I am reaching out to discuss an important challenge we are currently facing: matching 2 million resumes to 200 active job openings with a target accuracy of 100%. Given the scale of this task, we are exploring the best technologies and methodologies to achieve this goal effectively.

Problem Statement
We need an efficient and accurate system to match a vast number of resumes to a relatively smaller number of job descriptions. The primary objective is to streamline our recruitment process and ensure we are identifying the most suitable candidates for each position.

Goals
High Accuracy: Achieve at least 80% accuracy in matching resumes to job descriptions.
Scalability: Handle large volumes of data efficiently.
Automation: Minimize manual intervention and speed up the recruitment process.
Possible Technologies and Approaches
I would like to discuss and get your input on the following technologies and methods that could help us achieve our goals:

Natural Language Processing (NLP):

Text Preprocessing: Cleaning, tokenization, and normalization of text data.
Feature Extraction: Using TF-IDF, Word2Vec, GloVe, or BERT embeddings to convert text into numerical vectors.
Machine Learning Models:

Supervised Learning: Training models on labeled data to predict matches.
Unsupervised Learning: Clustering resumes and job descriptions to find natural groupings.
Deep Learning Models:

Transformer-based Models: Utilizing BERT, RoBERTa, or similar models for better contextual understanding and matching accuracy.
Similarity Measures:

Cosine Similarity: Measuring the cosine of the angle between two vectors to determine similarity.
Advanced Metrics: Exploring other similarity measures and custom metrics tailored to our data.
Request for Expertise
Have you worked on a similar problem or have experience with the technologies mentioned above? I would greatly appreciate your insights and any advice you might have on the best approach to achieve our target accuracy.

I have used Vector database and open ai embedding adda-002 model