id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
39,638 | import argparse
import gradio as gr
from chat_table import parsing_QA
args = parser.parse_args()
def predict(query, history=[]):
result = parsing_QA(args.api_key, args.secret_key, query)
history.append(["user: {}".format(query), "assistant: {}".format(result)])
return "", history, history | null |
39,639 | import json
import time
from create_index import columns_titles, index_name
from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieBot, ErnieRanker
from pipelines.pipelines import Pipeline
all_titles = [value for key, value in columns_titles.items()]
def text_re... | FistrParsing query to obtain keywords, then,text retrieval, and table Q&A |
39,640 | import argparse
import logging
import os
from typing import Optional
import fitz
import requests
from pipelines.document_stores import BaiduElasticsearchDocumentStore
from pipelines.nodes import EmbeddingRetriever, ErnieRanker
from pipelines.pipelines import Pipeline
import time
from functools import partial
from multi... | null |
39,641 | import argparse
import logging
import os
from typing import Optional
import fitz
import requests
from pipelines.document_stores import BaiduElasticsearchDocumentStore
from pipelines.nodes import EmbeddingRetriever, ErnieRanker
from pipelines.pipelines import Pipeline
import time
from functools import partial
from multi... | Get gradio chatbot. |
39,642 | import argparse
import logging
import os
from typing import Optional
import fitz
import requests
from pipelines.document_stores import BaiduElasticsearchDocumentStore
from pipelines.nodes import EmbeddingRetriever, ErnieRanker
from pipelines.pipelines import Pipeline
import time
from functools import partial
from multi... | null |
39,643 | import argparse
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
MilvusDocumentStore,
)
from pipelines.nodes import DensePassageRetriever, EmbeddingRetriever, SpacyTextSplitter
from pipe... | null |
39,644 | import argparse
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
MilvusDocumentStore,
)
from pipelines.nodes import DensePassageRetriever, EmbeddingRetriever, SpacyTextSplitter
from pipe... | null |
39,645 | import argparse
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
MilvusDocumentStore,
)
from pipelines.nodes import DensePassageRetriever, EmbeddingRetriever, SpacyTextSplitter
from pipe... | null |
39,646 | import argparse
def get_parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--api_type", type=str, default="qianfan")
parser.add_argument("--api_key", type=str, default="", help="The API Key.")
parser.add_argument("--secret_key", type=str, default="", help="The secret key.")
parse... | null |
39,647 | import json
import logging
import os
import re
import time
import arxiv
import erniebot as eb
import gradio as gr
from utils import (
_apply_token,
get_parse_args,
merge_summary,
pdf2image,
retrieval,
summarize_abstract,
tackle_history,
translate_part,
)
args = get_parse_args()
PROMPT_RE... | Retrieve papers |
39,648 | import json
import logging
import os
import re
import time
import arxiv
import erniebot as eb
import gradio as gr
from utils import (
_apply_token,
get_parse_args,
merge_summary,
pdf2image,
retrieval,
summarize_abstract,
tackle_history,
translate_part,
)
args = get_parse_args()
PROMPT_SY... | Model inference. |
39,649 | import json
import logging
import os
import re
import time
import arxiv
import erniebot as eb
import gradio as gr
from utils import (
_apply_token,
get_parse_args,
merge_summary,
pdf2image,
retrieval,
summarize_abstract,
tackle_history,
translate_part,
)
logger = logging.getLogger(__name... | Upload the file to bos or retrieve the json_file of the paper |
39,650 | import json
import logging
import os
import re
import time
import arxiv
import erniebot as eb
import gradio as gr
from utils import (
_apply_token,
get_parse_args,
merge_summary,
pdf2image,
retrieval,
summarize_abstract,
tackle_history,
translate_part,
)
def add_messaget_chatbot(message... | null |
39,651 | import json
import logging
import os
import re
import time
import arxiv
import erniebot as eb
import gradio as gr
from utils import (
_apply_token,
get_parse_args,
merge_summary,
pdf2image,
retrieval,
summarize_abstract,
tackle_history,
translate_part,
)
args = get_parse_args()
def tran... | null |
39,652 | import argparse
from pipelines.document_stores import (
BaiduElasticsearchDocumentStore,
ElasticsearchDocumentStore,
)
from pipelines.nodes import (
BM25Retriever,
DensePassageRetriever,
EmbeddingRetriever,
