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Retrieval augmented generation (RAG) has improved the function of large language models (LLM). It empowers generative AI to create more coherent and contextually relevant content. Let’s take a deeper look into understanding RAG.

What is retrieval augmented generation?

It is an AI framework and a type of natural language processing (NLP) model that enables the retrieval of information from an external knowledge base. It integrates retrieval-based and generation-based approaches to provide a robust database for LLMs.

A retrieval augmented generation model accesses a large pre-existing pool of knowledge to improve the quality of LLM-generated responses. It ensures that the information is more accurate and up-to-date by combining factual data with contextually relevant information.

By combining vector databases and LLM, the retrieval model has set up a standard for the search and navigation of data for generative AI. It has become one of the most used techniques for LLM.

Benefits of RAG

While retrieval augmented generation improves LLM responses, it offers multiple benefits to the generative AI efforts of an organization.

Explore RAG and its benefits, trade-offs, use cases, and enterprise adoption, in detail with our podcast! 

Improved contextual awareness

The retrieval component allows access to a large knowledge base, enabling the model to generate contextually relevant information. Due to improved awareness of the context, the output generated is more coherent and appropriate.

Enhanced accuracy

An LLM using a retrieval model can produce accurate results with proper attribution, including citations of relevant sources. Access to a large and accurate database ensures that factually correct results are generated.

Adaptability to dynamic knowledge

The knowledge base of a retrieval model is regularly updated to ensure access to the latest information. The system integrates new information without retraining the entire program, ensuring quick adaptability. It enables the generative models to access the latest statistics and research.

Resource efficiency

Retrieval mechanisms enable the model to retrieve information from a large information base. The contextual relevance of the data enhances the accuracy of the results, making the process resource-efficient. It makes handling of large data volumes easier and makes the system cost-efficient.

Increased developer control

Developers use a retrieval augmented generation model to control the information base of a LLM. They can adapt the data to the changing needs of the user. Moreover, they can also restrict the accessibility of the knowledge base, giving them control of data authorization.

Frameworks for retrieval augmented generation

A RAG system combines a retrieval model with a generation model. Developers use frameworks and libraries available online to implement the required retrieval system. Let’s take a look at some of the common resources used for it.

Hugging face transformers

It is a popular library of pre-trained models for different tasks. It includes retrieval models like Dense Passage Retrieval (DPR) and generation models like GPT. The transformer allows the integration of these systems to generate a unified retrieval augmented generation model.

Facebook AI similarity search (FAISS)

FAISS is used for similarity search and clustering dense vectors. It plays a crucial role in building retrieval components of a system. Its use is preferred in models where vector similarity is crucial for the system.

PyTorch and TensorFlow

These are commonly used deep learning frameworks that offer immense flexibility in building RAG models. They enable the developers to create retrieval and generation models separately. Both models can then be integrated into a larger framework to develop a RAG model.

Haystack

It is a Python framework that is built on Elasticsearch. It is suitable to build end-to-end conversational AI systems. The components of the framework are used for storage of information, retrieval models, and generation models.

Use cases of RAG

Some common use cases and real-world applications are listed below.

Content creation

It primarily deals with writing articles and blogs. It is one of the most common uses of LLM where the retrieval models are used to generate coherent and relevant content. It can lead to personalized results for users that include real-time trends and relevant contextual information.

Real-time commentary

A retriever uses APIs to connect real-time information updates with an LLM. It is used to create a virtual commentator which can be integrated further to create text-to-speech models. IBM used this mechanism during the US Open 2023 for live commentary.

Question answering system

The ability of LLMs to generate contextually relevant content enables the retrieval model to function as a question-answering machine. It can retrieve factual information from an extensive knowledge base to create a comprehensive answer.

Language translation

Translation is a tricky process. A retrieval model can detect the context of phrases and words, enabling the generation of relevant translations. Access to external databases ensures the results are accurate and fluent for the users. The extensive information on available idioms and phrases in multiple languages ensures this use case of the retrieval model.

Educational assistance

The application of a retrieval model in the educational arena is an extension of question answering systems. It uses the said system, particularly for educational queries of users. In answering questions and generating academic content, the system can create more comprehensive results with contextually relevant information.

Future of RAG

The integration of retrieval and generation models in LLM is expected to grow in the future. The current trends indicate their increasing use in technological applications. Some common areas of future development of RAG include:

• Improved architecture – the development of retrieval and generation models will result in the innovation of neural network architectures

• Enhanced conversational agents – improved adaptation of knowledge base into retrieval model databases will result in more sophisticated conversational agents that can adapt to domain-specific information in an improved manner

• Integration with multimodal information – including different types of information, including images and audio, can result in contextually rich responses that encompass a diverse range of media

• Increased focus on ethical concerns – since data privacy and ethics are becoming increasingly important in today’s digital world, the retrieval models will also focus more on mitigating biases and ethical concerns from the development systems

Hence, retrieval augmented generation is an important aspect of large language models within the arena of generative AI. It has improved the overall content processing and promises an improved architecture of LLMs in the future.

Traditional databases in healthcare struggle to grasp the complex relationships between patients and their clinical histories. This limitation hinders personalized medicine and hampers rapid diagnosis. Vector databases, with their ability to store and query high-dimensional patient data, emerge as a revolutionary solution.

This blog delves into the technical details of how AI in healthcare empowers patient similarity searches and paves the path for precision medicine.

Impact of AI on healthcare

The healthcare landscape is brimming with data such as demographics, medical records, lab results, imaging scans, – the list goes on. While these large datasets hold immense potential for personalized medicine and groundbreaking discoveries, traditional relational databases cannot store such high-dimensional data at a large scale and often fall short.

Their rigid structure struggles to represent the intricate connections and nuances inherent in patient data.

Vector databases are revolutionizing healthcare data management. Unlike traditional, table-like structures, they excel at handling the intricate, multi-dimensional nature of patient information.

Each patient becomes a unique point in a high-dimensional space, defined by their genetic markers, lab values, and medical history. This dense representation unlocks powerful capabilities discussed later.

Working with vector data is tough because regular databases, which usually handle one piece of information at a time, can’t handle the complexity and large amount of this type of data. This makes it hard to find important information and analyze it quickly.

That’s where vector databases come in handy—they are made on purpose to handle this special kind of data. They give you the speed, ability to grow, and flexibility you need to get the most out of your data.

Patient similarity search with vector databases in healthcare

The magic lies in the ability to perform a similarity search. By calculating the distance between patient vectors, we can identify individuals with similar clinical profiles. This opens a large span of possibilities.

Personalized treatment plans

By uncovering patients with comparable profiles and treatment outcomes, doctors can tailor interventions with greater confidence and optimize individual care. It also serves as handy for medical researchers to look for efficient cures or preventions for a disease diagnosed over multiple patients by analyzing their data, particularly for a certain period. 

Here’s how vector databases transform treatment plans:

• Precise Targeting: By comparing a patient’s vector to those of others who have responded well to specific treatments, doctors can identify the most promising options with laser-like accuracy. This reduces the guesswork and minimizes the risk of ineffective therapies.
• Predictive Insights: Vector databases enable researchers to analyze the trajectories of similar patients, predicting their potential responses to different treatments. This foresight empowers doctors to tailor interventions, preventing complications and optimizing outcomes proactively.
• Unlocking Untapped Potential: By uncovering hidden connections between seemingly disparate data points, vector databases can reveal new therapeutic targets and treatment possibilities. This opens doors for personalized medicine breakthroughs that were previously unimaginable.
• Dynamic Adaptation: As a patient’s health evolves, their vector map shifts and readjusts accordingly. This allows for real-time monitoring and continuous refinement of treatment plans, ensuring the best possible care at every stage of the journey.

Drug discovery and repurposing

Identifying patients similar to those successfully treated with a specific drug can accelerate clinical trials and uncover unexpected connections for existing medications.

