![Vector Databases Fundamentals to Production [2026 Edition]](https://img-c.udemycdn.com/course/750x422/6023278_65cf_4.jpg)
Master Pinecone, Chroma & pgvector for RAG Applications | LangChain Integration, Hybrid Search, Production Deployment
Instruction spans local development with Chroma, managed cloud solutions via Pinecone, and PostgreSQL extensions using pgvector.
The curriculum includes the mathematics behind vectors, embeddings, and similarity metrics such as Cosine Similarity and Dot Product.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This screenshot shows a VS Code environment where a Python script defines text documents and executes to retrieve results from a vector database, displaying calculated distances.
This diagram illustrates the Chroma database workflow, showing how queries are embedded and matched against stored documents before being passed to an LLM context window.
This IDE view demonstrates the Python syntax for defining document objects and using the upsert method to ingest text data into a ChromaDB collection.
Learners have noted a soft speaking volume and code windows that are excessively zoomed in, requiring horizontal scrolling. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
One signal suggests that slides in Section 2 may be outdated; note that the April 2026 update label does not guarantee a correction of this specific issue. Other reports indicate a need to verify demo code against the repository.
Aligns with the needs of developers and data scientists interested in vector databases and RAG.
