Description
I’m currently investigating some discrepancies in behaviors between Weaviate’s implementation of a pure vector similiarity search using HNSW + cosine distance metrics (e.g. the defaults according to docs) and a rival database (Redis) using a 3rd party integration library (LangChain).
I just wanted to confirm Weaviate’s baked-in behavior utilizing the text2VecCohere
integrations described here for managing Cohere embedding API calls.
Could someone confirm that input_type
is correctly being populated as search_document
when vectorizing new objects in the database and search_query
when a nearText
search is being performed?
I’ve dug through all the Cohere-related integration docs for Weaviate I could find as well as the client library but didn’t see any indications that I could specify input types for search/addition embedding operations. If there’s any integration docs I missed that explain this my apologies!
Server Setup Information
- Weaviate Server Version: 1.26.1
- Deployment Method: k8s
- Number of Running Nodes: 3
- Client Language and Version: Typescript 3.1.4