Build Your Own Auto-GPT Apps with LangChain (Python Tutorial)

Introduction to Lang Chain Library using Python

In this video, the speaker introduces the Lang Chain Library and explains how it can be used to develop applications using large language models. The speaker also discusses the benefits of learning this framework for data scientists and AI engineers.

What is Lang Chain?

• Lang Chain is a framework for developing applications powered by large language models like OpenAI's GPT models.

• It allows your application to become data aware and agentic, meaning that you can connect a language model to other data sources and allow it to interact with its environment.

• Using these pre-trained large language models for small businesses or even for large businesses will be a much more predictable way of doing AI projects because the model is already there.

Benefits of Learning Lang Chain

• Learning this framework can provide many great opportunities for data scientists and AI engineers.

• Using these large language models can be a more predictable way of doing AI projects, making it easier to work with smaller businesses or on larger machine learning projects.

• Understanding the underlying principles of this particular framework can set you up for many great opportunities in the future.

Modules in Lang Chain

• The modules in Lang Chain are listed in increasing order of complexity, starting with the model integrations that are supported.

• Other modules include tools for processing text, generating responses, and managing conversations.

Quick Start Guide

• The speaker provides an example from the quick start guide within VS Code to give viewers an idea of what it looks like in code and how it can be used.

• The Lang Chain Library has a GitHub page available for this project that viewers can go to, which explains how to set up the environment and install the necessary API keys.

Introduction to LangChain

In this section, the speaker introduces LangChain and explains how it can be used to interact with large language models.

Initializing a Model

• To use LangChain, you first need to load a model.

• There are many models available, including GPT4 (which is currently on a waitlist).

• Once you have loaded your model, you can provide it with a prompt and receive a response from the API.

Prompts

• Prompts allow you to manage and optimize your prompts for interacting with language models.

• You can use templates to create prompts that include input variables.

• The PromptTemplate class allows you to ask for user information and insert it into the prompt.

Memory

• LangChain allows you to provide long-term and short-term memory for your intelligent app.

• This helps the app remember previous interactions with users.

Conversation Chains

• Conversation chains allow for sequences of large language model calls.

• You can initialize a model, start a conversation, and call the .predict() method on the conversation object.

• This allows for more complex interactions between users and intelligent apps.

Working With Your Own Data

In this section, the speaker discusses how LangChain can be used with your own data.

Indexes

• Language models are more powerful when combined with your own text data.

• LangChain provides tools such as document loaders, text splitters, vector stores, and retrievers for working with your own data.

Chains

• Chains go beyond a single large language model call and allow for sequences of calls.

• This allows for more complex interactions with your own data.

Example

• The speaker provides an example of how LangChain can be used to build smart applications for companies using their existing data.

• This demonstrates the power of LangChain when working with your own data.

Chaining Prompts and Agents

In this section, the speaker discusses how to chain prompts and agents together to create an application. They also introduce the concept of agents and tools.

Chaining Prompts Together

• The LLM Chain class can be imported from langchain.chains to chain prompts together.

• The model and prompt are provided as input parameters to set up the chain.

• Predefined prompts can be combined with user input to run a chain.

Agents

• Agents involve making decisions about which actions to take, taking that action, seeing an observation, and repeating until it's done.

• Tools such as Google searches, Wikipedia, and SERP API are supported out of the box.

• The GPT model is used by agents to assess which tool to use based on the prompt given.

• An example of using an agent is shown using a Pandas data frame agent optimized for question answering.

Using Tools with Agents

• Initialize an agent type and load tools to provide it with some tools.

• Based on the prompt given, the agent will pick the best tool to solve the problem automatically.

• An example query is shown where an agent is given access only to Wikipedia and math tools.

Building an Assistant with LangChain Library

In this section, the speaker explains how to use the LangChain library to build an assistant that can answer questions about a specific YouTube video. The LangChain library has document loaders, text splitters, and vector stores that are used to load documents and split them into smaller chunks.

Using Document Loaders

• The LangChain library has document loaders that make it easy to load certain documents such as Discord, Figma, Git, Notion, Obsidian PDFs, PowerPoints and YouTube.

