|
| 1 | +import unittest |
| 2 | +import logging |
| 3 | +import sys |
| 4 | +import os |
| 5 | +import json |
| 6 | +import time |
| 7 | +from unittest.mock import patch, Mock |
| 8 | +import pprint |
| 9 | +import openai |
| 10 | +from langchain_core.tools import tool |
| 11 | +from langchain_community.agent_toolkits.load_tools import load_tools |
| 12 | +from langchain.agents import AgentExecutor, create_react_agent |
| 13 | +from typing import Any, List, Mapping, Optional |
| 14 | +from langchain.llms.base import BaseLLM |
| 15 | +from langchain_community.llms.fake import FakeListLLM |
| 16 | +from langchain.schema import LLMResult, Generation |
| 17 | +from langchain.chains import LLMChain |
| 18 | +from langchain.prompts import PromptTemplate |
| 19 | +from langchain import hub |
| 20 | +from langchain_core.messages import HumanMessage |
| 21 | + |
| 22 | +import graphsignal |
| 23 | +from graphsignal.uploader import Uploader |
| 24 | +from graphsignal.recorders.openai_recorder import OpenAIRecorder |
| 25 | +from test.model_utils import find_tag, find_usage, find_payload |
| 26 | + |
| 27 | +logger = logging.getLogger('graphsignal') |
| 28 | + |
| 29 | +@tool |
| 30 | +def multiply(first_int: int, second_int: int) -> int: |
| 31 | + """Multiply two integers together.""" |
| 32 | + return first_int * second_int |
| 33 | + |
| 34 | +class GraphsignalCallbackHandlerTest(unittest.IsolatedAsyncioTestCase): |
| 35 | + async def asyncSetUp(self): |
| 36 | + if len(logger.handlers) == 0: |
| 37 | + logger.addHandler(logging.StreamHandler(sys.stdout)) |
| 38 | + graphsignal.configure( |
| 39 | + api_key='k1', |
| 40 | + deployment='d1', |
| 41 | + upload_on_shutdown=False, |
| 42 | + debug_mode=True) |
| 43 | + |
| 44 | + async def asyncTearDown(self): |
| 45 | + graphsignal.shutdown() |
| 46 | + |
| 47 | + |
| 48 | + @patch.object(Uploader, 'upload_span') |
| 49 | + async def test_callback_tags(self, mocked_upload_span): |
| 50 | + from graphsignal.callbacks.langchain import GraphsignalCallbackHandler |
| 51 | + llm = FakeListLLM( |
| 52 | + responses=['Final Answer:42'], |
| 53 | + callbacks=[GraphsignalCallbackHandler(tags=dict(k1='v1'))] |
| 54 | + ) |
| 55 | + |
| 56 | + llm.invoke([HumanMessage(content="Tell me a joke")]) |
| 57 | + |
| 58 | + t1 = mocked_upload_span.call_args_list[0][0][0] |
| 59 | + |
| 60 | + self.assertEqual(find_tag(t1, 'k1'), 'v1') |
| 61 | + |
| 62 | + |
| 63 | + @patch.object(Uploader, 'upload_span') |
| 64 | + @patch('graphsignal.callbacks.langchain.v1.uuid_sha1', return_value='s1') |
| 65 | + async def test_chain(self, mocked_uuid_sha1, mocked_upload_span): |
| 66 | + os.environ["LANGCHAIN_TRACING_V2"] = "false" |
| 67 | + |
| 68 | + graphsignal.set_context_tag('ct1', 'v1') |
| 69 | + |
| 70 | + llm = FakeListLLM(responses=['Final Answer:42']) |
| 71 | + |
| 72 | + tools = [multiply] |
| 73 | + prompt = hub.pull("hwchase17/react") |
| 74 | + agent = create_react_agent(llm, tools, prompt) |
| 75 | + agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) |
| 76 | + agent_executor.invoke({"input": "What is 2 times 4?"}) |
| 77 | + |
| 78 | + def find_span(op_name): |
| 79 | + for call in mocked_upload_span.call_args_list: |
| 80 | + check_op_name = find_tag(call[0][0], 'operation') |
| 81 | + if check_op_name == op_name: |
| 82 | + return call[0][0] |
| 83 | + |
| 84 | + llm_span = find_span('langchain_community.llms.fake.FakeListLLM') |
| 85 | + self.assertEqual(find_tag(llm_span, 'model_type'), 'chat') |
| 86 | + self.assertEqual(find_tag(llm_span, 'ct1'), 'v1') |
| 87 | + |
| 88 | + @patch.object(Uploader, 'upload_span') |
| 89 | + @patch('graphsignal.callbacks.langchain.v1.uuid_sha1', return_value='s1') |
| 90 | + async def test_chain_async(self, mocked_uuid_sha1, mocked_upload_span): |
| 91 | + os.environ["LANGCHAIN_TRACING_V2"] = "false" |
| 92 | + |
| 93 | + graphsignal.set_context_tag('ct1', 'v1') |
| 94 | + |
| 95 | + llm = FakeListLLM(responses=['Final Answer:42']) |
| 96 | + tools = [multiply] |
| 97 | + prompt = hub.pull("hwchase17/react") |
| 98 | + agent = create_react_agent(llm, tools, prompt) |
| 99 | + agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) |
| 100 | + await agent_executor.ainvoke({"input": "What is 2 times 4?"}) |
| 101 | + |
| 102 | + def find_span(op_name): |
| 103 | + for call in mocked_upload_span.call_args_list: |
| 104 | + check_op_name = find_tag(call[0][0], 'operation') |
| 105 | + if check_op_name == op_name: |
| 106 | + return call[0][0] |
| 107 | + |
| 108 | + llm_span = find_span('langchain_community.llms.fake.FakeListLLM') |
| 109 | + self.assertEqual(find_tag(llm_span, 'model_type'), 'chat') |
| 110 | + |
| 111 | + @patch.object(Uploader, 'upload_span') |
| 112 | + @patch('graphsignal.callbacks.langchain.v1.uuid_sha1', return_value='s1') |
| 113 | + async def test_chain_async_with_decorator(self, mocked_uuid_sha1, mocked_upload_span): |
| 114 | + prompt = PromptTemplate( |
| 115 | + input_variables=["product"], |
| 116 | + template="What is a good name for a company that makes {product}?", |
| 117 | + ) |
| 118 | + |
| 119 | + llm = FakeListLLM(responses=['Final Answer:42']) |
| 120 | + chain = LLMChain(llm=llm, prompt=prompt) |
| 121 | + |
| 122 | + @graphsignal.trace_function |
| 123 | + async def run_chain(): |
| 124 | + graphsignal.set_context_tag('session_id', 's2') |
| 125 | + await chain.ainvoke("colorful socks") |
| 126 | + |
| 127 | + await run_chain() |
| 128 | + |
| 129 | + def find_span(op_name): |
| 130 | + for call in mocked_upload_span.call_args_list: |
| 131 | + check_op_name = find_tag(call[0][0], 'operation') |
| 132 | + if check_op_name == op_name: |
| 133 | + return call[0][0] |
| 134 | + |
| 135 | + run_chain_span = find_span('run_chain') |
| 136 | + self.assertIsNotNone(run_chain_span) |
| 137 | + |
| 138 | + llm_span = find_span('langchain_community.llms.fake.FakeListLLM') |
| 139 | + self.assertEqual(find_tag(llm_span, 'library'), 'langchain') |
| 140 | + self.assertEqual(find_tag(llm_span, 'session_id'), 's2') |
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