From 0308d11c4747fa4dedde7c2b6b6501bbb27a02cb Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Fri, 12 Apr 2024 11:10:54 -0700 Subject: [PATCH] [Python] Release W/ LLM Expectations (#596) - [x] change default dataset creation (should be unique per file) - [x] change env vars to use `LANGSMITH_` prefix Want feedback on: - [x] ergonomics: is this a general UX we want to support - [x] imports: should i re-implement string, embedding distance, etc.? - [x] Do we want default implementations? - [x] Any other we ought to include at the outset? - [x] I could also do a general `expect(value).is(...)` or something: want anything super generic like that? Example: ```python @unit(inputs=x, outputs=y) def test_output_semantically_close(): response = oai_client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Say hello!"}, ], ) # The embedding_distance call logs the embedding distance to LangSmith expect.embedding_distance( prediction=response.choices[0].message.content, reference="Hello!", # The following optional assertion logs a # pass/fail score to LangSmith # and raises an AssertionError if the assertion fails. ).to_be_less_than(0.5) # Compute damerau_levenshtein distance expect.string_distance( prediction=response.choices[0].message.content, reference="Hello!", # And then log a pass/fail score to LangSmith ).to_be_less_than(0.5) ``` The idea is it's still an easy onramp for developers to quickly write some scoring functions and get it running regularly in CI --- .../python-integration-tests/action.yml | 16 +- python/Makefile | 5 +- python/README.md | 12 +- python/langsmith/__init__.py | 18 + python/langsmith/_expect.py | 366 ++ python/langsmith/_internal/_edit_distance.py | 67 + .../_internal/_embedding_distance.py | 191 + python/langsmith/_testing.py | 182 +- python/langsmith/cli/main.py | 8 +- python/langsmith/client.py | 10 +- python/langsmith/env/_git.py | 18 +- python/langsmith/env/_runtime_env.py | 2 +- python/langsmith/evaluation/_arunner.py | 57 +- python/langsmith/evaluation/_runner.py | 61 +- python/langsmith/utils.py | 57 +- python/pyproject.toml | 2 +- .../39641eac-b198-4ff3-8840-b80d41596878.yaml | 74 - .../6127babf-9e14-49e2-934a-e966feb37220.yaml | 4872 ++++++----------- .../7dc87e56-553e-4b4d-8caf-161d5e8d1f8a.yaml | 353 ++ .../99bbea20-5911-435e-96dc-7c6d967f59a0.yaml | 146 - python/tests/evaluation/__init__.py | 0 python/tests/evaluation/test_evaluation.py | 97 + 22 files changed, 3099 insertions(+), 3515 deletions(-) create mode 100644 python/langsmith/_expect.py create mode 100644 python/langsmith/_internal/_edit_distance.py create mode 100644 python/langsmith/_internal/_embedding_distance.py delete mode 100644 python/tests/cassettes/39641eac-b198-4ff3-8840-b80d41596878.yaml create mode 100644 python/tests/cassettes/7dc87e56-553e-4b4d-8caf-161d5e8d1f8a.yaml delete mode 100644 python/tests/cassettes/99bbea20-5911-435e-96dc-7c6d967f59a0.yaml create mode 100644 python/tests/evaluation/__init__.py create mode 100644 python/tests/evaluation/test_evaluation.py diff --git a/.github/actions/python-integration-tests/action.yml b/.github/actions/python-integration-tests/action.yml index 5ab1a8bb2..0b7d8250b 100644 --- a/.github/actions/python-integration-tests/action.yml +++ b/.github/actions/python-integration-tests/action.yml @@ -33,7 +33,7 @@ runs: - name: Install dependencies run: | poetry install --with dev - poetry run pip install -U langchain langchain_anthropic tiktoken rapidfuzz vcrpy + poetry run pip install -U langchain langchain_anthropic tiktoken rapidfuzz vcrpy numpy shell: bash working-directory: python @@ -52,7 +52,19 @@ runs: LANGCHAIN_API_KEY: ${{ inputs.langchain-api-key }} OPENAI_API_KEY: ${{ inputs.openai-api-key }} ANTHROPIC_API_KEY: ${{ inputs.anthropic-api-key }} - LANGCHAIN_TEST_CACHE: "tests/cassettes" run: make doctest shell: bash working-directory: python + + + - name: Run Evaluation + env: + LANGCHAIN_TRACING_V2: "true" + LANGCHAIN_API_KEY: ${{ inputs.langchain-api-key }} + OPENAI_API_KEY: ${{ inputs.openai-api-key }} + ANTHROPIC_API_KEY: ${{ inputs.anthropic-api-key }} + LANGCHAIN_TEST_CACHE: "tests/cassettes" + run: make evals + shell: bash + working-directory: python + diff --git a/python/Makefile b/python/Makefile index 6586c6d47..274e08f72 100644 --- a/python/Makefile +++ b/python/Makefile @@ -1,4 +1,4 @@ -.PHONY: tests lint format +.PHONY: tests lint format build publish doctest integration_tests integration_tests_fast evals tests: poetry run pytest -n auto --durations=10 tests/unit_tests @@ -15,6 +15,9 @@ integration_tests_fast: doctest: poetry run pytest -n auto --durations=10 --doctest-modules langsmith +evals: + poetry run pytest tests/evaluation + lint: poetry run ruff check . poetry run mypy . diff --git a/python/README.md b/python/README.md index 71f53960c..97fbfb296 100644 --- a/python/README.md +++ b/python/README.md @@ -8,8 +8,8 @@ To install: ```bash pip install -U langsmith -export LANGCHAIN_TRACING_V2=true -export LANGCHAIN_API_KEY=ls_... +export LANGSMITH_TRACING=true +export LANGSMITH_API_KEY=ls_... ``` Then trace: @@ -68,10 +68,10 @@ Tracing can be activated by setting the following environment variables or by ma ```python import os -os.environ["LANGCHAIN_TRACING_V2"] = "true" -os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com" -os.environ["LANGCHAIN_API_KEY"] = "" -# os.environ["LANGCHAIN_PROJECT"] = "My Project Name" # Optional: "default" is used if not set +os.environ["LANGSMITH_TRACING_V2"] = "true" +os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com" +os.environ["LANGSMITH_API_KEY"] = "" +# os.environ["LANGSMITH_PROJECT"] = "My Project Name" # Optional: "default" is used if not set ``` > **Tip:** Projects are groups of traces. All runs are logged to a project. If not specified, the project is set to `default`. diff --git a/python/langsmith/__init__.py b/python/langsmith/__init__.py index 52a9251c2..d53de8a51 100644 --- a/python/langsmith/__init__.py +++ b/python/langsmith/__init__.py @@ -3,8 +3,10 @@ from typing import TYPE_CHECKING, Any if TYPE_CHECKING: + from langsmith._expect import expect from langsmith._testing import unit from langsmith.client import Client + from langsmith.evaluation import aevaluate, evaluate from langsmith.evaluation.evaluator import EvaluationResult, RunEvaluator from langsmith.run_helpers import trace, traceable from langsmith.run_trees import RunTree @@ -47,6 +49,19 @@ def __getattr__(name: str) -> Any: return unit + elif name == "expect": + from langsmith._expect import expect + + return expect + elif name == "evaluate": + from langsmith.evaluation import evaluate + + return evaluate + elif name == "aevaluate": + from langsmith.evaluation import aevaluate + + return aevaluate + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") @@ -59,4 +74,7 @@ def __getattr__(name: str) -> Any: "traceable", "trace", "unit", + "expect", + "evaluate", + "aevaluate", ] diff --git a/python/langsmith/_expect.py b/python/langsmith/_expect.py new file mode 100644 index 000000000..9c9e1bbc6 --- /dev/null +++ b/python/langsmith/_expect.py @@ -0,0 +1,366 @@ +"""Make approximate assertions as "expectations" on test results. + +This module is designed to be used within test cases decorated with the `@unit` decorator +It allows you to log scores about a test case and optionally make assertions that log as +"expectation" feedback to LangSmith. + +Example usage: + + from langsmith import expect, unit + + @unit + def test_output_semantically_close(): + response = oai_client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Say hello!"