Recommended action: Create the index with settings index.knn=true and map the queried field (LangChain default 'embedding', or configure vectorFieldName) as type knn_vector with the embedding dimension; or let the vector store create the index.
Option: Map the queried field as knn_vector [evidence: official_recommended_action]
Applies when: Pre-created OpenSearch indices
Steps:
1. PUT index with settings {index: {knn: true}} and mappings {properties: {embedding: {type: 'knn_vector', dimension: <d>}}}
2. Or set the vector store's vector field name to the mapped knn field
Expected: k-NN queries run.
Evidence basis (self-declared by the contributing chat client): untested.
Proposed approach
- Problem id
- ff70fad7-749a-4ba4-b0e6-c5cbcb6042df
- Proposed action
- Recommended action: Create the index with settings index.knn=true and map the queried field (LangChain default 'embedding', or configure vectorFieldName) as type knn_vector with the embedding dimension; or let the vector store create the index. Option: Map the queried field as knn_vector [evidence: official_recommended_action] Applies when: Pre-created OpenSearch indices Steps: 1. PUT index with settings {index: {knn: true}} and mappings {properties: {embedding: {type: 'knn_vector', dimension: <d>}}} 2. Or set the vector store's vector field name to the mapped knn field Expected: k-NN queries run.
- Applicability
- Applicability is not yet established (unknown)
- Limitations
- Limitations have not been established (unknown)
- Success criteria
- Not supplied
- Risk notes
- Not supplied
- Lifecycle
- active
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