Update model_pipelineV2.py

model_pipelineV2.py

@@ -13,53 +13,24 @@ from langchain_core.runnables import RunnableLambda, RunnablePassthrough
 from operator import itemgetter
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 
-class VectorStoreSingleton:
-    _instance = None
-
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = create_vectorstore()  # Your existing function to create the vectorstore
-        return cls._instance
-
-class LanguageModelSingleton:
-    _instance = None
-
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = load_llm()  # Your existing function to load the LLM
-        return cls._instance
-
 
 class ModelPipeLine:
     DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
     def __init__(self):
         self.curr_dir = os.path.dirname(__file__)
-        self.knowledge_dir = os.path.join(os.path.dirname(os.path.dirname(self.curr_dir)), 'data', 'Knowledge_base')
-        self.prompt_dir = os.path.join(os.path.dirname(os.path.dirname(self.curr_dir)), 'src', 'prompts')
+        self.knowledge_dir = "knowledge"
+        print("Knowledge Directory:", self.knowledge_dir)
+        self.prompt_dir = 'prompts'
         self.child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)
         self.parent_splitter = RecursiveCharacterTextSplitter(chunk_size=500)
-        self._documents = None  # Initialize as None for lazy loading
-        self.vectorstore, self.store = VectorStoreSingleton.get_instance()
-        self._retriever = None  # Corrected: Initialize _retriever as None for lazy loading
-        self.llm = LanguageModelSingleton.get_instance()
-        self.memory = ConversationBufferMemory(return_messages=True, output_key="answer", input_key="question")
-
-    @property
-    def documents(self):
-        if self._documents is None:
-            self._documents = process_pdf_document([
-                os.path.join(self.knowledge_dir, 'depression_1.pdf'),
-                os.path.join(self.knowledge_dir, 'depression_2.pdf')
-            ])
-        return self._documents
-
-    @property
-    def retriever(self):
-        if self._retriever is None:
-            self._retriever = rag_retriever(self.vectorstore, self.store, self.documents, self.parent_splitter, self.child_splitter)
-        return self._retriever
+        self.documents = process_pdf_document([os.path.join(self.knowledge_dir, 'depression_1.pdf'), os.path.join(self.knowledge_dir, 'depression_2.pdf')])
+        self.vectorstore, self.store = create_vectorstore()
+        self.retriever = rag_retriever(self.vectorstore, self.store, self.documents, self.parent_splitter, self.child_splitter) # Create the retriever
+        self.llm = load_llm() # Load the LLM model
+        self.memory = ConversationBufferMemory(return_messages=True, 
+                                               output_key="answer", 
+                                               input_key="question") # Instantiate ConversationBufferMemory
+
     
     def get_prompts(self, system_file_path='system_prompt_template.txt', 
                     condense_file_path='condense_question_prompt_template.txt'):