• Accelerated exploration: They transform complex drug and disease data into dense vectors, allowing for rapid similarity searches and the identification of promising drug candidates. Imagine sifting through millions of molecules at a single glance, pinpointing those with similar properties to known effective drugs.
• Repurposing potential: Vector databases can unearth hidden connections between existing drugs and potential new applications. By comparing drug vectors to disease vectors, they can reveal unexpected repurposing opportunities, offering a faster and cheaper path to new treatments. 
• Personalization insights: By weaving genetic and patient data into the drug discovery tapestry, vector databases can inform the development of personalized medications tailored to individual needs and responses. This opens the door to a future where treatments are as unique as the patients themselves. 
• Predictive power: Analyzing the molecular dance within the vector space can unveil potential side effects and predict drug efficacy before entering clinical trials. This helps navigate the treacherous waters of development, saving time and resources while prioritizing promising candidates. 

Cohort analysis in research

Grouping patients with similar characteristics facilitates targeted research efforts, leading to faster breakthroughs in disease understanding and treatment development.

• Exploring Disease Mechanisms: Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. This can shed light on underlying disease mechanisms and guide the development of novel diagnostic markers and therapeutic target 
• Unveiling Hidden Patterns: Vector databases excel at similarity search, enabling researchers to pinpoint patients with similar clinical trajectories, even if they don’t share the same diagnosis or traditional risk factors. This reveals hidden patterns that might have been overlooked in traditional data analysis methods.

Technicalities of vector databases

Using a vector database enables the incorporation of advanced functionalities into our artificial intelligence, such as semantic information retrieval and long-term memory. The diagram provided below enhances our comprehension of the significance of vector databases in such applications.

Let’s break down the illustrated process:

• Initially, we employ the embedding model to generate vector embeddings for the content intended for indexing.
• The resulting vector embedding is then placed into the vector database, referencing the original content from which the embedding was derived. 
• Upon receiving a query from the application, we utilize the same embedding model to create embeddings for the query. These query embeddings are subsequently used to search the database for similar vector embeddings. As previously noted, these analogous embeddings are linked to the initial content from which they were created.

In comparison to the working of a traditional database, where data is stored as common data types like string, integer, date, etc. Users query the data by comparison with each row; the result of this query is the rows where the condition of the query is withheld.

In vector databases, this process of querying is more optimized and efficient with the use of a similarity metric for searching the most similar vector to our query. The search involves a combination of various algorithms, like approximate nearest neighbor optimization, which uses hashing, quantization, and graph-based detection.

Here are a few key components of the discussed process described below:

• Feature engineering: Transforming raw clinical data into meaningful numerical representations suitable for vector space. This may involve techniques like natural language processing for medical records or dimensionality reduction for complex biomolecular data. 
• Distance metrics: Choosing the appropriate distance metric to calculate the similarity between patient vectors. Popular options include Euclidean distance, cosine similarity, and Manhattan distance, each capturing different aspects of the data relationships.

• • Cosine Similarity: Calculates the cosine of the angle between two vectors in a vector space. It varies from -1 to 1, with 1 indicating identical vectors, 0 denoting orthogonal vectors, and -1 representing diametrically opposed vectors.

• • Euclidean Distance: Measures the straight-line distance between two vectors in a vector space. It ranges from 0 to infinity, where 0 signifies identical vectors and larger values indicate increasing dissimilarity between vectors.

• • Dot Product: Evaluate the product of the magnitudes of two vectors and the cosine of the angle between them. Its range is from -∞ to ∞, with a positive value indicating vectors pointing in the same direction, 0 representing orthogonal vectors, and a negative value signifying vectors pointing in opposite directions. 

• Nearest neighbor search algorithms: Efficiently retrieving the closest patient vectors to a given query. Techniques like k-nearest neighbors (kNN) and Annoy trees excel in this area, enabling rapid identification of similar patients.

A general pipeline from storing vectors to querying them is shown in the figure below:

• Indexing: The vector database utilizes algorithms like PQ, LSH, or HNSW (detailed below) to index vectors. This process involves mapping vectors to a data structure that enhances search speed. 
• Querying: The vector database examines the indexed query vector against the dataset’s indexed vectors, identifying the nearest neighbors based on a similarity metric employed by that specific index. 
• Post Processing: In certain instances, the vector database retrieves the ultimate nearest neighbors from the dataset and undergoes post-processing to deliver the final results. This step may involve re-evaluating the nearest neighbors using an alternative similarity measure.

Challenges and considerations

While vector databases offer immense potential, challenges remain:

• Data privacy and security: Safeguarding patient data while harnessing its potential for enhanced healthcare outcomes requires the implementation of robust security protocols and careful consideration of ethical standards.

This involves establishing comprehensive measures to protect sensitive information, ensuring secure storage, and implementing stringent access controls.

Additionally, ethical considerations play a pivotal role, emphasizing the importance of transparent data handling practices, informed consent procedures, and adherence to privacy regulations. As healthcare organizations leverage the power of data to advance patient care, a meticulous approach to security and ethics becomes paramount to fostering trust and upholding the integrity of the healthcare ecosystem. 

• Explainability and interpretability: Gaining insight into the reasons behind patient similarity is essential for informed clinical decision-making. It is crucial to develop transparent models that not only analyze the “why” behind these similarities but also offer insights into the importance of features within the vector space.

This transparency ensures a comprehensive understanding of the factors influencing patient similarities, contributing to more effective and reasoned clinical decisions. Integration with existing infrastructure: Seamless integration with legacy healthcare systems is essential for the practical adoption of vector database technology.

Revolution of medicine – AI in healthcare

In summary, the integration of vector databases in healthcare is revolutionizing patient care and diagnostics. Overcoming the limitations of traditional systems, these databases enable efficient handling of complex patient data, leading to precise treatment plans, accelerated drug discovery, and enhanced research capabilities.

While the technical aspects showcase the sophistication of these systems, challenges such as data privacy and seamless integration with existing infrastructure need attention. Despite these hurdles, the potential benefits promise a significant impact on personalized medicine and improved healthcare outcomes.

Large language models (LLMs) are a fascinating aspect of machine learning.

Regarding selective prediction in large language models, it refers to the model’s ability to generate specific predictions or responses based on the given input.

This means that the model can focus on certain aspects of the input text to make more relevant or context-specific predictions. For example, if asked a question, the model will selectively predict an answer relevant to that question, ignoring unrelated information.

They function by employing deep learning techniques and analyzing vast datasets of text. Here’s a simple breakdown of how they work:

1. Architecture: LLMs use a transformer architecture, which is highly effective in handling sequential data like language. This architecture allows the model to consider the context of each word in a sentence, enabling more accurate predictions and the generation of text.
2. Training: They are trained on enormous amounts of text data. During this process, the model learns patterns, structures, and nuances of human language. This training involves predicting the next word in a sentence or filling in missing words, thereby understanding language syntax and semantics.
3. Capabilities: Once trained, LLMs can perform a variety of tasks such as translation, summarization, question answering, and content generation. They can understand and generate text in a way that is remarkably similar to human language.

How selective predictions work in LLMs

Selective prediction in the context of large language models (LLMs) is a technique aimed at enhancing the reliability and accuracy of the model’s outputs. Here’s how it works in detail:

1. Decision to Predict or Abstain: At its core, selective prediction involves the model making a choice about whether to make a prediction or to abstain from doing so. This decision is based on the model’s confidence in its ability to provide a correct or relevant answer.
2. Improving Reliability: By allowing LLMs to abstain from making predictions in cases where they are unsure, selective prediction improves the overall reliability of the model. This is crucial in applications where providing incorrect information can have serious consequences.
3. Self-Evaluation: Some selective prediction techniques involve self-evaluation mechanisms. These allow the model to assess its own predictions and decide whether they are likely to be accurate or not. For example, experiments with models like PaLM-2 and GPT-3 have shown that self-evaluation-based scores can improve accuracy and correlation with correct answers.
4. Advanced Techniques like ASPIRE: Google’s ASPIRE framework is an example of an advanced approach to selective prediction. It enhances the ability of LLMs to make more confident predictions by effectively assessing when to predict and when to withhold a response.
5. Selective Prediction in Applications: This technique can be particularly useful in applications like conformal prediction, multi-choice question answering, and filtering out low-quality predictions. It ensures that the model provides responses only when it has a high degree of confidence, thereby reducing the risk of disseminating incorrect information.

