"""The LangSmith Client.""" from __future__ import annotations import collections import dataclasses import datetime import functools import importlib import io import json import logging import os import random import socket import sys import threading import time import uuid import weakref from dataclasses import dataclass, field from queue import Empty, PriorityQueue, Queue from typing import ( TYPE_CHECKING, Any, Callable, DefaultDict, Dict, Iterable, Iterator, List, Mapping, Optional, Sequence, Tuple, Type, Union, cast, ) from urllib import parse as urllib_parse import requests from requests import adapters as requests_adapters from urllib3.util import Retry import langsmith from langsmith import env as ls_env from langsmith import schemas as ls_schemas from langsmith import utils as ls_utils from langsmith.evaluation import evaluator as ls_evaluator if TYPE_CHECKING: import pandas as pd logger = logging.getLogger(__name__) # Filter the Connection pool is full warnings from urllib3 logging.getLogger("urllib3.connectionpool").addFilter(ls_utils.FilterPoolFullWarning()) def _is_localhost(url: str) -> bool: """Check if the URL is localhost. Parameters ---------- url : str The URL to check. Returns ------- bool True if the URL is localhost, False otherwise. """ try: netloc = urllib_parse.urlsplit(url).netloc.split(":")[0] ip = socket.gethostbyname(netloc) return ip == "127.0.0.1" or ip.startswith("0.0.0.0") or ip.startswith("::") except socket.gaierror: return False def _parse_token_or_url(url_or_token: str, api_url: str) -> Tuple[str, str]: """Parse a public dataset URL or share token.""" try: uuid.UUID(url_or_token) return api_url, url_or_token except ValueError: pass # Then it's a URL parsed_url = urllib_parse.urlparse(url_or_token) # Extract the UUID from the path path_parts = parsed_url.path.split("/") if len(path_parts) >= 2: token_uuid = path_parts[-2] else: raise ls_utils.LangSmithUserError(f"Invalid public dataset URL: {url_or_token}") return api_url, token_uuid def _is_langchain_hosted(url: str) -> bool: """Check if the URL is langchain hosted. Parameters ---------- url : str The URL to check. Returns ------- bool True if the URL is langchain hosted, False otherwise. """ try: netloc = urllib_parse.urlsplit(url).netloc.split(":")[0] return netloc.endswith("langchain.com") except Exception: return False ID_TYPE = Union[uuid.UUID, str] def _default_retry_config() -> Retry: """Get the default retry configuration. If urllib3 version is 1.26 or greater, retry on all methods. Returns ------- Retry The default retry configuration. """ retry_params = dict( total=3, status_forcelist=[502, 503, 504, 408, 425, 429], backoff_factor=0.5, # Sadly urllib3 1.x doesn't support backoff_jitter raise_on_redirect=False, raise_on_status=False, ) # the `allowed_methods` keyword is not available in urllib3 < 1.26 # check to see if urllib3 version is 1.26 or greater urllib3_version = importlib.metadata.version("urllib3") use_allowed_methods = tuple(map(int, urllib3_version.split("."))) >= (1, 26) if use_allowed_methods: # Retry on all methods retry_params["allowed_methods"] = None return Retry(**retry_params) # type: ignore _PRIMITIVE_TYPES = (str, int, float, bool) _MAX_DEPTH = 2 def _serialize_json(obj: Any, depth: int = 0) -> Any: try: if depth >= _MAX_DEPTH: try: return json.loads(json.dumps(obj)) except BaseException: return repr(obj) if isinstance(obj, datetime.datetime): return obj.isoformat() if isinstance(obj, uuid.UUID): return str(obj) if obj is None or isinstance(obj, _PRIMITIVE_TYPES): return obj if isinstance(obj, bytes): return obj.decode("utf-8") if isinstance(obj, (set, list, tuple)): return [_serialize_json(x, depth + 1) for x in list(obj)] if isinstance(obj, dict): return {k: _serialize_json(v, depth + 1) for k, v in obj.items()} serialization_methods = [ ("model_dump_json", True), # Pydantic V2 ("json", True), # Pydantic V1 ("to_json", False), # dataclass_json ] for attr, exclude_none in serialization_methods: if hasattr(obj, attr) and callable(getattr(obj, attr)): try: method = getattr(obj, attr) json_str = ( method(exclude_none=exclude_none) if exclude_none else method() ) return json.loads(json_str) except Exception as e: logger.debug(f"Failed to serialize {type(obj)} to JSON: {e}") return repr(obj) if dataclasses.is_dataclass(obj): # Regular dataclass return dataclasses.asdict(obj) if hasattr(obj, "__slots__"): all_attrs = {slot: getattr(obj, slot, None) for slot in obj.__slots__} elif hasattr(obj, "__dict__"): all_attrs = vars(obj) else: return repr(obj) return { k: _serialize_json(v, depth=depth + 1) if v is not obj else repr(v) for k, v in all_attrs.items() } except BaseException as e: logger.debug(f"Failed to serialize {type(obj)} to JSON: {e}") return repr(obj) def close_session(session: requests.Session) -> None: """Close the session. Parameters ---------- session : Session The session to close. """ logger.debug("Closing Client.session") session.close() def _validate_api_key_if_hosted(api_url: str, api_key: Optional[str]) -> None: """Verify API key is provided if url not localhost. Parameters ---------- api_url : str The API URL. api_key : str or None The API key. Raises ------ LangSmithUserError If the API key is not provided when using the hosted service. """ # If the domain is langchain.com, raise error if no api_key if not api_key: if _is_langchain_hosted(api_url): raise ls_utils.LangSmithUserError( "API key must be provided when using hosted LangSmith API" ) def _get_tracing_sampling_rate() -> float | None: """Get the tracing sampling rate. Returns ------- float The tracing sampling rate. """ sampling_rate_str = os.getenv("LANGCHAIN_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." f" Got: {sampling_rate}" ) return sampling_rate def _get_api_key(api_key: Optional[str]) -> Optional[str]: api_key = api_key if api_key is not None else os.getenv("LANGCHAIN_API_KEY") if api_key is None or not api_key.strip(): return None return api_key.strip().strip('"').strip("'") def _get_api_url(api_url: Optional[str], api_key: Optional[str]) -> str: _api_url = ( api_url if api_url is not None else os.getenv( "LANGCHAIN_ENDPOINT", "https://api.smith.langchain.com", ) ) if not _api_url.strip(): raise ls_utils.LangSmithUserError("LangSmith API URL cannot be empty") return _api_url.strip().strip('"').strip("'").rstrip("/") def _hide_inputs(inputs: Dict[str, Any]) -> Dict[str, Any]: if os.environ.get("LANGCHAIN_HIDE_INPUTS") == "true": return {} return inputs def _hide_outputs(outputs: Dict[str, Any]) -> Dict[str, Any]: if os.environ.get("LANGCHAIN_HIDE_OUTPUTS") == "true": return {} return outputs def _as_uuid(value: ID_TYPE, var: str) -> uuid.UUID: try: return uuid.UUID(value) if not isinstance(value, uuid.UUID) else value except ValueError as e: raise ls_utils.LangSmithUserError( f"{var} must be a valid UUID or UUID string. Got {value}" ) from e @dataclass(order=True) class TracingQueueItem: priority: str action: str item: Any = field(compare=False) class Client: """Client for interacting with the LangSmith API.""" __slots__ = [ "__weakref__", "api_url", "api_key", "retry_config", "timeout_ms", "session", "_get_data_type_cached", "_web_url", "_tenant_id", "tracing_sample_rate", "_sampled_post_uuids", "tracing_queue", ] def __init__( self, api_url: Optional[str] = None, *, api_key: Optional[str] = None, retry_config: Optional[Retry] = None, timeout_ms: Optional[int] = None, web_url: Optional[str] = None, session: Optional[requests.Session] = None, auto_batch_tracing: bool = True, ) -> None: """Initialize a Client instance. Parameters ---------- api_url : str or None, default=None URL for the LangSmith API. Defaults to the LANGCHAIN_ENDPOINT environment variable or https://api.smith.langchain.com if not set. api_key : str or None, default=None API key for the LangSmith API. Defaults to the LANGCHAIN_API_KEY environment variable. retry_config : Retry or None, default=None Retry configuration for the HTTPAdapter. timeout_ms : int or None, default=None Timeout in milliseconds for the HTTPAdapter. web_url : str or None, default=None URL for the LangSmith web app. Default is auto-inferred from the ENDPOINT. session: requests.Session or None, default=None The session to use for requests. If None, a new session will be created. Raises ------ LangSmithUserError If the API key is not provided when using the hosted service. """ self.tracing_sample_rate = _get_tracing_sampling_rate() self._sampled_post_uuids: set[uuid.UUID] = set() self.api_key = _get_api_key(api_key) self.api_url = _get_api_url(api_url, self.api_key) _validate_api_key_if_hosted(self.api_url, self.api_key) self.retry_config = retry_config or _default_retry_config() self.timeout_ms = timeout_ms or 10000 self._web_url = web_url self._tenant_id: Optional[uuid.UUID] = None # Create a session and register a finalizer to close it self.session = session if session else requests.Session() weakref.finalize(self, close_session, self.session) # Initialize auto batching if auto_batch_tracing: self.tracing_queue: Optional[PriorityQueue] = PriorityQueue() threading.Thread( target=_tracing_control_thread_func, # arg must be a weakref to self to avoid the Thread object # preventing garbage collection of the Client object args=(weakref.ref(self),), ).start() else: self.tracing_queue = None # Mount the HTTPAdapter with the retry configuration adapter = requests_adapters.HTTPAdapter(max_retries=self.retry_config) self.session.mount("http://", adapter) self.session.mount("https://", adapter) self._get_data_type_cached = functools.lru_cache(maxsize=10)( self._get_data_type ) def _repr_html_(self) -> str: """Return an HTML representation of the instance with a link to the URL. Returns ------- str The HTML representation of the instance. """ link = self._host_url return f'LangSmith Client' def __repr__(self) -> str: """Return a string representation of the instance with a link to the URL. Returns ------- str The string representation of the instance. """ return f"Client (API URL: {self.api_url})" @property def _host_url(self) -> str: """The web host url.""" if self._web_url: link = self._web_url else: parsed_url = urllib_parse.urlparse(self.api_url) if _is_localhost(self.api_url): link = "http://localhost" elif parsed_url.path.endswith("/api"): new_path = parsed_url.path.rsplit("/api", 1)[0] link = urllib_parse.urlunparse(parsed_url._replace(path=new_path)) elif parsed_url.netloc.startswith("dev."): link = "https://dev.smith.langchain.com" else: link = "https://smith.langchain.com" return link @property def _headers(self) -> Dict[str, str]: """Get the headers for the API request. Returns ------- Dict[str, str] The headers for the API request. """ headers = {"User-Agent": f"langsmith-py/{langsmith.