ai-powered-search
Repository for codebase associated with Manning Publications book AI-Powered Search and related Maven course
GraphCanon updated 1mo · GitHub synced 1mo
Decision brief
ai-powered-search is designed for developers and researchers interested in implementing advanced search techniques using machine learning models.
Good fit when
- When you require robust click models to enhance understanding of user interactions with search results
- For projects that aim to integrate generative search methods, allowing users to receive more dynamic and context-aware responses
Avoid when
- Not recommended if you are working on projects requiring direct integration with Elasticsearch, as this tool focuses more on general machine learning techniques
- May not be ideal for real-time production environments where immediate updates and high scalability in search operations are critical, due to its academic focus
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (2d since push)
- As of 1mo
- Provenance
- Not a fork · Personal account
- As of 1mo
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/treygrainger/ai-powered-searchSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Code related to topics such as click models, generative search, hybrid search methods, large language models, learning-to-rank techniques, and personalized search solutions.
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Jul 24, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Jul 24, 2026
- Languages
- jupyter notebook
Source: github.language · Jul 24, 2026
Categories
Tags
README
License
All code in this repository is open source under the Apache License, Version 2.0 (ASL 2.0), unless otherwise specified.
Note that when executing the code, it may pull additional dependencies that follow alternate licenses, so please be sure to inspect those licenses before using them in your projects to ensure they are suitable. The code may also pull in datasets subject to various licenses, some of which may be derived from AI models and some of which may be derived from web crawls of data subject to fair use under the copyright laws in the country of publication (the USA). Any such datasets are published "as-is", for the sole purpose of demonstrating the concepts in the book, and these datasets and their associated licenses may be subject to change over time.
For agents
This page has a .md twin and JSON over the API.