Automate Invoice Extraction with Langchain's LLAMA 2

Table of Contents

1. Introduction
2. Implementation Steps
   1. Step 1: Implementing the Files
   2. Step 2: Front-end Implementation
   3. Step 3: Back-end Implementation
3. Extracting Data from PDF Files
   1. Get PDF Text
   2. Extracted Data
   3. Prompt Template
   4. Using the LLM Model
4. Uploading and Processing PDF Files
5. Conclusion

Introduction

In this article, we will explore the process of building an invoice extraction bot. We will provide a step-by-step guide on how to implement this application using Python and various libraries such as Streamlit, LamFit, and Hugging Face. The invoice extraction bot will be used to extract data from PDF invoices, including information such as invoice number, description, quantity, date, unit price, phone number, and address. We will also cover the implementation of the front-end user interface and the back-end logic required for Data Extraction.

Implementation Steps

Step 1: Implementing the Files

The first step in building the invoice extraction bot is to implement the necessary files. This includes creating the front-end interface using Streamlit and defining the back-end logic using Python. We will also communicate with the large language models (LLMs) and external services such as OpenAI and Replicate.ai.

Step 2: Front-end Implementation

In the front-end implementation, we will focus on creating the user interface using Streamlit. This will include designing the layout, adding buttons for file upload, and displaying the extracted data in a tabular format. The user will be able to browse and select PDF files, and upon clicking the "Extract Data" button, the extraction process will begin.

Step 3: Back-end Implementation

The back-end implementation is where the actual data extraction will take place. We will define functions to extract text from PDF files, process the extracted data using LLMs, and format the final output. We will also handle the integration with external services such as OpenAI and Replicate.ai. The extracted data will be stored in a dataframe for easy manipulation and display.

Extracting Data from PDF Files

Get PDF Text

The first function we will implement is "get PDF text," which will extract the text content from PDF files. We will use the PyPDF2 library to Read each page of the PDF and append the extracted text to a STRING variable. This function will return the raw text data from the PDF file.

Extracted Data

Once we have the raw data, we will pass it to another function called "extracted data." This function will use a large language model (LLM) to extract specific information from the invoice, such as the invoice number, description, quantity, date, etc. We will provide the LLM with a prompt template and Context to guide its extraction process. The extracted data will be returned as a string.

Prompt Template

To effectively utilize the LLM, we will Create a prompt template that instructs the model on which values to extract from the invoice. The template will define the expected output format and any additional instructions. This template will be passed to the LLM as part of the context.

Using the LLM Model

We have the option to use different LLM models for our data extraction. This can include models provided by OpenAI or Hugging Face. We can either use a pre-trained LLM model, such as GPT-3, or experiment with our own trained models using services like Replicate.ai. We will explore the steps to use LLM models and the associated costs.

Uploading and Processing PDF Files

Once we have implemented the necessary functions for data extraction, we will tie them together in the main function. This function will handle the file upload process, extract the data from each uploaded PDF file, and display the results in a tabular format. The extracted data will be stored in a dataframe, and the user will have the option to download the data as a CSV file.

Conclusion

In this article, we have covered the implementation process for an invoice extraction bot. We have explored the steps to implement the front-end user interface, the back-end logic for data extraction, and the integration with external services such as OpenAI and Replicate.ai. By following the provided steps, You will be able to build your own invoice extraction bot capable of extracting data from PDF invoices efficiently and accurately.

Highlights

• Build an invoice extraction bot using Python and various libraries.
• Extract data from PDF invoices, including invoice number, description, quantity, date, unit price, phone number, and address.
• Implement a front-end user interface using Streamlit.
• Define the back-end logic for data extraction, including text extraction from PDF files and processing with large language models (LLMs).
• Use prompt templates and LLMs to extract specific information from invoices.
• Display the extracted data in a tabular format and allow users to download the data as a CSV file.

Frequently Asked Questions (FAQ)

Q: How accurate is the invoice extraction process? A: The accuracy of the invoice extraction process depends on various factors such as the quality and structure of the PDF invoices, the training of the LLM model, and the effectiveness of the prompt template. It is recommended to fine-tune the LLM model with relevant data to improve accuracy.

Q: Can I use my own LLM model for data extraction? A: Yes, you can use your own pre-trained LLM model for data extraction. However, it requires additional setup and integration with the invoice extraction bot. Tools like Replicate.ai provide a platform to host and use your own LLM models.

Q: Are there any limitations to the number of PDF files that can be processed? A: The limitations on the number of PDF files that can be processed depend on factors such as the processing power, memory, and storage available. It is recommended to test with a smaller number of files initially and scale up based on the system's capacity.

Q: What are the costs associated with using external services like OpenAI and Replicate.ai? A: The costs associated with using external services like OpenAI and Replicate.ai can vary depending on factors such as the model used, the number of API calls made, and the processing power required. It is important to review the pricing details of the respective services before usage.

Q: Can the invoice extraction bot be extended to extract data from other formats, such as Word documents or images? A: Yes, the invoice extraction bot can be extended to extract data from other formats. Additional libraries and functions may be required to handle different file formats. It is recommended to research and explore specific solutions for extracting data from different file formats.

Q: How can I improve the performance and accuracy of the invoice extraction bot? A: To improve the performance and accuracy of the invoice extraction bot, you can experiment with different LLM models, fine-tune the models with relevant data, optimize the prompt templates, and handle edge cases or exceptions effectively. It is also important to gather feedback and continuously iterate on the extraction process.