suggestion for gogle drive integration app

[OmidGhozat]

I want to make a chatbot in which each user asks LLM and get an answer based on user files on drive.

[sahilnaircool]

To create a chatbot where each user can ask questions and get answers based on their files stored on Google Drive, you can follow these steps:-

1. Authentication and Access to Google Drive

2. Processing Files

3. Embedding and Indexing Data

4. LLM Integration

5. Fine-tuning Responses

6. User Interface

[OmidGhozat]

Thank you for your suggestion. How can it be faster? Should embedding and indexing data be in each given query? 

[AjayJ]

I think u can try with RAG on the drive files if u can able to acess the drive files, instead of the knowledge base prepared for RAG, u can directly use the drive's content as knowledge base

[dawnberdan · Accepted solution]

Hi @OmidGhozat,

Welcome to Google Cloud Community!

Making a chatbot that answers questions using user files includes several steps. Here’s a simple overview of the process:

1. User Interface: You’ll need a platform for users to interact with the chatbot. This could be a website, mobile app, or a straightforward command-line interface. The interface should enable users to submit their questions.

2. Google Drive Integration:

• Establish a connection between your chatbot and Google Drive.
• This usually requires using the Google Drive API and obtaining the appropriate authentication credentials.
• Also need to develop code that can access and analyze files stored in Google Drive.

3. LLM Integration: Select a Large Language Model (LLM) that fits your requirements. Popular options include:

• PaLM (Google AI)
• LaMDA (Google AI)

Note: Each LLM comes with its own API and documentation. Be sure to create an account with the provider and acquire the necessary API keys.

4. Chatbot Logic: When a user poses a question:

1. Extract key information from the user’s inquiry.
2. Retrieve relevant files from Google Drive.
3. Utilize your selected LLM to process these files.
4. Merge the LLM’s output with the information from the files to formulate a personalized response.

For Technical Details:

1. Google Drive API:

• Set up your Google Cloud Platform (GCP) project.
• Enable the Google Drive API for that project.
• Acquire OAuth 2.0 credentials to authenticate your chatbot with Google Drive.
• Use a client library (like Python’s google-api-python-client) for API interaction.

2. LLM API:

• Choose your LLM provider and create an account.
• Obtain your API keys and integrate them into your application.
• Familiarize yourself with the specific API calls and parameters needed for your chosen LLM.

I hope the above information is helpful.