id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
16,497 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Binarize the dict by setting the values that are 1 to 0. Values greater than 1 stay untouched. |
16,498 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Takes in a dataclass and outputs all of its fields and values in a list. |
16,499 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Write lines out to a file at path file_path. |
16,500 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Add `count` spaces before each line in `lines`. |
16,501 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Return a version of the target_word where the case matches the source_word. |
16,502 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | A wrapper for applying `process` to all `items`. |
16,503 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Given `items` (a list of dictionaries), remove any (key, value) pairs that aren't necessary to distinguish the items, removing the keys not in `priority_keys` and then from the end of `priority_keys` first. Example: items = [{"model": "M1", stop: "#", n: 3}, {"model": "M1", stop: "\n", n: 3}, {"model": "M2", stop: "\n"... |
16,504 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Generate a unique ID (e.g., 77437ea482144bf7b9275a0acee997db). |
16,505 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Get the file name from a path (e.g., /path/to/image.png => image.png). |
16,506 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | Creates a symlink at `dest`. `src` and `dest` can be relative paths. |
16,507 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | null |
16,508 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | null |
16,509 | from filelock import FileLock
import json
import os
import shlex
import subprocess
import urllib
import uuid
import zstandard
from typing import Any, Callable, Dict, List, Optional, TypeVar
from datetime import datetime, date
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import pyhocon
from da... | null |
16,510 | import sys
import time
from typing import Any, Callable, List, Optional
The provided code snippet includes necessary dependencies for implementing the `format_time` function. Write a Python function `def format_time(s: float) -> str` to solve the following problem:
Return a nice string representation of `s` seconds.
... | Return a nice string representation of `s` seconds. |
16,511 | from typing import Any, Optional, Dict
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists
from helm.clients.auto_client import AutoClient
from helm.benchmark.model_deployment_registry import ModelDeployment, get_model_deployment
from helm.tokeni... | null |
16,512 | from typing import Any, Optional, Dict
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists
from helm.clients.auto_client import AutoClient
from helm.benchmark.model_deployment_registry import ModelDeployment, get_model_deployment
from helm.tokeni... | null |
16,513 | from typing import Any, Optional, Dict
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists
from helm.clients.auto_client import AutoClient
from helm.benchmark.model_deployment_registry import ModelDeployment, get_model_deployment
from helm.tokeni... | null |
16,514 | from typing import Any, Optional, Dict
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists
from helm.clients.auto_client import AutoClient
from helm.benchmark.model_deployment_registry import ModelDeployment, get_model_deployment
from helm.tokeni... | null |
16,515 | from typing import Any, Optional, Dict
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists
from helm.clients.auto_client import AutoClient
from helm.benchmark.model_deployment_registry import ModelDeployment, get_model_deployment
from helm.tokeni... | null |
16,516 | import argparse
import json
from collections import defaultdict
import os
from typing import Dict
from helm.common.cache import (
KeyValueStoreCacheConfig,
MongoCacheConfig,
SqliteCacheConfig,
create_key_value_store,
)
from helm.common.key_value_store import request_to_key
from helm.common.hierarchical_... | Given a jsonl file with request and results, uploads request/result pairs to the cache at `cache_path`. We assume each line of the input jsonl file is structured {request: ..., result: ...}. |
