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from typing import Any, Optional import torch def flash_attn_func(q, k, v, dropout=0.0, bias=None, softmax_scale=None, is_causal=False): assert bias is None attn, lse, _ = _flash_attn_func(q, k, v, dropout_p=dropout, softmax_scale=softmax_scale, causal=is_causal, return_attn_probs=True)...
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import math from typing import Callable, Dict, Optional, Tuple import torch import torch.nn.functional as F from torch import Tensor from .moe_layer import fused_cumsum_sub_one, has_tutel EVAL_CAPACITY_TOKEN_FRACTION = 0.25 SAMPLE_FRACTION = 0.2 def one_hot(indices: torch.Tensor, num_classes: int, unsqueeze_indices=Fal...
Implements Top2Gating on logits.
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import math from typing import Callable, Dict, Optional, Tuple import torch import torch.nn.functional as F from torch import Tensor from .moe_layer import fused_cumsum_sub_one, has_tutel SAMPLE_FRACTION = 0.2 def gumbel_rsample(shape: Tuple, device: torch.device) -> Tensor: gumbel = gumbel_map.get(device) if g...
Implements Top2Gating on logits.
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import torch.distributed as dist def _find_my_group_index(grouped_ranks): my_rank = dist.get_rank() for i, group in enumerate(grouped_ranks): if my_rank in group: return i raise RuntimeError def get_all2all_group(moe_expert_count): if dist.is_initialized(): if not hasattr(ge...
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import copy import torch import torch.nn as nn class MultiwayNetwork(nn.Module): def __init__(self, module, dim=1): super().__init__() self.dim = dim self.A = module self.B = copy.deepcopy(module) self.B.reset_parameters() self.split_position = -1 def forward(self...
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import copy import torch import torch.nn as nn def set_split_position(position): def apply_fn(module): if hasattr(module, "split_position"): module.split_position = position return apply_fn
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import torch import torch.nn.functional as F from torch import nn from .rms_norm import RMSNorm from .multiway_network import MultiwayWrapper The provided code snippet includes necessary dependencies for implementing the `duplicate_interleave` function. Write a Python function `def duplicate_interleave(m)` to solve th...
A simple version of `torch.repeat_interleave` for duplicating a matrix while interleaving the copy.
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import torch import torch.nn.functional as F from torch import nn from .rms_norm import RMSNorm from .multiway_network import MultiwayWrapper def rotate_every_two(x): x1 = x[:, :, :, ::2] x2 = x[:, :, :, 1::2] x = torch.stack((-x2, x1), dim=-1) return x.flatten(-2) # in einsum notation: rearrange(x, '....
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import torch import torch.nn.functional as F from torch import nn from .rms_norm import RMSNorm from .multiway_network import MultiwayWrapper def get_activation_fn(activation): if activation == "swish": return F.silu elif activation == "gelu": return F.gelu else: raise NotImplemente...
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import torch.nn as nn from torchscale.component.multihead_attention import MultiheadAttention from torchscale.component.multiway_network import MultiwayNetwork class MultiheadAttention(nn.Module): def __init__( self, args, embed_dim, num_heads, dropout=0.0, self_atte...
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from flask import Flask import time import random import boto3 def log_metric(metric_name, value): # Send custom metric to CloudWatch cloudwatch.put_metric_data( Namespace='OnlineStore', MetricData=[{ 'MetricName': metric_name, 'Value': value, 'Unit': 'Count' ...
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from flask import Flask import time import random import boto3 products = { '1': {'name': 'Product 1', 'price': 10.99}, '2': {'name': 'Product 2', 'price': 19.99}, '3': {'name': 'Product 3', 'price': 5.49} } def log_metric(metric_name, value): # Send custom metric to CloudWatch cloudwatch.put_metric...
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import time def simulate_cpu_spike(duration=30, cpu_percent=80): print(f"Simulating CPU spike at {cpu_percent}%...") start_time = time.time() # Calculate the number of iterations needed to achieve the desired CPU utilization target_percent = cpu_percent / 100 total_iterations = int(target_percent ...
