add rerank endpoint

2026-01-20 20:44:48 +00:00
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+# Ollama Reranker Workaround
+
+> **⚠️ Important:** This is a **workaround/hack**, not a proper solution. It exploits an undocumented behavior of embedding magnitudes and should be used with caution.
+
+A FastAPI service that provides document reranking using Ollama's embedding endpoint. This exists because Ollama does not natively support a `/api/rerank` endpoint for cross-encoder reranker models.
+
+## The Problem
+
+Cross-encoder reranker models (like BGE-reranker-v2-m3) are designed to score query-document pairs for relevance. However:
+
+- **Ollama has no `/api/rerank` endpoint** - reranker models can't be used as intended
+- **`/api/embeddings`** - returns embeddings, not classification scores
+- **`/api/generate`** - reranker models can't generate text (they output uniform scores like 0.5)
+
+## The Workaround
+
+This service uses a magnitude-based approach:
+
+1. Concatenates query and document in cross-encoder format: `"Query: {query}\n\nDocument: {doc}\n\nRelevance:"`
+2. Gets embedding vector from Ollama's `/api/embeddings` endpoint
+3. Calculates the L2 norm (magnitude) of the embedding vector
+4. **Key discovery:** For BGE-reranker-v2-m3, **lower magnitude = more relevant**
+5. Inverts and normalizes to 0-1 range where higher score = more relevant
+
+### Why This Works (Sort Of)
+
+When a cross-encoder model processes a query-document pair through the embedding endpoint, the embedding's magnitude appears to correlate with relevance for some models. This is:
+- **Not documented behavior**
+- **Not guaranteed across models**
+- **Not the intended use of the embedding endpoint**
+- **Less accurate than proper cross-encoder scoring**
+
+But it's the only way to use reranker models with Ollama right now.
+
+## Limitations
+
+### ⚠️ Critical Limitations
+
+1. **Model-Specific Behavior**
+   - Magnitude ranges differ between models (BGE: 15-28, others: unknown)
+   - Correlation direction may vary (lower/higher = more relevant)
+   - Requires manual calibration per model
+
+2. **No Theoretical Foundation**
+   - Exploits accidental behavior, not designed functionality
+   - Could break with model updates
+   - No guarantee of correctness
+
+3. **Less Accurate Than Proper Methods**
+   - Native cross-encoder scoring is more accurate
+   - sentence-transformers library is the gold standard
+   - This is a compromise for GPU scheduling benefits
+
+4. **Embedding Dimension Dependency**
+   - Magnitude scales with dimensionality (384 vs 768 vs 1024)
+   - Models with different dimensions need different calibration
+
+5. **Performance**
+   - Requires one API call per document (40 docs = 40 calls)
+   - Slower than native reranking would be
+   - Fast but not optimal
+
+## When To Use This
+
+✅ **Use if:**
+- You need Ollama's GPU scheduling for multiple models
+- VRAM is constrained and you can't run separate services
+- You're okay with reduced accuracy vs sentence-transformers
+- You can tolerate model-specific calibration
+
+❌ **Don't use if:**
+- You need reliable, production-grade reranking
+- You need cross-model consistency
+- You have VRAM for sentence-transformers (~200MB for reranker only)
+- Accuracy is critical
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/yourusername/ollama-reranker-workaround.git
+cd ollama-reranker-workaround
+
+# Create virtual environment
+python3 -m venv .venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Ensure Ollama is running with a reranker model
+ollama pull qllama/bge-reranker-v2-m3
+```
+
+## Usage
+
+### Start the Service
+
+```bash
+python api.py
+```
+
+The service runs on `http://0.0.0.0:8080`
+
+### API Request
+
+```bash
+curl -X POST http://localhost:8080/v1/rerank \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "qllama/bge-reranker-v2-m3:latest",
+    "query": "What is machine learning?",
+    "documents": [
+      "Machine learning is a subset of artificial intelligence.",
+      "The weather today is sunny.",
+      "Neural networks are used in deep learning."
+    ],
+    "top_n": 2
+  }'
+```
+
+### Response
+
+```json
+{
+  "results": [
+    {
+      "index": 0,
+      "relevance_score": 0.9234,
+      "document": "Machine learning is a subset of artificial intelligence."
+    },
+    {
+      "index": 2,
+      "relevance_score": 0.7845,
+      "document": "Neural networks are used in deep learning."
+    }
+  ]
+}
+```
+
+## Configuration & Tunables
+
+### Model Calibration
+
+The most critical parameters are in `score_document_cross_encoder_workaround()`:
+
+```python
+# Magnitude bounds (model-specific!)