__version__}"} if self.api_key: headers["x-api-key"] = self.api_key return headers @property def info(self) -> Optional[ls_schemas.LangSmithInfo]: """Get the information about the LangSmith API. Returns ------- Optional[ls_schemas.LangSmithInfo] The information about the LangSmith API, or None if the API is not available. """ info = Client._get_info(self.session, self.api_url, self.timeout_ms) if info is None and self.tracing_queue is not None: self.tracing_queue = None return cast(Optional[ls_schemas.LangSmithInfo], info) @staticmethod @functools.lru_cache(maxsize=1) def _get_info( session: requests.Session, api_url: str, timeout_ms: int ) -> Optional[ls_schemas.LangSmithInfo]: try: response = session.get( api_url + "/info", headers={"Accept": "application/json"}, timeout=timeout_ms / 1000, ) ls_utils.raise_for_status_with_text(response) return ls_schemas.LangSmithInfo(**response.json()) except requests.HTTPError: return None except BaseException as e: logger.warning(f"Failed to get info from {api_url}: {repr(e)}") return None def request_with_retries( self, request_method: str, url: str, request_kwargs: Mapping, stop_after_attempt: int = 1, retry_on: Optional[Sequence[Type[BaseException]]] = None, to_ignore: Optional[Sequence[Type[BaseException]]] = None, ) -> requests.Response: """Send a request with retries. Parameters ---------- request_method : str The HTTP request method. url : str The URL to send the request to. request_kwargs : Mapping Additional request parameters. stop_after_attempt : int, default=1 The number of attempts to make. retry_on : Sequence[Type[BaseException]] or None, default=None The exceptions to retry on. In addition to: [LangSmithConnectionError, LangSmithAPIError]. to_ignore : Sequence[Type[BaseException]] or None, default=None The exceptions to ignore / pass on. Returns ------- Response The response object. Raises ------ LangSmithAPIError If a server error occurs. LangSmithUserError If the request fails. LangSmithConnectionError If a connection error occurs. LangSmithError If the request fails. """ retry_on_: Tuple[Type[BaseException], ...] = ( *(retry_on or []), *(ls_utils.LangSmithConnectionError, ls_utils.LangSmithAPIError), ) to_ignore_: Tuple[Type[BaseException], ...] = (*(to_ignore or ()),) response = None for idx in range(stop_after_attempt): try: try: response = self.session.request( request_method, url, stream=False, **request_kwargs ) ls_utils.raise_for_status_with_text(response) return response except requests.HTTPError as e: if response is not None: if response.status_code == 500: raise ls_utils.LangSmithAPIError( f"Server error caused failure to {request_method}" f" {url} in" f" LangSmith API. {repr(e)}" ) elif response.status_code == 429: raise ls_utils.LangSmithRateLimitError( f"Rate limit exceeded for {url}. {repr(e)}" ) elif response.status_code == 401: raise ls_utils.LangSmithAuthError( f"Authentication failed for {url}. {repr(e)}" ) elif response.status_code == 404: raise ls_utils.LangSmithNotFoundError( f"Resource not found for {url}. {repr(e)}" ) elif response.status_code == 409: raise ls_utils.LangSmithConflictError( f"Conflict for {url}. {repr(e)}" ) else: raise ls_utils.LangSmithError( f"Failed to {request_method} {url} in LangSmith" f" API. {repr(e)}" ) else: raise ls_utils.LangSmithUserError( f"Failed to {request_method} {url} in LangSmith API." f" {repr(e)}" ) except requests.ConnectionError as e: raise ls_utils.LangSmithConnectionError( f"Connection error caused failure to {request_method} {url}" " in LangSmith API. Please confirm your LANGCHAIN_ENDPOINT." f" {repr(e)}" ) from e except Exception as e: args = list(e.args) msg = args[1] if len(args) > 1 else "" msg = msg.replace("session", "session (project)") emsg = "\n".join([args[0]] + [msg] + args[2:]) raise ls_utils.LangSmithError( f"Failed to {request_method} {url} in LangSmith API. {emsg}" ) from e except to_ignore_ as e: if response is not None: logger.debug("Passing on exception %s", e) return response # Else we still raise an error except retry_on_: if idx + 1 == stop_after_attempt: raise sleep_time = 2**idx + (random.random() * 0.5) time.sleep(sleep_time) continue raise ls_utils.LangSmithError( f"Failed to {request_method} {url} in LangSmith API." ) def _get_with_retries( self, path: str, params: Optional[Dict[str, Any]] = None ) -> requests.Response: return self.request_with_retries( "get", f"{self.api_url}{path}", request_kwargs={ "params": params, "headers": self._headers, "timeout": self.timeout_ms / 1000, }, ) def _get_paginated_list( self, path: str, *, params: Optional[dict] = None ) -> Iterator[dict]: """Get a paginated list of items. Parameters ---------- path : str The path of the request URL. params : dict or None, default=None The query parameters. Yields ------ dict The items in the paginated list. """ params_ = params.copy() if params else {} offset = params_.get("offset", 0) params_["limit"] = params_.get("limit", 100) while True: params_["offset"] = offset response = self._get_with_retries(path, params=params_) items = response.json() if not items: break yield from items if len(items) < params_["limit"]: # offset and limit isn't respected if we're # querying for specific values break offset += len(items) def _get_cursor_paginated_list( self, path: str, *, body: Optional[dict] = None, request_method: str = "post", data_key: str = "runs", ) -> Iterator[dict]: """Get a cursor paginated list of items. Parameters ---------- path : str The path of the request URL. body : dict or None, default=None The query body. request_method : str, default="post" The HTTP request method. data_key : str, default="runs" Yields ------ dict The items in the paginated list. """ params_ = body.copy() if body else {} while True: response = self.request_with_retries( request_method, f"{self.api_url}{path}", request_kwargs={ "data": json.dumps(params_, default=_serialize_json), "headers": self._headers, "timeout": self.timeout_ms / 1000, }, ) response_body = response.json() if not response_body: break if not response_body.get(data_key): break yield from response_body[data_key] cursors = response_body.get("cursors") if not cursors: break if not cursors.get("next"): break params_["cursor"] = cursors["next"] def upload_dataframe( self, df: pd.DataFrame, name: str, input_keys: Sequence[str], output_keys: Sequence[str], *, description: Optional[str] = None, data_type: Optional[ls_schemas.DataType] = ls_schemas.DataType.kv, ) -> ls_schemas.Dataset: """Upload a dataframe as individual examples to the LangSmith API. Parameters ---------- df : pd.DataFrame The dataframe to upload. name : str The name of the dataset. input_keys : Sequence[str] The input keys. output_keys : Sequence[str] The output keys. description : str or None, default=None The description of the dataset. data_type : DataType or None, default=DataType.kv The data type of the dataset. Returns ------- Dataset The uploaded dataset. Raises ------ ValueError If the csv_file is not a string or tuple. """ csv_file = io.BytesIO() df.to_csv(csv_file, index=False) csv_file.seek(0) return self.upload_csv( ("data.csv", csv_file), input_keys=input_keys, output_keys=output_keys, description=description, name=name, data_type=data_type, ) def upload_csv( self, csv_file: Union[str, Tuple[str, io.BytesIO]], input_keys: Sequence[str], output_keys: Sequence[str], *, name: Optional[str] = None, description: Optional[str] = None, data_type: Optional[ls_schemas.DataType] = ls_schemas.DataType.kv, ) -> ls_schemas.Dataset: """Upload a CSV file to the LangSmith API. Parameters ---------- csv_file : str or Tuple[str, BytesIO] The CSV file to upload. If a string, it should be the path If a tuple, it should be a tuple containing the filename and a BytesIO object. input_keys : Sequence[str] The input keys. output_keys : Sequence[str] The output keys. name : str or None, default=None The name of the dataset. description : str or None, default=None The description of the dataset. data_type : DataType or None, default=DataType.kv The data type of the dataset. Returns ------- Dataset The uploaded dataset. Raises ------ ValueError If the csv_file is not a string or tuple. """ data = { "input_keys": input_keys, "output_keys": output_keys, } if name: data["name"] = name if description: data["description"] = description if data_type: data["data_type"] = ls_utils.get_enum_value(data_type) if isinstance(csv_file, str): with open(csv_file, "rb") as f: file_ = {"file": f} response = self.session.post( self.api_url + "/datasets/upload", headers=self._headers, data=data, files=file_, ) elif isinstance(csv_file, tuple): response = self.session.post( self.api_url + "/datasets/upload", headers=self._headers, data=data, files={"file": csv_file}, ) else: raise ValueError("csv_file must be a string or tuple") ls_utils.raise_for_status_with_text(response) result = response.json() # TODO: Make this more robust server-side if "detail" in result and "already exists" in result["detail"]: file_name = csv_file if isinstance(csv_file, str) else csv_file[0] file_name = file_name.split("/")[-1] raise ValueError(f"Dataset {file_name} already exists") return ls_schemas.Dataset( **result, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) @staticmethod def _run_transform( run: Union[ls_schemas.Run, dict, ls_schemas.RunLikeDict], ) -> dict: """ Transforms the given run object into a dictionary representation. Args: run (Union[ls_schemas.Run, dict]): The run object to transform. Returns: dict: The transformed run object as a dictionary. """ if hasattr(run, "dict") and callable(getattr(run, "dict")): run_create = run.dict() # type: ignore else: run_create = cast(dict, run) if "id" not in run_create: run_create["id"] = uuid.uuid4() elif isinstance(run["id"], str): run["id"] = uuid.UUID(run["id"]) if "inputs" in run_create: run_create["inputs"] = _hide_inputs(run_create["inputs"]) if "outputs" in run_create: run_create["outputs"] = _hide_outputs(run_create["outputs"]) if not run_create.get("start_time"): run_create["start_time"] = datetime.datetime.utcnow() return run_create @staticmethod def _insert_runtime_env(runs: Sequence[dict]) -> None: runtime_env = ls_env.get_runtime_and_metrics() for run_create in runs: run_extra = cast(dict, run_create.setdefault("extra", {})) # update runtime runtime: dict = run_extra.setdefault("runtime", {}) run_extra["runtime"] = {**runtime_env, **runtime} # update metadata metadata: dict = run_extra.setdefault("metadata", {}) langchain_metadata = ls_env.get_langchain_env_var_metadata() metadata.update( {k: v for k, v in langchain_metadata.items() if k not in metadata} ) def _filter_for_sampling( self, runs: Iterable[dict], *, patch: bool = False ) -> list[dict]: if self.tracing_sample_rate is None: return list(runs) if patch: sampled = [] for run in runs: if run["id"] in self._sampled_post_uuids: sampled.append(run) self._sampled_post_uuids.remove(run["id"]) return sampled else: sampled = [] for run in runs: if random.random() < self.tracing_sample_rate: sampled.append(run) self._sampled_post_uuids.add(run["id"]) return sampled def create_run( self, name: str, inputs: Dict[str, Any], run_type: str, *, project_name: Optional[str] = None, revision_id: Optional[ID_TYPE] = None, **kwargs: Any, ) -> None: """Persist a run to the LangSmith