Multimodal Chat Assistant with GPT-4o Analysis - n8n Workflow

AI Developers: Individuals looking to deploy advanced multimodal chatbots using n8n.
Technical Teams: Groups needing an automated system to analyze documents or visual content.
n8n Automation Experts: Users seeking a complex n8n template demonstrating the integration of LangChain nodes and conditional routing.
Content Managers: Anyone requiring immediate, contextual analysis of uploaded files.

This comprehensive n8n workflow leverages the cutting-edge capabilities of OpenAI’s GPT-4o model to deliver a truly multimodal chat experience. Unlike standard text-only chatbots, this solution allows users to upload files (images or PDFs) directly through the dedicated n8n trigger. The system automatically detects the file via an If n8n node, sends the file to GPT-4o for deep analysis, summarizes the content, and stores this initial finding in conversation memory.

For subsequent follow-up questions, the AI Agent utilizes the stored memory, ensuring a contextual and cohesive conversation, whether the initial input was text or a file. This sophisticated use of specialized n8n templates and custom LangChain components showcases the power of n8n in building complex, stateful AI solutions.

This advanced n8n workflow starts with the chat n8n trigger, which is designed specifically for stateful AI conversations and allows file uploads.

1. Trigger & File Check: The workflow is initiated by the chat n8n trigger. An If n8n node immediately checks if a file (like an image or PDF) was included in the user's request.


2. File Analysis Path (If True): If a file is detected, the flow moves to the OpenAI n8n node. This node uses GPT-4o to analyze the binary data, describe the content in detail, and suggest follow-up questions. The resulting descriptive text is then processed by a Basic LLM Chain for formatting.


3. Memory Insertion: The detailed analysis from the file is captured by the chatmem node, which inserts this critical context into the user's session history via the Simple Memory 1 n8n node. This ensures the chat 'remembers' the file content.


4. Text Chat Path (If False/Follow-up): If the user is sending a standard text message or a follow-up query, the If n8n node sends the execution to the retrieval path. The chatmem1 node retrieves the existing conversation history (including previous file analyses) from the Simple Memory 2 n8n node.


5. AI Agent Response: The final stage involves the AI Agent n8n node, which receives the user's input, the conversation memory, and the context retrieved by chatmem1. It uses the OpenAI Chat Model1 (GPT-4o) to generate a contextual and informed response, completing the cycle of this robust n8n workflow.

To set up this powerful n8n template, follow these steps:

1. Import: Copy the provided n8n workflow JSON and paste it into your n8n canvas using the 'New' -> 'Import from JSON' option.


2. Credentials: You will need a working OpenAI API key credential set up in n8n. Edit the OpenAI n8n node, the OpenAI Chat Model n8n node, and the OpenAI Chat Model1 n8n node to ensure they are using your correct credential.


3. Activation: The chat n8n trigger is publicly accessible. Activate the workflow by setting the toggle to 'Active'.


4. Testing: Access the public URL provided by the chat n8n node to begin interacting with your multimodal assistant. Test by uploading a simple image or a short PDF, followed by asking questions about the content.

chat (Chat Trigger): This specific n8n trigger initiates the conversation and allows users to upload files. It handles session management for stateful chat.
If (Conditional Logic): The crucial n8n node that routes the workflow execution based on the presence of an uploaded file ($json.files[0].fileName).
OpenAI (Multimodal Analysis): A LangChain n8n node configured with GPT-4o. Its function is to perform detailed analysis (operation: analyze, detail: high) on the binary data of the uploaded file.
Basic LLM Chain (Contextual Summary): This chain uses the analytical output and formats it based on the user's input before passing it to memory. It uses the OpenAI Chat Model n8n node as its underlying LLM.
chatmem & chatmem1 (Memory Manager): Specialized n8n nodes used to explicitly insert (Path True) or retrieve (Path False) conversation messages and contextual content (like the file analysis) from the memory buffer.
Simple Memory, Simple Memory 1, Simple Memory 2 (Memory Buffer Window): These n8n nodes store the conversation history, keyed by the user's sessionId, ensuring the AI maintains context across multiple messages. This is vital for a stateful n8n workflow.

• AI Agent (Core Chat Logic): The primary n8n node responsible for processing standard text queries and generating the final response, utilizing the conversation history retrieved from memory.