Building a RAG System: Chat with Your Business Data (Part 6)

· GSD 500 BPO · 22 min read · Artificial Intelligence

Computers don't understand text. They understand numbers. To let an AI "search" your documents, we need to convert your text into lists of numbers called Embeddings.

Imagine a 3D space.

  • The word "Dog" is at coordinate [1, 1, 1].
  • The word "Puppy" is at [1, 1, 2].
  • The word "Car" is at [9, 9, 9].

    • "Dog" is close to "Puppy". "Dog" is far from "Car". Vector Search algorithms calculate the Cosine Similarity (distance) between these points.

    The Workflow: 1. Ingestion: Load your PDF. 2. Chunking: Split it into 1000-character paragraphs. 3. Embedding: Send each paragraph to OpenAI's \`text-embedding-3-small\` model. It returns a vector. 4. Upsert: Save that vector + the text into Pinecone (Vector DB).

    Related Reading

  • [BDR vs SDR: What's the Difference and Which Do You Need?](/resources/blog/bdr-vs-sdr-difference-which-do-you-need)
  • [How to Build a Remote Sales Team in 2025](/resources/blog/how-to-build-remote-sales-team-2025)
  • [Building Your Personal AI Assistant with n8n (Part 3)](/resources/blog/n8n-building-ai-assistant-part-3)

    • Step 1: The Ingestion Pipeline (Admin Only)

    This workflow only runs when you have new documents.

    1. Google Drive Trigger: Watch for new files in folder "Knowledge Base". 2. Download File: Get the binary data. 3. Default Data Loader: Extract text from the PDF/Docx. 4. Text Splitter: This is critical. You can't feed a 100-page book to ChatGPT in one go. You must split it. - Chunk Size: 1000 characters. - Overlap: 100 characters (preserves context between chunks). 5. Embeddings (OpenAI): Convert chunks to vectors. 6. Pinecone (Upsert): Save them.

    Now your knowledge is indexed.

    Step 2: The Retrieval Pipeline (The Chatbot)

    Now, let's update our Agent from Part 3.

    1. Chat Trigger: User asks "How do I process a refund?". 2. Embeddings: We take the user's question and convert it to a vector. 3. Vector Search (Pinecone): We ask Pinecone: "Give me the 3 paragraphs in our database that are mathematically closest to this question." 4. Context Injection: We get those 3 paragraphs (e.g., the Refund Policy text). 5. LLM Call: We send a prompt to GPT-4: > "User Question: How do I process a refund? > Context: [Refund Policy Text...] > Answer the question using ONLY the provided context."

    Result: The AI answers accurately, citing your specific policy, without making things up.

    Advanced RAG: Metadata Filtering

    What if you have documents for "HR" and documents for "Sales"? You don't want the Sales AI reading HR salary data. Metadata is the key.

    When you upload the document to Pinecone, you tag it: \

    Conclusion: The End of the Beginning

    Congratulations. You have gone from a blank canvas to building a Autonomous, Multi-Modal, RAG-Enabled AI Agent System.

    You are no longer just an "Operator." You are an Architect. The systems you built in this series—Lead Gen, CRM Automation, AI Assistants—are the foundation of the modern enterprise.

    But technology moves fast. n8n releases new updates every week. AI models get smarter every month. The only way to stay ahead is to Never Stop Building.

    Thank you for taking this journey with me. This is Manuel from GSD BPO. Now, go automate the world.