How can I build an advanced RAG system with OCR and image retrieval in n8n?

Below is one way to approach your requirements entirely within n8n:

---

1. OCR/Processing Complex PDFs

Since n8n doesn’t include a dedicated native PDF-OCR node, you might consider:

• Using an HTTP Request Node: Call an external OCR API (for example, Google Cloud Vision, Microsoft OCR APIs, or any service you prefer) from n8n.
• Custom Code: Leverage the Function or Execute Command node (if available in your setup) to run a locally installed OCR engine like Tesseract.

Once you extract the textual data, you can also split the content using text splitter nodes or custom code to distinguish between the main manual text and any captions associated with images.

---

2. Storing & Retrieving Images

To ensure you retrieve the actual image content (and not just vectors):

• Separate Pipelines for Text and Images:
  • For text, use an embeddings workflow (storing vectors in your preferred vector database) to enable similarity search.
  • For images, store the binary image data directly. n8n makes it possible to store binary data in a field (for example, by using the “Put Output in Field” option in certain nodes).

• Using Built-in Integrations:
  The OpenAI image operations node can analyze images (for instance, identify features or answer questions about images). This means you might trigger a call to analyze the image and use that metadata to enrich your search queries.

• Workflow for Retrieving:
  When querying, make sure your workflow retrieves:
  • The associated text embeddings for semantic search.
  • The binary data or image URL for the actual image content.
  Pairing these two outputs in your response ensures users see the relevant image along with any related textual information.

---

3. Keeping the Entire Workflow in n8n

To build an integrated solution within n8n:

• Chain Nodes Together:
  • Use a node (or series of nodes) to fetch a PDF file (or manually trigger an upload).
  • Process the PDF by splitting it into sections with an OCR/HTTP Request node.
  • Separate out embedded images (if needed, you may need a custom function or third-party service for PDF parsing that identifies and extracts images).
  • Store text and binary data in distinct fields.
  • Use additional nodes to embed text data (for similarity search) and to store binary image data as needed.

• Error Handling & Modular Workflows:
  Consider splitting the process into sub-workflows:

  1. One workflow handles document ingestion and extraction.
  2. Another manages the vectorization (embeddings for text) and storage of binary files.
  3. A final workflow handles querying and retrieval, ensuring images come back as actual binary fields or URLs.

Remember to validate each step (for example, by using manual trigger nodes or test runs) to ensure each integration and custom function works as expected.

---

Example High-Level Workflow

MERMAIDCopied
flowchart TD
    A[Trigger: Upload PDF/Start Workflow] --> B[Extract PDF Content (HTTP Request to OCR API)]
    B --> C[Function Node: Split Text and Identify Image Data]
    C --> D1[Process Text]
    C --> D2[Extract & Store Image Binary]
    D1 --> E[Embed Text Data (Vector Database node or HTTP Request to embedding API)]
    D2 --> F[Store Binary Image Data]
    E & F --> G[Combine/Join Data for Search Results]
    G --> H[Query Workflow: Return matching text & actual images]

Note: Since n8n supports custom and HTTP Request nodes, you can plug in almost any external service you need (OCR engines, PDF parsers, embedding generators, etc.) while keeping all the processes managed within your n8n instance.

---

By splitting your manual’s content into separate pipelines for text and binary images and using built-in HTTP integrations and custom logic, you can create a robust RAG system that meets your requirements—all within n8n.

Feel free to ask if you need further guidance based on specific nodes or configuration details!