}, + ], + ) + response_txt = response.choices[0].message.content + # Intended usage + expect.embedding_distance( + prediction=response_txt, + reference="Hello!", + ).to_be_less_than(0.9) + + # Score the test case + matcher = expect.edit_distance( + prediction=response_txt, + reference="Hello!", + ) + # Apply an assertion and log 'expectation' feedback to LangSmith + matcher.to_be_less_than(1) + + # You can also directly make assertions on values directly + expect.value(response_txt).to_contain("Hello!") + # Or using a custom check + expect.value(response_txt).against(lambda x: "Hello" in x) +""" # noqa: E501 + +from __future__ import annotations + +import atexit +import concurrent.futures +import inspect +from typing import TYPE_CHECKING, Any, Callable, Optional, Union, overload + +from langsmith import client as ls_client +from langsmith import run_helpers as rh +from langsmith import utils as ls_utils + +if TYPE_CHECKING: + from langsmith._internal._edit_distance import EditDistanceConfig + from langsmith._internal._embedding_distance import EmbeddingConfig + + +class _Matcher: + """A class for making assertions on expectation values.""" + + def __init__( + self, + client: ls_client.Client, + key: str, + value: Any, + _executor: Optional[concurrent.futures.ThreadPoolExecutor] = None, + run_id: Optional[str] = None, + ): + self.client = client + self.key = key + self.value = value + self._executor = _executor or concurrent.futures.ThreadPoolExecutor( + max_workers=3 + ) + rt = rh.get_current_run_tree() + self._run_id = rt.id if rt else run_id + + def _submit_feedback(self, score: int, message: Optional[str] = None) -> None: + if not ls_utils.test_tracking_is_disabled(): + self._executor.submit( + self.client.create_feedback, + run_id=self._run_id, + key="expectation", + score=score, + comment=message, + ) + + def _assert(self, condition: bool, message: str, method_name: str) -> None: + try: + assert condition, message + self._submit_feedback(1, message=f"Success: {self.key}.{method_name}") + except AssertionError as e: + self._submit_feedback(0, repr(e)) + raise e from None + + def to_be_less_than(self, value: float) -> None: + """Assert that the expectation value is less than the given value. + + Args: + value: The value to compare against. + + Raises: + AssertionError: If the expectation value is not less than the given value. + """ + self._assert( + self.value < value, + f"Expected {self.key} to be less than {value}, but got {self.value}", + "to_be_less_than", + ) + + def to_be_greater_than(self, value: float) -> None: + """Assert that the expectation value is greater than the given value. + + Args: + value: The value to compare against. + + Raises: + AssertionError: If the expectation value is not + greater than the given value. + """ + self._assert( + self.value > value, + f"Expected {self.key} to be greater than {value}, but got {self.value}", + "to_be_greater_than", + ) + + def to_be_between(self, min_value: float, max_value: float) -> None: + """Assert that the expectation value is between the given min and max values. + + Args: + min_value: The minimum value (exclusive). + max_value: The maximum value (exclusive). + + Raises: + AssertionError: If the expectation value + is not between the given min and max. + """ + self._assert( + min_value < self.value < max_value, + f"Expected {self.key} to be between {min_value} and {max_value}," + f" but got {self.value}", + "to_be_between", + ) + + def to_be_approximately(self, value: float, precision: int = 2) -> None: + """Assert that the expectation value is approximately equal to the given value. + + Args: + value: The value to compare against. + precision: The number of decimal places to round to for comparison. + + Raises: + AssertionError: If the rounded expectation value + does not equal the rounded given value. + """ + self._assert( + round(self.value, precision) == round(value, precision), + f"Expected {self.key} to be approximately {value}, but got {self.value}", + "to_be_approximately", + ) + + def to_equal(self, value: float) -> None: + """Assert that the expectation value equals the given value. + + Args: + value: The value to compare against. + + Raises: + AssertionError: If the expectation value does + not exactly equal the given value. + """ + self._assert( + self.value == value, + f"Expected {self.key} to be equal to {value}, but got {self.value}", + "to_equal", + ) + + def to_contain(self, value: Any) -> None: + """Assert that the expectation value contains the given value. + + Args: + value: The value to check for containment. + + Raises: + AssertionError: If the expectation value does not contain the given value. + """ + self._assert( + value in self.value, + f"Expected {self.key} to contain {value}, but it does not", + "to_contain", + ) + + # Custom assertions + def against(self, func: Callable, /) -> None: + """Assert the expectation value against a custom function. + + Args: + func: A custom function that takes the expectation value as input. + + Raises: + AssertionError: If the custom function returns False. + """ + func_signature = inspect.signature(func) + self._assert( + func(self.value), + f"Assertion {func_signature} failed for {self.key}", + "against", + ) + + +class _Expect: + """A class for setting expectations on test results.""" + + def __init__(self, *, client: Optional[ls_client.Client] = None): + self.client = client or ls_client.Client() + self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=3) + atexit.register(self.executor.shutdown, wait=True) + + def embedding_distance( + self, + prediction: str, + reference: str, + *, + config: Optional[EmbeddingConfig] = None, + ) -> _Matcher: + """Compute the embedding distance between the prediction and reference. + + This logs the embedding distance to LangSmith and returns a `_Matcher` instance + for making assertions on the distance value. + + By default, this uses the OpenAI API for computing embeddings. + + Args: + prediction: The predicted string to compare. + reference: The reference string to compare against. + config: Optional configuration for the embedding distance evaluator. + Supported options: + - `encoder`: A custom encoder function to encode the list of input + strings to embeddings. Defaults to the OpenAI API. + - `metric`: The distance metric to use for comparison. + Supported values: "cosine", "euclidean", "manhattan", + "chebyshev", "hamming". + + Returns: + A `_Matcher` instance for the embedding distance value. + + + Examples: + >>> expect.embedding_distance( + ... prediction="hello", + ... reference="hi", + ... ).to_be_less_than(1.0) + """ # noqa: E501 + from langsmith._internal._embedding_distance import EmbeddingDistance + + config = config or {} + encoder_func = "custom" if config.get("encoder") else "openai" + evaluator = EmbeddingDistance(config=config) + score = evaluator.evaluate(prediction=prediction, reference=reference) + src_info = {"encoder": encoder_func, "metric": evaluator.distance} + self._submit_feedback( + "embedding_distance", + { + "score": score, + "source_info": src_info, + "comment": f"Using {encoder_func}, Metric: {evaluator.distance}", + }, + ) + return _Matcher( + self.client, "embedding_distance", score, _executor=self.executor + ) + + def edit_distance( + self, + prediction: str, + reference: str, + *, + config: Optional[EditDistanceConfig] = None, + ) -> _Matcher: + """Compute the string distance between the prediction and reference. + + This