feat(examples): Add RAGAS evaluation to RAG chain

examples/genai-rag-multimodal/README.md

@@ -59,6 +59,108 @@ When running the Notebook, you will reach a step that downloads an example PDF f
       - projects/200612033880 # Google Cloud Example Project
 ```
 
+## Deploying infrastructure using Machine Learning Infra Pipeline
+
+### Required Permissions for pipeline Service Account
+
+- Give `roles/compute.networkUser` to the Service Account that runs the Pipeline.
+
+  ```bash
+  SERVICE_ACCOUNT=$(terraform -chdir="./gcp-projects/ml_business_unit/shared" output -json terraform_service_accounts | jq -r '."ml-machine-learning"')
+
+  gcloud projects add-iam-policy-binding <INSERT_HOST_VPC_NETWORK_PROJECT_HERE> --member="serviceAccount:$SERVICE_ACCOUNT" --role="roles/compute.networkUser"
+  ```
+
+- Add the following ingress rule to the Service Perimeter.
+
+  ```yaml
+  ingressPolicies:
+  - ingressFrom:
+      identities:
+      - serviceAccount:<SERVICE_ACCOUNT>
+      sources:
+      - accessLevel: '*'
+    ingressTo:
+      operations:
+      - serviceName: '*'
+      resources:
+      - '*'
+  ```
+
+### Deployment steps
+
+**IMPORTANT:** Please note that the steps below are assuming you are checked out on the same level as `terraform-google-enterprise-genai/` and the other repos (`gcp-bootstrap`, `gcp-org`, `gcp-projects`...).
+
+- Retrieve the Project ID where the Machine Learning Pipeline Repository is located in.
+
+  ```bash
+  export INFRA_PIPELINE_PROJECT_ID=$(terraform -chdir="gcp-projects/ml_business_unit/shared/" output -raw cloudbuild_project_id)
+  echo ${INFRA_PIPELINE_PROJECT_ID}
+  ```
+
+- Clone the repository.
+
+  ```bash
+  gcloud source repos clone ml-machine-learning --project=${INFRA_PIPELINE_PROJECT_ID}
+  ```
+
+- Navigate into the repo and the desired branch. Create directories if they don't exist.
+
+  ```bash
+  cd ml-machine-learning
+  git checkout -b development
+
+  mkdir -p ml_business_unit/development
+  mkdir -p modules
+  ```
+
+- Copy required files to the repository.
+
+  ```bash
+  cp -R ../terraform-google-enterprise-genai/examples/genai-rag-multimodal ./modules
+  cp ../terraform-google-enterprise-genai/build/cloudbuild-tf-* .
+  cp ../terraform-google-enterprise-genai/build/tf-wrapper.sh .
+  chmod 755 ./tf-wrapper.sh
+
+  cat ../terraform-google-enterprise-genai/examples/genai-rag-multimodal/terraform.tfvars >> ml_business_unit/development/genai_example.auto.tfvars
+  cat ../terraform-google-enterprise-genai/examples/genai-rag-multimodal/variables.tf >> ml_business_unit/development/variables.tf
+  ```
+
+  > NOTE: Make sure there are no variable name collision for variables under `terraform-google-enterprise-genaiexamples/genai-rag-multimodal/variables.tf` and that your `terraform.tfvars` is updated with values from your environment.
+
+- Validate that variables under `ml_business_unit/development/genai_example.auto.tfvars` are correct.
+
+  ```bash
+  cat ml_business_unit/development/genai_example.auto.tfvars
+  ```
+
+- Create a file named `genai_example.tf` under `ml_business_unit/development` path that calls the module.
+
+  ```terraform
+  module "genai_example" {
+    source = "../../modules/genai-rag-multimodal"
+
+    kms_key                   = var.kms_key
+    network                   = var.network
+    subnet                    = var.subnet
+    machine_learning_project  = var.machine_learning_project
+    vector_search_vpc_project = var.vector_search_vpc_project
+  }
+  ```
+
+- Commit and push
+
+  ```terraform
+  git add .
+  git commit -m "Add GenAI example"
+
+  git push origin development
+  ```
+
+## Deploying infrastructure using terraform locally
+
+Run `terraform init && terraform apply -auto-approve`.
+
 ## Usage
 
 Once all the requirements are set up, you can start by running and adjusting the notebook step-by-step.

