Update local changes

seminar_edition_ai.py

@@ -113,7 +113,7 @@ def predictProclamando(queryKey):
 ####
 #
 ####
-def predictFromInit( sermonTopic, llmModelList):
+def predictFromInit( sermonTopic, llmModelList = []):
   global HISTORY_ANSWER
   keyStr = 'SERMON_TOPIC'
   templates = SermonGeminiPromptTemplate()
@@ -178,9 +178,11 @@ def predictFromInit( sermonTopic, llmModelList):
 ####
 #
 ####
-def predictQuestionBuild(sermonTopic):
+def predictQuestionBuild(sermonTopic, llmModelList = []):
+  llm = llmModelList[0] if len(llmModelList) > 0 else None
   templates = SermonGeminiPromptTemplate()
   chain = updatePromptTemplate(
+      llm,
       templates.getSermonPromptTemplates()['BUILD_QUESTION'],
        ['SERMON_IDEA', 'context']
       )
@@ -217,15 +219,37 @@ def predictQuestionBuild(sermonTopic):
 ####
 #
 ####
-def predictDevotionBuild(sermonTopic):
+def predictDevotionBuild(sermonTopic, llmModelList = []):
   templates = SermonGeminiPromptTemplate()
+  llm = llmModelList[0] if len(llmModelList) > 0 else None
 
   chain = updatePromptTemplate(
+      llm,
       templates.getSermonPromptTemplate()['BUILD_REFLECTIONS'],
        ['SERMON_IDEA', 'context']
       )
+
   global retriever
   global HISTORY_ANSWER
+  global embed_model
+
+  if embed_model == None:
+      llmBuilder = GeminiLLM()
+      embed_model = llmBuilder.getEmbeddingsModel()
+
+  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(
       HISTORY_ANSWER,
       chain,