16,517 | import argparse
import json
import os
import typing
from typing import List
from collections import Counter
from dacite import from_dict
from helm.common.request import Request
from helm.common.cache import (
KeyValueStoreCacheConfig,
MongoCacheConfig,
SqliteCacheConfig,
create_key_value_store,
)
from h... | Given a run suite folder, generates a jsonl file at `output_path` with raw queries where each line represents a single request. |
16,518 | import argparse
from pymongo import MongoClient
from helm.common.cache import create_key_value_store, MongoCacheConfig
from helm.common.hierarchical_logger import hlog, htrack, htrack_block
class MongoCacheConfig(KeyValueStoreCacheConfig):
def cache_stats_key(self) -> str:
def create_key_value_store(config:... | null |
16,519 | import argparse
import json
import os
from sqlitedict import SqliteDict
from helm.common.mongo_key_value_store import MongoKeyValueStore
from helm.common.hierarchical_logger import hlog, htrack
from typing import Optional
_SQLITE_FILE_SUFFIX = ".sqlite"
def copy_cache(
cache_dir: str,
mongo_host: str,
organ... | null |
16,520 | import argparse
import json
import time
from typing import Any, Callable, Dict, List
from helm.common.cache import create_key_value_store, MongoCacheConfig
from helm.common.general import parse_hocon
from helm.common.hierarchical_logger import hlog, htrack
from helm.clients.anthropic_client import AnthropicLegacyClient... | null |
16,521 | import argparse
import json
import time
from typing import Any, Callable, Dict, List
from helm.common.cache import create_key_value_store, MongoCacheConfig
from helm.common.general import parse_hocon
from helm.common.hierarchical_logger import hlog, htrack
from helm.clients.anthropic_client import AnthropicLegacyClient... | null |
16,522 | import argparse
from helm.common.cache import create_key_value_store, MongoCacheConfig
from helm.common.hierarchical_logger import hlog, htrack
class MongoCacheConfig(KeyValueStoreCacheConfig):
"""Configuration for a cache backed by a MongoDB collection."""
# URL for the MongoDB database that contains the col... | null |
16,523 | import argparse
from datetime import datetime
def generate_spec(scenario, model, tokenizer, num_prompt_tokens, num_output_tokens, random):
random_str: str = ""
if random is not None:
random_str = f",random={random}"
return (
f'"{scenario}:model={model},tokenizer={tokenizer},'
f"num_... | null |
16,524 | import os
from typing import Dict, List, Tuple
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists, ensure_file_downloaded, write, get_credentials
from helm.common.tokenization_request import (
TokenizationRequest,
TokenizationRequestResu... | null |
16,525 | import os
from typing import Dict, List, Tuple
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists, ensure_file_downloaded, write, get_credentials
from helm.common.tokenization_request import (
TokenizationRequest,
TokenizationRequestResu... | Tokenizes each book using the requested tokenizer service. |
16,526 | import os
from typing import Dict, List, Tuple
from helm.common.cache_backend_config import SqliteCacheBackendConfig
from helm.common.general import ensure_directory_exists, ensure_file_downloaded, write, get_credentials
from helm.common.tokenization_request import (
TokenizationRequest,
TokenizationRequestResu... | Generates the synthetic efficiency instances given the tokenized book sources. |
16,527 | import argparse
import csv
import json
import os
import random
import shutil
import requests
import statistics
from typing import Any, Dict, List
from tqdm import tqdm
from helm.common.hierarchical_logger import hlog, htrack_block
random.seed(0)
QUESTION_TYPE_TO_INFOS = {
"alignment": {
"instruction": "Plea... | Given a human eval results folder from HEIM, generates a dataset that can be used to evaluate VLMs. vhelm_image_critique: reasoning and knowledge scenarios - alignment vhelm_image_critique_aesthetics: MSCOCO perturbations - alignment - aesthetics vhelm_image_critique_originality_subject_aesthetics: originality scenario... |
16,528 | import os
from typing import Dict
from scaleapi import ScaleClient
