temporarily not using lock w/update for vectorstore

A2rchi/chains/chain.py

@@ -50,20 +50,26 @@ def __init__(self):
 
         self.chain = BaseChain.from_llm(self.llm, self.vectorstore.as_retriever(), return_source_documents=True)
 
-        update_vectorstore_thread = Thread(target=self.update_vectorstore)
-        update_vectorstore_thread.start()
+    #     update_vectorstore_thread = Thread(target=self.update_vectorstore)
+    #     update_vectorstore_thread.start()
+
+
+    # def update_vectorstore(self):
+    #     while not self.kill:
+    #         time.sleep(1000)
+    #         self.lock.acquire()
+    #         self.vectorstore = self.dataManager.fetch_vectorstore()
+    #         self.chain = BaseChain.from_llm(self.llm, self.vectorstore.as_retriever(), return_source_documents=True)
+    #         print("Updated chain with new vectorstore")
+    #         self.lock.release()
+    #     return None
+
 
     def update_vectorstore(self):
-        while not self.kill:
-            time.sleep(1000)
-            self.lock.acquire()
-            self.vectorstore = self.dataManager.fetch_vectorstore()
-            self.chain = BaseChain.from_llm(self.llm, self.vectorstore.as_retriever(), return_source_documents=True)
-            print("Updated chain with new vectorstore")
-            self.lock.release()
-        return None
-
+        self.vectorstore = self.dataManager.fetch_vectorstore()
+        self.chain = BaseChain.from_llm(self.llm, self.vectorstore.as_retriever(), return_source_documents=True)
 
+        return
 
 
     def __call__(self, history):
@@ -89,5 +95,3 @@ def __call__(self, history):
         answer = self.chain({"question": question, "chat_history": prev_history})
         self.lock.release()
         return answer
-
-

A2rchi/interfaces/chat_app.py

@@ -130,6 +130,7 @@ def __call__(self, inp: str, history: Optional[Tuple[str, str]], discussion_id:
             # Get similarity score to see how close the input is to the source
             # Low score means very close (it's a distance between embedding vectors approximated
             # by an approximate k-nearest neighbors algoirthm)
+            self.chain.update_vectorstore()
             score = self.chain.vectorstore.similarity_search_with_score(inp)[0][1]
 
             #Get the closest source to the document
@@ -205,4 +206,4 @@ def launch(self, _debug = True, _share = True):
 
             message.submit(fn = self.chat, inputs = [message, state, id_state], outputs=[chatbot, state, id_state])
 
-        self.block.launch(debug=_debug, share=_share)
+        self.block.launch(debug=_debug, share=_share)

deploy/compose.yaml

@@ -39,7 +39,7 @@ services:
     volumes:
       - a2rchi-data:/root/data/
     ports:
-      - 7861:7860
+      - 7861:7860  # host:container
 
   mailbox:
     build: