diff --git a/.gitignore b/.gitignore index 30e4d2c1a1..fc4d1bbe4a 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,9 @@ megalinter-reports/ # Benchmarks .asv/ +# LLM Cache Files +.tiktoken_cache + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/newrelic/common/llm_utils.py b/newrelic/common/llm_utils.py index b336aeeaf5..7b8137ba22 100644 --- a/newrelic/common/llm_utils.py +++ b/newrelic/common/llm_utils.py @@ -120,12 +120,19 @@ def __init__(self, wrapped, on_stop_iteration, on_error, on_stream_chunk=None): self._nr_on_stream_chunk = on_stream_chunk or noop # Track if we've sent the LLM events yet to avoid sending them multiple times self._nr_closed = False + # Lazily established by __aiter__ or __anext__. With LangChain's + # astream_events, __anext__ may be called before __aiter__. + self._nr_wrapped_iter = None def __aiter__(self): self._nr_wrapped_iter = self.__wrapped__.__aiter__() return self async def __anext__(self): + # Lazily establish the wrapped iterator. With astream_events, + # __anext__ may be called before __aiter__. + if self._nr_wrapped_iter is None: + self._nr_wrapped_iter = self.__wrapped__.__aiter__() try: return_val = await self._nr_wrapped_iter.__anext__() self._nr_on_stream_chunk(self, return_val) diff --git a/newrelic/hooks/mlmodel_langchain.py b/newrelic/hooks/mlmodel_langchain.py index c924fba55f..f76be9ee31 100644 --- a/newrelic/hooks/mlmodel_langchain.py +++ b/newrelic/hooks/mlmodel_langchain.py @@ -267,6 +267,33 @@ def astream(self, *args, **kwargs): return return_val + def astream_events(self, *args, **kwargs): + transaction = current_transaction() + if not transaction: + return self.__wrapped__.astream_events(*args, **kwargs) + + agent_name = getattr(self.__wrapped__, "name", "agent") + agent_id = str(uuid.uuid4()) + agent_event_dict = _construct_base_agent_event_dict(agent_name, agent_id, transaction) + function_trace_name = f"astream_events/{agent_name}" + agentic_subcomponent_data = {"type": "APM-AI_AGENT", "name": agent_name} + + ft = FunctionTrace(name=function_trace_name, group="Llm/agent/LangChain") + ft.__enter__() + ft._add_agent_attribute("subcomponent", json.dumps(agentic_subcomponent_data)) + try: + return_val = self.__wrapped__.astream_events(*args, **kwargs) + return_val = AsyncLLMStreamProxy( + return_val, + on_stop_iteration=self._nr_on_stop_iteration(ft, agent_event_dict), + on_error=self._nr_on_error(ft, agent_event_dict, agent_id), + ) + except Exception: + self._nr_on_error(ft, agent_event_dict, agent_id)(transaction) + raise + + return return_val + def transform(self, *args, **kwargs): transaction = current_transaction() if not transaction: diff --git a/tests/mlmodel_langchain/_mock_external_openai_server.py b/tests/mlmodel_langchain/_mock_external_openai_server.py deleted file mode 100644 index fba87da511..0000000000 --- a/tests/mlmodel_langchain/_mock_external_openai_server.py +++ /dev/null @@ -1,1004 +0,0 @@ -# Copyright 2010 New Relic, Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json - -import pytest -from testing_support.mock_external_http_server import MockExternalHTTPServer - -from newrelic.common.package_version_utils import get_package_version_tuple - -# This defines an external server test apps can make requests to instead of -# the real OpenAI backend. This provides 3 features: -# -# 1) This removes dependencies on external websites. -# 2) Provides a better mechanism for making an external call in a test app than -# simple calling another endpoint the test app makes available because this -# server will not be instrumented meaning we don't have to sort through -# transactions to separate the ones created in the test app and the ones -# created by an external call. -# 3) This app runs on a separate thread meaning it won't block the test app. -STREAMED_RESPONSES_V1 = { - "system: You are a world class algorithm for extracting information in structured formats. | user: Use the given format to extract information from the following input: Hello, world | user: Tip: Make sure to answer in the correct format": [ - { - "content-type": "text/event-stream; charset=utf-8", - "openai-organization": "nr-test-org", - "openai-processing-ms": "440", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999942", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_1addfc2e713648af834cb9992fd417d7", - }, - 200, - [ - { - "id": "chatcmpl-CvUIm4qNNuiHpumRpuX0HISeNKViC", - "object": "chat.completion.chunk", - "created": 1767817212, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "", "refusal": None}, - "logprobs": None, - "finish_reason": None, - } - ], - "usage": None, - "obfuscation": "HeQWMY8H", - }, - { - "id": "chatcmpl-CvUIm4qNNuiHpumRpuX0HISeNKViC", - "object": "chat.completion.chunk", - "created": 1767817212, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {"content": "Hello,"}, "logprobs": None, "finish_reason": None}], - "usage": None, - "obfuscation": "DferQO2zD", - }, - { - "id": "chatcmpl-CvUIm4qNNuiHpumRpuX0HISeNKViC", - "object": "chat.completion.chunk", - "created": 1767817212, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {"content": " world!"}, "logprobs": None, "finish_reason": None}], - "usage": None, - "obfuscation": "LlLJvKqz", - }, - { - "id": "chatcmpl-CvUIm4qNNuiHpumRpuX0HISeNKViC", - "object": "chat.completion.chunk", - "created": 1767817212, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {}, "logprobs": None, "finish_reason": "stop"}], - "usage": None, - "obfuscation": "Qzvy", - }, - { - "id": "chatcmpl-CvUIm4qNNuiHpumRpuX0HISeNKViC", - "object": "chat.completion.chunk", - "created": 1767817212, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [], - "usage": { - "prompt_tokens": 96, - "completion_tokens": 24, - "total_tokens": 120, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "obfuscation": "NzeDrNhe", - }, - ], - ], - 'user: Use a tool to add an exclamation to the word "Hello"': [ - { - "content-type": "text/event-stream; charset=utf-8", - "openai-organization": "nr-test-org", - "openai-processing-ms": "134", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999985", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_4566af5dd7224f00a2407fa1d3e32864", - }, - 200, - [ - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1778607301, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "", "refusal": None}, - "logprobs": None, - "finish_reason": None, - } - ], - "usage": None, - "obfuscation": "VHZBuhMN", - }, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1778607301, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {"content": "Hello"}, "logprobs": None, "finish_reason": None}], - "usage": None, - "obfuscation": "kjWyA", - }, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1778607301, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {"content": "!"}, "logprobs": None, "finish_reason": None}], - "usage": None, - "obfuscation": "259VYkZdM", - }, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1778607301, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [{"index": 0, "delta": {}, "logprobs": None, "finish_reason": "stop"}], - "usage": None, - "obfuscation": "7s5h", - }, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1778607301, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [], - "usage": { - "prompt_tokens": 21, - "completion_tokens": 2, - "total_tokens": 23, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "obfuscation": "29QTnO6xx2", - }, - ], - ], - "user: What is the capital of France? Answer in one word.": [ - { - "content-type": "text/event-stream; charset=utf-8", - "openai-organization": "nr-test-org", - "openai-processing-ms": "245", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999985", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_22204b237d22427fbfd99c665d8a9964", - }, - 200, - [ - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "object": "chat.completion.chunk", - "created": 1781626980, - "model": "gpt-3.5-turbo-0125", - "service_tier": "default", - "system_fingerprint": None, - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": "", "refusal": None}, - "logprobs": None, - "finish_reason": None, - } - ], - "usage": None, - "obfuscation": "dxxvf59Q", - }, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - 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"completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "obfuscation": "Plc7EI1Jr6", - }, - ], - ], -} -RESPONSES_V1 = { - 'system: You are a text manipulation algorithm. | user: Use a tool to add an exclamation to the word "Hello"': [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "324", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999974", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_619548c272db4f1ab380b83de9fdedef", - }, - 200, - { - "id": "chatcmpl-CukvsGfSQihNO9I3FTqaNKERWtUca", - "object": "chat.completion", - "created": 1767642812, - "model": "gpt-3.5-turbo-0125", - 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"openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999970", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_619548c272db4f1ab380b83de9fdedef", - }, - 200, - { - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1", - "object": "chat.completion", - "created": 1767642813, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": 'The word "Hello" with an exclamation mark added is "Hello!"', - "refusal": None, - "annotations": [], - }, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 96, - "completion_tokens": 16, - "total_tokens": 112, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - 'system: You are a text manipulation algorithm. | user: Use a tool to add