logs the string distance (Damerau-Levenshtein) to LangSmith and returns + a `_Matcher` instance for making assertions on the distance value. + + This depends on the `rapidfuzz` package for string distance computation. + + Args: + prediction: The predicted string to compare. + reference: The reference string to compare against. + config: Optional configuration for the string distance evaluator. + Supported options: + - `metric`: The distance metric to use for comparison. + Supported values: "damerau_levenshtein", "levenshtein", + "jaro", "jaro_winkler", "hamming", "indel". + - `normalize_score`: Whether to normalize the score between 0 and 1. + + Returns: + A `_Matcher` instance for the string distance value. + + Examples: + >>> expect.edit_distance("hello", "helo").to_be_less_than(1) + """ + from langsmith._internal._edit_distance import EditDistance + + config = config or {} + metric = config.get("metric") or "damerau_levenshtein" + normalize = config.get("normalize_score", True) + evaluator = EditDistance(config=config) + score = evaluator.evaluate(prediction=prediction, reference=reference) + src_info = {"metric": metric, "normalize": normalize} + self._submit_feedback( + "edit_distance", + { + "score": score, + "source_info": src_info, + "comment": f"Using {metric}, Normalize: {normalize}", + }, + ) + return _Matcher( + self.client, + "edit_distance", + score, + _executor=self.executor, + ) + + def value(self, value: Any) -> _Matcher: + """Create a `_Matcher` instance for making assertions on the given value. + + Args: + value: The value to make assertions on. + + Returns: + A `_Matcher` instance for the given value. + + Examples: + >>> expect.value(10).to_be_less_than(20) + """ + return _Matcher(self.client, "value", value, _executor=self.executor) + + ## Private Methods + + @overload + def __call__(self, value: Any, /) -> _Matcher: ... + + @overload + def __call__(self, /, *, client: ls_client.Client) -> _Expect: ... + + def __call__( + self, value: Optional[Any] = None, /, client: Optional[ls_client.Client] = None + ) -> Union[_Expect, _Matcher]: + expected = _Expect(client=client) + if value is not None: + return expected.value(value) + return expected + + def _submit_feedback(self, key: str, results: dict): + current_run = rh.get_current_run_tree() + run_id = current_run.id if current_run else None + if not ls_utils.test_tracking_is_disabled(): + self.executor.submit( + self.client.create_feedback, run_id=run_id, key=key, **results + ) + + +expect = _Expect() + +__all__ = ["expect"] diff --git a/python/langsmith/_internal/_edit_distance.py b/python/langsmith/_internal/_edit_distance.py new file mode 100644 index 000000000..bf812dbbf --- /dev/null +++ b/python/langsmith/_internal/_edit_distance.py @@ -0,0 +1,67 @@ +from typing import Any, Callable, Dict, Literal, Optional + +from typing_extensions import TypedDict + +METRICS = Literal[ + "damerau_levenshtein", + "levenshtein", + "jaro", + "jaro_winkler", + "hamming", + "indel", +] + + +class EditDistanceConfig(TypedDict, total=False): + metric: METRICS + normalize_score: bool + + +class EditDistance: + def __init__( + self, + config: Optional[EditDistanceConfig] = None, + ): + config = config or {} + metric = config.get("metric") or "damerau_levenshtein" + self.metric = self._get_metric( + metric, normalize_score=config.get("normalize_score", True) + ) + + def evaluate( + self, + prediction: str, + reference: Optional[str] = None, + ) -> float: + return self.metric(prediction, reference) + + @staticmethod + def _get_metric(distance: str, normalize_score: bool = True) -> Callable: + try: + from rapidfuzz import ( # type: ignore[import-not-found] + distance as rf_distance, + ) + except ImportError: + raise ImportError( + "This operation requires the rapidfuzz library to use." + "Please install it with `pip install -U rapidfuzz`." + ) + + module_map: Dict[str, Any] = { + "damerau_levenshtein": rf_distance.DamerauLevenshtein, + "levenshtein": rf_distance.Levenshtein, + "jaro": rf_distance.Jaro, + "jaro_winkler": rf_distance.JaroWinkler, + "hamming": rf_distance.Hamming, + "indel": rf_distance.Indel, + } + if distance not in module_map: + raise ValueError( + f"Invalid distance metric: {distance}" + f"\nMust be one of: {list(module_map)}" + ) + module = module_map[distance] + if normalize_score: + return module.normalized_distance + else: + return module.distance diff --git a/python/langsmith/_internal/_embedding_distance.py b/python/langsmith/_internal/_embedding_distance.py new file mode 100644 index 000000000..4eb9fc83b --- /dev/null +++ b/python/langsmith/_internal/_embedding_distance.py @@ -0,0 +1,191 @@ +from __future__ import annotations + +import logging +from typing import ( + TYPE_CHECKING, + Any, + Callable, + List, + Literal, + Optional, + Sequence, + Union, +) + +from typing_extensions import TypedDict + +if TYPE_CHECKING: + import numpy as np + + +logger = logging.getLogger(__name__) + +Matrix = Union[List[List[float]], List[Any], Any] + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + import numpy as np + + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd # type: ignore + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return np.array(Z) + except ImportError: + logger.debug( + "Unable to import simsimd, defaulting to NumPy implementation. If you want " + "to use simsimd please install with `pip install simsimd`." + ) + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity + + +def _get_openai_encoder() -> Callable[[Sequence[str]], Sequence[Sequence[float]]]: + """Get the OpenAI GPT-3 encoder.""" + try: + from openai import Client + except ImportError: + raise ImportError( + "THe default encoder for the EmbeddingDistance class uses the OpenAI API. " + "Please either install the openai library with `pip install openai` or " + "provide a custom encoder function (Callable[[str], Sequence[float]])." + ) + + def encode_text(texts: Sequence[str]) -> Sequence[Sequence[float]]: + client = Client() + response = client.embeddings.create( + input=list(texts), model="text-embedding-3-small" + ) + return [d.embedding for d in response.data] + + return encode_text + + +class EmbeddingConfig(TypedDict, total=False): + encoder: Callable[[List[str]], Sequence[Sequence[float]]] + metric: Literal["cosine", "euclidean", "manhattan", "chebyshev", "hamming"] + + +class EmbeddingDistance: + def __init__( + self, + config: Optional[EmbeddingConfig] = None, + ): + config = config or {} + self.distance = config.get("metric") or "cosine" + self.encoder = config.get("encoder") or _get_openai_encoder() + + def evaluate( + self, + prediction: str, + reference: str, + ) -> float: + try: + import numpy as np + except ImportError: + raise ImportError( + "The EmbeddingDistance class requires NumPy. Please install it with " + "`pip install numpy`." + ) + embeddings = self.encoder([prediction, reference]) + vector = np.array(embeddings) + return self._compute_distance(vector[0], vector[1]).item() + + def _compute_distance(self, a: np.ndarray, b: np.ndarray) -> np.floating: + if self.distance == "cosine": + return self._cosine_distance(a, b) # type: ignore + elif self.distance == "euclidean": + return self._euclidean_distance(a, b) + elif self.distance == "manhattan": + return self._manhattan_distance(a, b) + elif self.distance == "chebyshev": + return self._chebyshev_distance(a, b) + elif self.distance == "hamming": + return self._hamming_distance(a, b) + else: + raise ValueError(f"Invalid distance metric: {self.distance}") + + @staticmethod + def _cosine_distance(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """Compute the cosine distance between two vectors. + + Args: + a (np.ndarray): The first vector. + b (np.ndarray): The second vector. + + Returns: + np.ndarray: The cosine distance. + """ + return 1.0 - cosine_similarity([a], [b]) + + @staticmethod + def _euclidean_distance(a: np.ndarray, b: np.ndarray) -> np.floating: + """Compute the Euclidean distance between two vectors. + + Args: + a (np.ndarray): The first vector. + b (np.ndarray): The second vector. + + Returns: + np.floating: The Euclidean distance. + """ + return np.linalg.norm(a - b) + + @staticmethod + def _manhattan_distance(a: np.ndarray, b: np.ndarray) -> np.floating: + """Compute the Manhattan distance between two vectors. + + Args: + a (np.ndarray): The first vector. + b (np.ndarray): The second vector. + + Returns: + np.floating: The Manhattan distance. + """ + return np.sum(np.abs(a - b)) + + @staticmethod + def _chebyshev_distance(a: np.ndarray, b: np.ndarray) -> np.floating: + """Compute the Chebyshev distance between two vectors. + + Args: + a (np.ndarray): The first vector. + b (np.ndarray): The second vector. + + Returns: + np.floating: The Chebyshev distance. + """ + return np.max(np.abs(a - b)) + + @staticmethod + def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating: + """Compute the Hamming distance between two vectors. + + Args: + a (np.ndarray): The first vector. + b (np.ndarray): The second vector. + + Returns: + np.floating: The Hamming distance. + """ + return np.mean(a != b) diff --git a/python/langsmith/_testing.py b/python/langsmith/_testing.py index 59279a1d4..d3d44a937 100644 --- a/python/langsmith/_testing.py +++ b/python/langsmith/_testing.py @@ -7,7 +7,6 @@ import inspect import json import logging -import os import threading import uuid import warnings @@ -71,11 +70,11 @@ def unit(*args: Any, **kwargs: Any) -> Callable: Callable: The decorated test function. Environment: - - LANGCHAIN_TEST_CACHE: If set, API calls will be cached to disk to + - LANGSMITH_TEST_CACHE: If set, API calls will be cached to disk to save time and costs during testing. Recommended to commit the cache files to your repository for faster CI/CD runs. Requires the 'langsmith[vcr]' package to be installed. - - LANGCHAIN_TEST_TRACKING: Set this variable to the path of a directory + - LANGSMITH_TEST_TRACKING: Set this variable to the path of a directory to enable caching of test results. This is useful for re-running tests without re-executing the code. Requires the 'langsmith[vcr]' package. @@ -103,7 +102,7 @@ def unit(*args: Any, **kwargs: Any) -> Callable: ... assert a + b == 7 LLM calls are expensive! Cache requests by setting - `LANGCHAIN_TEST_CACHE=path/to/cache`. Check in these files to speed up + `LANGSMITH_TEST_CACHE=path/to/cache`. Check in these files to speed up CI/CD pipelines, so your results only change when your prompt or requested model changes. @@ -114,13 +113,13 @@ def unit(*args: Any, **kwargs: Any) -> Callable: Caching is faster if you install libyaml. See https://vcrpy.readthedocs.io/en/latest/installation.html#speed for more details. - >>> os.environ["LANGCHAIN_TEST_CACHE"] = "tests/cassettes" + >>> # os.environ["LANGSMITH_TEST_CACHE"] = "tests/cassettes" >>> import openai >>> from langsmith.wrappers import wrap_openai + >>> oai_client = wrap_openai(openai.Client()) >>> @unit ... def test_openai_says_hello(): ... # Traced code will be included in the test case - ... oai_client = wrap_openai(openai.Client()) ... response = oai_client.chat.completions.create( ... model="gpt-3.5-turbo", ... messages=[ @@ -130,6 +129,34 @@ def unit(*args: Any, **kwargs: Any) -> Callable: ... ) ... assert "hello" in response.choices[0].message.content.lower() + LLMs are stochastic. Naive assertions are flakey. You can use langsmith's + `expect` to score and make approximate assertions on your results. + + >>> from langsmith import expect + >>> @unit + ... def test_output_semantically_close(): + ... response = oai_client.chat.completions.create( + ... model="gpt-3.5-turbo", + ... messages=[ + ... {"role": "system", "content": "You are a helpful assistant."}, + ... {"role": "user", "content": "Say hello!"}, + ... ], + ... ) + ... # The embedding_distance call logs the embedding distance to LangSmith + ... expect.embedding_distance( + ... prediction=response.choices[0].message.content, + ... reference="Hello!", + ... # The following optional assertion logs a + ... # pass/fail score to LangSmith + ... # and raises an AssertionError if the assertion fails. + ... ).to_be_less_than(1.0) + ... # Compute damerau_levenshtein distance + ... expect.edit_distance( + ... prediction=response.choices[0].message.content, + ... reference="Hello!", + ... # And then log a pass/fail score to LangSmith + ... ).to_be_less_than(1.0) + The `@unit` decorator works natively with pytest fixtures. The values will populate the "inputs" of the corresponding example in LangSmith. @@ -160,8 +187,7 @@ def unit(*args: Any, **kwargs: Any) -> Callable: By default, each test case will be assigned a consistent, unique identifier based on the function name and module. You can also provide a custom identifier using the `id` argument: - - >>> @unit(id=uuid.uuid4()) + >>> @unit(id="1a77e4b5-1d38-4081-b829-b0442cf3f145") ... def test_multiplication(): ... assert 3 * 4 == 12 @@ -178,6 +204,7 @@ def unit(*args: Any, **kwargs: Any) -> Callable: To run these tests, use the pytest CLI. Or directly run the test functions. + >>> test_output_semantically_close() >>> test_addition() >>> test_nested() >>> test_with_fixture("Some input") @@ -195,10 +222,10 @@ def unit(*args: Any, **kwargs: Any) -> Callable: ) if kwargs: warnings.warn(f"Unexpected keyword arguments: {kwargs.keys()}") - disable_tracking = os.environ.get("LANGCHAIN_TEST_TRACKING") == "false" + disable_tracking = ls_utils.test_tracking_is_disabled() if disable_tracking: warnings.warn( - "LANGCHAIN_TEST_TRACKING is set to 'false'." + "LANGSMITH_TEST_TRACKING is set to 'false'." " Skipping LangSmith test tracking." ) @@ -240,29 +267,34 @@ def _get_experiment_name() -> str: return name -def _get_test_suite_name() -> str: - # TODO: This naming stuff is inelegant - test_suite_name = os.environ.get("LANGCHAIN_TEST_SUITE") +def _get_test_suite_name(func: Callable) -> str: + test_suite_name = ls_utils.get_env_var("TEST_SUITE") if test_suite_name: return test_suite_name - if __package__: - return __package__ + " Test Suite" - git_info = ls_env.get_git_info() - if git_info: - if git_info["remote_url"]: - repo_name = git_info["remote_url"].split("/")[-1].split(".")[0] - if repo_name: - return repo_name + " Test Suite" - raise ValueError("Please set the LANGCHAIN_TEST_SUITE environment variable.") + repo_name = ls_env.get_git_info()["repo_name"] + try: + mod = inspect.getmodule(func) + if mod: + return f"{repo_name}.{mod.