Alex Medick

Looking for a guide to the best NLP tools to build LLM Agents and RAG applications? Then look no further. You're in the right place.

Ask any non-native speaker: English is a confusing language to learn. We have words that are spelled the same but have different meanings and pronunciations (“minute,” meaning 1/60th of an hour, and “minute,” meaning a small amount), words that sound the same but are spelled differently and have different meanings (“to,” “too,” “two”), and words that sound the sam

A Guide to the Best NLP Tools for Building Advanced LLM Agents and RAG Applications

Looking for a guide to the best NLP tools to build LLM Agents and RAG applications? Then look no further. You're in the right place.

Ask any non-native speaker: English is a confusing language to learn. We have words that are spelled the same but have different meanings and pronunciations (“minute,” meaning 1/60th of an hour, and “minute,” meaning a small amount), words that sound the same but are spelled differently and have different meanings (“to,” “too,” “two”), and words that sound the same and are spelled the same but have different, even opposite, meanings in different contexts (“cleave,” “sanction”).

Not to mention all the idioms, slang terms, and regional dialects and usages.

English isn’t tricky just for non-native speakers. For decades, researchers have pursued natural language processing (NLP)—the ability of a machine to extract meaning from written or spoken language. Historically, these researchers have met the same challenges that non-native speakers encounter, but with less success.

Multiply this by the hundreds of languages spoken around the world, and you have a daunting task.

But we have made tremendous progress in NLP in recent years, thanks mainly to artificial intelligence (AI). These complementary technologies are driving new products such as machine translation and chatbots. In this article, we explore these concepts and how NLP and AI now work together to open a new era in human-computer interaction.


Understanding NLP and Its Applications in AI

NLP is a broad area of research that involves numerous considerations, such as:

 * Syntax: The rules of grammar and word order prevalent in a given language
 * Semantics: The meanings of words, phrases, and sentences in a given context
 * Pragmatics: How context influences meaning in real-world situations

Research in these and other areas is used in AI to build algorithms to interpret, understand, and generate human language. Key applications include:

 * Speech-to-text, or automatic generation of transcripts of spoken words
 * Text-to-speech, or “reading” text aloud, with proper pronunciation and inflections that convey meaning and sound natural
 * Machine translation from one language to another
 * Sentiment analysis to determine whether, for example, a Yelp review is positive or negative
 * Automated telephone agents that interpret your spoken requests to get you the answers or services you need
 * Chatbots that can carry on conversations with humans
 * Text or image generation based on user prompts


The Role of NLP in Building LLM Agents and RAG Applications

A large language model (LLM) is a machine-learning model that performs NLP tasks and is trained on immense quantities of text data. An LLP agent, in turn, is an application that leverages an LLM, memory, reasoning ability, and other tools to complete a specific type of task.

An important tool used in the design of LLM agents is called retrieval-augmented generation (RAG). RAG is a technique that connects an LLM agent to a knowledge base, such as a document repository or newspaper archive. An LLP agent combines what it has learned about language (how to compose meaningful sentences) with information it finds in the RAG pipeline to respond to user prompts.

NLP techniques, such as lexical semantics, terminology extraction, and word-sense disambiguation, are critical to develop these advanced AI applications. NLP techniques help them make “sense” of what the user is asking for and to improve the accuracy of the responses.


Top NLP Tools for AI Projects

In just the last few years, numerous tools have emerged to enable developers to integrate NLP techniques into their AI projects. Here, we briefly describe some of the top tools.


General-Purpose NLP Libraries

Several general-purpose code libraries are now available for NLP tasks. Some of these include:

 * Natural Language Toolkit (NLTK): A Python platform with interfaces to numerous lexical resources and processing libraries for tasks such as classification, stemming, parsing, tokenization, and semantic reasoning.
 * spaCy: More advanced than NLTK, spaCy offers support for over 75 languages and comes with pre-trained pipelines, transformers, and word vectors.
 * Stanford NLP: Stanford University’s NLP research group has developed a number of different NLP tools, such as CoreNLP (a Java suite of basic NLP tools), stanza (a Python library), and GloVe (software for word vectors and embeddings). These resources and more are available on GitHub.


Machine-Learning Frameworks for NLP

The two main ML frameworks for NLP are TensorFlow and PyTorch, both of which are general-purpose ML frameworks. Training an ML model on text data requires the text to be pre-processed with techniques such as tokenization and word vectoring. Both TensorFlow and PyTorch offer text pre-processing tools.

