id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
39,524 | import argparse
import json
import os
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--source_file_path', type=str, default=None, help='the source json file path')
parser.add_argument('--target_dir_path', type=str, default=None, help='the target dir path')
parser.add_a... | null |
39,525 | import argparse
import json
import os
def convert_json_to_data(json_file, out_dir, test_sample_num, train_sample_num, all_sample_num=None):
with open(json_file, "r", encoding="utf-8") as rf, open(
os.path.join(out_dir, "qa_pair.csv"), "w", encoding="utf-8"
) as qa_pair_wf, open(os.path.join(out_dir, "q... | null |
39,526 | import json
def json_format_indent(json_file, output_json):
with open(output_json, "w", encoding="utf-8") as wf:
with open(json_file, "r", encoding="utf-8") as rf:
all_lines = []
for json_line in rf:
line_dict = json.loads(json_line)
all_lines.append(... | null |
39,527 | import argparse
import json
import multiprocessing
import os
import time
from tqdm import tqdm
from tqdm.contrib import tzip
from paddlenlp.metrics import BLEU
from paddlenlp.transformers import BasicTokenizer
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--true_file_path', t... | null |
39,528 | import argparse
import json
import multiprocessing
import os
import time
from tqdm import tqdm
from tqdm.contrib import tzip
from paddlenlp.metrics import BLEU
from paddlenlp.transformers import BasicTokenizer
def calc_bleu_n(preds, targets, n_size=4):
assert len(preds) == len(targets), (
"The length of pre... | null |
39,529 | import argparse
import json
from tqdm import tqdm
from paddlenlp import Taskflow
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--model_path', type=str, default=None, help='the model path to be loaded for question_generation taskflow')
parser.add_argument('--max_length', t... | null |
39,530 | import argparse
import json
from tqdm import tqdm
from paddlenlp import Taskflow
def create_fake_question(json_file, out_json, num_return_sequences, all_sample_num=None, batch_size=8):
with open(json_file, "r", encoding="utf-8") as rf, open(out_json, "w", encoding="utf-8") as wf:
all_lines = rf.readlines()... | null |
39,531 | import argparse
import json
import os
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--do_create_test_qq_pair", action='store_true', help="Whether to do create_test_qq_pair")
parser.add_argument('--qq_pair_source_ori_file_path', type=str, default=None, help='the original s... | null |
39,532 | import argparse
import json
import os
def extract_q_from_json_file(json_file, out_file=None, test_sample_num=None, query_answer_path=None):
with open(json_file, "r", encoding="utf-8") as rf:
if out_file:
wf = open(os.path.join(out_file), "w", encoding="utf-8")
if query_answer_path:
... | null |
39,533 | import argparse
import json
from tqdm import tqdm
from paddlenlp import Taskflow
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--model_path', type=str, default=None, help='the model path to be loaded for question_generation taskflow')
parser.add_argument('--source_file_pa... | null |
39,534 | import argparse
import json
from tqdm import tqdm
from paddlenlp import Taskflow
The provided code snippet includes necessary dependencies for implementing the `answer_generation_from_paragraphs` function. Write a Python function `def answer_generation_from_paragraphs(paragraphs, batch_size=16, model=None, wf=None)` t... | Generate answer from given paragraphs. |
39,535 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from data import (
convert_example,
create_dataloader,
read_simcse_text,
read_text_pair,
word_repetition,
)
from model import SimCSE
from scipy import stats
from paddlenlp.data import P... | null |
39,536 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from data import (
convert_example,
create_dataloader,
read_simcse_text,
read_text_pair,
word_repetition,
)
from model import SimCSE
from scipy import stats
from paddlenlp.data import P... | null |
39,537 | import argparse
import os
import numpy as np
import paddle
from paddle import inference
from tqdm import tqdm
from paddlenlp.data import Pad, Tuple
from paddlenlp.transformers import AutoTokenizer
