diff --git a/langchain/evaluation/run_evaluators/base.py b/langchain/evaluation/run_evaluators/base.py index 0bc1ccbe..31aa047c 100644 --- a/langchain/evaluation/run_evaluators/base.py +++ b/langchain/evaluation/run_evaluators/base.py @@ -6,10 +6,13 @@ from typing import Any, Dict, List, Optional from langchainplus_sdk import EvaluationResult, RunEvaluator from langchainplus_sdk.schemas import Example, Run -from langchain.callbacks.manager import CallbackManagerForChainRun +from langchain.callbacks.manager import ( + AsyncCallbackManagerForChainRun, + CallbackManagerForChainRun, +) from langchain.chains.base import Chain from langchain.chains.llm import LLMChain -from langchain.schema import BaseOutputParser +from langchain.schema import RUN_KEY, BaseOutputParser class RunEvalInputMapper: @@ -59,8 +62,33 @@ class RunEvaluatorChain(Chain, RunEvaluator): example: Optional[Example] = inputs.get("example") chain_input = self.input_mapper.map(run, example) _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() - chain_output = self.eval_chain(chain_input, callbacks=_run_manager.get_child()) + callbacks = _run_manager.get_child() + chain_output = self.eval_chain( + chain_input, callbacks=callbacks, include_run_info=True + ) + run_info = chain_output[RUN_KEY] feedback = self.output_parser.parse_chain_output(chain_output) + feedback.evaluator_info[RUN_KEY] = run_info + return {"feedback": feedback} + + async def _acall( + self, + inputs: Dict[str, Any], + run_manager: AsyncCallbackManagerForChainRun | None = None, + ) -> Dict[str, Any]: + run: Run = inputs["run"] + example: Optional[Example] = inputs.get("example") + chain_input = self.input_mapper.map(run, example) + _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + chain_output = await self.eval_chain.acall( + chain_input, + callbacks=callbacks, + include_run_info=True, + ) + run_info = chain_output[RUN_KEY] + feedback = self.output_parser.parse_chain_output(chain_output) + feedback.evaluator_info[RUN_KEY] = run_info return {"feedback": feedback} def evaluate_run( @@ -68,3 +96,10 @@ class RunEvaluatorChain(Chain, RunEvaluator): ) -> EvaluationResult: """Evaluate an example.""" return self({"run": run, "example": example})["feedback"] + + async def aevaluate_run( + self, run: Run, example: Optional[Example] = None + ) -> EvaluationResult: + """Evaluate an example.""" + result = await self.acall({"run": run, "example": example}) + return result["feedback"] diff --git a/langchain/evaluation/run_evaluators/implementations.py b/langchain/evaluation/run_evaluators/implementations.py index cdd2bfee..a202d89d 100644 --- a/langchain/evaluation/run_evaluators/implementations.py +++ b/langchain/evaluation/run_evaluators/implementations.py @@ -43,7 +43,7 @@ class StringRunEvalInputMapper(RunEvalInputMapper, BaseModel): def map(self, run: Run, example: Optional[Example] = None) -> Dict[str, str]: """Maps the Run and Optional[Example] to a dictionary""" if run.outputs is None: - raise ValueError("Run outputs cannot be None.") + raise ValueError(f"Run {run.id} has no outputs.") data = { value: run.outputs.get(key) for key, value in self.prediction_map.items() diff --git a/langchain/experimental/client/tracing_datasets.ipynb b/langchain/experimental/client/tracing_datasets.ipynb index 7b3103ea..bcabf517 100644 --- a/langchain/experimental/client/tracing_datasets.ipynb +++ b/langchain/experimental/client/tracing_datasets.ipynb @@ -120,11 +120,11 @@ }, "outputs": [], "source": [ - "from langchain.llms import OpenAI\n", + "from langchain.chat_models import ChatOpenAI\n", "from langchain.agents import initialize_agent, load_tools\n", "from langchain.agents import AgentType\n", "\n", - "llm = OpenAI(temperature=0)\n", + "llm = ChatOpenAI(temperature=0)\n", "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n", "agent = initialize_agent(\n", " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False\n", @@ -138,51 +138,7 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n", - "Retrying langchain.llms.openai.acompletion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unknown format from LLM: This question cannot be answered using the numexpr library, as it does not involve any mathematical expressions.