Update memory component documentation

documentation/v0.2.0/components/memory/quickstart.md

@@ -7,10 +7,18 @@ A Conversation Buffer Memory component temporarily stores recent messages and in
 Conversation Buffer Memory doesn't require any specific configuration from the user.
 
 ```py
+from genai_stack.stack.stack import Stack
+from genai_stack.model import OpenAIGpt35Model
 from genai_stack.memory import ConversationBufferMemory
 
+model = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "<ADD_OPENAI_KEY>"})
 memory = ConversationBufferMemory.from_kwargs()
 
+stack = Stack(
+    model=model
+    memory=memory
+)
+
 # Storing few conversation
 memory.add_text(user_text="Hi my name is Jhon",model_text="Hello, Jhon! How can I assist you today?")
 memory.add_text(user_text="Which is the smallest month of the year?",model_text="The smallest month of the year is February")
@@ -23,24 +31,37 @@ memory.get_chat_history()
 
 **VectorDBMemory**
 
-VectorDBMemory supports both `ChromaDB` and `Weaviate`, which one is used to store the conversations is totally depends on the vectordb that is initialized and passed to the stack for storing the documents.
+VectorDBMemory supports both `ChromaDB` and `Weaviate`, which one is used to store the conversations is totally depends on the vectordb that is initialized and passed to the stack for storing the documents. by default `k=4`, so `get_chat_history()` returns last 4 conversations and you can also change this default value when initializing the `VectorDBMemory` component.
 
 ```py
 from genai_stack.stack.stack import Stack
+from genai_stack.model import OpenAIGpt35Model
+from genai_stack.embedding.langchain import LangchainEmbedding
 from genai_stack.vectordb import ChromaDB, Weaviate
 from genai_stack.memory import VectorDBMemory
 
-vectordb = ChromaDB.from_kwargs()
+model = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "<ADD_OPENAI_KEY>"})
+
+config = {
+    "model_name": "sentence-transformers/all-mpnet-base-v2",
+    "model_kwargs": {"device": "cpu"},
+    "encode_kwargs": {"normalize_embeddings": False},
+}
+embedding = LangchainEmbedding.from_kwargs(name="HuggingFaceEmbeddings", fields=config)
+
+vectordb = ChromaDB.from_kwargs(index_name="Testing")
 
     or
 
 vectordb = Weaviate.from_kwargs(
     url="http://localhost:8080/", index_name="Testing", text_key="test"
 )
 
-memory = VectorDBMemory.from_kwargs(index_name = "Conversations")
+memory = VectorDBMemory.from_kwargs(index_name = "Conversation", k=2)
 
-stack = Stack(
+Stack(
+    model=model,
+    embedding=embedding,
     vectordb=vectordb,
     memory=memory
 )
@@ -50,5 +71,7 @@ memory.add_text(user_text="Hi my name is Jhon",model_text="Hello, Jhon! How can
 memory.add_text(user_text="Which is the smallest month of the year?",model_text="The smallest month of the year is February")
 memory.add_text(user_text="What is my name?",model_text="Your name is Jhon.")
 
-memory.get_chat_history(query = "What is my name?")
+memory.get_chat_history()
 ```
+
+**Important Note**:Once the total conversations are 40, the first 20 conversations are removed from vectordb memory. This is because to make sure the context length doesn't exceeds.