Clean Dirty Data with Structured Output from OpenAI

Clean Dirty Data with Structured Output from OpenAI

Table of Contents

1. Introduction
2. Benefits of Structured Data from Language Models
3. Overview of Langchain Functionality
4. Handling Dirty Data in CRMs
5. Example: Matching User Input Industries with Standardized List
6. The Value of Structuring Unstructured Data
7. Opportunities for Monetization
8. Initializing the Language Model
9. Importing Packages
10. Setting Temperature to Zero
11. Providing the Open API Key
12. Formatting the Output with Response Schema
13. Creating the Chat Prompt Template
14. Generating a List of Standardized Names
15. Passing the Input and Formatting to Language Model
16. Parsing and Structuring the Output
17. Using Pandas for Easier Data Analysis
18. Adding Confidence Scores to the Matching
19. Implementing the Solution in Production
20. Conclusion

Article

How to Get Structured Data from Your Language Model

Do You want to leverage the power of your language model to extract structured data? With Langchain's new functionality, you can now instruct your language model to provide the specific data format you need. In this article, we will guide you through the process of transforming dirty data into structured information using Langchain. We will use a real-life example of matching user input industries with a standardized list to demonstrate the capabilities of this approach.

Introduction

Unstructured data can often pose challenges, especially when dealing with customer relationship management (CRM) systems. User input and other unstructured data can result in messy records that are difficult to analyze. However, with the help of language models and Langchain, you can effortlessly convert unstructured data into a clean and structured format.

Benefits of Structured Data from Language Models

Structured data offers several benefits, including improved data management, better analytics, and enhanced decision-making. By structuring unformatted or dirty data, you can easily compare it against standardized lists and extract Meaningful insights. This allows you to provide value to others by cleaning up and structuring their data, opening up opportunities for monetization.

Overview of Langchain Functionality

Langchain provides a convenient wrapper for the ChatGPT language model, enabling you to Interact with it easily. By instructing the language model using a defined data format, Langchain allows you to obtain the structured output you desire.

Handling Dirty Data in CRMs

CRMs often contain messy data, particularly in industry-related fields. User input and manual data entry can introduce inconsistencies and variations in industry names. In this tutorial, we will focus on cleaning and matching user input industries with a standardized industry list.

Example: Matching User Input Industries with Standardized List

Imagine you have a list of user input industries that you want to match against a standardized list. We will use LinkedIn data as an example. The goal is to find the best match for each user input and map it to a standardized industry. For instance, an input of "Airlines capital Z" would be mapped to "Transportation and Logistics."

To achieve this, we will utilize Langchain's functionality and demonstrate how easy it is to clean and structure unformatted data using language models.

The Value of Structuring Unstructured Data

There is a significant demand for structured data from unstructured sources. By transforming messy data into a structured format, you can unlock its true potential. Structured data enables others to utilize and analyze the information effectively, leading to improved decision-making and enhanced productivity.

Opportunities for Monetization

Not only does providing structured data add value to others, but it also presents opportunities for monetization. By offering data cleaning and structuring services, you can generate income by leveraging language models like ChatGPT and Langchain.

Initializing the Language Model

To begin, you need to initialize your language model using Langchain. In this example, we will be using the ChatGPT model. You can set the desired temperature, which controls the level of creativity in responses. Since we want structured output, we will set the temperature to zero. Additionally, make sure to provide your API key for authentication.

Importing Packages

To make the code implementation smoother, we need to import several packages. These packages include the necessary tools for data manipulation and the Langchain functionality. Importing these packages will ensure that we have all the required resources at our disposal.

Setting Temperature to Zero

The temperature parameter determines the randomness of the language model's responses. To obtain structured data, we want to avoid unnecessary creativity. Therefore, we will set the temperature to zero, ensuring that the model provides structured and deterministic outputs.

Providing the Open API Key

To establish a connection with the language model, you need to provide your open API key. This key authenticates your access to the model and allows Langchain to communicate with it effectively.

