chunks
- Add Embeddings Cohere1 node for multilingual embeddings
- Add Pinecone Vector Store1 node in insert mode to add documents to "demokb" index
12. **Set up credentials for:**
- IMAP email account
- SMTP email for sending (used in all emailSend nodes)
- Groq AI API for language model nodes
- Cohere API for embeddings nodes
- Pinecone API for vector store nodes
- Twitter API for posting tweets
13. **Test end-to-end:**
- Send test emails for each category
- Trigger manual ingestion for documents
- Verify AI classification fallback triggers on unknown subjects
- Check email notifications and Twitter posting
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| Workflow integrates Groq AI LLMs with Pinecone vector database for advanced email automation | |
| Uses LangChain nodes for chaining prompts and handling AI outputs | |
| Twitter node posts positive customer feedback as social proof | Twitter API documentation: https://developer.twitter.com/en/docs/twitter-api |
| Pinecone used for vector similarity search and document retrieval | Pinecone docs: https://docs.pinecone.io/ |
| Cohere embeddings used for both English and multilingual text | Cohere embeddings docs: https://docs.cohere.ai/ |
| Manual trigger allows batch document ingestion to update knowledge base | |
| Email credentials must be securely configured with OAuth2 or app passwords depending on provider | |
---
**Disclaimer:**
The text provided is exclusively derived from an automated workflow created with n8n, a workflow automation tool. All content complies with applicable content policies and contains no illegal, offensive, or protected material. All processed data is legal and public.