ErnieRanker,
JoinDocuments,
)
from pipelines.pipelines import Pipeline
from pipelines... | null |
39,653 | import json
import erniebot
import gradio as gr
from prompt_utils import functions, get_parse_args
from pipelines.document_stores import BaiduElasticsearchDocumentStore
from pipelines.nodes import EmbeddingRetriever
from pipelines.pipelines import Pipeline
args = get_parse_args()
def prediction(history):
logs = []
... | null |
39,654 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_files, pipelines_is_ready, semantic_search, upload_doc
def set_state_if_absent(key, value):
if key not in st.session_sta... | null |
39,655 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_files, pipelines_is_ready, semantic_search, upload_doc
def on_change_text():
st.session_state.question = st.session_stat... | null |
39,656 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_files, pipelines_is_ready, semantic_search, upload_doc
def upload_doc(file):
url = f"{API_ENDPOINT}/{DOC_UPLOAD}"
fi... | null |
39,657 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Used to show the "pipelines is loading..." message |
39,658 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Get the pipelines version from the REST API |
39,659 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Get the pipelines files from the REST API # http://server_ip:server_port/files?file_name=8f6435d7ff1f1913dbcd74feb47e2fdb_0.png |
39,660 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Send a query to the REST API and parse the answer. Returns both a ready-to-use representation of the results and the raw JSON. |
39,661 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Send a query to the REST API and parse the answer. Returns both a ready-to-use representation of the results and the raw JSON. |
39,662 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Send a query to the REST API and parse the answer. Returns both a ready-to-use representation of the results and the raw JSON. |
39,663 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Send a prompt text and corresponding parameters to the REST API |
39,664 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | Send a feedback (label) to the REST API |
39,665 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | null |
39,666 | import json
import logging
import os
import socket
from time import sleep
from typing import Any, Dict, List, Optional, Tuple
import requests
import streamlit as st
from pipelines.document_stores import ElasticsearchDocumentStore, MilvusDocumentStore
from pipelines.nodes import DensePassageRetriever
from pipelines.util... | null |
39,667 | import argparse
import io
import os
from pathlib import Path
from typing import NamedTuple
import gradio as gr
from utils import ChatFile, upload_chatfile
def clear_session():
return "", None, None, None, None, [] | null |
39,668 | import argparse
import io
import os
from pathlib import Path
from typing import NamedTuple
import gradio as gr
from utils import ChatFile, upload_chatfile
def upload(data_files, chunk_size, separator, filters):
for index, data_file in enumerate(data_files):
if data_file.name not in loaded_path:
... | null |
39,669 | import argparse
from io import BytesIO
import gradio as gr
from PIL import Image, ImageFile
from utils import image_to_text_search
def pil_base64(image, img_format="JPEG"):
Image.MAX_IMAGE_PIXELS = 1000000000
ImageFile.LOAD_TRUNCATED_IMAGES = True
img_buffer = BytesIO()
image.save(img_buffer, format=img... | null |
39,670 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
multi_recall_semantic_search,
pipelines_files,
pipelines_is_ready,
upload_doc,
)
def set_state_if_absent(key, val... | null |
39,671 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
multi_recall_semantic_search,
pipelines_files,
pipelines_is_ready,
upload_doc,
)
def on_change_text():
st.ses... | null |
39,672 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
multi_recall_semantic_search,
pipelines_files,
pipelines_is_ready,
upload_doc,
)
def upload_doc(file):
url = ... | null |
39,673 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from annotated_text import annotation
from markdown import markdown
from ui.utils import get_backlink, pipelines_is_ready, query, upload_doc
def set_state_if_absent(key, value):
... | null |
39,674 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from annotated_text import annotation
from markdown import markdown
from ui.utils import get_backlink, pipelines_is_ready, query, upload_doc
def on_change_text():
st.session_stat... | null |