__name__}" + except BaseException: + logger.debug("Could not determine test suite name from file path.") + raise ValueError("Please set the LANGSMITH_TEST_SUITE environment variable.") -def _get_test_suite(client: ls_client.Client) -> ls_schemas.Dataset: - test_suite_name = _get_test_suite_name() +def _get_test_suite( + client: ls_client.Client, test_suite_name: str +) -> ls_schemas.Dataset: if client.has_dataset(dataset_name=test_suite_name): return client.read_dataset(dataset_name=test_suite_name) else: - return client.create_dataset(dataset_name=test_suite_name) + repo = ls_env.get_git_info().get("remote_url") or "" + description = "Unit test suite" + if repo: + description += f" for {repo}" + return client.create_dataset( + dataset_name=test_suite_name, description=description + ) def _start_experiment( @@ -270,17 +302,29 @@ def _start_experiment( test_suite: ls_schemas.Dataset, ) -> ls_schemas.TracerSession: experiment_name = _get_experiment_name() - return client.create_project(experiment_name, reference_dataset_id=test_suite.id) + try: + return client.create_project( + experiment_name, + reference_dataset_id=test_suite.id, + description="Test Suite Results.", + metadata={ + "revision_id": ls_env.get_langchain_env_var_metadata().get( + "revision_id" + ) + }, + ) + except ls_utils.LangSmithConflictError: + return client.read_project(project_name=experiment_name) -def _get_id(func: Callable, inputs: dict) -> uuid.UUID: +def _get_id(func: Callable, inputs: dict, suite_id: uuid.UUID) -> uuid.UUID: try: file_path = str(Path(inspect.getfile(func)).relative_to(Path.cwd())) except ValueError: # Fall back to module name if file path is not available file_path = func.__module__ input_json = json.dumps(inputs, sort_keys=True) - identifier = f"{file_path}::{func.__name__}{input_json}" + identifier = f"{suite_id}{file_path}::{func.__name__}{input_json}" return uuid.uuid5(uuid.NAMESPACE_DNS, identifier) @@ -297,7 +341,7 @@ def _end_tests( class _LangSmithTestSuite: - _instance = None + _instances: Optional[dict] = None _lock = threading.RLock() def __init__( @@ -321,26 +365,37 @@ def id(self): def experiment_id(self): return self._experiment.id + @property + def experiment(self): + return self._experiment + @classmethod - def get_singleton(cls, client: Optional[ls_client.Client]) -> _LangSmithTestSuite: + def from_test( + cls, client: Optional[ls_client.Client], func: Callable + ) -> _LangSmithTestSuite: client = client or ls_client.Client() + test_suite_name = _get_test_suite_name(func) with cls._lock: - if not cls._instance: - test_suite = _get_test_suite(client) + if not cls._instances: + cls._instances = {} + if test_suite_name not in cls._instances: + test_suite = _get_test_suite(client, test_suite_name) experiment = _start_experiment(client, test_suite) - cls._instance = cls(client, experiment, test_suite) - return cls._instance + cls._instances[test_suite_name] = cls(client, experiment, test_suite) + return cls._instances[test_suite_name] @property def name(self): return self._experiment.name def update_version(self, version: datetime.datetime) -> None: - if self._version is None or version > self._version: - self._version = version + with self._lock: + if self._version is None or version > self._version: + self._version = version def get_version(self) -> Optional[datetime.datetime]: - return self._version + with self._lock: + return self._version def submit_result(self, run_id: uuid.UUID, error: Optional[str] = None) -> None: self._executor.submit(self._submit_result, run_id, error) @@ -357,17 +412,29 @@ def _submit_result(self, run_id: uuid.UUID, error: Optional[str] = None) -> None score=1, ) - def sync_example(self, example_id: uuid.UUID, inputs: dict, outputs: dict) -> None: - self._executor.submit(self._sync_example, example_id, inputs, outputs) + def sync_example( + self, example_id: uuid.UUID, inputs: dict, outputs: dict, metadata: dict + ) -> None: + self._executor.submit( + self._sync_example, example_id, inputs, outputs, metadata.copy() + ) - def _sync_example(self, example_id: uuid.UUID, inputs: dict, outputs: dict) -> None: + def _sync_example( + self, example_id: uuid.UUID, inputs: dict, outputs: dict, metadata: dict + ) -> None: try: example = self.client.read_example(example_id=example_id) - if inputs != example.inputs or outputs != example.outputs: + if ( + inputs != example.inputs + or outputs != example.outputs + or str(example.dataset_id) != str(self.id) + ): self.client.update_example( example_id=example.id, inputs=inputs, outputs=outputs, + metadata=metadata, + dataset_id=self.id, ) except ls_utils.LangSmithNotFoundError: example = self.client.create_example( @@ -375,6 +442,7 @@ def _sync_example(self, example_id: uuid.UUID, inputs: dict, outputs: dict) -> N inputs=inputs, outputs=outputs, dataset_id=self.id, + metadata=metadata, ) if example.modified_at: self.update_version(example.modified_at) @@ -391,6 +459,14 @@ class _UTExtra(TypedDict, total=False): cache: Optional[str] +def _get_test_repr(func: Callable, sig: inspect.Signature) -> str: + name = getattr(func, "__name__", None) or "" + description = getattr(func, "__doc__", None) or "" + if description: + description = f" - {description.strip()}" + return f"{name}{sig}{description}" + + def _ensure_example( func: Callable, *args: Any, langtest_extra: _UTExtra, **kwargs: Any ) -> Tuple[_LangSmithTestSuite, uuid.UUID]: @@ -401,14 +477,18 @@ def _ensure_example( signature = inspect.signature(func) # 2. Create the example inputs: dict = rh._get_inputs_safe(signature, *args, **kwargs) - example_id = langtest_extra["id"] or _get_id(func, inputs) outputs = {} if output_keys: for k in output_keys: outputs[k] = inputs.pop(k, None) - # TODO: Support multiple test suites - test_suite = _LangSmithTestSuite.get_singleton(client) - test_suite.sync_example(example_id, inputs, outputs) + test_suite = _LangSmithTestSuite.from_test(client, func) + example_id = langtest_extra["id"] or _get_id(func, inputs, test_suite.id) + test_suite.sync_example( + example_id, + inputs, + outputs, + metadata={"signature": _get_test_repr(func, signature)}, + ) return test_suite, example_id @@ -443,7 +523,17 @@ def _test(): if langtest_extra["cache"] else None ) - with ls_utils.with_optional_cache( + current_context = rh.get_tracing_context() + metadata = { + **(current_context["metadata"] or {}), + **{ + "experiment": test_suite.experiment.name, + "reference_example_id": str(example_id), + }, + } + with rh.tracing_context( + **{**current_context, "metadata": metadata} + ), ls_utils.with_optional_cache( cache_path, ignore_hosts=[test_suite.client.api_url] ): _test() diff --git a/python/langsmith/cli/main.py b/python/langsmith/cli/main.py index 7a52c4e3c..15bc3fc02 100644 --- a/python/langsmith/cli/main.py +++ b/python/langsmith/cli/main.py @@ -48,8 +48,8 @@ def pprint_services(services_status: List[Mapping[str, Union[str, List[str]]]]) service_message.append( "\nTo connect, set the following environment variables" " in your LangChain application:" - "\nLANGCHAIN_TRACING_V2=true" - "\nLANGCHAIN_ENDPOINT=http://localhost:80/api" + "\nLANGSMITH_TRACING_V2=true" + "\nLANGSMITH_ENDPOINT=http://localhost:80/api" ) logger.info("\n".join(service_message)) @@ -94,8 +94,8 @@ def _start_local(self) -> None: " when running your LangChain application.