def get_credentials(path: str) -> Dict[str, str]:
# Reads the credentials from the given path
with open(path, "r") as f:
# Read line by line, replaces the spaces, splits on the first ":"
# The first part is the key, the second pa... | null |
16,529 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `batch_line_generator` function. Write a Python function `def batch_line_generator(fname, batch_size)` to solve the following problem... | Returns generator for jsonl file with batched lines |
16,530 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `append_to_jsonl_file` function. Write a Python function `def append_to_jsonl_file(data, file)` to solve the following problem:
Appen... | Appends json dictionary as new line to file |
16,531 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `get_batch_files` function. Write a Python function `def get_batch_files(fdir: Path) -> List[str]` to solve the following problem:
Fo... | For each file in fdir, returns full filepath. Args: fdir (str): path to directory Returns: List[str]: filepaths for files in fdir |
16,532 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `create_dir` function. Write a Python function `def create_dir(out_dir)` to solve the following problem:
Creates new directory if it ... | Creates new directory if it doesn't already exist |
16,533 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `load_seed_relations` function. Write a Python function `def load_seed_relations(fdir: Path) -> pd.DataFrame` to solve the following ... | Returns a dataframe containing seed relations and associated information. Args: fdir (str): path to folder containined seed relations TSV files Returns: pd.DataFrame: dataframe for data in tsv files. |
16,534 | import os
import json
from typing import Iterator, Dict, List
import pandas as pd
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `save_jsonl` function. Write a Python function `def save_jsonl(fpath: Path, data: List[Dict[str, str]]) -> None` to solve the followi... | Saves data to file in JSONL format. Args: fpath (Path): path to file. data (List[Dict[str, str]]): data to save. Must be list of dictionaries. |
16,535 | import argparse
from collections import defaultdict
import numpy as np
from tqdm import tqdm
from pathlib import Path
from utils import jsonl_generator, load_seed_relations, save_jsonl
def get_arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--benchmark_folder", type=str, default=... | null |
16,536 | import argparse
from collections import defaultdict
import numpy as np
from tqdm import tqdm
from pathlib import Path
from utils import jsonl_generator, load_seed_relations, save_jsonl
def jsonl_generator(fname: str) -> Iterator[Dict[str, str]]:
"""Returns an iterator over a jsonl file."""
for line in open(fna... | null |
16,537 | import argparse
from collections import defaultdict
import numpy as np
from tqdm import tqdm
from pathlib import Path
from utils import jsonl_generator, load_seed_relations, save_jsonl
def load_relations(fpath):
rels = []
with open(fpath) as in_file:
for line in in_file:
rels.append(line.st... | null |
16,538 | import argparse
from tqdm import tqdm
from typing import Set, List, Dict
from pathlib import Path
from utils import get_batch_files, jsonl_generator, load_seed_relations, save_jsonl
def get_arg_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument(
"--processed_wik... | null |
16,539 | import argparse
from tqdm import tqdm
from typing import Set, List, Dict
from pathlib import Path
from utils import get_batch_files, jsonl_generator, load_seed_relations, save_jsonl
def jsonl_generator(fname: str) -> Iterator[Dict[str, str]]:
"""Returns an iterator over a jsonl file."""
for line in open(fname,... | Returns triples with relation in seed_rels Args: seed_rels (Set[str]): filtering set of relations. filepath (str): path to jsonl file with triple data Returns: List[Dict[str, str]]: list of filtered triples. |
16,540 | import argparse
from tqdm import tqdm
from typing import Set, List, Dict
from pathlib import Path
from utils import get_batch_files, jsonl_generator, load_seed_relations, save_jsonl
def jsonl_generator(fname: str) -> Iterator[Dict[str, str]]:
"""Returns an iterator over a jsonl file."""
