1/10 π§΅π‘ Ever wondered how to handle token limitations of LLMs in text summarization? Here's the elegant idea of the "refine" technique in @LangChainAI π¦π, inspired by the "reduce" concept in functional programming. Let's deep dive! π @hwchase17's your PR is under review π
2/10 "Reduce" in pythonπ 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 π¦π 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. @LangChainAIπ¦π 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 @LangChainAIπ¦π 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 π¦π an excellent solution for meaningful summarization of large texts with just a single chain!
π§΅π Following my last thread on "in-context learning", now it's time to explain how we can digest our custom data so that LLMβs π€ can use it. Spoiler alert- @LangChainAI π¦ π and a vector store like @pinecone π² will do all the work for us.
1/12 This is a laser focused thread 𧡠for devs and software engineers. Even if you have zero AI knowledge (like I did just 6 months ago)- I will be simplifying key data concepts for any gen ai applicationπ‘
2/12 Let's talk custom data digestion for LLMs π€
First off: Embedding models. These condense complex data into meaningful vectors, capturing relationships and semantic meaning. Think of it as a black box for text β‘ vector conversion. (vector = list of floats)
𧡠Ever wanted to talk with your LLMπ€ on some custom data that it wasn't originally trained on? @LangChainAI π¦π+ @pinecone π²vectorstore will do all the heavy lifting for you. Here's a simplified explanation using a series of 8 illustrations I made.
1/8 Assume you've got documentation of an internal library π. When you directly ask the LLM about the library, it can't answer as it wasn't trained on it π€·ββοΈ. No worries! @LangChainAI + @pinecone is here to help π
2/8: We load the entire package documentation into a vectorstore like @pinecone π². This involves transforming the text into vectors, aka 'embeddings'. Now, these vectors hover around, representing our texts ποΈ