\n" ) - logger.info("\tLANGCHAIN_TRACING_V2=true") - logger.info("\tLANGCHAIN_ENDPOINT=http://localhost:80/api\n") + logger.info("\tLANGSMITH_TRACING=true") + logger.info("\tLANGSMITH_ENDPOINT=http://localhost:80/api\n") self._open_browser("http://localhost") def pull( diff --git a/python/langsmith/client.py b/python/langsmith/client.py index f0b3dd20d..45b8eda2c 100644 --- a/python/langsmith/client.py +++ b/python/langsmith/client.py @@ -285,13 +285,13 @@ def _get_tracing_sampling_rate() -> float | None: float The tracing sampling rate. """ - sampling_rate_str = os.getenv("LANGCHAIN_TRACING_SAMPLING_RATE") + sampling_rate_str = ls_utils.get_env_var("TRACING_SAMPLING_RATE") if sampling_rate_str is None: return None sampling_rate = float(sampling_rate_str) if sampling_rate < 0 or sampling_rate > 1: raise ls_utils.LangSmithUserError( - "LANGCHAIN_TRACING_SAMPLING_RATE must be between 0 and 1 if set." + "LANGSMITH_TRACING_SAMPLING_RATE must be between 0 and 1 if set." f" Got: {sampling_rate}" ) return sampling_rate @@ -523,12 +523,12 @@ def __init__( self._hide_inputs = ( hide_inputs if hide_inputs is not None - else os.environ.get("LANGCHAIN_HIDE_INPUTS") == "true" + else ls_utils.get_env_var("HIDE_INPUTS") == "true" ) self._hide_outputs = ( hide_outputs if hide_outputs is not None - else os.environ.get("LANGCHAIN_HIDE_OUTPUTS") == "true" + else ls_utils.get_env_var("HIDE_OUTPUTS") == "true" ) def _repr_html_(self) -> str: @@ -1233,7 +1233,7 @@ def batch_ingest_runs( if not raw_body["post"] and not raw_body["patch"]: return - self._insert_runtime_env(raw_body["post"]) + self._insert_runtime_env(raw_body["post"] + raw_body["patch"]) info = self.info size_limit_bytes = (info.batch_ingest_config or {}).get( diff --git a/python/langsmith/env/_git.py b/python/langsmith/env/_git.py index 77ec08999..705f53c1e 100644 --- a/python/langsmith/env/_git.py +++ b/python/langsmith/env/_git.py @@ -22,23 +22,32 @@ def exec_git(command: List[str]) -> Optional[str]: class GitInfo(TypedDict, total=False): + repo_name: Optional[str] remote_url: Optional[str] commit: Optional[str] branch: Optional[str] author_name: Optional[str] author_email: Optional[str] - commit_message: Optional[str] commit_time: Optional[str] dirty: Optional[bool] tags: Optional[str] @functools.lru_cache(maxsize=1) -def get_git_info(remote: str = "origin") -> Optional[GitInfo]: +def get_git_info(remote: str = "origin") -> GitInfo: """Get information about the git repository.""" - if not exec_git(["rev-parse", "--is-inside-work-tree"]): - return None + return GitInfo( + remote_url=None, + commit=None, + branch=None, + author_name=None, + author_email=None, + commit_time=None, + dirty=None, + tags=None, + repo_name=None, + ) return { "remote_url": exec_git(["remote", "get-url", remote]), @@ -51,4 +60,5 @@ def get_git_info(remote: str = "origin") -> Optional[GitInfo]: "dirty": exec_git(["status", "--porcelain"]) != "", "author_name": exec_git(["log", "-1", "--format=%an"]), "author_email": exec_git(["log", "-1", "--format=%ae"]), + "repo_name": (exec_git(["rev-parse", "--show-toplevel"]) or "").split("/")[-1], } diff --git a/python/langsmith/env/_runtime_env.py b/python/langsmith/env/_runtime_env.py index 80b54c91b..5646263c2 100644 --- a/python/langsmith/env/_runtime_env.py +++ b/python/langsmith/env/_runtime_env.py @@ -168,7 +168,7 @@ def get_langchain_env_var_metadata() -> dict: langchain_metadata = { k: v for k, v in os.environ.items() - if k.startswith("LANGCHAIN_") + if (k.startswith("LANGCHAIN_") or k.startswith("LANGSMITH_")) and k not in excluded and "key" not in k.lower() and "secret" not in k.lower() diff --git a/python/langsmith/evaluation/_arunner.py b/python/langsmith/evaluation/_arunner.py index db7d9ebbc..b61596348 100644 --- a/python/langsmith/evaluation/_arunner.py +++ b/python/langsmith/evaluation/_arunner.py @@ -90,7 +90,7 @@ async def aevaluate( AsyncIterator[ExperimentResultRow]: An async iterator over the experiment results. Environment: - - LANGCHAIN_TEST_CACHE: If set, API calls will be cached to disk to save time and + - LANGSMITH_TEST_CACHE: If set, API calls will be cached to disk to save time and cost during testing. Recommended to commit the cache files to your repository for faster CI/CD runs. Requires the 'langsmith[vcr]' package to be installed. @@ -629,26 +629,43 @@ async def _aapply_summary_evaluators( examples.append(example) aggregate_feedback = [] project_id = self._get_experiment().id - for evaluator in summary_evaluators: - try: - # TODO: Support async evaluators - summary_eval_result = evaluator(runs, examples) - flattened_results = self.client._select_eval_results( - summary_eval_result, - fn_name=evaluator.__name__, - ) - aggregate_feedback.extend(flattened_results) - for result in flattened_results: - feedback = result.dict(exclude={"target_run_id"}) - evaluator_info = feedback.pop("evaluator_info", None) - self.client.create_feedback( - **feedback, - run_id=None, - project_id=project_id, - source_info=evaluator_info, + current_context = rh.get_tracing_context() + metadata = { + **(current_context["metadata"] or {}), + **{ + "experiment": self.experiment_name, + "experiment_id": project_id, + }, + } + with rh.tracing_context( + **{ + **current_context, + "project_name": "evaluators", + "metadata": metadata, + } + ): + for evaluator in summary_evaluators: + try: + # TODO: Support async evaluators + summary_eval_result = evaluator(runs, examples) + flattened_results = self.client._select_eval_results( + summary_eval_result, + fn_name=evaluator.__name__, + ) + aggregate_feedback.extend(flattened_results) + for result in flattened_results: + feedback = result.dict(exclude={"target_run_id"}) + evaluator_info = feedback.pop("evaluator_info", None) + self.client.create_feedback( + **feedback, + run_id=None, + project_id=project_id, + source_info=evaluator_info, + ) + except Exception as e: + logger.error( + f"Error running summary evaluator {repr(evaluator)}: {e}" ) - except Exception as e: - logger.error(f"Error running summary evaluator {repr(evaluator)}: {e}") yield {"results": aggregate_feedback} async def _get_dataset_version(self) -> Optional[str]: diff --git a/python/langsmith/evaluation/_runner.py b/python/langsmith/evaluation/_runner.py index 205f937a6..aa9ad06c5 100644 --- a/python/langsmith/evaluation/_runner.py +++ b/python/langsmith/evaluation/_runner.py @@ -257,7 +257,7 @@ def evaluate_existing( ExperimentResults: The evaluation results. Environment: - - LANGCHAIN_TEST_CACHE: If set, API calls will be cached to disk to save time and + - LANGSMITH_TEST_CACHE: If set, API calls will be cached to disk to save time and cost during testing. Recommended to commit the cache files to your repository for faster CI/CD runs. Requires the 'langsmith[vcr]' package to be installed. @@ -909,29 +909,44 @@ def _apply_summary_evaluators( aggregate_feedback = [] with cf.ThreadPoolExecutor() as executor: project_id = self._get_experiment().id - for evaluator in summary_evaluators: - try: - summary_eval_result = evaluator(runs, examples) - # TODO: Expose public API for this. - flattened_results = self.client._select_eval_results( - summary_eval_result, - fn_name=evaluator.__name__, - ) - aggregate_feedback.extend(flattened_results) - for result in flattened_results: - feedback = result.dict(exclude={"target_run_id"}) - evaluator_info = feedback.pop("evaluator_info", None) - executor.submit( - self.client.create_feedback, - **feedback, - run_id=None, - project_id=project_id, - source_info=evaluator_info, + current_context = rh.get_tracing_context() + metadata = { + **(current_context["metadata"] or {}), + **{ + "experiment": self.experiment_name, + "experiment_id": project_id, + }, + } + with rh.tracing_context( + **{ + **current_context, + "project_name": "evaluators", + "metadata": metadata, + } + ): + for evaluator in summary_evaluators: + try: + summary_eval_result = evaluator(runs, examples) + # TODO: Expose public API for this. + flattened_results = self.client._select_eval_results( + summary_eval_result, + fn_name=evaluator.