LangChain 實作加速器（LangChain 实战速成加速器）：打造大型語言模型 LLM 驅動的應用程式

單元目標：

在這個單元，我們將透過 clone a branch、設定 PyCharm、安裝相關套件和模組以及建立 Icebreaker 檔案來設置我們的專案。我們也會介紹 LangChain 框架的基本概念。

Topics covered:

涵蓋主題：

• Cloning a Git branch:
  我們將使用 Git 指令介面從 main repository 中 clone a branch 到本地端。這個步驟非常重要，因為我們需要在特定版本的程式碼上進行。

• 設定 PyCharm:
  我們將設定 PyCharm，包括設定 Python interpreter 和建立虛擬環境。這對於確保專案與一致且正確的 interpreter，並且有一個乾淨的工作環境來進行開發是至關重要的。.

• 安裝相關模組套件:

  我們將使用 pip 安裝專案所需的依賴套件，包括 LangChain 框架。這個步驟非常關鍵，能確保我們有所有必要的套件來開發應用程式。

• 介紹 LangChain 框架:
  我們將簡要介紹 LangChain 框架，說明其抽象概念如「chain」等，並解釋為何這個框架在開發者之間如此受歡迎。

• 建立 ice_breaker.py 檔案:
  我們將建立一個 Icebreaker 檔案，用來儲存所有 LangChain 的程式碼。我們會從一個預設的函式開始，並透過 Python 終端機運行它，確保一切正常運作。We'll create an Icebreaker file, which will hold all of our LanChain code. We'll start with a default function and run it using the Python terminal to make sure everything works as expected.

• Configuring runners:
  We'll add a new runner to PyCharm and configure it to run our Icebreaker 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 LanChain framework, build an Icebreaker 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 LanChain code.

Goal:
In this video, we will learn about the basics of the LangChain framework and how to write a simple prompt program that executes a large language model. We will also introduce some cool topics of the LangChain framework, such as chat models,  PromptTemplates, and chains.

Topics Covered:

• Introduction to prompts:
  We will define what a prompt is and how it is used to receive input and output from a large language model.

• PromptTemplates:
  We will cover the concept of  PromptTemplates and how they allow us to create customizable prompts that can receive different inputs.

• Chat models:
  We will introduce the new concept of chat models and how they are wrappers around large language models to interact with them through text.

• Chains:
  We will explain the purpose of chains, which is to combine multiple components to create a single coherent application.

• Creating a prompt and PromptTemplate:
  We will create a prompt template object and introduce the concept of input variables and keys.

• Using a ChatModel:
  We will create an instance of a chat model and plug it into the prompt template.

• Creating and Running the chain:
  We will run the chain with the prompt template and input variables and show how the LangChain framework automatically handles the process for us.

  
  

• Open Source Models in GenAI Applications

• Ollama

• Mistral by Mistral AI

• Llama3 by Meta

The course is filmed in version V0.1.14

In this video, we will be focusing on building an application that connects with LinkedIn and utilizes LLMs to analyze data that we scrape from Linked.

Here are the topics that we will cover:

1. Scraping LinkedIn API Data:
   We will write a code that fetches and scrapes information from a LinkedIn URL using the function called "scrape_linkedIn_profile".

2. Using ProxyURL:
   We will learn how to use ProxyURL to scrape LinkedIn API data. We will see how it's advantageous to use this tool.

3. Cleaning Response Payload:
   We will learn how to clean unnecessary data from the payload. We will discuss the importance of being frugal with tokens to avoid exceeding the token limit and how it affects the API call made to LLMs.

4. Leveraging summarization  Power of LLMs:
   We will give the LinkedIn data that we got as an input to an LLM chain so it can output a coherent summary.

LangChain Agent Theory

In this video, we’ll dive into the concept of LangChain agents and how they can be used for reasoning and performing tasks and interact with external services.

Topics:

• Introduction to LangChain agents

• The concept of subtasks and how agents split them to complete bigger tasks

• Chain Of Thought Prompting

• ReAct Prompt Prompting

• How agents can be used to perform tasks and interact with external services

• Understanding the structure of agents and their tools, Toolkits

• A practical example of building an agent

LangChain agents
LangChain agents are entities that can perform actions autonomously by interacting with the environment.
For this, an LLM (large language model) is used to determine which actions to take and in what order. There are several agents available in LangChain, including the zero-shot-react-description, react-docstore, self-ask-with-search, and conversational-react-description.

Tools

Tools are ways that an agent can use to interact with the outside world. Examples of tools include search engines, APIs, and other datastores.
Tools are useful because LLMs only have knowledge of what they were trained on, but tools can fetch up-to-date data and take actions that can be observed and factored into an agent's decision on what to do next.

LangChain AgentType
The LangChain AgentType is derived from prompt engineering techniques and determines how the agent will calculate its strategies to achieve a task. Memory helps the agent remember previous interactions, and an AgentExecutor is the logic for running an Agent in a while-loop until some stopping criteria is met.

The default LangChain Agent is based on the ReAct framework proposed by Yao et al.
This approach is characterized by the following algorithm:

1. user gives an agent a task

2. thought (the agent "thinks" about what to do),

3. action/action input (the agent decides what action to take and what the input to that tool should be),

4. observation (the output of the tool),

5. and repeating steps 2-4 until the agent "thinks" it is done.

Overall, LangChain agents, tools, and AgentType are important concepts in creating autonomous agents that can perform tasks and interact with the outside world.

• Initializing the Agent

• Defining the Tools

  We create a list of tools that the agent can use- It will need to search for the LinkedIn profile URL. Each tool has a unique name, function, and description. We also set a description for each tool, which is used by the agent reasoning algorithm to decide which tool to use.

• Initializing the Agent with Tools

  We initialize the agent with the list of tools and an LLM model. We also set the agent type to zero-shot react. The agent type is critical as it determines the reasoning process for performing a task. We can use different agent types for different tasks to get different results.

• Verbose Logging

  We set the verbose flag to true, which tells the agent to log every step it takes.
  This is useful for understanding the agent's reasoning process and how it performs a task.

• Creating a Custom Tool

  We create a custom tool that uses the LinkedIn API to search for the LinkedIn profile URL.
  We use the decorator method to define the tool so that the LangChain agent can use it.

• Testing the Agent

  We test the agent by passing it a name and running the code. The agent returns the LinkedIn profile URL for the given name. We also scrape the LinkedIn data from the URL using a proxy URL.

Testing the Agent

• We test the agent by passing it a name and running the code. The agent returns the LinkedIn profile URL for the given name. We also scrape the LinkedIn data from the URL using a proxy URL.

Introduction

In this video, we will learn how to enrich data using Twitter API. We will incorporate Twitter into our application, making it more robust. After watching the two videos, our application will have the ability to extract Twitter usernames and scrape data from Twitter using an agent.

Twitter Processing:
We will provide the same code as we use in this lecture, which we will call Twitter's API. We will also use a package called tweepy, which will help us access Twitter API.

Scraping User Tweets:
We will create a new function called "scrape_user_tweets" that will receive the username of the user and a number of tweets. It will get the tweets from this user and filter out retweets and replies.

Testing the Function
We will test the function by getting the tweets of Elon Musk. As Elon Musk is super popular on Twitter, we will have to filter out a couple of those messages. We will change the number of tweets to 100 to fix the problem.

Introduction:

In this video, we will create the LangChain agent that takes a name searches online and extracts the username from Twitter.

We'll use Python to create the Twitter Lookup Agent and adjust it to use Twitter instead of LinkedIn like we did in previous videos.

In this video, we'll cover the following topics:

• Creating the Twitter Lookup Agent

• Testing the application and overcoming token limitations

• Generating topics of interest and icebreakers for conversation starters

Introduction:
An Output Parser is a class that structures language model responses by implementing two methods: get_format_instructions() and parse(). The former method returns a string that contains instructions on how the language model's output should be formatted, while the latter method takes in a string response from the language model and parses it into a specific structure.

