Apache Answer Plugin: Qdrant Vector Search

This plugin enables semantic/vector search in Apache Answer using Qdrant.

Prerequisites

Qdrant instance (self-hosted or Qdrant Cloud)
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-qdrant

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

Field	Description	Example
Qdrant Endpoint	Qdrant gRPC endpoint	`localhost:6334`
Qdrant API Key	API key for Qdrant authentication (optional for local instances)
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`

Note: This plugin uses a dual API key pattern -- one for Qdrant authentication and a separate one for the embedding API.

Embedding dimensions are auto-detected from the configured model. No manual dimension configuration is needed.
On first configuration, the plugin creates a collection answer_vector_embeddings with cosine distance metric.
If the embedding model changes and produces different dimensions, the collection is automatically deleted and recreated.
Uses deterministic UUIDs derived from object IDs for consistent upsert behavior.
Communicates with Qdrant via gRPC for high performance.
A full sync of all questions/answers is triggered when the plugin starts.

Using Docker:

docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

The gRPC endpoint will be localhost:6334 and the REST API at localhost:6333.