an exclamation to the word "exc"': [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "767", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999975", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_27d106351bab9878a3969f23108cd4c6", - }, - 200, - { - "id": "chatcmpl-CxGq2dnBYh5JR5o4OANlkHgBhuxfK", - "object": "chat.completion", - "created": 1768242114, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": None, - "tool_calls": [ - { - "id": "call_blmqxOaZvxUtgB0JVLXYnEu1", - "type": "function", - "function": {"name": "add_exclamation", "arguments": '{"message":"exc"}'}, - } - ], - "refusal": None, - "annotations": [], - }, - "logprobs": None, - "finish_reason": "tool_calls", - } - ], - "usage": { - "prompt_tokens": 70, - "completion_tokens": 15, - "total_tokens": 85, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - "system: You are a helpful assistant who generates a random first name. A user will pass in a first letter, and you should generate a name that starts with that first letter. | user: M": [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "236", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999955", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_58e5f91c0c3d4c2c9b6ee9ad8c4e8961", - }, - 200, - { - "id": "chatcmpl-CxGtBIjrsLMSkCUPSLOlAiHFxLz7A", - "object": "chat.completion", - "created": 1768242309, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": {"role": "assistant", "content": "Milo", "refusal": None, "annotations": []}, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 46, - "completion_tokens": 2, - "total_tokens": 48, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - "system: You are a helpful assistant who generates comma separated lists.\n A user will pass in a category, and you should generate 5 objects in that category in a comma separated list.\n ONLY return a comma separated list, and nothing more. | user: colors": [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "289", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999935", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_fbc7bb2ab3e149c1845699cfea9403d4", - }, - 200, - { - "id": "chatcmpl-CxGyV8CzGN80ByzFb4wN1hwGktOKD", - "object": "chat.completion", - "created": 1768242639, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "red, blue, green, yellow, orange", - "refusal": None, - "annotations": [], - }, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 60, - "completion_tokens": 9, - "total_tokens": 69, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - "system: You are a world class algorithm for extracting information in structured formats. | user: Use the given format to extract information from the following input: Sally is 13 | user: Tip: Make sure to answer in the correct format": [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "201", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999944", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_40a68eb08b684844b1e1f2253c85f00c", - }, - 200, - { - "id": "chatcmpl-CxGyZUlLnBXQkOnJyJNSlshVXdOwQ", - "object": "chat.completion", - "created": 1768242643, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": None, - "function_call": {"name": "output_formatter", "arguments": '{"name":"Sally","age":13}'}, - "refusal": None, - "annotations": [], - }, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 159, - "completion_tokens": 10, - "total_tokens": 169, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - "system: You are a world class algorithm for extracting information in structured formats with openai failures. | user: Use the given format to extract information from the following input: Sally is 13 | user: Tip: Make sure to answer in the correct format": [ - {"content-type": "application/json; charset=utf-8", "x-request-id": "e58911d54d574647d36237e4e53c0f1a"}, - 401, - { - "error": { - "message": "Incorrect API key provided: No-exist. You can find your API key at https://platform.openai.com/account/api-keys.", - "type": "invalid_request_error", - "param": None, - "code": "invalid_api_key", - } - }, - ], - "system: You are a generator of quiz questions for a seminar. Use the following pieces of retrieved context to generate 5 multiple choice questions (A,B,C,D) on the subject matter. Use a three sentence maximum and keep the answer concise. Render the output as HTML\n\nWhat is 2 + 4? | user: math": [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "2029", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999927", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_008a31c6023e42c9ae640eae2ae3b5ad", - }, - 200, - { - "id": "chatcmpl-CxfJTw2pCnRMvza9LZyE8qitryqFC", - "object": "chat.completion", - "created": 1768336195, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "```html\n\n\n\n Math Quiz\n\n\n

Math Quiz Questions

\n
    \n
  1. What is the result of 5 + 3?
    2. \n \n
  2. What is the product of 6 x 7?
    3. \n \n
  3. What is the square root of 64?
    4. \n \n
  4. What is the result of 12 / 4?
    5. \n \n
  5. What is the sum of 15 + 9?
    6. \n \n
\n\n\n```", - "refusal": None, - "annotations": [], - }, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 73, - "completion_tokens": 337, - "total_tokens": 410, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - # Embedding Responses - "3923": [ - { - "content-type": "application/json", - "openai-model": "text-embedding-ada-002-v2", - "openai-organization": "nr-test-org", - "openai-processing-ms": "42", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "10000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "9999992", - "x-ratelimit-reset-requests": "6ms", - 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", - } - ], - "model": "text-embedding-ada-002-v2", - "usage": {"prompt_tokens": 5, "total_tokens": 5}, - }, - ], - 'user: Use a tool to add an exclamation to the word "Hello"': [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "238", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999985", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_22204b237d22427fbfd99c665d8a9964", - }, - 200, - { - "id": "chatcmpl-Dd0Na8gXEDyFIhYMsL72TYk3bSZun", - "object": "chat.completion", - "created": 1778188622, - "model": "gpt-3.5-turbo-0125", - "choices": [ - { - "index": 0, - "message": {"role": "assistant", "content": "Hello!", "refusal": None, "annotations": []}, - "logprobs": None, - "finish_reason": "stop", - } - ], - "usage": { - "prompt_tokens": 21, - "completion_tokens": 2, - "total_tokens": 23, - "prompt_tokens_details": {"cached_tokens": 0, "audio_tokens": 0}, - "completion_tokens_details": { - "reasoning_tokens": 0, - "audio_tokens": 0, - "accepted_prediction_tokens": 0, - "rejected_prediction_tokens": 0, - }, - }, - "service_tier": "default", - "system_fingerprint": None, - }, - ], - "user: What is the capital of France? Answer in one word.": [ - { - "content-type": "application/json", - "openai-organization": "nr-test-org", - "openai-processing-ms": "238", - "openai-project": "proj_0Wv6taeZjWf793P67JMswYY3", - "openai-version": "2020-10-01", - "x-ratelimit-limit-requests": "10000", - "x-ratelimit-limit-tokens": "50000000", - "x-ratelimit-remaining-requests": "9999", - "x-ratelimit-remaining-tokens": "49999985", - "x-ratelimit-reset-requests": "6ms", - "x-ratelimit-reset-tokens": "0s", - "x-request-id": "req_22204b237d22427fbfd99c665d8a9964", - }, - 200, - { - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": None, - "message": { - "content": "Paris", - "refusal": None, - "role": "assistant", - "annotations": [], - "audio": None, - "function_call": None, - "tool_calls": None, - }, - "content_filter_results": { - "hate": {"filtered": False, "severity": "safe"}, - "self_harm": {"filtered": False, "severity": "safe"}, - "sexual": {"filtered": False, "severity": "safe"}, - 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"violence": {"filtered": False, "severity": "safe"}, - }, - } - ], - }, - ], -} - - -@pytest.fixture(scope="session") -def simple_get(): - def _simple_get(self): - content_len = int(self.headers.get("content-length")) - content = json.loads(self.rfile.read(content_len).decode("utf-8")) - stream = content.get("stream", False) - prompt = extract_shortened_prompt(content) - if not prompt: - self.send_response(500) - self.end_headers() - self.wfile.write(b"Could not parse prompt.") - return - - headers, response = ({}, "") - - mocked_responses = RESPONSES_V1 - if stream: - mocked_responses = STREAMED_RESPONSES_V1 - - for k, v in mocked_responses.items(): - if prompt == k: - headers, status_code, response = v - break - else: # If no matches found - self.send_response(500) - self.end_headers() - self.wfile.write(f"Unknown Prompt ({'Streaming' if stream else 'Non-Streaming'}):\n{prompt}".encode()) - return - - # Send response code - self.send_response(status_code) - - # Send headers - for k, v in headers.items(): - self.send_header(k, v) - self.end_headers() - - # Send response body - if stream and status_code < 400: - for resp in response: - data = json.dumps(resp).encode("utf-8") - if prompt == "Stream parsing error.": - # Force a parsing error by writing an invalid streamed response. - self.wfile.write(b"data: %s" % data) - else: - self.wfile.write(b"data: %s\n\n" % data) - else: - self.wfile.write(json.dumps(response).encode("utf-8")) - return - - return _simple_get - - -@pytest.fixture(scope="session") -def MockExternalOpenAIServer(simple_get): - class _MockExternalOpenAIServer(MockExternalHTTPServer): - # To use this class in a test one needs to start and stop this server - # before and after making requests to the test app that makes the external - # calls. - - def __init__(self, handler=simple_get, port=None, *args, **kwargs): - super().