Background Information

Project overview

Setup

Introduction

Prompt template

LCEL

Introduction to prompt engineering

Prompts

Memory

Conversation memory

Supabase

Restoring memory

Vectorstores

Supabase vector

Ingest

Retrieval

Generation

Retrieval Augmented Generation

Output Parsing

Tools

Agents

Langsmith

Farewell

( the primaryDescriptionMarkdown is long - several lines, include images, as long as you want )

In this course we <headline of what we do>. <why this technology is good> but <here's how learning it is problem>. 

In <the length of these lessons> we cover <three salient points about what we cover. Your BEST points>. Its <however long it is> and comes with <these bonus features, and reasons to buy. code, etc.>

It's taught by <author name> who <credibility, reason you should listen to them>. <social proof, if you have it yet>

<then you can list other supporting stuff you want to add, anything you want here>

-------
Example:
-------

We created a free course to create your first GraphQL server (from scratch) using Node and TypeScript. TypeScript and GraphQL are a powerful combo, but there's a lot of confusing edges if you're getting it setup the first time.

In this course we cover the basics of getting TypeScript setup with Node, how GraphQL works, and how to set up a GraphQL server using Apollo server. It's two hours long, it comes with complete code examples, and we walk through the code step-by-step.

It's taught by my friend, Shopify-engineer Hassan Djirdeh and he's integrated what he's learned using GraphQL in production as an engineer at Shopify. We've already had over 1,300 students enroll and it's been getting great reviews so far.

// ... and so on

In this course we <headline of what we do>. <why this technology is good> but <here's how learning it is problem>. 

( the summary is short - one or two lines )

Langchain.js Bootcamp

What is GPT? GPT is a language model that looks at an input token array and predicts the next token. It then appends that token and continues until a stop condition is met.

What is a token? Language models are programs and they need inputs in the format of numbers. A tokenizer turns text into number representations. Imagine that you can choose to represent the English alphabet with each character being a token from 1-26. There are different tokenizers but most of the time, tokens represent characters, words, word pieces, and special instructions.

An array of tokens is not meaningful in itself, but one of the first steps of the language model is to embed the tokens. What you need to know is that an embedding is in a consistent dimension space which means that you can calculate differences between embeddings. Similar to how we can plot things along a simple 2d graph and some points are closer together.

Tokens are also important because models typically have a maximum number of tokens they can take in called "context size". And Tokens are also what typically gets priced in APIs. APIs have gotten a lot cheaper in general, but efficiency will still matter at scale.

In addition, chat models receive additional human aided fine-tuning in a way that restricts them to performing certain tasks and prevents unwanted behavior. This is called 

Reinforcement Learning from Human Feedback (RLHF)

What goes on under the hood of an RLHF training process is complex but it's fairly easy to understand the dataset. This is a sample RLHF dataset, be warned, it's not safe for work and it may be inflammatory. Link: 

https://huggingface.co/datasets/Anthropic/hh-rlhf

Given this dataset, you can probably see how you can finetune a model yourself and there is growing support for doing so now. Both with open source models and proprietary models.

The convention for most chat models is that there is a system message, a user role, and an assistant role. These roles are used in training and are meaningful to the model.

Another model is a function model, these were trained to use helper functions like tools, and call them when provided.

More model types are likely to come out in the future so it’s best to stay up to date and connected with a community.

Let’s talk about how you can change the behavior of the model without training.

To influence the output of the model, you can only change the input, or the prompt. You can influence the prompt at each step of the process. Here are some common ways to add to the prompt:

Also one thing to note is that models often have a maximum amount of tokens accepted as input. This is called the maximum context size.

It is also useful for finding similar embeddings as we’ll see later on.

aligned models - instruct models, chat models fine tuned to do specific tasks often with special prompt formats - user, assistant, system

We will only work with aligned chat models in this course.

Most of us are familiar with databases as a persistant store of data and the way we obtain information is to query the database. There is a parallel when it comes to the internet. There is a vast amount of data in the internet, and realistically we query it through Google Search (

 of all web search is done through Google).

The data used to pre-train the language model is sourced from the internet, and so the model has learned a compressed representation of the entire internet. When you query a language model through a prompt, you are tapping into a compressed version of the internet and it is also predicting what you wish to understand about the information. Let's illustrate this.

Language models have interesting parallels with information retrieval. Most of the time, when we query in natural language we receive ranked document results. The process usually has a 

. The Query is provided in natural language. The Retrieval process is usually optimized for relevance and performance. This step usually includes something like an index.

Information Retrieval

The most ubiquitous search experience is Google Search. When you interact with google, you have to think carefully about your input. You receive a limited ranked list of results. And you have to read through the documents to satisfy your intent.