39,675 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from annotated_text import annotation
from markdown import markdown
from ui.utils import get_backlink, pipelines_is_ready, query, upload_doc
def upload_doc(file):
def upload():
... | null |
39,676 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_is_ready, semantic_search, upload_doc
def set_state_if_absent(key, value):
if key not in st.session_state:
st.se... | null |
39,677 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_is_ready, semantic_search, upload_doc
def on_change_text():
st.session_state.question = st.session_state.quest
st.se... | null |
39,678 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from utils import pipelines_is_ready, semantic_search, upload_doc
def upload_doc(file):
url = f"{API_ENDPOINT}/{DOC_UPLOAD}"
files = [("files", ... | null |
39,679 | import argparse
import os
import gradio as gr
import requests
with gr.Blocks() as demo:
file_path = gr.Textbox(visible=False)
gr.Markdown(value="# Sentiment Analysis Application\n----")
upload_file = gr.File(label="Select a file", interactive=True, elem_id="file-upload-box")
with gr.Row():
reset... | null |
39,680 | import argparse
import os
import gradio as gr
import requests
args = parser.parse_args()
with gr.Blocks() as demo:
file_path = gr.Textbox(visible=False)
gr.Markdown(value="# Sentiment Analysis Application\n----")
upload_file = gr.File(label="Select a file", interactive=True, elem_id="file-upload-box")
w... | null |
39,681 | import argparse
import os
import gradio as gr
import requests
with gr.Blocks() as demo:
file_path = gr.Textbox(visible=False)
gr.Markdown(value="# Sentiment Analysis Application\n----")
upload_file = gr.File(label="Select a file", interactive=True, elem_id="file-upload-box")
with gr.Row():
reset... | null |
39,682 | import argparse
import gradio as gr
from utils import text_to_image_search
def text_to_image_search(
query, resolution="1024*1024", top_k_images=5, style="探索无限"
) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
"""
Send a prompt text and corresponding parameters to the REST API
"""
url = f"{API_END... | null |
39,683 | import argparse
import base64
import traceback
from io import BytesIO
import cv2
import fitz
import gradio as gr
import numpy as np
import requests
from PIL import Image
def process_path(path):
error = None
if path:
try:
images_list = load_document(path)
return (
... | null |
39,684 | import argparse
import base64
import traceback
from io import BytesIO
import cv2
import fitz
import gradio as gr
import numpy as np
import requests
from PIL import Image
def np2base64(image_np):
image = cv2.imencode(".jpg", image_np)[1]
base64_str = str(base64.b64encode(image))[2:-1]
return base64_str | null |
39,685 | import argparse
import base64
import traceback
from io import BytesIO
import cv2
import fitz
import gradio as gr
import numpy as np
import requests
from PIL import Image
args = parser.parse_args()
def get_base64(path):
if path.startswith("http://") or path.startswith("https://"):
resp = requests.get(path, a... | null |
39,686 | import argparse
import base64
import traceback
from io import BytesIO
import cv2
import fitz
import gradio as gr
import numpy as np
import requests
from PIL import Image
The provided code snippet includes necessary dependencies for implementing the `read_content` function. Write a Python function `def read_content(fil... | read the content of target file |
39,687 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
file_upload_qa_generate,
offline_ann,
pipelines_is_ready,
semantic_search,
text_to_qa_pair_search,
)
def set_... | null |
39,688 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
file_upload_qa_generate,
offline_ann,
pipelines_is_ready,
semantic_search,
text_to_qa_pair_search,
)
def on_c... | null |
39,689 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
file_upload_qa_generate,
offline_ann,
pipelines_is_ready,
semantic_search,
text_to_qa_pair_search,
)
def on_c... | null |
39,690 | import logging
import os
import sys
from json import JSONDecodeError
from pathlib import Path
import pandas as pd
import streamlit as st
from markdown import markdown
from ui.utils import (
file_upload_qa_generate,
offline_ann,
pipelines_is_ready,
semantic_search,
text_to_qa_pair_search,
)
def file... | null |
39,691 | import argparse
import gradio as gr
from utils import image_text_search
def image_text_search(query, filters={}, top_k_retriever=5) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
"""
Send a query to the REST API and parse the answer.
