A lot of people have asked us for ideas of how they can leverage Large Language Models (LLMs) for their business applications. A common example is to use the native language comprehension capabilities of LLMs to find matching content. This makes LLMs an excellent tool for search!
In this video, ThinkNimble CTO William Huster demonstrates a prototype application that enables searching for job descriptions using an unstructured, English-language description of a job seeker.
The code for this demo can be found here:
– https://github.com/thinknimble/embeddings-search-demo
Chapters
00:00 Intro – Why Build an LLM-based Search Engine?
01:00 Demo of Searching Job Descriptions
01:46 What is an Embedding?
03:06 Search by Meaning, not Content
03:52 Search with Unstructured Data
05:10 How Search with Embeddings Works
06:01 Set Up Database, Data Models, and Data
08:33 Generating Embeddings for JDs
11:04 How the Search Code Works
12:05 Creative Ways to Use Search Results
12:37 Outro – Other Use Case Examples
13:40 Outro – Final Words
Technologies used in this demo:
– Django
– PostgreSQL + pgvector
– Python sentence-transformers library
Links and Resources:
– https://www.sbert.net/ – Sentence Transformers package for Python
– https://github.com/pgvector/pgvector
– https://www.djangoproject.com/
If you’re looking for a technical team to integrate AI into your business, email hello@thinknimble.com
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