Harnessing the Power of Large Language Models: A Closer Look at LangChain and Pandas-AI

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Understanding the Mechanism Behind Generating Python Code from User Queries
In today's world of data science, two libraries that stand out for their popularity and ability to fetch insights directly from structured datasets are LangChain (42.4k stars) and Pandas-AI (6.6k stars). But the real intrigue lies not just in using these libraries, but in understanding the mechanics at play behind the scenes.
Take for instance, how these libraries interact with Large Language Models (LLMs). Rather than answering queries directly, they generate Python code based on user prompts which then delivers the required insights. This approach is rooted in the fact that LLMs, being essentially text-to-text models, excel in some areas like mathematics where they can solve complex problems, but may falter at seemingly trivial ones.
Consider LangChain's prompt:
"""You are an agent designed to write and execute python code to answer questions.You have access to a python REPL, which you can use to execute python code.If you get an error, debug your code and try again.Only use the output of your code to answer the question. You might know the answer without running any code, but you should still run the code to get the answer.If it does not seem like you can write code to answer the question, just return "I don't know" as the answer."""
Similarly, Pandas-AI works on a prompt that generates Python code: """Prompt to generate Python code"""text: str="""Today is {today_date}.You are provided with a pandas dataframe (df) with {num_rows} rows and {num_columns} columns.This is the result of `print(df.head({rows_to_display}))`:{df_head}.When asked about the data, your response should include a python code that describes the dataframe `df`.Using the provided dataframe, df, return the python code and make sure to prefix the requested python code with {START_CODE_TAG} exactly and suffix the code with {END_CODE_TAG} exactly to get the answer to the following question:"""
While these libraries are efficient tools for data analytics, their real magic lies in understanding the underlying mechanism they employ, leveraging the strengths and mitigating the limitations of LLMs. Let's embrace the wisdom of not just using, but also comprehending our tools.
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Do you ever feel overwhelmed by the rapid advancements in AI, especially Gen AI?
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