Chain that carries on a conversation and calls an LLM.
Abstract base class for memory in Chains.
Memory refers to state in Chains. Memory can be used to store information about past executions of a Chain and inject that information into the inputs of future executions of the Chain. For example, for conversational Chains Memory can be used to store conversations and automatically add them to future model prompts so that the model has the necessary context to respond coherently to the latest input.
Chain to run queries against LLMs.
This class is deprecated. See below for an example implementation using LangChain runnables:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()
chain.invoke("your adjective here")A basic memory implementation that simply stores the conversation history.
This stores the entire conversation history in memory without any additional processing.
Note that additional processing may be required in some situations when the conversation history is too large to fit in the context window of the model.
Chain to have a conversation and load context from memory.
This class is deprecated in favor of RunnableWithMessageHistory. Please refer
to this tutorial for more detail: https://python.langchain.com/docs/tutorials/chatbot/
RunnableWithMessageHistory offers several benefits, including:
Below is a minimal implementation, analogous to using ConversationChain with
the default ConversationBufferMemory:
from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
store = {} # memory is maintained outside the chain
def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
if session_id not in store:
store[session_id] = InMemoryChatMessageHistory()
return store[session_id]
model = ChatOpenAI(model="gpt-3.5-turbo-0125")
chain = RunnableWithMessageHistory(model, get_session_history)
chain.invoke(
"Hi I'm Bob.",
config={"configurable": {"session_id": "1"}},
) # session_id determines threadMemory objects can also be incorporated into the get_session_history callable:
from langchain_classic.memory import ConversationBufferWindowMemory
from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
store = {} # memory is maintained outside the chain
def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
if session_id not in store:
store[session_id] = InMemoryChatMessageHistory()
return store[session_id]
memory = ConversationBufferWindowMemory(
chat_memory=store[session_id],
k=3,
return_messages=True,
)
assert len(memory.memory_variables) == 1
key = memory.memory_variables[0]
messages = memory.load_memory_variables({})[key]
store[session_id] = InMemoryChatMessageHistory(messages=messages)
return store[session_id]
model = ChatOpenAI(model="gpt-3.5-turbo-0125")
chain = RunnableWithMessageHistory(model, get_session_history)
chain.invoke(
"Hi I'm Bob.",
config={"configurable": {"session_id": "1"}},
) # session_id determines thread