The provided code snippet includes necessary dependencies for implementing the `convert_example` function. Write a Python ... | Builds model inputs from a sequence. A BERT sequence has the following format: - single sequence: ``[CLS] X [SEP]`` Args: example(obj:`list(str)`): The list of text to be converted to ids. tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` which conta... |
39,538 | import argparse
import os
import numpy as np
import paddle
from paddle import inference
from tqdm import tqdm
from paddlenlp.data import Pad, Tuple
from paddlenlp.transformers import AutoTokenizer
def read_text(file_path):
file = open(file_path)
id2corpus = {}
for idx, data in enumerate(file.readlines()):
... | null |
39,539 | import argparse
import os
from functools import partial
import paddle
from ann_util import build_index
from data import convert_example_test, create_dataloader, gen_id2corpus, gen_text_file
from model import SimCSE
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import MapDataset
from paddlenlp.transforme... | null |
39,540 | import argparse
from paddle_serving_server.web_service import Op, WebService
def convert_example(example, tokenizer, max_seq_length=512, pad_to_max_seq_len=False):
result = []
for text in example:
encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, pad_to_max_seq_len=pad_to_max_seq_len)
... | null |
39,541 | import argparse
from paddle_serving_server.web_service import WebService
def convert_example(example, tokenizer, max_seq_length=512, pad_to_max_seq_len=False):
result = []
for text in example:
encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, pad_to_max_seq_len=pad_to_max_seq_len)
... | null |
39,542 | import random
import numpy as np
import paddle
def gen_id2corpus(corpus_file):
id2corpus = {}
with open(corpus_file, "r", encoding="utf-8") as f:
for idx, line in enumerate(f):
id2corpus[idx] = line.rstrip()
return id2corpus | null |
39,543 | import random
import numpy as np
import paddle
def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
if trans_fn:
dataset = dataset.map(trans_fn)
shuffle = True if mode == "train" else False
if mode == "train":
batch_sampler = paddle.io.DistributedBat... | null |
39,544 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `convert_example` function. Write a Python function `def convert_example(example, tokenizer, max_seq_length=512, do_evalute=False)` to solve the following problem:
Builds model inputs from a se... | Builds model inputs from a sequence. A BERT sequence has the following format: - single sequence: ``[CLS] X [SEP]`` Args: example(obj:`list(str)`): The list of text to be converted to ids. tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` which conta... |
39,545 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `convert_example_test` function. Write a Python function `def convert_example_test(example, tokenizer, max_seq_length=512, pad_to_max_seq_len=False)` to solve the following problem:
Builds mode... | Builds model inputs from a sequence. A BERT sequence has the following format: - single sequence: ``[CLS] X [SEP]`` Args: example(obj:`list(str)`): The list of text to be converted to ids. tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` which conta... |
39,546 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `read_simcse_text` function. Write a Python function `def read_simcse_text(data_path)` to solve the following problem:
Reads data.
Here is the function:
def read_simcse_text(data_path):
"... | Reads data. |
39,547 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `read_text_pair` function. Write a Python function `def read_text_pair(data_path, is_test=False)` to solve the following problem:
Reads data.
Here is the function:
def read_text_pair(data_pat... | Reads data. |
39,548 | import random
import numpy as np
import paddle
def gen_text_file(similar_text_pair_file):
text2similar_text = {}
texts = []
with open(similar_text_pair_file, "r", encoding="utf-8") as f:
for line in f:
splited_line = line.rstrip().split("\t")
if len(splited_line) != 2:
... | null |
39,549 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `word_repetition` function. Write a Python function `def word_repetition(input_ids, token_type_ids, dup_rate=0.32)` to solve the following problem:
Word Repetition strategy.
