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640 lines
24 KiB
Python
640 lines
24 KiB
Python
from __future__ import annotations
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import asyncio
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import functools
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import logging
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import socket
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from datetime import datetime
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from io import BytesIO
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Coroutine,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Union,
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)
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from urllib.parse import urlsplit
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from uuid import UUID
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import requests
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from pydantic import BaseSettings, Field, root_validator
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from requests import Response
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.tracers.langchain import LangChainTracer
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from langchain.callbacks.tracers.schemas import Run, TracerSession
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from langchain.chains.base import Chain
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from langchain.chat_models.base import BaseChatModel
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from langchain.client.models import (
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Dataset,
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DatasetCreate,
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Example,
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ExampleCreate,
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ListRunsQueryParams,
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)
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from langchain.llms.base import BaseLLM
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from langchain.schema import ChatResult, LLMResult, messages_from_dict
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from langchain.utils import raise_for_status_with_text, xor_args
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if TYPE_CHECKING:
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import pandas as pd
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logger = logging.getLogger(__name__)
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MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel]
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def _get_link_stem(url: str) -> str:
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scheme = urlsplit(url).scheme
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netloc_prefix = urlsplit(url).netloc.split(":")[0]
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return f"{scheme}://{netloc_prefix}"
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def _is_localhost(url: str) -> bool:
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"""Check if the URL is localhost."""
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try:
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netloc = urlsplit(url).netloc.split(":")[0]
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ip = socket.gethostbyname(netloc)
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return ip == "127.0.0.1" or ip.startswith("0.0.0.0") or ip.startswith("::")
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except socket.gaierror:
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return False
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class LangChainPlusClient(BaseSettings):
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"""Client for interacting with the LangChain+ API."""
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api_key: Optional[str] = Field(default=None, env="LANGCHAIN_API_KEY")
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api_url: str = Field(..., env="LANGCHAIN_ENDPOINT")
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tenant_id: str = Field(..., env="LANGCHAIN_TENANT_ID")
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@root_validator(pre=True)
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def validate_api_key_if_hosted(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Verify API key is provided if url not localhost."""
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api_url: str = values.get("api_url", "http://localhost:8000")
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api_key: Optional[str] = values.get("api_key")
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if not _is_localhost(api_url):
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if not api_key:
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raise ValueError(
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"API key must be provided when using hosted LangChain+ API"
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)
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else:
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tenant_id = values.get("tenant_id")
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if not tenant_id:
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values["tenant_id"] = LangChainPlusClient._get_seeded_tenant_id(
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api_url, api_key
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)
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return values
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@staticmethod
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def _get_seeded_tenant_id(api_url: str, api_key: Optional[str]) -> str:
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"""Get the tenant ID from the seeded tenant."""
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url = f"{api_url}/tenants"
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headers = {"authorization": f"Bearer {api_key}"} if api_key else {}
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response = requests.get(url, headers=headers)
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try:
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raise_for_status_with_text(response)
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except Exception as e:
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raise ValueError(
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"Unable to get seeded tenant ID. Please manually provide."
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) from e
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results: List[dict] = response.json()
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if len(results) == 0:
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raise ValueError("No seeded tenant found")
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return results[0]["id"]
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@staticmethod
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def _get_session_name(
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session_name: Optional[str],
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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dataset_name: str,
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) -> str:
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if session_name is not None:
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return session_name
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current_time = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
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if isinstance(llm_or_chain_factory, BaseLanguageModel):
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model_name = llm_or_chain_factory.__class__.__name__
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else:
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model_name = llm_or_chain_factory().__class__.__name__
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return f"{dataset_name}-{model_name}-{current_time}"
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def _repr_html_(self) -> str:
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"""Return an HTML representation of the instance with a link to the URL."""
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link = _get_link_stem(self.api_url)
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return f'<a href="{link}", target="_blank" rel="noopener">LangChain+ Client</a>'
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def __repr__(self) -> str:
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"""Return a string representation of the instance with a link to the URL."""
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return f"LangChainPlusClient (API URL: {self.api_url})"
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@property
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def _headers(self) -> Dict[str, str]:
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"""Get the headers for the API request."""
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headers = {}
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if self.api_key:
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headers["authorization"] = f"Bearer {self.api_key}"
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return headers
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@property
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def query_params(self) -> Dict[str, str]:
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"""Get the headers for the API request."""