Pydantic Output Parser

The Pydantic Output Parser is a type of Output Parser that allows users to specify a schema and request LLMs for  outputs that conform to that schema.
We can use Pydantic to declare the python data model, and the Pydantic Output Parser will convert the LLM's response into a structured object that conforms to the schema. This can be useful for handling complex output from LLMs, or if we want our output to be ingested with other systems.

Using Pydantic Output Parser

To use the Pydantic Output Parser, users can define their desired data structure using Pydantic's BaseModel, similar to a Python dataclass but with actual type checking and coercion.
We can then define a query intended to prompt the LLM to populate the data structure.

The Pydantic Output Parser can be instantiated with the Pydantic object representing the desired data structure. A PromptTemplate can be set up with instructions for how the LLM should generate a response, and the Pydantic Output Parser can be used to generate format instructions for the PromptTemplate.

The PromptTemplate can then be used to format the user query and format instructions into a prompt, which can be passed to the LLM to generate a response.
The response can then be parsed using the PydanticOutputParser, which will convert it into a structured object that conforms to the specified schema.

Introduction

In this video, we're going to build the server-side for our project, as well as the front-end.
By the end of it, we will have fully integrated it into a Lansing-based generative application.

Technical Details

We also want to return from the ice_breaker function, the profile pic URL, so we're going to change the type of notation in the return value to a tuple.

We will be using Flask to create a Flask application. We need the Flask to render the template to render the HTML code that we're going to be writing.
Building the Flask Web Server

We will create a new file and name it app.py. This file will hold our Flask web server. We will use the main endpoint '/process' which will be using a post request with the name of the person we want to get info about.
When we call the endpoint, we will invoke the process function.

Creating the Front-End

We need to write the front-end.
We will create a new directory and name it static. Inside it, we will create a new directory named CSS. We will create a new file named style.css and we'll place our CSS content inside it.

Next, we will create a new directory named templates. In the templates directory, we will create an index.html file. Flask will be looking for the HTML5 to render in the templates directory.

• Introduction
  In this video, we will review the classes and objects that we will use for this session. We will explain each class related to the theoretical particle reviewed earlier, including Document Loaders, Text Splitters, OpenAI Embeddings, Pinecone, and VectorDBQA Chain.

  
  

• Document Loaders
  Document Loaders are classes that implement how to load and process data to make it digestible by the language model. We will be using the class of TextLoader, which helps us send data to LLMS.
  This abstraction allows us to attach other things to the text, such as processing WhatsApp messages, downloading PDF files, or working with a Notion notebook.
  

• TextSplitter
  TextSplitter us with long pieces of text.
  Large text files often contain more tokens than the modeling forces allow, causing the request to fail.
  TextSplitter solves this problem by splitting the text into smaller chunks, which can be sent to the language model. TextSplitter has a lot of logic, including splitting strategies and calculating appropriate chunk size. The chunk size may change according to what we want to accomplish, depending on the different weighting systems. We can specify the chunk size and overlap between the chunks to ensure that the text isn't sped up in a way that disturbs the context or meaning.

  
  

• OpenAIEmbeddings
  OpenAIEmbeddings is a black box that takes inputs as text and outputs vectors in an embedded Vector space.
  We can use OpenAI Embeddings' API to embed a text, and we will get back a vector. There are many embedding providers available, but OpenAI Embeddings is one of the best. The Embedding abstraction It creates a uniform interface for us to access different embeddings from different providers. It is straightforward to switch between different providers by just changing a parameter.

  
  

• Pinecone
  Pinecone is an excellent Vector database that has recently gone viral. It provides a persistent storage solution for the vectors we receive from embedding like OpenAI Embeddings.
  Pinecone  allows us to search in the vector space for the closest vectors of the current one. We can add new vectors to the vector space and retrieve relevant documents from the vector database.

