How To Overcome Context Limits in Large Language Models

How To Overcome Context Limits in Large Language Models

Understanding the Challenge of Context Limits in LLMs

Ever wondered how to overcome context limits in LLMs? Large Language Models (LLMs) are powerful tools for processing and understanding vast amounts of text. However, they come with a challenge - context limits.

These limits restrict the amount of data that can be processed at once, often leading to errors when the limit is exceeded.

The Impact of Context Limits on Data Processing

Context limits can significantly impact data processing. For instance, if you're trying to process a 42MB PDF file with over 160 pages of dense documentation, you'll likely encounter a context error. This is because the context limit of most LLMs, like GPT 3.5, is around 4000 tokens, which is approximately 2000 words. So, how do we bypass these LLMs context limits?

The Power of Vector Embeddings and Vector Search

Breaking down text into manageable chunks is one effective way to overcome context limits in LLMs is by using vector embeddings and vector search. For additional insights, we've crafted a detailed blog post on how to generate Open AI embeddings with your data.

This technique involves breaking down the text into manageable chunks, each containing a specific number of tokens. For instance, you can split a large text into chunks of 500 tokens each.

Once the text is broken down into chunks, you can then use vector search to retrieve the most relevant pieces of information to a specific input query. This process basically works as a semantic cache for the LLM, involving embedding each chunk of data and sorting them using cosine similarity to detect the closest match to the search query.

Enhancing Accuracy and Efficiency in LLMs

Adjust context window and chunk sizes. To improve accuracy, you can adjust the context window and chunk sizes.

For instance, instead of using six pages, you might want to increase it to 10 or 20 pages. This increases the chance of the chunk containing the information you want, thereby helping you bypass LLMs context limits more effectively.

Step-by-step Guide to Bypassing LLMs Context Limits

Ready to put this into practice? Here's a step-by-step guide to help you overcome context limits in LLMs:

1. Break down the text into manageable chunks.

Truncate text

2. Use vector search to retrieve the most relevant pieces of information.

Vector search

3. Adjust the context window and chunk sizes to improve accuracy.

Adjust chunk sizes

4. Leverage larger models for greater context limits.

GPT 3.5 16k context limit

Real-world Application: Processing Large PDFs with LLMs

This technique is not just theoretical; it has real-world applications. For instance, you can use it to process large PDFs with LLMs, transforming them into text and retrieving relevant information based on specific queries.

Next Steps: Optimizing Your Use of LLMs

While vector embeddings and search are effective, there are other techniques you can explore to overcome context limits in LLMs. For instance, you can use the map reduce technique, which involves summarizing each chunk of data into a smaller chunk and processing those summaries instead of the full page.

Leveraging Larger Models for Greater Context Limits

Another way to overcome context limits in LLMs is by leveraging larger models. For instance, if you're using GPT 3.5 and you've hit a limit of 4000 tokens, you might choose to move to the 3.5 16K model. This increases the context limit by four times, allowing you to process larger amounts of data.

Choose your LLM

Getting Support and Further Learning

If you're interested in learning more about how to bypass LLMs context limits, consider signing up for Relevance AI. It's free, and you can get started in just a few minutes. You'll gain access to a wealth of resources and support to help you optimize your use of LLMs. Plus, you'll be able to apply the techniques discussed in this blog post to your own projects.

Embarking on Your Journey to Master LLMs

Overcoming context limits in LLMs may seem daunting, but with the right techniques and tools, it's entirely possible. By breaking down text into manageable chunks, using vector search, adjusting context window and chunk sizes, and leveraging larger models, you can bypass LLMs context limits and process large amounts of data with ease. So why wait? Start your journey to mastering LLMs today.

Daniel Vassilev
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