Workflow · Production
Building a first semantic search system with Milvus-lite and Cohere Embed — step-by-step guide with code
The problem
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.
Workflow diagram · grounded in source
1
Load research paper dataset
integration
“I gathered a collection of research papers and abstracts”
2
Embed document abstracts
ai_action
“I called Cohere's client since I used Cohere's new version of the Embed model”
3
Create ANN index in Milvus
integration
“the choice was to implement the IVF_FLAT (Inverted File Index) because it offers faster search times, particularly with high-dimensional data like that used in this project”
4
User submits search query
trigger
“I provided the list of search terms, call the embed function, and wait for the results”
5
Embed search query
ai_action
“input_type, where you tell the function whether you are embedding a search document or a search query. This additional parameter ensures the highest quality of the user search query and performs more efficiently”
6
Ranked results returned
output
“The model returns the similarity score, along with the results, and ranks the results accordingly”
Reported 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.
Reported metrics
Embedding cost for 10,000 abstracts$2.232
Reported stack
Milvus-liteCohere Embed
Source
https://mlops.community/blog/how-to-build-your-first-semantic-search-system-my-step-by-step-guide-with-code
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
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.
What tools did this team use?
Milvus-lite, Cohere Embed.
What results were reported?
Embedding cost for 10,000 abstracts: $2.232 (source-reported, not independently verified).
How is this workflow AI workflow structured?
Load research paper dataset → Embed document abstracts → Create ANN index in Milvus → User submits search query → Embed search query → Ranked results returned.