RAG Conversational Agent for Corporate Files Utilizing Google Drive and Gemini
Develops a Retrieval-Augmented Generation (RAG) chatbot that responds to inquiries utilizing company documents stored in Google Drive, powered by Google Gemini.
How it works
The workflow titled "RAG Conversational Agent for Corporate Files Utilizing Google Drive and Gemini" is designed to create a chatbot that leverages company documents stored in Google Drive to respond to user inquiries. The workflow operates through a series of interconnected nodes that facilitate data retrieval, processing, and response generation.
1. Trigger Node:
The workflow begins with a webhook trigger that listens for incoming requests. This allows the chatbot to receive user queries in real-time.
2. Google Drive Node:
Upon receiving a query, the workflow utilizes a Google Drive node to search for relevant documents. This node is configured to filter files based on specific criteria, such as file type or keywords that match the user’s inquiry.
3. Data Processing Node:
Once the relevant documents are retrieved, the workflow processes the data using a function node. This node extracts the necessary information from the documents, preparing it for the next step.
4. Gemini Node:
The processed data is then sent to the Google Gemini node, which is responsible for generating a response based on the retrieved information. This node utilizes the capabilities of the Gemini AI model to formulate a coherent and contextually relevant answer.
5. Response Node:
Finally, the generated response is sent back to the user through the webhook response node, completing the interaction.
The flow of data is seamless, moving from the user’s query through document retrieval and processing, to the final response generation, ensuring that users receive accurate and timely information based on corporate documents.
Key Features
- Real-Time Interaction:
The workflow allows for immediate responses to user inquiries, enhancing user engagement and satisfaction.
- Document Retrieval:
It efficiently searches and retrieves relevant documents from Google Drive, ensuring that the chatbot has access to the most pertinent information.
- AI-Powered Responses:
By integrating with Google Gemini, the workflow leverages advanced AI capabilities to generate responses that are not only accurate but also contextually appropriate.
- Customizable Search Parameters:
Users can define specific search criteria, allowing for tailored document retrieval based on their needs.
- Seamless Integration:
The workflow integrates multiple services (Google Drive and Gemini) into a cohesive system, simplifying the process of accessing corporate knowledge.
Tools Integration
- Webhook Node:
Used to trigger the workflow based on incoming user queries.
- Google Drive Node:
Facilitates the search and retrieval of documents stored in Google Drive.
- Function Node:
Processes the retrieved data to extract relevant information for response generation.
- Google Gemini Node:
Generates AI-driven responses based on the processed document data.
- Webhook Response Node:
Sends the final response back to the user.
API Keys Required
• Google Drive API Key: Required for accessing and retrieving documents from Google Drive.
• Google Gemini API Key: Necessary for utilizing the Gemini AI model to generate responses.
No additional API keys or authentication configurations are required beyond those mentioned above.