  
  

In this video we will write a python script script that demonstrates how to use various libraries and tools to create an LLM powered assistant that can answer questions based on the content of a given text document.

This is the ingestion part.

Here are the main steps that the code follows:

• Import necessary libraries including LangChain modules:
  TextLoader, CharacterTextSplitter, OpenAIEmbeddings, PineconeVectorStore, and OpenAI.

• Initialize Pinecone by providing an API key and environment.

• Load a text document from a file using TextLoader.

• Split the document into smaller chunks using CharacterTextSplitter.

• Use OpenAIEmbeddings to create embeddings for each text chunk.

• Index the embeddings in Pinecone using the PineconeVectorStore.from_documents method.

Overall, this code demonstrates how to leverage several powerful libraries and tools to build a simple but effective search engine that can provide answers to natural language queries.

Introduction:
In this video, we'll be exploring how to use local vectorstores to load and retrieve PDF documents using 
  LangChain and FAISS

Setting up the Environment

• Creating a new directory and virtual environment

• Installing required packages including PyPDF and FAISS

Loading and Processing the PDF Document

• Using PyPDF to load the PDF file

• Chunking the document with LangChain's CharacterSplitter

• Using OpenAI embeddings to convert the document into vectors

• Storing vectors in a vector store

FAISS

• Python package to help with similarity search in our local environment

• Cloning the Repository

  First, we'll clone the repository by running the command
  "git clone https://github.com/emarco177/documentation-helper.git -b 1-start-here"
  in the terminal. This will clone the repository and start us off on the beginning branch.

  
  

• Creating the Download Directory

  Next, we'll create a new directory called "langchain-docs" to download the Python documentation of LangChain to. We'll use the wget utility to download the documentation of LangChain.

  This will take some time as it will download a lot of files, so feel free to take a break.

• Creating the Pinecone Index

  Once we have all the files downloaded, we'll move on to creating a Pinecone index.
  We'll log in to our user and create an index that will store the embeddings of the documentation of LangChain.  We'll search for embeddings of OpenAI to determine the dimension of the vectors that are returned, which is 1536.

• Initializing and Adding Documents to the Pinecone Index

  After the index is initialized, we'll start adding documents to it.
  

• Splitting the Documents into Chunks

  We'll then split the documents into chunks using the text feature split documents. We'll print a debugging message to count how many documents we have now after splitting them.

• Insert to Pincone vectorstore:
  Then we will use the Pincone wrapper of LangChain to insert all the embeddings of the chunks we created.

In this video, we will be implementing a RetrievalQA chain in a backend powered by LangChain and Pinecone. We will be discussing how to retrieve context from a vectorstore and use it to augment a prompt to get a better answer from an LLM.

Setting up Pinecone and the Python client

• We will start by importing Pinecone and the Python client to create an index for our vector store.

Retrieving context from a vector store

• We will write code to retrieve context from a vector store using LinkChain and Pinecone.

• We will discuss how the vector store uses similarity search to retrieve relevant vectors.

Augmenting a prompt with context

• We will modify our prompt to include the retrieved context from the vector store.

• We will discuss how this helps improve the accuracy of our LM.

Using create_retrieval_chain + create_stuff_documents_chain

• We will discuss how to use a retriever object to retrieve relevant vectors from the vectorstore.

Running a demo

• We will run a demo to see the RetrievalQA LangChain chain in action.

• We will discuss the output and how to access the source documents.

To summerize, implementing a retrieval Q&A chain in a backend using LinkChain and Pinecone is a powerful way to improve the accuracy of an LLM. With this approach, we can retrieve context from a vector store and use it to augment a prompt, resulting in more accurate and relevant answers.

In this video, we will be implementing a RetrievalQA chain in a backend powered by LangChain and Pinecone. We will be discussing how to retrieve context from a vectorstore and use it to augment a prompt to get a better answer from an LLM.

Setting up Pinecone and the Python client

• We will start by importing Pinecone and the Python client to create an index for our vector store.