• To get the transcript of a YouTube video using the document loader in Python code:

• Import the YouTube loader from document loaders

• Input the video URL

• Call the loader.load() method to get the transcript

• The transcript is returned as a long string within a list. Use page_content to access the actual transcript.

Using Text Splitters

• Large transcripts cannot be provided in full to API of large language models like GPT-3.5 Turbo because they have token limits.

• Text splitters can be used to split up large transcripts into smaller chunks.

• To split up a transcript using text splitter in Python code:

• Define text splitter object

• Call text_splitter.split_documents() method and input transcript object created earlier

• Split documents will return a list of split-up documents.

Conclusion

The LangChain library provides useful tools for loading and splitting up large documents like transcripts. These tools are essential when building assistants that rely on large language models like GPT-3.5 Turbo.

Creating a Database of YouTube Video Transcripts

In this section, the speaker explains how to use embeddings from OpenAI and the FAISS library to create a database of YouTube video transcripts that can be used to answer specific questions.

Using Embeddings and FAISS Library

• The embeddings from OpenAI are used to convert text into numerical representations called factors.

• The FAISS library is used for efficient similarity search.

• A similarity search is performed on the database using the user's query to find relevant pieces of information.

• A filter or lookup table is created to get only the information needed, which is then provided to a large language model with the user's question.

Creating a Database and Answering Questions

• The function "create DB from YouTube video URL" loads the transcript, splits it into chunks of 1000 tokens, and puts it into a vector database object.

• The function "get response from query" uses this database to answer specific questions by performing a similarity search on the database using the user's query. It returns K documents (default: 4).

• All documents are joined into one single string, which is then used to create a model using GPT 3.5 turbo model.

• A template for the prompt is defined based on the application being created. Factual information from the transcript is used to answer questions.

• By creating minor changes within this template, entirely different apps for all kinds of industries can be created.

Chaining Chat Prompts

• The chat function using the GPT 3.5 turbo model is used to chain all of this together.

• A system message prompt and a human message prompt are defined, with the former explaining what the AI agent should do and the latter providing the user's input.

• All of that is combined into a chat prompt, which is then run with a query and the docs.

Querying Video Transcripts with OpenAI's GPT-3

In this section, the speaker explains how to use OpenAI's GPT-3 to query video transcripts and get specific information from them.

Converting Video Transcript into Database Object

• The video transcript is converted into a database object.

• A query can be filled in and the get response from query function can be called to answer a specific question about the video transcript.

Example Queries

• An example of querying for information about AGI (Artificial General Intelligence).

• Another example of querying for information about the hosts of the podcast.

Retrieving Response and Docs

• The get response from query function not only returns the response but also provides access to the documents used to generate that response.

• This feature is useful for fact-checking or additional research.

Possibilities with GPT-3

• The possibilities of using GPT-3 are endless, such as creating a list of channels that talk about a specific topic and automatically processing their videos with these functions.

• It is an exciting opportunity for freelancers to work on AI projects.

Data Freelancer

• Data Freelancer is recommended as a resource for those interested in starting freelance projects but don't know where to start.

Systemizing Freelancing in Data

In this section, the speaker talks about the systems and models he has developed over the years to systemize freelancing in data. He also mentions that by signing up for Data Freelancer, one can become part of a community of other data professionals working on their freelancing career.

Developing Systems for Freelancing

• The speaker has developed systems and models over the years to systemize freelancing in data.

• These systems help ensure that one never runs out of clients and can make more money working on fun projects.

• By using these systems, one can create freedom in their work life.

Joining a Community of Data Professionals

• By signing up for Data Freelancer, one can become part of a community of other data professionals who are working on their freelancing career.

• This community is focused on achieving the same goals: making more money, working on fun projects, and creating freedom.

• The speaker describes it as feeling like hanging out with friends but with real business results.

Opportunities in AI Freelancing

In this section, the speaker talks about the amazing opportunities available right now in the world of AI freelancing. He encourages those interested to check out Data Freelancer.

Exploring AI Freelancing Opportunities

• There are amazing opportunities available right now in the world of AI freelancing.

• Those interested should consider exploring these opportunities further.

Check Out Data Freelancer

• If you're interested in taking advantage of these opportunities but don't know where to start, check out Data Freelancer.

• The link to sign up for the waitlist is provided in the video description.