Workflow · workflow
Building a first semantic search system with Milvus-lite and Cohere Embed — step-by-step guide with code
Traditional keyword-based search fails to understand query context and intent, returning irrelevant or decontextualized results when search terms do not exactly match the target content.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Load research paper dataset
Ten thousand research paper abstracts from the arxiv dataset are loaded as the searchable corpus.
Tools used
Milvus-liteCohere Embed
Outcome
The author built a working semantic search engine over 10,000 research paper abstracts that returns ranked, context-rich results using vector embeddings and approximate nearest neighbor search.
Results
Cost replaced$2.232
Grounding & classification
Source type: technical build writeup
7 fields verified against source quotes, 1 dropped as unverifiable.
enterprise searchknowledge searchknowledge basesource backedtools describedtechnical build writeup