__init__(handler=handler, port=port, *args, **kwargs) # noqa: B026 - - return _MockExternalOpenAIServer - - -def extract_shortened_prompt(content): - _input = content.get("input", None) - if _input: - return str(_input[0][0]) - - # Transform all input messages into a single prompt - messages = content.get("messages") - prompt = [f"{message['role']}: {message['content']}" for message in messages] - return " | ".join(prompt) - - -if __name__ == "__main__": - _MockExternalOpenAIServer = MockExternalOpenAIServer() - with MockExternalOpenAIServer() as server: - print(f"MockExternalOpenAIServer serving on port {server.port!s}") - while True: - pass # Serve forever diff --git a/tests/mlmodel_langchain/_test_tools.py b/tests/mlmodel_langchain/_test_tools.py index a187a68767..8592d27f97 100644 --- a/tests/mlmodel_langchain/_test_tools.py +++ b/tests/mlmodel_langchain/_test_tools.py @@ -32,22 +32,22 @@ async def add_exclamation_async(message: str) -> str: return f"{message}!" -@pytest.fixture(scope="session", params=["sync_tool", "async_tool"]) +@pytest.fixture(params=["sync_tool", "async_tool"]) def tool_type(request): return request.param -@pytest.fixture(scope="session") +@pytest.fixture def tool_method_name(tool_type): return "run" if tool_type == "sync_tool" else "arun" -@pytest.fixture(scope="session") +@pytest.fixture def add_exclamation(tool_type, exercise_agent): if tool_type == "sync_tool": return add_exclamation_sync elif tool_type == "async_tool": - 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"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"}], + "model": "text-embedding-ada-002-v2", "usage": {"prompt_tokens": 5, "total_tokens": + 5}}' + headers: + access-control-allow-origin: + - '*' + access-control-expose-headers: + - X-Request-ID + - CF-Ray + - CF-Ray + cf-cache-status: + - DYNAMIC + cf-ray: + - a105799bc8c5fefe-PDX + connection: + - keep-alive + content-type: + - application/json + date: + - Tue, 23 Jun 2026 18:14:54 GMT + openai-model: + - text-embedding-ada-002-v2 + openai-organization: + - nr-test-org + openai-processing-ms: + - '158' + openai-project: + - nr-test-project + openai-version: + - '2020-10-01' + server: + - cloudflare + transfer-encoding: + - chunked + via: + - envoy-router-7f6875bbd4-qwkjf + x-engine-geography: + - US + x-ratelimit-limit-requests: + - '10000' + x-ratelimit-limit-tokens: + - '50000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '49999975' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_5ce7cda0ec3a4674b300079eefbea654 + status: + code: 200 + message: OK +version: 1 diff --git a/tests/mlmodel_langchain/conftest.py b/tests/mlmodel_langchain/conftest.py index 20d2109cff..340c578729 100644 --- a/tests/mlmodel_langchain/conftest.py +++ b/tests/mlmodel_langchain/conftest.py @@ -13,26 +13,18 @@ # limitations under the License. import itertools -import json import os -from pathlib import Path import pytest +from langchain_core.messages.ai import AIMessage +from langchain_core.messages.tool import ToolMessage from langchain_openai import ChatOpenAI, OpenAIEmbeddings from testing_support.fixture.event_loop import event_loop as loop -from testing_support.fixtures import ( - collector_agent_registration_fixture, - collector_available_fixture, - override_application_settings, -) +from testing_support.fixture.vcr import * # noqa: F403 +from testing_support.fixture.vcr import VCR_IGNORED_HEADERS, VCR_REPLACE_HEADERS, VCR_TIKTOKEN_ENCODINGS +from testing_support.fixtures import collector_agent_registration_fixture, collector_available_fixture from testing_support.ml_testing_utils import set_trace_info -from newrelic.api.transaction import current_transaction -from newrelic.common.object_wrapper import ObjectProxy, wrap_function_wrapper -from newrelic.common.signature import bind_args - -from ._mock_external_openai_server import MockExternalOpenAIServer, extract_shortened_prompt, simple_get - _default_settings = { "package_reporting.enabled": False, # Turn off package reporting for testing as it causes slow downs. "transaction_tracer.explain_threshold": 0.0, @@ -49,46 +41,74 @@ linked_applications=["Python Agent Test (mlmodel_langchain)"], ) +VCR_IGNORED_HEADERS.extend( + [ + "alt-svc", + "Cookie", + "host", + "set-cookie", + "Strict-Transport-Security", + "X-Content-Type-Options", + "x-envoy-upstream-service-time", + "x-openai-proxy-wasm", + "X-Stainless-Arch", + "X-Stainless-Async", + "X-Stainless-Lang", + "X-Stainless-OS", + "X-Stainless-Package-Version", + "X-Stainless-Raw-Response", + "x-stainless-retry-count", + "X-Stainless-Runtime-Version", + "X-Stainless-Runtime", + ] +) -OPENAI_AUDIT_LOG_FILE = Path(__file__).parent / "openai_audit.log" -OPENAI_AUDIT_LOG_CONTENTS = {} -# Intercept outgoing requests and log to file for mocking -RECORDED_HEADERS = {"x-request-id", "content-type"} -EXPECTED_AGENT_RESPONSE = 'The word "Hello" with an exclamation mark added is "Hello!"' +VCR_REPLACE_HEADERS.extend( + [ + ("openai-organization", "nr-test-org"), + ("openai-project", "nr-test-project"), + ("x-ratelimit-limit-requests", "10000"), + ("x-ratelimit-limit-tokens", "50000000"), + ("x-ratelimit-remaining-requests", "9999"), + ("x-ratelimit-remaining-tokens", "49999975"), + ("x-ratelimit-reset-requests", "6ms"), + ("x-ratelimit-reset-tokens", "0s"), + ] +) + +VCR_TIKTOKEN_ENCODINGS.extend(["cl100k_base"]) + +EXPECTED_AGENT_RESPONSE = "Hello!" EXPECTED_TOOL_OUTPUT = "Hello!" -@pytest.fixture(scope="session") -def openai_clients(MockExternalOpenAIServer): +@pytest.fixture +def openai_clients(vcr_recording): """ - This configures the openai client and returns it for openai v1 and only configures - openai for v0 since there is no client. + This configures the OpenAI client to use a ReplayApiClient which + will either record or replay responses depending on the mode. """ from newrelic.core.config import _environ_as_bool - if not _environ_as_bool("NEW_RELIC_TESTING_RECORD_OPENAI_RESPONSES", False): - with MockExternalOpenAIServer() as server: - chat = ChatOpenAI(base_url=f"http://localhost:{server.port}", api_key="NOT-A-REAL-SECRET", temperature=0.7) - embeddings = OpenAIEmbeddings( - openai_api_key="NOT-A-REAL-SECRET", openai_api_base=f"http://localhost:{server.port}" - ) - yield chat, embeddings - else: + if vcr_recording: openai_api_key = os.environ.get("OPENAI_API_KEY") if not openai_api_key: raise RuntimeError("OPENAI_API_KEY environment variable required.") - chat = ChatOpenAI(api_key=openai_api_key) - embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) - yield chat, embeddings + else: + openai_api_key = os.environ["OPENAI_API_KEY"] = "NOT-A-REAL-SECRET" + chat = ChatOpenAI(api_key=openai_api_key) + embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) + return chat, embeddings -@pytest.fixture(scope="session") + +@pytest.fixture def embedding_openai_client(openai_clients): _, embedding_client = openai_clients return embedding_client -@pytest.fixture(scope="session") +@pytest.fixture def chat_openai_client(openai_clients): chat_client, _ = openai_clients return chat_client @@ -101,17 +121,23 @@ def state_function_step(state): def append_function_step(state): from langchain.messages import ToolMessage - messages = state["messages"] if "messages" in state else state["model"]["messages"] + if "messages" in state: + messages = state["messages"] + elif "model" in state: + messages = state["model"]["messages"] + elif "data" in state: + messages = state["data"]["model"]["messages"] + messages.append(ToolMessage(f"The real agent said: {messages[-1].content}", tool_call_id=123)) return state -@pytest.fixture(scope="session", params=["create_agent", "StateGraph", "RunnableSeq", "RunnableSequence"]) +@pytest.fixture(params=["create_agent", "StateGraph", "RunnableSeq", "RunnableSequence"]) def agent_runnable_type(request): return request.param -@pytest.fixture(scope="session") +@pytest.fixture def create_agent_runnable(agent_runnable_type, chat_openai_client): """Create different runnable forms of the same agent and model as a fixture.""" @@ -162,7 +188,7 @@ def _create_runnable_sequence(*args, **kwargs): raise NotImplementedError -@pytest.fixture(scope="session") +@pytest.fixture def validate_agent_output(agent_runnable_type): def _unpack_messages(response): if isinstance(response, list) and not any(response): @@ -173,7 +199,20 @@ def _unpack_messages(response): # Messages are packaged into nested dicts with a "model" or "tool_call" key, a "message" key, # which contains a list with one or more messages in order. To unpack everything, # we need to unpack the dictionaries values and extract the messasges lists, then flatten them. - messages_packed = [next(iter(event.values()))["messages"] for event in response] + try: + messages_packed = [next(iter(event.values()))["messages"] for event in response] + except TypeError: + messages_packed = [] + for event in response: + for value in event.values(): + if isinstance(value, dict): + try: + dict_content = next(iter(value.values())) + if dict_content and ("messages" in dict_content): + messages_packed.append(dict_content["messages"]) + except StopIteration: + pass + return list(itertools.chain.from_iterable(messages_packed)) # invoke returns a Response object that contains the messages directly @@ -230,39 +269,137 @@ def _validate_agent_output(response): return _validate_agent_output -@pytest.fixture(scope="session", params=["invoke", "ainvoke", "stream", "astream"]) -def exercise_agent(request, loop, validate_agent_output, agent_runnable_type): +# This is used for `stream_events` and `astream_events` +@pytest.fixture +def validate_agent_event_output(agent_runnable_type): + def _unpack_messages(response): + if isinstance(response, list) and not any(response): + # Only None are returned from RunnableSeq.stream(), avoid the crash + return [] + elif isinstance(response, list): + # stream returns a list of events + # Messages are packaged into nested dicts with a "model" or "tool_call" key, a "message" key, + # which contains a list with one or more messages in order. To unpack everything, + # we need to unpack the dictionaries values and extract the messasges lists, then flatten them. + try: + messages_packed = [next(iter(event.values()))["messages"] for event in response] + except TypeError: + messages_packed = [] + for event in response: + for value in event.values(): + if isinstance(value, dict): + try: + dict_content = next(iter(value.values())) + if dict_content and ("messages" in dict_content): + messages_packed.append(dict_content["messages"]) + except StopIteration: + pass + + return list(itertools.chain.from_iterable(messages_packed)) + + # invoke returns a Response object that contains the messages directly + return response["messages"] + + def _validate_agent_event_output(response): + messages = _unpack_messages(response) + if agent_runnable_type == "create_agent": + for event in messages: + if isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "stop": + assert event.content == EXPECTED_AGENT_RESPONSE + elif isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "tool_calls": + assert event.tool_calls + elif isinstance(event, ToolMessage): + assert event.content == EXPECTED_TOOL_OUTPUT + + elif agent_runnable_type == "RunnableSeq": + # stream and astream do not directly output anything for RunnableSeq, and can't be validated. + for event in messages: + if isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "stop": + assert event.content == EXPECTED_AGENT_RESPONSE + elif isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "tool_calls": + assert event.tool_calls + elif isinstance(event, ToolMessage): + assert event.content == EXPECTED_TOOL_OUTPUT + + elif agent_runnable_type == "StateGraph": + for event in messages: + if isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "stop": + assert event.content == EXPECTED_AGENT_RESPONSE + elif isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "tool_calls": + assert event.tool_calls + elif isinstance(event, ToolMessage): + assert event.content == EXPECTED_TOOL_OUTPUT + + elif agent_runnable_type == "RunnableSequence": + for event in messages: + if isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "stop": + assert event.content == EXPECTED_AGENT_RESPONSE + elif isinstance(event, AIMessage) and event.response_metadata["finish_reason"] == "tool_calls": + assert event.tool_calls + elif isinstance(event, ToolMessage): + assert event.content == EXPECTED_TOOL_OUTPUT + + else: + raise NotImplementedError + + return _validate_agent_event_output + + +@pytest.fixture( + params=[ + {"run_method": "invoke", "version": "v1"}, + {"run_method": "invoke", "version": "v2"}, + {"run_method": "ainvoke", "version": "v1"}, + {"run_method": "ainvoke", "version": "v2"}, + {"run_method": "stream", "version": "v1"}, + {"run_method": "stream", "version": "v2"}, + {"run_method": "astream", "version": "v1"}, + {"run_method": "astream", "version": "v2"}, + {"run_method": "astream_events", "version": "v1"}, + {"run_method": "astream_events", "version": "v2"}, + ] +) +def exercise_agent(request, loop, validate_agent_output, validate_agent_event_output, agent_runnable_type): def _exercise_agent(agent, prompt): - if request.param == "invoke": - response = agent.invoke(prompt) + if method == "invoke": + response = agent.invoke(prompt, version=version) validate_agent_output(response) return response - elif request.param == "ainvoke": - response = loop.run_until_complete(agent.ainvoke(prompt)) + elif method == "ainvoke": + response = loop.run_until_complete(agent.ainvoke(prompt, version=version)) validate_agent_output(response) return response - elif request.param == "stream": - response = list(agent.stream(prompt)) + elif method == "stream": + response = list(agent.stream(prompt, version=version)) validate_agent_output(response) return response - elif request.param == "astream": + elif method == "astream": async def _exercise_agen(): - return [event async for event in agent.astream(prompt)] + return [event async for event in agent.astream(prompt, version=version)] response = loop.run_until_complete(_exercise_agen()) validate_agent_output(response) return response + elif method == "astream_events": + + async def _exercise_agen(): + return [event async for event in agent.astream_events(prompt, version=version)] + + response = loop.run_until_complete(_exercise_agen()) + validate_agent_event_output(response) + return response else: raise NotImplementedError - _exercise_agent._called_method = request.param # Used for metric names + method, version = request.param.get("run_method"), request.param.get("version") + _exercise_agent._called_method = method # Used for metric names # Expected number of events for a full run of the agent if agent_runnable_type != "RunnableSequence": _exercise_agent._expected_event_count = 11 _exercise_agent._expected_event_count_error = 5 - elif request.param in {"invoke", "ainvoke"}: + elif method in ("invoke", "ainvoke"): _exercise_agent._expected_event_count = 14 _exercise_agent._expected_event_count_error = 7 else: @@ -272,124 +409,14 @@ async def _exercise_agen(): return _exercise_agent -@pytest.fixture(scope="session") +@pytest.fixture def method_name(exercise_agent, agent_runnable_type): + method = exercise_agent._called_method + # Only AgentObjectProxy (used for create_agent) instruments `astream_events` directly. + # For StateGraph/RunnableSeq/RunnableSequence, `astream_events` delegates to `astream`, + # so the recorded metric is "astream". + if agent_runnable_type != "create_agent" and method == "astream_events": + return "astream" if agent_runnable_type == "StateGraph": - return "invoke" if exercise_agent._called_method in {"invoke", "stream"} else "ainvoke" - return exercise_agent._called_method - - -@pytest.fixture(autouse=True, scope="session") -def openai_server(wrap_httpx_client_send, wrap_stream_iter_events): - """ - This fixture will either create a mocked backend for testing purposes, or will - set up an audit log file to log responses of the real OpenAI backend to a file. - The behavior can be controlled by setting NEW_RELIC_TESTING_RECORD_OPENAI_RESPONSES=1 as - an environment variable to run using the real OpenAI backend. (Default: mocking) - """ - from newrelic.core.config import _environ_as_bool - - if _environ_as_bool("NEW_RELIC_TESTING_RECORD_OPENAI_RESPONSES", False): - wrap_function_wrapper("httpx._client", "Client.send", wrap_httpx_client_send) - wrap_function_wrapper("openai._streaming", "Stream._iter_events", wrap_stream_iter_events) - yield # Run tests - # Write responses to audit log - with OPENAI_AUDIT_LOG_FILE.open("w") as audit_log_fp: - json.dump(OPENAI_AUDIT_LOG_CONTENTS, fp=audit_log_fp, indent=4) - else: - # We are mocking openai responses so we don't need to do anything in this case. - yield - - -@pytest.fixture(scope="session") -def wrap_httpx_client_send(): - def _wrap_httpx_client_send(wrapped, instance, args, kwargs): - bound_args = bind_args(wrapped, args, kwargs) - stream = bound_args.get("stream", False) - request = bound_args["request"] - - if not request: - return wrapped(*args, **kwargs) - - params = json.loads(request.content.decode("utf-8")) - prompt = extract_shortened_prompt(params) - - # Send request - response = wrapped(*args, **kwargs) - - if response.status_code >= 400 or response.status_code < 200: - prompt = "error" - - rheaders = response.headers - - headers = dict( - filter( - lambda k: ( - k[0].lower() in RECORDED_HEADERS - or k[0].lower().startswith("openai") - or k[0].lower().startswith("x-ratelimit") - ), - rheaders.items(), - ) - ) - - # Append response data to log - if stream: - OPENAI_AUDIT_LOG_CONTENTS[prompt] = [headers, response.status_code, []] - if prompt == "error": - OPENAI_AUDIT_LOG_CONTENTS[prompt][2] = json.loads(response.read()) - else: - body = json.loads(response.content.decode("utf-8")) - OPENAI_AUDIT_LOG_CONTENTS[prompt] = headers, response.status_code, body - return response - - return _wrap_httpx_client_send - - -class LLMStreamAuditLogProxy(ObjectProxy): - def __init__(self, wrapped): - super().__init__(wrapped) - - def __iter__(self): - return self - - # Make this Proxy a pass through to our instrumentation's proxy by passing along - # get attr and set attr calls to our instrumentation's proxy. - def __getattr__(self, attr): - return self.__wrapped__.__getattr__(attr) - - def __setattr__(self, attr, value): - return self.__wrapped__.__setattr__(attr, value) - - def __next__(self): - transaction = current_transaction() - if not transaction: - return self.__wrapped__.__next__() - - try: - return_val = self.__wrapped__.__next__() - if return_val: - prompt = list(OPENAI_AUDIT_LOG_CONTENTS.keys())[-1] - if not getattr(return_val, "data", "").startswith("[DONE]"): - OPENAI_AUDIT_LOG_CONTENTS[prompt][2].append(return_val.json()) - return return_val - except Exception: - raise - - def close(self): - return self.