Returns both a ready-to-use representation of the results and the raw JS... | null |
39,692 | import csv
import math
import time
from collections import defaultdict
from typing import Dict, List, cast
from datasets import load_dataset
from mteb.abstasks import AbsTaskRetrieval
from paddlenlp import Taskflow
def load_t2ranking_for_retraviel(num_max_passages: float):
collection_dataset = load_dataset("THUIR/... | null |
39,693 | import json
import logging
import typing
from datetime import datetime
from typing import Dict, Generator, List, Optional, Tuple, Union
from elasticsearch.helpers import scan
from tqdm.auto import tqdm
from pipelines.document_stores.filter_utils import LogicalFilterClause
from pipelines.nodes.preprocessor import PrePro... | Read Documents + Labels from a SQuAD-style file. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. :param filename: Path to file in SQuAD format :param max_docs: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available e... |
39,694 | import json
import logging
import typing
from datetime import datetime
from typing import Dict, Generator, List, Optional, Tuple, Union
from elasticsearch.helpers import scan
from tqdm.auto import tqdm
from pipelines.document_stores.filter_utils import LogicalFilterClause
from pipelines.nodes.preprocessor import PrePro... | Read Documents + Labels from a SQuAD-style file in jsonl format, i.e. one document per line. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. This is a generator which will yield one tuple per iteration containing a list of batch_size documents and a list with the documents' labe... |
39,695 | import json
import logging
import typing
from datetime import datetime
from typing import Dict, Generator, List, Optional, Tuple, Union
from elasticsearch.helpers import scan
from tqdm.auto import tqdm
from pipelines.document_stores.filter_utils import LogicalFilterClause
from pipelines.nodes.preprocessor import PrePro... | Converts a SQuAD-json-file into jsonl format with one document per line. :param squad_file: SQuAD-file in json format. :param output_file: Name of output file (SQuAD in jsonl format) |
39,696 | import json
import logging
import typing
from datetime import datetime
from typing import Dict, Generator, List, Optional, Tuple, Union
from elasticsearch.helpers import scan
from tqdm.auto import tqdm
from pipelines.document_stores.filter_utils import LogicalFilterClause
from pipelines.nodes.preprocessor import PrePro... | Converts a date to RFC3339 format, as Weaviate requires dates to be in RFC3339 format including the time and timezone. If the provided date string does not contain a time and/or timezone, we use 00:00 as default time and UTC as default time zone. This method cannot be part of WeaviateDocumentStore, as this would result... |
39,697 | import json
import logging
import typing
from datetime import datetime
from typing import Dict, Generator, List, Optional, Tuple, Union
from elasticsearch.helpers import scan
from tqdm.auto import tqdm
from pipelines.document_stores.filter_utils import LogicalFilterClause
from pipelines.nodes.preprocessor import PrePro... | This function provides brownfield support of existing Elasticsearch indexes by converting each of the records in the provided index to pipelines `Document` objects and writing them to the specified `DocumentStore`. It can be used on a regular basis in order to add new records of the Elasticsearch index to the `Document... |
39,698 | from abc import ABC, abstractmethod
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union
from sqlalchemy import and_, or_
from sqlalchemy.sql import select
from pipelines.document_stores import utils
The provided code snippet includes necessary dependencies for implementing the `ne... | Data structure that recursively adds a dictionary as value if a key does not exist. Advantage: In nested dictionary structures, we don't need to check if a key already exists (which can become hard to maintain in nested dictionaries with many levels) but access the existing value if a key exists and create an empty dic... |
39,699 | import collections
import logging
from abc import abstractmethod
from itertools import islice
from pathlib import Path
from typing import Dict, Generator, List, Optional, Set, Union
import numpy as np
from pipelines.document_stores.utils import (
eval_data_from_json,
eval_data_from_jsonl,
squad_json_to_json... | null |
39,700 | import collections
import logging
from abc import abstractmethod
from itertools import islice
from pathlib import Path
from typing import Dict, Generator, List, Optional, Set, Union
import numpy as np
from pipelines.document_stores.utils import (
eval_data_from_json,
eval_data_from_jsonl,
squad_json_to_json... | null |
39,701 | import collections
import logging
from abc import abstractmethod
from itertools import islice
from pathlib import Path
from typing import Dict, Generator, List, Optional, Set, Union
import numpy as np
from pipelines.document_stores.utils import (
eval_data_from_json,
