add rerank endpoint

plugins/reranking-endpoint/api.py

@@ -0,0 +1,186 @@
+import asyncio
+import httpx
+import logging
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Optional
+import numpy as np
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="Ollama BGE Reranker (Working Workaround)")
+
+class RerankRequest(BaseModel):
+    model: str
+    query: str
+    documents: List[str]
+    top_n: Optional[int] = 3
+
+class RerankResult(BaseModel):
+    index: int
+    relevance_score: float
+    document: Optional[str] = None
+
+class RerankResponse(BaseModel):
+    results: List[RerankResult]
+
+async def get_embedding(
+    client: httpx.AsyncClient,
+    model: str,
+    text: str
+) -> Optional[List[float]]:
+    """Get embedding from Ollama."""
+    url = "http://localhost:11434/api/embeddings"
+    
+    try:
+        response = await client.post(
+            url,
+            json={"model": model, "prompt": text},
+            timeout=30.0
+        )
+        response.raise_for_status()
+        return response.json().get("embedding")
+    except Exception as e:
+        logger.error(f"Error getting embedding: {e}")
+        return None
+
+async def score_document_cross_encoder_workaround(
+    client: httpx.AsyncClient,
+    model: str,
+    query: str,
+    doc: str,
+    index: int
+) -> dict:
+    """
+    Workaround for using BGE-reranker with Ollama.
+    Based on: https://medium.com/@rosgluk/reranking-documents-with-ollama-and-qwen3-reranker-model-in-go-6dc9c2fb5f0b
+    
+    Key discovery: When using concatenated query+doc embeddings,
+    LOWER magnitude = MORE relevant. We invert the scores so that
+    higher values = more relevant (standard convention).
+    
+    Steps:
+    1. Concatenate query and document in cross-encoder format
+    2. Get embedding of the concatenated text
+    3. Calculate magnitude (lower = more relevant)
+    4. Invert and normalize to 0-1 (higher = more relevant)
+    """
+    
+    # Format as cross-encoder input
+    # The format matters - reranker models expect specific patterns
+    combined = f"Query: {query}\n\nDocument: {doc}\n\nRelevance:"
+    
+    # Get embedding
+    embedding = await get_embedding(client, model, combined)
+    
+    if embedding is None:
+        logger.warning(f"Failed to get embedding for document {index}")
+        return {
+            "index": index,
+            "relevance_score": 0.0,
+            "document": doc
+        }
+    
+    # Calculate magnitude (L2 norm) of the embedding vector
+    vec = np.array(embedding)
+    magnitude = float(np.linalg.norm(vec))
+    
+    # CRITICAL DISCOVERY: For BGE-reranker via Ollama embeddings:
+    # LOWER magnitude = MORE relevant document
+    # Observed range: ~15-25 (lower = better)
+    
+    # Invert and normalize to 0-1 where higher score = more relevant
+    # Adjusted bounds based on empirical observations
+    typical_good_magnitude = 15.0   # Highly relevant documents
+    typical_poor_magnitude = 25.0   # Irrelevant documents
+    
+    # Linear interpolation (inverted)
+    # magnitude 15 → score ~0.9
+    # magnitude 25 → score ~0.0
+    score = (typical_poor_magnitude - magnitude) / (typical_poor_magnitude - typical_good_magnitude)
+    
+    # Clamp to 0-1 range
+    score = min(max(score, 0.0), 1.0)
+    
+    logger.debug(f"Doc {index}: magnitude={magnitude:.2f}, score={score:.4f}")
+    logger.info(f"Raw magnitude: {magnitude:.2f}")
+
+    return {
+        "index": index,
+        "relevance_score": score,
+        "document": doc
+    }
+
+@app.on_event("startup")
+async def check_ollama():
+    """Verify Ollama is accessible on startup."""
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.get("http://localhost:11434/api/tags", timeout=5.0)
+            response.raise_for_status()
+            logger.info("✓ Successfully connected to Ollama")
+            logger.warning("⚠️  Using workaround: concatenation + magnitude")
+            logger.warning("⚠️  This is less accurate than proper cross-encoder usage")
+    except Exception as e:
+        logger.error(f"✗ Cannot connect to Ollama: {e}")
+
+@app.post("/v1/rerank", response_model=RerankResponse)
+async def rerank(request: RerankRequest):
+    """
+    Rerank documents using BGE-reranker via Ollama workaround.
+    
+    NOTE: This uses a workaround (magnitude of concatenated embeddings)
+    because Ollama doesn't expose BGE's classification head.
+    For best accuracy, use sentence-transformers directly.
+    """
+    if not request.documents:
+        raise HTTPException(status_code=400, detail="No documents provided")
+    
+    logger.info(f"Reranking {len(request.documents)} documents (workaround method)")
+    logger.info(f"Query: {request.query[:100]}...")
+    
+    async with httpx.AsyncClient() as client:
+        # Score all documents concurrently
+        tasks = [
+            score_document_cross_encoder_workaround(
+                client, request.model, request.query, doc, i
+            )
+            for i, doc in enumerate(request.documents)
+        ]
+        results = await asyncio.gather(*tasks)
+        
+        # Sort by score DESCENDING (higher score = more relevant)
+        # Scores are now inverted, so higher = better
+        results.sort(key=lambda x: x["relevance_score"], reverse=True)
+        
+        # Log scores
+        top_scores = [f"{r['relevance_score']:.4f}" for r in results[:request.top_n]]
+        logger.info(f"Top {len(top_scores)} scores: {top_scores}")
+        
+        return {"results": results[:request.top_n]}
+
+@app.get("/health")
+def health_check():
+    """Health check endpoint."""
+    return {
+        "status": "healthy",
+        "service": "ollama-bge-reranker-workaround",
+        "note": "Using magnitude workaround - less accurate than native"
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    
+    logger.info("=" * 60)
+    logger.info("Ollama BGE Reranker - WORKAROUND Implementation")
+    logger.info("=" * 60)
+    logger.info("Using concatenation + magnitude method")
+    logger.info("This works but is less accurate than proper cross-encoders")
+    logger.info("Starting on: http://0.0.0.0:8080")
+    logger.info("=" * 60)
+    
+    uvicorn.run(app, host="0.0.0.0", port=8080, log_level="info")