__wrapped__.close() - - -@pytest.fixture(scope="session") -def wrap_stream_iter_events(): - def _wrap_stream_iter_events(wrapped, instance, args, kwargs): - transaction = current_transaction() - - if not transaction: - return wrapped(*args, **kwargs) - - return_val = wrapped(*args, **kwargs) - proxied_return_val = LLMStreamAuditLogProxy(return_val) - return proxied_return_val - - return _wrap_stream_iter_events + return "invoke" if method in ("invoke", "stream", "stream_events") else "ainvoke" + return method diff --git a/tests/mlmodel_langchain/test_agents.py b/tests/mlmodel_langchain/test_agents.py index 6a1c471ecd..49fd8d47a2 100644 --- a/tests/mlmodel_langchain/test_agents.py +++ b/tests/mlmodel_langchain/test_agents.py @@ -33,7 +33,11 @@ from newrelic.common.object_names import callable_name from newrelic.common.object_wrapper import transient_function_wrapper -PROMPT = {"messages": [HumanMessage('Use a tool to add an exclamation to the word "Hello"')]} +PROMPT = { + "messages": [ + HumanMessage('Use a tool to add an exclamation to the word "Hello". Answer in one word with no formatting.') + ] +} ERROR_PROMPT = {"messages": [HumanMessage('Use a tool to add an exclamation to the word "exc"')]} SYNC_METHODS = {"invoke", "stream"} diff --git a/tests/mlmodel_langchain/test_chain.py b/tests/mlmodel_langchain/test_chain.py index cd693d1a69..bad3d0971e 100644 --- a/tests/mlmodel_langchain/test_chain.py +++ b/tests/mlmodel_langchain/test_chain.py @@ -18,6 +18,7 @@ import openai import pytest from langchain_community.vectorstores.faiss import FAISS +from langchain_openai import ChatOpenAI from testing_support.fixtures import reset_core_stats_engine, validate_attributes from testing_support.ml_testing_utils import ( disabled_ai_monitoring_record_content_settings, @@ -38,18 +39,29 @@ from newrelic.common.object_names import callable_name try: - from langchain_classic.chains import create_retrieval_chain from langchain_classic.chains.combine_documents import create_stuff_documents_chain from langchain_classic.chains.openai_functions import ( create_structured_output_chain, create_structured_output_runnable, ) + from langchain_classic.chains.retrieval import create_retrieval_chain from langchain_core.output_parsers import BaseOutputParser except ImportError: - from langchain.chains import create_retrieval_chain - from langchain.chains.combine_documents import create_stuff_documents_chain - from langchain.chains.openai_functions import create_structured_output_chain, create_structured_output_runnable - from langchain.schema import BaseOutputParser + # Before Langchain v1.3.9 + try: + from langchain_classic.chains import create_retrieval_chain + from langchain_classic.chains.combine_documents import create_stuff_documents_chain + from langchain_classic.chains.openai_functions import ( + create_structured_output_chain, + create_structured_output_runnable, + ) + from langchain_core.output_parsers import BaseOutputParser + except ImportError: + # Before Langchain v1.0.0 + from langchain.chains import create_retrieval_chain + from langchain.chains.combine_documents import create_stuff_documents_chain + from langchain.chains.openai_functions import create_structured_output_chain, create_structured_output_runnable + from langchain.schema import BaseOutputParser chat_completion_recorded_events_invoke_langchain_error = [ @@ -386,298 +398,552 @@ recorded_events_retrieval_chain_response = [ [ - {"type": "LlmEmbedding"}, + {"timestamp": None, "type": "LlmEmbedding"}, { + "duration": None, "id": None, - "span_id": None, - "trace_id": "trace-id", + "ingest_source": "Python", + "input": "[[3923, 374, 220, 17, 489, 220, 19, 30]]", "request.model": "text-embedding-ada-002", "request_id": None, - "duration": None, "response.model": "text-embedding-ada-002-v2", "response.organization": "nr-test-org", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, - "response.headers.ratelimitLimitTokens": 10000000, + "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 9999992, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.usage.total_tokens": 8, + "span_id": None, + "trace_id": "trace-id", "vendor": "openai", - "ingest_source": "Python", - "input": "[[3923, 374, 220, 17, 489, 220, 19, 30]]", }, ], [ - {"type": "LlmEmbedding"}, + {"timestamp": None, "type": "LlmEmbedding"}, { + "duration": None, "id": None, - "span_id": None, - "trace_id": "trace-id", + "ingest_source": "Python", + "input": "[[10590]]", "request.model": "text-embedding-ada-002", "request_id": None, - "duration": None, "response.model": "text-embedding-ada-002-v2", "response.organization": "nr-test-org", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, - "response.headers.ratelimitLimitTokens": 10000000, + "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 9999998, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.usage.total_tokens": 1, + "span_id": None, + "trace_id": "trace-id", "vendor": "openai", - "ingest_source": "Python", - "input": "[[10590]]", }, ], [ - {"type": "LlmVectorSearch"}, + {"timestamp": None, "type": "LlmVectorSearch"}, { - "request.k": 4, "duration": None, + "id": None, + "ingest_source": "Python", + "request.k": 4, + "request.query": "math", "response.number_of_documents": 1, "span_id": None, "trace_id": "trace-id", - "id": None, "vendor": "langchain", - "ingest_source": "Python", - "request.query": "math", }, ], [ - {"type": "LlmVectorSearchResult"}, + {"timestamp": None, "type": "LlmVectorSearchResult"}, { "id": None, + "ingest_source": "Python", + "page_content": "What is 2 + 4?", "search_id": None, "sequence": 0, "span_id": None, "trace_id": "trace-id", "vendor": "langchain", - "ingest_source": "Python", - "page_content": "What is 2 + 4?", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, - "request_id": None, + "ingest_source": "Python", + "request_id": "", + "response.number_of_messages": 2, "span_id": None, + "timestamp": None, "trace_id": "trace-id", + "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { "completion_id": None, - "sequence": 1, + "content": "{'input': 'math'}", + "id": None, + "ingest_source": "Python", + "request_id": "", + "role": "user", + "sequence": 0, + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { + "completion_id": None, + "content": "page_content='What is 2 + 4?'", + "id": None, "ingest_source": "Python", "is_response": True, + "request_id": "", "role": "assistant", + "sequence": 1, + "span_id": None, + "trace_id": "trace-id", + "vendor": "langchain", "virtual_llm": True, - "content": "page_content='What is 2 + 4?'", }, ], [ - {"type": "LlmChatCompletionSummary"}, + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, - "timestamp": None, - "span_id": None, - "trace_id": "trace-id", - "request.model": "gpt-3.5-turbo", - "request.temperature": 0.7, - "vendor": "openai", "ingest_source": "Python", + "request.model": "gpt-3.5-turbo", "request_id": None, - "duration": None, "response.model": "gpt-3.5-turbo-0125", - "response.organization": "nr-test-org", "response.choices.finish_reason": "stop", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 49999927, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", + "response.usage.completion_tokens": 210, "response.usage.prompt_tokens": 73, - "response.usage.completion_tokens": 337, - "response.usage.total_tokens": 410, + "response.usage.total_tokens": 283, "response.number_of_messages": 3, + "response.organization": "nr-test-org", + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", + "vendor": "openai", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionMessage"}, { + "completion_id": None, "id": None, - "timestamp": None, + "ingest_source": "Python", "request_id": None, - "span_id": None, - "trace_id": "trace-id", + "response.model": "gpt-3.5-turbo-0125", "role": "system", - "completion_id": None, "sequence": 0, - "response.model": "gpt-3.5-turbo-0125", + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", "vendor": "openai", "token_count": 0, - "ingest_source": "Python", "content": "You are a generator of quiz questions for a seminar. Use the following pieces of retrieved context to generate 5 multiple choice questions (A,B,C,D) on the subject matter. Use a three sentence maximum and keep the answer concise. Render the output as HTML\n\nWhat is 2 + 4?", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionMessage"}, { + "completion_id": None, + "content": "math", "id": None, - "timestamp": None, "request_id": None, - "span_id": None, - "trace_id": "trace-id", + "response.model": "gpt-3.5-turbo-0125", "role": "user", - "completion_id": None, "sequence": 1, - "response.model": "gpt-3.5-turbo-0125", + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", "vendor": "openai", "token_count": 0, "ingest_source": "Python", - "content": "math", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionMessage"}, { + "completion_id": None, + "content": "```html\n" + "1. What is the result of 5 x 3?\n" + " A) 12\n" + " B) 15\n" + " C) 18\n" + " D) 20\n" + "\n" + "2. What is the solution to 10 / 2?\n" + " A) 3\n" + " B) 4\n" + " C) 5\n" + " D) 6\n" + "\n" + "3. If a rectangle has a length of 8 and a width of 6, what is its area?\n" + " A) 12\n" + " B) 24\n" + " C) 36\n" + " D) 48\n" + "\n" + "4. What is the value of 3 squared?\n" + " A) 6\n" + " B) 8\n" + " C) 9\n" + " D) 12\n" + "\n" + "5. If a store sells a shirt for $20 and a customer buys 3 shirts, how much " + "do they owe?\n" + " A) $40\n" + " B) $50\n" + " C) $60\n" + " D) $70\n" + "```\n", "id": None, "request_id": None, - "span_id": None, - "trace_id": "trace-id", + "response.model": "gpt-3.5-turbo-0125", "role": "assistant", - "completion_id": None, "sequence": 2, - "response.model": "gpt-3.5-turbo-0125", + "span_id": None, + "trace_id": "trace-id", "vendor": "openai", "token_count": 0, "ingest_source": "Python", "is_response": True, - "content": "```html\n\n\n\n Math Quiz\n\n\n

Math Quiz Questions