Retrieving context from a vector store

• We will write code to retrieve context from a vector store using LinkChain and Pinecone.

• We will discuss how the vector store uses similarity search to retrieve relevant vectors.

Augmenting a prompt with context

• We will modify our prompt to include the retrieved context from the vector store.

• We will discuss how this helps improve the accuracy of our LM.

Implementing a RetrievalQA LangChain chain

• We will create a RetrievalQA chain using LinkChain and the LM.

• We will discuss how to use a retriever object to retrieve relevant vectors from the vectorstore.

Running a demo

• We will run a demo to see the RetrievalQA LangChain chain in action.

• We will discuss the output and how to access the source documents.

To summerize, implementing a retrieval Q&A chain in a backend using LinkChain and Pinecone is a powerful way to improve the accuracy of an LLM. With this approach, we can retrieve context from a vector store and use it to augment a prompt, resulting in more accurate and relevant answers.

• Introduction:

  In this video, we will go over the steps to create a new file called main.py and use Streamlit to build an elegant and simple user interface.

• Creating a Streamlit Runner
  We will create a Streamlit Runner to run the Streamlit CLI with our main.py file. We will select Python as our language, name the runner "Streamlit Runner", and include the path to the Streamlit CLI.

• Creating a User Prompt
  We will then create a text input for the user to input their prompt. The prompt will be stored in a variable called "prompt".

• Generating a Response
  We will then generate a response to the user's prompt by running the run_llm function. The response will be stored in a variable called "generated_response".

• Updating the Session State

  We will then update the session state to include the user's prompt and the response from the AI. This will allow us to maintain chat history and provide context to the AI.

• Displaying Chat History
  If the chat history is not empty, we will display the chat history to the user. This will allow the user to see the questions they've asked and the AI's responses.

• Intro- Integrating Conversation Memory into a Language Model with LangChain

  In this video, we will explore how to integrate conversation memory into a language model using LangChain.
  

• Setting Up the Repository

  First, commit the current code and create a new branch to implement memory support.

  
  

• Updating Session States

  We will add a new key called "chat_history" to the Streamlit session_states variable.
  This key will store a list containing the entire conversation history between the user and the language model.

  
  

• Updating the Function to Support Memory

  We will modify the function to accept a chat_history parameter. Pass this parameter to the run_llm function, which will refactor to process the chat history as well.

  
  

• Implementing Conversational Retrieval Chain

  We will replace the RetrievalQA chain with a ConversationalRetrieval chain which support memory.
  Initialize it using the from_llm function from LangChain instead of the from_type function used before in RetrievalQA.
  We will also pass the chat history object to the conversational retrieval chain.

  
  

• Understanding the Augmented Prompt

  We'll dive into LangChain source code.
  We will examine the augmented prompt sent to the language model by the ConversationalRetrieval chain. The prompt is modified to include the chat history, allowing the language model to understand the context of the conversation.

Now we know how to integrate conversation memory into a language model using LangChain.
This process can be applied to any language model that requires context-aware conversation support.

• ConversationBufferMemory

  ConversationBufferMemory is a memory utility in the langchain package that allows for storing messages in a buffer and extracting them as a string or a list of messages. It is useful for storing conversation history in a chatbot or conversational AI system.

  It simply keeps a buffer of all the interactions in a conversation. It does not have a limit on the number of interactions it can store

  
  

• ConversationBufferWindowMemory

  ConversationBufferWindowMemory is a type of memory in the LangChain package that keeps a list of the interactions of a conversation over time, but only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large. On the other hand, ConversationBufferMemory is a different type of memory that simply keeps a buffer of all the interactions in a conversation. It does not have a limit on the number of interactions it can store.

  
  

• ConversationTokenBufferMemory

  ConversationTokenBufferMemory is a buffer for storing conversation memory that keeps a buffer of recent interactions in memory, and uses token length rather than the number of interactions to determine when to flush interactions. It is a class in the langchain.memory.token_buffer module and is used in conversational AI applications to store and retrieve conversation history.