eval_data_from_jsonl,
squad_json_to_json... | Batch elements of an iterable into fixed-length chunks or blocks. |
39,702 | from __future__ import annotations
import logging
import re
from collections.abc import Callable, Iterable
from hashlib import md5
from typing import Any, Dict, List, Optional, Tuple, Union
from events import Events
from pipelines import (
Answer,
BaseComponent,
BaseStandardPipeline,
Document,
Extra... | Print text with optional color. :param text: Text to print. :param end: End character to use (defaults to ""). :param color: Color to print text in (defaults to None). |
39,703 | import inspect
import logging
import re
import sys
import networkx as nx
from typing import Any, Dict, List, Optional
from networkx import DiGraph
from pipelines.pipelines.config import (
build_component_dependency_graph,
get_component_definitions,
get_pipeline_definition,
validate_config,
)
def camel_t... | Generates code to create a pipeline. :param pipeline_config: The pipeline config that specifies components and pipelines. :param pipeline_variable_name: The variable name of the pipeline to be generated. Defaults to "pipeline". :param pipeline_name: The name of the pipeline defined in pipeline_config to be generated. I... |
39,704 | import inspect
import logging
import re
import sys
import networkx as nx
from typing import Any, Dict, List, Optional
from networkx import DiGraph
from pipelines.pipelines.config import (
build_component_dependency_graph,
get_component_definitions,
get_pipeline_definition,
validate_config,
)
def _format... | null |
39,705 | import inspect
import logging
import re
import sys
import networkx as nx
from typing import Any, Dict, List, Optional
from networkx import DiGraph
from pipelines.pipelines.config import (
build_component_dependency_graph,
get_component_definitions,
get_pipeline_definition,
validate_config,
)
def _format... | null |
39,706 | import copy
import logging
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional
from networkx import DiGraph
import yaml
The provided code snippet includes necessary dependencies for implementing the `get_pipeline_definition` function. Write a Python function `def get_pipeline_defi... | Get the definition of Pipeline from a given pipeline config. If the config contains more than one Pipeline, then the pipeline_name must be supplied. :param pipeline_config: Dict Pipeline config parsed as a dictionary. :param pipeline_name: name of the Pipeline. |
39,707 | import copy
import logging
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional
from networkx import DiGraph
import yaml
def _overwrite_with_env_variables(component_definition: Dict[str, Any]):
"""
Overwrite the pipeline config with environment variables. For example, to cha... | Returns the definitions of all components from a given pipeline config. :param pipeline_config: Dict Pipeline config parsed as a dictionary. :param overwrite_with_env_variables: Overwrite the YAML configuration with environment variables. For example, to change index name param for an ElasticsearchDocumentStore, an env... |
39,708 | import copy
import logging
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional
from networkx import DiGraph
import yaml
def read_pipeline_config_from_yaml(path: Path):
with open(path, "r", encoding="utf-8") as stream:
return yaml.safe_load(stream) | null |
39,709 | import copy
import logging
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional
from networkx import DiGraph
import yaml
def _validate_user_input(input: str):
def validate_config(pipeline_config: Dict[str, Any]):
for component in pipeline_config["components"]:
_validate... | null |
39,710 | import copy
import logging
import os
from pathlib import Path
import re
from typing import Any, Dict, List, Optional
from networkx import DiGraph
import yaml
The provided code snippet includes necessary dependencies for implementing the `build_component_dependency_graph` function. Write a Python function `def build_co... | Builds a dependency graph between components. Dependencies are: - referenced components during component build time (e.g. init params) - predecessor components in the pipeline that produce the needed input This enables sorting the components in a working and meaningful order for instantiation using topological sorting.... |
39,711 | import ast
import json
import logging
from abc import ABC
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from uuid import uuid4
from pipelines.nodes.base import BaseComponent
from pipelines.nodes.prompt.shapers import AnswerParser, BaseOutputParser
from pipelines.schema import Document, MultiLabel... | null |
39,712 | import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
import requests
import sseclient
from pipelines.nodes.prompt.invocation_layer.base import PromptModelInvocationLayer
from pipelines.nodes.prompt.invocation_layer.handlers import (
DefaultTokenStreamingHandler,
