A conversational search engine is a type of search interface that allows users to interact with a search system using natural language conversations. This type of search engine can interpret user queries, understand their intent, and respond with relevant results. The project “https://github.com/pingcap/autoflow/” utilizes several key technologies and dependencies to implement a conversational search engine.
The Big Picture
The conversational search engine in this project is designed to provide a fast, efficient, and user-centric search experience. The system is optimized for performance, with a response time of less than 100 milliseconds to ensure a smooth user experience. The architecture is composed of several backend services that are optimized for specific types of results.
Design Philosophy
The conversational search engine is designed to be AI-proof, with the ability to understand user queries and provide relevant results. The system is also designed to be customizable, allowing developers to integrate external data sources and tailor the search experience to their specific needs.
Programming Languages
The conversational search engine is built using several programming languages, including TypeScript, Python, and JavaScript. These languages were chosen for their performance, scalability, and ease of use.
TiDB
TiDB is a distributed SQL database that is used in this project to store and manage data. TiDB is designed to be highly available, scalable, and consistent, making it an ideal choice for a conversational search engine.
LlamaIndex
LlamaIndex is a library for building and using large language models. It is used in this project to provide natural language processing capabilities to the conversational search engine.
DSPy
DSPy is a library for building and using deep learning models. It is used in this project to provide machine learning capabilities to the conversational search engine.
Next.js
Next.js is a framework for building server-rendered React applications. It is used in this project to provide a fast and efficient user interface for the conversational search engine.
shadcn/ui
shadcn/ui is a library of reusable UI components for React. It is used in this project to provide a consistent and user-friendly interface for the conversational search engine.
Redis
Redis is an in-memory data structure store that is used in this project to provide fast and efficient caching capabilities.
FastAPI
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is used in this project to provide a fast and efficient backend for the conversational search engine.
Nginx
Nginx is a web server and reverse proxy server that is used in this project to provide a fast and efficient frontend for the conversational search engine.
Supervisor
Supervisor is a client/server system that allows its users to control a number of processes on UNIX-like operating systems. It is used in this project to manage and monitor the conversational search engine.
JSON-file
JSON-file is a simple and lightweight file format for storing data. It is used in this project to store and manage configuration data for the conversational search engine.
Capabilities
The conversational search engine in this project is capable of handling complex questions and natural language queries. The system uses natural language processing and machine learning algorithms to interpret user queries and provide relevant results.
External Data Sources
The conversational search engine can be integrated with external data sources, allowing developers to extend its capabilities and provide customized search experiences. For example, a plugin can be developed to index and search data from a specific data source, such as a database or an API.
Customization
The conversational search engine is designed to be customizable, allowing developers to tailor the search experience to their specific needs. Developers can integrate external data sources, customize the search UI, and refine search queries to meet their requirements.
Examples
Here are some examples of how the conversational search engine can be used:
- A developer can use the conversational search engine to search for code snippets, documentation, and other resources in their software catalog.
- A user can use the conversational search engine to search for answers to questions, such as “How do I reset my password?” or “What is the return policy for this product?”
- A team can use the conversational search engine to search for documents, files, and other resources that are relevant to their project.
Sources
- https://engineering.fb.com/2010/05/17/web/the-life-of-a-typeahead-query/
- https://www.educative.io/blog/embracing-change-ai-proof-your-career
- https://backstage.io/docs/features/search
- https://backstage.io/docs/plugins/integrating-search-into-plugins
- https://opensource.com/article/18/10/conversational-interface-design-and-usability
- https://engineering.fb.com/2020/10/09/data-infrastructure/nemo
- https://opensource.com/education/16/7/intro-widernet-project
- https://engineering.fb.com/2018/10/02/ml-applications/under-the-hood-facebook-marketplace-powered-by-artificial-intelligence
- https://grafana.com/blog/2023/07/25/lessons-learned-from-integrating-openai-into-a-grafana-data-source
- https://backstage.io/docs/features/search/architecture
- https://sweetcode.io/bert-vs-ernie-the-natural-language-processing-revolution
- https://backstage.io/blog/2021/06/24/announcing-backstage-search-platform
