# Using Specialized Processors with Document AI (Python) - GSP1140 ## Overview [Document AI](https://cloud.google.com/document-ai/docs) is a document understanding solution that takes unstructured data (e.g. documents, emails, invoices, forms, etc.) and makes the data easier to understand, analyze, and consume. The API provides structure through content classification, entity extraction, advanced searching, and more. In this lab, you will learn how to use Document AI Specialized Processors to classify and parse specialized documents with Python. For the parsing and entity extraction, you will use an invoice as an example. This procedure and example code will work with any [specialized document](https://cloud.google.com/document-ai/docs/processors-list) supported by Document AI. ## Objectives In this lab, you will learn how to perform the following tasks: * Extract schematized entities using specialized processors. ## Setup and requirements ### Before you click the Start Lab button Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click **Start Lab**, shows how long Google Cloud resources are made available to you. This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab. To complete this lab, you need: * Access to a standard internet browser (Chrome browser recommended). **Note:** Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account. * Time to complete the lab—remember, once you start, you cannot pause a lab. **Note:** Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account. ### How to start your lab and sign in to the Google Cloud console 1. Click the **Start Lab** button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following: * The Open Google Cloud console button * Time remaining * The temporary credentials that you must use for this lab * Other information, if needed, to step through this lab 2. Click **Open Google Cloud console** (or right-click and select **Open Link in Incognito Window** if you are running the Chrome browser). The lab spins up resources, and then opens another tab that shows the Sign in page. ***Tip:*** Arrange the tabs in separate windows, side-by-side. **Note:** If you see the **Choose an account** dialog, click **Use Another Account**. 3. If necessary, copy the **Username** below and paste it into the **Sign in** dialog. ```apache student-00-b132012bdd59@qwiklabs.net ``` Copied! You can also find the Username in the Lab Details pane. 4. Click **Next**. 5. Copy the **Password** below and paste it into the **Welcome** dialog. ```apache f1iWnFssPEc1 ``` Copied! You can also find the Password in the Lab Details pane. 6. Click **Next**. **Important:** You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. **Note:** Using your own Google Cloud account for this lab may incur extra charges. 7. Click through the subsequent pages: * Accept the terms and conditions. * Do not add recovery options or two-factor authentication (because this is a temporary account). * Do not sign up for free trials. After a few moments, the Google Cloud console opens in this tab. **Note:** To access Google Cloud products and services, click the **Navigation menu** or type the service or product name in the **Search** field. ![Navigation menu icon and Search field](https://cdn.qwiklabs.com/9Fk8NYFp3quE9mF%2FilWF6%2FlXY9OUBi3UWtb2Ne4uXNU%3D align="left") ### Activate Cloud Shell Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. 1. Click **Activate Cloud Shell** at the top of the Google Cloud console. 2. Click through the following windows: * Continue through the Cloud Shell information window. * Authorize Cloud Shell to use your credentials to make Google Cloud API calls. When you are connected, you are already authenticated, and the project is set to your **Project\_ID**, `qwiklabs-gcp-03-f14d60f7f072`. The output contains a line that declares the **Project\_ID** for this session: ```apache Your Cloud Platform project in this session is set to qwiklabs-gcp-03-f14d60f7f072 ``` `gcloud` is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion. 3. (Optional) You can list the active account name with this command: ```apache gcloud auth list ``` Copied! 