Apache Answer Plugin: ChromaDB Vector Search

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

Prerequisites

ChromaDB instance (self-hosted)
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-chromadb

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

Field	Description	Example
ChromaDB Endpoint	ChromaDB HTTP API URL	`http://localhost:8000`
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 a collection answer_vector_embeddings with hnsw:space set to cosine.
Uses the ChromaDB REST API directly (no external Go SDK required).
ChromaDB cosine distance (0 = identical, 2 = opposite) is converted to a similarity score (1.0 = identical, 0.0 = opposite).
A full sync of all questions/answers is triggered when the plugin starts.

Using Docker:

docker run -p 8000:8000 chromadb/chroma

The REST API will be available at http://localhost:8000.