Thread by @EdenEmarco177

1/10 🧵💡 Ever wondered how to handle token limitations of LLMs in text summarization? Here's the elegant idea of the "refine" technique in @LangChainAI 🦜🔗, inspired by the "reduce" concept in functional programming. Let's deep dive! 🚀 @hwchase17's your PR is under review 😎

2/10 "Reduce" in python🐍 or "foldl" as it's known in Haskell, is a critical element in functional programming. this is a high order function that has 3 parameters: an iterable, a reduction function, and a starting value.

3/10
"foldl" / "reduce" applies a specified binary operation to successive elements of an iterable, accumulating the result to produce a single output. "reducing the list"

Let's simplify it with an example:

4/10 Now, how does @LangChainAI 🦜🔗 leverage this concept for handling LLM token limitations?
When faced with a large piece of text, it can first chop it up into manageable chunks. This forms our list for the "reduce" operation.

5/10 Then, the reduction function is @LangChainAI calling our LLM. It uses a specific prompt to ask the LLM to either refine an existing summary with additional context, or, if the context isn't useful, to retain the original summary.

6/10 So, the LLM's job is to take each chunk, and refine the summary so far, based on the new context provided. It's essentially leveraging the "reduce" concept to distill a meaningful, concise summary from the large text. @LangChainAI🦜🔗 implements all the heavy lifting.

7/10 As for the starting value in this "reduce" operation, it's just an empty string. This allows the @LangChainAI🦜🔗 and the LLM to build up the final summary chunk by chunk, refining along the way.

9/10 One thing to note here: The "refine" technique is sequential and can't run in parallel (like @LangChainAI MapReduce which we will cover in future posts). This could be a downside if you're dealing with a really large volume of data. It also performs several LLM calls.

10/10 However, the upside is, by taking this approach, the summarization output is super meaningful, making it a worthwhile trade-off. This makes @LangChainAI 🦜🔗 an excellent solution for meaningful summarization of large texts with just a single chain!

python.langchain.com/en/latest/modu…

11/10 This elegant combination of functional programming and AI truly makes @LangChainAI a powerful tool in LLM-powered applications.

Checkout the source code by @hwchase17
implementing this
github.com/hwchase17/lang…

Until next time, happy coding! 🚀
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