API. Parameters ---------- name : str The name of the run. inputs : Dict[str, Any] The input values for the run. run_type : str The type of the run, such as tool, chain, llm, retriever, embedding, prompt, or parser. revision_id : ID_TYPE or None, default=None The revision ID of the run. **kwargs : Any Additional keyword arguments. Raises ------ LangSmithUserError If the API key is not provided when using the hosted service. """ project_name = project_name or kwargs.pop( "session_name", # if the project is not provided, use the environment's project ls_utils.get_tracer_project(), ) run_create = { **kwargs, "session_name": project_name, "name": name, "inputs": inputs, "run_type": run_type, } if not self._filter_for_sampling([run_create]): return run_create = self._run_transform(run_create) self._insert_runtime_env([run_create]) if revision_id is not None: run_create["extra"]["metadata"]["revision_id"] = revision_id if ( self.tracing_queue is not None # batch ingest requires trace_id and dotted_order to be set and run_create.get("trace_id") is not None and run_create.get("dotted_order") is not None # Checked last since it makes a (cached) API call and self.info is not None # Older versions don't support batch ingest ): return self.tracing_queue.put( TracingQueueItem(run_create["dotted_order"], "create", run_create) ) headers = { **self._headers, "Accept": "application/json", "Content-Type": "application/json", } self.request_with_retries( "post", f"{self.api_url}/runs", request_kwargs={ "data": json.dumps(run_create, default=_serialize_json), "headers": headers, "timeout": self.timeout_ms / 1000, }, to_ignore=(ls_utils.LangSmithConflictError,), ) def batch_ingest_runs( self, create: Optional[ Sequence[Union[ls_schemas.Run, ls_schemas.RunLikeDict, Dict]] ] = None, update: Optional[ Sequence[Union[ls_schemas.Run, ls_schemas.RunLikeDict, Dict]] ] = None, *, pre_sampled: bool = False, ): """ Batch ingest/upsert multiple runs in the Langsmith system. Args: create (Optional[Sequence[Union[ls_schemas.Run, RunLikeDict]]]): A sequence of `Run` objects or equivalent dictionaries representing runs to be created / posted. update (Optional[Sequence[Union[ls_schemas.Run, RunLikeDict]]]): A sequence of `Run` objects or equivalent dictionaries representing runs that have already been created and should be updated / patched. pre_sampled (bool, optional): Whether the runs have already been subject to sampling, and therefore should not be sampled again. Defaults to False. Returns: None: If both `create` and `update` are None. Raises: LangsmithAPIError: If there is an error in the API request. Note: - The run objects MUST contain the dotted_order and trace_id fields to be accepted by the API. """ if not create and not update: return # transform and convert to dicts create_dicts = [self._run_transform(run) for run in create or []] update_dicts = [self._run_transform(run) for run in update or []] # combine post and patch dicts where possible if update_dicts and create_dicts: create_by_id = {run["id"]: run for run in create_dicts} standalone_updates: list[dict] = [] for run in update_dicts: if run["id"] in create_by_id: create_by_id[run["id"]].update( {k: v for k, v in run.items() if v is not None} ) else: standalone_updates.append(run) update_dicts = standalone_updates for run in create_dicts: if not run.get("trace_id") or not run.get("dotted_order"): raise ls_utils.LangSmithUserError( "Batch ingest requires trace_id and dotted_order to be set." ) for run in update_dicts: if not run.get("trace_id") or not run.get("dotted_order"): raise ls_utils.LangSmithUserError( "Batch ingest requires trace_id and dotted_order to be set." ) # filter out runs that are not sampled if pre_sampled: body = { "post": create_dicts, "patch": update_dicts, } else: body = { "post": self._filter_for_sampling(create_dicts), "patch": self._filter_for_sampling(update_dicts, patch=True), } if not body["post"] and not body["patch"]: return self._insert_runtime_env(body["post"]) self.request_with_retries( "post", f"{self.api_url}/runs/batch", request_kwargs={ "data": json.dumps(body, default=_serialize_json), "timeout": self.timeout_ms / 1000, "headers": { **self._headers, "Accept": "application/json", "Content-Type": "application/json", }, }, to_ignore=(ls_utils.LangSmithConflictError,), ) def update_run( self, run_id: ID_TYPE, *, end_time: Optional[datetime.datetime] = None, error: Optional[str] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, events: Optional[Sequence[dict]] = None, **kwargs: Any, ) -> None: """Update a run in the LangSmith API. Parameters ---------- run_id : str or UUID The ID of the run to update. end_time : datetime or None The end time of the run. error : str or None, default=None The error message of the run. inputs : Dict or None, default=None The input values for the run. outputs : Dict or None, default=None The output values for the run. events : Sequence[dict] or None, default=None The events for the run. **kwargs : Any Kwargs are ignored. """ headers = { **self._headers, "Accept": "application/json", "Content-Type": "application/json", } data: Dict[str, Any] = { "id": _as_uuid(run_id, "run_id"), "trace_id": kwargs.pop("trace_id", None), "parent_run_id": kwargs.pop("parent_run_id", None), "dotted_order": kwargs.pop("dotted_order", None), } if not self._filter_for_sampling([data], patch=True): return if end_time is not None: data["end_time"] = end_time.isoformat() if error is not None: data["error"] = error if inputs is not None: data["inputs"] = _hide_inputs(inputs) if outputs is not None: data["outputs"] = _hide_outputs(outputs) if events is not None: data["events"] = events if ( self.tracing_queue is not None # batch ingest requires trace_id and dotted_order to be set and data["trace_id"] is not None and data["dotted_order"] is not None # Checked last since it makes an API call and self.info is not None # Older versions don't support batch ingest ): return self.tracing_queue.put( TracingQueueItem(data["dotted_order"], "update", data) ) self.request_with_retries( "patch", f"{self.api_url}/runs/{data['id']}", request_kwargs={ "data": json.dumps(data, default=_serialize_json), "headers": headers, "timeout": self.timeout_ms / 1000, }, ) def _load_child_runs(self, run: ls_schemas.Run) -> ls_schemas.Run: """Load child runs for a given run. Parameters ---------- run : Run The run to load child runs for. Returns ------- Run The run with loaded child runs. Raises ------ LangSmithError If a child run has no parent. """ child_runs = self.list_runs(id=run.child_run_ids) treemap: DefaultDict[uuid.UUID, List[ls_schemas.Run]] = collections.defaultdict( list ) runs: Dict[uuid.UUID, ls_schemas.Run] = {} for child_run in sorted( child_runs, key=lambda r: r.dotted_order, ): if child_run.parent_run_id is None: raise ls_utils.LangSmithError(f"Child run {child_run.id} has no parent") treemap[child_run.parent_run_id].append(child_run) runs[child_run.id] = child_run run.child_runs = treemap.pop(run.id, []) for run_id, children in treemap.items(): runs[run_id].child_runs = children return run def read_run( self, run_id: ID_TYPE, load_child_runs: bool = False ) -> ls_schemas.Run: """Read a run from the LangSmith API. Parameters ---------- run_id : str or UUID The ID of the run to read. load_child_runs : bool, default=False Whether to load nested child runs. Returns ------- Run The run. """ response = self._get_with_retries(f"/runs/{_as_uuid(run_id, 'run_id')}") run = ls_schemas.Run(**response.json(), _host_url=self._host_url) if load_child_runs and run.child_run_ids: run = self._load_child_runs(run) return run def list_runs( self, *, project_id: Optional[ID_TYPE] = None, project_name: Optional[str] = None, run_type: Optional[str] = None, reference_example_id: Optional[ID_TYPE] = None, query: Optional[str] = None, filter: Optional[str] = None, execution_order: Optional[int] = None, parent_run_id: Optional[ID_TYPE] = None, start_time: Optional[datetime.datetime] = None, error: Optional[bool] = None, run_ids: Optional[List[ID_TYPE]] = None, **kwargs: Any, ) -> Iterator[ls_schemas.Run]: """List runs from the LangSmith API. Parameters ---------- project_id : UUID or None, default=None The ID of the project to filter by. project_name : str or None, default=None The name of the project to filter by. run_type : str or None, default=None The type of the runs to filter by. reference_example_id : UUID or None, default=None The ID of the reference example to filter by. query : str or None, default=None The query string to filter by. filter : str or None, default=None The filter string to filter by. execution_order : int or None, default=None The execution order to filter by. Execution order is the position of the run in the full trace's execution sequence. All root run traces have execution_order 1. parent_run_id : UUID or None, default=None The ID of the parent run to filter by. start_time : datetime or None, default=None The start time to filter by. error : bool or None, default=None Whether to filter by error status. run_ids : List[str or UUID] or None, default=None The IDs of the runs to filter by. **kwargs : Any Additional keyword arguments. Yields ------ Run The runs. """ if project_name is not None: if project_id is not None: raise ValueError("Only one of project_id or project_name may be given") project_id = self.read_project(project_name=project_name).id body_query: Dict[str, Any] = { "session": [project_id] if project_id else None, "run_type": run_type, "reference_example": ( [reference_example_id] if reference_example_id else None ), "query": query, "filter": filter, "execution_order": execution_order, "parent_run": parent_run_id, "start_time": start_time.isoformat() if start_time else None, "error": error, "id": run_ids, **kwargs, } body_query = {k: v for k, v in body_query.items() if v is not None} yield from ( ls_schemas.Run(**run, _host_url=self._host_url) for run in self._get_cursor_paginated_list( "/runs/query", body=body_query, request_method="post" ) ) def get_run_url( self, *, run: ls_schemas.RunBase, project_name: Optional[str] = None, project_id: Optional[ID_TYPE] = None, ) -> str: """Get the URL for a run. Parameters ---------- run : Run The run. project_name : str or None, default=None The name of the project. project_id : UUID or None, default=None The ID of the project. Returns ------- str The URL for the run. """ if hasattr(run, "session_id") and run.session_id is not None: session_id = run.session_id elif project_id is not None: session_id = project_id elif project_name is not None: session_id = self.read_project(project_name=project_name).id else: project_name = ls_utils.get_tracer_project() session_id = self.read_project(project_name=project_name).id session_id_ = _as_uuid(session_id, "session_id") return ( f"{self._host_url}/o/{self._get_tenant_id()}/projects/p/{session_id_}/" f"r/{run.id}?poll=true" ) def share_run(self, run_id: ID_TYPE, *, share_id: Optional[ID_TYPE] = None) -> str: """Get a share link for a run.""" run_id_ = _as_uuid(run_id, "run_id") data = { "run_id": str(run_id_), "share_token": share_id or str(uuid.uuid4()), } response = self.session.put( f"{self.api_url}/runs/{run_id_}/share", headers=self._headers, json=data, ) ls_utils.raise_for_status_with_text(response) share_token = response.json()["share_token"] return f"{self._host_url}/public/{share_token}/r" def unshare_run(self, run_id: ID_TYPE) -> None: """Delete share link for a run.""" response = self.session.delete( f"{self.api_url}/runs/{_as_uuid(run_id, 'run_id')}/share", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def read_run_shared_link(self, run_id: ID_TYPE) -> Optional[str]: response = self.session.get( f"{self.api_url}/runs/{_as_uuid(run_id, 'run_id')}/share", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) result = response.json() if result is None or "share_token" not in result: return None return f"{self._host_url}/public/{result['share_token']}/r" def run_is_shared(self, run_id: ID_TYPE) -> bool: """Get share state for a run.""" link = self.read_run_shared_link(_as_uuid(run_id, "run_id")) return link is not None def list_shared_runs( self, share_token: ID_TYPE, run_ids: Optional[List[str]] = None ) -> List[ls_schemas.Run]: """Get shared runs.""" params = {"id": run_ids, "share_token": str(share_token)} response = self.session.get( f"{self.api_url}/public/{_as_uuid(share_token, 'share_token')}/runs", headers=self._headers, params=params, ) ls_utils.raise_for_status_with_text(response) return [ ls_schemas.Run(**run, _host_url=self._host_url) for run in response.json() ] def read_dataset_shared_schema( self, dataset_id: Optional[ID_TYPE] = None, *, dataset_name: Optional[str] = None, ) -> ls_schemas.DatasetShareSchema: if dataset_id is None and dataset_name is None: raise ValueError("Either dataset_id or dataset_name must be given") if dataset_id is None: dataset_id = self.read_dataset(dataset_name=dataset_name).id response = self.session.get( f"{self.api_url}/datasets/{_as_uuid(dataset_id, 'dataset_id')}/share", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) d = response.json() return cast( ls_schemas.DatasetShareSchema, { **d, "url": f"{self._host_url}/public/" f"{_as_uuid(d['share_token'], 'response.share_token')}/d", }, ) def share_dataset( self, dataset_id: Optional[ID_TYPE] = None, *, dataset_name: Optional[str] = None, ) -> ls_schemas.DatasetShareSchema: """Get a share link for a dataset.""" if dataset_id is None and dataset_name is None: raise ValueError("Either dataset_id or dataset_name must be given") if dataset_id is None: dataset_id = self.read_dataset(dataset_name=dataset_name).id data = { "dataset_id": str(dataset_id), } response = self.session.put( f"{self.api_url}/datasets/{_as_uuid(dataset_id, 'dataset_id')}/share", headers=self._headers, json=data, ) ls_utils.raise_for_status_with_text(response) d: dict = response.json() return cast( ls_schemas.DatasetShareSchema, {**d, "url": f"{self._host_url}/public/{d['share_token']}/d"}, ) def unshare_dataset(self, dataset_id: ID_TYPE) -> None: """Delete share link for a dataset.""" response = self.session.delete( f"{self.api_url}/datasets/{_as_uuid(dataset_id, 'dataset_id')}/share", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def read_shared_dataset( self, share_token: str, ) -> ls_schemas.Dataset: """Get shared datasets.""" response = self.session.get( f"{self.api_url}/public/{_as_uuid(share_token, 'share_token')}/datasets", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) return ls_schemas.Dataset( **response.json(), _host_url=self._host_url, _public_path=f"/public/{share_token}/d", ) def list_shared_examples( self, share_token: str, *, example_ids: Optional[List[ID_TYPE]] = None ) -> List[ls_schemas.Example]: """Get shared examples.""" params = {} if example_ids is not None: params["id"] = [str(id) for id in example_ids] response = self.session.get( f"{self.api_url}/public/{_as_uuid(share_token, 'share_token')}/examples", headers=self._headers, params=params, ) ls_utils.raise_for_status_with_text(response) return [ ls_schemas.Example(**dataset, _host_url=self._host_url) for dataset in response.json() ] def list_shared_projects( self, *, dataset_share_token: str, project_ids: Optional[List[ID_TYPE]] = None, name: Optional[str] = None, name_contains: Optional[str] = None, ) -> Iterator[ls_schemas.TracerSessionResult]: params = {"id": project_ids, "name": name, "name_contains": name_contains} share_token = _as_uuid(dataset_share_token, "dataset_share_token") yield from [ ls_schemas.TracerSessionResult(**project, _host_url=self._host_url) for project in self._get_paginated_list( f"/public/{share_token}/datasets/sessions", params=params, ) ] def create_project( self, project_name: str, *, description: Optional[str] = None, metadata: Optional[dict] = None, upsert: bool = False, project_extra: Optional[dict] = None, reference_dataset_id: Optional[ID_TYPE] = None, ) -> ls_schemas.TracerSession: """Create a project on the LangSmith API. Parameters ---------- project_name : str The name of the project. project_extra : dict or None, default=None Additional project information. metadata: dict or None, default=None Additional metadata to associate with the project. description : str or None, default=None The description of the project. upsert : bool, default=False Whether to update the project if it already exists. reference_dataset_id: UUID or None, default=None The ID of the reference dataset to associate with the project. Returns ------- TracerSession The created project. """ endpoint = f"{self.api_url}/sessions" extra = project_extra if metadata: extra = {**(extra or {}), "metadata": metadata} body: Dict[str, Any] = { "name": project_name, "extra": extra, "description": description, } params = {} if upsert: params["upsert"] = True if reference_dataset_id is not None: body["reference_dataset_id"] = reference_dataset_id response = self.session.post( endpoint, headers={**self._headers, "Content-Type": "application/json"}, data=json.dumps(body, default=_serialize_json), ) ls_utils.raise_for_status_with_text(response) return ls_schemas.TracerSession(**response.json(), _host_url=self._host_url) def update_project( self, project_id: ID_TYPE, *, name: Optional[str] = None, description: Optional[str] = None, metadata: Optional[dict] = None, project_extra: Optional[dict] = None, end_time: Optional[datetime.datetime] = None, ) -> ls_schemas.TracerSession: """Update a LangSmith project. Parameters ---------- project_id : UUID The ID of the project to update. name : str or None, default=None The new name to give the project. This is only valid if the project has been assigned an end_time, meaning it has been completed/closed. description : str or None, default=None The new description to give the project. metadata: dict or None, default=None project_extra : dict or None, default=None Additional project information. Returns ------- TracerSession The updated project. """ endpoint = f"{self.api_url}/sessions/{_as_uuid(project_id, 'project_id')}" extra = project_extra if metadata: extra = {**(extra or {}), "metadata": metadata} body: Dict[str, Any] = { "name": name, "extra": extra, "description": description, "end_time": end_time.isoformat() if end_time else None, } response = self.session.patch( endpoint, headers={**self._headers, "Content-Type": "application/json"}, data=json.dumps(body, default=_serialize_json), ) ls_utils.raise_for_status_with_text(response) return ls_schemas.TracerSession(**response.json(), _host_url=self._host_url) def _get_tenant_id(self) -> uuid.UUID: if self._tenant_id is not None: return self._tenant_id response = self._get_with_retries("/sessions", params={"limit": 1}) result = response.json() if isinstance(result, list): tracer_session = ls_schemas.TracerSessionResult( **result[0], _host_url=self._host_url ) self._tenant_id = tracer_session.tenant_id return self._tenant_id raise ls_utils.LangSmithError("No projects found") @ls_utils.xor_args(("project_id", "project_name")) def read_project( self, *, project_id: Optional[str] = None, project_name: Optional[str] = None, include_stats: bool = False, ) -> ls_schemas.TracerSessionResult: """Read a project from the LangSmith API. Parameters ---------- project_id : str or None, default=None The ID of the project to read. project_name : str or None, default=None The name of the project to read. Note: Only one of project_id or project_name may be given. include_stats : bool, default=False Whether to include a project's aggregate statistics in the response. Returns ------- TracerSessionResult The project. """ path = "/sessions" params: Dict[str, Any] = {"limit": 1} if project_id is not None: path += f"/{_as_uuid(project_id, 'project_id')}" elif project_name is not None: params["name"] = project_name else: raise ValueError("Must provide project_name or project_id") params["include_stats"] = include_stats response = self._get_with_retries(path, params=params) result = response.json() if isinstance(result, list): if len(result) == 0: raise ls_utils.LangSmithNotFoundError( f"Project {project_name} not found" ) return ls_schemas.TracerSessionResult(**result[0], _host_url=self._host_url) return ls_schemas.TracerSessionResult( **response.json(), _host_url=self._host_url ) def has_project( self, project_name: str, *, project_id: Optional[str] = None ) -> bool: """Check if a project exists. Parameters ---------- project_name : str The name of the project to check for. project_id : str or None, default=None The ID of the project to check for. Returns ------- bool Whether the project exists. """ try: self.read_project(project_name=project_name) except ls_utils.LangSmithNotFoundError: return False return True def get_test_results( self, *, project_id: Optional[ID_TYPE] = None, project_name: Optional[str] = None, ) -> "pd.DataFrame": """Read the record-level information from a test project into a Pandas DF. Note: this will fetch whatever data exists in the DB. Results are not immediately available in the DB upon evaluation run completion. Returns ------- pd.DataFrame A dataframe containing the test results. """ import pandas as pd # type: ignore runs = self.list_runs( project_id=project_id, project_name=project_name, execution_order=1 ) results = [] example_ids = [] for r in runs: row = { "example_id": r.reference_example_id, **{f"input.{k}": v for k, v in r.inputs.items()}, **{f"outputs.{k}": v for k, v in (r.outputs or {}).items()}, } if r.feedback_stats: for k, v in r.feedback_stats.items(): row[f"feedback.{k}"] = v.get("avg") row.update( { "execution_time": ( (r.end_time - r.start_time).total_seconds() if r.end_time else None ), "error": r.error, "id": r.id, } ) if r.reference_example_id: example_ids.append(r.reference_example_id) results.append(row) result = pd.DataFrame(results).set_index("example_id") batch_size = 100 example_outputs = [] for batch in [ example_ids[i : i + batch_size] for i in range(0, len(example_ids), batch_size) ]: for example in self.list_examples(example_ids=batch): example_outputs.append( { "example_id": example.id, **{ f"reference.