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from typing import List, Optional, Any, Iterator
from unittest.mock import Mock
import pytest
try:
# Langchain >= 0.2
from langchain_openai import ChatOpenAI
except ImportError:
# Langchain < 0.2
from langchain_community.chat_models import ChatOpenAI
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.messages import BaseMessage, AIMessageChunk
from langchain_core.outputs import ChatGenerationChunk
from sentry_sdk import start_transaction
from sentry_sdk.integrations.langchain import LangchainIntegration
from langchain.agents import tool, AgentExecutor, create_openai_tools_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
global stream_result_mock # type: Mock
global llm_type # type: str
class MockOpenAI(ChatOpenAI):
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
for x in stream_result_mock():
yield x
@property
def _llm_type(self) -> str:
return llm_type
def tiktoken_encoding_if_installed():
try:
import tiktoken # type: ignore # noqa # pylint: disable=unused-import
return "cl100k_base"
except ImportError:
return None
@pytest.mark.parametrize(
"send_default_pii, include_prompts, use_unknown_llm_type",
[
(True, True, False),
(True, False, False),
(False, True, False),
(False, False, True),
],
)
def test_langchain_agent(
sentry_init, capture_events, send_default_pii, include_prompts, use_unknown_llm_type
):
global llm_type
llm_type = "acme-llm" if use_unknown_llm_type else "openai-chat"
sentry_init(
integrations=[
LangchainIntegration(
include_prompts=include_prompts,
tiktoken_encoding_name=tiktoken_encoding_if_installed(),
)
],
traces_sample_rate=1.0,
send_default_pii=send_default_pii,
)
events = capture_events()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are very powerful assistant, but don't know current events",
),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
global stream_result_mock
stream_result_mock = Mock(
side_effect=[
[
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": "call_BbeyNhCKa6kYLYzrD40NGm3b",
"function": {
"arguments": "",
"name": "get_word_length",
},
"type": "function",
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": None,
"function": {
"arguments": '{"word": "eudca"}',
"name": None,
},
"type": None,
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(content="5"),
generation_info={"finish_reason": "function_call"},
),
],
[
ChatGenerationChunk(
text="The word eudca has 5 letters.",
type="ChatGenerationChunk",
message=AIMessageChunk(content="The word eudca has 5 letters."),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
generation_info={"finish_reason": "stop"},
message=AIMessageChunk(content=""),
),
],
]
)
llm = MockOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
openai_api_key="badkey",
)
agent = create_openai_tools_agent(llm, [get_word_length], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[get_word_length], verbose=True)
with start_transaction():
list(agent_executor.stream({"input": "How many letters in the word eudca"}))
tx = events[0]
assert tx["type"] == "transaction"
chat_spans = list(
x for x in tx["spans"] if x["op"] == "ai.chat_completions.create.langchain"
)
tool_exec_span = next(x for x in tx["spans"] if x["op"] == "ai.tool.langchain")
assert len(chat_spans) == 2
# We can't guarantee anything about the "shape" of the langchain execution graph
assert len(list(x for x in tx["spans"] if x["op"] == "ai.run.langchain")) > 0
if use_unknown_llm_type:
assert "ai_prompt_tokens_used" in chat_spans[0]["measurements"]
assert "ai_total_tokens_used" in chat_spans[0]["measurements"]
else:
# important: to avoid double counting, we do *not* measure
# tokens used if we have an explicit integration (e.g. OpenAI)
assert "measurements" not in chat_spans[0]
if send_default_pii and include_prompts:
assert (
"You are very powerful"
in chat_spans[0]["data"]["ai.input_messages"][0]["content"]
)
assert "5" in chat_spans[0]["data"]["ai.responses"]
assert "word" in tool_exec_span["data"]["ai.input_messages"]
assert 5 == int(tool_exec_span["data"]["ai.responses"])
assert (
"You are very powerful"
in chat_spans[1]["data"]["ai.input_messages"][0]["content"]
)
assert "5" in chat_spans[1]["data"]["ai.responses"]
else:
assert "ai.input_messages" not in chat_spans[0].get("data", {})
assert "ai.responses" not in chat_spans[0].get("data", {})
assert "ai.input_messages" not in chat_spans[1].get("data", {})
assert "ai.responses" not in chat_spans[1].get("data", {})
assert "ai.input_messages" not in tool_exec_span.get("data", {})
assert "ai.responses" not in tool_exec_span.get("data", {})
def test_langchain_error(sentry_init, capture_events):
sentry_init(
integrations=[LangchainIntegration(include_prompts=True)],
traces_sample_rate=1.0,
send_default_pii=True,
)
events = capture_events()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are very powerful assistant, but don't know current events",
),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
global stream_result_mock
stream_result_mock = Mock(side_effect=Exception("API rate limit error"))
llm = MockOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
openai_api_key="badkey",
)
agent = create_openai_tools_agent(llm, [get_word_length], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[get_word_length], verbose=True)
with start_transaction(), pytest.raises(Exception):
list(agent_executor.stream({"input": "How many letters in the word eudca"}))
error = events[0]
assert error["level"] == "error"
def test_span_origin(sentry_init, capture_events):
sentry_init(
integrations=[LangchainIntegration()],
traces_sample_rate=1.0,
)
events = capture_events()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are very powerful assistant, but don't know current events",
),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
global stream_result_mock
stream_result_mock = Mock(
side_effect=[
[
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": "call_BbeyNhCKa6kYLYzrD40NGm3b",
"function": {
"arguments": "",
"name": "get_word_length",
},
"type": "function",
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(
content="",
additional_kwargs={
"tool_calls": [
{
"index": 0,
"id": None,
"function": {
"arguments": '{"word": "eudca"}',
"name": None,
},
"type": None,
}
]
},
),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
message=AIMessageChunk(content="5"),
generation_info={"finish_reason": "function_call"},
),
],
[
ChatGenerationChunk(
text="The word eudca has 5 letters.",
type="ChatGenerationChunk",
message=AIMessageChunk(content="The word eudca has 5 letters."),
),
ChatGenerationChunk(
type="ChatGenerationChunk",
generation_info={"finish_reason": "stop"},
message=AIMessageChunk(content=""),
),
],
]
)
llm = MockOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
openai_api_key="badkey",
)
agent = create_openai_tools_agent(llm, [get_word_length], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[get_word_length], verbose=True)
with start_transaction():
list(agent_executor.stream({"input": "How many letters in the word eudca"}))
(event,) = events
assert event["contexts"]["trace"]["origin"] == "manual"
for span in event["spans"]:
assert span["origin"] == "auto.ai.langchain"
|
