Update documentation: Replace Qdrant with Supabase references

- Updated vector store provider references throughout documentation
- Changed default vector store from Qdrant to Supabase (pgvector)
- Updated configuration examples to use Supabase connection strings
- Modified navigation structure to remove qdrant-specific references
- Updated examples in mem0-with-ollama and llama-index integration
- Corrected API reference and architecture documentation

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

docs/examples/mem0-with-ollama.mdx

@@ -26,11 +26,10 @@ from mem0 import Memory
 
 config = {
     "vector_store": {
-        "provider": "qdrant",
+        "provider": "supabase", 
         "config": {
-            "collection_name": "test",
-            "host": "localhost",
-            "port": 6333,
+            "connection_string": "postgresql://supabase_admin:your_password@localhost:5435/postgres",
+            "collection_name": "memories",
             "embedding_model_dims": 768,  # Change this according to your local model's dimensions
         },
     },
@@ -66,7 +65,7 @@ memories = m.get_all(user_id="john")
 ### Key Points
 
 - **Configuration**: The setup involves configuring the vector store, language model, and embedding model to use local resources.
-- **Vector Store**: Qdrant is used as the vector store, running on localhost.
+- **Vector Store**: Supabase with pgvector is used as the vector store, running on localhost.
 - **Language Model**: Ollama is used as the LLM provider, with the "llama3.1:latest" model.
 - **Embedding Model**: Ollama is also used for embeddings, with the "nomic-embed-text:latest" model.