4. Click **Authorize**. **Output:** ```apache ACTIVE: * ACCOUNT: student-00-b132012bdd59@qwiklabs.net To set the active account, run: $ gcloud config set account `ACCOUNT` ``` 5. (Optional) You can list the project ID with this command: ```apache gcloud config list project ``` Copied! **Output:** ```apache [core] project = qwiklabs-gcp-03-f14d60f7f072 ``` **Note:** For full documentation of `gcloud`, in Google Cloud, refer to [the gcloud CLI overview guide](https://cloud.google.com/sdk/gcloud). ## Task 1. Enable the Document AI API Before you can begin using Document AI, you must enable the API. 1. Open Cloud Shell by clicking the **Activate Cloud Shell** button at the top of the console. 2. In Cloud Shell, run the following commands to enable the API for Document AI. ```apache gcloud services enable documentai.googleapis.com ``` Copied! You should see something like this: ```apache Operation "operations/..." finished successfully. ``` You will also need to install [Pandas](https://pandas.pydata.org/), an Open Source Data Analysis library for Python. 3. Run the following command to install Pandas. ```apache pip3 install --upgrade pandas ``` Copied! 4. Run the following command to install the Python client libraries for Document AI. ```apache pip3 install --upgrade google-cloud-documentai ``` Copied! You should see something like this: ```apache ... Installing collected packages: google-cloud-documentai Successfully installed google-cloud-documentai-2.15.0 ``` Now, you're ready to use the Document AI API! Click **Check my progress** to verify the objective. Enable the Document AI API ## Task 2. Create a Form Parser processor You must first create a Form Parser processor instance to use in the Document AI Platform for this tutorial. 1. From the Navigation Menu, click **VIEW ALL PRODUCTS** under **Artificial Intelligence**, select **Document AI**. ![Document AI Overview Console](https://cdn.qwiklabs.com/DrloY2MFgVSuxoip2X%2BiebqoLJi89TCKqVTaHfkt9zM%3D align="left") 2. Click **Explore Processors**, scroll down to **Specialized** and and inside **Invoice Parser**, click **Create Processor**. ![Procurement Doc Splitter](https://cdn.qwiklabs.com/JxN11uvN6dRFHak%2FL41JUFoPovWre8ajZ%2B40bvKrn6Y%3D align="left") 3. Give it the name `lab-invoice-parser` and select the closest region on the list. 4. Click **Create** to create your processor. 5. **Copy** your Processor ID. You must use this in your code later ![Lab Invoice Parser](https://cdn.qwiklabs.com/pE83xdzEgnJiivlsUk9%2Bk8Or1GuK834vEzJ0%2FzqQOMo%3D align="left") Create a processor ### Download sample documents We have a few sample documents which you can use for this lab. 1. Run the following command to download the sample forms to Cloud Shell. ```apache gcloud storage cp gs://cloud-samples-data/documentai/codelabs/specialized-processors/procurement_multi_document.pdf . gcloud storage cp gs://cloud-samples-data/documentai/codelabs/specialized-processors/google_invoice.pdf . ``` Copied! 2. Confirm the files are downloaded to Cloud Shell using the below command: ```apache ls ``` Copied! You should see something like this: ```apache google_invoice.pdf procurement_multi_document.pdf ``` ## Task 3. Extract the entities Now you can extract the schematized entities from the files, including confidence scores, properties, and normalized values. The code for making the API request is identical to the previous step, and it can be done with online or batch requests. You will access the following information from the entities: * **Entity Type** * (e.g. `invoice_date`, `receiver_name`, `total_amount`) * **Raw Values** * Data values as presented in the original document file. * **Normalized Values** * Data values in a normalized and standard format, if applicable. * Also can include enrichment from [Enterprise Knowledge Graph](https://cloud.google.com/blog/products/ai-machine-learning/improves-document-ai-accuracy-and-consistency-with-ekg) * **Confidence Values** * How "sure" the model is that the values are accurate. Some entity types, such as `line_item` can also include [properties](https://cloud.google.com/document-ai/docs/reference/rest/v1/Document#:~:text=supported%20document%20types.