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How to use ChatGPT and LLMs for data extraction

Artificial intelligence (AI) has taken huge leaps forward in the last 18 months with the development of sophisticated large language models. These models, including GPT-3.5, GPT-4, and open source LLM OpenChat 3.5 7B, are reshaping the landscape of data extraction. This process, which involves pulling out key pieces of information like names and organizations from text, is crucial for a variety of analytical tasks. As we explore the capabilities of these AI tools, we find that they differ in how well they perform, how cost-effective they are, and how efficiently they handle structured data formats such as JSON and YAML.

These advanced models are designed to understand and process large volumes of text in a way that resembles human cognition. By simply entering a prompt, they can filter through the text and deliver structured data. This makes the task of extracting names and organizations much smoother and allows for easy integration into further data analysis processes.

Data Extraction using ChatGPT and OpenChat locally

The examples below show how to save your extracted data to JSON and YAML files. Because they are easy to read and work well with many programming languages. JSON is particularly good for organizing hierarchical data with its system of key-value pairs, while YAML is preferred for its straightforward handling of complex configurations.

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However, extracting data is not without challenges. Issues like incorrect syntax, unnecessary context, and redundant data can affect the accuracy of the information retrieved. It’s crucial to adjust these large language models carefully to avoid these problems and ensure the responses are syntactically correct.

When we look at different models, proprietary ones like GPT-3.5 and GPT-4 from OpenAI are notable. GPT-4 is the more advanced of the two, with better context understanding and more detailed outputs. OpenChat 3.5 7B offers an open-source option that is less expensive, though it may not be as powerful as its proprietary counterparts.

Data extraction efficiency can be greatly improved by using parallel processing. This method sends multiple extraction requests to the model at the same time. It not only makes the process more efficient but also reduces the time needed for large data extraction projects.

Token Costs

The cost of using these models is an important factor to consider. Proprietary models have fees based on usage, which can add up in big projects. Open-source models can lower these costs but might require more setup and maintenance. The amount of context given to the model also affects its performance. Models like GPT-4 can handle more context, which leads to more accurate extractions in complex situations. However, this can also mean longer processing times and higher costs.

Creating effective prompts and designing a good schema are key to guiding the model’s responses. A well-crafted prompt can direct the model’s focus to the relevant parts of the text, and a schema can organize the data in a specific way. This is important for reducing redundancy and keeping the syntax precise.

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Large language models are powerful tools for data extraction, capable of quickly processing text to find important information. Choosing between models like GPT-3.5, GPT-4, and OpenChat 3.5 7B depends on your specific needs, budget, and the complexity of the task. With the right setup and a deep understanding of their capabilities, these models can provide efficient and cost-effective solutions for extracting names and organizations from text.

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My lisa Nichols is an accomplished article writer with a flair for crafting engaging and informative content. With a deep curiosity for various subjects and a dedication to thorough research, lisa Nichols brings a unique blend of creativity and accuracy to every piece

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