Workaround OpenAI's Token Limit With Chain Types

Name: Workaround OpenAI's Token Limit With Chain Types
Uploaded: 2023-03-01T20:30:05.000Z
Duration: 30 min 53 s

How to Fix OpenAI Token Length Error

In this video, the speaker discusses how to fix the OpenAI token length error. The video covers four different methods for managing prompts and working around the 4K token limit.

Understanding the Problem

OpenAI has a 4K token limit for processing requests.

Exceeding this limit can be an issue for businesses or products based on OpenAI.

The speaker introduces four different methods for managing prompts and working around the 4K token limit.

Method 1 - Stuffing

This method involves summarizing a document that is under 4K characters long by feeding it directly into OpenAI.

If the document is longer than 4K characters, this method will not work.

Method 2 - MapReduce

This method involves slicing up a document into individual pieces and passing each one over to OpenAI in separate API calls.

After receiving summaries from each piece, a final summary is generated using all of them.

Method 3 - Chunking

This method involves breaking down a long prompt into smaller chunks and sending them separately to OpenAI.

The resulting summaries are then combined to create a final summary.

Method 4 - Fine-Tuning GPT Models

This method involves fine-tuning GPT models with your own data to generate more accurate summaries.

It requires some technical expertise but can be very effective.

Conclusion

The speaker concludes by summarizing the four methods and encouraging viewers to experiment with them to find the best solution for their needs.

Methods for Summarizing Text

In this section, the speaker discusses four methods for summarizing text. The first method is extractive summarization, which involves selecting important sentences from the original text. The second method is abstractive summarization, which involves generating new sentences that capture the essence of the original text. The third method is refine summarization, which involves combining summaries generated from different parts of the original text. The fourth method is map re-rank, which involves ranking answers to specific questions posed to different parts of the original text.

Extractive Summarization

Extractive summarization involves selecting important sentences from the original text.

This method can be done manually or through algorithms that use natural language processing techniques.

One advantage of extractive summarization is that it preserves the meaning and context of the original text.

However, one disadvantage is that it may not capture all important information in a long document.

Abstractive Summarization

Abstractive summarization involves generating new sentences that capture the essence of the original text.

This method requires more advanced natural language processing techniques than extractive summarization.

One advantage of abstractive summarization is that it can generate more concise summaries than extractive summarization.

However, one disadvantage is that it may introduce errors or inaccuracies in the summary.

Refine Summarization

Refine summarization involves combining summaries generated from different parts of the original text.

This method involves passing each part of the original text through a summarization algorithm and then combining the resulting summaries.

One advantage of refine summarization is that it can provide more relevant context than other methods.

However, one disadvantage is that it can be a slow process because each summary must be generated independently.

Map Re-rank

Map re-rank involves ranking answers to specific questions posed to different parts of the original text.

This method is better suited for answering specific questions rather than generating general summaries.

One advantage of map re-rank is that it scales well and can handle large amounts of data.

However, one disadvantage is that it does not combine information across documents.

Lang Chain Prompt

In this section, the speaker explains how to use Lang Chain to summarize a large document. They demonstrate how to use the mapreduce and recursive text splitter methods to split a large document into smaller chunks and then summarize those chunks.

Using MapReduce Method

To summarize a large document using the mapreduce method, set chain type equal to mapreduce and for both equals true.

The summary of the small document is similar to that of the stuffing method.

To summarize a larger document, use Lang Chain's recursive text splitter method with chunk size equals 400 and chunk overlap equals zero.

This splits the large document into smaller chunks, which can be summarized using the mapreduce method.

Using Recursive Text Splitter Method

The first five sections are summarized using write a concise summary of the following prompt.

All summaries are combined into one final summary using write a concise summary of the following prompt.

Refine method is used on each chunk by inserting prompts in talking to OpenAI.

Conclusion

The speaker demonstrates how to use Lang Chain's mapreduce and recursive text splitter methods to summarize large documents. By splitting up larger documents into smaller chunks, it becomes possible to generate accurate summaries without exceeding model context length limits.

Four Methods of Prompt Management

In this section, the speaker discusses four different methods of prompt management and how to chain different commands together in order to fit your use case.

Summarization Method

The first method discussed is the Summarization Method.

This method involves summarizing a given text or document into a shorter version while still retaining its key points.

The speaker suggests trying out this method for your use case to see if it works.

Generative Method

The second method discussed is the Generative Method.

This method involves generating new text based on a given prompt or input.

It can be used for tasks such as language translation or text completion.

Retrieval Method

The third method discussed is the Retrieval Method.

This method involves retrieving relevant information from a large dataset based on a given query or question.

It can be used for tasks such as search engines or recommendation systems.

Map Re-rank Method

The fourth and final method discussed is the Map Re-rank Method.

This method involves ranking answers to a given question based on their relevance to the query.

The speaker demonstrates how this can be done using an example with five documents and a specific question.

Using Context to Score Answers

In this section, the speaker discusses how context can be used to score answers in the Map Re-rank Method.

Scoring Examples

The speaker provides examples of how scoring can be done in the Map Re-rank Method using context.

A piece of context is provided along with a question, and both are used to determine how fully an answer addresses the question.

The speaker notes that this is a good example of prompt engineering.

Intermediate Steps

The speaker shows the intermediate steps involved in using the Map Re-rank Method to answer a specific question.

The method involves going through different documents, asking questions, and ranking answers based on their relevance to the query.

Conclusion

In this section, the speaker concludes by encouraging viewers to try out different methods of prompt management and see which ones work best for their use case.

Four Methods of Prompt Management

The speaker summarizes the four methods of prompt management discussed in the video: Summarization, Generative, Retrieval, and Map Re-rank.

Viewers are encouraged to experiment with these methods and find which ones work best for them.