TensorFlow includes the KerasNLP and TensorFlow Text libraries. KerasNLP is the higher-level library, and the lower-level TensorFlow Text enables more fine-grained control. Together, these libraries help developers build robust text pre-processors to train text-based ML models.

PyTorch, an open-source Python ML framework developed and supported by Facebook, includes the TorchText library, which includes text data processing utilities and popular natural-language datasets.


Specialized Tools for LLM Agent and RAG Development

In addition to the general-purpose frameworks and NLP tools, some specialized tools are available to develop LLM agents and RAG pipelines:

 * Hugging Face Transformers: A transformer is a specific type of machine-learning architecture. Hugging Face is a community of ML developers who have built numerous NLP-based transformers for various types of tasks.
 * AllenNLP: The Allen Institute for AI, an AI research organization, has developed its own PyTorch NLP library for text processing and model development.
 * OpenAI GPT: One of the best-known organizations focusing on LLMs, OpenAI’s Generative Pretrained Transformer (GPT) models power a variety of NLP-based tasks, most notably its ChatGPT model for chatbots and other natural-language interaction applications.
 * BART and T5: These LLMs, developed by Facebook, are similar but intended for different use cases. BART (Bi-directional AutoRegressive Transformers) is more for generating text “from scratch,” changing the tone of input text, or translating text from one language to another. T5 (Text-to-Text Transfer Transformer) is more useful for RAG pipelines to find answers to specific questions in a connected repository. Hybrid applications can be developed to leverage the strengths of both BART and T5.


Choose the Right NLP Tool

How do you choose the right NLP tool for your AI project? As with any software development project, the right tool depends on a number of factors, such as:

 * Intended use: What problem are you trying to solve? Who are the users, and what will they be able to do with an NLP-based AI application that they can’t do well (or efficiently, or at all) now?
 * NLP tasks: What specific NLP techniques need to be applied to make your proposed application work?
 * Interaction channel: Will users interact with this application by point-and-click, keyboard, microphone, or something more exotic like drawing, photographs, or video?
 * Data availability: What data is available to train your model(s)? Where did it come from? Has it been cleansed to reduce biases and inaccuracies? For RAG pipelines, what are your target data repositories?
 * Scalability: What is the scope of the problem space you are addressing? Is there value in scaling your proposed solution to address additional problem spaces?
 * Technical expertise: How experienced is your development team in AI in general and NLP in particular?

Armed with the answers to these questions, you should be able to leverage the large and growing AI development community to point you toward the right NLP tools.


Success Stories

Here are some examples of successful NLP-based AI applications:

Global pharmaceutical giant Eli Lilly developed a content translation tool for its specialized pharma and life sciences content. This tool enables them to exchange highly-technical information internally, and it shares this information with external health ministries and other regulatory bodies around the world.

Note-taking software provider Waspnote has leveraged a custom chatbot from SharurAI to enhance the note-taking experience, including summary automation and personalization to cater to individual user style and presentation preferences.

International consultancy Accenture uses NLP and RAG to help its legal affairs team analyze millions of contract documents in its repository. It helps them understand their contractual risk under various scenarios, such as natural disasters and financial crises.

Applications such as ChatGPT make the most headlines, but the technology is about more than that. The pace of NLP-based AI development has accelerated, and companies around the world now find innovative ways to apply the technology.


Conclusion

Once seen as an intractable problem in computer science, NLP (with the help of AI) is now a mature approach to real-world problems, and it continues to evolve. With much of the world’s data immortalized in unstructured text, it’s clear that NLP-based AI tools are the fastest, most reliable way to unlock the value of that data.

Businesses that do not explore the possibilities of NLP-powered AI may miss important opportunities to gain competitive advantage. A search for success stories such as those mentioned above can inspire you to find NLP-based solutions to your problems as well.


Further Reading and Resources

Here are some resources to learn more about NLP technology:


Books

 * Introduction to Natural Language Processing by Jacob Eisenstein
 * Deep Learning for NLP and Speech Recognition by Uday Kamath, John Liu, James Whitaker
 * Natural Language Processing in Action by Hobson Lane, Cole Howard, Hannes Hapke


Communities

 * Hugging Face
 * NLPlanet


Courses, Guides, and Sites

 * What is Natural Language Processing (NLP)? A Comprehensive Guide for Beginners
 * A Complete Guide to Natural Language Processing
 * Jay Alammar’s blog


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