LlamaIndex- Develop LLM powered applications with LlamaIndex

Goal of video:

In this video, we will set up our project by cloning a branch, configuring PyCharm, installing dependencies, and building an Icebreaker file. We'll also introduce the LanChain framework.

Topics covered:

• Configuring PyCharm:
  We'll configure PyCharm by setting up the Python interpreter and creating a virtual environment. This is important to ensure our project is aligned with the correct interpreter and that we have a clean environment to work in.

• Installing dependencies: We'll use pip to install the necessary dependencies for our project, including the Lanchang framework. This step is essential to ensure we have all the necessary packages to build our application.

• Introduction to the LlamaIndex framework:
  We'll provide a quick introduction to the LlamaIndex framework, explaining its abstractions like chains and why it's become so popular among developers.

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• Configuring runners:
  We'll add a new runner to PyCharm and configure it to run our main.py file.

• Installing a code formatter:
  We'll install the code formatter Black, which will help us maintain consistent formatting in our Python code. This is a best practice in software development and will help ensure that our code is clean, readable, and maintainable.

To summarize: In this video, we'll show you how to set up your project by cloning a Git branch, configuring PyCharm, and installing dependencies. We'll also provide a quick introduction to the LlamaIndex framework, build an main.py file, and configure runners to run it. Finally, we'll install a code formatter to ensure our code is consistent and readable. By the end of this video, you'll be ready to start writing your first LlamaIndex code.

Data Agents

Data Agents are LLM-powered knowledge workers in LlamaIndex that can intelligently perform various tasks over your data, in both a “read” and “write” function. They are capable of the following:

• Perform automated search and retrieval over different types of data - unstructured, semi-structured, and structured.

• Calling any external service API in a structured fashion, and processing the response + storing it for later.

In that sense, agents are a step beyond our query engines in that they can not only “read” from a static source of data, but can dynamically ingest and modify data from a variety of different tools.

Building a data agent requires the following core components:

• A reasoning loop

• Tool abstractions

A data agent is initialized with set of APIs, or Tools, to interact with; these APIs can be called by the agent to return information or modify state. Given an input task, the data agent uses a reasoning loop to decide which tools to use, in which sequence, and the parameters to call each tool.

Tools

Having proper tool abstractions is at the core of building data agents. Defining a set of Tools is similar to defining any API interface, with the exception that these Tools are meant for agent rather than human use. We allow users to define both a Tool as well as a ToolSpec containing a series of functions under the hood.

A Tool implements a very generic interface - simply define __call__ and also return some basic metadata (name, description, function schema).

A Tool Spec defines a full API specification of any service that can be converted into a list of Tools.

LlamaIndex offer a few different types of Tools:

• FunctionTool: A function tool allows users to easily convert any user-defined function into a Tool. It can also auto-infer the function schema.

• QueryEngineTool: A tool that wraps an existing query engine. Note: since our agent abstractions inherit from BaseQueryEngine, these tools can also wrap other agents.

What is Language Modeling?

• Language modeling is the task of predicting the next word in a sentence.

• It is similar to autocomplete or word suggestions we see in our day-to-day life.

• The language model predicts the probability of the next word based on the previous words in the sentence.

  
  

Formal Definition of Language Modeling

• Language modeling involves computing the probability distribution of the next word in a sequence of words.

• The probability of the next word (x t+1) is calculated based on the sequence of words before it (X1, X2, ..., XT) and needs to be a part of the vocabulary (V).

  
  

Large Language Models: A Brief Overview

• A large language model (LLM) is a language model trained on a huge amount of data.

• LLMs are capable of predicting the probability of the next word with high accuracy.

• They have gained immense popularity in recent times due to their ability to perform a variety of language-related tasks.

How Large Language Models Work

• LLMs work by taking an input of words and predicting the probability of the next word.

• They make their predictions based on the input provided and the probabilities learned during the training phase.

• LLMs can sometimes generate output that is far-fetched from reality and simply not true due to the limitations of probability-based predictions.
  