for line in open(fname,... | null |
16,541 | import argparse
from collections import defaultdict
import json
from tqdm import tqdm
from pathlib import Path
from utils import get_batch_files, jsonl_generator, save_jsonl
def get_arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--processed_wikidata", type=str, help="Path to pro... | null |
16,542 | import argparse
from collections import defaultdict
import json
from tqdm import tqdm
from pathlib import Path
from utils import get_batch_files, jsonl_generator, save_jsonl
The provided code snippet includes necessary dependencies for implementing the `bad_alias` function. Write a Python function `def bad_alias(alias... | Returns true if an entity has a "bad" alias and false otherwise. An alias is bad if it corresponds to a category, stub, disambiguation, stub, template, or list page. We just check for these keywords in the title. Args: aliases (list): list of aliases for an entity. Returns: bool: whether or not the entity has a bad ali... |
16,543 | import argparse
import os
import shutil
import subprocess
from typing import List, Optional
from helm.common.hierarchical_logger import hlog, htrack, htrack_block
OUTPUT_PATH_TEMPLATE = "benchmark_output/runs/{suite}"
DRYRUN_SUITE1: str = "dryrun_results1"
DRYRUN_SUITE2: str = "dryrun_results2"
def do_dry_run(
dryr... | null |
16,544 | from django.shortcuts import render, get_object_or_404, redirect
from django.views import generic
from .models import Todo
from django.http import HttpResponseRedirect
class Todo(models.Model):
title = models.CharField(max_length=100)
created_at = models.DateTimeField('Created', auto_now_add=True)
update_a... | null |
16,545 | from django.shortcuts import render, get_object_or_404, redirect
from django.views import generic
from .models import Todo
from django.http import HttpResponseRedirect
class Todo(models.Model):
title = models.CharField(max_length=100)
created_at = models.DateTimeField('Created', auto_now_add=True)
update_a... | null |
16,546 | from django.shortcuts import render, get_object_or_404, redirect
from django.views import generic
from .models import Todo
from django.http import HttpResponseRedirect
class Todo(models.Model):
title = models.CharField(max_length=100)
created_at = models.DateTimeField('Created', auto_now_add=True)
update_a... | null |
16,547 | from django.shortcuts import redirect
def index(request):
return redirect('/todos') | null |
16,548 | from typing import Optional
from nemoguardrails.actions import action
async def check_blocked_terms(context: Optional[dict] = None):
bot_response = context.get("bot_message")
# A quick hard-coded list of proprietary terms. You can also read this from a file.
proprietary_terms = ["proprietary", "proprietar... | null |
16,553 | import csv
import json
from nemoguardrails.server.api import register_logger
The provided code snippet includes necessary dependencies for implementing the `custom_logger` function. Write a Python function `async def custom_logger(item)` to solve the following problem:
Custom logger that writes the ratings to a CSV fi... | Custom logger that writes the ratings to a CSV file in the current directory. |
16,554 | from functools import lru_cache
from torch import bfloat16
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
The provided code snippet includes necessary dependencies for implementing the `get_falco... | Loads the Falcon 7B Instruct LLM. |
16,555 | from functools import lru_cache
from torch import bfloat16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
def ge... | null |
16,556 | import os
import os.path
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_pro... | null |
16,557 | from functools import lru_cache
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
def get_dolly_v2_3b_llm(streaming... | null |
16,558 | from functools import lru_cache
from torch import float16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
The pro... | Loads the Vicuna 7B LLM. |
16,559 | from functools import lru_cache
from torch import float16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
The pro... | Loads the Vicuna 13B LLM. |
16,560 | from functools import lru_cache
from torch import float16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import (
HuggingFacePipelineCompatible,
register_llm_provider,
)
def _loa... | Loads the Vicuna 13B LLM from a local path. |
16,561 | import logging
import os
from datetime import datetime
from typing import Optional
import pinecone
from langchain.chains import RetrievalQA
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores import Pine... | null |
16,562 | import asyncio
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
The provided code snippet includes necessary dependencies for implementing the `query_tabular_data` function. Writ... | Answer a question based on some tabular data. |
16,563 | import os.path
