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community[patch]: update use of deprecated llm methods (#20393)

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.predict and .predict_messages for BaseLanguageModel and BaseChatModel
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ccurme 2024-04-12 17:28:23 -04:00 committed by GitHub
parent 3a068b26f3
commit 38faa74c23
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13 changed files with 25 additions and 24 deletions
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2
libs/community/langchain_community/callbacks/labelstudio_callback.py
View File

@ -98,7 +98,7 @@ class LabelStudioCallbackHandler(BaseCallbackHandler):
... mode='prompt'
... )
>>> llm = OpenAI(callbacks=[handler])
>>> llm.predict('Tell me a story about a dog.')
>>> llm.invoke('Tell me a story about a dog.')
"""
DEFAULT_PROJECT_NAME: str = "LangChain-%Y-%m-%d"

2
libs/community/langchain_community/callbacks/llmonitor_callback.py
View File

@ -204,7 +204,7 @@ class LLMonitorCallbackHandler(BaseCallbackHandler):
llmonitor_callback = LLMonitorCallbackHandler()
llm = OpenAI(callbacks=[llmonitor_callback],
metadata={"userId": "user-123"})
llm.predict("Hello, how are you?")
llm.invoke("Hello, how are you?")
```
"""

6
libs/community/langchain_community/llms/opaqueprompts.py
View File

@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.language_models.llms import LLM
from langchain_core.messages import AIMessage
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env
@ -95,10 +96,11 @@ class OpaquePrompts(LLM):
# TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
# call the LLM with the sanitized prompt and get the response
llm_response = self.base_llm.predict(
llm_response = self.base_llm.bind(stop=stop).invoke(
sanitized_prompt_value_str,
stop=stop,
)
if isinstance(llm_response, AIMessage):
llm_response = llm_response.content
# desanitize the response by restoring the original sensitive information
desanitize_response: op.DesanitizeResponse = op.desanitize(

4
libs/community/tests/unit_tests/chat_models/test_openai.py
View File

@ -96,8 +96,8 @@ def test_openai_predict(mock_completion: dict) -> None:
"client",
mock_client,
):
res = llm.predict("bar")
assert res == "Bar Baz"
res = llm.invoke("bar")
assert res.content == "Bar Baz"
assert completed

2
libs/core/tests/unit_tests/language_models/llms/test_base.py
View File

@ -60,7 +60,7 @@ def test_batch_size() -> None:
llm = FakeListLLM(responses=["foo"] * 1)
with collect_runs() as cb:
llm.predict("foo")
llm.invoke("foo")
assert len(cb.traced_runs) == 1
assert (cb.traced_runs[0].extra or {}).get("batch_size") == 1

2
libs/core/tests/unit_tests/tracers/test_run_collector.py
View File

@ -9,7 +9,7 @@ from langchain_core.tracers.context import collect_runs
def test_collect_runs() -> None:
llm = FakeListLLM(responses=["hello"])
with collect_runs() as cb:
llm.predict("hi")
llm.invoke("hi")
assert cb.traced_runs
assert len(cb.traced_runs) == 1
assert isinstance(cb.traced_runs[0].id, uuid.UUID)

2
libs/experimental/langchain_experimental/llms/anthropic_functions.py
View File

@ -183,7 +183,7 @@ class AnthropicFunctions(BaseChatModel):
raise ValueError(
"if `function_call` provided, `functions` must also be"
)
response = self.model.predict_messages(
response = self.model.invoke(
messages, stop=stop, callbacks=run_manager, **kwargs
)
completion = cast(str, response.content)

2
libs/experimental/langchain_experimental/llms/ollama_functions.py
View File

@ -89,7 +89,7 @@ function in "functions".'
)
if "functions" in kwargs:
del kwargs["functions"]
response_message = self.llm.predict_messages(
response_message = self.llm.invoke(
[system_message] + messages, stop=stop, callbacks=run_manager, **kwargs
)
chat_generation_content = response_message.content

18
libs/experimental/tests/unit_tests/chat_models/test_llm_wrapper_llama2chat.py
View File

@ -49,7 +49,7 @@ def model_cfg_sys_msg() -> Llama2Chat:
def test_default_system_message(model: Llama2Chat) -> None:
messages = [HumanMessage(content="usr-msg-1")]
actual = model.predict_messages(messages).content # type: ignore
actual = model.invoke(messages).content # type: ignore
