🔥Quickstart

ConversationBufferMemory

A Conversation Buffer Memory component temporarily stores recent messages and interactions in a conversation. It acts as a short-term memory buffer, holding onto messages for a brief period to facilitate real-time conversations. This component help in maintaining a sense of continuity and context within the conversation.

Conversation Buffer Memory doesn't require any specific configuration from the user.

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")
memory.add_text(user_text="What is my name?",model_text="Your name is Jhon.")

memory.get_chat_history()

Important Note: The ConversationBufferMemory uses the main memory of the system to store the conversations and it will be lost once the process gets terminated.

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. 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.

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

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 = "Conversation", k=2)

Stack(
    model=model,
    embedding=embedding,
    vectordb=vectordb,
    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")
memory.add_text(user_text="What is my name?",model_text="Your name is Jhon.")

memory.get_chat_history()

Important Note:Once the total number of conversations are 40, the first 20 conversations are removed from vectordb memory. This is because to make sure the context length doesn't exceeds.