Fix gemini and add results

README.md

@@ -289,6 +289,25 @@ For high-level information about these models, see the [MTEB Leaderboard](https:
 
 *... and 522 more queries*
 
+### google-gemini/text-embedding-004 (BSB)
+
+**Accuracy: 76.1%** (1214/1596 points across 532 queries)
+
+| Query | Expected | Top Result | Score | ✓ |
+|-------|----------|------------|-------|---|
+| isn't there a verse about god is love? | ['1 John 4:8', '1 John 4:16'] | 1 John 4:8 | 0.7386 | ✅ |
+| god is love | ['1 John 4:8', '1 John 4:16'] | 1 John 4:8 | 0.7778 | ✅ |
+| verse about they meant bad but god meant good (rou... | ['Genesis 50:20'] | Genesis 50:20 | 0.7120 | ✅ |
+| they meant bad but god meant good | ['Genesis 50:20'] | Genesis 50:20 | 0.6916 | ✅ |
+| isn't there a verse about god loves the world? | ['John 3:16'] | 1 John 4:8 | 0.6559 | ❌ |
+| god loves the world | ['John 3:16'] | 1 John 2:15 | 0.6578 | ❌ |
+| isn't there a verse about god loved the whole worl... | ['John 3:16'] | 1 John 4:8 | 0.6519 | ❌ |
+| god loved the whole world | ['John 3:16'] | John 3:16 | 0.6571 | ✅ |
+| isn't there a verse about in the beginning god cre... | ['Genesis 1:1'] | Genesis 1:1 | 0.7756 | ✅ |
+| in the beginning god created | ['Genesis 1:1'] | Genesis 1:1 | 0.8750 | ✅ |
+
+*... and 522 more queries*
+
 ### openai/text-embedding-ada-002 (BSB)
 
 **Accuracy: 74.3%** (1186/1596 points across 532 queries)

main.py

@@ -124,8 +124,37 @@ class GeminiProvider(EmbeddingProvider):
     async def embed_batch(self, texts: List[str]) -> List[List[float]]:
         client = await self._get_client()
         result = client.embed_content(model=f"models/{self.model_name}", content=texts)
+        
         # Gemini embeddings are automatically normalized to unit length
-        return [embedding["embedding"] for embedding in result["embedding"]]
+        # The result is a dictionary with an 'embedding' key containing the embedding vectors
+        if isinstance(result, dict) and 'embedding' in result:
+            embeddings = result['embedding']
+            
+            # Handle case where embeddings is a list of embedding vectors
+            if isinstance(embeddings, list):
+                # Check if we have multiple embeddings (batch) or a single embedding
+                if len(embeddings) > 0:
+                    # If first element is a list of numbers, we have direct embedding vectors
+                    if isinstance(embeddings[0], (list, tuple)) and all(isinstance(x, (int, float)) for x in embeddings[0][:5]):
+                        return embeddings
+                    # If first element is a dict with 'embedding' key, extract them
+                    elif isinstance(embeddings[0], dict) and 'embedding' in embeddings[0]:
+                        return [emb['embedding'] for emb in embeddings]
+                    # If first element is an object with embedding attribute
+                    elif hasattr(embeddings[0], 'embedding'):
+                        return [emb.embedding for emb in embeddings]
+                    else:
+                        # Assume it's a single embedding vector for all texts (unlikely but handle it)
+                        return [embeddings] * len(texts)
+                else:
+                    return []
+            # Handle case where embeddings is a single vector
+            elif isinstance(embeddings, (list, tuple)):
+                return [embeddings]
+            else:
+                return []
+        else:
+            raise ValueError(f"Unexpected Gemini API response format: {type(result)}")
     
     async def embed_queries_batch(self, queries: List[str], batch_size: int = 100) -> List[List[float]]:
         """Efficiently embed multiple queries using Gemini's batch capability."""