Here’s how it works and improves response quality:

Example:

Imagine using a language model for a task like answering trivia questions. The LLM is prompted with a question: “What is the capital of France?” Normally, the model would generate a response based on its training.

However, with selective prediction, the model first evaluates its confidence in its knowledge about the answer. If it’s highly confident (knowing that Paris is the capital), it proceeds with the response. If not, it may abstain from answering or express uncertainty rather than providing a potentially incorrect answer.

Improvement in response quality:

1. Reduces Misinformation: By abstaining from answering when uncertain, selective prediction minimizes the risk of spreading incorrect information.
2. Enhances Reliability: It improves the overall reliability of the model by ensuring that responses are given only when the model has high confidence in their accuracy.
3. Better User Trust: Users can trust the model more, knowing that it avoids guessing when unsure, leading to higher quality and more dependable interactions.

Selective prediction, therefore, plays a vital role in enhancing the quality and reliability of responses in real-world applications of LLMs.

ASPIRE framework for selective predictions

The ASPIRE framework, particularly in the context of selective prediction for Large Language Models (LLMs), is a sophisticated process designed to enhance the model’s prediction capabilities. It comprises three main stages:

1. Task-Specific Tuning: In this initial stage, the LLM is fine-tuned for specific tasks. This means adjusting the model’s parameters and training it on data relevant to the tasks it will perform. This step ensures that the model is well-prepared and specialized for the type of predictions it will make.
2. Answer Sampling: After tuning, the LLM engages in answer sampling. Here, the model generates multiple potential answers or responses to a given input. This process allows the model to explore a range of possible predictions rather than settle on the first plausible option.
3. Self-Evaluation Learning: The final stage involves self-evaluation learning. The model evaluates the generated answers from the previous stage, assessing their quality and relevance. It learns to identify which answers are most likely to be correct or useful based on its training and the specific context of the question or task.

Helping businesses with informed decision-making

Businesses and industries can greatly benefit from adopting selective prediction frameworks like ASPIRE in several ways:

1. Enhanced Decision Making: By using selective prediction, businesses can make more informed decisions. The framework’s focus on task-specific tuning and self-evaluation allows for more accurate predictions, which is crucial in strategic planning and market analysis.
2. Risk Management: Selective prediction helps in identifying and mitigating risks. By accurately predicting market trends and customer behavior, businesses can proactively address potential challenges.
3. Efficiency in Operations: In industries such as manufacturing, selective prediction can optimize supply chain management and production processes. This leads to reduced waste and increased efficiency.
4. Improved Customer Experience: In service-oriented sectors, predictive frameworks can enhance customer experience by personalizing services and anticipating customer needs more accurately.
5. Innovation and Competitiveness: Selective prediction aids in fostering innovation by identifying new market opportunities and trends. This helps businesses stay competitive in their respective industries.
6. Cost Reduction: By making more accurate predictions, businesses can reduce costs associated with trial and error and inefficient processes.

Learn more about how DALLE, GPT 3, and MuseNet are reshaping industries.

Enhance trust with LLMs

Selective prediction frameworks like ASPIRE offer businesses and industries a strategic advantage by enhancing decision-making, improving operational efficiency, managing risks, fostering innovation, and ultimately leading to cost savings.

Overall, the ASPIRE framework is designed to refine the predictive capabilities of LLMs, making them more accurate and reliable by focusing on task-specific tuning, exploratory answer generation, and self-assessment of generated responses

In summary, selective prediction in LLMs is about the model’s ability to judge its own certainty and decide when to provide a response. This enhances the trustworthiness and applicability of LLMs in various domains.

Mistral AI, a startup co-founded by individuals with experience at Google’s DeepMind and Meta, made a significant entrance into the world of LLMs with Mistral 7B.

This model can be easily accessed and downloaded from GitHub or via a 13.4-gigabyte torrent, emphasizing accessibility. Mistral 7b, a 7.3 billion parameter model with the sheer size of some of its competitors, Mistral 7b punches well above its weight in terms of capability and efficiency. 

What makes Mistral 7b a great competitor? 

One of the key strengths of Mistral 7b lies in its architecture. Unlike many LLMs relying solely on transformer networks, Mistral 7b incorporates a hybrid approach, leveraging transformers and recurrent neural networks (RNNs). This unique blend allows Mistral 7b to excel at tasks that require both long-term memory and context awareness, such as question answering and code generation. 

Furthermore, Mistral 7b utilizes innovative attention mechanisms like group query attention and sliding window attention. These techniques enable the model to focus on relevant parts of the input data more effectively, improving performance and efficiency. 

Learn in detail about llm evaluation method

Mistral 7b architecture 

Mistral 7B is an architecture based on transformer architecture and introduces several innovative features and parameters. Here’s a gist of the architectural details: 

1. Sliding window attention: 

Mistral 7B addresses the quadratic complexity of vanilla attention by implementing Sliding Window Attention (SWA). 

SWA allows each token to attend to a maximum of W tokens from the previous layer (here, W = 3). 

Tokens outside the sliding window still influence next-word prediction. 

Information can propagate forward by up to k × W tokens after k attention layers. 

Parameters include dim = 4096, n_layers = 32, head_dim = 128, hidden_dim = 14336, n_heads = 32, n_kv_heads = 8, window_size = 4096, context_len = 8192, and vocab_size = 32000. 

Source:E2Enetwork 

2. Rolling Buffer Cache: 

This fixed-size cache serves as the “memory” for the sliding window attention. It efficiently stores key-value pairs for recent timesteps, eliminating the need for recomputing that information. A set attention span stays constant, managed by a rolling buffer cache limiting its size. 

Within the cache, each time step’s keys and values are stored at a specific location, determined by i mod W, where W is the fixed cache size. When the position i exceeds W, previous values in the cache get replaced. 

This method slashes cache memory usage by 8 times while maintaining the model’s effectiveness. 

Source:E2Enetwork 

3. Pre-fill and chunking: 

During sequence generation, the cache is pre-filled with the provided prompt to enhance context. For long prompts, chunking divides them into smaller segments, each treated with both cache and current chunk attention, further optimizing the process.

When creating a sequence, tokens are guessed step by step, with each token relying on the ones that came before it. The starting information, known as the prompt, lets us fill the (key, value) cache beforehand with this prompt.

The chunk size can determine the window size, and the attention mask is used across both the cache and the chunk. This ensures the model gets the necessary information while staying efficient. 

Source:E2Enetwork 

Comparison of performance: Mistral 7B vs Llama2-13B  

The true test of any LLM lies in its performance on real-world tasks. Mistral 7b has been benchmarked against several established models, including Llama 2 (13B parameters) and Llama 1 (34B parameters).

The results are impressive, with Mistral 7b outperforming both models on all tasks tested. It even approaches the performance of CodeLlama 7B (also 7B parameters) on code-related tasks while maintaining strong performance on general language tasks. Performance comparisons were conducted across a wide range of benchmarks, encompassing various aspects.

1. Performance comparison 

Mistral 7B surpasses Llama2-13B across various benchmarks, excelling in commonsense reasoning, world knowledge, reading comprehension, and mathematical tasks. Its dominance isn’t marginal; it’s a robust demonstration of its capabilities. 

2. Equivalent Model Capacity 

In reasoning, comprehension, and STEM tasks, Mistral 7B functions akin to a Llama2 model over three times its size. This not only highlights its efficiency in memory usage but also its enhanced processing speed. Essentially, it offers immense power within an elegantly streamlined design. 

3. Knowledge-based assessments 

Mistral 7B demonstrates superiority in most assessments and competes equally with Llama2-13B in knowledge-based benchmarks. This parallel performance in knowledge tasks is especially intriguing, given Mistral 7B’s comparatively restrained parameter count. 

Source:MistralAI 

Beyond benchmarks: Practical applications 

The capabilities of Mistral 7b extend far beyond benchmark scores Mistral 7B isn’t limited to a single skill. It performs exceptionally well across various tasks, spanning code-related fields and English language tasks. Remarkably, it matches CodeLlama-7B’s performance in coding tasks, highlighting its adaptability and wide-ranging abilities.  Some of the common works in each field are mentioned below: 

• Natural Language Processing (NLP): Machine translation, text summarization, question answering, and sentiment analysis. 
• Code Generation and Analysis: Generate code snippets, translate natural language to code, and analyze existing code for potential issues. 
• Creative Writing: Compose poems, scripts, musical pieces, and other creative text formats. 
• Education and Research: Assist with research tasks, generate educational materials, and personalize learning experiences. 

