reduce import time by 6x (#1396)

* reduce import by 6x

* fix linting

src/crewai/memory/storage/rag_storage.py

@@ -5,11 +5,6 @@ import os
 import shutil
 from typing import Any, Dict, List, Optional
 
-from embedchain import App
-from embedchain.llm.base import BaseLlm
-from embedchain.models.data_type import DataType
-from embedchain.vectordb.chroma import InvalidDimensionException
-
 from crewai.memory.storage.interface import Storage
 from crewai.utilities.paths import db_storage_path
 
@@ -29,10 +24,6 @@ def suppress_logging(
     logger.setLevel(original_level)
 
 
-class FakeLLM(BaseLlm):
-    pass
-
-
 class RAGStorage(Storage):
     """
     Extends Storage to handle embeddings for memory entries, improving
@@ -74,9 +65,19 @@ class RAGStorage(Storage):
         if embedder_config:
             config["embedder"] = embedder_config
         self.type = type
-        self.app = App.from_config(config=config)
+        self.config = config
+        self.allow_reset = allow_reset
+
+    def _initialize_app(self):
+        from embedchain import App
+        from embedchain.llm.base import BaseLlm
+
+        class FakeLLM(BaseLlm):
+            pass
+
+        self.app = App.from_config(config=self.config)
         self.app.llm = FakeLLM()
-        if allow_reset:
+        if self.allow_reset:
             self.app.reset()
 
     def _sanitize_role(self, role: str) -> str:
@@ -86,6 +87,8 @@ class RAGStorage(Storage):
         return role.replace("\n", "").replace(" ", "_").replace("/", "_")
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        if not hasattr(self, "app"):
+            self._initialize_app()
         self._generate_embedding(value, metadata)
 
     def search(  # type: ignore # BUG?: Signature of "search" incompatible with supertype "Storage"
@@ -95,6 +98,10 @@ class RAGStorage(Storage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Any]:
+        if not hasattr(self, "app"):
+            self._initialize_app()
+        from embedchain.vectordb.chroma import InvalidDimensionException
+
         with suppress_logging():
             try:
                 results = (
@@ -108,6 +115,10 @@ class RAGStorage(Storage):
         return [r for r in results if r["metadata"]["score"] >= score_threshold]
 
     def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:
+        if not hasattr(self, "app"):
+            self._initialize_app()
+        from embedchain.models.data_type import DataType
+
         self.app.add(text, data_type=DataType.TEXT, metadata=metadata)
 
     def reset(self) -> None: