mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
acd86d33bc
Provide shared memory capability for the Agent. Inspired by #1293 . ## Problem If both Agent and Tools (i.e., LLMChain) use the same memory, both of them will save the context. It can be annoying in some cases. ## Solution Create a memory wrapper that ignores the save and clear, thereby preventing updates from Agent or Tools.
103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
"""Test conversation chain and memory."""
|
|
import pytest
|
|
|
|
from langchain.chains.conversation.base import ConversationChain
|
|
from langchain.memory.buffer import ConversationBufferMemory
|
|
from langchain.memory.buffer_window import ConversationBufferWindowMemory
|
|
from langchain.memory.summary import ConversationSummaryMemory
|
|
from langchain.prompts.prompt import PromptTemplate
|
|
from langchain.schema import BaseMemory
|
|
from tests.unit_tests.llms.fake_llm import FakeLLM
|
|
|
|
|
|
def test_memory_ai_prefix() -> None:
|
|
"""Test that ai_prefix in the memory component works."""
|
|
memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant")
|
|
memory.save_context({"input": "bar"}, {"output": "foo"})
|
|
assert memory.buffer == "Human: bar\nAssistant: foo"
|
|
|
|
|
|
def test_memory_human_prefix() -> None:
|
|
"""Test that human_prefix in the memory component works."""
|
|
memory = ConversationBufferMemory(memory_key="foo", human_prefix="Friend")
|
|
memory.save_context({"input": "bar"}, {"output": "foo"})
|
|
assert memory.buffer == "Friend: bar\nAI: foo"
|
|
|
|
|
|
def test_conversation_chain_works() -> None:
|
|
"""Test that conversation chain works in basic setting."""
|
|
llm = FakeLLM()
|
|
prompt = PromptTemplate(input_variables=["foo", "bar"], template="{foo} {bar}")
|
|
memory = ConversationBufferMemory(memory_key="foo")
|
|
chain = ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="bar")
|
|
chain.run("foo")
|
|
|
|
|
|
def test_conversation_chain_errors_bad_prompt() -> None:
|
|
"""Test that conversation chain raise error with bad prompt."""
|
|
llm = FakeLLM()
|
|
prompt = PromptTemplate(input_variables=[], template="nothing here")
|
|
with pytest.raises(ValueError):
|
|
ConversationChain(llm=llm, prompt=prompt)
|
|
|
|
|
|
def test_conversation_chain_errors_bad_variable() -> None:
|
|
"""Test that conversation chain raise error with bad variable."""
|
|
llm = FakeLLM()
|
|
prompt = PromptTemplate(input_variables=["foo"], template="{foo}")
|
|
memory = ConversationBufferMemory(memory_key="foo")
|
|
with pytest.raises(ValueError):
|
|
ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="foo")
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"memory",
|
|
[
|
|
ConversationBufferMemory(memory_key="baz"),
|
|
ConversationBufferWindowMemory(memory_key="baz"),
|
|
ConversationSummaryMemory(llm=FakeLLM(), memory_key="baz"),
|
|
],
|
|
)
|
|
def test_conversation_memory(memory: BaseMemory) -> None:
|
|
"""Test basic conversation memory functionality."""
|
|
# This is a good input because the input is not the same as baz.
|
|
good_inputs = {"foo": "bar", "baz": "foo"}
|
|
# This is a good output because these is one variable.
|
|
good_outputs = {"bar": "foo"}
|
|
memory.save_context(good_inputs, good_outputs)
|
|
# This is a bad input because there are two variables that aren't the same as baz.
|
|
bad_inputs = {"foo": "bar", "foo1": "bar"}
|
|
with pytest.raises(ValueError):
|
|
memory.save_context(bad_inputs, good_outputs)
|
|
# This is a bad input because the only variable is the same as baz.
|
|
bad_inputs = {"baz": "bar"}
|
|
with pytest.raises(ValueError):
|
|
memory.save_context(bad_inputs, good_outputs)
|
|
# This is a bad output because it is empty.
|
|
with pytest.raises(ValueError):
|
|
memory.save_context(good_inputs, {})
|
|
# This is a bad output because there are two keys.
|
|
bad_outputs = {"foo": "bar", "foo1": "bar"}
|
|
with pytest.raises(ValueError):
|
|
memory.save_context(good_inputs, bad_outputs)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"memory",
|
|
[
|
|
ConversationBufferMemory(memory_key="baz"),
|
|
ConversationSummaryMemory(llm=FakeLLM(), memory_key="baz"),
|
|
ConversationBufferWindowMemory(memory_key="baz"),
|
|
],
|
|
)
|
|
def test_clearing_conversation_memory(memory: BaseMemory) -> None:
|
|
"""Test clearing the conversation memory."""
|
|
# This is a good input because the input is not the same as baz.
|
|
good_inputs = {"foo": "bar", "baz": "foo"}
|
|
# This is a good output because there is one variable.
|
|
good_outputs = {"bar": "foo"}
|
|
memory.save_context(good_inputs, good_outputs)
|
|
|
|
memory.clear()
|
|
assert memory.load_memory_variables({}) == {"baz": ""}
|