[cckolon]

This is really cool. How is reranking built in? Is there a model that runs inside the database? If so, how did you choose it?

[svcrunch]

Thanks for your interest. The rerankers are external, GoodMem is a unified API layer that calls out to various providers. There's no model running inside the database or the GoodMem server.

We support both commercial APIs and self-hosted options:

  - Cohere (rerank-english-v3.0, etc.)
  - Voyage AI (rerank-2.5)
  - Jina AI (jina-reranker-v3)

Self-hosted (no API key needed):

  - TEI - https://github.com/huggingface/text-embeddings-inference
  - vLLM - https://docs.vllm.ai/en/v0.8.1/serving/openai_compatible_server.html#rerank-api

You register a reranker once with the CLI:

  # Cohere
  goodmem reranker create \
    --display-name "Cohere" \
    --provider-type COHERE \
    --endpoint-url "https://api.cohere.com" \
    --model-identifier "rerank-english-v3.0" \
    --cred-api-key "YOUR_API_KEY"

  # Self-hosted TEI (e.g., BAAI/bge-reranker-v2-m3)
  goodmem reranker create \
    --display-name "TEI Local" \
    --provider-type TEI \
    --endpoint-url "http://localhost:8081" \
    --model-identifier "BAAI/bge-reranker-v2-m3"

Then you can experiment interactively through the TUI.

  goodmem memory retrieve \
    --space-id <your-space> \
    --post-processor-interactive \
    "your query"

For your setup, I think TEI is probably the path of least resistance, it has first-class reranker support and runs well on CPU.

[cckolon]

Nice, that’s really cool.