updates

docs/source/reference/optimizations.rst

@@ -5,10 +5,10 @@ EvaDB Optimizations 🛠️
 
 EvaDB optimizes the evaluation of AI functions using these optimizations:
 
-1️⃣ Result Caching: EvaDB caches outcomes from expensive function invocations during query processing. This approach facilitates faster retrieval in subsequent queries. 📂
+1️⃣ Result Caching: EvaDB caches outcomes from expensive function invocations during query processing. This approach facilitates faster retrieval in subsequent queries. 📂⚡
 
-2️⃣ Predicate Reordering: Efficiency is key. EvaDB strategically reorders predicates to prioritize lower-cost and more selective evaluations. ⚖️
+2️⃣ Predicate Reordering: Efficiency is key. EvaDB strategically reorders predicates to prioritize lower-cost and more selective evaluations. 🔀🕰️
 
-3️⃣ Parallel Processing with Ray: Leveraging the Ray framework, EvaDB runs AI models in parallel, optimizing GPU utilization. Additionally, an AI pipeline is established for concurrent CPU tasks, such as data loading and decoding. 🚀
+3️⃣ Parallel Processing with Ray: Leveraging the Ray framework, EvaDB runs AI models in parallel, optimizing GPU utilization. Additionally, an AI pipeline is established for concurrent CPU tasks, such as data loading and decoding. 🚄🎩
 
-These techniques ensure superior performance and responsiveness in EvaDB's AI function evaluations.
+These techniques ensure superior performance and responsiveness in EvaDB's AI function evaluations. Dive in and experience the EvaDB difference! 🌟🎉