Here is the functi... | Word Repetition strategy. |
39,550 | import argparse
import time
import numpy as np
from config import collection_name, embedding_name, partition_tag
from milvus_util import RecallByMilvus, VecToMilvus, text_max_len
from tqdm import tqdm
def read_text(file_path):
file = open(file_path)
id2corpus = []
for idx, data in enumerate(file.readlines()... | null |
39,551 | import argparse
import time
import numpy as np
from config import collection_name, embedding_name, partition_tag
from milvus_util import RecallByMilvus, VecToMilvus, text_max_len
from tqdm import tqdm
collection_name = "multi_label"
partition_tag = "partition_2"
embedding_name = "embeddings"
class RecallByMilvus:
... | null |
39,552 | import hnswlib
import numpy as np
from paddlenlp.utils.log import logger
logger = Logger()
def build_index(args, data_loader, model):
index = hnswlib.Index(space="ip", dim=args.output_emb_size if args.output_emb_size > 0 else 768)
# Initializing index
# max_elements - the maximum number of elements (cap... | null |
39,553 | import time
import numpy as np
import pandas as pd
from config import collection_name, embedding_name, partition_tag
from milvus_util import RecallByMilvus
from paddle_serving_server.pipeline import PipelineClient
def recall_result(list_data):
client = PipelineClient()
client.connect(["127.0.0.1:8080"])
fe... | null |
39,554 | import time
import numpy as np
import pandas as pd
from config import collection_name, embedding_name, partition_tag
from milvus_util import RecallByMilvus
from paddle_serving_server.pipeline import PipelineClient
collection_name = "multi_label"
partition_tag = "partition_2"
embedding_name = "embeddings"
class Recal... | null |
39,556 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from data import convert_example, create_dataloader, read_simcse_text, word_repetition
from model import SimCSE
from scipy import stats
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import ... | null |
39,557 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from data import convert_example, create_dataloader, read_simcse_text, word_repetition
from model import SimCSE
from scipy import stats
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import ... | null |
39,561 | from paddle_serving_server.web_service import Op, WebService
def convert_example(example, tokenizer, max_seq_length=512, pad_to_max_seq_len=False):
result = []
for text in example:
encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, pad_to_max_seq_len=pad_to_max_seq_len)
input_ids... | null |
39,567 | import random
import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `read_text_pair` function. Write a Python function `def read_text_pair(data_path, is_test=False)` to solve the following problem:
Reads data.
Here is the function:
def read_text_pair(data_pat... | Reads data. |
39,570 | import numpy as np
from milvus_util import VecToMilvus
from tqdm import tqdm
class VecToMilvus:
def __init__(self):
self.client = Milvus(host=MILVUS_HOST, port=MILVUS_PORT)
def has_collection(self, collection_name):
try:
status, ok = self.client.has_collection(collection_name)
... | null |
39,572 | import time
import numpy as np
import pandas as pd
from data import gen_id2corpus
from milvus_util import RecallByMilvus
from paddle_serving_server.pipeline import PipelineClient
def gen_id2corpus(corpus_file):
id2corpus = {}
with open(corpus_file, "r", encoding="utf-8") as f:
for idx, line in enumerat... | null |
39,574 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
def set_seed(seed):
paddle.seed(seed)
random.seed(seed)
np.random.seed(seed) | null |
39,575 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
class ASRError(Exception):
pass
The provided code snippet includes necessary dependencies for imp... | Mandarin ASR Args: audio_file (str): Audio file of Mandarin with sampling rate 16000. audio_format (str): The file extension of audio_file, 'wav' by default. Please refer to https://github.com/Baidu-AIP/speech-demo for more demos. |
39,576 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `evaluate` function. W... | Given a dataset, it evals model and computes the metric. Args: model(obj:`paddle.nn.Layer`): A model to classify texts. metric(obj:`paddle.metric.Metric`): The evaluation metric. data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches. |
39,577 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
def map_offset(ori_offset, offset_mapping):
"""
map ori offset to token offset
"""
for... | example: { title prompt content result_list } |
