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feat(example): Add Generative AI example using RAG and Vector Search (#…
…46) * feat(module): add genai example * feat: add README.md * edit README.md * add known issues * add fix * add service agent role assignment * update terraform.tfvars * Update README with VPC-SC Instruction * fix render * Update module * add regional bucket and fix deployed index access * add Google Header to file * update README.md with terraform docs * lint fixes * implement PR review changes * update host_vpc projectid
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| # Multimodal RAG Langchain Example | ||
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| ## Overview | ||
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| Retrieval Augmented Generation (RAG) has become a popular paradigm for enabling LLMs to access external data and also as a mechanism for [Grounding](https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview), to mitigate against hallucinations. | ||
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| In this notebook, you will perform multimodal RAG by performing Q&A over a financial document filled with both text and images. | ||
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| This example is an adapted version of the sample Generative AI notebook from the Google Cloud codebase. You can find the original example and other notebooks in the [Google Cloud Platform Generative AI](https://github.com/GoogleCloudPlatform/generative-ai/tree/main) repository. | ||
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| The main modifications to the original example include: | ||
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| - Adaptations to comply with Cloud Foundation Toolkit security measures. | ||
| - Installation of additional libraries in the Conda environment. | ||
| - Use of Vertex AI Workbench to run the notebook with a custom Service Account. | ||
| - Implementation of Vector Search on Vertex AI with [Private Service Connect](https://cloud.google.com/vpc/docs/private-service-connect). | ||
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| ## Requirements | ||
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| - Terraform v1.7.5 | ||
| - [Authenticated Google Cloud SDK 469.0.0](https://cloud.google.com/sdk/docs/authorizing) | ||
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| ### Provision Infrastructure with Terraform | ||
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| - Update the `terraform.tfvars` file with values from your environment. | ||
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| ```terraform | ||
| kms_key = "projects/KMS-PROJECT-ID/locations/REGION/keyRings/ML-ENV-KEYRING/cryptoKeys/ML-ENV-KEY" | ||
| network = "projects/NETWORK-PROJECT-ID/global/networks/NETWORK-NAME" | ||
| subnet = "projects/NETWORK-PROJECT-ID/regions/REGION/subnetworks/SUBNET-NAME" | ||
| machine_learning_project = "MACHINE-LEARNING-PROJECT-ID" | ||
| vector_search_vpc_project = "NETWORK-PROJECT-ID" | ||
| ``` | ||
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| - Assuming you are deploying the example on top of the development environment, the following instructions will provide you more insight on how to retrieve these values: | ||
| - **NETWORK-PROJECT-ID**: Run `terraform output -raw restricted_host_project_id` on `gcp-networks` repository, inside the development environment directory and branch. | ||
| - **NETWORK-NAME**: Run `terraform output -raw restricted_network_name` on `gcp-networks` repository, inside the development environment directory and branch. | ||
| - **MACHINE-LEARNING-PROJECT-ID**: Run `terraform output -raw machine_learning_project_id` on `gcp-projects` repository, inside the Machine Learning business unit directory and on the development branch. | ||
| - **KMS-PROJECT-ID**, **ML-ENV-KEYRING**, **ML-ENV-KEY**: Run `terraform output machine_learning_kms_keys` on `gcp-projects` repository, inside the Machine Learning business unit directory and on the development branch. | ||
| - **REGION**: The chosen region. | ||
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| ### Allow file download from Google Notebook Examples Bucket on VPC-SC Perimeter | ||
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| When running the Notebook, you will reach a step that downloads an example PDF file from a bucket, you need to add the egress rule below on the VPC-SC perimeter to allow the operation. | ||
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| ```yaml | ||
| - egressFrom: | ||
| identities: | ||
| - serviceAccount:rag-notebook-runner@<INSERT_YOUR_MACHINE_LEARNING_PROJECT_ID_HERE>.iam.gserviceaccount.com | ||
| egressTo: | ||
| operations: | ||
| - methodSelectors: | ||
| - method: google.storage.buckets.list | ||
| - method: google.storage.buckets.get | ||
| - method: google.storage.objects.get | ||
| - method: google.storage.objects.list | ||
| serviceName: storage.googleapis.com | ||
| resources: | ||
| - projects/200612033880 # Google Cloud Example Project | ||
| ``` | ||
| ## Usage | ||
| Once all the requirements are set up, you can start by running and adjusting the notebook step-by-step. | ||