\n
    \n
  1. What is the result of 5 + 3?
    2. \n \n
  2. What is the product of 6 x 7?
    3. \n \n
  3. What is the square root of 64?
    4. \n \n
  4. What is the result of 12 / 4?
    5. \n \n
  5. What is the sum of 15 + 9?
    6. \n \n
\n\n\n```", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, - "timestamp": None, - "request_id": None, + "ingest_source": "Python", + "request.model": "gpt-3.5-turbo", + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "response.number_of_messages": 2, "span_id": None, + "timestamp": None, "trace_id": "trace-id", - "completion_id": None, - "sequence": 0, - "response.model": "gpt-3.5-turbo-0125", "vendor": "langchain", - "ingest_source": "Python", - "role": "user", "virtual_llm": True, - "content": "{'input': 'math', 'context': [Document(id='1234', metadata={}, page_content='What is 2 + 4?')]}", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionMessage"}, { + "completion_id": None, + "content": "{'input': 'math', 'context': [Document(id='1234', metadata={}, " + "page_content='What is 2 + 4?')]}", "id": None, - "request_id": None, + "ingest_source": "Python", + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "role": "user", + "sequence": 0, "span_id": None, + "timestamp": None, "trace_id": "trace-id", - "completion_id": None, - "sequence": 1, - "response.model": "gpt-3.5-turbo-0125", "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { + "completion_id": None, + "content": "```html\n" + "1. What is the result of 5 x 3?\n" + " A) 12\n" + " B) 15\n" + " C) 18\n" + " D) 20\n" + "\n" + "2. What is the solution to 10 / 2?\n" + " A) 3\n" + " B) 4\n" + " C) 5\n" + " D) 6\n" + "\n" + "3. If a rectangle has a length of 8 and a width of 6, what is " + "its area?\n" + " A) 12\n" + " B) 24\n" + " C) 36\n" + " D) 48\n" + "\n" + "4. What is the value of 3 squared?\n" + " A) 6\n" + " B) 8\n" + " C) 9\n" + " D) 12\n" + "\n" + "5. If a store sells a shirt for $20 and a customer buys 3 " + "shirts, how much do they owe?\n" + " A) $40\n" + " B) $50\n" + " C) $60\n" + " D) $70\n" + "```\n", + "id": None, "ingest_source": "Python", - "role": "assistant", "is_response": True, + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "role": "assistant", + "sequence": 1, + "span_id": None, + "trace_id": "trace-id", + "vendor": "langchain", "virtual_llm": True, - "content": "```html\n\n\n\n Math Quiz\n\n\n

Math Quiz Questions

\n
    \n
  1. What is the result of 5 + 3?
    2. \n \n
  2. What is the product of 6 x 7?
    3. \n \n
  3. What is the square root of 64?
    4. \n \n
  4. What is the result of 12 / 4?
    5. \n \n
  5. What is the sum of 15 + 9?
    6. \n \n
\n\n\n```", }, ], [ - {"type": "LlmChatCompletionMessage"}, + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, - "request_id": None, + "ingest_source": "Python", + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "response.number_of_messages": 2, "span_id": None, + "timestamp": None, "trace_id": "trace-id", + "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { "completion_id": None, - "sequence": 1, + "content": "{'input': 'math'}", + "id": None, + "ingest_source": "Python", + "request_id": "", "response.model": "gpt-3.5-turbo-0125", + "role": "user", + "sequence": 0, + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { + "completion_id": None, + "content": "{'input': 'math', 'context': [Document(id='1234', metadata={}, " + "page_content='What is 2 + 4?')], 'answer': '```html\\n1. What " + "is the result of 5 x 3?\\n A) 12\\n B) 15\\n C) 18\\n " + "D) 20\\n\\n2. What is the solution to 10 / 2?\\n A) 3\\n B) " + "4\\n C) 5\\n D) 6\\n\\n3. If a rectangle has a length of 8 " + "and a width of 6, what is its area?\\n A) 12\\n B) 24\\n " + "C) 36\\n D) 48\\n\\n4. What is the value of 3 squared?\\n " + "A) 6\\n B) 8\\n C) 9\\n D) 12\\n\\n5. If a store sells a " + "shirt for $20 and a customer buys 3 shirts, how much do they " + "owe?\\n A) $40\\n B) $50\\n C) $60\\n D) $70\\n```\\n'}", + "id": None, "ingest_source": "Python", - "role": "assistant", "is_response": True, + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "role": "assistant", + "sequence": 1, + "span_id": None, + "trace_id": "trace-id", + "vendor": "langchain", "virtual_llm": True, - "content": "{'input': 'math', 'context': [Document(id='1234', metadata={}, page_content='What is 2 + 4?')], 'answer': '```html\\n\\n\\n\\n Math Quiz\\n\\n\\n

Math Quiz Questions

\\n
    \\n
  1. What is the result of 5 + 3?
    2. \\n \\n
  2. What is the product of 6 x 7?
    3. \\n \\n
  3. What is the square root of 64?
    4. \\n \\n
  4. What is the result of 12 / 4?
    5. \\n \\n
  5. What is the sum of 15 + 9?
    6. \\n \\n
\\n\\n\\n```'}", }, ], ] chat_completion_recorded_events_str_response = [ - ( - {"type": "LlmChatCompletionSummary"}, + [ + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, - "timestamp": None, + "ingest_source": "Python", + "llm.context": "attr", "llm.conversation_id": "my-awesome-id", "llm.foo": "bar", + "request.model": "gpt-3.5-turbo", + "request_id": None, + "response.choices.finish_reason": "stop", + "response.headers.llmVersion": "2020-10-01", + "response.headers.ratelimitLimitRequests": 10000, + "response.headers.ratelimitLimitTokens": 50000000, + "response.headers.ratelimitRemainingRequests": 9999, + "response.headers.ratelimitRemainingTokens": 49999975, + "response.headers.ratelimitResetRequests": "6ms", + "response.headers.ratelimitResetTokens": "0s", + "response.model": "gpt-3.5-turbo-0125", + "response.number_of_messages": 3, + "response.organization": "nr-test-org", + "response.usage.completion_tokens": 2, + "response.usage.prompt_tokens": 46, + "response.usage.total_tokens": 48, "span_id": None, + "timestamp": None, "trace_id": "trace-id", - "vendor": "langchain", + "vendor": "openai", + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { + "completion_id": None, + "content": "You are a helpful assistant who generates a random first name. A user will pass in a first letter, and " + "you should generate a name that starts with that first letter.", + "id": None, "ingest_source": "Python", - "virtual_llm": True, + "llm.context": "attr", + "llm.conversation_id": "my-awesome-id", + "llm.foo": "bar", "request_id": None, - "duration": None, - "request.model": "gpt-3.5-turbo", "response.model": "gpt-3.5-turbo-0125", - "response.number_of_messages": 2, - "metadata.id": "123", + "role": "system", + "sequence": 0, + "span_id": None, + "timestamp": None, + "token_count": 0, + "trace_id": "trace-id", + "vendor": "openai", }, - ), - ( - {"type": "LlmChatCompletionMessage"}, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, { + "completion_id": None, + "content": "M", "id": None, - "timestamp": None, + "ingest_source": "Python", + "llm.context": "attr", "llm.conversation_id": "my-awesome-id", "llm.foo": "bar", "request_id": None, + "response.model": "gpt-3.5-turbo-0125", + "role": "user", + "sequence": 1, "span_id": None, + "timestamp": None, + "token_count": 0, "trace_id": "trace-id", - "content": "{'text': 'M'}", + "vendor": "openai", + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { "completion_id": None, - "sequence": 0, - "response.model": "gpt-3.5-turbo-0125", - "vendor": "langchain", + "content": "Matilda", + "id": None, "ingest_source": "Python", - "role": "user", - "virtual_llm": True, + "is_response": True, + "llm.context": "attr", + "llm.conversation_id": "my-awesome-id", + "llm.foo": "bar", + "request_id": None, + "response.model": "gpt-3.5-turbo-0125", + "role": "assistant", + "sequence": 2, + "span_id": None, + "token_count": 0, + "trace_id": "trace-id", + "vendor": "openai", }, - ), - ( - {"type": "LlmChatCompletionMessage"}, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionSummary"}, { + "duration": None, "id": None, + "ingest_source": "Python", + "llm.context": "attr", "llm.conversation_id": "my-awesome-id", "llm.foo": "bar", - "request_id": None, + "metadata.id": "123", + "request.model": "gpt-3.5-turbo", + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "response.number_of_messages": 2, "span_id": None, + "timestamp": None, "trace_id": "trace-id", - "content": "Milo", + "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { "completion_id": None, - "sequence": 1, + "content": "{'text': 