39,578 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `reader` function. Wri... | read json |
39,579 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
def add_negative_example(examples, texts, prompts, label_set, negative_ratio):
with tqdm(total=len... | null |
39,580 | import json
import math
import random
import time
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
import numpy as np
import paddle
from tqdm import tqdm
def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
if t... | null |
39,581 | import argparse
import os
import time
from functools import partial
import paddle
from utils import convert_example, create_dataloader, evaluate, reader, set_seed
from paddlenlp.datasets import load_dataset
from paddlenlp.metrics import SpanEvaluator
from paddlenlp.transformers import UIE, AutoTokenizer
def set_seed(s... | null |
39,582 | import os
import time
import argparse
import json
import numpy as np
from utils import set_seed, convert_ext_examples
def set_seed(seed):
def convert_ext_examples(
raw_examples,
negative_ratio,
prompt_prefix="情感倾向",
options=["正向", "负向"],
separator="##",
is_train=True,
... | null |
39,583 | import io
import os
import setuptools
with open("requirements.txt") as fin:
REQUIRED_PACKAGES = fin.read()
def read(*names, **kwargs):
with io.open(os.path.join(os.path.dirname(__file__), *names), encoding=kwargs.get("encoding", "utf8")) as fp:
return fp.read() | null |
39,584 | import json
import logging
import os
import shutil
import uuid
from pathlib import Path
from typing import List, Optional
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
from fastapi.responses import FileResponse
from pydantic import BaseModel
from rest_api.config import (
FILE_PARSE_P... | You can use this endpoint to upload a file for indexing |
39,585 | import json
import logging
import os
import shutil
import uuid
from pathlib import Path
from typing import List, Optional
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
from fastapi.responses import FileResponse
from pydantic import BaseModel
from rest_api.config import (
FILE_PARSE_P... | You can use this endpoint to upload a file for indexing |
39,586 | import json
import logging
import os
import shutil
import uuid
from pathlib import Path
from typing import List, Optional
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
from fastapi.responses import FileResponse
from pydantic import BaseModel
from rest_api.config import (
FILE_PARSE_P... | null |
39,587 | import json
import logging
import os
import shutil
import uuid
from pathlib import Path
from typing import List, Optional
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
from fastapi.responses import FileResponse
from pydantic import BaseModel
from rest_api.config import (
FILE_PARSE_P... | null |
39,588 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint can be used during startup to understand if the server is ready to take any requests, or is still loading. The recommended approach is to call this endpoint with a short timeout, like 500ms, and in case of no reply, consider the server busy. |
39,589 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | Get the running pipelines version. |
39,590 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,591 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,592 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,593 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,594 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,595 | import json
import logging
import shutil
import time
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional
from fastapi import APIRouter, File, Form, UploadFile
from numpy import ndarray
from pydantic import BaseConfig
from rest_api.config import (
CONCURRENT_REQUEST_PER_WORKER,
FILE... | This endpoint receives the question as a string and allows the requester to set additional parameters that will be passed on to the pipelines pipeline. |
39,596 | from typing import Dict, Union, Optional
import json
import logging
from fastapi import APIRouter
from pipelines.schema import Label
from rest_api.schema import FilterRequest, LabelSerialized, CreateLabelSerialized
from rest_api.controller.search import DOCUMENT_STORE
class LabelSerialized(Label, BaseModel):
docum... | This endpoint allows the API user to submit feedback on an answer for a particular query. For example, the user can send feedback on whether the answer was correct and whether the right snippet was identified as the answer. Information submitted through this endpoint is used to train the underlying QA model. |
