Build a Semantic Job Search Engine with Bright Data, LanceDB, and Cohere

A tutorial demonstrates how to build and evaluate a semantic job-search engine using 200 LinkedIn postings collected through Bright Data, Cohere embeddings and reranking, and LanceDB as a local vector database. It contrasts exact-match keyword search with vector search, which retrieves jobs based on meaning, and shows how structured fields such as salary, seniority, and employment type can be applied as SQL-style filters alongside semantic queries. The pipeline includes asynchronous scraping, salary normalization, incremental upserts, full-text and vector indexing, optional hybrid search with reranking, a Streamlit interface, and LanceDB version snapshots for historical data access. In an evaluation across 10 hand-labeled queries, vector search achieved average precision@3 of 70%, compared with 43% for keyword search and 67% for hybrid search with reranking, suggesting that vector-only retrieval was adequate for this small dataset while hybrid approaches may become more useful at larger scale. The example project reportedly costs about $0.34 to run, identifies implementation caveats involving API behavior, scraped error rows, salaries, and UI rendering, and presents the architecture as reusable for other scraped domains such as e-commerce, real estate, news, and business prospecting.

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