70% of developers are adopting AI today: Familiarizing themselves with the rise of big language models, LangChain and vector databases in today's technology landscape

Artificial Intelligence has limitless possibilities, as evidenced by the new releases and developments it introduces everyone to. With the release of the latest chatbot developed by OpenAI called ChatGPT, the field of Artificial Intelligence has taken over the world as ChatGPT, thanks to its transformational GPT architecture, is constantly making headlines. From deep learning, natural language processing (NLP) and natural language understanding (NLU) to computer vision, AI is leading everyone into the future with endless innovations. Almost every industry is harnessing the potential of AI and revolutionizing it. Excellent technological advances are responsible for this remarkable development, especially in the areas of large language models (LLM), LangChain and vector databases.

Large language models

The development of large language models (LLMs) represents a huge step forward for artificial intelligence. These models, based on deep learning, demonstrate impressive accuracy and fluency in natural language processing and comprehension. LLMs are trained using vast amounts of textual data from a variety of sources including books, magazines, web pages, and other textual resources. In the process, they perceive linguistic structures, patterns, and semantic relationships that help them understand the complexity of human communication.

The LLM architecture is usually based on a deep neural network with several layers. Based on the patterns and relationships found in the training data, this network analyzes the input text and produces a prediction. In order to reduce the discrepancy between expected and inferred results, the model parameters are tuned during the training phase. During training, the LLM consumes the text data and tries to predict the next word or series of words depending on the context.

Application of LLM

1. Answering Questions: LLMs are adept at answering questions, and in order to provide an accurate and concise answer to a question, they search an extensive corpus of texts such as books, articles, or websites.
2. Content Creation - M.A. students have proven to be useful in activities related to content creation. They are able to create grammatically correct and coherent articles, blog posts, and other written content.
3. Text Summarization: LLMs are excellent at text summarization, which is the task of retaining important information while compressing lengthy texts into shorter, more digestible summaries.
4. Chatbots - LLMs are often used when building chatbots and systems that utilize conversational AI. They allow these systems to interact with users in ordinary language by understanding their questions, answering them appropriately, and maintaining context throughout the interaction.
5. Language Translation - LLMs are able to accurately translate text from one language to another, facilitating successful communication despite language barriers.

Stages of study in the LLM program

1. In the initial phase of LLM training, it is necessary to collect a large textual dataset that will be used by the model to identify linguistic patterns and structures.
2. Once the data set has been collected, pre-processing is required to prepare it for training. For this purpose, the data must be cleaned by removing all unnecessary and redundant records.
3. Choosing an appropriate model architecture is very important for LLM learning. Transformer-based architectures have shown high performance in natural language processing and generation, including the GPT model.
4. The model parameters are customized for LLM training and their accuracy is improved using deep learning techniques such as backpropagation. During training, the model processes the input data and produces predictions based on recognized patterns.
5. After initial training, the LLM is further refined on specific tasks or domains to improve performance in those areas.
6. To determine the effectiveness of a trained LLM, it is necessary to evaluate its performance, for which a number of metrics are used, including perplexity and accuracy.
7. After training and evaluation, the LLM is put into operation in a production environment to solve real-world problems.

Some known language models

1. GPT (Generative Pre-trained Transformer) is a prominent member of the OpenAI family of GPT models, serving as the basis for the well-known ChatGPT model. It is a unidirectional autoregressive model designed only for the decoder, as it generates text by predicting the next word based on previously generated words. With 175 billion parameters, GPT is widely used for content generation, question answering, etc.
2. BERT - Bidirectional Encoder Representations from Transformers (BERT) is one of the first self-supervised language models based on transformers. It is a powerful model for understanding and processing natural language with 340 million parameters.
3. PaLM - Google's Pathways Language Model (PaLM) with 540 billion parameters used a modified version of the common encoder-decoder-transformer model architecture and showed high performance in the tasks of natural language processing, code generation, question answering, etc.

LangChain

Despite their adaptability and ability to solve a wide range of language problems, LLMs have their limitations when preparing precise answers or solving tasks that require in-depth knowledge or expertise. LangChain in this case serves as a bridge between LLMs and subject matter experts. By incorporating the specialized knowledge of subject matter experts, it leverages the capabilities of the LLM. It provides more accurate, complete, and contextually appropriate answers on specialized topics by combining the LLM's general understanding of the language with specialized knowledge in a particular field.

Meaning of LangChain

If one were to query the LLM for a list of the top performing stores from the previous week, without using the LangChain framework, the LLM would come up with a logical SQL query to get the desired result with fake but plausible column names. With the LangChain architecture, programmers can give the LLM a range of options and capabilities. They can ask the LLM to create a workflow that breaks the problem into multiple parts and navigates the LLM's questions and intermediate steps, resulting in a comprehensive answer for the LLM.

For drug discovery, an LLM can provide general information on medical issues, but may not have the in-depth understanding needed to make a specific diagnosis or treatment suggestion. LangChain, on the other hand, can add medical knowledge from specialists or medical information databases to enhance the LLM's answers.

Vector databases

A vector database is a completely new and distinctive database that is rapidly gaining acceptance in the field of artificial intelligence and machine learning. They differ from traditional relational databases, originally designed to store tabular data as rows and columns, and from more modern NoSQL databases such as MongoDB, which store data as JSON documents. This is because a vector database is only designed to store and retrieve vector embeddings as data.

A vector database is based on vector embedding, a data encoding that carries semantic information that allows artificial intelligence systems to interpret and maintain data in the long term. In vector databases, data is organized and stored using its geometric properties, where its coordinates in space and other defining qualities are used to identify each object. Such databases help to search for similar objects and perform advanced analysis of large amounts of data.

Best vector databases

1. Pinecone - Pinecone is a cloud-based vector database designed to store, index and quickly search large collections of high-dimensional vectors. One of its main features is its real-time indexing and search capability. It can handle both sparse and dense vectors.
2. Chroma - Chroma is an open source vector database that provides a fast and scalable way to store and retrieve nested images. It is user-friendly and easy to use, offers a simple API and supports many backends, including popular options such as RocksDB and Faiss.
3. Milvus - Milvus is a vector database system specifically designed to efficiently process large amounts of complex data. For a variety of applications including similarity search, anomaly detection and natural language processing, it is a strong and adaptable solution that provides high speed, performance, scalability and specialized functionality.
4. Redis is an amazing vector database with features like indexing and searching, distance calculation, high performance, data storage and analysis, fast response time.
5. Vespa - Vespa supports real-time geospatial search and analytics, fast query results, high data availability and multiple ranking options.

In conclusion, this year will see an unprecedented growth in the widespread application of artificial intelligence. This remarkable development is due to outstanding technological developments, particularly in the areas of large language models (LLMs), LangChain, and vector databases. LLMs have transformed natural language processing, LangChain has provided programmers with a framework for creating intelligent agents, and vector databases enable efficient storage, indexing, and retrieval of high-dimensional data. Collectively, these technological innovations have paved the way for a future based on artificial intelligence.