39,597 | from typing import Dict, Union, Optional
import json
import logging
from fastapi import APIRouter
from pipelines.schema import Label
from rest_api.schema import FilterRequest, LabelSerialized, CreateLabelSerialized
from rest_api.controller.search import DOCUMENT_STORE
DOCUMENT_STORE = PIPELINE.get_document_store()
Th... | This endpoint allows the API user to retrieve all the feedback that has been submitted through the `POST /feedback` endpoint. |
39,598 | from typing import Dict, Union, Optional
import json
import logging
from fastapi import APIRouter
from pipelines.schema import Label
from rest_api.schema import FilterRequest, LabelSerialized, CreateLabelSerialized
from rest_api.controller.search import DOCUMENT_STORE
DOCUMENT_STORE = PIPELINE.get_document_store()
Th... | This endpoint allows the API user to delete all the feedback that has been sumbitted through the `POST /feedback` endpoint |
39,599 | from typing import Dict, Union, Optional
import json
import logging
from fastapi import APIRouter
from pipelines.schema import Label
from rest_api.schema import FilterRequest, LabelSerialized, CreateLabelSerialized
from rest_api.controller.search import DOCUMENT_STORE
class FilterRequest(BaseModel):
filters: Optio... | This endpoint returns basic accuracy metrics based on user feedback, e.g., the ratio of correct answers or correctly identified documents. You can filter the output by document or label. Example: `curl --location --request POST 'http://127.0.0.1:8000/eval-doc-qa-feedback' \ --header 'Content-Type: application/json' \ -... |
39,600 | from typing import Dict, Union, Optional
import json
import logging
from fastapi import APIRouter
from pipelines.schema import Label
from rest_api.schema import FilterRequest, LabelSerialized, CreateLabelSerialized
from rest_api.controller.search import DOCUMENT_STORE
logger = logging.getLogger(__name__)
DOCUMENT_STOR... | This endpoint returns JSON output in the SQuAD format for question/answer pairs that were marked as "relevant" by user feedback through the `POST /feedback` endpoint. The context_size param can be used to limit response size for large documents. |
39,601 | from typing import List
import logging
from fastapi import APIRouter
from rest_api.controller.search import DOCUMENT_STORE
from rest_api.config import LOG_LEVEL
from rest_api.schema import FilterRequest, DocumentSerialized
DOCUMENT_STORE = PIPELINE.get_document_store()
class FilterRequest(BaseModel):
filters: Opt... | This endpoint allows you to retrieve documents contained in your document store. You can filter the documents to delete by metadata (like the document's name), or provide an empty JSON object to clear the document store. Example of filters: `'{"filters": {{"name": ["some", "more"], "category": ["only_one"]}}'` To get a... |
39,602 | from typing import List
import logging
from fastapi import APIRouter
from rest_api.controller.search import DOCUMENT_STORE
from rest_api.config import LOG_LEVEL
from rest_api.schema import FilterRequest, DocumentSerialized
DOCUMENT_STORE = PIPELINE.get_document_store()
class FilterRequest(BaseModel):
filters: Opt... | This endpoint allows you to delete documents contained in your document store. You can filter the documents to delete by metadata (like the document's name), or provide an empty JSON object to clear the document store. Example of filters: `'{"filters": {{"name": ["some", "more"], "category": ["only_one"]}}'` To get all... |
39,603 | import logging
import sys
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.openapi.utils import get_openapi
from fastapi.routing import APIRoute
from starlette.middleware.cors import CORSMiddleware
from rest_api.config import ROOT_PATH
from rest_api.controller.errors.http_error import http_error_h... | Used to autogenerate OpenAPI specs file to use in the documentation. See `docs/_src/api/openapi/generate_openapi_specs.py` |
39,604 | import logging
import sys
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.openapi.utils import get_openapi
from fastapi.routing import APIRoute
from starlette.middleware.cors import CORSMiddleware
from rest_api.config import ROOT_PATH
from rest_api.controller.errors.http_error import http_error_h... | Simplify operation IDs so that generated API clients have simpler function names (see https://fastapi.tiangolo.com/advanced/path-operation-advanced-configuration/#using-the-path-operation-function-name-as-the-operationid). The operation IDs will be the same as the route names (i.e. the python method names of the endpoi... |