-,properties%5B%5D,-object%20\(Entity\)) which are nested entities such as `line_item/unit_price` and `line_item/description`. This example flattens out the nested structure for ease of viewing. ### Invoice Parser 1. In Cloud Shell, create a file called `extraction.py` and paste the following code into it: ```apache import pandas as pd from google.cloud import documentai_v1 as documentai def online_process( project_id: str, location: str, processor_id: str, file_path: str, mime_type: str, ) -> documentai.Document: """ Processes a document using the Document AI Online Processing API. """ opts = {"api_endpoint": f"{location}-documentai.googleapis.com"} # Instantiates a client documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts) # The full resource name of the processor, e.g.: # projects/project-id/locations/location/processor/processor-id # You must create new processors in the Cloud Console first resource_name = documentai_client.processor_path(project_id, location, processor_id) # Read the file into memory with open(file_path, "rb") as file: file_content = file.read() # Load Binary Data into Document AI RawDocument Object raw_document = documentai.RawDocument(content=file_content, mime_type=mime_type) # Configure the process request request = documentai.ProcessRequest(name=resource_name, raw_document=raw_document) # Use the Document AI client to process the sample form result = documentai_client.process_document(request=request) return result.document PROJECT_ID = "YOUR_PROJECT_ID" LOCATION = "YOUR_PROJECT_LOCATION" # Format is 'us' or 'eu' PROCESSOR_ID = "INVOICE_PARSER_ID" # Create processor in Cloud Console # The local file in your current working directory FILE_PATH = "google_invoice.pdf" # Refer to https://cloud.google.com/document-ai/docs/processors-list # for supported file types MIME_TYPE = "application/pdf" document = online_process( project_id=PROJECT_ID, location=LOCATION, processor_id=PROCESSOR_ID, file_path=FILE_PATH, mime_type=MIME_TYPE, ) types = [] raw_values = [] normalized_values = [] confidence = [] # Grab each key/value pair and their corresponding confidence scores. for entity in document.entities: types.append(entity.type_) raw_values.append(entity.mention_text) normalized_values.append(entity.normalized_value.text) confidence.append(f"{entity.confidence:.0%}") # Get Properties (Sub-Entities) with confidence scores for prop in entity.properties: types.append(prop.type_) raw_values.append(prop.mention_text) normalized_values.append(prop.normalized_value.text) confidence.append(f"{prop.confidence:.0%}") # Create a Pandas Dataframe to print the values in tabular format. df = pd.DataFrame( { "Type": types, "Raw Value": raw_values, "Normalized Value": normalized_values, "Confidence": confidence, } ) print(df) ``` Copied! 2. Replace `INVOICE_PARSER_ID` with the ID for the Invoice Parser Processor you created earlier and use the file `google_invoice.pdf`. 3. Replace `YOUR_PROJECT_ID` and `YOUR_PROJECT_LOCATION` with your Google Cloud Project ID and Processor location, respectively. 4. Run the script: ```apache python3 extraction.py ``` Copied! Your output should look something like this: ```apache Type Raw Value Normalized Value Confidence 0 due_date Sep 30, 2019 2019-09-30 99% 1 net_amount 22,379.39 22379.39 99% 2 total_amount 19,647.68 19647.68 99% 3 invoice_date Sep 24, 2019 2019-09-24 98% 4 total_tax_amount 1,767.97 1767.97 94% 5 receiver_name Jane Smith, 88% 6 receiver_address 1600 Amphitheatre Pkway Mountain View, CA 94043 77% 7 invoice_id 23413561D 60% 8 freight_amount 199.99 199.99 60% 9 invoice_type invoice_statement 59% 10 currency $ USD 58% 11 supplier_name Google Google 37% 12 line_item 9.99 12 12 ft HDMI cable 119.88 100% 13 line_item/unit_price 9.99 9.99 95% 14 line_item/quantity 12 12 75% 15 line_item/description 12 ft HDMI cable 64% 16 line_item/amount 119.88 119.88 90% 17 line_item 12 399.99 27" Computer Monitor 4,799.88 100% 18 line_item/quantity 12 12 76% 19 line_item/unit_price 399.99 399.99 