+typical_good_magnitude = 15.0   # Highly relevant documents
+typical_poor_magnitude = 25.0   # Irrelevant documents
+
+# For BGE-reranker-v2-m3, observed range is ~15-28
+# Lower magnitude = more relevant (inverted correlation)
+```
+
+### How to Calibrate for a New Model
+
+1. **Enable magnitude logging:**
+   ```python
+   logger.info(f"Raw magnitude: {magnitude:.2f}")
+   ```
+
+2. **Test with known relevant/irrelevant documents:**
+   ```python
+   # Send queries with obviously relevant and irrelevant docs
+   # Observe magnitude ranges in logs
+   ```
+
+3. **Determine correlation direction:**
+   - If relevant docs have **lower** magnitudes → set `invert = True`
+   - If relevant docs have **higher** magnitudes → set `invert = False`
+
+4. **Set bounds:**
+   ```python
+   # Find 90th percentile of relevant doc magnitudes
+   typical_good_magnitude = <observed_value>
+   
+   # Find 10th percentile of irrelevant doc magnitudes  
+   typical_poor_magnitude = <observed_value>
+   ```
+
+### Prompt Format Tuning
+
+The concatenation format may affect results:
+
+```python
+# Current format (works for BGE-reranker-v2-m3)
+combined = f"Query: {query}\n\nDocument: {doc}\n\nRelevance:"
+
+# Alternative formats to try:
+combined = f"{query} [SEP] {doc}"
+combined = f"query: {query} document: {doc}"
+combined = f"<query>{query}</query><document>{doc}</document>"
+```
+
+Test different formats and check if score distributions improve.
+
+### Concurrency Settings
+
+```python
+# In the rerank() endpoint
+# Process all documents concurrently (default)
+tasks = [score_document(...) for doc in documents]
+results = await asyncio.gather(*tasks)
+
+# Or batch for rate limiting:
+batch_size = 10
+for i in range(0, len(documents), batch_size):
+    batch = documents[i:i+batch_size]
+    # process batch...
+```
+
+## Technical Details
+
+### Magnitude Calculation
+
+```python
+import numpy as np
+
+# Get embedding from Ollama
+embedding = await get_embedding(client, model, combined_text)
+
+# Calculate L2 norm (Euclidean length)
+vec = np.array(embedding)
+magnitude = float(np.linalg.norm(vec))
+# magnitude = sqrt(sum(x_i^2 for all dimensions))
+```
+
+### Score Normalization
+
+```python
+# Linear interpolation (inverted for BGE-reranker-v2-m3)
+score = (typical_poor_magnitude - magnitude) / (typical_poor_magnitude - typical_good_magnitude)
+
+# Clamp to [0, 1]
+score = min(max(score, 0.0), 1.0)
+```
+
+### Example Magnitude Distributions
+
+From real queries to BGE-reranker-v2-m3:
+
+```
+Query: "Was ist eine Catalog Node ID?"
+
+Highly relevant docs: magnitude ~15.30 - 15.98 → score 0.95-0.97
+Moderately relevant:   magnitude ~17.00 - 19.00 → score 0.70-0.85
+Weakly relevant:       magnitude ~20.00 - 24.00 → score 0.20-0.50
+Irrelevant:           magnitude ~25.00 - 28.00 → score 0.00-0.10
+```
+
+## Alternatives
+
+### 1. Use sentence-transformers (Recommended for Production)
+
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('BAAI/bge-reranker-v2-m3', device='cpu')
+scores = model.predict([(query, doc) for doc in documents])
+```
+
+**Pros:** Accurate, reliable, proper implementation  
+**Cons:** ~200MB VRAM/RAM, separate from Ollama
+
+### 2. Request Ollama Feature
+
+Open an issue on [Ollama's GitHub](https://github.com/ollama/ollama) requesting native `/api/rerank` support.
+
+### 3. Use API Services
+
+Services like Cohere, Jina AI, or Voyage AI offer reranking APIs.
+
+## Requirements
+
+```
+fastapi>=0.104.0
+uvicorn>=0.24.0
+httpx>=0.25.0
+pydantic>=2.0.0
+numpy>=1.24.0
+```
+
+## Contributing
+
+This is a workaround for a missing feature. Contributions welcome for:
+- Calibration configs for additional models
+- Auto-calibration logic
+- Alternative prompt formats
+- Better normalization strategies
+
+But remember: **The best contribution would be native Ollama support.**
+
+## License
+
+MIT
+
+## Disclaimer
+
+This is an **experimental workaround** that exploits undocumented behavior. It is:
+- Not endorsed by Ollama or BAAI
+- Not guaranteed to work across models or versions
+- Not suitable for production use without extensive testing
+- A temporary solution until native reranking support exists
+
+**Use at your own risk and always validate results against ground truth.**