Source:E2Enetwork 

Source:MistralAI 

A cost-effective Solution 

One of the most compelling aspects of Mistral 7b is its cost-effectiveness. Compared to models of similar size, Mistral 7b requires significantly less computational resources to run. This makes it a more accessible option for individuals and organizations with limited budgets. Additionally, Mistral AI offers flexible deployment options, allowing users to run the model on their own infrastructure or through the cloud. 

Versatile deployment 

Mistral 7B stands out due to its Apache 2.0 license, granting broad accessibility for diverse users, including individuals, major corporations, and governmental bodies.

This open-source license not only ensures inclusivity but also permits customization and adaptation to suit specific needs. It empowers users to modify, share, and utilize Mistral 7B for a wide array of applications, fostering innovation and collaboration in the community. 

The decentralization issue vs transparency 

Mistral AI prioritizes transparency and open access, yet safety concerns arise due to the fully decentralized ‘Mistral-7B-v0.1’ model, capable of unmoderated response generation.

Unlike models such as GPT and Llama, it lacks mechanisms to discern appropriate responses, posing potential exploitation risks. However, despite safety concerns, decentralized Language Model Models (LLMs) offer advantages, democratizing AI access and enabling positive applications. 

Are large language models the zero shot reasoners? Read more here

Conclusion 

Mistral 7b is a testament to the power of innovation in the LLM domain. Despite its relatively small size, it has established itself as a force to be reckoned with, delivering impressive performance across a wide range of tasks. With its focus on efficiency and cost-effectiveness, Mistral 7b is poised to democratize access to cutting-edge language technology and shape the future of how we interact with machines. 

 Large language models (LLMs), such as OpenAI’s GPT-4, are swiftly metamorphosing from mere text generators into autonomous, goal-oriented entities displaying intricate reasoning abilities. This crucial shift carries the potential to revolutionize the manner in which humans connect with AI, ushering us into a new frontier.

This blog will break down the working of these agents, illustrating the impact they impart on what is known as the ‘Lang Chain’. 

Working of the agents 

Our exploration into the realm of LLM agents begins with understanding the key elements of their structure, namely the LLM core, the Prompt Recipe, the Interface and Interaction, and Memory. The LLM core forms the fundamental scaffold of an LLM agent. It is a neural network trained on a large dataset, serving as the primary source of the agent’s abilities in text comprehension and generation. 

The functionality of these agents heavily relies on prompt engineering. Prompt recipes are carefully crafted sets of instructions that shape the agent’s behaviors, knowledge, goals, and persona and embed them in prompts. 

The agent’s interaction with the outer world is dictated by its user interface, which could vary from command-line, graphical, to conversational interfaces. In the case of fully autonomous agents, prompts are programmatically received from other systems or agents.

Another crucial aspect of their structure is the inclusion of memory, which can be categorized into short-term and long-term. While the former helps the agent be aware of recent actions and conversation histories, the latter works in conjunction with an external database to recall information from the past. 

Learn in detail about LangChain

Ingredients involved in agent creation 

Creating robust and capable LLM agents demands integrating the core LLM with additional components for knowledge, memory, interfaces, and tools.

The LLM forms the foundation, while three key elements are required to allow these agents to understand instructions, demonstrate essential skills, and collaborate with humans: the underlying LLM architecture itself, effective prompt engineering, and the agent’s interface. 

Tools 

Tools are functions that an agent can invoke. There are two important design considerations around tools: 

• Giving the agent access to the right tools 
• Describing the tools in a way that is most helpful to the agent 

Without thinking through both, you won’t be able to build a working agent. If you don’t give the agent access to a correct set of tools, it will never be able to accomplish the objectives you give it. If you don’t describe the tools well, the agent won’t know how to use them properly. Some of the vital tools a working agent needs are:

1. SerpAPI : This page covers how to use the SerpAPI search APIs within Lang Chain. It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper. Here are the details for its installation and setup:

• Install requirements with pip install google-search-results 
• Get a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY) 

You can also easily load this wrapper as a tool (to use with an agent). You can do this with:

2. Math-tool: The llm-math tool wraps an LLM to do math operations. It can be loaded into the agent tools like: 

Python-REPL tool: Allows agents to execute Python code. To load this tool, you can use: 

The action of python REPL allows agent to execute the input code and provide the response. 

The impact of agents: 

A noteworthy advantage of LLM agents is their potential to exhibit self-initiated behaviors ranging from purely reactive to highly proactive. This can be harnessed to create versatile AI partners capable of comprehending natural language prompts and collaborating with human oversight. 

LLM agents leverage LLMs innate linguistic abilities to understand instructions, context, and goals, operate autonomously and semi-autonomously based on human prompts, and harness a suite of tools such as calculators, APIs, and search engines to complete assigned tasks, making logical connections to work towards conclusions and solutions to problems. Here are few of the services that are highly dominated by the use of Lang Chain agents:

Facilitating language services 

Agents play a critical role in delivering language services such as translation, interpretation, and linguistic analysis. Ultimately, this process steers the actions of the agent through the encoding of personas, instructions, and permissions within meticulously constructed prompts.

Users effectively steer the agent by offering interactive cues following the AI’s responses. Thoughtfully designed prompts facilitate a smooth collaboration between humans and AI. Their expertise ensures accurate and efficient communication across diverse languages. 

Quality assurance and validation 

Ensuring the accuracy and quality of language-related services is a core responsibility. Agents verify translations, validate linguistic data, and maintain high standards to meet user expectations. Agents can manage relatively self-contained workflows with human oversight.

Use internal validation to verify the accuracy and coherence of their generated content. Agents undergo rigorous testing against various datasets and scenarios. These tests validate the agent’s ability to comprehend queries, generate accurate responses, and handle diverse inputs. 

Types of agents 

Agents use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning a response to the user. Here are the agents available in Lang Chain.  

Zero-Shot ReAct: This agent uses the ReAct framework to determine which tool to use based solely on the tool’s description. Any number of tools can be provided. This agent requires that a description is provided for each tool. Below is how we can set up this Agent: 

Let’s invoke this agent and check if it’s working in chain 

This will invoke the agent. 

Structured-Input ReAct: The structured tool chat agent is capable of using multi-input tools. Older agents are configured to specify an action input as a single string, but this agent can use a tool’s argument schema to create a structured action input. This is useful for more complex tool usage, like precisely navigating around a browser. Here is how one can setup the React agent:

The further necessary imports required are:

Setting up parameters:

Creating the agent:

Improving performance of an agent 

Enhancing the capabilities of agents in Large Language Models (LLMs) necessitates a multi-faceted approach. Firstly, it is essential to keep refining the art and science of prompt engineering, which is a key component in directing these systems securely and efficiently. As prompt engineering improves, so does the competencies of LLM agents, allowing them to venture into new spheres of AI assistance.

Secondly, integrating additional components can expand agents’ reasoning and expertise. These components include knowledge banks for updating domain-specific vocabularies, lookup tools for data gathering, and memory enhancement for retaining interactions.

Thus, increasing the autonomous capabilities of agents requires more than just improved prompts; they also need access to knowledge bases, memory, and reasoning tools.

Lastly, it is vital to maintain a clear iterative prompt cycle, which is key to facilitating natural conversations between users and LLM agents. Repeated cycling allows the LLM agent to converge on solutions, reveal deeper insights, and maintain topic focus within an ongoing conversation. 

Conclusion 

The advent of large language model agents marks a turning point in the AI domain. With increasing advances in the field, these agents are strengthening their footing as autonomous, proactive entities capable of reasoning and executing tasks effectively.

The application and impact of Large Language Model agents are vast and game-changing, from conversational chatbots to workflow automation. The potential challenges or obstacles include ensuring the consistency and relevance of the information the agent processes, and the caution with which personal or sensitive data should be treated. The promising future outlook of these agents is the potentially increased level of automated and efficient interaction humans can have with AI. 

