Analyze images, videos, documents & audio with Gemini Tools and Qwen LLM Agent
π Analyze uploaded images, videos, audio, and documents with specialized tools β powered by a lightweight language-only agent.
π§ What It Does
This workflow enables multimodal file analysis using Google Gemini tools connected to a text-only LLM agent. Users can upload images, videos, audio files, or documents via a chat interface. The workflow will:
- Upload each file to Google Gemini and obtain an accessible URL.
- Dynamically generate contextual prompts based on the file(s) and user message.
- Allow the agent to invoke Gemini tools for specific media types as needed.
- Return a concise, helpful response based on the analysis.
π Use Cases
- Customer support: Let users upload screenshots, documents, or recordings and get helpful insights or summaries.
- Multimedia QA: Review visual, audio, or video content for correctness or compliance.
- Educational agents: Interpret content from PDFs, diagrams, or audio recordings on the fly.
- Low-cost multimodal assistants: Achieve multimodal functionality without relying on large vision-language models.
π― Why This Architecture Matters
Unlike end-to-end multimodal LLMs (like Gemini 1.5 or GPT-4o), this template:
- Uses a text-only LLM (Qwen 32B via Groq) for reasoning.
- Delegates media analysis to specialized Gemini tools.
β Advantages
| Feature | Benefit | | ----------------------- | --------------------------------------------------------------------- | | π§© Modular | LLM + Tools are decoupled; can update them independently | | πΈ Cost-Efficient | No need to pay for full multimodal models; only use tools when needed | | π§ Tool-based Reasoning | Agent invokes tools on demand, just like OpenAIβs Toolformer setup | | β‘ Fast | Groq LLMs offer ultra-fast responses with low latency | | π Memory | Includes context buffer for multi-turn chats (15 messages) |
π§ͺ How It Works
πΉ Input via Chat
- Users submit a message and (optionally) files via the
chatTrigger.
πΉ File Handling
-
If no files: prompt is passed directly to the agent.
-
If files are included:
- Files are split, uploaded to Gemini (to get public URLs).
- Metadata (name, type, URL) is collected and embedded into the prompt.
πΉ Prompt Construction
-
A new
chatInputis dynamically generated:User message Media: [array of file data]
πΉ Agent Reasoning
-
The
Langchain Agentreceives:-
The enriched prompt
-
File URLs
-
Memory context (15 turns)
-
Access to 4 Gemini tools:
IMG: analyze imageVIDEO: analyze videoAUDIO: analyze audioDOCUMENT: analyze document
-
The agent autonomously decides whether and how to use tools, then responds with concise output.
π§± Nodes & Services
| Category | Node / Tool | Purpose |
| --------------- | ---------------------------- | ------------------------------------- |
| Chat Input | chatTrigger | User interface with file support |
| File Processing | splitOut, splitInBatches | Process each uploaded file |
| Upload | googleGemini | Uploads each file to Gemini, gets URL |
| Metadata | set, aggregate | Builds structured file info |
| AI Agent | Langchain Agent | Receives context + file data |
| Tools | googleGeminiTool | Analyze media with Gemini |
| LLM | lmChatGroq (Qwen 32B) | Text reasoning, high-speed |
| Memory | memoryBufferWindow | Maintains session context |
βοΈ Setup Instructions
1. π Required Credentials
- Groq API key (for Qwen 32B model)
- Google Gemini API key (Palm / Gemini 1.5 tools)
2. π§© Nodes That Need Setup
-
Replace existing credentials on:
Upload a file- Each
GeminiTool(IMG, VIDEO, AUDIO, DOCUMENT) lmChatGroq
3. β οΈ File Size & Format Considerations
- Some Gemini tools have file size or format restrictions.
- You may add validation nodes before uploading if needed.
π οΈ Optional Improvements
- Add logging and error handling (e.g., for upload failures).
- Add MIME-type filtering to choose the right tool explicitly.
- Extend to include OCR or transcription services pre-analysis.
- Integrate with Slack, Telegram, or WhatsApp for chat delivery.
π§ͺ Example Use Case
> "Hola, ΒΏquΓ© dice este PDF?"
Uploads a document β Agent routes it to Gemini DOCUMENT tool β Receives extracted content β LLM summarizes it in Spanish.
π§° Tags
multimodal, agent, langchain, groq, gemini, image analysis, audio analysis, document parsing, video analysis, file uploader, chat assistant, LLM tools, memory, AI tools
π Files
- This template is ready to use as-is in n8n.
- No external webhooks or integrations required.
n8n AI Agent for Multimodal Analysis with Gemini and Groq
This n8n workflow demonstrates a powerful AI agent capable of analyzing various content types (images, videos, documents, audio) using Google Gemini and Groq's Qwen LLM. It's designed to process incoming chat messages, determine the content type, and route the analysis to the appropriate AI model.
What it does:
- Listens for Chat Messages: The workflow is triggered by an incoming chat message.
- Initial Data Preparation: It then prepares the incoming message by setting a default
messagefield. - Determines Content Type: The workflow uses an "If" node to intelligently identify if the received message contains an image, video, document, or audio based on predefined conditions (though the conditions are not explicitly defined in the provided JSON, this is the logical step).
- Routes to AI Agent:
- If the content is identified as an image, video, document, or audio, it routes the data to a Google Gemini node for analysis.
- If the content is not one of the above, it routes the data to a Groq Chat Model (likely using Qwen LLM) for general text-based AI agent processing.
- AI Agent Processing:
- The Google Gemini node (for multimodal content) or the Groq Chat Model (for text) acts as an AI agent, performing the requested analysis or generating a response.
- A "Simple Memory" node is included, suggesting the AI agent can maintain context across interactions.
- Aggregates Results: After the AI agent processes the content, the results are merged and then aggregated.
- Splits Out Results: Finally, the aggregated results are split out, making them ready for further processing or display.
- Loops for Multiple Items: The "Loop Over Items" and "Merge" nodes suggest that the workflow can handle multiple chat messages or multiple items within a single message, processing them in batches and then combining the results.
Prerequisites/Requirements:
- n8n Instance: A running n8n instance.
- Google Gemini API Key: Required for the Google Gemini node to analyze multimodal content.
- Groq API Key: Required for the Groq Chat Model node (likely Qwen LLM) for text-based AI processing.
- Chat Platform Integration: The "Chat Trigger" node implies integration with a chat platform (e.g., Slack, Telegram, Discord) from which messages are received.
Setup/Usage:
- Import the Workflow: Import the provided JSON into your n8n instance.
- Configure Credentials:
- Set up credentials for Google Gemini with your API key.
- Set up credentials for Groq with your API key.
- Configure the Chat Trigger node to connect to your desired chat platform and listen for incoming messages.
- Define "If" Node Conditions: Edit the "If" node (ID 20) to define the logic for identifying different content types (e.g., checking for specific keywords, file extensions, or metadata in the incoming chat message payload).
- Customize AI Agent Logic: Adjust the configurations of the "Google Gemini" and "Groq Chat Model" nodes to specify the desired analysis tasks or conversational prompts.
- Activate the Workflow: Once configured, activate the workflow to start processing incoming chat messages.
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