{k}": v for k, v in (example.outputs or {}).items() }, } ) if example_outputs: df = pd.DataFrame(example_outputs).set_index("example_id") result = df.merge(result, left_index=True, right_index=True) # Flatten dict columns into dot syntax for easier access return pd.json_normalize(result.to_dict(orient="records")) def list_projects( self, project_ids: Optional[List[ID_TYPE]] = None, name: Optional[str] = None, name_contains: Optional[str] = None, reference_dataset_id: Optional[ID_TYPE] = None, reference_dataset_name: Optional[str] = None, reference_free: Optional[bool] = None, ) -> Iterator[ls_schemas.TracerSession]: """ List projects from the LangSmith API. Parameters ---------- project_ids : Optional[List[ID_TYPE]], optional A list of project IDs to filter by, by default None name : Optional[str], optional The name of the project to filter by, by default None name_contains : Optional[str], optional A string to search for in the project name, by default None reference_dataset_id : Optional[List[ID_TYPE]], optional A dataset ID to filter by, by default None reference_dataset_name : Optional[str], optional The name of the reference dataset to filter by, by default None reference_free : Optional[bool], optional Whether to filter for only projects not associated with a dataset. Yields ------ TracerSession The projects. """ params: Dict[str, Any] = {} if project_ids is not None: params["id"] = project_ids if name is not None: params["name"] = name if name_contains is not None: params["name_contains"] = name_contains if reference_dataset_id is not None: if reference_dataset_name is not None: raise ValueError( "Only one of reference_dataset_id or" " reference_dataset_name may be given" ) params["reference_dataset"] = reference_dataset_id elif reference_dataset_name is not None: reference_dataset_id = self.read_dataset( dataset_name=reference_dataset_name ).id params["reference_dataset"] = reference_dataset_id if reference_free is not None: params["reference_free"] = reference_free yield from ( ls_schemas.TracerSessionResult(**project, _host_url=self._host_url) for project in self._get_paginated_list("/sessions", params=params) ) @ls_utils.xor_args(("project_name", "project_id")) def delete_project( self, *, project_name: Optional[str] = None, project_id: Optional[str] = None ) -> None: """Delete a project from LangSmith. Parameters ---------- project_name : str or None, default=None The name of the project to delete. project_id : str or None, default=None The ID of the project to delete. """ if project_name is not None: project_id = str(self.read_project(project_name=project_name).id) elif project_id is None: raise ValueError("Must provide project_name or project_id") response = self.session.delete( self.api_url + f"/sessions/{_as_uuid(project_id, 'project_id')}", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def create_dataset( self, dataset_name: str, *, description: Optional[str] = None, data_type: ls_schemas.DataType = ls_schemas.DataType.kv, ) -> ls_schemas.Dataset: """Create a dataset in the LangSmith API. Parameters ---------- dataset_name : str The name of the dataset. description : str or None, default=None The description of the dataset. data_type : DataType or None, default=DataType.kv The data type of the dataset. Returns ------- Dataset The created dataset. """ dataset = ls_schemas.DatasetCreate( name=dataset_name, description=description, data_type=data_type, ) response = self.session.post( self.api_url + "/datasets", headers={**self._headers, "Content-Type": "application/json"}, data=dataset.json(), ) ls_utils.raise_for_status_with_text(response) return ls_schemas.Dataset( **response.json(), _host_url=self._host_url, _tenant_id=self._get_tenant_id(), ) def has_dataset( self, *, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None ) -> bool: """Check whether a dataset exists in your tenant. Parameters ---------- dataset_name : str or None, default=None The name of the dataset to check. dataset_id : str or None, default=None The ID of the dataset to check. Returns ------- bool Whether the dataset exists. """ try: self.read_dataset(dataset_name=dataset_name, dataset_id=dataset_id) return True except ls_utils.LangSmithNotFoundError: return False @ls_utils.xor_args(("dataset_name", "dataset_id")) def read_dataset( self, *, dataset_name: Optional[str] = None, dataset_id: Optional[ID_TYPE] = None, ) -> ls_schemas.Dataset: """Read a dataset from the LangSmith API. Parameters ---------- dataset_name : str or None, default=None The name of the dataset to read. dataset_id : UUID or None, default=None The ID of the dataset to read. Returns ------- Dataset The dataset. """ path = "/datasets" params: Dict[str, Any] = {"limit": 1} if dataset_id is not None: path += f"/{_as_uuid(dataset_id, 'dataset_id')}" elif dataset_name is not None: params["name"] = dataset_name else: raise ValueError("Must provide dataset_name or dataset_id") response = self._get_with_retries( path, params=params, ) result = response.json() if isinstance(result, list): if len(result) == 0: raise ls_utils.LangSmithNotFoundError( f"Dataset {dataset_name} not found" ) return ls_schemas.Dataset( **result[0], _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) return ls_schemas.Dataset( **result, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) def read_dataset_openai_finetuning( self, dataset_id: Optional[str] = None, *, dataset_name: Optional[str] = None ) -> list: """ Download a dataset in OpenAI Jsonl format and load it as a list of dicts. Parameters ---------- dataset_id : str The ID of the dataset to download. dataset_name : str The name of the dataset to download. Returns ------- list The dataset loaded as a list of dicts. """ path = "/datasets" if dataset_id is not None: pass elif dataset_name is not None: dataset_id = self.read_dataset(dataset_name=dataset_name).id else: raise ValueError("Must provide dataset_name or dataset_id") response = self._get_with_retries( f"{path}/{_as_uuid(dataset_id, 'dataset_id')}/openai_ft", ) dataset = [json.loads(line) for line in response.text.strip().split("\n")] return dataset def list_datasets( self, *, dataset_ids: Optional[List[ID_TYPE]] = None, data_type: Optional[str] = None, dataset_name: Optional[str] = None, dataset_name_contains: Optional[str] = None, ) -> Iterator[ls_schemas.Dataset]: """List the datasets on the LangSmith API. Yields ------ Dataset The datasets. """ params: Dict[str, Any] = {} if dataset_ids is not None: params["id"] = dataset_ids if data_type is not None: params["data_type"] = data_type if dataset_name is not None: params["name"] = dataset_name if dataset_name_contains is not None: params["name_contains"] = dataset_name_contains yield from ( ls_schemas.Dataset( **dataset, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) for dataset in self._get_paginated_list("/datasets", params=params) ) @ls_utils.xor_args(("dataset_id", "dataset_name")) def delete_dataset( self, *, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, ) -> None: """Delete a dataset from the LangSmith API. Parameters ---------- dataset_id : UUID or None, default=None The ID of the dataset to delete. dataset_name : str or None, default=None The name of the dataset to delete. """ if dataset_name is not None: dataset_id = self.read_dataset(dataset_name=dataset_name).id if dataset_id is None: raise ValueError("Must provide either dataset name or ID") response = self.session.delete( f"{self.api_url}/datasets/{_as_uuid(dataset_id, 'dataset_id')}", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def clone_public_dataset( self, token_or_url: str, *, source_api_url: Optional[str] = None, dataset_name: Optional[str] = None, ) -> None: """Clone a public dataset to your own langsmith tenant. This operation is idempotent. If you already have a dataset with the given name, this function will do nothing. Args: token_or_url (str): The token of the public dataset to clone. source_api_url: The URL of the langsmith server where the data is hosted. Defaults to the API URL of your current client. dataset_name (str): The name of the dataset to create in your tenant. Defaults to the name of the public dataset. """ source_api_url = source_api_url or self.api_url source_api_url, token_uuid = _parse_token_or_url(token_or_url, source_api_url) source_client = Client( # Placeholder API key not needed anymore in most cases, but # some private deployments may have API key-based rate limiting # that would cause this to fail if we provide no value. api_url=source_api_url, api_key="placeholder", ) ds = source_client.read_shared_dataset(token_uuid) dataset_name = dataset_name or ds.name if self.has_dataset(dataset_name=dataset_name): logger.info( f"Dataset {dataset_name} already exists in your tenant. Skipping." ) return try: # Fetch examples first examples = list(source_client.list_shared_examples(token_uuid)) dataset = self.create_dataset( dataset_name=dataset_name, description=ds.description, data_type=ds.data_type or ls_schemas.DataType.kv, ) try: self.create_examples( inputs=[e.inputs for e in examples], outputs=[e.outputs for e in examples], dataset_id=dataset.id, ) except BaseException as e: # Let's not do automatic clean up for now in case there might be # some other reasons why create_examples fails (i.e., not network issue # or keyboard interrupt). # The risk is that this is an existing dataset that has valid examples # populated from another source so we don't want to delete it. logger.error( f"An error occurred while creating dataset {dataset_name}. " "You should delete it manually." ) raise e finally: del source_client def _get_data_type(self, dataset_id: ID_TYPE) -> ls_schemas.DataType: dataset = self.read_dataset(dataset_id=dataset_id) return dataset.data_type @ls_utils.xor_args(("dataset_id", "dataset_name")) def create_llm_example( self, prompt: str, generation: Optional[str] = None, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, created_at: Optional[datetime.datetime] = None, ) -> ls_schemas.Example: """Add an example (row) to an LLM-type dataset.""" return self.create_example( inputs={"input": prompt}, outputs={"output": generation}, dataset_id=dataset_id, dataset_name=dataset_name, created_at=created_at, ) @ls_utils.xor_args(("dataset_id", "dataset_name")) def create_chat_example( self, messages: List[Union[Mapping[str, Any], ls_schemas.BaseMessageLike]], generations: Optional[ Union[Mapping[str, Any], ls_schemas.BaseMessageLike] ] = None, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, created_at: Optional[datetime.datetime] = None, ) -> ls_schemas.Example: """Add an example (row) to a Chat-type dataset.""" final_input = [] for message in messages: if ls_utils.is_base_message_like(message): final_input.append( ls_utils.convert_langchain_message( cast(ls_schemas.BaseMessageLike, message) ) ) else: final_input.append(cast(dict, message)) final_generations = None if generations is not None: if ls_utils.is_base_message_like(generations): final_generations = ls_utils.convert_langchain_message( cast(ls_schemas.BaseMessageLike, generations) ) else: final_generations = cast(dict, generations) return self.create_example( inputs={"input": final_input}, outputs=( {"output": final_generations} if final_generations is not None else None ), dataset_id=dataset_id, dataset_name=dataset_name, created_at=created_at, ) def create_example_from_run( self, run: ls_schemas.Run, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, created_at: Optional[datetime.datetime] = None, ) -> ls_schemas.Example: """Add an example (row) to an LLM-type dataset.""" if dataset_id is None: dataset_id = self.read_dataset(dataset_name=dataset_name).id dataset_name = None # Nested call expects only 1 defined dataset_type = self._get_data_type_cached(dataset_id) if dataset_type == ls_schemas.DataType.llm: if run.run_type != "llm": raise ValueError( f"Run type {run.run_type} is not supported" " for dataset of type 'LLM'" ) try: prompt = ls_utils.get_prompt_from_inputs(run.inputs) except ValueError: raise ValueError( "Error converting LLM run inputs to prompt for run" f" {run.id} with inputs {run.inputs}" ) inputs: Dict[str, Any] = {"input": prompt} if not run.outputs: outputs: Optional[Dict[str, Any]] = None else: try: generation = ls_utils.get_llm_generation_from_outputs(run.outputs) except ValueError: raise ValueError( "Error converting LLM run outputs to generation for run" f" {run.id} with outputs {run.outputs}" ) outputs = {"output": generation} elif dataset_type == ls_schemas.DataType.chat: if run.run_type != "llm": raise ValueError( f"Run type {run.run_type} is not supported" " for dataset of type 'chat'" ) try: inputs = {"input": ls_utils.get_messages_from_inputs(run.inputs)} except ValueError: raise ValueError( "Error converting LLM run inputs to chat messages for run" f" {run.id} with inputs {run.inputs}" ) if not run.outputs: outputs = None else: try: outputs = { "output": ls_utils.get_message_generation_from_outputs( run.outputs ) } except ValueError: raise ValueError( "Error converting LLM run outputs to chat generations" f" for run {run.id} with outputs {run.outputs}" ) elif dataset_type == ls_schemas.DataType.kv: # Anything goes inputs = run.inputs outputs = run.outputs else: raise ValueError(f"Dataset type {dataset_type} not recognized.") return self.create_example( inputs=inputs, outputs=outputs, dataset_id=dataset_id, dataset_name=dataset_name, created_at=created_at, ) def create_examples( self, *, inputs: Sequence[Mapping[str, Any]], outputs: Optional[Sequence[Optional[Mapping[str, Any]]]] = None, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, **kwargs: Any, ) -> None: """Create examples in a dataset. Parameters ---------- inputs : Sequence[Mapping[str, Any]] The input values for the examples. outputs : Optional[Sequence[Optional[Mapping[str, Any]]]], default=None The output values for the examples. dataset_id : Optional[ID_TYPE], default=None The ID of the dataset to create the examples in. dataset_name : Optional[str], default=None The name of the dataset to create the examples in. Returns ------- None Raises ------ ValueError If both `dataset_id` and `dataset_name` are `None`. """ if dataset_id is None and dataset_name is None: raise ValueError("Either dataset_id or dataset_name must be provided.") if dataset_id is None: dataset_id = self.read_dataset(dataset_name=dataset_name).id examples = [ { "inputs": in_, "outputs": out_, "dataset_id": dataset_id, } for in_, out_ in zip(inputs, outputs or [None] * len(inputs)) ] response = self.session.post( f"{self.api_url}/examples/bulk", headers={**self._headers, "Content-Type": "application/json"}, data=json.dumps(examples, default=_serialize_json), ) ls_utils.raise_for_status_with_text(response) @ls_utils.xor_args(("dataset_id", "dataset_name")) def create_example( self, inputs: Mapping[str, Any], dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, created_at: Optional[datetime.datetime] = None, outputs: Optional[Mapping[str, Any]] = None, example_id: Optional[ID_TYPE] = None, ) -> ls_schemas.Example: """Create a dataset example in the LangSmith API. Examples are rows in a dataset, containing the inputs and expected outputs (or other reference information) for a model or chain. Parameters ---------- inputs : Mapping[str, Any] The input values for the example. dataset_id : UUID or None, default=None The ID of the dataset to create the example in. dataset_name : str or None, default=None The name of the dataset to create the example in. created_at : datetime or None, default=None The creation timestamp of the example. outputs : Mapping[str, Any] or None, default=None The output values for the example. exemple_id : UUID or None, default=None The ID of the example to create. If not provided, a new example will be created. Returns ------- Example The created example. """ if dataset_id is None: dataset_id = self.read_dataset(dataset_name=dataset_name).id data = { "inputs": inputs, "outputs": outputs, "dataset_id": dataset_id, } if created_at: data["created_at"] = created_at.isoformat() if example_id: data["id"] = example_id example = ls_schemas.ExampleCreate(**data) response = self.session.post( f"{self.api_url}/examples", headers={**self._headers, "Content-Type": "application/json"}, data=example.json(), ) ls_utils.raise_for_status_with_text(response) result = response.json() return ls_schemas.Example( **result, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) def read_example(self, example_id: ID_TYPE) -> ls_schemas.Example: """Read an example from the LangSmith API. Parameters ---------- example_id : str or UUID The ID of the example to read. Returns ------- Example The example. """ response = self._get_with_retries( f"/examples/{_as_uuid(example_id, 'example_id')}", ) return ls_schemas.Example( **response.json(), _host_url=self._host_url, _tenant_id=self._get_tenant_id(), ) def list_examples( self, dataset_id: Optional[ID_TYPE] = None, dataset_name: Optional[str] = None, example_ids: Optional[Sequence[ID_TYPE]] = None, inline_s3_urls: bool = True, ) -> Iterator[ls_schemas.Example]: """Retrieve the example rows of the specified dataset. Parameters ---------- dataset_id : UUID or None, default=None The ID of the dataset to filter by. dataset_name : str or None, default=None The name of the dataset to filter by. example_ids : List[UUID] or None, default=None The IDs of the examples to filter by. Yields ------ Example The examples. """ params: Dict[str, Any] = {} if dataset_id is not None: params["dataset"] = dataset_id elif dataset_name is not None: dataset_id = self.read_dataset(dataset_name=dataset_name).id params["dataset"] = dataset_id else: pass if example_ids is not None: params["id"] = example_ids params["inline_s3_urls"] = inline_s3_urls yield from ( ls_schemas.Example( **example, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) for example in self._get_paginated_list("/examples", params=params) ) def update_example( self, example_id: str, *, inputs: Optional[Dict[str, Any]] = None, outputs: Optional[Mapping[str, Any]] = None, dataset_id: Optional[ID_TYPE] = None, ) -> Dict[str, Any]: """Update a specific example. Parameters ---------- example_id : str or UUID The ID of the example to update. inputs : Dict[str, Any] or None, default=None The input values to update. outputs : Mapping[str, Any] or None, default=None The output values to update. dataset_id : UUID or None, default=None The ID of the dataset to update. Returns ------- Dict[str, Any] The updated example. """ example = ls_schemas.ExampleUpdate( inputs=inputs, outputs=outputs, dataset_id=dataset_id, ) response = self.session.patch( f"{self.api_url}/examples/{_as_uuid(example_id, 'example_id')}", headers={**self._headers, "Content-Type": "application/json"}, data=example.json(exclude_none=True), ) ls_utils.raise_for_status_with_text(response) return response.json() def delete_example(self, example_id: ID_TYPE) -> None: """Delete an example by ID. Parameters ---------- example_id : str or UUID The ID of the example to delete. """ response = self.session.delete( f"{self.api_url}/examples/{_as_uuid(example_id, 'example_id')}", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def _resolve_run_id( self, run: Union[ls_schemas.Run, ls_schemas.RunBase, str, uuid.UUID], load_child_runs: bool, ) -> ls_schemas.Run: """Resolve the run ID. Parameters ---------- run : Run or RunBase or str or UUID The run to resolve. load_child_runs : bool Whether to load child runs. Returns ------- Run The resolved run. Raises ------ TypeError If the run type is invalid. """ if isinstance(run, (str, uuid.UUID)): run_ = self.read_run(run, load_child_runs=load_child_runs) else: run_ = run return run_ def _resolve_example_id( self, example: Union[ls_schemas.Example, str, uuid.UUID, dict, None], run: ls_schemas.Run, ) -> Optional[ls_schemas.Example]: """Resolve the example ID. Parameters ---------- example : Example or str or UUID or dict or None The example to resolve. run : Run The run associated with the example. Returns ------- Example or None The resolved example. """ if isinstance(example, (str, uuid.UUID)): reference_example_ = self.read_example(example) elif isinstance(example, ls_schemas.Example): reference_example_ = example elif isinstance(example, dict): reference_example_ = ls_schemas.Example( **example, _host_url=self._host_url, _tenant_id=self._get_tenant_id() ) elif run.reference_example_id is not None: reference_example_ = self.read_example(run.reference_example_id) else: reference_example_ = None return reference_example_ def _select_eval_results( self, results: Union[ls_evaluator.EvaluationResult, ls_evaluator.EvaluationResults], ) -> List[ls_evaluator.EvaluationResult]: if isinstance(results, ls_evaluator.EvaluationResult): results_ = [results] elif isinstance(results, dict) and "results" in results: results_ = cast(List[ls_evaluator.EvaluationResult], results["results"]) else: raise TypeError( f"Invalid evaluation result type {type(results)}." " Expected EvaluationResult or EvaluationResults." ) return results_ def evaluate_run( self, run: Union[ls_schemas.Run, ls_schemas.RunBase, str, uuid.UUID], evaluator: ls_evaluator.RunEvaluator, *, source_info: Optional[Dict[str, Any]] = None, reference_example: Optional[ Union[ls_schemas.Example, str, dict, uuid.UUID] ] = None, load_child_runs: bool = False, ) -> ls_evaluator.EvaluationResult: """Evaluate a run. Parameters ---------- run : Run or RunBase or str or UUID The run to evaluate. evaluator : RunEvaluator The evaluator to use. source_info : Dict[str, Any] or None, default=None Additional information about the source of the evaluation to log as feedback metadata. reference_example : Example or str or dict or UUID or None, default=None The example to use as a reference for the evaluation. If not provided, the run's reference example will be used. load_child_runs : bool, default=False Whether to load child runs when resolving the run ID. Returns ------- Feedback The feedback