TokenStreamingHa... | Make a request to the OpenAI API given a `url`, `headers`, `payload`, and `timeout`. :param url: The URL of the OpenAI API. :param headers: Dictionary of HTTP Headers to send with the :class:`Request`. :param payload: The payload to send with the request. :param timeout: The timeout length of the request. The default i... |
39,713 | import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
import requests
import sseclient
from pipelines.nodes.prompt.invocation_layer.base import PromptModelInvocationLayer
from pipelines.nodes.prompt.invocation_layer.handlers import (
DefaultTokenStreamingHandler,
TokenStreamingHa... | Check the `finish_reason` the answers returned by OpenAI completions endpoint. If the `finish_reason` is `length` or `content_filter`, log a warning to the user. :param result: The result returned from the OpenAI API. :param payload: The payload sent to the OpenAI API. |
39,714 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def format_document(
document: Document,
... | null |
39,715 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
The provided code snippet includes necessary dep... | An identity function. You can use it to rename values in the invocation context without changing them. Example: ```python assert rename(1) == 1 ``` |
39,716 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def string_to_answer(
string: str,
prompt... | Transforms a list of strings into a list of answers. Specify `reference_pattern` to populate the answer's `document_ids` by extracting document references from the strings. :param strings: The list of strings to transform. :param prompts: The prompts used to generate the answers. :param documents: The documents used to... |
39,717 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
The provided code snippet includes necessary dep... | Transforms a list of documents with scores in their metadata into a list containing a single document. The resulting document contains the scores and the contents of all the original documents. All metadata is dropped. Example: ```python assert join_documents_and_scores( documents=[ Document(content="first", meta={"sco... |
39,718 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def format_answer(
answer: Answer,
patter... | Extracts the content field of answers and returns a list of strings. Example: ```python assert answers_to_strings( answers=[ Answer(answer="first"), Answer(answer="second"), Answer(answer="third") ], pattern="[$idx] $answer", str_replace={"r": "R"} ) == ["[1] fiRst", "[2] second", "[3] thiRd"] ``` |
39,719 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def format_document(
document: Document,
... | Extracts the content field of documents and returns a list of strings. Use regext in the `pattern` parameter to control how the documents are represented. Example: ```python assert documents_to_strings( documents=[ Document(content="first"), Document(content="second"), Document(content="third") ], pattern="[$idx] $cont... |
39,720 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
The provided code snippet includes necessary dep... | Transforms a list of strings into a list of documents. If you pass the metadata in a single dictionary, all documents get the same metadata. If you pass the metadata as a list, the length of this list must be the same as the length of the list of strings, and each document gets its own metadata. You can specify `id_has... |
39,721 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def join_documents_to_string(
documents: List... | Transforms a list of documents into a list containing a single document. The content of this document is the joined result of all original documents, separated by the delimiter you specify. Use regex in the `pattern` parameter to control how each document is represented. You can use the following placeholders: - $conte... |
39,722 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
def format_string(string: str, str_replace: Optio... | Transforms a list of strings into a single string. The content of this string is the content of all of the original strings separated by the delimiter you specify. Example: ```python assert join_strings(strings=["first", "second", "third"], delimiter=" - ", str_replace={"r": "R"}) == "fiRst - second - thiRd" ``` |
39,723 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
The provided code snippet includes necessary dep... | Joins the lists you pass to it into a single list. Example: ```python assert join_lists(lists=[[1, 2, 3], [4, 5]]) == [1, 2, 3, 4, 5] ``` |
39,724 | import inspect
import logging
import re
from functools import reduce
from string import Template
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pipelines.nodes.base import BaseComponent
from pipelines.schema import Answer, Document, MultiLabel
The provided code snippet includes necessary dep... | Transforms a value into a list containing this value as many times as the length of the target list. Example: ```python assert value_to_list(value=1, target_list=list(range(5))) == [1, 1, 1, 1, 1] ``` |
39,725 | import logging
