Build Your Own Chatbot System with Text Embedding and Matching Engine

Build Your Own Chatbot System with Text Embedding and Matching Engine

Table of Contents:

  1. Introduction
  2. What is a Chatbot System?
  3. Text Embedding for Text API
  4. Matching Engine: Lang Chain
  5. Using Vex AI Form API for Document Search
  6. Retriever and Generator Mechanisms
  7. Advantages of Large Language Models (LLMs)
  8. Overview of the Chatbot System
  9. Setting up the Environment for Python
  10. Authenticating with Google Cloud Platform
  11. Installing Vex AI SDK and Dependencies
  12. Initializing the Lang Chain Model
  13. Creating the Matching Engine Index Endpoint
  14. Injecting and Loading PDF Documents
  15. Embedding Text and Metadata into the Vector Store
  16. Validating Semantic Search with Matching Engine
  17. Configuring Question and Answering Chain
  18. Customizing the Retrieval Prompt Format
  19. Configuring Red Question and Answer Retrieval
  20. Enabling Web Browsing OpenAI System
  21. Asking Questions and Retrieving Information
  22. Creating Personal Chatbots using Vex AI API
  23. Conclusion

🤖 Article: Chatbot System Using Text Embedding for Text API and Matching Engine: Lang Chain

In this article, we will explore the concept of a chatbot system implemented in a notebook. We will focus on utilizing text embedding for the Text API and a matching engine called Lang Chain. Additionally, we will employ the Vex AI Form API to search for documents and generate responses based on user input. By combining these elements, we can build a chatbot that can search for the most Relevant responses using vector-based matching. Let's dive into the details of each component step by step.

1. Introduction

Chatbots have become an increasingly popular tool for providing automated customer support and generating responses based on user input. In this article, we will discuss the implementation of a chatbot system using text embedding for the Text API and a matching engine called Lang Chain. By leveraging the capabilities of Vex AI and other related technologies, we can create a powerful chatbot that can retrieve information from a document and provide appropriate responses to user queries.

2. What is a Chatbot System?

Before delving into the technical aspects, let's first understand what a chatbot system is. A chatbot system is a computer program designed to simulate human conversation through text or voice interactions. It utilizes artificial intelligence techniques to understand and respond to user queries in a natural language format. The goal of a chatbot system is to provide prompt and accurate information to users without the need for human intervention.

3. Text Embedding for Text API

Text embedding is a technique that converts text data into numerical representations, often in the form of vectors. These representations capture the semantic meaning of the text and enable algorithms to perform tasks such as similarity search and pattern matching. In our chatbot system, we will use text embedding to transform user queries and document contents into vector representations that can be used for efficient retrieval of relevant information.

4. Matching Engine: Lang Chain

Lang Chain is a matching engine that operates on text embeddings. It allows for efficient retrieval of the most similar vectors based on a given query. Lang Chain is particularly useful for question answering systems as it can quickly find the most relevant information from a large document corpus. By utilizing Lang Chain as the core of our chatbot system, we can ensure accurate and efficient retrieval of answers to user queries.

5. Using Vex AI Form API for Document Search

To facilitate document search and retrieval in our chatbot system, we will utilize the Vex AI Form API. This API allows us to search for documents based on user queries and retrieve the most relevant information from them. By combining the power of Vex AI's text matching engine with the form API, we can provide accurate and contextually appropriate responses to user queries.

6. Retriever and Generator Mechanisms

In our chatbot system, we will employ two essential mechanisms: the retriever and the generator. The retriever is responsible for retrieving relevant questions based on the related text, while the generator generates answers based on the information stored in the backend document. These mechanisms work HAND in hand to ensure that our chatbot can provide accurate and informative answers to user queries.

7. Advantages of Large Language Models (LLMs)

Large language models (LLMs) play a crucial role in the success of modern chatbot systems. LLMs have improved both quantitatively and qualitatively, making them readily available and highly effective. By leveraging the power of LLMs and incorporating Vex AI's text embedding and matching engine, we can enhance our chatbot system's performance and ensure accurate responses to user queries.

8. Overview of the Chatbot System

Before diving into the implementation details, let's take a high-level look at the flow of our chatbot system. First, we inject a PDF document and split it into smaller chunks. These chunks are then transformed into embedded vectors and indexed using Vex AI's matching engine. When a user enters a query, the system matches the query against the vector store and retrieves the most relevant answers. This combination of document processing, text embedding, and matching engine forms the core of our chatbot system.

9. Setting up the Environment for Python

To begin implementing our chatbot system, we need to set up the Python environment. We will install the necessary dependencies and packages, such as the Vex AI SDK and the Google Cloud Platform. These tools will provide us with the required libraries and APIs to work with Vex AI and perform text embedding and retrieval operations.

10. Authenticating with Google Cloud Platform

To access the resources and services provided by Google Cloud Platform, we need to authenticate our account. This authentication process ensures secure and authorized access to the necessary APIs and functionalities. Once authenticated, we can proceed with the installation and configuration of the required components for our chatbot system.

11. Installing Vex AI SDK and Dependencies

To work with Vex AI and its related components, we need to install the Vex AI SDK and other dependencies. These packages will provide us with the necessary tools to utilize Vex AI's text embedding and matching engine. Once installed, we can proceed with initializing the Lang Chain model and configuring the matching engine for our chatbot system.

12. Initializing the Lang Chain Model

In our chatbot system, we will make use of the Lang Chain model for text embedding and matching operations. By initializing the Lang Chain model, we can leverage its pre-trained capabilities and integrate it seamlessly into our chatbot system. This step is crucial for ensuring accurate and efficient retrieval of relevant information from the document corpus.

13. Creating the Matching Engine Index Endpoint

To facilitate retrieval of relevant information from the document corpus, we need to establish a matching engine index endpoint. This endpoint acts as a vector-based database, enabling us to store and retrieve high-dimensional vectors based on their relevance to a given query. By creating the index endpoint, we can ensure efficient and accurate retrieval of answers to user queries.

14. Injecting and Loading PDF Documents

To provide our chatbot system with a corpus of documents to search and retrieve information from, we need to inject and load PDF documents. We can obtain a list of research PDFs from a reliable source and copy them to a designated Google Cloud Storage bucket. This step sets the foundation for the retrieval process and enables us to proceed with embedding the text and metadata into the vector store.

15. Embedding Text and Metadata into the Vector Store

In this step, we will embed the text and metadata from the PDF documents into the vector store. This process involves transforming the document chunks into embedded vectors using the Vex AI embedding API. Once embedded, the vectors are added to the index with streaming updates, ensuring that the vector store is up-to-date and capable of providing accurate responses to user queries.

(Continued in the article...)

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