'M'}", + "id": None, + "ingest_source": "Python", + "llm.context": "attr", + "llm.conversation_id": "my-awesome-id", + "llm.foo": "bar", + "request_id": "", "response.model": "gpt-3.5-turbo-0125", + "role": "user", + "sequence": 0, + "span_id": None, + "timestamp": None, + "trace_id": "trace-id", "vendor": "langchain", + "virtual_llm": True, + }, + ], + [ + {"timestamp": None, "type": "LlmChatCompletionMessage"}, + { + "completion_id": None, + "content": "Matilda", + "id": None, "ingest_source": "Python", - "role": "assistant", "is_response": True, + "llm.context": "attr", + "llm.conversation_id": "my-awesome-id", + "llm.foo": "bar", + "request_id": "", + "response.model": "gpt-3.5-turbo-0125", + "role": "assistant", + "sequence": 1, + "span_id": None, + "trace_id": "trace-id", + "vendor": "langchain", "virtual_llm": True, }, - ), + ], ] + chat_completion_recorded_events_list_response = [ ( {"type": "LlmChatCompletionSummary"}, @@ -1077,7 +1343,6 @@ def _test(): ) def test_langchain_chain_error_in_openai( set_trace_info, - chat_openai_client, json_schema, prompt_openai_error, create_function, @@ -1085,6 +1350,7 @@ def test_langchain_chain_error_in_openai( call_function_args, call_function_kwargs, expected_events, + monkeypatch, ): @reset_core_stats_engine() @validate_transaction_error_event_count(1) @@ -1104,7 +1370,11 @@ def _test(): add_custom_attribute("llm.foo", "bar") add_custom_attribute("non_llm_attr", "python-agent") - runnable = create_function(json_schema, chat_openai_client, prompt_openai_error) + # Ensure the OpenAI key is set incorrectly for this test + monkeypatch.delenv("OPENAI_API_KEY") + chat_openai_client_no_api_key = ChatOpenAI(api_key="FAKE-OPENAI-KEY") + + runnable = create_function(json_schema, chat_openai_client_no_api_key, prompt_openai_error) with pytest.raises(openai.AuthenticationError): with WithLlmCustomAttributes({"context": "attr"}): @@ -1438,6 +1708,8 @@ def _test(): _test() +# NOTE: To reproduce these tests, set the `OPENAI_API_KEY` +# environment variable to an invalid key. @pytest.mark.parametrize( "create_function,call_function,call_function_args,call_function_kwargs,expected_events", ( @@ -1477,7 +1749,6 @@ def _test(): ) def test_async_langchain_chain_error_in_openai( set_trace_info, - chat_openai_client, json_schema, prompt_openai_error, create_function, @@ -1485,6 +1756,7 @@ def test_async_langchain_chain_error_in_openai( call_function_args, call_function_kwargs, expected_events, + monkeypatch, loop, ): @reset_core_stats_engine() @@ -1505,7 +1777,11 @@ def _test(): add_custom_attribute("llm.foo", "bar") add_custom_attribute("non_llm_attr", "python-agent") - runnable = create_function(json_schema, chat_openai_client, prompt_openai_error) + # Ensure the OpenAI key is set incorrectly for this test + monkeypatch.delenv("OPENAI_API_KEY") + chat_openai_client_no_api_key = ChatOpenAI(api_key="FAKE-OPENAI-KEY") + + runnable = create_function(json_schema, chat_openai_client_no_api_key, prompt_openai_error) with pytest.raises(openai.AuthenticationError): with WithLlmCustomAttributes({"context": "attr"}): diff --git a/tests/mlmodel_langchain/test_state_graph.py b/tests/mlmodel_langchain/test_state_graph.py index 4c857e2206..6f571ae10b 100644 --- a/tests/mlmodel_langchain/test_state_graph.py +++ b/tests/mlmodel_langchain/test_state_graph.py @@ -21,7 +21,11 @@ from newrelic.api.background_task import background_task CLIENT_PROMPT = {"messages": [HumanMessage("What is the capital of France? Answer in one word.")]} -AGENT_PROMPT = {"messages": [HumanMessage('Use a tool to add an exclamation to the word "Hello"')]} +AGENT_PROMPT = { + "messages": [ + HumanMessage('Use a tool to add an exclamation to the word "Hello". Answer in one word with no formatting.') + ] +} client_recorded_events = [ [ @@ -31,22 +35,21 @@ "id": None, "ingest_source": "Python", "request.model": "gpt-3.5-turbo", - "request.temperature": 0.7, - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.choices.finish_reason": "stop", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 49999985, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.model": "gpt-3.5-turbo-0125", "response.number_of_messages": 2, "response.organization": "nr-test-org", - "response.usage.completion_tokens": 2, + "response.usage.completion_tokens": 1, "response.usage.prompt_tokens": 19, - "response.usage.total_tokens": 21, + "response.usage.total_tokens": 20, "span_id": None, "timestamp": None, "trace_id": None, @@ -58,9 +61,9 @@ { "completion_id": None, "content": "What is the capital of France? Answer in one word.", - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i-0", + "id": None, "ingest_source": "Python", - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "user", "sequence": 0, @@ -76,10 +79,10 @@ { "completion_id": None, "content": "Paris", - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i-1", + "id": None, "ingest_source": "Python", "is_response": True, - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "assistant", "sequence": 1, @@ -99,14 +102,13 @@ "id": None, "ingest_source": "Python", "request.model": "gpt-3.5-turbo", - "request.temperature": 0.7, - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.choices.finish_reason": "stop", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 49999985, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.model": "gpt-3.5-turbo-0125", @@ -114,9 +116,9 @@ "response.organization": "nr-test-org", # langchain's ChatOpenAI.stream() passes stream_options={"include_usage": True} # by default, so the final usage chunk is captured and these are populated. - "response.usage.completion_tokens": 2, + "response.usage.completion_tokens": 1, "response.usage.prompt_tokens": 19, - "response.usage.total_tokens": 21, + "response.usage.total_tokens": 20, "span_id": None, "time_to_first_token": None, "timestamp": None, @@ -129,9 +131,9 @@ { "completion_id": None, "content": "What is the capital of France? Answer in one word.", - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i-0", + "id": None, "ingest_source": "Python", - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "user", "sequence": 0, @@ -147,10 +149,10 @@ { "completion_id": None, "content": "Paris", - "id": "chatcmpl-DelITaJCJy951hwON0psdz2H9dF7i-1", + "id": None, "ingest_source": "Python", "is_response": True, - "request_id": "req_22204b237d22427fbfd99c665d8a9964", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "assistant", "sequence": 1, @@ -171,22 +173,21 @@ "id": None, "ingest_source": "Python", "request.model": "gpt-3.5-turbo", - "request.temperature": 0.7, - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": None, "response.choices.finish_reason": "tool_calls", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 49999974, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.model": "gpt-3.5-turbo-0125", "response.number_of_messages": 2, "response.organization": "nr-test-org", "response.usage.completion_tokens": 15, - "response.usage.prompt_tokens": 70, - "response.usage.total_tokens": 85, + "response.usage.prompt_tokens": 78, + "response.usage.total_tokens": 93, "span_id": None, "timestamp": None, "trace_id": None, @@ -198,9 +199,9 @@ { "completion_id": None, "content": "You are a text manipulation algorithm.", - "id": "chatcmpl-CukvsGfSQihNO9I3FTqaNKERWtUca-0", + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": "req_c78b73e8e4c84348bd4effe1cf6eff49", "response.model": "gpt-3.5-turbo-0125", "role": "system", "sequence": 0, @@ -215,10 +216,10 @@ {"timestamp": None, "type": "LlmChatCompletionMessage"}, { "completion_id": None, - "content": 'Use a tool to add an exclamation to the word "Hello"', - "id": "chatcmpl-CukvsGfSQihNO9I3FTqaNKERWtUca-1", + "content": 'Use a tool to add an exclamation to the word "Hello". Answer in one word with no formatting.', + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": "req_c78b73e8e4c84348bd4effe1cf6eff49", "response.model": "gpt-3.5-turbo-0125", "role": "user", "sequence": 1, @@ -239,7 +240,7 @@ "input": "{'message': 'Hello'}", "name": "add_exclamation", "output": "Hello!", - "run_id": "call_ymnsNurMgr3atFVr7BnJ2XYK", + "run_id": None, "span_id": None, "trace_id": None, "vendor": "langchain", @@ -252,22 +253,21 @@ "id": None, "ingest_source": "Python", "request.model": "gpt-3.5-turbo", - "request.temperature": 0.7, - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": None, "response.choices.finish_reason": "stop", "response.headers.llmVersion": "2020-10-01", "response.headers.ratelimitLimitRequests": 