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import boto3 import json def lambda_handler(event, context): # Get the specific EC2 instance. ec2_client = boto3.client('ec2') # Assume compliant by default compliance_status = "COMPLIANT" # Extract the configuration item from the invokingEvent config = json.loads(event['invokingEv...
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from flask import Flask def hello(): return 'Hello, world!'
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import boto3 def lambda_handler(event, context): ec2 = boto3.client('ec2') # Get all EBS snapshots response = ec2.describe_snapshots(OwnerIds=['self']) # Get all active EC2 instance IDs instances_response = ec2.describe_instances(Filters=[{'Name': 'instance-state-name', 'Values': ['running']}]) ...
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from flask import Flask def hello(): return 'Hello, Flask on Docker!'
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from flask import Flask def greet(name): return f'Hello, {name}! Welcome to Flask on Docker.'
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server_config = { 'server1': {'ip': '192.168.1.1', 'port': 8080, 'status': 'active'}, 'server2': {'ip': '192.168.1.2', 'port': 8000, 'status': 'inactive'}, 'server3': {'ip': '192.168.1.3', 'port': 9000, 'status': 'active'} } def get_server_status(server_name): return server_config.get(server_name, {})....
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from flask import Flask def hello_world(): return 'Hello, World!'
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import requests from requests.auth import HTTPBasicAuth import json from flask import Flask def createJira(): url = "https://veeramallaabhishek.atlassian.net/rest/api/3/issue" API_TOKEN="" auth = HTTPBasicAuth("", API_TOKEN) headers = { "Accept": "application/json", "Content-Type": ...
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def update_server_config(file_path, key, value): # Read the existing content of the server configuration file with open(file_path, 'r') as file: lines = file.readlines() # Update the configuration value for the specified key with open(file_path, 'w') as file: for line in lines: ...
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import os def list_files_in_folder(folder_path): try: files = os.listdir(folder_path) return files, None except FileNotFoundError: return None, "Folder not found" except PermissionError: return None, "Permission denied"
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import importlib import inspect import os import sys import typing from enum import Enum from pathlib import Path from typing import Dict, List, Literal, Optional, Sequence, TypedDict, Union import toml from pydantic import BaseModel def _load_package_modules( package_directory: Union[str, Path], submodule: Optiona...
Create a rst file for building of documentation. Args: package_name: Can be either "langchain" or "core" or "experimental".
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import json import os import sys from pathlib import Path import toml from docutils import nodes from sphinx.util.docutils import SphinxDirective class ExampleLinksDirective(SphinxDirective): """Directive to generate a list of links to examples. We have a script that extracts links to API reference docs fro...
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import os from pathlib import Path from langchain_community import chat_models, llms from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel from langchain_core.language_models.llms import LLM, BaseLLM LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat") LLM_FEAT_TABLE_CORRE...
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import os from pathlib import Path from langchain_community import chat_models, llms from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel from langchain_core.language_models.llms import LLM, BaseLLM CHAT_MODEL_IGNORE = ("FakeListChatModel", "HumanInputChatModel") CHAT_MODEL_FEAT_TABLE_C...
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import os import re import sys from pathlib import Path DOCS_DIR = Path(os.path.abspath(__file__)).parents[1] def update_links(doc_path, docs_link): with open(DOCS_DIR / doc_path, "r") as f: content = f.read() # replace relative links content = re.sub("\]\(\.\/", f"]({docs_link}", content) wi...
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import argparse import importlib import inspect import json import logging import os import re from pathlib import Path The provided code snippet includes necessary dependencies for implementing the `find_files` function. Write a Python function `def find_files(path)` to solve the following problem: Find all MDX files...
Find all MDX files in the given path
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import argparse import importlib import inspect import json import logging import os import re from pathlib import Path _DOCS_DIR = _CURRENT_PATH / "docs" def get_args(): parser = argparse.ArgumentParser() parser.add_argument( "--docs_dir", type=str, default=_DOCS_DIR, help="Dir...
