terraform-genai-knowledge-base
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GoogleCloudPlatform
Fine tune an LLM model to answer questions from your documents.
Generative AI Knowledge Base
Description
Tagline
Fine tune an LLM model to answer questions from your documents.
Detailed
This solution showcases how to extract question & answer pairs out of documents using Generative AI. It provides an end-to-end demonstration of QA extraction and fine-tuning of a large language model (LLM) on Vertex AI. Along the way, the solution utilizes Document AI Character Recognition (OCR), Firestore, Vector Search, Vertex AI Studio, and Cloud Functions.
Architecture
- Uploading a new document triggers the webhook Cloud Function.
- Document AI extracts the text from the document file.
- Indexes the document text in Vector Search.
- A Vertex AI Large Language Model generates questions and answers from the document text.
- The questions and answers pairs are saved into Firestore.
- A fine tuning dataset is generated from the Firestore database.
- After human validation, a fine tuned Large Language Model is deployed and saved in the Model Registry.
Prerequisites
Documentation
- Knowledge Base using Generative AI
Deployment Duration
Configuration: 2 mins Deployment: 6 mins
Cost
Inputs
| Name | Description | Type | Default | Required |
|---|---|---|---|---|
| disable_services_on_destroy | Whether project services will be disabled when the resources are destroyed. | bool |
false |
no |
| documentai_location | Document AI location, see https://cloud.google.com/document-ai/docs/regions | string |
"us" |
no |
| firestore_location | Firestore location, see https://firebase.google.com/docs/firestore/locations | string |
"nam5" |
no |
| labels | A set of key/value label pairs to assign to the resources deployed by this blueprint. | map(string) |
{} |
no |
| project_id | The Google Cloud project ID to deploy to | string |
n/a | yes |
| region | The Google Cloud region to deploy to | string |
"us-central1" |
no |
| unique_names | Whether to use unique names for resources | bool |
false |
no |
Outputs
| Name | Description |
|---|---|
| bucket_docs_name | The name of the docs bucket created |
| bucket_main_name | The name of the main bucket created |
| docs_index_endpoint_id | The ID of the docs index endpoint |
| docs_index_id | The ID of the docs index |
| documentai_processor_id | The full Document AI processor path ID |
| firestore_database_name | The name of the Firestore database created |
| neos_tutorial_url | The URL to launch the in-console tutorial for the Generative AI Knowledge Base solution |
| predictions_notebook_url | The URL to open the notebook for model predictions in Colab |
| unique_id | The unique ID for this deployment |
Requirements
These sections describe requirements for using this module.
Software
The following dependencies must be available:
- Terraform v0.13
- Terraform Provider for GCP plugin v5.8
Service Account
A service account with the following roles must be used to provision the resources of this module:
- Storage Admin:
roles/storage.admin
The Project Factory module and the IAM module may be used in combination to provision a service account with the necessary roles applied.
APIs
A project with the following APIs enabled must be used to host the resources of this module:
- Google Cloud Storage JSON API:
storage-api.googleapis.com
The Project Factory module can be used to provision a project with the necessary APIs enabled.
Contributing
Refer to the contribution guidelines for information on contributing to this module.
Security Disclosures
Please see our security disclosure process.
Metadata
Owner
GoogleCloudPlatform
Metadata
Fine tune an LLM model to answer questions from your documents.