examples/genai-rag-multimodal/multimodal_rag_langchain.ipynb

@@ -599,7 +599,7 @@
     "\n",
     "\n",
     "# Image summaries\n",
-    "img_base64_list, image_summaries = generate_img_summaries(\".\")"
+    "img_base64_list, image_summaries = generate_img_summaries(\"./intro_multimodal_rag_old_version\")"
    ]
   },
   {
@@ -824,8 +824,17 @@
     "    for i, s in enumerate(text_summaries + table_summaries + image_summaries)\n",
     "]\n",
     "\n",
-    "retriever_multi_vector_img.docstore.mset(list(zip(doc_ids, doc_contents)))\n",
+    "list_of_docs = list(zip(doc_ids, doc_contents))\n",
     "\n",
+    "retriever_multi_vector_img.docstore.mset(list_of_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# If using Vertex AI Vector Search, this will take a while to complete.\n",
     "# You can cancel this cell and continue later.\n",
     "retriever_multi_vector_img.vectorstore.add_documents(summary_docs)"
@@ -1000,6 +1009,166 @@
     "Markdown(result)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RAGAS Evaluation\n",
+    "\n",
+    "On the cells below we will be using RAGAS to evaluate the RAG pipeline for text-based context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install ragas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "questions = [\n",
+    "    \"How did COVID-19 initially impact Google's advertising revenue in 2020?\",\n",
+    "    \"How did Google's advertising revenue recover from the initial COVID-19 impact?\",\n",
+    "    \"What was the primary driver of Google's operating cash flow in 2020?\",\n",
+    "    \"How did Google's share repurchases compare to the previous year in 2020?\"\n",
+    "]\n",
+    "\n",
+    "golden_answers = [\n",
+    "    \"COVID-19 initially impacted Google's advertising revenue in 2020 in two ways, Users searched for less commercially-driven topics, reducing the relevance and value of ads displayed and Businesses cut back on advertising budgets due to the economic downturn caused by the pandemic.\",\n",
+    "    \"Google's advertising revenue recovered from the initial COVID-19 impact through a combination of factors, User search activity shifted back to more commercially-driven topics, increasing the effectiveness of advertising and As the economic climate improved, businesses began to invest more heavily in advertising again.\",\n",
+    "    \"The primary driver of Google's operating cash flow in 2020 was revenue generated from its advertising products, totaling $91.7 billion\",\n",
+    "    \"Google's share repurchases in 2020 were $50.3 billion, reflecting a significant increase of 62% compared to the prior year.\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def summarize_image_context(doc_base64):\n",
+    "    prompt = \"\"\"You are an assistant tasked with summarizing images for retrieval. \\\n",
+    "    These summaries will be embedded and used to retrieve the raw image. \\\n",
+    "    Give a concise summary of the image that is well optimized for retrieval.\n",
+    "    If it's a table, extract all elements of the table.\n",
+    "    If it's a graph, explain the findings in the graph.\n",
+    "    Do not include any numbers that are not mentioned in the image.\n",
+    "    \"\"\"\n",
+    "    return image_summarize(doc_base64, prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_samples = {\n",
+    "        \"contexts\": [],\n",
+    "        \"question\": [],\n",
+    "        \"answer\": [],\n",
+    "        \"ground_truth\": []\n",
+    "    }\n",
+    "\n",
+    "for i, question in enumerate(questions):    \n",
+    "    docs = retriever_multi_vector_img.get_relevant_documents(question, limit=10)    \n",
+    "    image_contexts = []\n",
+    "    \n",
+    "    source_docs = split_image_text_types(docs)\n",
+    "    \n",
+    "    if len(source_docs[\"images\"]) > 0:        \n",
+    "        for image in source_docs[\"images\"]:\n",
+    "            image_contexts.append(summarize_image_context(image))\n",
+    "    \n",
+    "    text_context = source_docs[\"texts\"]\n",
+    "    \n",
+    "    data_samples[\"contexts\"].append(text_context + image_contexts)\n",
+    "    data_samples[\"question\"].append(question)\n",
+    "    data_samples[\"answer\"].append(chain_multimodal_rag.invoke(question))\n",
+    "    data_samples[\"ground_truth\"].append(golden_answers[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "dataset = Dataset.from_dict(data_samples)\n",
+    "\n",
+    "\n",
+    "from ragas.metrics import (\n",
+    "    context_precision,\n",
+    "    answer_relevancy,\n",
+    "    faithfulness,\n",
+    "    context_recall,\n",
+    "    answer_similarity,\n",
+    "    answer_correctness,\n",
+    ")\n",
+    "from ragas.metrics.critique import harmfulness\n",
+    "\n",
+    "# list of metrics we're going to use\n",
+    "metrics = [\n",
+    "    faithfulness,\n",
+    "    answer_relevancy,\n",
+    "    context_recall,\n",
+    "    context_precision,\n",
+    "    harmfulness,\n",
+    "    answer_similarity,\n",
+    "    answer_correctness,\n",
+    "]\n",
+    "\n",
+    "from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings\n",
+    "\n",
+    "config = {    \n",
+    "    \"chat_model_id\": \"gemini-1.0-pro-002\",\n",
+    "    \"embedding_model_id\": \"textembedding-gecko\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "vertextai_llm = ChatVertexAI(model_name=config[\"chat_model_id\"],)\n",
+    "vertextai_embeddings = VertexAIEmbeddings(model_name=config[\"embedding_model_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from ragas import evaluate\n",
+    "\n",
+    "result = evaluate(\n",
+    "    dataset,  # using 1 as example due to quota constrains\n",
+    "    metrics=metrics,\n",
+    "    llm=vertextai_llm,\n",
+    "    embeddings=vertextai_embeddings,\n",
+    ")\n",
+    "\n",
+    "result.to_pandas()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

examples/genai-rag-multimodal/outputs.tf

@@ -26,12 +26,12 @@ output "host_vpc_project_id" {
 
 output "host_vpc_network" {
   description = "This is the Self-link of the Host VPC network"
-  value       = var.network
+  value       = google_workbench_instance.instance.gce_setup[0].network_interfaces[0].network
 }
 
 output "notebook_project_id" {
   description = "The Project ID where the notebook will be run on"
-  value       = var.machine_learning_project
+  value       = google_workbench_instance.instance.project
 }
 
 output "vector_search_bucket_name" {

examples/genai-rag-multimodal/versions.tf

@@ -0,0 +1,23 @@
+/**
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+terraform {
+  required_providers {
+    google = {
+      version = "~> 5.34.0"
+    }
+  }
+}