Large language models (LLMs) have revolutionized the field of natural language processing (NLP), enabling machines to generate human-quality text, translate languages, and answer questions in an informative way. These advancements have opened up a world of possibilities for applications in various domains, from customer service to education.  

Want to build a custom llm application? Check out our in-person Large Language Model bootcamp. 

However, mastering LLMs requires a comprehensive understanding of their underlying principles, architectures, and training techniques. 

This 7-step guide will provide you with a structured approach to mastering LLMs: 

Step 1: Understand LLM basics 

Before diving into the complexities of LLMs, it’s crucial to establish a solid foundation in the fundamental concepts. This includes understanding the following: 

• Natural Language Processing (NLP): NLP is the field of computer science that deals with the interaction between computers and human language. It encompasses tasks like machine translation, text summarization, and sentiment analysis. 

Read more about attention mechanisms in natural language processing

• Deep Learning: LLMs are powered by deep learning, a subfield of machine learning that utilizes artificial neural networks to learn from data. Familiarize yourself with the concepts of neural networks, such as neurons, layers, and activation functions. 

• Transformer: The transformer architecture is a cornerstone of modern LLMs. Understand the components of the transformer architecture, including self-attention, encoder-decoder architecture, and positional encoding. 

Step 2: Explore LLM architectures 

LLMs come in various architectures, each with its strengths and limitations. Explore different LLM architectures, such as: 

• BERT (Bidirectional Encoder Representations from Transformers): BERT is a widely used LLM that excels in natural language understanding tasks, such as question answering and sentiment analysis. 

• GPT (Generative Pre-training Transformer): GPT is known for its ability to generate human-quality text, making it suitable for tasks like creative writing and chatbots. 

• XLNet (Generalized Autoregressive Pre-training for Language Understanding): XLNet is an extension of BERT that addresses some of its limitations, such as its bidirectional nature. 

Step 3: Pre-training LLMs 

Pre-training is a crucial step in the development of LLMs. It involves training the LLM on a massive dataset of text and code to learn general language patterns and representations. Explore different pre-training techniques, such as: 

• Masked Language Modeling (MLM): In MLM, random words are masked in the input text, and the LLM is tasked with predicting the missing words. 

• Next Sentence Prediction (NSP): In NSP, the LLM is given two sentences and asked to determine whether they are consecutive sentences from a text or not. 

• Contrastive Language-Image Pre-training (CLIP): CLIP involves training the LLM to match text descriptions with their corresponding images. 

Step 4: Fine-tuning LLMs 

Fine-tuning involves adapting a pre-trained LLM to a specific task or domain. This is done by training the LLM on a smaller dataset of task-specific data. Explore different fine-tuning techniques, such as:

• Task-specific loss functions: Define loss functions that align with the specific task, such as accuracy for classification tasks or BLEU score for translation tasks. 

• Data augmentation: Augment the task-specific dataset to improve the LLM’s generalization ability. 

• Early stopping: Implement early stopping to prevent overfitting and optimize the LLM’s performance. 

This talk below can help you get started with fine-tuning GPT 3.5 Turbo. 

Step 5: Alignment and post-training 

Alignment and post-training are essential steps to ensure that LLMs are aligned with human values and ethical considerations. This includes: 

• Bias mitigation: Identify and mitigate biases in the LLM’s training data and outputs. 

• Fairness evaluation: Evaluate the fairness of the LLM’s decisions and identify potential discriminatory patterns. 

• Explainability: Develop methods to explain the LLM’s reasoning and decision-making processes. 

Step 6: Evaluating LLMs 

Evaluating LLMs is crucial to assess their performance and identify areas for improvement. Explore different evaluation metrics, such as: 

• Accuracy: Measure the proportion of correct predictions for classification tasks. 

• Fluency: Assess the naturalness and coherence of the LLM’s generated text. 

• Relevance: Evaluate the relevance of the LLM’s outputs to the given prompts or questions. 

Read more about: Evaluating large language models

Step 7: Build LLM apps 

With a strong understanding of LLMs, you can start building applications that leverage their capabilities. Explore different application scenarios, such as:

• Chatbots: Develop chatbots that can engage in natural conversations with users. 

• Content creation: Utilize LLMs to generate creative content, such as poems, scripts, or musical pieces. 

• Machine translation: Build machine translation systems that can accurately translate languages. 

Start learning large language models

Mastering large language models (LLMs) is an ongoing journey that requires continuous learning and exploration. By following these seven steps, you can gain a comprehensive understanding of LLMs, their underlying principles, and the techniques involved in their development and application.  

As LLMs continue to evolve, stay informed about the latest advancements and contribute to the responsible and ethical development of these powerful tools. Here’s a list of YouTube channels that can help you stay updated in the world of large language models.

Multimodality refers to an AI model’s ability to understand, process, and generate multiple types of information, such as text, images, and potentially even sounds. It’s the capacity to interpret and interact with various data forms, where the model not only reads textual information but also comprehends visual or other types of data.  

How does multimodality increase the power of LLMs?

The significance of multimodality lies in its potential to greatly enhance the effectiveness and applications of AI models.  

Consider the human intellect and its capacity to comprehend the world and tackle unique challenges. This ability stems from processing diverse forms of information, including language, sight, and taste, among others.

If an individual lacks access to one of these sensory inputs from the outset, such as vision, their understanding of the real world is likely to be significantly impaired. 

Hence, multimodality in models, like GPT-4, allows them to develop intuition and understand complex relationships not just inside single modalities but across them, mimicking human-level cognizance to a higher degree.  

Read about: GPT 3.5 VS GPT 4

Here are a few examples where we see that GPT-4 Vision is capable of performing human-like tasks:

Example 1: GPT-4 Vision and understanding humor

  Source: OpenAI 

Example 2: GPT-4 Vision acing complex exams  

Why does vision help GPT-4 do better on tests? Well, think about it like this: you’d probably get more out of an exam if it’s written down for you to see, rather than just hearing it from someone, right?

It’s the same deal with a model like the GPT-4. Having that visual element just makes things a bit clearer and easier to work with. 

Hence, multimodal learning opens up newer opportunities, helps AI handle real-world data more efficiently, and brings us closer to developing AI models that act and think more like humans. 

How does the GPT-4 with Vision model combine text and image inputs to provide responses? 

GPT-4 with Vision combines natural language processing capabilities with computer vision. This means it can accept different forms of input, like text and images, and deliver outputs based on that mixture of information.

This model represents a significant advance in machine learning and natural language processing, as it bridges two traditionally separate fields: computer vision and natural language processing. 

Enabling models to understand different types of data enhances their performance and expands their application scope. For instance, in the real-world, they may be used for Visual Question Answering (VQA), wherein the model is given an image and a text query about the image, and it needs to provide a suitable answer. 

Use-cases of GPT-4 Vision 

GPT-4V can perform a variety of tasks, including data deciphering, multi-condition processing, text transcription from images, object detection, coding enhancement, design understanding, and more. Here are some mind-boggling use cases of GPT-4 Vision. Of course, as time progresses, its usability will keep increasing.

1. Data Deciphering and Visualization: GPT-4V is capable of processing infographics or charts and providing detailed breakdowns of the data presented. This means that complex visual data can be transformed into understandable insights, making it easier for users to comprehend complex information. Here’s an example:

Source: Datacamp 

Conversely, the technology demonstrates proficiency in interpreting the provided data and generating impactful visual representations. Here’s an example where GPT-4 successfully processed LATEX code to produce a Python plot.

This was achieved through interactive dialogue with the user. In this scenario, the model accurately extracted the necessary data and efficiently addressed all user queries. It adeptly reformatted the data and tailored the visualization to meet the specified requirements. 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

1. Multi-condition processing:

GPT-4V is excellent at analyzing images under varying conditions, such as different lighting or complex scenes, and can provide insightful details drawn from these varying contexts.  

Source: roboflow 

Text Transcription

The model is geared to transcribe text from images. It could be a game-changer in digitizing written or printed documents by converting images of text into a digital format. 

