AI-powered accounting reports from Sabre EDI with GPT-4 and Pinecone RAG

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