Add rag enabled setting

src/main/askai/core/askai_configs.py

@@ -110,6 +110,10 @@ def chunk_overlap(self) -> int:
     def rag_retrival_amount(self) -> int:
         return settings.get_int("askai.rag.retrival.amount")
 
+    @property
+    def is_rag(self) -> bool:
+        return settings.get_int("askai.rag.enabled")
+
     @property
     def language(self) -> Language:
         """Lookup order: Settings -> Locale -> Environment."""

src/main/askai/core/askai_settings.py

@@ -43,7 +43,7 @@ class AskAiSettings(metaclass=Singleton):
     INSTANCE: "AskAiSettings"
 
     # Current settings version. Updating this value will trigger a database recreation using the defaults.
-    _ACTUAL_VERSION: str = "0.1.9"
+    _ACTUAL_VERSION: str = "0.1.91"
 
     RESOURCE_DIR = str(classpath.resource_path())
 
@@ -103,6 +103,7 @@ def defaults(self) -> None:
         self._settings.put("askai.text.splitter.chunk.size", "askai", 1000)
         self._settings.put("askai.text.splitter.chunk.overlap", "askai", 100)
         self._settings.put("askai.rag.retrival.amount", "askai", 3)
+        self._settings.put("askai.rag.enabled", "askai", True)
         # Router
         self._settings.put("askai.max.short.memory.size", "askai", 15)
         self._settings.put("askai.max.router.iteractions", "askai", 30)

src/main/askai/core/support/rag_provider.py

@@ -48,13 +48,14 @@ def get_rag_examples(self, query: str, k: int = configs.rag_retrival_amount) ->
         :param k: The number of examples to retrieve (default is 3).
         :return: A list of strings representing the retrieved examples.
         """
-        if self._rag_db is None:
-            self._rag_db = FAISS.from_documents(self._rag_docs, lc_llm.create_embeddings())
-        example_docs: list[Document] = self._rag_db.similarity_search(query, k=k)
-        rag_examples = os.linesep.join([doc.page_content for doc in example_docs])
-        return dedent(f"""
-        **Examples:**
-        \"\"\"
-        {rag_examples}
-        \"\"\"
-        """).strip()
+        if configs.is_rag:
+            if self._rag_db is None:
+                self._rag_db = FAISS.from_documents(self._rag_docs, lc_llm.create_embeddings())
+            example_docs: list[Document] = self._rag_db.similarity_search(query, k=k)
+            rag_examples = os.linesep.join([doc.page_content for doc in example_docs])
+            return dedent(f"""
+            **Examples:**
+            \"\"\"
+            {rag_examples}
+            \"\"\"
+            """).strip()