Hands-On: Building a Semantic Search Pipeline
Explore building a semantic search pipeline that converts text into embeddings, stores vectors in ChromaDB, and queries with cosine similarity. Understand how hybrid retrieval with metadata filters improves search relevance and learn multi-tenancy strategies for data isolation in production systems.
Traditional keyword search breaks down when users describe problems in their own words. A customer types “my order never arrived,” but the relevant article in your knowledge base is titled “Shipping Delay Policy.” No keyword overlap exists between the query and the document, so a traditional search engine returns nothing useful. Semantic search solves this by comparing the meaning of text rather than matching individual tokens. It converts both documents and queries into numerical vectors (embeddings) that capture semantic intent, then finds the closest vectors in a high-dimensional space.
In this hands-on lab, you will build a complete semantic search pipeline from scratch. You will embed a small document corpus using OpenAI’s embedding model, store those vectors in ChromaDB, query them with cosine similarity, layer metadata filters for hybrid-style retrieval, and implement two chromadb client library, and the OpenAI embeddings API.
The following diagram illustrates the end-to-end architecture you are about to build.
Embedding and storing documents
Preparing the corpus and generating embeddings
The first phase of any semantic search pipeline is converting raw text into vectors that a database can index. You will work with a small corpus of eight knowledge-base articles, each tagged with metadata like category and tenant_id. These metadata fields are not decorative. They become the foundation for filtered retrieval and tenant isolation later in the lab.
You will generate ...