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import argparse import importlib import inspect import json import logging import os import re from pathlib import Path logger = logging.getLogger(__name__) _BASE_URL = "https://api.python.langchain.com/en/latest/" code_block_re = re.compile(r"^(```python\n)(.*?)(```\n)", re.DOTALL | re.MULTILINE) _IMPORT_RE = re.compi...
Replace imports in each Python code block with links to their documentation and append the import info in a comment
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import re import xml.etree.ElementTree as ET from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union from langchain_core.exceptions import OutputParserException from langchain_core.messages import BaseMessage from langchain_core.output_parsers.transform import BaseTransformOutputParser from langcha...
Get nested element from path.
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from __future__ import annotations import json import re from json import JSONDecodeError from typing import Any, Callable, List, Optional, Type import jsonpatch from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers.format_instructions import JSON_FORMAT_INSTRUCTIONS from langc...
Parse a JSON string from a Markdown string and check that it contains the expected keys. Args: text: The Markdown string. expected_keys: The expected keys in the JSON string. Returns: The parsed JSON object as a Python dictionary.
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from __future__ import annotations import re from abc import abstractmethod from collections import deque from typing import AsyncIterator, Deque, Iterator, List, TypeVar, Union from langchain_core.messages import BaseMessage from langchain_core.output_parsers.transform import BaseTransformOutputParser T = TypeVar("T")...
Drop the last n elements of an iterator.
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from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type from langchain_core.example_selectors.base import BaseExampleSelector from langchain_core.pydantic_v1 import BaseModel, Extra from langchain_core.vectorstores import VectorStore The provided code snippet includes neces...
Return a list of values in dict sorted by key.
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import re from typing import Callable, Dict, List from langchain_core.example_selectors.base import BaseExampleSelector from langchain_core.prompts.prompt import PromptTemplate from langchain_core.pydantic_v1 import BaseModel, validator def _get_length_based(text: str) -> int: return len(re.split("\n| ", text))
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from __future__ import annotations import asyncio import functools import inspect import json import logging import uuid import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, O...
Convert a sync iterator into an async iterator.
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from __future__ import annotations import asyncio import functools import inspect import json import logging import uuid import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, O...
Get prompts that are already cached.
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from __future__ import annotations import asyncio import functools import inspect import json import logging import uuid import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, O...
Get prompts that are already cached. Async version.
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from __future__ import annotations import asyncio import functools import inspect import json import logging import uuid import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, O...
Update the cache and get the LLM output.
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from __future__ import annotations import asyncio import functools import inspect import json import logging import uuid import warnings from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, O...
Update the cache and get the LLM output. Async version
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from __future__ import annotations from abc import ABC, abstractmethod from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence, Set, Type, TypeVar, Union, ) from typing_extensions import TypeAlias from langchain_core._a...
Encode the text into token IDs.
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from __future__ import annotations from abc import ABC, abstractmethod from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence, Set, Type, TypeVar, Union, ) from typing_extensions import TypeAlias from langchain_core._a...
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from __future__ import annotations import asyncio import inspect import uuid import warnings from abc import ABC, abstractmethod from typing import ( TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Union, cast, ) from...
Generate from a stream.
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from __future__ import annotations import asyncio import inspect import uuid import warnings from abc import ABC, abstractmethod from typing import ( TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Union, cast, ) from...
Async generate from a stream.
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from __future__ import annotations import asyncio import inspect import uuid import warnings from abc import ABC, abstractmethod from typing import ( TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Union, cast, ) from...
Convert a sync iterator into an async iterator.
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from __future__ import annotations import asyncio import inspect import uuid import warnings from abc import ABC, abstractmethod from typing import ( TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Optional, Sequence, Union, cast, ) from...
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from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Extra, Field from langchain_core.utils import get_bolded_text from langchain_core.utils._merge import merge_dic...
Merge two message contents. Args: first_content: The first content. second_content: The second content. Returns: The merged content.