39,605 | import argparse
from pipelines import DocPipeline
from pipelines.nodes import DocOCRProcessor, DocPrompter
args = parser.parse_args()
def docprompt_pipeline():
use_gpu = True if args.device == "gpu" else False
preprocessor = DocOCRProcessor(use_gpu=use_gpu)
docprompter = DocPrompter(use_gpu=use_gpu, batc... | null |
39,606 | import argparse
import os
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import MultiModalRetriever
from pipelines.pipelines import Pipeline
from pipelines.utils import convert_files_to_dicts, fetch_archive_from_http
args = parser.parse_args()
def image_text_retrieval_tutorial():
fai... | null |
39,607 | import argparse
import os
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import MultiModalRetriever
from pipelines.pipelines import Pipeline
from pipelines.schema import Document
from pipelines.utils import fetch_archive_from_http
args = parser.parse_args()
def image_text_retrieval_tutor... | null |
39,608 | import argparse
import os
from pipelines.nodes import AnswerExtractor, QAFilter, QuestionGenerator
from pipelines.pipelines import QAGenerationPipeline
args = parser.parse_args()
def offline_qa_generation():
answer_extractor = AnswerExtractor(
model="uie-base-answer-extractor",
device=args.device,
... | null |
39,610 | import argparse
import os
from pprint import pprint
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import (
AnswerExtractor,
DensePassageRetriever,
ErnieRanker,
QAFilter,
QuestionGenerator,
)
from pipelines.pipelines import QAGenerationPipeline, SemanticSearchPipeline
... | null |
39,611 | import argparse
from pipelines import TextToImagePipeline
from pipelines.nodes import ErnieTextToImageGenerator
args = parser.parse_args()
def text_to_image():
erine_image_generator = ErnieTextToImageGenerator(ak=args.api_key, sk=args.secret_key)
pipe = TextToImagePipeline(erine_image_generator)
prediction... | null |
39,612 | import argparse
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
)
from pipelines.nodes import (
BM25Retriever,
DensePassageRetriever,
ErnieRanker,
JoinDocuments,
)
from pipelines.pipelines import Pipeline
from pipelines.utils import (
conv... | null |
39,613 | import argparse
import os
from pipelines.document_stores import FAISSDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieRanker
from pipelines.utils import (
convert_files_to_dicts,
fetch_archive_from_http,
print_documents,
)
args = parser.parse_args()
def get_faiss_re... | null |
39,614 | import logging
import os
from src.llm import Ernie_llm_list, llamaChatCompletion, llm_config
def completions_with_backoff(**kwargs):
chatter = kwargs["chatter"]
return chatter.create(
messages=kwargs["messages"], temperature=kwargs["temperature"], max_gen_len=kwargs["max_tokens"]
) | null |
39,615 | import os
import re
import pandas as pd
import sympy
from src.tot.prompts.game24 import (
cot_prompt,
propose_prompt,
standard_prompt,
value_last_step_prompt,
value_prompt,
)
from src.tot.tasks.base import DATA_PATH, Task
def get_current_numbers(y: str) -> str:
last_line = y.strip().split("\n")... | null |
39,616 | import re
import time
import erniebot
def contains_number(input_string):
# 检查字符串中是否存在中文 和 数字
return bool(re.search(r"\d", input_string)) | null |
39,617 | import re
import time
import erniebot
def contains_chinese(input_string):
return bool(re.search(r"[\u4e00-\u9fff]", input_string)) | null |
39,618 | import re
import time
import erniebot
def contains_english(input_string):
return bool(re.search(r"[a-zA-Z]", input_string)) | null |
39,619 | import re
import time
import erniebot
def contains_math_symbols(input_string):
# 这里我们对特殊字符进行了转义，因为它们在正则表达式中有特殊含义
return bool(re.search(r"[\+\-\*/]", input_string)) | null |
39,620 | import argparse
import json
import os
import time
from src.llm.llama import Ernie, Ernie_llm_list, llamaChatCompletion, llm_config
from src.tot.methods.bfs import naive_solve, solve
from src.tot.models import gpt_usage
from src.tot.tasks import get_task
def solve(args, task, idx, to_print=True, chatter=None):
glob... | null |
39,621 | import argparse
import json
import os
import time
from src.llm.llama import Ernie, Ernie_llm_list, llamaChatCompletion, llm_config
from src.tot.methods.bfs import naive_solve, solve
from src.tot.models import gpt_usage
from src.tot.tasks import get_task
llm_backend_choices = list(llm_config.keys())
def parse_args():
... | null |
39,622 | import argparse
from pipelines.agents import Agent, Tool
from pipelines.agents.base import ToolsManager