__name__, + ) + aggregate_feedback.extend(flattened_results) + for result in flattened_results: + feedback = result.dict(exclude={"target_run_id"}) + evaluator_info = feedback.pop("evaluator_info", None) + executor.submit( + self.client.create_feedback, + **feedback, + run_id=None, + project_id=project_id, + source_info=evaluator_info, + ) + except Exception as e: + logger.error( + f"Error running summary evaluator {repr(evaluator)}: {e}" ) - except Exception as e: - logger.error( - f"Error running summary evaluator {repr(evaluator)}: {e}" - ) yield {"results": aggregate_feedback} def _get_dataset_version(self) -> Optional[str]: diff --git a/python/langsmith/utils.py b/python/langsmith/utils.py index 0b6dc8c23..56764b589 100644 --- a/python/langsmith/utils.py +++ b/python/langsmith/utils.py @@ -63,12 +63,13 @@ class LangSmithConnectionError(LangSmithError): def tracing_is_enabled() -> bool: """Return True if tracing is enabled.""" - return ( - os.environ.get( - "LANGCHAIN_TRACING_V2", os.environ.get("LANGCHAIN_TRACING", "") - ).lower() - == "true" - ) + var_result = get_env_var("TRACING_V2", default=get_env_var("TRACING", default="")) + return var_result == "true" + + +def test_tracking_is_disabled() -> bool: + """Return True if testing is enabled.""" + return get_env_var("TEST_TRACKING", default="") == "false" def xor_args(*arg_groups: Tuple[str, ...]) -> Callable: @@ -307,6 +308,33 @@ def is_base_message_like(obj: object) -> bool: ) +def get_env_var( + name: str, + default: Optional[str] = None, + *, + namespaces: Tuple = ("LANGSMITH", "LANGCHAIN"), +) -> Optional[str]: + """Retrieve an environment variable from a list of namespaces. + + Args: + name (str): The name of the environment variable. + default (Optional[str], optional): The default value to return if the + environment variable is not found. Defaults to None. + namespaces (Tuple, optional): A tuple of namespaces to search for the + environment variable. Defaults to ("LANGSMITH", "LANGCHAINs"). + + Returns: + Optional[str]: The value of the environment variable if found, + otherwise the default value. + """ + names = [f"{namespace}_{name}" for namespace in namespaces] + for name in names: + value = os.environ.get(name) + if value is not None: + return value + return default + + def get_tracer_project(return_default_value=True) -> Optional[str]: """Get the project name for a LangSmith tracer.""" return os.environ.get( @@ -315,13 +343,12 @@ def get_tracer_project(return_default_value=True) -> Optional[str]: # for a hosted langserve deployment even if the customer sets some # other project name in their environment. "HOSTED_LANGSERVE_PROJECT_NAME", - os.environ.get( - "LANGCHAIN_PROJECT", - os.environ.get( - # This is the legacy name for a LANGCHAIN_PROJECT, so it - # has lower precedence than LANGCHAIN_PROJECT - "LANGCHAIN_SESSION", - "default" if return_default_value else None, + get_env_var( + "PROJECT", + # This is the legacy name for a LANGCHAIN_PROJECT, so it + # has lower precedence than LANGCHAIN_PROJECT + default=get_env_var( + "SESSION", default="default" if return_default_value else None ), ), ) @@ -399,11 +426,11 @@ def get_cache_dir(cache: Optional[str]) -> Optional[str]: Returns: Optional[str]: The cache path if provided, otherwise the value - from the LANGCHAIN_TEST_CACHE environment variable. + from the LANGSMITH_TEST_CACHE environment variable. """ if cache is not None: return cache - return os.environ.get("LANGCHAIN_TEST_CACHE") + return get_env_var("TEST_CACHE", default=None) @contextlib.contextmanager diff --git a/python/pyproject.toml b/python/pyproject.toml index bb6fee59e..8c9fe1693 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langsmith" -version = "0.1.45" +version = "0.1.46" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." authors = ["LangChain "] license = "MIT" diff --git a/python/tests/cassettes/39641eac-b198-4ff3-8840-b80d41596878.yaml b/python/tests/cassettes/39641eac-b198-4ff3-8840-b80d41596878.yaml deleted file mode 100644 index 53de48806..000000000 --- a/python/tests/cassettes/39641eac-b198-4ff3-8840-b80d41596878.yaml +++ /dev/null @@ -1,74 +0,0 @@ -interactions: -- request: - body: '{"messages": [{"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Say hello!"}], 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x-request-id: - - req_25f48f2341b00ee3311159a6bdf5820e - status: - code: 200 - message: OK -version: 1 diff --git a/python/tests/cassettes/6127babf-9e14-49e2-934a-e966feb37220.yaml b/python/tests/cassettes/6127babf-9e14-49e2-934a-e966feb37220.yaml index a055bbc63..a5cbfb3cd 100644 --- a/python/tests/cassettes/6127babf-9e14-49e2-934a-e966feb37220.yaml +++ b/python/tests/cassettes/6127babf-9e14-49e2-934a-e966feb37220.yaml @@ -1,6 +1,6 @@ interactions: - request: - body: '{"input": [[8134], [2822]], "model": "text-embedding-ada-002", "encoding_format": + body: '{"input": [[9642], [9642]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' headers: {} method: POST @@ -8,225 +8,122 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA1SZSROqPJuG9/0rTr1bu0pFJA/fjklAwATFAXvFoAiIyJAA+f58F56u7uqNCwgS - kme47jv//o8/f/6p4+KR9P/8688/77zr//nP+Voa9dE///rzX//x58+fP//+/f6/kY8qfqRp/sl+ - w38380/6GP/515/V/175v0H/+vNPYlQSlmJTRasrsgVpWjpv5slZjUZJ0Gt4p2Rke6Kt2sGs1woa - 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submitted answer on a given task or input based on a set of criteria. Here is the data:\n[BEGIN DATA]\n***\n[Input]: {''context'': ''The man is not steering a car'', ''question'': - ''Can we logically conclude for sure that the man is not steering a SUV?''}\n***\n[Submission]: + ''Can we logically conclude for sure that the man is not steering a sedan?''}\n***\n[Submission]: Yes\n***\n[Criteria]: usefulness: The prediction is useful if it is correct and/or asks a useful followup question.\n***\n[Reference]: Yes\n***\n[END DATA]\nDoes the submission meet the Criteria? 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Here is the data:\n[BEGIN - DATA]\n***\n[Input]: {''context'': ''The man is not steering a SUV'', ''question'': - ''Can we logically conclude for sure that the man is not steering a car?''}\n***\n[Submission]: + DATA]\n***\n[Input]: {''context'': ''The man is not steering a car'', ''question'': + ''Can we logically conclude for sure that the man is not steering a jeep?''}\n***\n[Submission]: Yes\n***\n[Criteria]: usefulness: The prediction is useful if it is correct - and/or asks a useful followup question.\n***\n[Reference]: No\n***\n[END DATA]\nDoes + and/or asks a useful followup question.\n***\n[Reference]: Yes\n***\n[END DATA]\nDoes the submission meet the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. Then print only the @@ -3206,23 +2355,21 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNbxNBDL3nV1hz4bItSRqatheEKhDioxJQQIigajLrzU46O17GXqVV - 1f+OPLtJaCmXKLH9nt+zx7kbARhfmjMwrrbimjYcnJ5/OH7zbryevV9///TxdZh9ns/C/G14cTKe - WlMogpZrdLJFHTpq2oDiKfZpl9AKKutkPpnOpyfj+SQnGioxKGzVysHsYHw8ORoQNXmHbM7g5wgA - 4C5/qrZY4o05g3GxjTTIbFdoznZFACZR0IixzJ7FRjHFPukoCsYs97JGcMkLJk8RKkogtWcQy9fg - GTrGqgsRmQvY1N7VGiyx8hFLsAxSI7QJS+/ULCzRxxU4SgmdgI3lc0pg+VqjdiCDikKgTdfC7w5Z - YYeLuIgfiPoyyaQ+tp0U+WtWeyPaeZH1Njbqj0gCLIipZ//y9dvCaM8M2nL3qHMbYYMQaOWdDeFW - OV3oSsyOuUsIUg+dn2Z3Nr1cGND2CStMGB2CjbzBBEL90B72vKCFOYRLTXiGJTrbsUJUqEYsyG2L - QJVyF8AE645lV/hfKZmgAC9QEnJ8JtCgjaAze0q1NrBQ+iqLltw0D1ytcLdsvIjusreSlf9AXpjt - vnX6yeqC+20/dq8WMWFFCYvHz2EQNLyH3PVVWXrN6Rb6+qyBWevVUEbo67P/PpQ8pcF4rmsQ+6Ux - OooltDaJGpYHrxq91Ji2pv/W+lTvHedAYDPwYhEvzHBB97vTC7RqEy31TGMXwi5e+ei5vkpomaKe - GQu1Pfx+BPArn3j34GpNm6hp5UroGqMSTsezns/s/0322cnx6ZAVEhv2iaP50WiQaPiWBZuryscV - pjb5fPIqdHQ/+gMAAP//AwCfr+NS6QQAAA== + H4sIAAAAAAAAA2xTTU8bMRC951eMfMkloWygDc2lBy5F4ohKo4KQ453dNXhntp5ZhRTlv1f2bggI + LpY14/fh5/HLBMD40qzAuMaqa7sw/365Leur/va6LuTi39efRXn9uPl9g0X8dXVrZgnBm0d0ekCd + OG67gOqZhraLaBUTa7EsFhfLxdlykRstlxgSrO50fj4//VacjYiGvUMxK/gzAQB4yWvyRiU+mxWc + zg6VFkVsjWb1egjARA6pYqyIF7WkZnZsOiZFynZvGgQXvWL0TFBxBG28gFp5Ai/QC1Z9IBSZwbbx + rknFEitPWIIV0Aahi1h6ly4LG/RUg+MY0SlYKr9wBCtPqWpHMqg4BN72HfztURLs5I7u6Jp5OKaZ + 1FPX6yxvs9tnTcrTZLe1lPbECqKIcSB3Nk6TYoYcmDPm0hJsEQLX3tkQdonQhb7EfF3pI4I2o+zn + 3I+I3Y9ptpkMSL9pvWpKgGSLMausUaaHjFqrrsEhnYgVRiSH4+ETuEkBt2hJjrpvMvRyCPC9oEjq + loyDvfRA9mOWM9j0OrzheA9ClwYk7j55r49aESuOOAT/RrZF1AF9HBau3oxHxq/vaG3GMdu/zmfg + uou8SbNMfQiv9cqTl+YhohWmNIui3A3w/QTgPv+D/t1omy5y2+mD8hNSIlycng985vjljt2iuBi7 + ymrDsXG2WExGi0Z2otg+VJ5qjF30+V8ko5P95D8AAAD//wMAUoMU+w4EAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - - 872071cfa8d267f1-SJC + - 872e34c84a7b17ea-SJC Cache-Control: - no-cache, must-revalidate Connection: @@ -3232,14 +2379,14 @@ interactions: Content-Type: - application/json Date: - - Wed, 10 Apr 2024 05:47:56 GMT + - Thu, 11 Apr 2024 21:52:57 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=MDB2CRUw4Z5PTuI5_JX5qbZ_zstlCff5CgX.f8vDKfA-1712728076-1.0.1.1-4En9.keumRw1mdAUMxwi.u08xr3tdEuGAE4xHLwk326RRY7qhHyzEpk_4W1t6nfudA3NZGh8ZN1uKLi3V5eokg; - path=/; expires=Wed, 10-Apr-24 06:17:56 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=ER5kCl2QwvB_Y2ucvpkG05atInnH6AjdgOjLgc8xokQ-1712872377-1.0.1.1-rVbXxP4nlWdwi3njgiKLffIYeHNimi5.hCeFRfTUlDaKQ4968gUp3Mt3a3EUNfibsTjo2qn6rymT0YO.a3soUQ; + path=/; expires=Thu, 11-Apr-24 22:22:57 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=08UlY5LImYBiYHSk0o_f6CjOw9oUun4ptX5nm3ddbVE-1712728076815-0.0.1.1-604800000; + - _cfuvid=SkX7F6FLp8qjOYESeaPfJpl4I6ZRHKY9Xwq1WqiRYok-1712872377489-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Transfer-Encoding: - chunked @@ -3252,7 +2399,7 @@ interactions: openai-organization: - langchain openai-processing-ms: - - '5096' + - '4856' openai-version: - '2020-10-01' strict-transport-security: @@ -3262,25 +2409,25 @@ interactions: x-ratelimit-limit-tokens: - '300000' x-ratelimit-remaining-requests: - - '9999' + - '9998' x-ratelimit-remaining-tokens: - - '299760' + - '299518' x-ratelimit-reset-requests: - - 6ms + - 11ms x-ratelimit-reset-tokens: - - 48ms + - 96ms x-request-id: - - req_d542e023ebdeb23fa496786b421bc1c5 + - req_648bb00b6435e233b39fb4198b35cb96 status: code: 200 message: OK - request: body: '{"messages": [{"role": "user", "content": "You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:\n[BEGIN - DATA]\n***\n[Input]: {''context'': ''The man is not steering a jeep.'', ''question'': - ''Can we logically conclude for sure that the man is not steering a car?''}\n***\n[Submission]: + DATA]\n***\n[Input]: {''context'': ''The man is not steering a car'', ''question'': + ''Can we logically conclude for sure that the man is not steering a convertible?''}\n***\n[Submission]: Yes\n***\n[Criteria]: usefulness: The prediction is useful if it is correct - and/or asks a useful followup question.\n***\n[Reference]: No\n***\n[END DATA]\nDoes + and/or asks a useful followup question.\n***\n[Reference]: Yes\n***\n[END DATA]\nDoes the submission meet the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. 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Here is the data:\n[BEGIN - DATA]\n***\n[Input]: {''context'': ''The man is not steering a sedan'', ''question'': - ''Can we logically conclude for sure that the man is not steering a car?''}\n***\n[Submission]: + DATA]\n***\n[Input]: {''context'': ''The man is not steering a car'', ''question'': + ''Can we logically conclude for sure that the man is not steering a SUV?''}\n***\n[Submission]: Yes\n***\n[Criteria]: usefulness: The prediction is useful if it is correct - and/or asks a useful followup question.\n***\n[Reference]: No\n***\n[END DATA]\nDoes + and/or asks a useful followup question.\n***\n[Reference]: Yes\n***\n[END DATA]\nDoes the submission meet the Criteria? First, write out in a step by step manner your reasoning about each criterion to be sure that your conclusion is correct. Avoid simply stating the correct answers at the outset. 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At the end, repeat just the letter again + by itself on a new line."}], "model": "gpt-4", "n": 1, "seed": 42, "stream": + false, "temperature": 0.0}' headers: {} method: POST - uri: https://api.openai.com/v1/embeddings + uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | - H4sIAAAAAAAAA1SXyc6zvJaF5+cqXn1TSgqhs/lndCF0sUmAhNQIEkIgpKGxDT43f/TmK1WpJkjY - 2zbaXmvvh3//6+fnz7tsq8v055+fP10zTn/+63fsWkzFn39+/vtfPz8/P//+Pv9fZPUsq+u1edXf - 8O9k87pW859/fsT/Hfm/oH9+/qgjVMmlvOcDm5TEg2ulNejuEcnDPCnnB4x9NaBB18/Dcqm5A8+7 - dU+dq9UlzJRqRad+ZqLZv2wAs7khwJRSHS1WhFrxHN0UAJfDgtHqwMNZuy2WHh+Qg81sdwvnel0I - MMmzG3U3ls7JKzOYfsLuRF35/ky4oWq59hHyN7ak3A/ZUWyPsNsGN6J22rGdm+NF1LLrakUE//wE - Y5p8GuBoxw4t1txy9jC6B1wZKEIsFPZgObpqAJ2DPSIp1zmfN6l/AOHqOtJNvRtsLlEthUr3LDFe - st6m++IgaIyua2qMz9Ee80qrgScuNpLvjpOwyawbuKhoxMZNfXHGYjwCwYQBvuKJh+zkigcorDud - qMejZBNhMC8weOs7omWNxcXq2T6g79AXgZutxemLJW/IpueL6Mp2N8zn6oUgOFc6NVH3Kd/DxruA - g1sipBfTJuG+1j5BRSSbqFtwtpc86x14JfGIo9wlIUMhqxR/a0ECFq1rmfVAMejO5xzb+7q3iTeI - 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+ + def accuracy(run: Run, example: Example): + pred = run.outputs["output"] # type: ignore + expected = example.outputs["answer"] # type: ignore + return {"score": expected.lower() == pred.lower()} + + def precision(runs: Sequence[Run], examples: Sequence[Example]): + predictions = [run.outputs["output"].lower() for run in runs] # type: ignore + expected = [example.outputs["answer"].lower() for example in examples] # type: ignore + tp = sum([p == e for p, e in zip(predictions, expected) if p == "yes"]) + fp = sum([p == "yes" and e == "no" for p, e in zip(predictions, expected)]) + return {"score": tp / (tp + fp)} + + def predict(inputs: dict) -> dict: + return {"output": "Yes"} + + evaluate( + predict, + data=dataset_name, + evaluators=[accuracy], + summary_evaluators=[precision], + metadata={ + "my-prompt-version": "abcd-1234", + "function": "evaluate", + }, + ) + + +async def test_aevaluate(): + client = Client() + client.clone_public_dataset( + "https://smith.langchain.com/public/419dcab2-1d66-4b94-8901-0357ead390df/d" + ) + dataset_name = "Evaluate Examples" + + def accuracy(run: Run, example: Example): + pred = run.outputs["output"] # type: ignore + expected = example.outputs["answer"] # type: ignore + return {"score": expected.lower() == pred.lower()} + + def precision(runs: Sequence[Run], examples: Sequence[Example]): + predictions = [run.outputs["output"].lower() for run in runs] # type: ignore + expected = [example.outputs["answer"].lower() for example in examples] # type: ignore + tp = sum([p == e for p, e in zip(predictions, expected) if p == "yes"]) + fp = sum([p == "yes" and e == "no" for p, e in zip(predictions, expected)]) + return {"score": tp / (tp + fp)} + + async def apredict(inputs: dict) -> dict: + await asyncio.sleep(0.1) + return {"output": "Yes"} + + await aevaluate( + apredict, + data=dataset_name, + evaluators=[accuracy], + summary_evaluators=[precision], + experiment_prefix="My Experiment", + metadata={ + "my-prompt-version": "abcd-1234", + "function": "agevaluate", + }, + ) + + +@unit +def test_foo(): + expect(3 + 4).to_equal(7) + + +@pytest.fixture +def some_input(): + return "Some input" + + +@pytest.fixture +def expected_output(): + return "input" + + +@unit(output_keys=["expected_output"]) +def test_bar(some_input: str, expected_output: str): + expect(some_input).to_contain(expected_output)