Jina Embedding & Reranker Server

OpenAI-compatible API server for Jina embeddings and reranking models.

Features

• Embedding: jina-embeddings-v5-text-small (with LoRA task adapters)
• Reranking: jina-reranker-v3
• OpenAI-compatible API: Works with existing OpenAI client libraries
• Task-adaptive embeddings: Switch LoRA adapter per request (retrieval, text-matching, clustering, classification)

Installation

uv sync

Fetch_Models.bat  #This is for windows, it contains two 'hf download' commands, adapt for linux accordingly

Usage

Start the server:

uv run jina_server.py

Or activate the virtual environment:

uv venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
python jina_server.py

API Endpoints

POST /v1/embeddings

Create embeddings for input text. Supports task-adaptive LoRA adapters.

Parameters

Parameter	Type	Default	Description
input	string | string[]	(required)	Text(s) to embed
task	string	"retrieval"	Task adapter: retrieval, text-matching, clustering, classification
prompt_name	string	null	Required when task="retrieval": "query" or "document"
model	string	"jina-embeddings-v5-text-small"	Model name
batch_size	int	32	Processing batch size (1–128)

Examples

Retrieval (query side):

curl http://localhost:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": "What is machine learning?", "task": "retrieval", "prompt_name": "query"}'

Retrieval (document side):

curl http://localhost:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": "Machine learning is a field of AI...", "task": "retrieval", "prompt_name": "document"}'

Text matching:

curl http://localhost:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["Hello", "Hi"], "task": "text-matching"}'

Classification:

curl http://localhost:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": "This product is great!", "task": "classification"}'

Clustering:

curl http://localhost:8000/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": ["neural networks", "monetary policy"], "task": "clustering"}'

POST /v1/rerank

Rerank documents based on query relevance.

curl http://localhost:8000/v1/rerank \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is machine learning?",
    "documents": ["Doc 1", "Doc 2"],
    "top_n": 3
  }'

GET /v1/models

List available models.

GET /

Health check endpoint.

Testing

Run the test suite:

uv run test_server.py

WIP:

I'm telling my opencode to implement avx512 for my 9900x rig, brb

License

MIT