fix(query): removed from get_chat_history

genai_stack/memory/base.py

@@ -13,7 +13,7 @@ class BaseMemoryConfig(StackComponentConfig):
 
 
 class BaseMemory(StackComponent):
-    
+
     def get_user_text(self) -> str:
         """
         This method returns the user query
@@ -25,21 +25,21 @@ def get_model_text(self) -> str:
         This method returns the model response
         """
         raise NotImplementedError()
-    
+
     def get_text(self) -> dict:
         """
         This method returns both user query and model response
         """
         raise NotImplementedError()
-    
+
     def add_text(self, user_text:str, model_text:str) -> None:
         """
         This method stores both user query and model response
         """
         raise NotImplementedError()
-    
-    def get_chat_history(self, query:str) -> str:
+
+    def get_chat_history(self) -> str:
         """
         This method returns the chat conversation history
         """
-        raise NotImplementedError()
+        raise NotImplementedError()

genai_stack/memory/langchain.py

@@ -28,17 +28,17 @@ def get_user_text(self):
         if len(self.memory.chat_memory.messages) == 0:
             return None
         return self.memory.chat_memory.messages[-2].content
-    
+
     def get_model_text(self):
         if len(self.memory.chat_memory.messages) == 0:
             return None
         return self.memory.chat_memory.messages[-1].content
-    
+
     def get_text(self):
         return {
-            "user_text":self.get_user_text(), 
+            "user_text":self.get_user_text(),
             "model_text":self.get_model_text()
         }
 
-    def get_chat_history(self, query):
-        return parse_chat_conversation_history(self.memory.chat_memory.messages)
+    def get_chat_history(self):
+        return parse_chat_conversation_history(self.memory.chat_memory.messages)

genai_stack/memory/vectordb.py

@@ -2,7 +2,7 @@
 from langchain.memory import VectorStoreRetrieverMemory
 from genai_stack.memory.base import BaseMemory, BaseMemoryConfig, BaseMemoryConfigModel
 from genai_stack.memory.utils import (
-    parse_chat_conversation_history_search_result, 
+    parse_chat_conversation_history_search_result,
     format_index_name
 )
 
@@ -25,16 +25,16 @@ class VectorDBMemory(BaseMemory):
     def _post_init(self, *args, **kwargs):
         config:VectorDBMemoryConfigModel  = self.config.config_data
 
-        # We have to pass the index name in two places, one is to Vectordb and to 
+        # We have to pass the index name in two places, one is to Vectordb and to
         # VectorStoreRetriever.
         # in case of weaviate, if we pass the index name in lowercase, the weaviate will
         # internally convert it to pascal for schema/collection
         # eg passed index name => chatting, weaviate converted to Chatting,
-        # But if VectorStoreRetriever use the lowercased index name, 
+        # But if VectorStoreRetriever use the lowercased index name,
         # it throws index error, since the weaviate changed to pascal.
-        # To handle this we are converting the index name to pascal before intializing 
-        # the Vectordb and Vectorstoreretriever, and to 
-        # maintain the consistency, we are also converting the chromadb index name to 
+        # To handle this we are converting the index name to pascal before intializing
+        # the Vectordb and Vectorstoreretriever, and to
+        # maintain the consistency, we are also converting the chromadb index name to
         # pascal, instead of conditionally doing only for weaviate.
         kwarg_map, index_name = format_index_name(config=config)
 
@@ -53,8 +53,6 @@ def _post_init(self, *args, **kwargs):
     def add_text(self, user_text: str, model_text: str):
         self.memory.save_context({"input": user_text}, {"output": model_text})
 
-    def get_chat_history(self, query):
-        documents = self.memory.load_memory_variables({
-            "prompt": query
-        })[self.memory.memory_key]
-        return parse_chat_conversation_history_search_result(search_results=documents)
+    def get_chat_history(self):
+        documents = self.memory.load_memory_variables()[self.memory.memory_key]
+        return parse_chat_conversation_history_search_result(search_results=documents)

genai_stack/retriever/base.py

@@ -35,8 +35,8 @@ def get_context(self, query: str):
         """
         raise NotImplementedError()
 
-    def get_chat_history(self, query:str) -> str:
+    def get_chat_history(self) -> str:
         """
         This method returns the chat conversation history
         """
-        return self.mediator.get_chat_history(query=query)
+        return self.mediator.get_chat_history()

genai_stack/retriever/langchain.py

@@ -32,7 +32,7 @@ def retrieve(self, query: str, context: List[Document] = None):
             metadata = context[0].metadata if context else None
             prompt_dict["context"] = parse_search_results(context)
         if "history" in prompt_template.input_variables:
-            prompt_dict["history"] = self.get_chat_history(query=query)
+            prompt_dict["history"] = self.get_chat_history()
         else:
             # Cache and memory cannot co-exist. Memory is given priority.
             cache = self.mediator.get_cache(query=query, metadata=metadata)

genai_stack/stack/mediator.py

@@ -67,9 +67,9 @@ def add_text(self, user_text: str, model_text: str) -> None:
         if self._is_component_available("memory"):
             self._stack.memory.add_text(user_text, model_text)
 
-    def get_chat_history(self, query:str) -> str:
+    def get_chat_history(self) -> str:
         if self._is_component_available("memory"):
-            return self._stack.memory.get_chat_history(query=query)
+            return self._stack.memory.get_chat_history()
 
     # Vectordb
     def store_to_vectordb(self, documents: List[LangDocument]):

tests/test_memory.py

@@ -57,9 +57,9 @@ def chromadb_stack(self, index_name = "Chroma"):
         self.memory = VectorDBMemory.from_kwargs(index_name = index_name)
 
         self.chromadb_memory_stack = Stack(
-            model=None, 
-            embedding=self.embedding, 
-            vectordb=self.chromadb, 
+            model=None,
+            embedding=self.embedding,
+            vectordb=self.chromadb,
             memory=self.memory
         )
 
@@ -83,24 +83,24 @@ def weaviatedb_stack(self, index_name = "Weaviate"):
         self.memory = VectorDBMemory.from_kwargs(index_name = index_name)
 
         self.weaviatedb_memory_stack = Stack(
-            model=None, 
-            embedding=self.embedding, 
-            vectordb=self.weaviatedb, 
+            model=None,
+            embedding=self.embedding,
+            vectordb=self.weaviatedb,
             memory=self.memory
-        )  
-    
+        )
+
     def store_conversation_to_chromadb_memory(self, user_text:str, model_text:str):
         self.chromadb_memory_stack.memory.add_text(
             user_text=user_text,model_text=model_text
         )
-    
+
     def store_conversation_to_weaviate_memory(self, user_text:str, model_text:str):
         self.weaviatedb_memory_stack.memory.add_text(
             user_text=user_text,model_text=model_text
         )
-    
+
     def test_chromadb_memory(self, query:str = "what is my favourite car?"):
-        print(self.chromadb_memory_stack.memory.get_chat_history(query=query))
-    
+        print(self.chromadb_memory_stack.memory.get_chat_history())
+
     def test_weaviatedb_memory(self, query:str = "what is my favourite sport?"):
-        print(self.weaviatedb_memory_stack.memory.get_chat_history(query=query))
+        print(self.weaviatedb_memory_stack.memory.get_chat_history())