Apache Answer Plugin: Elasticsearch Vector Search

This plugin enables semantic/vector search in Apache Answer using Elasticsearch with dense vector fields.

Prerequisites

Elasticsearch 8.0+ (with dense vector and kNN search support)
An OpenAI-compatible embedding API (e.g., OpenAI, Azure OpenAI, or any compatible provider)

Build Apache Answer with this plugin:

./answer build --with github.com/apache/answer-plugins/vector-search-elasticsearch

After enabling the plugin in the Admin UI (Admin > Plugins > Vector Search), configure the following fields:

Field	Description	Example
Endpoints	Comma-separated Elasticsearch URLs	`http://localhost:9200`
Username	Elasticsearch username (optional)	`elastic`
Password	Elasticsearch password (optional)	`changeme`
Embedding API Host	OpenAI-compatible API base URL	`https://api.openai.com`
Embedding API Key	API key for the embedding service	`sk-...`
Embedding Model	Model name for generating embeddings	`text-embedding-3-small`
Embedding Level	`question` embeds question + all answers + comments together; `answer` embeds each answer separately	`question`
Similarity Threshold	Minimum cosine similarity score (0-1). Default `0` means no filtering	`0.5`

Embedding dimensions are auto-detected from the configured model. No manual dimension configuration is needed.
On first configuration, the plugin creates an index answer_vector with a dense_vector field matching the detected dimensions and cosine similarity.
If the embedding model changes and produces different dimensions, the index is automatically deleted and recreated.
Uses Elasticsearch kNN search for vector similarity queries.
A full sync of all questions/answers is triggered when the plugin starts.