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from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Extra, Field from langchain_core.utils import get_bolded_text from langchain_core.utils._merge import merge_dic...
Convert a sequence of Messages to a list of dictionaries. Args: messages: Sequence of messages (as BaseMessages) to convert. Returns: List of messages as dicts.
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from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Extra, Field from langchain_core.utils import get_bolded_text from langchain_core.utils._merge import merge_dic...
Get a title representation for a message. Args: title: The title. bold: Whether to bold the title. Returns: The title representation.
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Get a callback manager for a chain group in a context manager. Useful for grouping different calls together as a single run even if they aren't composed in a single chain. Args: group_name (str): The name of the chain group. callback_manager (CallbackManager, optional): The callback manager to use. inputs (Dict[str, An...
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Get an async callback manager for a chain group in a context manager. Useful for grouping different async calls together as a single run even if they aren't composed in a single chain. Args: group_name (str): The name of the chain group. callback_manager (AsyncCallbackManager, optional): The async callback manager to u...
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Makes so an awaitable method is always shielded from cancellation
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Generic event handler for CallbackManager. Note: This function is used by langserve to handle events. Args: handlers: The list of handlers that will handle the event event_name: The name of the event (e.g., "on_llm_start") ignore_condition_name: Name of the attribute defined on handler that if True will cause the handl...
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Generic event handler for AsyncCallbackManager. Note: This function is used by langserve to handle events. Args: handlers: The list of handlers that will handle the event event_name: The name of the event (e.g., "on_llm_start") ignore_condition_name: Name of the attribute defined on handler that if True will cause the ...
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from __future__ import annotations import asyncio import functools import logging import uuid from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager, contextmanager from contextvars import copy_context from typing import ( TYPE_CHECKING, ...
Configure the callback manager. Args: callback_manager_cls (Type[T]): The callback manager class. inheritable_callbacks (Optional[Callbacks], optional): The inheritable callbacks. Defaults to None. local_callbacks (Optional[Callbacks], optional): The local callbacks. Defaults to None. verbose (bool, optional): Whether ...
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from __future__ import annotations import inspect import uuid import warnings from abc import abstractmethod from inspect import signature from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union from langchain_core.callbacks import ( AsyncCallbackManager, AsyncCallbackManagerForToo...
Create a pydantic schema from a function's signature. Args: model_name: Name to assign to the generated pydandic schema func: Function to generate the schema from Returns: A pydantic model with the same arguments as the function
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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text The provided code snippet includes necessary dependencies for implementing the `try_json_stringif...
Try to stringify an object to JSON. Args: obj: Object to stringify. fallback: Fallback string to return if the object cannot be stringified. Returns: A JSON string if the object can be stringified, otherwise the fallback string.
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import json from typing import Any, Callable, List from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import Run from langchain_core.utils.input import get_bolded_text, get_colored_text The provided code snippet includes necessary dependencies for implementing the `elapsed` function...
Get the elapsed time of a run. Args: run: any object with a start_time and end_time attribute. Returns: A string with the elapsed time in seconds or milliseconds if time is less than a second.
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from __future__ import annotations import datetime import warnings from typing import Any, Dict, List, Optional, Type from uuid import UUID from langsmith.schemas import RunBase as BaseRunV2 from langsmith.schemas import RunTypeEnum as RunTypeEnumDep from langchain_core._api import deprecated from langchain_core.output...
RunTypeEnum.
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from __future__ import annotations import logging import os from typing import Any, Dict, Optional, Union import requests from langchain_core._api import deprecated from langchain_core.messages import get_buffer_string from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import ( C...
Get the headers for the LangChain API.
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from __future__ import annotations import logging import os from typing import Any, Dict, Optional, Union import requests from langchain_core._api import deprecated from langchain_core.messages import get_buffer_string from langchain_core.tracers.base import BaseTracer from langchain_core.tracers.schemas import ( C...
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from __future__ import annotations import logging import threading import weakref from concurrent.futures import Future, ThreadPoolExecutor, wait from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast from uuid import UUID import langsmith from langsmith.evaluation.evaluator import EvaluationResult,...