import pickle
from pathlib import Path
from typing import Optional
import faiss
import pandas as pd
import torch
from gpt4pandas import GPT4Pandas
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import BaseLLM
from langchain.text_splitte... | null |
16,564 | from langchain.llms.base import BaseLLM
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from nemoguardrails import LLMRails
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.kb.kb import KnowledgeBase
async def rag(context: dict, llm: Base... | null |
16,565 | import os.path
from nemoguardrails import LLMRails, RailsConfig
The provided code snippet includes necessary dependencies for implementing the `demo_input_checking` function. Write a Python function `def demo_input_checking()` to solve the following problem:
Demo using the Python API and a config that only has input r... | Demo using the Python API and a config that only has input rails. |
16,566 | import os.path
from nemoguardrails import LLMRails, RailsConfig
The provided code snippet includes necessary dependencies for implementing the `demo_output_checking` function. Write a Python function `def demo_output_checking()` to solve the following problem:
Demo using the Python API and a config that only has outpu... | Demo using the Python API and a config that only has output rails. |
16,567 | import asyncio
import logging
from typing import Optional
from langchain_core.language_models import BaseLLM
from langchain_core.runnables import RunnableConfig
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.context import streaming_handler_var
from nemogu... | Demo using the streaming of response chunks directly. |
16,568 | import asyncio
import logging
from typing import Optional
from langchain_core.language_models import BaseLLM
from langchain_core.runnables import RunnableConfig
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.context import streaming_handler_var
from nemogu... | Demo of using the streaming of chunks with the final response as well. |
16,569 | import asyncio
import logging
from typing import Optional
from langchain_core.language_models import BaseLLM
from langchain_core.runnables import RunnableConfig
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.context import streaming_handler_var
from nemogu... | Demo for streaming of response chunks directly with HuggingFacePipline deployed LLMs. |
16,570 | import asyncio
import logging
from typing import Optional
from langchain_core.language_models import BaseLLM
from langchain_core.runnables import RunnableConfig
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.context import streaming_handler_var
from nemogu... | Demo of using the streaming of chunks from custom actions. |
16,571 | from typing import Any, Callable, Coroutine
from langchain.llms.base import BaseLLM
from nemoguardrails import LLMRails, RailsConfig
COLANG_CONFIG = """
define user express greeting
"hi"
define user express ill intent
"I hate you"
"I want to destroy the world"
define bot express cannot respond
"I'm sorry I cann... | null |
16,572 | import os
from langchain.chains import LLMMathChain
from langchain.prompts import ChatPromptTemplate
from langchain_core.tools import Tool
from langchain_openai.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.integrations.langchain... | Basic setup with a prompt and a model. |
16,573 | import os
from langchain.chains import LLMMathChain
from langchain.prompts import ChatPromptTemplate
from langchain_core.tools import Tool
from langchain_openai.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.integrations.langchain... | Basic setup invoking LLM rails directly. |
16,574 | import os
from langchain.chains import LLMMathChain
from langchain.prompts import ChatPromptTemplate
from langchain_core.tools import Tool
from langchain_openai.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.integrations.langchain... | Basic setup combining the two above. Wraps the model with a rails configuration |
16,575 | import os
from langchain.chains import LLMMathChain
from langchain.prompts import ChatPromptTemplate
from langchain_core.tools import Tool
from langchain_openai.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.integrations.langchain... | Experiment with adding a tool as an action to a RunnableRails instance. This is essentially an Agent! An Agent is LangChain is a chain + an executor (AgentExecutor). - the chain is responsible for predicting the next step - the executor is responsible for invoking the tools if needed, and re-invoking the chain Since th... |
16,576 | import os
import subprocess
import traceback
EXAMPLES_FOLDER = os.path.join(os.path.dirname(os.path.dirname(__file__)), "examples")
The provided code snippet includes necessary dependencies for implementing the `create_chatter` function. Write a Python function `def create_chatter(name, configname, logger)` to solve t... | Create a NeMo Guardrails chatter specified with the configuration |