What is a Prompt in AI Language Models?

• A prompt is the input given to an AI model to produce an output.

• It guides the model to understand the context and generate a meaningful response.

Components of a Prompt:

Instruction

• The heart of the prompt that tells the AI model what task it needs to perform.

• It sets the stage for the model's response, whether it's text summary, translation, or classification.

Context

• Additional information that helps the AI model understand the task and generate more accurate responses.

• For some tasks, context may not be necessary, but for others, it can significantly improve the model's performance.

Input Data

• The information that the AI model will process to complete the task set in the prompt.

• It could be a piece of text, image, or anything relevant to the task.

Output Indicator

• Signals the AI model that we expect a response.

• Sometimes implicit in the instruction, but sometimes explicitly stated.

Here are the key points we'll cover:

• Large language models and their immense knowledge base

• What is zero shot prompting?

• An example of a zero shot prompt

• Why zero shot prompts are popular among AI beginners

• The limitations of zero shot prompting

With zero shot prompting, AI models can generate outputs for tasks they haven't been explicitly trained on, using their pre-existing knowledge to perform the task based on the information provided in the prompt. However, this kind of prompt comes with its own set of limitations, such as accuracy and scope.

In this video, we will explore the concept of Few Shot Prompting, a technique used in prompt engineering that allows AI models to generate or classify new data by presenting them with a small number of examples or shots of a particular task or concept along with a prompt or instruction. Here are the main points we will cover:

What is Few Shot Prompting?

Few Shot Prompting is a prompt engineering technique that involves presenting the AI model with a small number of examples or shots of a task or concept to generate or classify new data that is similar to the examples provided. It is particularly useful in scenarios where there is limited data available for a given task or domain where data may be scarce.

How Does Few Shot Prompting Work?

Few Shot Prompting works by providing the AI model with a few examples of a particular task or concept and a prompt or instruction on how to generate or classify new data similar to the examples provided. It can quickly adapt models to new tasks and domains by fine-tuning existing models without requiring a large amount of new data.

Case Study: Zero Shot, One Shot, and Few Shot Prompting in Action

We will demonstrate the effectiveness of zero shot, one shot, and few shot prompting techniques in generating text-to-text descriptions for Blue Willow, an open source AI tool that generates images from text prompts. By comparing the outputs generated by each technique, we will see which one performed better according to our task of generating a good description to paint a picture.

Introduction to Chain of Thought

• Explanation of Chain of Thought's purpose in improving LLM reasoning abilities

• How Chain of Thought allows models to decompose complex problems into manageable steps

Standard Prompting Limitations

• Examples of insufficient answers with standard zero-shot prompting

• Explanation of zero-shot prompting

Chain of Thought Prompting

• Explanation of Chain of Thought as a new prompting technique

• Examples of Chain of Thought's success in solving complex reasoning problems

• Comparison to human problem-solving methods

Zero-Shot and Few-Shot Chain of Thought Prompting

• Explanation of zero-shot Chain of Thought prompting

• Explanation of few-shot Chain of Thought prompting

• Benefits and limitations of each method

In this video, we will explore the ReAct Prompting technique, a powerful approach to prompt engineering that combines reasoning and acting to accomplish complex tasks. Here are the main points we will cover:

What is ReAct Prompting?

• ReAct Prompting is a technique that allows language models to reason and act upon a task to generate an output.

• It is based on the chain of thoughts that the model can generate to accomplish a task.

How Does ReAct Prompting Work?

• ReAct Prompting involves breaking down a task into multiple steps, reasoning the steps, acting upon them, and then completing the entire task.

• The model can derive an action by accessing external sources or APIs, allowing it to accomplish more complex tasks.

Case Study: ReAct Prompting in Action

• We will look at a research paper that demonstrates the power of ReAct Prompting in action.

• The paper shows how a language model was able to derive the correct answer to a complex question by reasoning and acting upon it.