Wait for all tracers to finish.
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from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client from langsmith import utils as ls_utils from tenacity import (...
Log an error once.
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from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client from langsmith import utils as ls_utils from tenacity import (...
Wait for all tracers to finish.
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from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client from langsmith import utils as ls_utils from tenacity import (...
Get the client.
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from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client from langsmith import utils as ls_utils from tenacity import (...
Get the executor.
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from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client from langsmith import utils as ls_utils from tenacity import (...
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from __future__ import annotations from contextlib import contextmanager from contextvars import ContextVar from typing import ( TYPE_CHECKING, Any, Generator, List, Optional, Tuple, Type, Union, cast, ) from uuid import UUID from langsmith import utils as ls_utils from langsmith.run...
Get the Deprecated LangChainTracer in a context manager. Args: session_name (str, optional): The name of the session. Defaults to "default". Returns: TracerSessionV1: The LangChainTracer session. Example: >>> with tracing_enabled() as session: ... # Use the LangChainTracer session
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from __future__ import annotations from contextlib import contextmanager from contextvars import ContextVar from typing import ( TYPE_CHECKING, Any, Generator, List, Optional, Tuple, Type, Union, cast, ) from uuid import UUID from langsmith import utils as ls_utils from langsmith.run...
Instruct LangChain to log all runs in context to LangSmith. Args: project_name (str, optional): The name of the project. Defaults to "default". example_id (str or UUID, optional): The ID of the example. Defaults to None. tags (List[str], optional): The tags to add to the run. Defaults to None. client (LangSmithClient, ...
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from __future__ import annotations from contextlib import contextmanager from contextvars import ContextVar from typing import ( TYPE_CHECKING, Any, Generator, List, Optional, Tuple, Type, Union, cast, ) from uuid import UUID from langsmith import utils as ls_utils from langsmith.run...
Collect all run traces in context. Returns: run_collector.RunCollectorCallbackHandler: The run collector callback handler. Example: >>> with collect_runs() as runs_cb: chain.invoke("foo") run_id = runs_cb.traced_runs[0].id
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from __future__ import annotations from contextlib import contextmanager from contextvars import ContextVar from typing import ( TYPE_CHECKING, Any, Generator, List, Optional, Tuple, Type, Union, cast, ) from uuid import UUID from langsmith import utils as ls_utils from langsmith.run...
Register a configure hook. Args: context_var (ContextVar[Optional[Any]]): The context variable. inheritable (bool): Whether the context variable is inheritable. handle_class (Optional[Type[BaseCallbackHandler]], optional): The callback handler class. Defaults to None. env_var (Optional[str], optional): The environment ...
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from __future__ import annotations import asyncio import copy import threading from collections import defaultdict from typing import ( Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, TypeVar, Union, overload, ) from uuid import UUID import jsonpatch from typing_ext...
Extract standardized inputs from a run. Standardizes the inputs based on the type of the runnable used. Args: run: Run object schema_format: The schema format to use. Returns: Valid inputs are only dict. By conventions, inputs always represented invocation using named arguments. A None means that the input is not yet k...
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from __future__ import annotations import asyncio import copy import threading from collections import defaultdict from typing import ( Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, TypeVar, Union, overload, ) from uuid import UUID import jsonpatch from typing_ext...
Extract standardized output from a run. Standardizes the outputs based on the type of the runnable used. Args: log: The log entry. schema_format: The schema format to use. Returns: An output if returned, otherwise a None
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from __future__ import annotations import asyncio import copy import threading from collections import defaultdict from typing import ( Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, TypeVar, Union, overload, ) from uuid import UUID import jsonpatch from typing_ext...
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from __future__ import annotations import asyncio import copy import threading from collections import defaultdict from typing import ( Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, TypeVar, Union, overload, ) from uuid import UUID import jsonpatch from typing_ext...
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from __future__ import annotations import asyncio import copy import threading from collections import defaultdict from typing import ( Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, TypeVar, Union, overload, ) from uuid import UUID import jsonpatch from typing_ext...