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return {"tenant_id": self.tenant_id}
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def _get(self, path: str, params: Optional[Dict[str, Any]] = None) -> Response:
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"""Make a GET request."""
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query_params = self.query_params
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if params:
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query_params.update(params)
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return requests.get(
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f"{self.api_url}{path}", headers=self._headers, params=query_params
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)
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def upload_dataframe(
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self,
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df: pd.DataFrame,
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name: str,
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description: str,
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input_keys: List[str],
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output_keys: List[str],
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) -> Dataset:
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"""Upload a dataframe as individual examples to the LangChain+ API."""
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dataset = self.create_dataset(dataset_name=name, description=description)
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for row in df.itertuples():
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inputs = {key: getattr(row, key) for key in input_keys}
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outputs = {key: getattr(row, key) for key in output_keys}
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self.create_example(inputs, outputs=outputs, dataset_id=dataset.id)
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return dataset
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def upload_csv(
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self,
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csv_file: Union[str, Tuple[str, BytesIO]],
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description: str,
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input_keys: List[str],
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output_keys: List[str],
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) -> Dataset:
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"""Upload a CSV file to the LangChain+ API."""
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files = {"file": csv_file}
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data = {
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"input_keys": ",".join(input_keys),
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"output_keys": ",".join(output_keys),
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"description": description,
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"tenant_id": self.tenant_id,
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}
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response = requests.post(
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self.api_url + "/datasets/upload",
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headers=self._headers,
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data=data,
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files=files,
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)
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raise_for_status_with_text(response)
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result = response.json()
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# TODO: Make this more robust server-side
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if "detail" in result and "already exists" in result["detail"]:
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file_name = csv_file if isinstance(csv_file, str) else csv_file[0]
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file_name = file_name.split("/")[-1]
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raise ValueError(f"Dataset {file_name} already exists")
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return Dataset(**result)
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def read_run(self, run_id: str) -> Run:
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"""Read a run from the LangChain+ API."""
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response = self._get(f"/runs/{run_id}")
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raise_for_status_with_text(response)
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return Run(**response.json())
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def list_runs(
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self,
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*,
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session_id: Optional[str] = None,
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session_name: Optional[str] = None,
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run_type: Optional[str] = None,
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**kwargs: Any,
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) -> List[Run]:
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"""List runs from the LangChain+ API."""
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if session_name is not None:
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if session_id is not None:
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raise ValueError("Only one of session_id or session_name may be given")
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session_id = self.read_session(session_name=session_name).id
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query_params = ListRunsQueryParams(
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session_id=session_id, run_type=run_type, **kwargs
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)
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filtered_params = {
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k: v for k, v in query_params.dict().items() if v is not None
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}
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response = self._get("/runs", params=filtered_params)
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raise_for_status_with_text(response)
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return [Run(**run) for run in response.json()]
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@xor_args(("session_id", "session_name"))
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def read_session(
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self, *, session_id: Optional[str] = None, session_name: Optional[str] = None
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) -> TracerSession:
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"""Read a session from the LangChain+ API."""
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path = "/sessions"
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params: Dict[str, Any] = {"limit": 1, "tenant_id": self.tenant_id}
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if session_id is not None:
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path += f"/{session_id}"
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elif session_name is not None:
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params["name"] = session_name
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else:
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raise ValueError("Must provide dataset_name or dataset_id")
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response = self._get(
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path,
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params=params,
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)
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raise_for_status_with_text(response)
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response = self._get(
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path,
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params=params,
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)
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raise_for_status_with_text(response)
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result = response.json()
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if isinstance(result, list):
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if len(result) == 0:
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raise ValueError(f"Dataset {session_name} not found")
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return TracerSession(**result[0])
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return TracerSession(**response.json())
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def list_sessions(self) -> List[TracerSession]:
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"""List sessions from the LangChain+ API."""
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response = self._get("/sessions")
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raise_for_status_with_text(response)
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return [TracerSession(**session) for session in response.json()]
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def create_dataset(self, dataset_name: str, description: str) -> Dataset:
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"""Create a dataset in the LangChain+ API."""