16,577 | import os
import subprocess
import traceback
The provided code snippet includes necessary dependencies for implementing the `close_chatter` function. Write a Python function `def close_chatter(chatter)` to solve the following problem:
Close the given chatter
Here is the function:
def close_chatter(chatter):
"""C... | Close the given chatter |
16,578 |
def are_strings_semantically_same(string1, string2):
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
vectorizer = TfidfVectorizer().fit_transform([string1, string2])
similarity = cosine_similarity(vectorizer)
# Determine if the si... | null |
16,579 | import os
import random
import time
import pandas as pd
from tqdm import tqdm
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.logging.stats import llm_stats
def build_run_configs():
run_configs = []
for test_config in TEST_CONFIGS:
config = RailsConfig.from_path(os.path.join(CONFIGS... | null |
16,580 | import logging
The provided code snippet includes necessary dependencies for implementing the `create_logger` function. Write a Python function `def create_logger(filename)` to solve the following problem:
Create a logger specified by the filename
Here is the function:
def create_logger(filename):
"""Create a lo... | Create a logger specified by the filename |
16,581 | import asyncio
import dataclasses
import json
import uuid
from collections import namedtuple
from datetime import datetime, timezone
from enum import Enum
from typing import Any, Dict, List, Tuple, Union
import yaml
Property = namedtuple("Property", ["name", "type"])
def _has_property(e: Dict[str, Any], p: Property) -... | null |
16,582 | import asyncio
import dataclasses
import json
import uuid
from collections import namedtuple
from datetime import datetime, timezone
from enum import Enum
from typing import Any, Dict, List, Tuple, Union
import yaml
_event_validators = [
Validator("Events need to provide 'type'", lambda e: "type" in e),
Validat... | Performs a basic event validation and returns True if the event conforms. |
16,583 | import asyncio
import dataclasses
import json
import uuid
from collections import namedtuple
from datetime import datetime, timezone
from enum import Enum
from typing import Any, Dict, List, Tuple, Union
import yaml
The provided code snippet includes necessary dependencies for implementing the `get_or_create_event_loo... | Helper to return the current asyncio loop. If one does not exist, it will be created. |
16,584 | import contextvars
from typing import List
from nemoguardrails.logging.explain import LLMCallInfo
from nemoguardrails.rails.llm.options import (
ActivatedRail,
ExecutedAction,
GenerationLog,
)
class ExecutedAction(BaseModel):
"""Information about an action that was executed."""
action_name: str = ... | Computes the GenerationLog based on the processing log. The processing log is a raw sequence of all the relevant events. The generation log is a more structured, curated, version of it. |
16,585 | from typing import List
import yaml
The provided code snippet includes necessary dependencies for implementing the `split_markdown_in_topic_chunks` function. Write a Python function `def split_markdown_in_topic_chunks( content: str, max_chunk_size: int = 400 ) -> List[dict]` to solve the following problem:
Splits ... | Splits a markdown content into topic chunks. This function takes a markdown content as input and divides it into topic chunks based on headings and subsections. Each chunk includes a title and body, with an optional maximum size. Parameters: - content (str): The markdown content to be split. - max_chunk_size (int): The... |
16,586 | import json
from typing import List
The provided code snippet includes necessary dependencies for implementing the `get_history_cache_key` function. Write a Python function `def get_history_cache_key(messages: List[dict]) -> str` to solve the following problem:
Compute the cache key for a sequence of messages. Args: m... | Compute the cache key for a sequence of messages. Args: messages: The list of messages. Returns: A unique string that can be used as a key for the provides sequence of messages. |
16,587 | import logging
import os
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import yaml
from pydantic import BaseModel, ValidationError, root_validator
from pydantic.fields import Field
from nemoguardrails.colang import parse_colang_file, parse_flow_elements
from nemoguardrails.colang.v2_x.lang.colang_ast ... | Load recursively all the imported path in the specified raw_config. Args: raw_config: The starting raw configuration (i.e., a dict) colang_files: The current set of colang files which will be extended as new configurations are loaded. |
16,588 | import logging
import os
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import yaml