Formatting the Output with Response Schema

Langchain offers the flexibility to define the desired output format using response schemas. By specifying the structure of the output, you can instruct the language model accordingly. In our example, we will define the response schema to include input industry and standardized industry fields.

Creating the Chat Prompt Template

The chat prompt template is a vital part of instructing the language model. It serves as a guide for generating the desired output. In our case, we will design the prompt template to match input industries with their corresponding standardized industries.

Generating a List of Standardized Names

To perform the matching process, you need a standardized list of industry names. We will use LinkedIn data as our reference list. By having a comprehensive list of standardized names, we can map user input industries accurately.

Passing the Input and Formatting to Language Model

With the prompt template and the list of user input industries, we can now pass the formatted input to the language model. Langchain handles the communication with the model and retrieves the structured output. The response will include the mappings of user input industries with their corresponding standardized industries.

Parsing and Structuring the Output

The output received from the language model may require some parsing and structuring to make it easier to work with. In our case, we will parse the output and load it into a pandas DataFrame. This allows us to analyze and manipulate the structured data efficiently.

Using Pandas for Easier Data Analysis

Pandas, a popular data analysis library, helps us handle the structured data more effectively. By creating a DataFrame from the parsed output, we can easily analyze and Visualize the results. Pandas provides a powerful set of tools for data manipulation and exploration.

Adding Confidence Scores to the Matching

To assess the quality of the matches between user input industries and standardized industries, we can incorporate confidence scores. By assigning scores Based on the closeness of the match, we can evaluate the reliability of the mapping. This additional information adds value and helps in decision-making.

Implementing the Solution in Production

When implementing this solution in a production environment, it is essential to optimize the process. Rather than calling the language model for every single industry name, it is more efficient to batch them together. By maintaining a proprietary database of mapped values, you can minimize API calls and speed up the response time.

Conclusion

Transforming unstructured data into structured information is a powerful capability offered by language models like ChatGPT and Langchain. By leveraging their functionality, you can clean and structure data from various sources, opening up opportunities for monetization and value creation. Follow the steps outlined in this article to extract structured data from your language model and make the most out of your unstructured data.

Highlights

• Langchain offers the functionality to extract structured data from language models.
• Structured data provides better data management, analytics, and decision-making capabilities.
• Cleaning and structuring unformatted data is crucial for achieving meaningful insights.
• Matching user input industries with standardized lists is a practical application of structured Data Extraction.
• Opportunities for monetization arise from providing data cleaning and structuring services.
• Initializing the language model, importing packages, and setting temperature are essential steps in the process.
• Establishing the chat prompt template and defining the response schema allow for instructing the language model effectively.
• The parsed output can be loaded into a pandas DataFrame for easier data analysis.
• Adding confidence scores helps assess the quality and reliability of the matches.
• Implementing the solution in production involves optimizing the process and utilizing proprietary databases for faster response times.

FAQ

Q: Can I use Langchain with other language models? A: Langchain is designed to work specifically with the ChatGPT language model.

Q: Can I adjust the temperature parameter for more creative responses? A: Yes, the temperature parameter allows you to control the level of randomness in the language model's outputs. Increase the temperature for more creative responses.

Q: How accurate are the matches between user input industries and standardized industries? A: The accuracy of the matches depends on the quality and comprehensiveness of the standardized list and the language model's training. Fuzzy matching algorithms can help find meaningful matches beyond simple string matching.

Q: What are the benefits of using pandas for data analysis? A: Pandas provides a powerful set of tools for data manipulation, analysis, and visualization. It simplifies working with structured data and allows for efficient exploration and insights generation.

Q: Are there any limitations to consider when implementing this solution in production? A: When using language models in production, consider possible costs associated with API calls and response times. Building a proprietary database of mapped values can optimize the process and reduce reliance on frequent API calls.

Q: Can I monetize the structured data generated using this approach? A: Yes, by offering data cleaning and structuring services, you can monetize the value provided by the structured data. Your clients can leverage the clean, usable data for analytics, decision-making, and other applications.