object created by the evaluation. """ run_ = self._resolve_run_id(run, load_child_runs=load_child_runs) reference_example_ = self._resolve_example_id(reference_example, run_) evaluator_response = evaluator.evaluate_run( run_, example=reference_example_, ) results = self._log_evaluation_feedback( evaluator_response, run_, source_info=source_info, ) # TODO: Return all results return results[0] def _log_evaluation_feedback( self, evaluator_response: Union[ ls_evaluator.EvaluationResult, ls_evaluator.EvaluationResults ], run: ls_schemas.Run, source_info: Optional[Dict[str, Any]] = None, ) -> List[ls_evaluator.EvaluationResult]: results = self._select_eval_results(evaluator_response) for res in results: source_info_ = source_info or {} if res.evaluator_info: source_info_ = {**res.evaluator_info, **source_info_} run_id_ = res.target_run_id if res.target_run_id else run.id self.create_feedback( run_id_, res.key, score=res.score, value=res.value, comment=res.comment, correction=res.correction, source_info=source_info_, source_run_id=res.source_run_id, feedback_source_type=ls_schemas.FeedbackSourceType.MODEL, ) return results async def aevaluate_run( self, run: Union[ls_schemas.Run, str, uuid.UUID], evaluator: ls_evaluator.RunEvaluator, *, source_info: Optional[Dict[str, Any]] = None, reference_example: Optional[ Union[ls_schemas.Example, str, dict, uuid.UUID] ] = None, load_child_runs: bool = False, ) -> ls_evaluator.EvaluationResult: """Evaluate a run asynchronously. Parameters ---------- run : Run or str or UUID The run to evaluate. evaluator : RunEvaluator The evaluator to use. source_info : Dict[str, Any] or None, default=None Additional information about the source of the evaluation to log as feedback metadata. reference_example : Optional Example or UUID, default=None The example to use as a reference for the evaluation. If not provided, the run's reference example will be used. load_child_runs : bool, default=False Whether to load child runs when resolving the run ID. Returns ------- EvaluationResult The evaluation result object created by the evaluation. """ run_ = self._resolve_run_id(run, load_child_runs=load_child_runs) reference_example_ = self._resolve_example_id(reference_example, run_) evaluator_response = await evaluator.aevaluate_run( run_, example=reference_example_, ) # TODO: Return all results and use async API results = self._log_evaluation_feedback( evaluator_response, run_, source_info=source_info, ) return results[0] def create_feedback( self, run_id: ID_TYPE, key: str, *, score: Union[float, int, bool, None] = None, value: Union[float, int, bool, str, dict, None] = None, correction: Union[dict, None] = None, comment: Union[str, None] = None, source_info: Optional[Dict[str, Any]] = None, feedback_source_type: Union[ ls_schemas.FeedbackSourceType, str ] = ls_schemas.FeedbackSourceType.API, source_run_id: Optional[ID_TYPE] = None, feedback_id: Optional[ID_TYPE] = None, eager: bool = False, stop_after_attempt: int = 10, ) -> ls_schemas.Feedback: """Create a feedback in the LangSmith API. Parameters ---------- run_id : str or UUID The ID of the run to provide feedback on. key : str The name of the metric, tag, or 'aspect' this feedback is about. score : float or int or bool or None, default=None The score to rate this run on the metric or aspect. value : float or int or bool or str or dict or None, default=None The display value or non-numeric value for this feedback. correction : dict or None, default=None The proper ground truth for this run. comment : str or None, default=None A comment about this feedback. source_info : Dict[str, Any] or None, default=None Information about the source of this feedback. feedback_source_type : FeedbackSourceType or str, default=FeedbackSourceType.API The type of feedback source, such as model (for model-generated feedback) or API. source_run_id : str or UUID or None, default=None, The ID of the run that generated this feedback, if a "model" type. feedback_id : str or UUID or None, default=None The ID of the feedback to create. If not provided, a random UUID will be generated. eager : bool, default=False Whether to skip the write queue when creating the feedback. This means that the feedback will be immediately available for reading, but may cause the write to fail if the API is under heavy load, since the target run_id may have not been created yet. stop_after_attempt : int, default=10 The number of times to retry the request before giving up. """ if not isinstance(feedback_source_type, ls_schemas.FeedbackSourceType): feedback_source_type = ls_schemas.FeedbackSourceType(feedback_source_type) if feedback_source_type == ls_schemas.FeedbackSourceType.API: feedback_source: ls_schemas.FeedbackSourceBase = ( ls_schemas.APIFeedbackSource(metadata=source_info) ) elif feedback_source_type == ls_schemas.FeedbackSourceType.MODEL: feedback_source = ls_schemas.ModelFeedbackSource(metadata=source_info) else: raise ValueError(f"Unknown feedback source type {feedback_source_type}") feedback_source.metadata = ( feedback_source.metadata if feedback_source.metadata is not None else {} ) if source_run_id is not None and "__run" not in feedback_source.metadata: feedback_source.metadata["__run"] = {"run_id": str(source_run_id)} if feedback_source.metadata and "__run" in feedback_source.metadata: # Validate that the linked run ID is a valid UUID # Run info may be a base model or dict. _run_meta: Union[dict, Any] = feedback_source.metadata["__run"] if hasattr(_run_meta, "dict") and callable(_run_meta): _run_meta = _run_meta.dict() if "run_id" in _run_meta: _run_meta["run_id"] = str( _as_uuid( feedback_source.metadata["__run"]["run_id"], "feedback_source.metadata['__run']['run_id']", ) ) feedback_source.metadata["__run"] = _run_meta feedback = ls_schemas.FeedbackCreate( id=feedback_id or uuid.uuid4(), run_id=run_id, key=key, score=score, value=value, correction=correction, comment=comment, feedback_source=feedback_source, created_at=datetime.datetime.now(datetime.timezone.utc), modified_at=datetime.datetime.now(datetime.timezone.utc), ) self.request_with_retries( "POST", self.api_url + "/feedback" + ("/eager" if eager else ""), request_kwargs={ "data": json.dumps( feedback.dict(exclude_none=True), default=_serialize_json ), "headers": { **self._headers, "Content-Type": "application/json", "Accept": "application/json", }, "timeout": self.timeout_ms / 1000, }, stop_after_attempt=stop_after_attempt, retry_on=(ls_utils.LangSmithNotFoundError,), ) return ls_schemas.Feedback(**feedback.dict()) def update_feedback( self, feedback_id: ID_TYPE, *, score: Union[float, int, bool, None] = None, value: Union[float, int, bool, str, dict, None] = None, correction: Union[dict, None] = None, comment: Union[str, None] = None, ) -> None: """Update a feedback in the LangSmith API. Parameters ---------- feedback_id : str or UUID The ID of the feedback to update. score : float or int or bool or None, default=None The score to update the feedback with. value : float or int or bool or str or dict or None, default=None The value to update the feedback with. correction : dict or None, default=None The correction to update the feedback with. comment : str or None, default=None The comment to update the feedback with. """ feedback_update: Dict[str, Any] = {} if score is not None: feedback_update["score"] = score if value is not None: feedback_update["value"] = value if correction is not None: feedback_update["correction"] = correction if comment is not None: feedback_update["comment"] = comment response = self.session.patch( self.api_url + f"/feedback/{_as_uuid(feedback_id, 'feedback_id')}", headers={**self._headers, "Content-Type": "application/json"}, data=json.dumps(feedback_update, default=_serialize_json), ) ls_utils.raise_for_status_with_text(response) def read_feedback(self, feedback_id: ID_TYPE) -> ls_schemas.Feedback: """Read a feedback from the LangSmith API. Parameters ---------- feedback_id : str or UUID The ID of the feedback to read. Returns ------- Feedback The feedback. """ response = self._get_with_retries( f"/feedback/{_as_uuid(feedback_id, 'feedback_id')}", ) return ls_schemas.Feedback(**response.json()) def list_feedback( self, *, run_ids: Optional[Sequence[ID_TYPE]] = None, feedback_key: Optional[Sequence[str]] = None, feedback_source_type: Optional[Sequence[ls_schemas.FeedbackSourceType]] = None, **kwargs: Any, ) -> Iterator[ls_schemas.Feedback]: """List the feedback objects on the LangSmith API. Parameters ---------- run_ids : List[str or UUID] or None, default=None The IDs of the runs to filter by. feedback_key: List[str] or None, default=None The feedback key(s) to filter by. Example: 'correctness' The query performs a union of all feedback keys. feedback_source_type: List[FeedbackSourceType] or None, default=None The type of feedback source, such as model (for model-generated feedback) or API. **kwargs : Any Additional keyword arguments. Yields ------ Feedback The feedback objects. """ params: dict = { "run": run_ids, **kwargs, } if feedback_key is not None: params["key"] = feedback_key if feedback_source_type is not None: params["source"] = feedback_source_type yield from ( ls_schemas.Feedback(**feedback) for feedback in self._get_paginated_list("/feedback", params=params) ) def delete_feedback(self, feedback_id: ID_TYPE) -> None: """Delete a feedback by ID. Parameters ---------- feedback_id : str or UUID The ID of the feedback to delete. """ response = self.session.delete( f"{self.api_url}/feedback/{_as_uuid(feedback_id, 'feedback_id')}", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) # Annotation Queue API def list_annotation_queues( self, *, queue_ids: Optional[List[ID_TYPE]] = None, name: Optional[str] = None, name_contains: Optional[str] = None, ) -> Iterator[ls_schemas.AnnotationQueue]: params: dict = { "ids": ( [_as_uuid(id_, f"queue_ids[{i}]") for i, id_ in enumerate(queue_ids)] if queue_ids is not None else None ), "name": name, "name_contains": name_contains, } yield from ( ls_schemas.AnnotationQueue(**queue) for queue in self._get_paginated_list("/annotation-queues", params=params) ) def create_annotation_queue( self, *, name: str, description: Optional[str] = None, queue_id: Optional[ID_TYPE] = None, ) -> ls_schemas.AnnotationQueue: body = { "name": name, "description": description, "id": queue_id, } response = self.request_with_retries( "post", f"{self.api_url}/annotation-queues", { "json": {k: v for k, v in body.items() if v is not None}, "headers": self._headers, }, ) ls_utils.raise_for_status_with_text(response) return ls_schemas.AnnotationQueue(**response.json()) def read_annotation_queue(self, queue_id: ID_TYPE) -> ls_schemas.AnnotationQueue: # TODO: Replace when actual endpoint is added return next(self.list_annotation_queues(queue_ids=[queue_id])) def update_annotation_queue( self, queue_id: ID_TYPE, *, name: str, description: Optional[str] = None ) -> None: response = self.request_with_retries( "patch", f"{self.api_url}/annotation-queues/{_as_uuid(queue_id, 