Implementation of astream_log for a given runnable. The implementation has been factored out (at least temporarily) as both astream_log and astream_events relies on it.
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from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core.load.serializable import Serializable from langchain_core.messages import ( AIMessage, BaseMessage, FunctionMessage, HumanMessage, ) class AgentAction(Serializable): """A full de...
Convert an agent action to a message. This code is used to reconstruct the original AI message from the agent action. Args: agent_action: Agent action to convert. Returns: AIMessage that corresponds to the original tool invocation.
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import warnings from typing import TYPE_CHECKING, Optional _verbose: bool = False The provided code snippet includes necessary dependencies for implementing the `set_verbose` function. Write a Python function `def set_verbose(value: bool) -> None` to solve the following problem: Set a new value for the `verbose` globa...
Set a new value for the `verbose` global setting.
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import warnings from typing import TYPE_CHECKING, Optional _debug: bool = False The provided code snippet includes necessary dependencies for implementing the `set_debug` function. Write a Python function `def set_debug(value: bool) -> None` to solve the following problem: Set a new value for the `debug` global settin...
Set a new value for the `debug` global setting.
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import warnings from typing import TYPE_CHECKING, Optional _llm_cache: Optional["BaseCache"] = None The provided code snippet includes necessary dependencies for implementing the `set_llm_cache` function. Write a Python function `def set_llm_cache(value: Optional["BaseCache"]) -> None` to solve the following problem: ...
Set a new LLM cache, overwriting the previous value, if any.
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from abc import ABC from typing import ( Any, Dict, List, Literal, Optional, TypedDict, Union, cast, ) from typing_extensions import NotRequired from langchain_core.pydantic_v1 import BaseModel, PrivateAttr The provided code snippet includes necessary dependencies for implementing the `...
Try to determine if a value is different from the default. Args: value: The value. key: The key. model: The model. Returns: Whether the value is different from the default.
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from abc import ABC from typing import ( Any, Dict, List, Literal, Optional, TypedDict, Union, cast, ) from typing_extensions import NotRequired from langchain_core.pydantic_v1 import BaseModel, PrivateAttr def _replace_secrets( root: Dict[Any, Any], secrets_map: Dict[str, str] ) ->...
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27,913
import asyncio import threading from collections import defaultdict from functools import partial from itertools import groupby from typing import ( Any, Awaitable, Callable, DefaultDict, Dict, List, Mapping, Optional, Sequence, Type, TypeVar, Union, ) from langchain_core...
Asynchronously patch a runnable config with context getters and setters. Args: config: The runnable config. steps: The runnable steps. Returns: The patched runnable config.
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import asyncio import threading from collections import defaultdict from functools import partial from itertools import groupby from typing import ( Any, Awaitable, Callable, DefaultDict, Dict, List, Mapping, Optional, Sequence, Type, TypeVar, Union, ) from langchain_core...
Patch a runnable config with context getters and setters. Args: config: The runnable config. steps: The runnable steps. Returns: The patched runnable config.
27,915
import asyncio import threading from collections import defaultdict from functools import partial from itertools import groupby from typing import ( Any, Awaitable, Callable, DefaultDict, Dict, List, Mapping, Optional, Sequence, Type, TypeVar, Union, ) from langchain_core...
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import asyncio import threading from collections import defaultdict from functools import partial from itertools import groupby from typing import ( Any, Awaitable, Callable, DefaultDict, Dict, List, Mapping, Optional, Sequence, Type, TypeVar, Union, ) from langchain_core...
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from __future__ import annotations from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, Dict, List, Optional, Sequence, Set, Tuple, Type, TypedDict, TypeVar, Union, cast, overload, ) from langchain_core._api import deprecated from lan...
Instantiate a message from a variety of message formats. The message format can be one of the following: - BaseMessagePromptTemplate - BaseMessage - 2-tuple of (role string, template); e.g., ("human", "{user_input}") - 2-tuple of (message class, template) - string: shorthand for ("human", template); e.g., "{user_input}...