from typing import Any, Dict, List, Optional, Tuple, Type, Union, overload
from pipelines.nodes.base import BaseComponent
from pipelines.nodes.prompt.invocation_layer import PromptModelInvocationLayer
from pipelines.schema import Document, MultiLabel
def instruction_following_models() -> List[str]:
... | null |
39,726 | import functools
import logging
import multiprocessing
import os
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional
import pypdf
from pipelines.nodes.file_converter import BaseConverter, ImageToTextConverter
def extract_pages(page_list, file_path):
def run_process(pages, file_path, p... | null |
39,727 | from typing import Any, List, Optional
The provided code snippet includes necessary dependencies for implementing the `calculate_ranking_scores` function. Write a Python function `def calculate_ranking_scores(list_items: List[Any], boost_first_factor: Optional[int] = None) -> List[float]` to solve the following proble... | Assigns scores to items in a list based on their rank position and ensures that the scores add up to 1. :param list_items: The list of items to score. :param boost_first_factor: The factor to boost the score of the first item by. |
39,728 | import logging
import multiprocessing
import time
from copy import deepcopy
from functools import partial
from multiprocessing import Pool
from typing import Dict, List, Optional, Union
import numpy as np
from tqdm.auto import tqdm
from tritonclient.http import InferenceServerClient, InferInput, InferRequestedOutput
fr... | null |
39,729 | import logging
import multiprocessing
import time
from copy import deepcopy
from functools import partial
from multiprocessing import Pool
from typing import Dict, List, Optional, Union
import numpy as np
from tqdm.auto import tqdm
from tritonclient.http import InferenceServerClient, InferInput, InferRequestedOutput
fr... | null |
39,730 | import logging
from typing import Any, Callable, List, Optional, Protocol, Tuple
from pipelines.nodes.combine_documents.base import BaseCombineDocuments
def _split_list_of_docs(docs: List[dict], length_func: Callable, token_max: int, **kwargs: Any) -> List[List[dict]]:
new_result_doc_list = []
_sub_result_docs... | null |
39,731 | import logging
from typing import Any, Callable, List, Optional, Protocol, Tuple
from pipelines.nodes.combine_documents.base import BaseCombineDocuments
logger = logging.getLogger(__name__)
class CombineDocsProtocol(Protocol):
def __call__(self, docs: List[dict], **kwargs: Any) -> str:
def _collapse_docs(
doc... | null |
39,732 | import logging
from typing import Any, List, Optional, Tuple
from pipelines.nodes import ErnieBot
from pipelines.nodes.combine_documents.base import BaseCombineDocuments
def format_document(doc: dict, prompt: str) -> str:
return prompt.format(**doc) | null |
39,733 | from __future__ import annotations
import typing
from dataclasses import asdict
from typing import Any, Dict, List, Optional, Union
import ast
import json
import logging
import time
from pathlib import Path
from uuid import uuid4
import mmh3
import numpy as np
import pandas as pd
from pydantic import BaseConfig
from py... | Constructs a pydantic dataclass from a dict incl. other nested dataclasses. This allows simple de-serialization of pydentic dataclasses from json. :param dict: Dict containing all attributes and values for the dataclass. :param pydantic_dataclass_type: The class of the dataclass that should be constructed (e.g. Documen... |
39,734 | from typing import Union, Optional, List
import logging
import numpy as np
def offset_to_token_idx_vecorized(token_offsets, ch_idx):
"""Returns the idx of the token at the given character idx"""
# case ch_idx is at end of tokens
if ch_idx >= np.max(token_offsets):
# TODO check "+ 1" (it is needed fo... | TODO Write Comment |
39,735 | from typing import Union, Optional, List
import logging
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_passage_offsets` function. Write a Python function `def get_passage_offsets(doc_offsets, doc_stride, passage_len_t, doc_text)` to solve the following problem:
G... | Get spans (start and end offsets) for passages by applying a sliding window function. The sliding window moves in steps of doc_stride. Returns a list of dictionaries which each describe the start, end and id of a passage that is formed when chunking a document using a sliding window approach. |
39,736 | from typing import Union, Optional, List
import logging
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `offset_to_token_idx` function. Write a Python function `def offset_to_token_idx(token_offsets, ch_idx) -> Optional[int]` to solve the following problem:
Returns the... | Returns the idx of the token at the given character idx |
39,737 | import json
import logging
import os
import random
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from pipelines.data_handler.dataset import convert_features_to_dataset
from pipelines.data_handler.samples import (
Sample,
SampleBask... | null |
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