'queue_id')}", { "json": { "name": name, "description": description, }, "headers": self._headers, }, ) ls_utils.raise_for_status_with_text(response) def delete_annotation_queue(self, queue_id: ID_TYPE) -> None: response = self.session.delete( f"{self.api_url}/annotation-queues/{_as_uuid(queue_id, 'queue_id')}", headers=self._headers, ) ls_utils.raise_for_status_with_text(response) def add_runs_to_annotation_queue( self, queue_id: ID_TYPE, *, run_ids: List[ID_TYPE] ) -> None: response = self.request_with_retries( "post", f"{self.api_url}/annotation-queues/{_as_uuid(queue_id, 'queue_id')}/runs", { "json": [ str(_as_uuid(id_, f"run_ids[{i}]")) for i, id_ in enumerate(run_ids) ], "headers": self._headers, }, ) ls_utils.raise_for_status_with_text(response) def list_runs_from_annotation_queue( self, queue_id: ID_TYPE ) -> Iterator[ls_schemas.RunWithAnnotationQueueInfo]: path = f"/annotation-queues/{_as_uuid(queue_id, 'queue_id')}/runs" yield from ( ls_schemas.RunWithAnnotationQueueInfo(**run) for run in self._get_paginated_list(path, params={"headers": self._headers}) ) async def arun_on_dataset( self, dataset_name: str, llm_or_chain_factory: Any, *, evaluation: Optional[Any] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, revision_id: Optional[str] = None, ) -> Dict[str, Any]: """ Asynchronously run the Chain or language model on a dataset and store traces to the specified project name. Args: dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Optional evaluation configuration to use when evaluating concurrency_level: The number of async tasks to run concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. project_metadata: Optional metadata to store with the project. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. revision_id: Optional revision identifier to assign this test run to track the performance of different versions of your system. Returns: A dictionary containing the run's project name and the resulting model outputs. For the synchronous version, see client.run_on_dataset. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() await client.arun_on_dataset( "", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator ` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) await client.arun_on_dataset( "", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 try: from langchain.smith import arun_on_dataset as _arun_on_dataset except ImportError: raise ImportError( "The client.arun_on_dataset function requires the langchain" "package to run.\nInstall with pip install langchain" ) return await _arun_on_dataset( dataset_name=dataset_name, llm_or_chain_factory=llm_or_chain_factory, client=self, evaluation=evaluation, concurrency_level=concurrency_level, project_name=project_name, project_metadata=project_metadata, verbose=verbose, tags=tags, input_mapper=input_mapper, revision_id=revision_id, ) def run_on_dataset( self, dataset_name: str, llm_or_chain_factory: Any, *, evaluation: Optional[Any] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, input_mapper: Optional[Callable[[Dict], Any]] = None, revision_id: Optional[str] = None, ) -> Dict[str, Any]: """ Run the Chain or language model on a dataset and store traces to the specified project name. Args: dataset_name: Name of the dataset to run the chain on. llm_or_chain_factory: Language model or Chain constructor to run over the dataset. The Chain constructor is used to permit independent calls on each example without carrying over state. evaluation: Configuration for evaluators to run on the results of the chain concurrency_level: The number of tasks to execute concurrently. project_name: Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}. project_metadata: Metadata to store with the project. verbose: Whether to print progress. tags: Tags to add to each run in the project. input_mapper: A function to map to the inputs dictionary from an Example to the format expected by the model to be evaluated. This is useful if your model needs to deserialize more complex schema or if your dataset has inputs with keys that differ from what is expected by your chain or agent. revision_id: Optional revision identifier to assign this test run to track the performance of different versions of your system. Returns: A dictionary containing the run's project name and the resulting model outputs. For the (usually faster) async version of this function, see `client.arun_on_dataset`. Examples -------- .. code-block:: python from langsmith import Client from langchain.chat_models import ChatOpenAI from langchain.chains import LLMChain from langchain.smith import RunEvalConfig # Chains may have memory. Passing in a constructor function lets the # evaluation framework avoid cross-contamination between runs. def construct_chain(): llm = ChatOpenAI(temperature=0) chain = LLMChain.from_string( llm, "What's the answer to {your_input_key}" ) return chain # Load off-the-shelf evaluators via config or the EvaluatorType (string or enum) evaluation_config = RunEvalConfig( evaluators=[ "qa", # "Correctness" against a reference answer "embedding_distance", RunEvalConfig.Criteria("helpfulness"), RunEvalConfig.Criteria({ "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?" }), ] ) client = Client() client.run_on_dataset( "", construct_chain, evaluation=evaluation_config, ) You can also create custom evaluators by subclassing the :class:`StringEvaluator ` or LangSmith's `RunEvaluator` classes. .. code-block:: python from typing import Optional from langchain.evaluation import StringEvaluator class MyStringEvaluator(StringEvaluator): @property def requires_input(self) -> bool: return False @property def requires_reference(self) -> bool: return True @property def evaluation_name(self) -> str: return "exact_match" def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict: return {"score": prediction == reference} evaluation_config = RunEvalConfig( custom_evaluators = [MyStringEvaluator()], ) client.run_on_dataset( "", construct_chain, evaluation=evaluation_config, ) """ # noqa: E501 try: from langchain.smith import run_on_dataset as _run_on_dataset except ImportError: raise ImportError( "The client.run_on_dataset function requires the langchain" "package to run.\nInstall with pip install langchain" ) return _run_on_dataset( dataset_name=dataset_name, llm_or_chain_factory=llm_or_chain_factory, concurrency_level=concurrency_level, client=self, evaluation=evaluation, project_name=project_name, project_metadata=project_metadata, verbose=verbose, tags=tags, input_mapper=input_mapper, revision_id=revision_id, ) def _tracing_thread_drain_queue( tracing_queue: Queue, limit: int = 100, block: bool = True ) -> List[TracingQueueItem]: next_batch: List[TracingQueueItem] = [] try: # wait 250ms for the first item, then # - drain the queue with a 50ms block timeout # - stop draining if we hit the limit # shorter drain timeout is used instead of non-blocking calls to # avoid creating too many small batches if item := tracing_queue.get(block=block, timeout=0.25): next_batch.append(item) while item := tracing_queue.get(block=block, timeout=0.05): next_batch.append(item) if limit and len(next_batch) >= limit: break except Empty: pass return next_batch def _tracing_thread_handle_batch( client: Client, tracing_queue: Queue, batch: List[TracingQueueItem] ) -> None: create = [it.item for it in batch if it.action == "create"] update = [it.item for it in batch if it.action == "update"] try: client.batch_ingest_runs(create=create, update=update, pre_sampled=True) except Exception: # exceptions are logged elsewhere, but we need to make sure the # background thread continues to run pass finally: for _ in batch: tracing_queue.task_done() _AUTO_SCALE_UP_QSIZE_TRIGGER = 1000 _AUTO_SCALE_UP_NTHREADS_LIMIT = 16 _AUTO_SCALE_DOWN_NEMPTY_TRIGGER = 4 def _ensure_ingest_config( info: Optional[ls_schemas.LangSmithInfo], ) -> ls_schemas.BatchIngestConfig: default_config = ls_schemas.BatchIngestConfig( size_limit=100, scale_up_nthreads_limit=_AUTO_SCALE_UP_NTHREADS_LIMIT, scale_up_qsize_trigger=_AUTO_SCALE_UP_QSIZE_TRIGGER, scale_down_nempty_trigger=_AUTO_SCALE_DOWN_NEMPTY_TRIGGER, ) if not info: return default_config try: if not info.batch_ingest_config: return default_config return info.batch_ingest_config except BaseException: return default_config def _tracing_control_thread_func(client_ref: weakref.ref[Client]) -> None: client = client_ref() if client is None: return try: if not client.info: print(f"no info: {client.info}", file=sys.stderr, flush=True) return except BaseException as e: logger.debug("Error in tracing control thread: %s", e) return tracing_queue = client.tracing_queue assert tracing_queue is not None batch_ingest_config = _ensure_ingest_config(client.info) size_limit: int = batch_ingest_config["size_limit"] scale_up_nthreads_limit: int = batch_ingest_config["scale_up_nthreads_limit"] scale_up_qsize_trigger: int = batch_ingest_config["scale_up_qsize_trigger"] sub_threads: List[threading.Thread] = [] # 1 for this func, 1 for getrefcount, 1 for _get_data_type_cached num_known_refs = 3 # loop until while ( # the main thread dies threading.main_thread().is_alive() # or we're the only remaining reference to the client and sys.getrefcount(client) > num_known_refs + len(sub_threads) ): for thread in sub_threads: if not thread.is_alive(): sub_threads.remove(thread) if ( len(sub_threads) < scale_up_nthreads_limit and tracing_queue.qsize() > scale_up_qsize_trigger ): new_thread = threading.Thread( target=_tracing_sub_thread_func, args=(weakref.ref(client),) ) sub_threads.append(new_thread) new_thread.start() if next_batch := _tracing_thread_drain_queue(tracing_queue, limit=size_limit): _tracing_thread_handle_batch(client, tracing_queue, next_batch) # drain the queue on exit while next_batch := _tracing_thread_drain_queue( tracing_queue, limit=size_limit, block=False ): _tracing_thread_handle_batch(client, tracing_queue, next_batch) def _tracing_sub_thread_func(client_ref: weakref.ref[Client]) -> None: client = client_ref() if client is None: return try: if not client.info: return except BaseException as e: logger.debug("Error in tracing control thread: %s", e) return tracing_queue = client.tracing_queue assert tracing_queue is not None batch_ingest_config = _ensure_ingest_config(client.info) size_limit = batch_ingest_config.get("size_limit", 100) seen_successive_empty_queues = 0 # loop until while ( # the main thread dies threading.main_thread().is_alive() # or we've seen the queue empty 4 times in a row and seen_successive_empty_queues <= batch_ingest_config["scale_down_nempty_trigger"] ): if next_batch := _tracing_thread_drain_queue(tracing_queue, limit=size_limit): seen_successive_empty_queues = 0 _tracing_thread_handle_batch(client, tracing_queue, next_batch) else: seen_successive_empty_queues += 1 # drain the queue on exit while next_batch := _tracing_thread_drain_queue( tracing_queue, limit=size_limit, block=False ): _tracing_thread_handle_batch(client, tracing_queue, next_batch)