How to Build an AI Document Chatbot in 10 Minutes

Dave Ebbelaar
26 May 202316:09

TLDRThis video tutorial demonstrates how to rapidly integrate chat GPT with company data using Flowwise, a visual UI Builder that simplifies the creation of large language model apps. The presenter guides viewers through setting up Flowwise, obtaining API keys from OpenAI and Pinecone, and then constructing a conversational AI capable of answering questions about proprietary data. The process is streamlined with the use of Langchain, enabling quick prototyping and scalability. The video also showcases additional features, such as document loaders and embedding capabilities, and concludes with embedding the chatbot into a Python environment for further interaction.

Takeaways

  • πŸ˜€ Flowwise is a visual UI Builder that simplifies the creation of large language model apps within minutes.
  • πŸ”§ It is an open-source tool, allowing users to download and use it directly from the GitHub repository.
  • πŸ“š The tool is built on top of Langchain, a powerful framework for developing applications with large language models.
  • πŸ”‘ To use Flowwise, you need an OpenAI API key and a Pinecone API key, both of which can be set up for free.
  • πŸ’» Flowwise can be started using either npm or Docker, with Docker offering more flexibility in terms of port usage.
  • πŸ“ The video demonstrates how to clone the Flowwise repository and set it up locally using Docker.
  • πŸ—‚οΈ Flowwise allows users to build conversational AI that can answer questions about their own data by integrating various building blocks.
  • πŸ“‘ The tutorial includes setting up API keys for OpenAI and Pinecone, and creating a document chatbot using a text file.
  • 🧐 The video also shows how to use Flowwise for building a conversational AI with internet access and memory capabilities.
  • πŸ” Flowwise can be used for rapid prototyping of AI applications, allowing for quick testing and validation of ideas.
  • πŸ”— The tool can be embedded into other applications using Python, and there's potential for deploying it to a cloud server for real-world use.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is to demonstrate how to build an AI document chatbot using Flowwise, a visual UI Builder, in just 10 minutes.

  • What is Flowwise and why is it significant?

    -Flowwise is an open-source visual UI Builder that allows users to quickly build large language model applications. It is significant because it enables rapid prototyping and testing of AI applications with a user-friendly visual interface.

  • What are the prerequisites to follow along with the tutorial?

    -To follow along with the tutorial, one needs an OpenAI API key, which is free to set up but requires a credit card for potential charges, and a Pinecone API key, which is also free and does not require a credit card.

  • How does one get started with Flowwise?

    -To get started with Flowwise, one needs to visit the Flowwise GitHub repository, clone the repository, and then either use npm or Docker to start the application locally.

  • What is the role of Docker in setting up Flowwise?

    -Docker is used to run Flowwise in a containerized environment, which provides flexibility and allows the user to specify the port for the local server to run on.

  • How does Flowwise help in building a conversational AI?

    -Flowwise provides a visual interface to connect building blocks, such as text splitters, OpenAI blocks for chat and embeddings, and Pinecone configuration, to create a conversational AI that can answer questions about your own data.

  • What is the purpose of the text splitter in the Flowwise application?

    -The text splitter is used to chunk documents into smaller parts so that they can be fed to the AI without surpassing the token limit.

  • What is the significance of Langchain in the context of Flowwise?

    -Langchain is the underlying technology that powers Flowwise, allowing users to build large language model applications quickly and efficiently through a visual builder.

  • How can the chatbot created with Flowwise be tested?

    -The chatbot can be tested by opening the chat interface in the Flowwise application and asking questions to see if it can provide accurate answers based on the uploaded documents.

  • What are some of the additional tools and document loaders available in Flowwise?

    -Flowwise offers additional tools such as a calculator, internet search capabilities, and document loaders for various formats like CSV, docx, PDF, and more.

  • How can the Flowwise application be integrated into custom code?

    -Flowwise applications can be embedded into custom code through the provided Python integration, allowing users to incorporate the chatbot functionality into their own projects.

Outlines

00:00

πŸ”§ Integrating ChatGPT with Company Data Using Flowwise

The video introduces Flowwise, a visual UI Builder for creating large language model apps quickly. The presenter will demonstrate how to set up Flowwise and build a conversational AI that can answer questions based on company data. Flowwise is open-source and utilizes LangChain, allowing rapid prototyping of AI apps. An OpenAI API key and a Pinecone API key are required for the tutorial. The first step involves cloning the Flowwise repository from GitHub and setting up either npm or Docker for installation.

05:00

βš™οΈ Setting Up Flowwise with Docker

The presenter explains the steps to set up Flowwise using Docker. After cloning the repository, users should rename the .env.example file to .env and change the port if necessary. The Docker setup process includes running Docker Compose to start a local server. Once Docker is running, users can access Flowwise through a browser at the specified local host port. The video demonstrates building a conversational retrieval QA chain using a pre-built template from the Flowwise marketplace, configuring it with OpenAI and Pinecone API keys, and uploading a text file to start chatting with data.