Search is actually quite similar to chatting with a model. The Query for LLMs is also natural language. And you can think of the generation step as similar to Retrieval because most of the data that Large Language Models are trained on is public data from the internet. At each instance the model is retrying it's best to complete the document instead of fetching the document.

# Background Information

What is GPT? GPT is a language model that looks at an input token array and predicts the next token. It then appends that token and continues until a stop condition is met.

What is a token? Language models are programs and they need inputs in the format of numbers. A tokenizer turns text into number representations. Imagine that you can choose to represent the English alphabet with each character being a token from 1-26. There are different tokenizers but most of the time, tokens represent characters, words, word pieces, and special instructions.

An array of tokens is not meaningful in itself, but one of the first steps of the language model is to embed the tokens. What you need to know is that an embedding is in a consistent dimension space which means that you can calculate differences between embeddings. Similar to how we can plot things along a simple 2d graph and some points are closer together.

:::important
Tokens are also important because models typically have a maximum number of tokens they can take in called "context size". And Tokens are also what typically gets priced in APIs. APIs have gotten a lot cheaper in general, but efficiency will still matter at scale.
:::

In addition, chat models receive additional human aided fine-tuning in a way that restricts them to performing certain tasks and prevents unwanted behavior. This is called [Reinforcement Learning from Human Feedback (RLHF)](https://huggingface.co/blog/rlhf).

:::caution
What goes on under the hood of an RLHF training process is complex but it's fairly easy to understand the dataset. This is a sample RLHF dataset, be warned, it's not safe for work and it may be inflammatory. Link: <https://huggingface.co/datasets/Anthropic/hh-rlhf>

Given this dataset, you can probably see how you can finetune a model yourself and there is growing support for doing so now. Both with open source models and proprietary models.
:::

The convention for most chat models is that there is a system message, a user role, and an assistant role. These roles are used in training and are meaningful to the model.

Another model is a function model, these were trained to use helper functions like tools, and call them when provided.

More model types are likely to come out in the future so it’s best to stay up to date and connected with a community.

Let’s talk about how you can change the behavior of the model without training.

To influence the output of the model, you can only change the input, or the prompt. You can influence the prompt at each step of the process. Here are some common ways to add to the prompt:

-   Adding memory
-   Document retrieval
-   Giving tools to the model - (this may not be obvious, the tool descriptions are inserted into the prompt and the prompt is formatted to encourage outputting in a JSON format, which gets parsed and runs code)

:::note
Also one thing to note is that models often have a maximum amount of tokens accepted as input. This is called the maximum context size.
:::

:::tip
Playing with tokens:
<https://platform.openai.com/tokenizer>
:::

It is also useful for finding similar embeddings as we’ll see later on.

aligned models - instruct models, chat models fine tuned to do specific tasks often with special prompt formats - user, assistant, system

We will only work with aligned chat models in this course.

## Understanding Large Language Models

Most of us are familiar with databases as a persistant store of data and the way we obtain information is to query the database. There is a parallel when it comes to the internet. There is a vast amount of data in the internet, and realistically we query it through Google Search ([roughly 90%](https://www.oberlo.com/statistics/search-engine-market-share#:~:text=Top%20search%20engines%20in%202023,done%20through%20the%20internet%20giant.) of all web search is done through Google).

The data used to pre-train the language model is sourced from the internet, and so the model has learned a compressed representation of the entire internet. When you query a language model through a prompt, you are tapping into a compressed version of the internet and it is also predicting what you wish to understand about the information. Let's illustrate this.

To summarize the training process:

1.  Cover words ahead of current position.
2.  Model has to predict the next word.
3.  Model is trained to predict correct word.
4.  Move to next word and Repeat.

### Information retrieval

Language models have interesting parallels with information retrieval. Most of the time, when we query in natural language we receive ranked document results. The process usually has a **Query**, a **Retrieval**, and returns **Documents**. The Query is provided in natural language. The Retrieval process is usually optimized for relevance and performance. This step usually includes something like an index.

![Information Retrieval](https://d2uusema5elisf.cloudfront.net/courses/langchainjs-bootcamp/module_01/lesson_01.02/public/assets/InformationRetrieval.png "Information Retrieval")

:::note
The most ubiquitous search experience is Google Search. When you interact with google, you have to think carefully about your input. You receive a limited ranked list of results. And you have to read through the documents to satisfy your intent.
:::

Search is actually quite similar to chatting with a model. The Query for LLMs is also natural language. And you can think of the generation step as similar to Retrieval because most of the data that Large Language Models are trained on is public data from the internet. At each instance the model is retrying it's best to complete the document instead of fetching the document.