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import json import logging from pathlib import Path from typing import Callable, Dict, Union import yaml from langchain_core.output_parsers.string import StrOutputParser from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import ChatPromptTemplate from langchain_core.prompts.few_...
Load the "few shot" prompt from the config.
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import json import logging from pathlib import Path from typing import Callable, Dict, Union import yaml from langchain_core.output_parsers.string import StrOutputParser from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import ChatPromptTemplate from langchain_core.prompts.few_...
Load the prompt template from config.
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import json import logging from pathlib import Path from typing import Callable, Dict, Union import yaml from langchain_core.output_parsers.string import StrOutputParser from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import ChatPromptTemplate from langchain_core.prompts.few_...
Load chat prompt from config
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from __future__ import annotations import json from abc import ABC, abstractmethod from pathlib import Path from typing import ( TYPE_CHECKING, Any, Callable, Dict, Generic, List, Mapping, Optional, Type, TypeVar, Union, ) import yaml from langchain_core.output_parsers.base i...
Format a document into a string based on a prompt template. First, this pulls information from the document from two sources: 1. `page_content`: This takes the information from the `document.page_content` and assigns it to a variable named `page_content`. 2. metadata: This takes information from `document.metadata` and...
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from typing import Any, Dict, List, Tuple from langchain_core.prompt_values import PromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import BaseChatPromptTemplate from langchain_core.pydantic_v1 import root_validator def _get_inputs(inputs: dict, input_variables: L...
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from __future__ import annotations import warnings from abc import ABC from string import Formatter from typing import Any, Callable, Dict, List, Set from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.utils import get_co...
Format a template using jinja2. *Security warning*: As of LangChain 0.0.329, this method uses Jinja2's SandboxedEnvironment by default. However, this sand-boxing should be treated as a best-effort approach rather than a guarantee of security. Do not accept jinja2 templates from untrusted sources as they may lead to arb...
27,924
from __future__ import annotations import warnings from abc import ABC from string import Formatter from typing import Any, Callable, Dict, List, Set from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.utils import get_co...
Validate that the input variables are valid for the template. Issues a warning if missing or extra variables are found. Args: template: The template string. input_variables: The input variables.
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from __future__ import annotations import warnings from abc import ABC from string import Formatter from typing import Any, Callable, Dict, List, Set from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.utils import get_co...
Check that template string is valid. Args: template: The template string. template_format: The template format. Should be one of "f-string" or "jinja2". input_variables: The input variables. Raises: ValueError: If the template format is not supported.
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from __future__ import annotations import warnings from abc import ABC from string import Formatter from typing import Any, Callable, Dict, List, Set from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.utils import get_co...
Get the variables from the template. Args: template: The template string. template_format: The template format. Should be one of "f-string" or "jinja2". Returns: The variables from the template. Raises: ValueError: If the template format is not supported.
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from typing import Sequence The provided code snippet includes necessary dependencies for implementing the `print_sys_info` function. Write a Python function `def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None` to solve the following problem: Print information about the environment for debugging p...
Print information about the environment for debugging purposes.
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import contextlib import functools import inspect import warnings from typing import Any, Callable, Generator, Type, TypeVar from langchain_core._api.internal import is_caller_internal T = TypeVar("T", Type, Callable) def warn_beta( *, message: str = "", name: str = "", obj_type: str = "", addendum:...
Decorator to mark a function, a class, or a property as beta. When marking a classmethod, a staticmethod, or a property, the ``@beta`` decorator should go *under* ``@classmethod`` and ``@staticmethod`` (i.e., `beta` should directly decorate the underlying callable), but *over* ``@property``. When marking a class ``C`` ...
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import contextlib import functools import inspect import warnings from typing import Any, Callable, Generator, Type, TypeVar from langchain_core._api.internal import is_caller_internal class LangChainBetaWarning(DeprecationWarning): """A class for issuing beta warnings for LangChain users.""" The provided code sni...
Context manager to suppress LangChainDeprecationWarning.