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dataset = DatasetCreate(
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tenant_id=self.tenant_id,
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name=dataset_name,
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description=description,
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)
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response = requests.post(
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self.api_url + "/datasets",
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headers=self._headers,
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data=dataset.json(),
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)
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raise_for_status_with_text(response)
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return Dataset(**response.json())
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@xor_args(("dataset_name", "dataset_id"))
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def read_dataset(
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self, *, dataset_name: Optional[str] = None, dataset_id: Optional[str] = None
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) -> Dataset:
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path = "/datasets"
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params: Dict[str, Any] = {"limit": 1, "tenant_id": self.tenant_id}
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if dataset_id is not None:
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path += f"/{dataset_id}"
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elif dataset_name is not None:
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params["name"] = dataset_name
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else:
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raise ValueError("Must provide dataset_name or dataset_id")
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response = self._get(
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path,
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params=params,
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)
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raise_for_status_with_text(response)
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result = response.json()
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if isinstance(result, list):
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if len(result) == 0:
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raise ValueError(f"Dataset {dataset_name} not found")
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return Dataset(**result[0])
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return Dataset(**result)
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def list_datasets(self, limit: int = 100) -> Iterable[Dataset]:
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"""List the datasets on the LangChain+ API."""
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response = self._get("/datasets", params={"limit": limit})
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raise_for_status_with_text(response)
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return [Dataset(**dataset) for dataset in response.json()]
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@xor_args(("dataset_id", "dataset_name"))
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def delete_dataset(
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self, *, dataset_id: Optional[str] = None, dataset_name: Optional[str] = None
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) -> Dataset:
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"""Delete a dataset by ID or name."""
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if dataset_name is not None:
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dataset_id = self.read_dataset(dataset_name=dataset_name).id
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if dataset_id is None:
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raise ValueError("Must provide either dataset name or ID")
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response = requests.delete(
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f"{self.api_url}/datasets/{dataset_id}",
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headers=self._headers,
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)
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raise_for_status_with_text(response)
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return response.json()
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@xor_args(("dataset_id", "dataset_name"))
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def create_example(
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self,
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inputs: Dict[str, Any],
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dataset_id: Optional[UUID] = None,
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dataset_name: Optional[str] = None,
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created_at: Optional[datetime] = None,
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outputs: Dict[str, Any] | None = None,
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) -> Example:
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"""Create a dataset example in the LangChain+ API."""
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if dataset_id is None:
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dataset_id = self.read_dataset(dataset_name).id
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data = {
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"inputs": inputs,
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"outputs": outputs,
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"dataset_id": dataset_id,
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}
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if created_at:
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data["created_at"] = created_at.isoformat()
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example = ExampleCreate(**data)
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response = requests.post(
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f"{self.api_url}/examples", headers=self._headers, data=example.json()
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)
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raise_for_status_with_text(response)
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result = response.json()
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return Example(**result)
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def read_example(self, example_id: str) -> Example:
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"""Read an example from the LangChain+ API."""
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response = self._get(f"/examples/{example_id}")
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raise_for_status_with_text(response)
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return Example(**response.json())
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def list_examples(
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self, dataset_id: Optional[str] = None, dataset_name: Optional[str] = None
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) -> Iterable[Example]:
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"""List the datasets on the LangChain+ API."""
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params = {}
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if dataset_id is not None:
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params["dataset"] = dataset_id
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elif dataset_name is not None:
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dataset_id = self.read_dataset(dataset_name=dataset_name).id
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params["dataset"] = dataset_id
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else:
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pass
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response = self._get("/examples", params=params)
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raise_for_status_with_text(response)
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return [Example(**dataset) for dataset in response.json()]
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@staticmethod
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async def _arun_llm(
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llm: BaseLanguageModel,
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inputs: Dict[str, Any],
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langchain_tracer: LangChainTracer,
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) -> Union[LLMResult, ChatResult]:
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if isinstance(llm, BaseLLM):
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if "prompt" not in inputs:
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raise ValueError(f"LLM Run requires 'prompt' input. Got {inputs}")
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llm_prompt: str = inputs["prompt"]
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llm_output = await llm.agenerate([llm_prompt], callbacks=[langchain_tracer])
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elif isinstance(llm, BaseChatModel):
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if "messages" not in inputs:
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raise ValueError(f"Chat Run requires 'messages' input. Got {inputs}")
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raw_messages: List[dict] = inputs["messages"]
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messages = messages_from_dict(raw_messages)
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llm_output = await llm.agenerate([messages], callbacks=[langchain_tracer])
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else:
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raise ValueError(f"Unsupported LLM type {type(llm)}")
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return llm_output
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@staticmethod
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async def _arun_llm_or_chain(
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example: Example,
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langchain_tracer: LangChainTracer,
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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n_repetitions: int,
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) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
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"""Run the chain asynchronously."""