Object Detection

GPT-4V has superior object detection capabilities. It can accurately identify different objects within an image, even abstract ones, providing a comprehensive analysis and comprehension of images. 

Source: roboflow 

Game Development:

GPT-4V can significantly impact the gaming industry as well. Here an example where it was provided with a comprehensive overview of a 3D game. GPT-4 demonstrated its capability to develop a functional game using HTML and JavaScript. This is accomplished without prior training or experience in related projects. 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

Web Development:

GPT-4 Vision significantly enhances web development by enabling the creation of websites from visual inputs like sketches. It interprets design elements and transforms them into functional HTML, CSS, and JavaScript code, including interactive features and specific themes, such as a ’90s hacker style with dynamic effects. Here’s an example where GPT-4 was prompted to write code for a website by only providing it a hand drawn sketch:  

Source: Datacamp 

Once the HTML and CSS files were created as instructed, this was the result: 

Source: Datacamp 

This advancement streamlines the web development process, making it more accessible and efficient, particularly for those with limited coding knowledge. It opens up new possibilities for creative design and can be applied across various domains, potentially evolving with continuous learning and improvement. 

Complex Mathematical Analysis: GPT-4V can process and analyze intricate mathematical expressions, especially when they are represented graphically or in handwritten forms. 

Source: roboflow 

Integrations with Other Systems: GPT-4 can be integrated with other systems through its API, expanding its application sphere to diverse domains like security, healthcare diagnostics, and entertainment. 

Educational Assistance: GPT-4V can help in the educational sector by analysing diagrams, illustrations, and visual aids, and transforming them into detailed textual explanations, making concepts easier to comprehend for students and educators alike. 

The innovation of incorporating visual capabilities, therefore, offers a dynamic and engaging method for users to interact with AI systems. 

Where does GPT 4 Vision perform less effectively? 

While the GPT-4 Vision is groundbreaking, it is important to recognize its limitations and risks. 

• Privacy Concerns: GPT-4 Vision’s ability to identify individuals and locations in images raises serious privacy issues. This poses a challenge for companies to balance innovation with adherence to privacy laws and ethical practices. 
• Bias in Image Analysis: The risk of biases in image interpretation could lead to unfair or discriminatory outcomes, particularly affecting diverse demographic groups. This necessitates careful oversight and continuous improvement of the AI’s algorithms to minimize biases. 
• Unreliable Medical Advice or Dangerous Instructions: The model might inadvertently provide inaccurate medical advice or instructions for potentially hazardous tasks. This limitation is significant, especially in contexts where precise and reliable information is critical for safety and health. 
• Cybersecurity Vulnerabilities: GPT-4 Vision could be exploited for tasks like solving CAPTCHAs, posing cybersecurity risks. This highlights the need for robust security measures to prevent malicious use. 
• Content Accuracy and Hallucination: The model, like other AI systems, can sometimes generate content that is not factually correct or based in reality, known as ‘hallucinations’. Users must be vigilant and verify the information provided by the AI. 
• Refusal to Analyze Certain Images: In some cases, GPT-4 Vision might refuse to analyze images, particularly those involving people, due to the sensitive nature of such data. This limitation can be viewed as a measure to prevent misuse or ethical breaches, but it also restricts the model’s functionality in certain scenarios. 
• Overall, these risks and limitations highlight the importance of cautious and responsible deployment of GPT-4 Vision, ensuring that its use aligns with ethical standards and societal norms. 

Conclusion 

GPT-4 Vision represents a monumental leap in AI technology, merging text and image processing to offer unprecedented capabilities. Its potential in fields like web development, content creation, and data analysis is immense.

However, this technology comes with responsibilities. The potential risks, including privacy concerns, biases, and safety issues, underscore the importance of using GPT-4 Vision with a mindful approach.

As we harness this powerful tool, it’s crucial to continuously evaluate and address these challenges to ensure ethical and responsible usage of AI. 

In this blog, we are enhancing our Language Model (LLM) experience by adopting the Retrieval-Augmented Generation (RAG) approach!

We’ll explore the fundamental architecture of RAG conceptually and delve deeper by implementing it through the Lang Chain orchestration framework and leveraging an open-source model from Hugging Face for both question answering and text embedding. 

So, let’s get started! 

Common hallucinations in large language models  

The most common problem faced by state-of-the-art LLMs is that they produce inaccurate or hallucinated responses. This mostly occurs when prompted with information not present in their training set, despite being trained on extensive data.

This discrepancy between the general knowledge embedded in the LLM’s weights and newer information can be bridged using RAG. The solution provided by RAG eliminates the need for computationally intensive and expertise-dependent fine-tuning, offering a more flexible approach to adapting to evolving information.

Read more about: AI hallucinations and risks associated with large language models

What is RAG? 

Retrieval-Augmented Generation involves enhancing the output of Large Language Models (LLMs) by providing them with additional information from an external knowledge source.

Explore LLM context augmentation techniques like RAG and fine-tuning in detail with out podcast now!

This method aims to improve the accuracy and contextuality of LLM-generated responses while minimizing factual inaccuracies. RAG empowers language models to sidestep the need for retraining, facilitating access to the most up-to-date information to produce trustworthy outputs through retrieval-based generation. 

Architecture of RAG approach

Figure from Lang chain documentation

Prerequisites for code implementation 

1. HuggingFace account and LLAMA2 model access:

• Create a Hugging Face account (free sign-up available) to access open-source Llama 2 and embedding models. 
• Request access to LLAMA2 models using this form (access is typically granted within a few hours). 
• After gaining access to Llama 2 models, please proceed to the provided link, select the checkbox to indicate your agreement to the information, and then click ‘Submit’. 

2. Google Colab account:

• Create a Google account if you don’t already have one. 
• Use Google Colab for code execution. 

3. Google Colab environment setup: 

• In Google Colab, go to Runtime > Change runtime type > Hardware accelerator > GPU > GPU type > T4 for faster execution of code. 

4. Library and dependency installation: 

• Install necessary libraries and dependencies using the following command: 

5. Authentication with HuggingFace: 

• Integrate your Hugging Face token into Colab’s environment:

• When prompted, enter your Hugging Face token obtained from the “Access Token” tab in your Hugging Face settings. 

Step 1: Document Loading 

Loading a document refers to the process of retrieving and storing data as documents in memory from a specified source. This process is typically facilitated by document loaders, which provide a “load” method for accessing and loading documents into the memory. 

Lang chain has number of document loaders in this example we will be using “WebBaseLoader” class from the “langchain.document_loaders” module to load content from a specific web page.

 
The code extracts content from the web page “https://lilianweng.github.io/posts/2023-06-23-agent/“. BeautifulSoup (`bs4`) is employed for HTML parsing, focusing on elements with the classes “post-content”, “post-title”, and “post-header.” The loaded content is stored in the variable `docs`. 

Step 2: Document transformation – Splitting/chunking document 

After loading the data, it can be transformed to fit the application’s requirements or to extract relevant portions. This involves splitting lengthy documents into smaller chunks that are compatible with the model and produce accurate and clear results. Lang Chain offers various text splitters, in this implementation we chose the “RecursiveCharacterTextSplitter” for generic text processing.

The code breaks documents into chunks of 1000 characters with a 200-character overlap. This chunking is employed for embedding and vector storage, enabling more focused retrieval of relevant content during runtime. The recursive splitter ensures chunks maintain contextual integrity by using common separators, like new lines, until the desired chunk size is achieved. 

Step 3: Storage in vector database 

After extracting text chunks, we store and index them for future searches using the RAG application. A common approach involves embedding the content of each split and storing these embeddings in a vector store. 

When searching, we embed the search query and perform a similarity search to identify stored splits with embeddings most similar to the query embedding. Cosine similarity, which measures the angle between embeddings, is a simple similarity measure. 

Using the Chroma vector store and open source “HuggingFaceEmbeddings” in Lang chain, we can embed and store all document splits in a single command. 

Text embedding: 

Text embedding converts textual data into numerical vectors that capture the semantic meaning of the text. This enables efficient identification of similar text pieces. An embedding model, which is a variant of Language Models (LLMs) specifically designed for this purpose. 

