AI-Powered Information Extraction and Matchmaking

This article explores using Large Language Models (LLMs) for information extraction from job seeker CVs and recommending suitable jobs. It leverages LlamaParse for document parsing and Pydantic for structured data extraction and validation, minimizing LLM hallucinations. The process involves: extracting key information (education, skills, experience), scoring skills based on their prominence in the CV, creating a job vector database, retrieving top job matches based on semantic similarity, and generating recommendations with explanations using an LLM.

A Streamlit application allows users to upload a CV (PDF), select LLMs (OpenAI's gpt-4o or open-source alternatives), and embedding models. The application then extracts the candidate's profile, computes skill scores (displayed as a star rating), and provides top job recommendations with explanations. The code uses OpenAI's API for the gpt-4o LLM and text-embedding-3-large embedding model, but offers flexibility to use open-source alternatives with a CUDA-enabled GPU.

The article details the Pydantic models for structured data extraction, showcasing their use in validating LLM output and ensuring data consistency. It explains the process of creating a job vector database from a curated JSON dataset (sample_jobs.json) and using cosine similarity to score skills based on their semantic relevance within the CV. The final job recommendations are generated using a Retrieval-Augmented Generation (RAG) approach, combining the extracted profile information with relevant job descriptions from the vector database.

The Streamlit application displays the extracted profile information (name, email, age, education, skills, experience) and the top job matches, including company details, job descriptions, location, employment type, salary range (if available), URL, and a brief explanation of the match. The skill scores are visually represented using a star rating system.

The article concludes by suggesting areas for improvement and expansion, including: improving the job database ingestion pipeline, expanding the profile information extracted from CVs, refining the skill-scoring method, extending the application to match job ads with candidate profiles, testing the application with diverse CV formats, and providing CV improvement and upskilling recommendations. The complete code is available on GitHub. The author encourages readers to clap, comment, and follow them on Medium and LinkedIn.

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