10000, "response.headers.ratelimitLimitTokens": 50000000, "response.headers.ratelimitRemainingRequests": 9999, - "response.headers.ratelimitRemainingTokens": 49999970, + "response.headers.ratelimitRemainingTokens": 49999975, "response.headers.ratelimitResetRequests": "6ms", "response.headers.ratelimitResetTokens": "0s", "response.model": "gpt-3.5-turbo-0125", "response.number_of_messages": 5, "response.organization": "nr-test-org", - "response.usage.completion_tokens": 16, - "response.usage.prompt_tokens": 96, - "response.usage.total_tokens": 112, + "response.usage.completion_tokens": 3, + "response.usage.prompt_tokens": 107, + "response.usage.total_tokens": 110, "span_id": None, "timestamp": None, "trace_id": None, @@ -279,9 +279,9 @@ { "completion_id": None, "content": "You are a text manipulation algorithm.", - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1-0", + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": "req_c78b73e8e4c84348bd4effe1cf6eff49", "response.model": "gpt-3.5-turbo-0125", "role": "system", "sequence": 0, @@ -296,10 +296,10 @@ {"timestamp": None, "type": "LlmChatCompletionMessage"}, { "completion_id": None, - "content": 'Use a tool to add an exclamation to the word "Hello"', - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1-1", + "content": 'Use a tool to add an exclamation to the word "Hello". Answer in one word with no formatting.', + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": "req_c78b73e8e4c84348bd4effe1cf6eff49", "response.model": "gpt-3.5-turbo-0125", "role": "user", "sequence": 1, @@ -314,9 +314,9 @@ {"timestamp": None, "type": "LlmChatCompletionMessage"}, { "completion_id": None, - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1-2", + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "assistant", "sequence": 2, @@ -332,9 +332,9 @@ { "completion_id": None, "content": "Hello!", - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1-3", + "id": None, "ingest_source": "Python", - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "tool", "sequence": 3, @@ -349,11 +349,11 @@ {"timestamp": None, "type": "LlmChatCompletionMessage"}, { "completion_id": None, - "content": 'The word "Hello" with an exclamation mark added is "Hello!"', - "id": "chatcmpl-CukvtgYHPS8HRHqCQiQgQrs7a2Tx1-4", + "content": "Hello!", + "id": None, "ingest_source": "Python", "is_response": True, - "request_id": "req_619548c272db4f1ab380b83de9fdedef", + "request_id": None, "response.model": "gpt-3.5-turbo-0125", "role": "assistant", "sequence": 4, @@ -396,7 +396,7 @@ def _build_graph(node): return builder.compile() -@pytest.fixture(scope="session") +@pytest.fixture def create_agent(chat_openai_client): def _create_agent(model="gpt-5.1", tools=None, system_prompt=None, name="my_agent"): from langchain.agents import create_agent diff --git a/tests/mlmodel_langchain/test_tools.py b/tests/mlmodel_langchain/test_tools.py index 3ad250fb45..e5a4b8dbef 100644 --- a/tests/mlmodel_langchain/test_tools.py +++ b/tests/mlmodel_langchain/test_tools.py @@ -34,7 +34,11 @@ from ._test_tools import add_exclamation, tool_method_name, tool_type -PROMPT = {"messages": [HumanMessage('Use a tool to add an exclamation to the word "Hello"')]} +PROMPT = { + "messages": [ + HumanMessage('Use a tool to add an exclamation to the word "Hello". Answer in one word with no formatting.') + ] +} ERROR_PROMPT = {"messages": [HumanMessage('Use a tool to add an exclamation to the word "exc"')]} SYNC_METHODS = {"invoke", "stream"} diff --git a/tests/testing_support/fixture/vcr.py b/tests/testing_support/fixture/vcr.py index 8d93a946aa..bf21995a62 100644 --- a/tests/testing_support/fixture/vcr.py +++ b/tests/testing_support/fixture/vcr.py @@ -79,6 +79,7 @@ raise ImportError("pytest-recording is required to use the vcr fixtures.") from exc import json +import os from pathlib import Path import pytest @@ -88,6 +89,7 @@ VCR_IGNORED_HEADERS = ["content-length", "traceparent", "tracestate", "user-agent", "x-goog-api-client"] VCR_REPLACE_HEADERS = [] # Must be tuples of (header_name, replacement_value) VCR_MATCH_ON = ["method", "scheme", "host", "port", "path", "body", "headers", "query"] +VCR_TIKTOKEN_ENCODINGS = [] # === Settings fixtures, required and overridable === @@ -217,6 +219,7 @@ def vcr_config( vcr_match_on, vcr_before_record_request, vcr_before_record_response, + vcr_cache_tiktoken_encodings, ): """ Combines the overridable settings fixtures into VCR.py's final configuration. @@ -297,3 +300,22 @@ def pytest_collection_modifyitems(items): """ for item in items: item.add_marker(pytest.mark.vcr) + + +@pytest.fixture +def vcr_cache_tiktoken_encodings(monkeypatch): + """Cache the tiktoken encodings before enabling VCR which blocks network access.""" + try: + import tiktoken + except ImportError: + return # tiktoken is not installed, skip caching + + # Set up temporary cache dir + tox_env_dir = os.environ.get("TOX_ENV_DIR", None) or Path.cwd() + cache_dir = Path(tox_env_dir) / ".tiktoken_cache" + monkeypatch.setenv("TIKTOKEN_CACHE_DIR", str(cache_dir)) + cache_dir.mkdir(parents=True, exist_ok=True) + + # Pre-fetch encodings used in tests + for encoding in VCR_TIKTOKEN_ENCODINGS: + tiktoken.get_encoding(encoding) diff --git a/tox.ini b/tox.ini index 30334daa1a..8f37351cce 100644 --- a/tox.ini +++ b/tox.ini @@ -168,9 +168,9 @@ envlist = python-framework_starlette-{py39,py310,py311,py312,py313,py314,py314t,pypy311}-starlettelatest, python-framework_strawberry-{py39,py310,py311,py312}-strawberry02352, python-framework_strawberry-{py39,py310,py311,py312,py313,py314,py314t}-strawberrylatest, - python-framework_tornado-{py39,py310,py311,py312,py313,py314,py314t}-tornadolatest, + ; python-framework_tornado-{py39,py310,py311,py312,py313,py314,py314t}-tornadolatest, ; Remove `python-framework_tornado-{py314,py314t}-tornadomaster` temporarily - python-framework_tornado-{py310,py311,py312,py313}-tornadomaster, + ; python-framework_tornado-{py310,py311,py312,py313}-tornadomaster, python-hybridagent_ariadne-{py39,py310,py311,py312,py313,py314,py314t}, python-hybridagent_dynamodb-{py39,py310,py311,py312,py313,py314,py314t}, python-hybridagent_fastapi-{py39,py310,py311,py312,py313,py314,py314t,pypy311}, @@ -214,6 +214,7 @@ deps = py39: pytest==8.4.2 WebTest==3.0.7 coverage + pytest-recording # Test Suite Dependencies adapter_asgiref-asgireflatest: asgiref @@ -399,9 +400,9 @@ deps = framework_strawberry: starlette framework_strawberry-strawberrylatest: strawberry-graphql framework_strawberry-strawberry02352: strawberry-graphql<0.236.0 - framework_tornado: pycurl - framework_tornado-tornadolatest: tornado - framework_tornado-tornadomaster: https://github.com/tornadoweb/tornado/archive/master.zip + ; framework_tornado: pycurl + ; framework_tornado-tornadolatest: tornado + ; framework_tornado-tornadomaster: https://github.com/tornadoweb/tornado/archive/master.zip hybridagent_aiopg: opentelemetry-api hybridagent_aiopg: opentelemetry-instrumentation-aiopg hybridagent_aiopg: aiopg @@ -469,6 +470,8 @@ deps = mlmodel_langchain: langchain-core mlmodel_langchain: langchain-community mlmodel_langchain: langchain-openai + mlmodel_langchain: langchain-classic + mlmodel_langchain: aiohttp<3.14.0 mlmodel_langchain: langgraph mlmodel_langchain: pypdf mlmodel_langchain: tiktoken @@ -500,9 +503,9 @@ setenv = without_extensions: NEW_RELIC_EXTENSIONS = false agent_features: NEW_RELIC_APDEX_T = 1000 framework_grpc: PYTHONPATH={toxinidir}/tests/:{toxinidir}/tests/framework_grpc/sample_application - framework_tornado: PYCURL_SSL_LIBRARY=openssl - framework_tornado: LDFLAGS=-L/usr/local/opt/openssl/lib - framework_tornado: CPPFLAGS=-I/usr/local/opt/openssl/include + ; framework_tornado: PYCURL_SSL_LIBRARY=openssl + ; framework_tornado: LDFLAGS=-L/usr/local/opt/openssl/lib + ; framework_tornado: CPPFLAGS=-I/usr/local/opt/openssl/include passenv = NEW_RELIC_DEVELOPER_MODE @@ -602,7 +605,7 @@ changedir = framework_sanic: tests/framework_sanic framework_starlette: tests/framework_starlette framework_strawberry: tests/framework_strawberry - framework_tornado: tests/framework_tornado + ; framework_tornado: tests/framework_tornado hybridagent_aiopg: tests/hybridagent_aiopg hybridagent_ariadne: tests/hybridagent_ariadne hybridagent_dynamodb: tests/hybridagent_dynamodb