Effortless RAG in n8n - Use ALL Your Files (PDFs, Excel, and More)

Creating RAG AI Agents with n8n

Overview of RAG AI Agents

• The process of creating RAG (Retrieval-Augmented Generation) AI agents using n8n is straightforward, leveraging the AI agent node alongside vector store retrieval and document inserter tools.

• Extracting text from various document types, particularly PDFs, poses challenges but is essential for building a knowledge base.

Challenges with Document Types

• Users have faced difficulties extending workflows to accommodate different file types like PDFs and Excel documents.

• The video aims to demonstrate how to work with these file types in n8n, enabling quick integration into the knowledge base.

Workflow Demonstration

• A previously built workflow for the RAG AI agent will be referenced as a foundation for modifications needed to support PDF and Excel documents.

• The new workflow introduces branching based on file type, allowing for different extraction nodes tailored to specific formats.

Interaction Triggers

• Two triggers initiate interactions: one for chat input within the n8n user interface and another webhook trigger that allows API usage of the agent.

• The tools agent defines user prompts and system messages while integrating models like GPT 4.0 mini or alternatives such as Claude.

Document Ingestion Process

• A Superbase PostgreSQL database stores chat history, while a retrieve documents tool facilitates RAG operations using OpenAI models for embeddings.

• The workflow includes triggers that activate when files are created or updated in a designated Google Drive folder.

File Type Handling

• Each time a file is created or updated, n8n checks this folder every minute to capture changes effectively.

• Important metadata about each file type is captured during these events, which informs subsequent processing steps in the workflow.

MIME Type Identification

• Understanding MIME types helps identify document formats; e.g., Google Docs are denoted by "application/vnd.google-apps.document," while PDFs use "application/pdf."

Understanding File Processing Workflows

Overview of File Attributes

• The file contains various attributes, including creation date and last updated date. One key parameter is the "mime type," which indicates the file type (e.g., application/pdf for PDF documents).

Importance of Mime Type

• The mime type is crucial as it determines how to branch in the workflow to reach the correct extraction node based on the file type.

Managing Old Records

• Before processing a new document, all old records in the vector store are deleted to avoid confusion with outdated versions that could mislead the language model.

Document Text Extraction Process

• The current workflow differs from previous ones by allowing multiple nodes for text extraction, accommodating complex file types like PDFs and Excel documents, unlike simpler formats such as text files or CSVs.

Extract Node Variations

• A variety of extract nodes are available for different file types (HTML, JSON, PDF, Excel), enabling flexibility in handling diverse document formats.

Branching Logic Based on Mime Type

• The branching logic uses mime types to determine which extraction node to utilize. For example:

• If mime type is application/pdf, it routes to a specific PDF extraction node.

• For Excel documents (application/vnd.openxmlformats-officedocument.spreadsheetml.sheet), it follows a different path.

• Google Docs use a simpler route for raw text extraction.

Fallback Options in Workflow

• A default fallback option exists if none of the specified mime types match; this allows for extracting data from common formats like CSV or plain text files.

Resource Reference for Mime Types

• A valuable resource is provided that lists exact strings for various mime types. This can help users identify appropriate branches when extending workflows with additional file types.

Testing with Different File Types

• During testing, uploading files into designated folders ensures accurate fetching of specific document types. For instance, confirming that a PDF's mime type is correctly identified as application/pdf.

Final Steps in Document Processing

Workflow Automation in Google Drive

Setting Up the Workflow

• The workflow is designed to operate automatically, triggering actions for any new or updated files in Google Drive without manual intervention. This demonstration illustrates each step's output for clarity.

Testing Event Triggers

• By clicking on "test event," the system processes everything leading up to a specific node, selecting the first branch based on the MIME type of the file (application/pdf).

Extracting Text from PDF Files

• The extraction process differentiates between outputs: data from general documents goes to a field called "Data," while PDFs output text to an attribute named "text." Understanding this distinction is crucial.

Important Output Considerations

• A significant insight is that when using a default document loader, the extracted data must match the output field from its respective extract node. For example, JSON data corresponds with raw text files and Google Docs.

Handling Different File Types

• When extracting from various file types like Excel, it's essential to adapt your setup according to their unique attributes. Excel files will output concatenated data, which requires careful handling during processing.

Processing Excel Documents

Fetching Test Events for Excel Files

• After uploading an Excel file into a designated folder, fetching test events confirms that it recognizes the correct MIME type associated with .xlsx files.

Branch Selection for Processing

• The system selects a specific branch tailored for processing Excel files. This involves more detailed handling compared to other formats due to varying record structures within CSV files.

Simplifying Data Extraction

• The approach taken here simplifies extraction by treating the entire Excel file as raw text and aggregating records into one item rather than creating multiple entries per row.

Summarizing Data for Database Insertion

• The summary process converts an array of records into a single string suitable for database insertion. This method can vary based on use cases—whether inserting one record per row or aggregating multiple rows together.

Finalizing Document Insertion and Querying

Inserting Records into Vector Database

• After treating the document as raw text, only one record is inserted into the vector database since it was processed as a singular entity rather than multiple records.

Verifying Inserted Records

• Upon checking the documents table in the database, all different records—including those from Google Docs and PDFs—are confirmed present alongside newly added vectors from the Excel document.

Querying Action Items

Building a RAG AI Agent

Overview of Document Ingestion for Knowledge Base

• The speaker references a previous video focused on constructing a RAG (Retrieval-Augmented Generation) AI agent, emphasizing its capability to handle various document types for knowledge bases.

• Viewers are encouraged to ask questions in the comments section regarding the ingestion of different document types and how to effectively organize them within their knowledge base.

• The speaker expresses willingness to assist viewers in understanding the best practices for splitting documents and optimizing their knowledge management strategies.

• There is an invitation for feedback, indicating that viewer appreciation is valued and can influence future content creation.