95% 20 line_item/description 27" Computer Monitor 42% 21 line_item/amount 4,799.88 4799.88 93% 22 line_item Ergonomic Keyboard 12 59.99 719.88 100% 23 line_item/description Ergonomic Keyboard 42% 24 line_item/quantity 12 12 75% 25 line_item/unit_price 59.99 59.99 94% 26 line_item/amount 719.88 719.88 85% 27 line_item Optical mouse 12 19.99 239.88 100% 28 line_item/description Optical mouse 55% 29 line_item/quantity 12 12 72% 30 line_item/unit_price 19.99 19.99 94% 31 line_item/amount 239.88 239.88 81% 32 line_item Laptop 12 1,299.99 15,599.88 100% 33 line_item/description Laptop 65% 34 line_item/quantity 12 12 71% 35 line_item/unit_price 1,299.99 1299.99 94% 36 line_item/amount 15,599.88 15599.88 91% 37 line_item Misc processing fees 899.99 899.99 1 100% 38 line_item/description Misc processing fees 54% 39 line_item/unit_price 899.99 899.99 92% 40 line_item/amount 899.99 899.99 82% 41 line_item/quantity 1 1 68% ``` 5. Create a Cloud Storage bucket, and upload the generated output of the command `docai_outputs.txt` to the bucket. ```apache # Create a bucket export PROJECT_ID=$(gcloud config get-value project) gsutil mb gs://$PROJECT_ID-docai # Create and upload the file python3 extraction.py > docai_outputs.txt gsutil cp docai_outputs.txt gs://$PROJECT_ID-docai ``` Copied! Create a cloud storage bucket and upload the output file ## Optional: Try out other specialized processors You've successfully used Document AI for Procurement to classify documents and parse an invoice. Document AI also supports the other specialized solutions listed here: * [Identity](https://cloud.google.com/solutions/identity-doc-ai) * [Lending](https://cloud.google.com/solutions/lending-doc-ai) * [Contracts](https://cloud.google.com/solutions/contract-doc-ai) You can follow the same procedure and use the same code to handle any specialized processor. If you would like to try out the other specialized solutions, you can re-run the lab with other processor types and specialized sample documents. **Note:** Some Identity, Lending, and Contract processors are currently in limited access. If you have a business use case for these processors, please fill out and submit the appropriate request form before proceeding. ### Sample Documents Here are some sample documents you can use to try out the other specialized processors. | **Solution** | **Processor Type** | **Document** | | --- | --- | --- | | Identity | [US Driver License Parser](https://cloud.google.com/document-ai/docs/processors-list#processor_us-driver-license-parser) | [Download license.pdf](https://storage.googleapis.com/cloud-samples-data/documentai/codelabs/specialized-processors/license.pdf) | | Lending | [Lending Splitter & Classifier](https://cloud.google.com/document-ai/docs/processors-list#processor_lending-splitter-classifier) | [Download lending\_multi\_document.pdf](https://storage.googleapis.com/cloud-samples-data/documentai/codelabs/specialized-processors/lending_multi_document.pdf) | | Lending | [W9 Parser](https://cloud.google.com/document-ai/docs/processors-list#processor_w9-parser) | [Download W9.pdf](https://storage.googleapis.com/cloud-samples-data/documentai/codelabs/specialized-processors/W9.pdf) | | Contracts | [Contract Parser](https://cloud.google.com/document-ai/docs/processors-list#processor_contract-parser) | [Download CymbalContract.pdf](https://storage.googleapis.com/cloud-samples-data/documentai/codelabs/specialized-processors/CymbalContract.pdf) | You can find other sample documents and processor output in the [documentation](https://cloud.google.com/document-ai/docs/output). --- ## Solution of Lab %[https://youtu.be/uRZgKAnKmKY] ```apache curl -LO raw.githubusercontent.com/ePlus-DEV/storage/refs/heads/main/labs/GSP1140/lab.sh source lab.sh ``` **Script Alternative** ```apache curl -LO raw.githubusercontent.com/QUICK-GCP-LAB/2-Minutes-Labs-Solutions/refs/heads/main/Using%20Specialized%20Processors%20with%20Document%20AI%20Python/gsp1140.sh sudo chmod +x *.sh ./*.sh ``` ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1770179525853/3b229753-6d1b-44b0-b215-eef46dfb9bd2.png align="center")