10:01

🧠 Building a Conversational Agent with Memory

The video showcases another example from Flowwise: a conversational AI agent with memory and tool integration, including a calculator and internet access via SerpAPI. The agent can remember previous interactions, enhancing its conversational abilities. The presenter demonstrates asking questions and performing calculations, showing the agent's capability to retain and use information from prior prompts. The video also highlights the flexibility of Flowwise in chaining various tools and functionalities, making it a robust platform for experimenting with AI applications.

15:03

πŸ“‚ Embedding and Extending Flowwise with Python

The presenter introduces the embedding feature in Flowwise, showing how to embed the application in HTML and Python. A Python example involves creating a simple script to interact with Flowwise's API. The video emphasizes the current lack of documentation for Flowwise but provides tips for integrating memory and input keys in custom applications. The presenter concludes by discussing the potential of Flowwise for rapid prototyping and testing of AI components, highlighting its role in the presenter's workflow for AI projects. The video ends with a call to action for viewers to like and subscribe, and to check out additional content on selling AI services.

Mindmap

Keywords

πŸ’‘Chatbot

A chatbot is an AI-powered computer program designed to simulate conversation with human users. In the context of the video, a chatbot is being built to integrate with a company's data and answer questions about it. The script mentions building a 'document chatbot' which utilizes large language models to process and retrieve information from documents.

πŸ’‘Flowwise

Flowwise is a visual UI Builder introduced in the video, which allows users to build applications for large language models in a short amount of time. It is used to create a conversational AI that can interact with company data. The script demonstrates how to set up Flowwise and use it to build a chatbot.

πŸ’‘Open Source

Open source refers to software whose source code is available to the public, allowing anyone to view, use, modify, and distribute the software. In the video, Flowwise is described as open source, meaning it can be downloaded directly from GitHub and used without cost for users to build their applications.

πŸ’‘Lang Chain

Lang Chain is mentioned as the underlying technology in the video. It is a framework for developing applications powered by large language models. The script explains that Flowwise uses Lang Chain to enable the quick creation of applications that can handle language tasks.

πŸ’‘API Key

An API key is a unique identifier used to authenticate requests to an API (Application Programming Interface). In the script, the presenter mentions needing an OpenAI API key and a Pinecone API key to set up and use the services required for the chatbot's functionality.

πŸ’‘Pinecone

Pinecone is a vector database service mentioned in the video. It is used for managing and performing operations on vectors, which are used in the context of the chatbot to store and retrieve document embeddings for similarity search.

πŸ’‘Document Loaders

Document loaders are components that can read and process various document formats. In the video, Flowwise is shown to have document loaders for formats like CSV, docx, and PDF, which are used to ingest and prepare data for the chatbot to understand and respond to queries.

πŸ’‘Embeddings

Embeddings in the context of the video refer to the process of converting text data into numerical vectors that can be understood and processed by AI models. The script explains using OpenAI to create embeddings from text, which are then used for similarity searches within Pinecone.

πŸ’‘Rapid Prototyping

Rapid prototyping is the fast creation of a working model of a product or service. The video emphasizes the ability of Flowwise to enable rapid prototyping of AI applications, allowing developers to quickly test and evaluate ideas, such as the document chatbot being built.

πŸ’‘Docker

Docker is a platform that allows developers to develop, ship, and run applications in containers. The script describes using Docker to run the Flowwise application, which provides flexibility and simplifies the deployment process.

πŸ’‘Conversational AI

Conversational AI refers to systems that can engage in a conversation with humans in a natural way. The video demonstrates building a conversational AI agent that can remember past interactions (using buffer memory) and utilize tools like a calculator or internet searches to provide responses.

Highlights

Flowwise is a visual UI Builder that enables the creation of large language model applications in minutes.

It is open source and can be downloaded directly from the GitHub repository.

Flowwise uses Lang chain under the hood, which is powerful for spinning up large language model apps.

An Open AI API key is required, which is free to set up but requires a credit card for minimal charges per query.

A Pinecone API key is also needed, which is free and does not require a credit card.

The tutorial involves cloning the Flowwise repository and setting up the project locally.

Docker or npm can be used to start the application, with Docker offering more flexibility.

A Dockerfile and docker-compose.yml are used to run the application in a Docker container.

Flowwise AI provides a visual interface to build conversational AI that can answer questions about your own data.

API keys and Pinecone configuration are necessary to set up the document chatbot.

The document chatbot uses a text splitter and Open AI blocks for chat and embeddings.

A simple txt file can be uploaded and used to test the chatbot's capabilities.

Flowwise allows for rapid prototyping of large language model applications.

The platform includes a marketplace with boilerplate examples to expedite the setup process.

Flowwise integrates with various document loaders, making it versatile for different file types.

The platform also supports embedding the application into other systems using HTML or Python.

Documentation for Flowwise is currently limited, which may pose a challenge for some users.

Flowwise can be used for testing and evaluating AI ideas before building custom solutions with Lang chain.

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