Introduction

DocuConvo is an innovative application that combines traditional documentation with conversational AI capabilities powered by GPT-3.5. This allows organizations to enhance their documentation search experience by enabling users to converse with the documentation.

How DocuConvo Works Internally

DocuConvo operates in the following steps:

1. Crawling Documentation Website:

   • Our application crawls the entire documentation website provided by the organization.

2. Creating Knowledge Base:

   • The crawled information is processed and converted into vector embeddings.
   • Vector embeddings are saved into the Pinecone vector database as an index.

3. Search Process:

   • When a search request is received from the organization's search bar, it is compared against the knowledge base using vector embeddings.
   • Similar vectors are passed to GPT3.5 as context, along with the search query.

Get Started

To create a knowledge base for their documentation website, organizations need to provide the following details:

1. Documentation Website URL:

   • Example: https://nextjs.org/docs

2. Documentation Website URL Match:

   • Example: https://nextjs.org/docs/**
   • This is a URL pattern that describes the structure of the documentation URLs. Use a wildcard (**) to capture variations.

3. CSS Selector for Main Text Content:

   • This selector helps identify the main content area of the documentation, increasing the accuracy of the context passed to GPT.

 Pinecone Details

 To store vector embeddings, ensuring complete ownership of your data:

1. Pinecone API Key
2. Pinecone Index Name
3. Pinecone Environment

 OpenAI API Key

 The last step is to enter the OpenAI API key, which will be used to generate responses for search queries with documentation context.

Usage

import { Docuconvo } from 'docuconvo'

const docuconvo = new Docuconvo({
  docuconvo_key: 'your-docuconvo-key'
})

const { answer, message, error } = await docuconvo.search(searchQuery)

Acknowledgments

DocuConvo draws inspiration from the BuilderIO/gpt-crawler project, GPT-Crawler focuses on crawling documentation websites to generate knowledge files for use with OpenAI assistants, DocuConvo takes it a step further by directly integrating the conversational search capability into the documentation website itself.

By combining the information retrieval capabilities of a web crawler with the natural language processing power of GPT-3.5, DocuConvo provides an immersive and interactive experience for users seeking information within documentation.

Contributors

A big thank you to the following contributors who have played a significant role in the development of DocuConvo: