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This workflow automates the process of reading EDI files generated by Sabre, parsing them using an AI Agent, and producing structured accounting reports like:
📌 Accounts Receivable (AR) Summary
📌 Tax and Surcharges Report
It also uses Retrieval-Augmented Generation (RAG) to vectorize the Sabre Interface User Record (IUR)—a 154-page technical document—so that the AI agent can reference it when clarification is required while generating reports.
⚙️ Tools & Integrations Used
Component:Tool/Service:Purpose:Workflow Engine:n8n:Automation & orchestration
LLM Model:OpenAI GPT-4 / Chat Model:Natural language understanding and parsing
Embeddings Model:OpenAI Embeddings:Convert text into semantic vector format
Vector Database:Pinecone:Store and retrieve document chunks semantically
Storage:Google Drive:Source of raw EDI text files and PDF documentation
DataLoader + Splitter:n8n Node + Recursive Splitter:Loads and prepares documents for embedding
AI Agents:n8n AI Agent Node:Runs context-aware prompts and parses reports
🧱 Workflow Breakdown
🧠 1. Vectorizing the Sabre IUR Document (RAG Setup)
📘 Objective: Enable the AI Agent to refer to the IUR document (154 pages) for detailed explanations of EDI terms, formats, and rules.
Flow Steps:
Google Drive Search + Download – Find and pull the IUR PDF file.
Default Data Loader – Load the file and preprocess it for semantic splitting.
Recursive Character Splitter – Break down large pages into meaningful chunks.
OpenAI Embeddings – Vectorize each chunk.
Pinecone Vector Store – Save into a Pinecone namespace for future retrieval.
✅ Result: The IUR is now searchable via semantic queries from the AI Agent.
📁 2. Reading and Extracting Data from EDI Files
📘 Objective: Parse raw EDI files for financial records and summaries.
Flow Steps:
Trigger – Manual or scheduled execution of the workflow.
Google Drive Search – Finds all new .edi or .txt files.
Download File Contents – Loads content of each file into memory.
Extract from File – Raw text extraction.
📊 3. Report Generation Using AI Agents
📘 Objective: AI Agents parse the extracted data to generate structured accounting reports.
a. Accounts Receivable Report Agent
The extracted text is passed to an AI Agent.
Model is connected to:
OpenAI Chat Model (LLM)
Pinecone Vector DB (IUR reference)
Outputs a structured AR Summary Report.
b. Tax and Surcharges Report Agent
Same steps as above.
Prompts adjusted to extract tax, fees, surcharges, and amounts.
✅ Output Format: Can be mapped to columns and inserted into a Google Sheet or exported as a CSV/JSON.
📑 Sample Reports You Can Build
Already implemented:
✅ Accounts Receivable (AR) Summary Report
✅ Tax and Surcharges Report
Can be extended to:
3. Accounts Payable (AP)
4. Passenger Revenue
5. Daily Sales
6. Commission Report
7. Net Profit Margin (if supplier cost + commission is available)
💡 Key Advantages
✅ No-code automation with n8n
✅ Semantic reasoning using AI + Vector DB (RAG)
✅ Can work with various Sabre outputs without manual parsing
✅ Modular: Easy to add new report types
✅ Cloud-integrated (Drive, Pinecone, OpenAI)
🧪 Potential Improvements
Area Suggestions
Testing Add a “Preview” step to validate extracted data before writing
Scalability Batch mode + Google Sheet batching for multiple reports
Audit Trail Log every file name, timestamp, report type in a Google Sheet
Notification Send Slack/Email when a new report is generated
Multi-model support Add Claude/Gemini fallback if OpenAI usage limit is hit