Travel Planning Assistant with MongoDB Atlas, Gemini LLM and Vector Search

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector

Help me set up the n8n workflow "Travel Planning Assistant with MongoDB Atlas, Gemini LLM and Vector Search" (https://n8n.io/workflows/3577). It uses: AI Agent, Embeddings OpenAI, Recursive Character Text Splitter, Default Data Loader, Google Gemini Chat Model, MongoDB Chat Memory, MongoDB Atlas Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.