expected = (
f"<s>[INST] <<SYS>>\n{DEFAULT_SYSTEM_PROMPT}\n<</SYS>>\n\nusr-msg-1 [/INST]"
)
@ -62,7 +62,7 @@ def test_configured_system_message(
) -> None:
messages = [HumanMessage(content="usr-msg-1")]
actual = model_cfg_sys_msg.predict_messages(messages).content # type: ignore
actual = model_cfg_sys_msg.invoke(messages).content # type: ignore
expected = "<s>[INST] <<SYS>>\nsys-msg\n<</SYS>>\n\nusr-msg-1 [/INST]"
assert actual == expected
@ -73,7 +73,7 @@ async def test_configured_system_message_async(
) -> None:
messages = [HumanMessage(content="usr-msg-1")]
actual = await model_cfg_sys_msg.apredict_messages(messages) # type: ignore
actual = await model_cfg_sys_msg.ainvoke(messages) # type: ignore
expected = "<s>[INST] <<SYS>>\nsys-msg\n<</SYS>>\n\nusr-msg-1 [/INST]"
assert actual.content == expected
@ -87,7 +87,7 @@ def test_provided_system_message(
HumanMessage(content="usr-msg-1"),
]
actual = model_cfg_sys_msg.predict_messages(messages).content
actual = model_cfg_sys_msg.invoke(messages).content
expected = "<s>[INST] <<SYS>>\ncustom-sys-msg\n<</SYS>>\n\nusr-msg-1 [/INST]"
assert actual == expected
@ -102,7 +102,7 @@ def test_human_ai_dialogue(model_cfg_sys_msg: Llama2Chat) -> None:
HumanMessage(content="usr-msg-3"),
]
actual = model_cfg_sys_msg.predict_messages(messages).content
actual = model_cfg_sys_msg.invoke(messages).content
expected = (
"<s>[INST] <<SYS>>\nsys-msg\n<</SYS>>\n\nusr-msg-1 [/INST] ai-msg-1 </s>"
"<s>[INST] usr-msg-2 [/INST] ai-msg-2 </s><s>[INST] usr-msg-3 [/INST]"
@ -113,14 +113,14 @@ def test_human_ai_dialogue(model_cfg_sys_msg: Llama2Chat) -> None:
def test_no_message(model: Llama2Chat) -> None:
with pytest.raises(ValueError) as info:
model.predict_messages([])
model.invoke([])
assert info.value.args[0] == "at least one HumanMessage must be provided"
def test_ai_message_first(model: Llama2Chat) -> None:
with pytest.raises(ValueError) as info:
model.predict_messages([AIMessage(content="ai-msg-1")])
model.invoke([AIMessage(content="ai-msg-1")])
assert (
info.value.args[0]
@ -136,7 +136,7 @@ def test_human_ai_messages_not_alternating(model: Llama2Chat) -> None:
]
with pytest.raises(ValueError) as info:
model.predict_messages(messages) # type: ignore
model.invoke(messages) # type: ignore
assert info.value.args[0] == (
"messages must be alternating human- and ai-messages, "
@ -151,6 +151,6 @@ def test_last_message_not_human_message(model: Llama2Chat) -> None:
]
with pytest.raises(ValueError) as info:
model.predict_messages(messages)
model.invoke(messages)
assert info.value.args[0] == "last message must be a HumanMessage"

2
libs/experimental/tests/unit_tests/chat_models/test_llm_wrapper_mixtral.py
View File

@ -23,7 +23,7 @@ def test_prompt(model: Mixtral) -> None:
HumanMessage(content="usr-msg-2"),
]
actual = model.predict_messages(messages).content # type: ignore
actual = model.invoke(messages).content # type: ignore
expected = (
"<s>[INST] sys-msg\nusr-msg-1 [/INST] ai-msg-1 </s> [INST] usr-msg-2 [/INST]" # noqa: E501
)

2
libs/experimental/tests/unit_tests/chat_models/test_llm_wrapper_orca.py
View File

@ -23,7 +23,7 @@ def test_prompt(model: Orca) -> None:
HumanMessage(content="usr-msg-2"),
]
actual = model.predict_messages(messages).content # type: ignore
actual = model.invoke(messages).content # type: ignore
expected = "### System:\nsys-msg\n\n### User:\nusr-msg-1\n\n### Assistant:\nai-msg-1\n\n### User:\nusr-msg-2\n\n" # noqa: E501
assert actual == expected

2
libs/experimental/tests/unit_tests/chat_models/test_llm_wrapper_vicuna.py
View File

@ -23,7 +23,7 @@ def test_prompt(model: Vicuna) -> None:
HumanMessage(content="usr-msg-2"),
]
actual = model.predict_messages(messages).content # type: ignore
actual = model.invoke(messages).content # type: ignore
expected = "sys-msg USER: usr-msg-1 ASSISTANT: ai-msg-1 </s>USER: usr-msg-2 "
assert actual == expected

3
libs/langchain/tests/integration_tests/cache/test_upstash_redis_cache.py vendored
View File

@ -86,6 +86,5 @@ def test_redis_cache_chat() -> None:
llm = FakeChatModel()
params = llm.dict()
params["stop"] = None
with pytest.warns():
llm.predict("foo")
llm.invoke("foo")
langchain.llm_cache.redis.flushall()