 Lang Chain’s Embeddings class facilitates interaction with various text embedding models. While any model can be used, we opted for “HuggingFaceEmbeddings”. 

This code initializes an instance of the HuggingFaceEmbeddings class, configuring it with an open-source pre-trained model located at “sentence-transformers/all-MiniLM-l6-v2“. By doing this text embedding is created for converting textual data into numerical vectors. 

Vector Stores: 

Vector stores are specialized databases designed to efficiently store and search for high-dimensional vectors, such as text embeddings. They enable the retrieval of the most similar embedding vectors based on a given query vector. Lang Chain integrates with various vector stores, and we are using “Chroma” vector store for this task.

This code utilizes the Chroma class to create a vector store (vectorstore) from the previously split documents (splits) using the specified embeddings (embeddings). The Chroma vector store facilitates efficient storage and retrieval of document vectors for further processing. 

Step 4: Retrieval of text chunks 

After storing the data, preparing the LLM model, and constructing the pipeline, we need to retrieve the data. Retrievers serve as interfaces that return documents based on a query. 

Retrievers cannot store documents; they can only retrieve them. Vector stores form the foundation of retrievers. Lang Chain offers a variety of retriever algorithms, here is the one we implement. 

Step 5: Generation of answer with RAG approach 

Preparing the LLM Model: 

In the context of Retrieval Augmented Generation (RAG), an LLM model plays a crucial role in generating comprehensive and informative responses to user queries. By leveraging its ability to process and understand natural language, the LLM model can effectively combine retrieved documents with the given query to produce insightful and relevant outputs.

These lines import the necessary libraries for handling pre-trained models and tokenization. The specific model “meta-llama/Llama-2-7b-chat-hf” is chosen for its question-answering capabilities.

This code defines a transformer pipeline, which encapsulates the pre-trained HuggingFace model and its associated configuration. It specifies the task as “text-generation” and sets various parameters to optimize the pipeline’s performance. 

This line creates a Lang Chain pipeline (HuggingFace Pipeline) that wraps the transformer pipeline. The model_kwargs parameter adjusts the model’s “temperature” to control its creativity and randomness. 

Retrieval QA Chain: 

To combine question-answering with a retrieval step, we employ the RetrievalQA chain, which utilizes a language model and a vector database as a retriever. By default, we process all data in a single batch and set the chain type to “stuff” when interacting with the language model. 

This code initializes a RetrievalQA instance by specifying a chain type (“stuff”), a HuggingFacePipeline (llm), and a retriever (retriever-initialize previously in the code from vectorstore). The return_source_documents parameter is set to True to include source documents in the output, enhancing contextual information retrieval.
 

Finally, we call this QA chain with the specific question we want to ask.

The result will be: 

We can print source documents to see which document chunks the model used to generate the answer to this specific query.

In this output, only 2 out of 4 document contents are shown as an example, that were retrieved to answer the specific question. 

Conclusion 

In conclusion, by embracing the Retrieval-Augmented Generation (RAG) approach, we have elevated our Language Model (LLM) experience to new heights.

Through a deep dive into the conceptual foundations of RAG and practical implementation using the Lang Chain orchestration framework, coupled with the power of an open-source model from Hugging Face, we have enhanced question answering capabilities of LLMs.

This journey exemplifies the seamless integration of innovative technologies to optimize LLM capabilities, paving the way for a more efficient and powerful language processing experience. Cheers to the exciting possibilities that arise from combining innovative approaches with open-source resources! 

GPT-3.5 and other large language models (LLMs) have transformed natural language processing (NLP). Trained on massive datasets, LLMs can generate text that is both coherent and relevant to the context, making them invaluable for a wide range of applications. 

Learning about LLMs is essential in today’s fast-changing technological landscape. These models are at the forefront of AI and NLP research, and understanding their capabilities and limitations can empower people in diverse fields. 

This blog lists steps and several tutorials that can help you get started with large language models. From understanding large language models to building your own ChatGPT, this roadmap covers it all. 

Want to build your own ChatGPT? Checkout our in-person Large Language Model Bootcamp. 

Step 1: Understand the real-world applications 

Building a large language model application on custom data can help improve your business in a number of ways. This means that LLMs can be tailored to your specific needs. For example, you could train a custom LLM on your customer data to improve your customer service experience.  

The talk below will give an overview of different real-world applications of large language models and how these models can assist with different routine or business activities. 

Step 2: Introduction to fundamentals and architectures of LLM applications 

Applications like Bard, ChatGPT, Midjourney, and DallE have entered some applications like content generation and summarization. However, there are inherent challenges for a lot of tasks that require a deeper understanding of trade-offs like latency, accuracy, and consistency of responses.

Any serious applications of LLMs require an understanding of nuances in how LLMs work, including embeddings, vector databases, retrieval augmented generation (RAG), orchestration frameworks, and more. 

This talk will introduce you to the fundamentals of large language models and their emerging architectures. This video is perfect for anyone who wants to learn more about Large Language Models and how to use LLMs to build real-world applications. 

Step 3: Understanding vector similarity search 

Traditional keyword-based methods have limitations, leaving us searching for a better way to improve search. But what if we could use deep learning to revolutionize search?

Imagine representing data as vectors, where the distance between vectors reflects similarity, and using Vector Similarity Search algorithms to search billions of vectors in milliseconds. It’s the future of search, and it can transform text, multimedia, images, recommendations, and more.  

The challenge of searching today is indexing billions of entries, which makes it vital to learn about vector similarity search. This talk below will help you learn how to incorporate vector search and vector databases into your own applications to harness deep learning insights at scale.  

Step 4: Explore the power of embedding with vector search 

 The total amount of digital data generated worldwide is increasing at a rapid rate. Simultaneously, approximately 80% (and growing) of this newly generated data is unstructured data—data that does not conform to a table- or object-based model.

Examples of unstructured data include text, images, protein structures, geospatial information, and IoT data streams. Despite this, the vast majority of companies and organizations do not have a way of storing and analyzing these increasingly large quantities of unstructured data.  

Embeddings—high-dimensional, dense vectors that represent the semantic content of unstructured data can remedy this issue. This makes it significant to learn about embeddings.  

The talk below will provide a high-level overview of embeddings, discuss best practices around embedding generation and usage, build two systems (semantic text search and reverse image search), and see how we can put our application into production using Milvus.  

Step 5: Discover the key challenges in building LLM applications 

As enterprises move beyond ChatGPT, Bard, and ‘demo applications’ of large language models, product leaders and engineers are running into challenges. The magical experience we observe on content generation and summarization tasks using ChatGPT is not replicated on custom LLM applications built on enterprise data. 

Enterprise LLM applications are easy to imagine and build a demo out of, but somewhat challenging to turn into a business application. The complexity of datasets, training costs, cost of token usage, response latency, context limit, fragility of prompts, and repeatability are some of the problems faced during product development. 

Delve deeper into these challenges with the below talk: 

Step 6: Building Your Own ChatGPT 

Learn how to build your own ChatGPT or a custom large language model using different AI platforms like Llama Index, LangChain, and more. Here are a few talks that can help you to get started:  

Build Agents Simply with OpenAI and LangChain 

Build Your Own ChatGPT with Redis and Langchain 

Build a Custom ChatGPT with Llama Index 

Step 7: Learn about Retrieval Augmented Generation (RAG)  

Learn the common design patterns for LLM applications, especially the Retrieval Augmented Generation (RAG) framework; What is RAG and how it works, how to use vector databases and knowledge graphs to enhance LLM performance, and how to prioritize and implement LLM applications in your business.  

The discussion below will not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also empower technical architects and engineers with practical insights and methodologies. 

Step 8: Understanding AI observability  

AI observability is the ability to monitor and understand the behavior of AI systems. It is essential for responsible AI, as it helps to ensure that AI systems are safe, reliable, and aligned with human values.  

The talk below will discuss the importance of AI observability for responsible AI and offer fresh insights for technical architects, engineers, and organizational leaders seeking to leverage Large Language Model applications and generative AI through AI observability.  

