Process documents & build semantic search with OpenAI, Gemini & Qdrant
This automation transforms static documents into a dynamic knowledge base by intelligently processing files from Google Drive and web forms. It leverages Gemini and OpenAI to extract deep insights and store searchable embeddings within a Qdrant vector database. The built-in chat interface empowers users to query their repository and receive instant, context-aware responses.
Run this with your team's AIWhat This Recipe Does
The Store Files in Qdrant CLOUD Fairwork automation streamlines the process of transforming unstructured business documents into searchable, AI-ready data. Manually indexing files for custom AI models or internal knowledge bases is time-consuming and prone to error. This workflow automates the entire pipeline: it captures files via a secure form or Google Drive upload, processes the content, and stores it directly in your Qdrant vector database. By automating the ingestion of company documents, policies, and research, you ensure your AI applications always have access to the most current information. This eliminates manual data entry, reduces the technical overhead of maintaining a vector store, and allows your team to focus on extracting insights rather than managing infrastructure. The result is a centralized, high-performance repository that powers intelligent search, customer support bots, and internal research tools with minimal human intervention.
What your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
Google Drive, Qdrant, Jotform, Google-gemini connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect Google Drive and Qdrant once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
Who Uses This
- Customer Support Leads use this to automatically feed new product manuals and help articles into their AI chatbots for more accurate automated responses.
- Legal and Compliance Teams use this to centralize and index large volumes of contracts and regulatory documents for rapid semantic search and retrieval.
- Product Managers use this to build a searchable knowledge base of user feedback and market research reports to inform future product roadmaps.
Frequently Asked Questions
What types of files can I upload through this automation?
The workflow is designed to handle common document formats stored in Google Drive, such as PDFs, Word documents, and text files, which are then processed for storage in Qdrant.
Do I need to manage the vector database manually?
No, once the initial connection to your Qdrant Cloud instance is established, the automation handles the indexing and storage of new files automatically.
Can I trigger this automation for existing folders in Google Drive?
Yes, you can configure the Google Drive trigger to watch specific folders, so any file added to those locations is automatically processed and stored.
What is the benefit of storing files in Qdrant versus a standard folder?
Qdrant allows for semantic search, meaning your AI can understand the context and meaning of your documents rather than just searching for specific keywords.
Coming from n8n?
This recipe uses nodes like GoogleDrive, Langchain.vectorStoreQdrant, Langchain.embeddingsOpenAi, StickyNote and 12 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork