OCI Generative AI Professional: Mastering Chatbot Development and Large Language Models

As an OCI Generative AI Professional, you'll learn how to develop chatbots that can understand and respond to user queries in a conversational manner. This involves working with large language models that can generate human-like text based on input prompts.

OCI Generative AI provides a range of pre-trained models that can be fine-tuned for specific use cases, such as customer service or language translation. These models can be integrated into chatbots to create more engaging and personalized user experiences.

With OCI Generative AI, you'll be able to build chatbots that can understand the nuances of human language and respond accordingly. This requires a deep understanding of natural language processing (NLP) and machine learning concepts.

By mastering chatbot development and large language models, you'll be able to create more effective and efficient AI solutions that can drive business outcomes and improve customer satisfaction.

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To configure chatbot code, start by downloading the repository as a zip file from the provided link.

Unzip the contents to your .venv folder, then right-click the project and select Reload from Disk.

It's essential to check the project structure, which should resemble the one shown in the article.

To add PDFs to the project, download some PDFs and add them to a new pdf-docs folder, which should be a sub-folder of module4.

You'll need to open demo-chroma-create.py and change the URL and Compartment ID to the desired values.

Note that the manual persist() method is deprecated in Chroma version 0.4.x, so you can ignore this warning.

To run the file, check that you have a non-zero number of documents loaded into chromadb.

You can run the chroma server by executing the command: `chroma run --path .venv\ou-generativeai-pro-main\demos\module4\chromadb`

To start the chatbot, open demo-ou-chatbot-chroma-final.py in PyCharm and change the URL and Compartment ID.

You'll need to open another Terminal and run the chatbot by executing the command: `streamlit run .venv\ou-generativeai-pro-main\demos\module4\demo-ou-chatbot-chroma-final.py`

To interact with the chatbot, simply ask it a question, such as "What consoles does Matt Mulvaney own?"

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Large Language Models are a type of artificial intelligence model designed to understand and generate human-like text based on the data they've been trained on.

These models are characterized by their vast size, typically consisting of billions or even trillions of parameters, which are the adjustable weights that help the model make predictions or generate text.

The vast size of LLMs is what allows them to understand and generate complex text, making them a powerful tool for tasks like language translation, text summarization, and content creation.

LLMs are designed to learn from large amounts of data, which enables them to recognize patterns and relationships in language that humans may not even notice.

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Decoding is a crucial step in generating text with generative AI models. Greedy decoding is a straightforward approach that selects the token with the highest probability at each step, continuing until an end-of-sequence token is produced or a maximum length is reached.

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Nucleus sampling, also known as top-p sampling, is a more sophisticated decoding strategy that considers a dynamic subset of the top probable tokens, allowing for more nuanced and varied text generation. This approach can lead to more interesting and diverse outputs.

Beam search is an extension of greedy decoding that aims to improve the quality of generated sequences by considering a set of candidate sequences instead of just the single most probable one. This can be particularly useful when working with complex or open-ended prompts.

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In-context learning is a type of prompting that involves constructing a prompt with demonstrations of the task the model is meant to complete.

To achieve in-context learning, you need to think about how to present the task in a way that shows the model what it needs to do. For example, if you're asking a model to summarize a text, you could include a summary of a similar text in the prompt.

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K-shot prompting includes k examples of the task you want the model to complete in the prompt. This can be a helpful way to give the model some context and guidance.

Including multiple examples in the prompt can help the model learn from different perspectives and approaches. However, it's essential to strike a balance between providing too much information and not enough.

Chain-of-thought prompting involves breaking down the steps of solving a problem into small chunks. This can help the model think more clearly and logically.

By breaking down complex problems into smaller, manageable parts, you can help the model focus on one step at a time. This can be especially helpful for tasks that require a series of logical steps.

Least to most prompting involves solving simpler problems first and using the solutions to the simple problems to solve more difficult problems. This can be a helpful way to build a foundation of knowledge and understanding.

By starting with simple problems, you can create a stepping stone for more complex tasks. This can help the model build its confidence and ability to tackle more challenging problems.

Additional reading: Generative Ai Prompt Examples

Step-back prompting involves identifying high-level concepts pertinent to a task. This can help the model understand the broader context and significance of the task.

By taking a step back and looking at the big picture, you can help the model see the relevance and importance of the task. This can be especially helpful for tasks that require a high level of understanding and insight.

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Decoding is a crucial step in the generation process, and it's where the model decides what to output. There are several decoding techniques to choose from.

Greedy decoding is a straightforward approach where the model selects the token with the highest probability as its next output, and this process continues until an end-of-sequence token is produced. This method is simple but can lead to repetitive and unvaried text.

Nucleus sampling, on the other hand, is a more sophisticated strategy that considers a dynamic subset of the top probable tokens. This allows for more nuanced and varied text generation, making it a popular choice for creative writing and other applications.

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Beam search is an extension of greedy decoding that aims to improve the quality of generated sequences by considering a set of candidate sequences instead of just the single most probable one. This approach can produce more accurate and diverse text, but it can also be more computationally expensive.

Decoding techniques can be adjusted to suit specific needs, and understanding the options available is key to getting the best results.

Summarization Models are a crucial part of the OCI Generative AI Service, and they can be really helpful in getting to the point of a long piece of text.

The Cohere Command Model is one such model that generates a succinct version of the original text, relaying the most important information.

This model is essentially the same as one of the pretrained text generation models, but with hyperparameters specified for text summarization, which makes it particularly well-suited for this task.

If you're working with a lot of text data, a summarization model like Cohere Command Model can be a real time-saver, allowing you to quickly get a sense of the main points without having to read through every word.

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Retrieval Augmented Generation (RAG) is an approach in natural language processing that combines retrieval-based and generative models to produce high-quality, contextually relevant text.

RAG is particularly useful for tasks that require a deep understanding of context, such as generating entire sequences of text like paragraphs or documents. This is because it allows the model to retrieve relevant information from a large external knowledge source and use it to guide the generation process.

There are two main ways to implement RAG: sequence model and token model. The sequence model focuses on generating entire sequences of text, while the token model operates at the token level and is used for tasks that require fine-grained control over individual tokens.

The sequence model is great for tasks like generating paragraphs or documents, but the token model is more suitable for tasks like text completion, question answering, or dialogue generation where precision is key.

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The OCI Generative AI service is a fully-managed platform that allows you to leverage generative AI models for various text-based tasks.

It provides access to state-of-the-art large language models (LLMs) from Cohere and Meta, which you can use for tasks like summarization, text generation, translation, and information extraction.

The service allows you to fine-tune these pre-trained models on your own data, which can significantly improve the model's performance on specific tasks relevant to your business needs.

You can create endpoints, update them, or even delete them as needed, and manage the compute resources allocated to your custom models.

OCI Generative AI utilizes isolated AI clusters for both fine-tuning and hosting custom models, ensuring security and optimal performance for your workloads.

Here are some key features of the OCI Generative AI service:

This service offers a lot of flexibility and control, allowing you to customize your models to meet your specific business needs.

Fine-tuning your models on your own data can significantly improve their performance on specific tasks, but it requires labeled data, which can be expensive and time-consuming to acquire.

Large Language Models, like those in OCI's Generative AI Service, are characterized by their vast size, typically consisting of billions or even trillions of parameters.

These parameters, also known as adjustable weights, help the model make predictions or generate text based on the data it has been trained on.

To control the output of these models, you can adjust various hyperparameters. For example, the Maximum Output Token setting determines the maximum number of tokens generated per response, up to 4,000 tokens in OCI.

Low Temperature hyperparameters result in more deterministic and predictable outputs, while High Temperature hyperparameters produce more creative and diverse outputs.

You can also use Top K and Top P hyperparameters to influence the next token chosen by the model. Top K picks the next token from the top K tokens based on probability, while Top P picks the next token based on the cumulative probability, ensuring the sum of probabilities is below a certain threshold.

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The Stop Sequence setting is useful for controlling the length of the output. For example, if the stop sequence is a period (.), the model stops generating text once it reaches the end of the first sentence.

Penalty Hyperparameters, such as Frequency Penalty and Presence Penalty, can be used to reduce the probability of a token based on how often it has appeared in the text.

Here are the Penalty Hyperparameters in more detail:

Finally, the Show Likelihoods parameter can be used to assign a score indicating how likely a token is to follow the current token. This parameter does not influence the generation process itself but rather serves as a diagnostic tool to help understand the model's behavior.