Oct 19 • Support Needed 💻
Best approach for extracting structured data from AI chat conversations in n8n workflow?
I'm building an automotive damage assessment chatbot workflow in n8n that collects customer information through conversation. I'm facing a challenge with data extraction and storage strategy.
## Current Setup
- **n8n workflow** with AI Agent (Google Gemini)
- **PostgreSQL database** (`claim_sessions` table with ~50 columns)
- **Two data sources:**
1. **OCR extraction** from uploaded documents (ID, vehicle registration, police reports) - field names are known and consistent
2. **Chat conversations** where AI collects data like name, address, accident details, etc. - field names are unpredictable
## The Problem
For OCR data, I can easily normalize field names since I control the OCR prompt. But for chat-collected data, the AI agent might name fields arbitrarily:
- User: "My name is Max Mustermann"
- AI might store as: `{name: "Max Mustermann"}` or `{vorname: "Max", nachname: "Mustermann"}` or `{customer_name: "..."}`
I need consistent field names matching my database schema (e.g., `vorname`, `nachname`, `strasse_nr`, `plz`, `kennzeichen`, etc.)
## Options I'm Considering
### Option 1: Structured Output Parser
- Add Output Parser node to AI Agent
- Force AI to output JSON with exact schema every response
- **Pros:** Guaranteed structure, direct DB insert
- **Cons:** Verbose (AI outputs full schema every message), complex setup, might limit natural conversation
### Option 2: Post-Processing Data Extraction
- AI Agent generates natural conversation
- After each response, run separate AI call to extract structured data from the conversation
- Use Claude/Gemini API with prompt: "Extract all mentioned data into this exact JSON schema: {vorname, nachname, ...}"
- **Pros:** Natural conversation, flexible
- **Cons:** Extra API call per message, costs more tokens, slight latency
### Option 3: Hybrid Approach
- OCR data → Automatic normalization & DB save (works great)
- Chat data → Keep in Chat Memory only
- Final step → AI summarizes all collected info, user confirms, then save to DB
- **Pros:** Simple, works immediately, fewer API calls
- **Cons:** Data only persists after full conversation completion
### Option 4: In-Prompt Schema Enforcement
- Add detailed field naming instructions to AI system prompt
- List all 50+ database fields with descriptions
- Hope AI follows naming convention consistently
- **Pros:** No extra nodes/API calls
- **Cons:** Unreliable, AI might still use different names, hard to validate
## Current Workflow Structure
```
Webhook → Extract Input → Detect Support/Claim Agent
→ [IF has files] → OCR Processing → Normalize OCR Data → Save to DB
→ AI Agent (with Chat Memory) → Send Response
```
## Questions
1. Which approach is most reliable for production use?
2. Is there a better n8n-native solution I'm missing?
3. How do others handle incremental data collection in conversational AI workflows?
4. Would a combination approach work better (e.g., OCR = auto-save, Chat = manual confirmation)?
## Constraints
- Using n8n Cloud (latest version)
- PostgreSQL + Supabase for vector store
- Google Gemini 2.0 Flash as main LLM
- Conversations can be 20-50+ messages long
- Need to support chat history compression (summarize every 20 messages)
Any advice from those who've built similar conversational data collection workflows?
7
3 comments
Best approach for extracting structured data from AI chat conversations in n8n workflow?
skool.com/ai-automation-society
A community built to master no-code AI automations. Join to learn, discuss, and build the systems that will shape the future of work.
Leaderboard (30-day)
1
🔥
+2521
2
+1231
3
🔥
+1209
4
🔥
+1189
5
🔥
+783
Powered by