from pydantic import BaseModel, ValidationError, root_validator
from pydantic.fields import Field
from nemoguardrails.colang import parse_colang_file, parse_flow_elements
from nemoguardrails.colang.v2_x.lang.colang_ast ... | Helper to join two rails configuration. |
16,589 | import logging
from typing import Optional
from langchain.chains import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import (
get_multiline_response,
... | Checks if the last bot response is a hallucination by checking multiple completions for self-consistency. :return: True if hallucination is detected, False otherwise. |
16,590 | import logging
from functools import lru_cache
import spacy
from nemoguardrails import RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.rails.llm.config import (
SensitiveDataDetection,
SensitiveDataDetectionOptions,
)
def _get_analyzer():
try:
from presidio_analyzer import ... | Checks whether the provided text contains any sensitive data. Args source: The source for the text, i.e. "input", "output", "retrieval". text: The text to check. config: The rails configuration object. Returns True if any sensitive data has been detected, False otherwise. |
16,591 | import logging
from functools import lru_cache
import spacy
from nemoguardrails import RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.rails.llm.config import (
SensitiveDataDetection,
SensitiveDataDetectionOptions,
)
def _get_analyzer():
try:
from presidio_analyzer import ... | Checks whether the provided text contains any sensitive data. Args source: The source for the text, i.e. "input", "output", "retrieval". text: The text to check. config: The rails configuration object. Returns The altered text, if applicable. |
16,592 | import json
import logging
import os
from typing import Optional
import aiohttp
from nemoguardrails.actions import action
from nemoguardrails.colang.v1_0.lang.utils import new_uuid
log = logging.getLogger(__name__)
def new_uuid() -> str:
"""Helper to generate new UUID v4.
In testing mode, it will generate a p... | null |
16,593 | import logging
from typing import Optional
from langchain.llms import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.library.factchecking.align_score.request import alignscore_request
from nemoguardrails.library.self_check.facts.actions import self_check_facts
from nemoguardrails.llm.taskmanager ... | Checks the facts for the bot response using an information alignment score. |
16,594 | import os
from functools import lru_cache
from typing import List
import nltk
import typer
import uvicorn
from alignscore import AlignScore
from fastapi import FastAPI
from pydantic import BaseModel
def hello_world():
welcome_str = (
f"This is a development server to host AlignScore models.\n"
+ f"... | null |
16,595 | import os
from functools import lru_cache
from typing import List
import nltk
import typer
import uvicorn
from alignscore import AlignScore
from fastapi import FastAPI
from pydantic import BaseModel
def get_model(model: str):
"""Initialize a model.
Args
model: The type of the model to be loaded, i.e. "b... | null |
16,596 | import os
from functools import lru_cache
from typing import List
import nltk
import typer
import uvicorn
from alignscore import AlignScore
from fastapi import FastAPI
from pydantic import BaseModel
def get_model(model: str):
class AlignScoreRequest(BaseModel):
def get_alignscore(model, evidence: str, claim: str) -> di... | null |
16,597 | import os
from functools import lru_cache
from typing import List
import nltk
import typer
import uvicorn
from alignscore import AlignScore
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
def get_model(model: str):
"""Initialize a model.
Args
model: The type of the model to be... | null |
16,598 | import logging
from typing import Optional
from nemoguardrails.actions import action
from nemoguardrails.library.jailbreak_detection.request import (
jailbreak_detection_heuristics_request,
)
from nemoguardrails.llm.taskmanager import LLMTaskManager
log = logging.getLogger(__name__)
async def jailbreak_detection_h... | Checks the user's prompt to determine if it is attempt to jailbreak the model. |
16,599 | import os
from typing import Optional
import typer
import uvicorn
from fastapi import FastAPI
from heuristics import checks
from pydantic import BaseModel
def hello_world():
welcome_str = (
"This is a development server for jailbreak detection.\n"
"Hit the /heuristics endpoint to run all heuristics... | null |
16,600 | import os
from typing import Optional
import typer
import uvicorn
from fastapi import FastAPI
from heuristics import checks
from pydantic import BaseModel
class JailbreakCheckRequest(BaseModel):
def lp_heuristic_check(request: JailbreakCheckRequest):
return checks.check_jailbreak_length_per_perplexity(
req... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.