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previous_example_id = langchain_tracer.example_id
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langchain_tracer.example_id = example.id
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outputs = []
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for _ in range(n_repetitions):
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try:
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if isinstance(llm_or_chain_factory, BaseLanguageModel):
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output: Any = await LangChainPlusClient._arun_llm(
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llm_or_chain_factory, example.inputs, langchain_tracer
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)
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else:
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chain = llm_or_chain_factory()
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output = await chain.arun(
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example.inputs, callbacks=[langchain_tracer]
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)
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outputs.append(output)
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except Exception as e:
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logger.warning(f"Chain failed for example {example.id}. Error: {e}")
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outputs.append({"Error": str(e)})
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langchain_tracer.example_id = previous_example_id
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return outputs
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@staticmethod
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async def _gather_with_concurrency(
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n: int,
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initializer: Callable[[], Coroutine[Any, Any, LangChainTracer]],
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*async_funcs: Callable[[LangChainTracer, Dict], Coroutine[Any, Any, Any]],
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) -> List[Any]:
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"""
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Run coroutines with a concurrency limit.
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Args:
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n: The maximum number of concurrent tasks.
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initializer: A coroutine that initializes shared resources for the tasks.
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async_funcs: The async_funcs to be run concurrently.
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Returns:
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A list of results from the coroutines.
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"""
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semaphore = asyncio.Semaphore(n)
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job_state = {"num_processed": 0}
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tracer_queue: asyncio.Queue[LangChainTracer] = asyncio.Queue()
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for _ in range(n):
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tracer_queue.put_nowait(await initializer())
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async def run_coroutine_with_semaphore(
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async_func: Callable[[LangChainTracer, Dict], Coroutine[Any, Any, Any]]
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) -> Any:
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async with semaphore:
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tracer = await tracer_queue.get()
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try:
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result = await async_func(tracer, job_state)
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finally:
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tracer_queue.put_nowait(tracer)
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return result
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return await asyncio.gather(
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*(run_coroutine_with_semaphore(function) for function in async_funcs)
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)
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async def _tracer_initializer(self, session_name: str) -> LangChainTracer:
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"""
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Initialize a tracer to share across tasks.
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Args:
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session_name: The session name for the tracer.
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Returns:
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A LangChainTracer instance with an active session.
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"""
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tracer = LangChainTracer(session_name=session_name)
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tracer.ensure_session()
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return tracer
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async def arun_on_dataset(
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self,
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dataset_name: str,
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llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
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*,
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concurrency_level: int = 5,
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num_repetitions: int = 1,
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session_name: Optional[str] = None,
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verbose: bool = False,
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) -> Dict[str, Any]:
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"""
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|
Run the chain on a dataset and store traces to the specified session 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.
|
|
concurrency_level: The number of async tasks to run concurrently.
|
|
num_repetitions: Number of times to run the model on each example.
|
|
This is useful when testing success rates or generating confidence
|
|
intervals.
|
|
session_name: Name of the session to store the traces in.
|
|
Defaults to {dataset_name}-{chain class name}-{datetime}.
|
|
verbose: Whether to print progress.
|
|
|
|
Returns:
|
|
A dictionary mapping example ids to the model outputs.
|
|
"""
|
|
session_name = LangChainPlusClient._get_session_name(
|
|
session_name, llm_or_chain_factory, dataset_name
|
|
)
|
|
dataset = self.read_dataset(dataset_name=dataset_name)
|
|
examples = self.list_examples(dataset_id=str(dataset.id))
|
|
results: Dict[str, List[Any]] = {}
|
|
|
|
async def process_example(
|
|
example: Example, tracer: LangChainTracer, job_state: dict
|
|
) -> None:
|
|
"""Process a single example."""
|
|
result = await LangChainPlusClient._arun_llm_or_chain(
|
|
example,
|
|
tracer,
|
|
llm_or_chain_factory,
|
|
num_repetitions,
|
|
)
|
|
results[str(example.id)] = result
|
|
job_state["num_processed"] += 1
|
|
if verbose:
|
|
print(
|
|
f"Processed examples: {job_state['num_processed']}",
|
|
end="\r",
|
|
flush=True,
|
|
)
|
|
|
|
await self._gather_with_concurrency(
|
|
concurrency_level,
|
|
functools.partial(self._tracer_initializer, session_name),
|
|
*(functools.partial(process_example, e) for e in examples),
|
|
)
|
|
return results
|
|
|
|
@staticmethod
|
|
def run_llm(
|
|
llm: BaseLanguageModel,
|
|
inputs: Dict[str, Any],
|
|
langchain_tracer: LangChainTracer,
|
|
) -> Union[LLMResult, ChatResult]:
|
|
"""Run the language model on the example."""
|
|
if isinstance(llm, BaseLLM):
|
|
if "prompt" not in inputs:
|
|
raise ValueError(f"LLM Run must contain 'prompt' key. Got {inputs}")
|
|
llm_prompt: str = inputs["prompt"]
|
|
llm_output = llm.generate([llm_prompt], callbacks=[langchain_tracer])
|
|
elif isinstance(llm, BaseChatModel):
|
|
if "messages" not in inputs:
|
|
raise ValueError(
|
|
f"Chat Model Run must contain 'messages' key. Got {inputs}"
|
|
)
|
|
raw_messages: List[dict] = inputs["messages"]
|
|
messages = messages_from_dict(raw_messages)
|
|
llm_output = llm.generate([messages], callbacks=[langchain_tracer])
|
|
else:
|
|
raise ValueError(f"Unsupported LLM type {type(llm)}")
|
|
return llm_output
|
|
|
|
@staticmethod
|
|
def run_llm_or_chain(
|
|
example: Example,
|
|
langchain_tracer: LangChainTracer,
|
|
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
|
|
n_repetitions: int,
|
|
) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
|
|
"""Run the chain synchronously."""
|
|
previous_example_id = langchain_tracer.example_id
|
|
langchain_tracer.example_id = example.id
|
|
outputs = []
|
|
for _ in range(n_repetitions):
|
|
try:
|
|
if isinstance(llm_or_chain_factory, BaseLanguageModel):
|
|
output: Any = LangChainPlusClient.run_llm(
|
|
llm_or_chain_factory, example.inputs, langchain_tracer
|
|
)
|
|
else:
|
|
chain = llm_or_chain_factory()
|
|
output = chain.run(example.inputs, callbacks=[langchain_tracer])
|
|
outputs.append(output)
|
|
except Exception as e:
|
|
logger.warning(f"Chain failed for example {example.id}. Error: {e}")
|
|
outputs.append({"Error": str(e)})
|
|
langchain_tracer.example_id = previous_example_id
|
|
return outputs
|
|
|
|
def run_on_dataset(
|
|
self,
|
|
dataset_name: str,
|
|
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
|
|
*,
|
|
num_repetitions: int = 1,
|
|
session_name: Optional[str] = None,
|
|
verbose: bool = False,
|
|
) -> Dict[str, Any]:
|
|
"""Run the chain on a dataset and store traces to the specified session 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.
|
|
concurrency_level: Number of async workers to run in parallel.
|
|
num_repetitions: Number of times to run the model on each example.
|
|
This is useful when testing success rates or generating confidence
|
|
intervals.
|
|
session_name: Name of the session to store the traces in.
|
|
Defaults to {dataset_name}-{chain class name}-{datetime}.
|
|
verbose: Whether to print progress.
|
|
|
|
Returns:
|
|
A dictionary mapping example ids to the model outputs.
|
|
"""
|
|
session_name = LangChainPlusClient._get_session_name(
|
|
session_name, llm_or_chain_factory, dataset_name
|
|
)
|
|
dataset = self.read_dataset(dataset_name=dataset_name)
|
|
examples = list(self.list_examples(dataset_id=str(dataset.id)))
|
|
results: Dict[str, Any] = {}
|
|
tracer = LangChainTracer(session_name=session_name)
|
|
tracer.ensure_session()
|
|
for i, example in enumerate(examples):
|
|
result = self.run_llm_or_chain(
|
|
example,
|
|
tracer,
|
|
llm_or_chain_factory,
|
|
num_repetitions,
|
|
)
|
|
if verbose:
|
|
print(f"{i+1} processed", flush=True, end="\r")
|
|
results[str(example.id)] = result
|
|
return results
|