  
  

• ConversationSummaryMemory

  ConversationSummaryMemory is a type of memory utility in the LangChain package that creates a summary of the conversation over time. It condenses information from the conversation over time and can be useful for keeping track of the conversation history. It differs from other memory utilities in LangChain in that it specifically focuses on creating a summary of the conversation, whereas other memory utilities may focus on different aspects of the conversation, such as keeping a buffer of recent interactions or using token length to determine when to flush interactions.

  
  

• ConversationSummaryBufferMemory

  ConversationSummaryBufferMemory is a type of memory utility in the LangChain package that keeps a buffer of recent interactions in memory and compiles them into a summary, using token length rather than the number of interactions to determine when to flush interactions. It differs from other memory utilities in that it combines the functionality of keeping a buffer of recent interactions with the ability to summarize the conversation over time. Other memory utilities in the package include ConversationSummaryMemory, which creates a summary of the conversation over time, and ConversationBufferMemory, which keeps a buffer of recent interactions in memory.

  
  

• Entity Memory

  Entity Memory is a memory module in LangChain that remembers information about specific entities. It uses LLMs to extract information on entities and builds up its knowledge about that entity over time. It is designed to be used in chains and provides easy ways to incorporate its utilities into chains. Compared to other memory utilities in LangChain, Entity Memory is focused on remembering information about specific entities, while other memory utilities are more general and can be used to manage and manipulate previous chat messages. Additionally, Entity Memory is designed to be used in chains, while other memory utilities can be used in a variety of contexts.

  
  

• VectorStore-Backed Memory

  VectorStore-Backed Memory is a memory component in LangChain that stores memories in a VectorDB and queries the top-K most “salient” docs every time it is called. It differs from most of the other Memory classes in that it doesn’t explicitly track the order of interactions. In this case, the “docs” are previous conversation snippets. This can be useful to refer to relevant pieces of information that the AI was told earlier in the conversation.

  
  

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.

How to Produce Prompts for Better LLM Responses

Incorporating Context for Coherent and Accurate Responses

• Context provides relevance for generating better responses

• Leaving context to the AI model could lead to off-topic or irrelevant responses

  
  

Defining a Clear Task for Unambiguous Results

• Clear and precise task definitions provide better results

• Ambiguous tasks can lead to confusion and lower performance

• Example: Improving user experience of an e-commerce website

Iterating for Optimized Prompts

• Iterations involve refining prompts and evaluating output

• Refining prompts over iterations leads to optimized prompts

• Taking the time to write effective prompts saves time in the long run

By following these low-hanging fruit tips for prompt engineering, you can improve the performance of your AI models and get better responses. Incorporating context, defining clear tasks, and iterating to refine prompts will lead to more accurate and relevant responses, saving you time and improving the overall performance of your AI models.

1. Exception has occurred: Forbidden

   403 Forbidden

   453 - You currently have Essential access which includes access to Twitter API v2 endpoints only. If you need access to this endpoint, you’ll need to apply for Elevated access via the Developer Portal. You can learn more here: https://developer.twitter.com/en/docs/twitter-api/getting-started/about-twitter-api#v2-access-leve

     File "/Users/justin/ownCloud/__LLM/udemy_langchain/ice_breaker/third_parties/twitter.py", line 27, in scrape_user_tweets

       tweets = api.user_timeline(screen_name=username, count=num_tweets)

                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

     File "/Users/justin/ownCloud/__LLM/udemy_langchain/ice_breaker/ice_breaker.py", line 37, in <module>

       print(scrape_user_tweets(username="@elonmusk"))

             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

   tweepy.errors.Forbidden: 403 Forbidden

   453 - You currently have Essential access which includes access to Twitter API v2 endpoints only. If you need access to this endpoint, you’ll need to apply for Elevated access via the Developer Portal. You can learn more here: https://developer.twitter.com/en/docs/twitter-api/getting-started/about-twitter-api#v2-access-lev