from pipelines.nodes import PromptNode, WebRetriever
from pipelines.nodes.prompt.prompt_template import PromptTemplate
from pipelines.pipelines import WebQAPipeline
few_shot_prompt = """
You are a helpful and knowled... | null |
39,623 | import argparse
import glob
import os
from pipelines.agents import Agent, Tool
from pipelines.agents.base import ToolsManager
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import (
CharacterTextSplitter,
DensePassageRetriever,
DocxToTextConverter,
FileTypeClassifier,
... | null |
39,624 | import argparse
import glob
import os
from pipelines.agents import Agent, Tool
from pipelines.agents.base import ToolsManager
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import (
CharacterTextSplitter,
DensePassageRetriever,
DocxToTextConverter,
FileTypeClassifier,
... | null |
39,625 | os
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieRanker
from pipelines.utils import (
convert_files_to_dicts,
fetch_archive_from_http,
print_documents,
)
args = parser.parse_args()
def dense_faq_pipeline():
use_gpu = True if args.devic... | null |
39,626 | os
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieRanker, ErnieReader
from pipelines.utils import (
convert_files_to_dicts,
fetch_archive_from_http,
print_answers,
)
args = parser.parse_args()
def dense_qa_pipeline():
use_gpu = True if ... | null |
39,627 | import argparse
import os
from pipelines.document_stores import FAISSDocumentStore, MilvusDocumentStore
from pipelines.nodes import (
DensePassageRetriever,
ErnieBot,
PromptTemplate,
TruncatedConversationHistory,
)
from pipelines.pipelines import Pipeline
from pipelines.utils import convert_files_to_dic... | null |
39,628 | import argparse
import glob
import time
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import (
CharacterTextSplitter,
DensePassageRetriever,
ErnieBot,
ErnieRanker,
MarkdownConverter,
PromptTemplate,
TruncatedConversationHistory,
)
from pipelines.nodes.file_con... | null |
39,629 | import argparse
import glob
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
FAISSDocumentStore,
)
from pipelines.nodes import (
ErnieBot,
ErnieRanker,
PromptTemplate,
TruncatedConversationHistory,
)
from pipelines.nodes.file_converter import TextConverter
from pipelines.... | null |
39,630 | import argparse
import glob
import time
from pipelines.document_stores import ElasticsearchDocumentStore
from pipelines.nodes import (
CharacterTextSplitter,
ChatGLMBot,
DensePassageRetriever,
ErnieBot,
ErnieRanker,
PDFToTextConverter,
PromptTemplate,
)
from pipelines.pipelines import Pipeli... | null |
39,631 | import argparse
import glob
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import (
CharacterTextSplitter,
DensePassageRetriever,
ErnieBot,
ErnieRanker,
PDFToTextConverter,
PromptTemplate,
)
from pipelines.pipelines import Pipeline
args = parser.parse_args()
def c... | null |
39,632 | import argparse
import glob
import time
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
)
from pipelines.nodes import (
BM25Retriever,
CharacterTextSplitter,
ChatGLMBot,
DensePassageRetriever,
EmbeddingRetriever,
ErnieBot,
ErnieRan... | null |
39,633 | import argparse
import glob
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieBot, ErnieRanker, PromptTemplate
from pipelines.nodes.file_converter.docx import DocxTotxtConverter
from pipelines.nodes.preprocessor.text_splitter import SpacyTextSplitter
from p... | null |
39,634 | import argparse
from pipelines import SentaPipeline
from pipelines.nodes import SentaProcessor, SentaVisualization, UIESenta
def format_print(results):
"""
Print Information in results.
"""
if "sr_save_path" in results:
print("\nText Result: ", results["sr_save_path"])
if "img_dict" in resul... | Sentiment Analysis with Pipeline. |
39,635 | import glob
import json
import logging
import os
import re
import shutil
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever
from collections import defaultdict
def preprocess(path):
"""
Preprocessing json file
"""
with open(path, mode="r", encoding... | Process json files to obtain text and table information |
39,636 | import glob
import json
import logging
import os
import re
import shutil
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever
from collections import defaultdict
The provided code snippet includes necessary dependencies for implementing the `create_index` function.... | Creating indexes |
39,637 | import argparse
import gradio as gr
from chat_table import parsing_QA
def reset_state():
return "", [] | null |
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