| To run the notebook, open the Google Cloud Console on Vertex AI Workbench, open JupyterLab and upload the notebook (`multimodal_rag_langchain.ipynb`) to it. | ||
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| ### Optional: Use `terraform output` and bash command to fill in fields in the notebook | ||
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| You can save some time adjusting the notebook by running the commands below: | ||
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| - Extract values from `terraform output` and validate. | ||
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| ```bash | ||
| export private_endpoint_ip_address=$(terraform output -raw private_endpoint_ip_address) | ||
| echo private_endpoint_ip_address=$private_endpoint_ip_address | ||
| export host_vpc_project_id=$(terraform output -raw host_vpc_project_id) | ||
| echo host_vpc_project_id=$host_vpc_project_id | ||
| export notebook_project_id=$(terraform output -raw notebook_project_id) | ||
| echo notebook_project_id=$notebook_project_id | ||
| export vector_search_bucket_name=$(terraform output -raw vector_search_bucket_name) | ||
| echo vector_search_bucket_name=$vector_search_bucket_name | ||
| export host_vpc_network=$(terraform output -raw host_vpc_network) | ||
| echo host_vpc_network=$host_vpc_network | ||
| ``` | ||
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| - Search and Replace using `sed` command. | ||
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| ```bash | ||
| sed -i "s/<INSERT_PRIVATE_IP_VALUE_HERE>/$private_endpoint_ip_address/g" multimodal_rag_langchain.ipynb | ||
| sed -i "s/<INSERT_HOST_VPC_PROJECT_ID>/$host_vpc_project_id/g" multimodal_rag_langchain.ipynb | ||
| sed -i "s/<INSERT_NOTEBOOK_PROJECT_ID>/$notebook_project_id/g" multimodal_rag_langchain.ipynb | ||
| sed -i "s/<INSERT_BUCKET_NAME>/$vector_search_bucket_name/g" multimodal_rag_langchain.ipynb | ||
| sed -i "s:<INSERT_HOST_VPC_NETWORK>:$host_vpc_network:g" multimodal_rag_langchain.ipynb | ||
| ``` | ||
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| ## Known Issues | ||
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| - `Error: Error creating Instance: googleapi: Error 400: value_to_check(https://compute.googleapis.com/compute/v1/projects/...) is not found`. | ||
| - When creating the VertexAI Workbench Instance through terraform you might face this issue. The issue is being tracked on this [link](https://github.com/hashicorp/terraform-provider-google/issues/17904). | ||
| - If you face this issue you will not be able to use terraform to create the instance, therefore, you will need to manually create it on [Google Cloud Console](https://console.cloud.google.com/vertex-ai/workbench/instances) using the same parameters. | ||
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| <!-- BEGINNING OF PRE-COMMIT-TERRAFORM DOCS HOOK --> | ||
| ## Inputs | ||
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| | Name | Description | Type | Default | Required | | ||
| |------|-------------|------|---------|:--------:| | ||
| | instance\_location | Vertex Workbench Instance Location | `string` | `"us-central1-a"` | no | | ||
| | kms\_key | The KMS key to use for disk encryption | `string` | n/a | yes | | ||
| | machine\_learning\_project | Machine Learning Project ID | `string` | n/a | yes | | ||
| | machine\_name | The name of the machine instance | `string` | `"rag-notebook-instance"` | no | | ||
| | machine\_type | The type of machine to use for the instance | `string` | `"e2-standard-2"` | no | | ||
| | network | The Host VPC network ID to connect the instance to | `string` | n/a | yes | | ||
| | service\_account\_name | The name of the service account | `string` | `"rag-notebook-runner"` | no | | ||
| | subnet | The subnet ID within the Host VPC network to use in Vertex Workbench and Private Service Connect | `string` | n/a | yes | | ||
| | vector\_search\_address\_name | The name of the address to create | `string` | `"vector-search-endpoint"` | no | | ||
| | vector\_search\_bucket\_location | Bucket Region | `string` | `"US-CENTRAL1"` | no | | ||
| | vector\_search\_ip\_region | The region to create the address in | `string` | `"us-central1"` | no | | ||
| | vector\_search\_vpc\_project | The project ID where the Host VPC network is located | `string` | n/a | yes | | ||
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| ## Outputs | ||
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| | Name | Description | | ||
| |------|-------------| | ||
| | host\_vpc\_network | This is the Self-link of the Host VPC network | | ||
| | host\_vpc\_project\_id | This is the Project ID where the Host VPC network is located | | ||
| | notebook\_project\_id | The Project ID where the notebook will be run on | | ||
| | private\_endpoint\_ip\_address | The private IP address of the vector search endpoint | | ||
| | vector\_search\_bucket\_name | The name of the bucket that Vector Search will use | | ||
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| <!-- END OF PRE-COMMIT-TERRAFORM DOCS HOOK --> |
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