Step 9: Prevent large language models hallucination  

It important to evaluate user interactions to monitor prompts and responses, configure acceptable limits to indicate things like malicious prompts, toxic responses, llm hallucinations, and jailbreak attempts, and set up monitors and alerts to help prevent undesirable behaviour. Tools like WhyLabs and Hugging Face play a vital role here.  

The talk below will use Hugging Face + LangKit to effectively monitor Machine Learning and LLMs like GPT from OpenAI. This session will equip you with the knowledge and skills to use LangKit with Hugging Face models. 

Step 10: Learn to fine-tune LLMs 

Fine-tuning GPT-3.5 Turbo allows you to customize the model to your specific use case, improving performance on specialized tasks, achieving top-tier performance, enhancing steerability, and ensuring consistent output formatting. It important to understand what fine-tuning is, why it’s important for GPT-3.5 Turbo, how to fine-tune GPT-3.5 Turbo for specific use cases, and some of the best practices for fine-tuning GPT-3.5 Turbo.  

Whether you’re a data scientist, machine learning engineer, or business user, this talk below will teach you everything you need to know about fine-tuning GPT-3.5 Turbo to achieve your goals and using a fine tuned GPT3.5 Turbo model to solve a real-world problem. 

Step 11: Become ChatGPT prompting expert 

Learn advanced ChatGPT prompting techniques essential to upgrading your prompt engineering experience. Use ChatGPT prompts in all formats, from freeform to structured, to get the most out of large language models. Explore the latest research on prompting and discover advanced techniques like chain-of-thought, tree-of-thought, and skeleton prompts. 

Explore scientific principles of research for data-driven prompt design and master prompt engineering to create effective prompts in all formats.

Step 12: Master LLMs for more 

Large Language Models assist with a number of tasks like analysing the data while creating engaging and informative data visualizations and narratives or to easily create and customize AI-powered PowerPoint presentations.  

Start mastering LLMs for tasks that can ease up your business activities.  

To learn more about large language models, checkout this playlist; from tutorials to crash courses, it is your one-stop learning spot for LLMs and Generative AI.  

RAG integration revolutionized search with LLM, boosting dynamic retrieval.

Within the implementation of a RAG system, a pivotal factor governing its efficiency and performance lies in the determination of the optimal chunk size. How does one identify the most effective chunk size for seamless and efficient retrieval? This is precisely where the comprehensive assessment provided by the LlamaIndex Response Evaluation tool becomes invaluable.

In this article, we will provide a comprehensive walkthrough, enabling you to discern the ideal chunk size through the powerful features of LlamaIndex’s Response Evaluation module. 

Tune in to Co-founder and CEO of LlamaIndex, Jerry Liu, and learn all about LLMs, RAG, fine-tuning and more!

Why chunk size matters in RAG system

Selecting the appropriate chunk size is a crucial determination that holds sway over the effectiveness and precision of a RAG system in various ways: 

Pertinence and detail:

Opting for a smaller chunk size, such as 256, results in more detailed segments. However, this heightened detail brings the potential risk that pivotal information might not be included in the most retrieved segments.

On the contrary, a chunk size of 512 is likely to encompass all vital information within the leading chunks, ensuring that responses to inquiries are readily accessible. To navigate this challenge, we will employ the faithfulness and relevance metrics.

These metrics gauge the absence of ‘hallucinations’ and the ‘relevancy’ of responses concerning the query and the contexts retrieved, respectively. 

Generation time for responses:

With an increase in the chunk size, the volume of information directed into the LLM for generating a response also increases. While this can guarantee a more comprehensive context, it might potentially decelerate the system. Ensuring that the added depth doesn’t compromise the system’s responsiveness is pivotal.

Ultimately, finding the ideal chunk size boils down to achieving a delicate equilibrium. Capturing all crucial information while maintaining operational speed It’s essential to conduct comprehensive testing with different sizes to discover a setup that aligns with the unique use case and dataset requirements. 

Why evaluation? 

The discussion surrounding evaluation in the field of NLP has been contentious, particularly with the advancements in NLP methodologies.

Traditional evaluation techniques like BLEU or F1 are now unreliable for assessing models because they have limited correspondence with human evaluations.

As a result, the landscape of evaluation practices continues to shift, emphasizing the need for cautious application. 

In this blog, our focus will be on configuring the gpt-3.5-turbo model to serve as the central tool for evaluating the responses in our experiment.

To facilitate this, we establish two key evaluators, the faithfulness evaluator and the relevance evaluator, utilizing the service context. This approach aligns with the evolving standards of LLM evaluation, reflecting the need for more sophisticated and reliable evaluation mechanisms. 

 Faithfulness evaluator: This evaluator is instrumental in determining whether the response was artificially generated and checks if the response from a query engine corresponds with any source nodes. 

Relevancy evaluator: This evaluator is crucial for gauging whether the query was effectively addressed by the response and examines whether the response, combined with source nodes, matches the query. 

In order to determine the appropriate chunk size, we will calculate metrics such as average response time, average faithfulness, and average relevancy across different chunk sizes.  

 

Downloading dataset 

We will be using the IRS armed forces tax guide for this experiment. 

• mkdir is used to make a folder. Here we are making a folder named dataset in the root directory. 

• wget command is used for non-interactive downloading of files from the web. It allows users to retrieve content from web servers, supporting various protocols like HTTP, HTTPS, and FTP. 

 

Load dataset 

• SimpleDirectoryReader class will help us to load all the files in the dataset directory. 
• document[0:10] represents that we will only be loading the first 10 pages of the file for the sake of simplicity. 

 

Defining question bank 

These questions will help us to evaluate metrics for different chunk sizes. 

 

Establishing evaluators  

This code initializes an OpenAI language model (gpt-3.5-turbo) with temperature=0 settings and instantiate evaluators for measuring faithfulness and relevancy, utilizing the ServiceContext module with default configurations. 

 

Main evaluator method 

We will be evaluating each chunk size based on 3 metrics. 

1. Average Response Time 
2. Average Faithfulness 
3. Average Relevancy 

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• The function evaluator takes two parameters, chunkSize and questionBank. 
• It first initializes an OpenAI language model (llm) with the model set to gpt-3.5-turbo. 
• Then, it creates a serviceContext using the ServiceContext.from_defaults method, specifying the language model (llm) and the chunk size (chunkSize). 
• The function uses the VectorStoreIndex.from_documents method to create a vector index from a set of documents, with the service context specified. 
• It builds a query engine (queryEngine) from the vector index. 

• The total number of questions in the question bank is determined and stored in the variable totalQuestions. 

Next, the function initializes variables for tracking various metrics: 

• totalResponseTime: Tracks the cumulative response time for all questions. 
• totalFaithfulness: Tracks the cumulative faithfulness score for all questions. 
• totalRelevancy: Tracks the cumulative relevancy score for all questions. 

• It records the start time before querying the queryEngine for a response to the current question. 
• It calculates the elapsed time for the query by subtracting the start time from the current time. 
• The function evaluates the faithfulness of the response using faithfulnessLLM.evaluate_response and stores the result in the faithfulnessResult variable. 
• Similarly, it evaluates the relevancy of the response using relevancyLLM.evaluate_response and stores the result in the relevancyResult variable. 
• The function accumulates the elapsed time, faithfulness result, and relevancy result in their respective total variables. 

• After evaluating all the questions, the function computes the averages 

 

Testing different chunk sizes 

To find out the best chunk size for our data, we have defined a list of chunk sizes then we will traverse through the list of chunk sizes and find out the average response time, average faithfulness, and average relevance with the help of evaluator method. After this, we will convert our data list into a data frame with the help of Pandas DataFrame class to view it in a fine manner. 

 

From the illustration, it is evident that the chunk size of 128 exhibits the highest average faithfulness and relevancy while maintaining the second-lowest average response time. 

Use LlamaIndex to construct a RAG system 

Identifying the best chunk size for a RAG system depends on a combination of intuition and empirical data. By utilizing LlamaIndex’s Response Evaluation module, we can experiment with different sizes and make well-informed decisions.

When constructing a RAG system, it is crucial to remember that the chunk size plays a pivotal role. Therefore, it is essential to invest the necessary time to thoroughly evaluate and fine-tune the chunk size for optimal outcomes. 

You can find the complete code here