With the exponential increase in data generated every day, it becomes essential to have effective tools to interpret and use this information strategically. We have already seen in our previous articles how the Retrieval Augmented Generation (RAG) technique can use the power of LLMs to support information search and reprocessing. Now the time has come to see how all this can be put into practice by adopting the LlamaIndex framework.
What is LlamaIndex?
LlamaIndex is a complete and versatile framework for developing AI applications that make use of Context-Augmentation. For example, in the case of an NLP model, in addition to the input prompts and the generative capacity of the model itself, it is possible to draw on information from external documents or databases to generate contextualized, precise and more detailed output.
Let’s imagine we need to use an NLP model to analyze product reviews. Without Context-Augmentation, the model only works with review text. With Context-Augmentation, each review can be enriched with additional information such as the product category, price, technical specifications, date of the review and the profile of the user who wrote the review (age, gender, past preferences, etc.). This additional context allows the model to make more accurate predictions and offer more relevant answers.
What are the benefits of Context-Augmentation?
The benefits introduced by Context-Augmentation are different but the most obvious ones are the following:
- By enriching data with contextual information, AI models can generate more accurate outputs. This is especially useful in industries like healthcare, where accurate interpretation of medical data can make the difference between a correct diagnosis and an incorrect one.
- Adding context can help reduce bias in models. For example, an NLP model that analyzes texts from different cultures can benefit from cultural context to avoid misinterpretations.
- Context-Augmentation makes models more robust against changes in input data. With richer context, models can better adapt to new or changing situations, improving their ability to generalize.
What are the main features of LlamaIndex?
One of the features of LlamaIndex that I consider most interesting is the ability to extract information from different sources. The framework has extractors for most common document formats such as PDF, CSV, Word or PowerPoint.
The documents provided as input are processed and indexed in a standardized format (normalization) which can then be converted into vectors using the embeddings functionality and saved both in-memory and within external vector stores.
Large files are split into smaller, more manageable units called “chunks.” By processing only a portion of the dataset at a time, LlamaIndex avoids memory overload, improving system stability. Furthermore, dividing data into smaller units facilitates the parallelization of processing operations, reducing execution times.
Once the vectorstore has been populated, it is possible to use the information to query the LLM and obtain contextualized answers according to the Retrieval Augmented Generation pattern.
With its accuracy, scalability and ease of use, LlamaIndex is destined to become a point of reference for all organizations looking to make the most of their data. If the topic interests you, don’t miss the next articles in which we will see how to use LlamaIndex to develop AI applications.