mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
a408ed3ea3
Add MemoryChain and ConversationChain as chains that take a docstore in addition to the prompt, and use the docstore to stuff context into the prompt. This can be used to have an ongoing conversation with a chatbot. Probably needs a bit of refactoring for code quality Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
"""Test conversation chain and memory."""
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import pytest
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from langchain.chains.base import Memory
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from langchain.chains.conversation.base import ConversationChain
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from langchain.chains.conversation.memory import (
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ConversationBufferMemory,
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ConversationSummaryMemory,
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)
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from langchain.prompts.prompt import PromptTemplate
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from tests.unit_tests.llms.fake_llm import FakeLLM
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def test_conversation_chain_works() -> None:
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"""Test that conversation chain works in basic setting."""
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llm = FakeLLM()
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prompt = PromptTemplate(input_variables=["foo", "bar"], template="{foo} {bar}")
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memory = ConversationBufferMemory(dynamic_key="foo")
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chain = ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="bar")
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chain.run("foo")
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def test_conversation_chain_errors_bad_prompt() -> None:
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"""Test that conversation chain works in basic setting."""
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llm = FakeLLM()
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prompt = PromptTemplate(input_variables=[], template="nothing here")
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with pytest.raises(ValueError):
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ConversationChain(llm=llm, prompt=prompt)
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def test_conversation_chain_errors_bad_variable() -> None:
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"""Test that conversation chain works in basic setting."""
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llm = FakeLLM()
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prompt = PromptTemplate(input_variables=["foo"], template="{foo}")
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memory = ConversationBufferMemory(dynamic_key="foo")
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with pytest.raises(ValueError):
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ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="foo")
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@pytest.mark.parametrize(
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"memory",
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[
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ConversationBufferMemory(dynamic_key="baz"),
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ConversationSummaryMemory(llm=FakeLLM(), dynamic_key="baz"),
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],
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)
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def test_conversation_memory(memory: Memory) -> None:
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"""Test basic conversation memory functionality."""
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# This is a good input because the input is not the same as baz.
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good_inputs = {"foo": "bar", "baz": "foo"}
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# This is a good output because these is one variable.
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good_outputs = {"bar": "foo"}
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memory._save_context(good_inputs, good_outputs)
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# This is a bad input because there are two variables that aren't the same as baz.
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bad_inputs = {"foo": "bar", "foo1": "bar"}
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with pytest.raises(ValueError):
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memory._save_context(bad_inputs, good_outputs)
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# This is a bad input because the only variable is the same as baz.
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bad_inputs = {"baz": "bar"}
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with pytest.raises(ValueError):
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memory._save_context(bad_inputs, good_outputs)
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# This is a bad output because it is empty.
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with pytest.raises(ValueError):
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memory._save_context(good_inputs, {})
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# This is a bad output because there are two keys.
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bad_outputs = {"foo": "bar", "foo1": "bar"}
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with pytest.raises(ValueError):
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memory._save_context(good_inputs, bad_outputs)
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