text-embeddings-inference

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

> what does the rag for uploaded files do in discourse? You can upload files that will act as RAG files for an AI bot. The bot can also have access to forum content, plus the ability to run tools in our sandboxed JS environment, making it possible for Discourse to host AI bots. > also, when i run a discourse search does it really do both a regular keyword search and a vector search? how do you combine results? Yes, it does both. In the full page search it does keyword first, then vector asynchronously, which can be toggled by the user in the UI. It's auto toggled when keyword has zero results now. Results are combined using reciprocal rank fusion. In the quick header search we simply append vector search to keyword search results when keyword returns less than 4 results. > does all discourse instances have those features? for example, int

Just migrated all embeddings to this same model a few weeks ago in my company, and it's a game changer. Having 32k context is a 64x increase when compared with our previous used model. Plus being natively multilingual and producing very standard 1024 long arrays made it a seamless transition even with millions of embeddings across thousands of databases. I do recommend using https://github.com/huggingface/text-embeddings-inference for fast inference.

Hugging Face does maintain a package named Text Embedding Inference (TEI) with GPU/CPU-optimized container images. While I have only used this for hosting embedding models, it does appear to support Roberta architecture classifiers (specifically sentiment analysis). https://github.com/huggingface/text-embeddings-inference You can always run a zero shot pipeline in HF with a simple Flask/FastAPI application.

Worth noting that this also impacts the great https://github.com/huggingface/text-embeddings-inference, which allows anyone to run state of the art embeddings with great performance.