Generative AI applications for document extraction
Utilise Gemini 2.0 to reduce expenses and expedite document extraction.
Gemini 2.0, comprising Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, and Gemini 2.0 Pro (Experimental), was made available to all users by Google DeepMind a few weeks ago. Every model can handle at least one million input tokens, which facilitates a variety of tasks, including creative writing and image generation. It has also altered how Google turns papers into structured data. Gemini 2.0 revolutionised chunking PDFs for RAG systems and can even turn PDFs into insights, addressing the tedious and costly issue of manual document processing.
The multi-step generative AI technique that combines language models (LLMs) with structured, externalised rules to enhance document extraction using Gemini 2.0.
A simple, multi-step method for document extraction
For effective extraction, there are several benefits to using a multi-step architecture rather than a single, monolithic prompt. This method starts with modular extraction, in which preliminary duties are divided into more manageable, targeted prompts that target particular content areas in a document. In addition to improving accuracy, its modularity lessens the LLM’s cognitive strain.
The externalised rule management is an additional advantage of a multi-step strategy. The advantages of simple CRUD (Create, Read, Update, Delete) actions are obtained by maintaining post-processing rules externally, such as with Google Sheets or a BigQuery table. This enhances the rules’ maintainability and version control. Additionally, by divorcing the logic of extraction from the logic of processing, each may be independently modified and optimised.
In the end, Google’s hybrid strategy blends the advantages of a hierarchical rules engine with LLM-powered extraction. While the rules engine offers a clear and controllable framework for implementing business logic and decision-making, LLMs manage the difficulties of comprehending and extracting information from unstructured data. A real-world implementation is described in the steps that follow.
First Step: Extraction
To extract data, it uses Gemini with the first prompt. Utilise Gemini’s controlled generation feature to produce particular response schemas.
The results of Gemini’s initial data extraction are assigned to the variable extracted_data. Applying its established business rules is the next crucial step that this organised data is now prepared for.
Step 2: Provide a rules engine with the extracted data
This extracted_data will then be fed into a rules engine, which in our implementation is just another call to Gemini, which serves as a strong and adaptable rules processor. We’ll supply a set of validation rules specified in the analysis_rules variable in addition to the extracted data. With the help of Gemini, this engine will methodically verify that the extracted data satisfies our predetermined standards for accuracy and consistency.
The business rules we wish to apply to the collected receipt data are contained in the JSON object analysis_rules. Every rule specifies a particular condition to be checked, a course of action to follow in the event that the condition is satisfied, and an optional alert message to be sent in the event of a violation. The flexibility of these rules you can add, change, or remove them without affecting the main extraction procedure is what gives this strategy its strength. The best thing about utilising Gemini is that non-programmers can maintain the rules because they are written in a language that is easy for people to understand.
Step 3: Integrate your insights
Lastly, and most importantly, incorporate the rules engine’s insights and alerts into current workflows and data pipelines. The true worth of this multi-step procedure is revealed at this point. As an illustration, it may use Google Cloud tools to create reliable APIs and systems that will automate further steps brought on by the rule-based analysis. Here are a few instances of downstream tasks:
- Automated task creation: Assign data verification to the relevant teams by using Cloud Functions to initiate tasks in project management systems.
- Data quality pipelines: Use Dataflow integration to identify possible discrepancies in data in BigQuery tables and initiate validation procedures.
- Vertex AI integration: Track data lineage and model performance in relation to extracted metrics and corrections by utilising the Vertex AI Model Registry.
- Integration of the dashboard To show notifications, use Looker, Google Sheets, or Data Studio.
- Human in the loop trigger: Using Cloud Tasks, create a trigger system that indicates which extractions need attention and double-checking.
The process of document extraction
A strong basis for creating reliable, rule-driven document extraction pipelines is provided by this practical method. Start by looking through these resources:
- Gemini for document understanding: Take a look at Gemini for document understanding for a complete, one-stop solution to your document processing requirements. It makes a lot of typical extraction problems easier.
- Few-shot prompting: Use few-shot prompting to start your Gemini adventure. This effective method, which offers examples within the prompt itself, can greatly enhance the calibre of your extractions with little work.
- Gemini model fine-tuning: Take into consideration fine-tuning Gemini models when you require highly specialised, domain-specific extraction outputs. This enables you to precisely customise the model’s performance to meet your needs.
