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Run Bunker Guard NPC chat and RAG lore checks with Google Gemini and Google Drive

by Aslamul Fikri Alfirdausiadapted from n8n official workflow galleryUpdated Aug 2026
RequiresAAI AgentDDefault Data LoaderEmbeddings Google GeminiEmbeddings Google GeminiGoogle DriveGoogle DriveGoogle Gemini Chat ModelRecursive Character Text SplitterRecursive Character Text SplitterSSimple MemorySimple Vector StoreSimple Vector StoreVector Store Question Answer ToolVector Store Question Answer Tool
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WeWebhookGoogle Gemini Chat ModelGoogle Gemini C…Agnpc creatorAgChat GeneratorSwSwitchRTRespond to WebhookRespond to Webh…AgDayClue creatorMBSimple MemoryDDDefault Data LoaderDefault Data Lo…TSRecursive Character Text SplitterRecursive Chara…VSSimple Vector StoreSimple Vector S…TVDatabase_BunkerVSSimple Vector Store1Simple Vector S…EGEmbeddings Google GeminiEmbeddings Goog…Download File From Google DriveDownload File F…ChWhen chat message receivedWhen chat messa…MaWhen clicking ‘Execute workflow’When clicking ‘…AgAI AgentMBWindow Buffer MemoryWindow Buffer M…1234567891011121314151617181920212223242526
1/5
STEPS · 26
Starts on an incoming request

Quick overview This workflow powers dynamic character interactions for investigative games. It ingests lore from Google Drive into a vector database, using Google Gemini agents to generate daily briefings, spawn NPCs, and roleplay interrogations via a webhook. How it works Runs on a manual trigger to download a lore PDF from Google Drive, split it into chunks, create Google Gemini embeddings, and store them in an in-memory vector store for retrieval. Receives a POST webhook request from the game client containing game state and an action value (briefing, init, or chat). Routes the request based on action to either generate a morale-based daily intel briefing, create a new NPC profile for the day, or generate an in-character reply to the player’s interrogation message. When factual lore is needed (routes, locations, prices, rules, professions), the Gemini agent queries the Database_Bunker vector-search tool to ground the response in the ingested PDF. Maintains short conversation context per player using a buffer memory keyed by playerId for ongoing chat and NPC interactions. Returns the agent output to the caller as clean JSON via the webhook response.

Tags

n8nreference-onlyagentdocument-default-data-loaderembeddings-google-geminigoogle-drivelm-chat-google-geminimemory-buffer-windowtext-splitter-recursive-character-text-splittertool-vector-storevector-store-in-memory
Connects
AAI AgentDDefault Data LoaderembeddingsgooglegeminiEmbeddings Google GeminigoogledriveGoogle DriveGoogle Gemini Chat ModelrecursivecharactertextsplitterRecursive Character Text Splitter
CategoryAI Automation
Triggermanual
Complexitycomplex
Nodes19
AddedJul 6, 2026

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