\n" - ] - }, - { - "data": { - "text/plain": [ - "['39,566,248 people live in Canada as of 2023.',\n", - " \"Romain Gavras is Dua Lipa's boyfriend and his age raised to the .43 power is 4.9373857399466665.\",\n", - " '3.991298452658078',\n", - " 'The shortest distance (air line) between Boston and Paris is 3,437.00 mi (5,531.32 km).',\n", - " 'The total number of points scored in the 2023 Super Bowl raised to the .23 power is 2.3086081644669734.',\n", - " ValueError('unknown format from LLM: This question cannot be answered using the numexpr library, as it does not involve any mathematical expressions.'),\n", - " 'The 2023 Super Bowl scored 3 more points than the 2022 Super Bowl.',\n", - " '1.9347796717823205',\n", - " 'Devin Booker, Kendall Jenner\\'s boyfriend, is 6\\' 5\" tall and his height raised to the .13 power is 1.27335715306192.',\n", - " '1213 divided by 4345 is 0.2791714614499425']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import asyncio\n", "\n", @@ -206,13 +162,12 @@ " return await agent.arun(input_example)\n", " except Exception as e:\n", " # The agent sometimes makes mistakes! These will be captured by the tracing.\n", - " print(e)\n", " return e\n", "\n", "\n", "for input_example in inputs:\n", " results.append(arun(agent, input_example))\n", - "await asyncio.gather(*results)" + "results = await asyncio.gather(*results)" ] }, { @@ -479,27 +434,6 @@ "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain failed for example fb07a1d4-e96e-45fe-a3cd-5113e174b017. Error: unknown format from LLM: Sorry, I cannot answer this question as it requires information that is not currently available.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processed examples: 2\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain failed for example f088cda6-3745-4f83-b8fa-e5c1038e81b2. Error: unknown format from LLM: Sorry, as an AI language model, I do not have access to personal information such as someone's age. Please provide a different math problem.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -511,36 +445,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Chain failed for example abb7259c-8136-4903-80b3-04644eebcc82. Error: Parsing LLM output produced both a final answer and a parse-able action: I need to use the search engine to find out who Dua Lipa's boyfriend is and then use the calculator to raise his age to the .43 power.\n", - "Action 1: Search\n", - "Action Input 1: \"Dua Lipa boyfriend\"\n", - "Observation 1: Anwar Hadid is Dua Lipa's boyfriend.\n", - "Action 2: Calculator\n", - "Action Input 2: 21^0.43\n", - "Observation 2: Anwar Hadid's age raised to the 0.43 power is approximately 3.87.\n", - "Thought: I now know the final answer.\n", - "Final Answer: Anwar Hadid is Dua Lipa's boyfriend and his age raised to the 0.43 power is approximately 3.87.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processed examples: 7\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chain failed for example 2123b7f1-3d3d-4eca-ba30-faf0dff75399. Error: Could not parse LLM output: `I need to subtract the score of the`\n" + "Chain failed for example 59fb1b4d-d935-4e43-b2a7-bc33fde841bb. Error: LLMMathChain._evaluate(\"\n", + "round(0.2791714614499425, 2)\n", + "\") raised error: 'VariableNode' object is not callable. Please try again with a valid numerical expression\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Processed examples: 9\r" + "Processed examples: 5\r" ] } ], @@ -622,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "35db4025-9183-4e5f-ba14-0b1b380f49c7", "metadata": { "tags": [] @@ -644,39 +558,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "20ab5a84-1d34-4532-8b4f-b12407f42a0e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "LangChain+ Client" - ], - "text/plain": [ - "LangChainPlusClient (API URL: https://dev.api.langchain.plus)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# TODO: Use this one above as well\n", - "from langchainplus_sdk import LangChainPlusClient\n", - "\n", - "client = LangChainPlusClient()\n", - "runs = list(client.list_runs(session_name=evaluation_session_name, execution_order=1, error=False))\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c23a51-1e0a-46d8-b04b-0e0627983232", + "execution_count": 27, + "id": "4c94a738-dcd3-442e-b8e7-dd36459f56e3", "metadata": { "tags": [] }, @@ -684,12 +567,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddf4e207965345c7b1ac27a5e3e677e8", + "model_id": "a185493c1af74cbaa0f9b10f32cf81c6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/44 [00:00LangChain+ Client" + ], + "text/plain": [ + "LangChainPlusClient (API URL: https://dev.api.langchain.plus)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "client" ] @@ -718,7 +619,7 @@ { "cell_type": "code", "execution_count": null, - "id": "daf7dc7f-a5b0-49be-a695-2a87e283e588", + "id": "a5037e54-2c5a-4993-9b46-2a98773d3079", "metadata": {}, "outputs": [], "source": [] diff --git a/poetry.lock b/poetry.lock index 249729e2..7806d55f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1685,13 +1685,13 @@ files = [ [[package]] name = "deeplake" -version = "3.5.4" +version = "3.6.0" description = "Activeloop Deep Lake" category = "main" optional = false python-versions = "*" files = [ - {file = "deeplake-3.5.4.tar.gz", hash = "sha256:a05eba10141fe1e5be5f0ed115a42da1320d6675d644b49790dc28485a87aa32"}, + {file = "deeplake-3.6.0.tar.gz", hash = "sha256:bf502ed4fcd19624e750c649b8dd2fb892529a29384c8a816bbb09005b763db1"}, ] [package.dependencies] @@ -1708,11 +1708,12 @@ pyjwt = "*" tqdm = "*" [package.extras] -all = ["IPython", "av (>=8.1.0)", "flask", "google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)", "laspy", "libdeeplake (==0.0.54)", "nibabel", "oauth2client (>=4.1.3,<4.2.0)", "pydicom"] +all = ["IPython", "av (>=8.1.0)", "azure-cli", "azure-identity", "azure-storage-blob", "flask", "google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)", "laspy", "libdeeplake (==0.0.55)", "nibabel", "oauth2client (>=4.1.3,<4.2.0)", "pydicom"] audio = ["av (>=8.1.0)"] av = ["av (>=8.1.0)"] +azure = ["azure-cli", "azure-identity", "azure-storage-blob"] dicom = ["nibabel", "pydicom"] -enterprise = ["libdeeplake (==0.0.54)", "pyjwt"] +enterprise = ["libdeeplake (==0.0.55)", "pyjwt"] gcp = ["google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "google-cloud-storage (>=1.42.0,<1.43.0)"] gdrive = ["google-api-python-client (>=2.31.0,<2.32.0)", "google-auth (>=2.0.1,<2.1.0)", "google-auth-oauthlib (>=0.4.5,<0.5.0)", "oauth2client (>=4.1.3,<4.2.0)"] medical = ["nibabel", "pydicom"] @@ -2524,14 +2525,14 @@ grpc = ["grpcio (>=1.44.0,<2.0.0dev)"] [[package]] name = "gptcache" -version = "0.1.29.1" +version = "0.1.30" description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications." category = "main" optional = false python-versions = ">=3.8.1" files = [ - {file = "gptcache-0.1.29.1-py3-none-any.whl", hash = "sha256:e281f1c7bf1f2cc72e0193d6e15ad3d50e7cb358d04332ef96b98f732aee8a74"}, - {file = "gptcache-0.1.29.1.tar.gz", hash = "sha256:c7c07c5300422feed302f4a140ff5e25e01f04885c81fb3388e07b3c3a82647a"}, + {file = "gptcache-0.1.30-py3-none-any.whl", hash = "sha256:57c37babe85161fbbe547cb036f6780b33232d70557ae085daccf3c032bc7b14"}, + {file = "gptcache-0.1.30.tar.gz", hash = "sha256:a69f600e286dee3e7f3b151c8b269778a4e6a7d5da409c01fbfbedf3239d0cd9"}, ] [package.dependencies] @@ -3969,14 +3970,14 @@ tests = ["pytest", "pytest-mock"] [[package]] name = "langchainplus-sdk" -version = "0.0.4" +version = "0.0.6" description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform." category = "main" optional = false python-versions = ">=3.8.1,<4.0" files = [ - 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