Update local changes

seminar_edition_ai.py

@@ -112,7 +112,6 @@ def predictProclamando(queryKey):
 def predictFromInit( sermonTopic, llmModelList):
   global HISTORY_ANSWER
   keyStr = 'SERMON_TOPIC'
-
   templates = SermonGeminiPromptTemplate()
 
   llm = llmModelList[0] if len(llmModelList) > 0 else None
@@ -131,6 +130,21 @@ def predictFromInit( sermonTopic, llmModelList):
     keyStr = 'BIBLE_VERSICLE'
 
   global retriever
+
+  if retriever == None:
+      doc = Document(page_content="text", metadata={"source": "local"})
+
+      vectorstore = Chroma.from_documents(
+          documents=[doc],
+          embedding=embed_model,
+          persist_directory="chroma_db_dir_sermon",  # Local mode with in-memory storage only
+          collection_name="sermon_lab_ai"
+      )
+
+      retriever = vectorstore.as_retriever(
+          search_kwargs={"k": 3}
+      )
+
   answer = askQuestionInit(
       '',
       chain,
@@ -143,13 +157,6 @@ def predictFromInit( sermonTopic, llmModelList):
   if answer != '':
     doc =  Document(page_content="text", metadata = {"source": "local"})
 
-    vectorstore = Chroma.from_documents(
-      documents = [doc],
-      embedding = embed_model,
-      persist_directory="chroma_db_dir_sermon",  # Local mode with in-memory storage only
-      collection_name="sermon_lab_ai"
-    )
-
     retriever = vectorstore.as_retriever(
         search_kwargs = {"k": 3}
     )
@@ -169,6 +176,20 @@ def predictQuestionBuild(sermonTopic):
        ['SERMON_IDEA', 'context']
       )
   global retriever
+
+  if retriever == None:
+      doc = Document(page_content="text", metadata={"source": "local"})
+
+      vectorstore = Chroma.from_documents(
+          documents=[doc],
+          embedding=embed_model,
+          persist_directory="chroma_db_dir_sermon",  # Local mode with in-memory storage only
+          collection_name="sermon_lab_ai"
+      )
+      retriever = vectorstore.as_retriever(
+          search_kwargs={"k": 3}
+      )
+
   answer = askQuestionEx(
       '',
       chain,