Block 1 - OpenAI Chat Model
- Type / Role
- @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
- Config choices
- Version 1.3
This workflow is provided as-is. Please review and test before using in production.
Company Knowledge Base Assistant Who's it for This workflow is designed for companies looking to onboard new employees and interns efficiently. It's perfect for HR teams, team leaders, and organiza...
@n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.googledrivetrigger, n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.vectorstoreinmemory, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.agent
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Roshan Ramani.
Original n8n.io sourceThis workflow is designed for companies looking to onboard new employees and interns efficiently. It's perfect for HR teams, team leaders, and organizations that want to provide instant access to company knowledge without manual intervention. Whether you're a startup or an established company, this assistant helps your team find answers quickly from your existing documentation.
This AI-powered chatbot automatically learns from your company documents stored in Google Drive and provides accurate, contextual answers to employee questions. The system continuously monitors a designated Drive folder, processes new documents, and makes them instantly searchable through a conversational interface.
Key features:
The workflow has two main components:
Document Processing Pipeline: Monitors your Google Drive folder every minute for new files. When a document is added, it's automatically downloaded, split into searchable chunks, converted into vector embeddings, and stored in an in-memory knowledge base.
Chat Interface: Users send questions via webhook, the AI agent searches the knowledge base for relevant information, maintains conversation history for context, and returns accurate answers with source citations.
Configure Google Drive:
Configure OpenAI:
Upload Your Documents:
Test the Chat Interface:
{
"data": "Your question here",
"session_id": "unique-user-id"
}
Integrate with Your Tools:
topK parameter in "Search Company Documents" to retrieve more relevant sectionscontextWindowLength in "Conversation History" to remember more previous messagesThis catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | AI-powered company documents Q&A assistant with Google Drive and GPT-4 mini |
|---|---|
| Complexity | advanced |
| Nodes | 15 |
| Categories | Internal Wiki, AI RAG |
| Author | Roshan Ramani |
| Published | 18 Nov 2025 |
Use the JSON export at /data/workflows/10947/10947.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Company Knowledge Base Assistant Who's it for This workflow is designed for companies looking to onboard new employees and interns efficiently. It's perfect for HR teams, team leaders, and organiza...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Internal Wiki, AI RAG use case.