id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
146,183 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('agent_executions', 'permission_id')
op.drop_table('agent_execution_permissions')
# ### end Alembic commands ### | null |
146,184 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('users', sa.Column('first_login_source', sa.String(), nullable=True))
# ### end Alembic commands ### | null |
146,185 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('users', 'first_login_source')
# ### end Alembic commands ### | null |
146,186 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('agent_schedule',
sa.Column('created_at', sa.DateTime(), nullable=True),
sa.Column('updated_at', sa.DateTime(), nullable=True),
sa.Column('id', sa.Inte... | null |
146,187 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index(op.f('ix_agent_schedule_agent_id'), table_name='agent_schedule')
op.drop_index(op.f('ix_agent_schedule_status'), table_name='agent_schedule')
op.drop_index... | null |
146,188 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
op.rename_table('agent_workflows', 'iteration_workflows')
op.rename_table('agent_workflow_steps', 'iteration_workflow_steps')
with op.batch_alter_table('iteration_workflow_steps') as bop:
bop.alter_column('agent_workflow_id', ne... | null |
146,189 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
op.rename_table('iteration_workflows', 'agent_workflows')
op.rename_table('iteration_workflow_steps', 'agent_workflow_steps')
op.drop_column('agent_executions', 'iteration_workflow_step_id')
op.drop_column('agent_workflows', 'has_t... | null |
146,190 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('resources', sa.Column('agent_id', sa.Integer(), nullable=True))
op.drop_column('resources', 'project_id')
# ### end Alembic commands ### | null |
146,191 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('resources', sa.Column('project_id', sa.INTEGER(), autoincrement=False, nullable=True))
op.drop_column('resources', 'agent_id')
# ### end Alembic commands ... | null |
146,192 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('agent_workflow_step_waits',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('name', sa.String(), nullable=True),
sa.Column('description', sa.... | null |
146,193 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('agent_workflow_step_waits')
# ### end Alembic commands ### | null |
146,194 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('models',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('model_name', sa.String(), nullable=False),
sa.Column('description', sa.String(), nu... | null |
146,195 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('models')
# ### end Alembic commands ### | null |
146,196 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
op.create_table('agent_templates',
sa.Column('created_at', sa.DateTime(), nullable=True),
sa.Column('updated_at', sa.DateTime(), nullable=True),
sa.Column('id', sa.Integer(), nullable=F... | null |
146,197 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
op.drop_table('agent_template_configs')
op.drop_table('agent_templates') | null |
146,198 | from alembic import op
import sqlalchemy as sa
from sqlalchemy.engine.reflection import Inspector
tables = inspector.get_table_names()
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
if 'agent_configurations' not in tables:
op.create_table('agent_configurations',
... | null |
146,199 | from alembic import op
import sqlalchemy as sa
from sqlalchemy.engine.reflection import Inspector
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('users')
op.drop_table('tools')
op.drop_table('tool_configs')
op.drop_table('projects')
op.drop_... | null |
146,200 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('models', sa.Column('context_length', sa.Integer(), nullable=True))
# ### end Alembic commands ### | null |
146,201 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('models', 'context_length')
# ### end Alembic commands ### | null |
146,202 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('agent_execution_feeds', sa.Column('error_message', sa.String(), nullable=True))
op.add_column('agent_executions', sa.Column('last_shown_error_id', sa.Integer(),... | null |
146,203 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('agent_executions', 'last_shown_error_id')
op.drop_column('agent_execution_feeds', 'error_message')
# ### end Alembic commands ### | null |
146,204 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('oauth_tokens',
sa.Column('created_at', sa.DateTime(), nullable=True),
sa.Column('updated_at', sa.DateTime(), nullable=True),
sa.Column('id', sa.Intege... | null |
146,205 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_index('ix_agt_agnt_workflow_id', 'agent_templates', ['agent_workflow_id'], unique=False)
op.create_index('ix_agt_agnt_organisation_id', 'agent_templates', ['organi... | null |
146,206 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('resources', sa.Column('agent_execution_id', sa.Integer(), nullable=True))
# ### end Alembic commands ### | null |
146,207 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('resources', 'agent_execution_id')
# ### end Alembic commands ### | null |
146,208 | from alembic import op
import sqlalchemy as sa
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('configurations',
sa.Column('created_at', sa.DateTime(), nullable=True),
sa.Column('updated_at', sa.DateTime(), nullable=True),
sa.Column('id', sa.Inte... | null |
146,209 | from alembic import op
import sqlalchemy as sa
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_index('ix_agents_agnt_template_id', 'agents', ['agent_template_id'], unique=False)
op.create_index('ix_at_name', 'agent_templates', ['name'], unique=False)
op.... | null |
146,210 | from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
from urllib.parse import urlparse
config = context.config
if config.config_file_name is not None:
fileConfig(config.config_file_name)
from superagi.models.base_model import DBBaseM... | Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. |
146,211 | from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
from urllib.parse import urlparse
config = context.config
if config.config_file_name is not None:
fileConfig(config.config_file_name)
from superagi.models.base_model import DBBaseM... | Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. |
146,212 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.exit(1) | null |
146,213 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def run_npm_commands():
os.chdir("gui")
try:
subprocess.run(["npm", "install"], check=True)
except subprocess.CalledProcessError:
logger.error(f"E... | null |
146,214 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
def run_server():
api_process = subprocess.Popen(["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"])
os.chdir("gui")
ui_process = subprocess.Popen(["npm", "run", "dev"])
os.chd... | null |
146,215 | import os
import sys
import subprocess
from time import sleep
import shutil
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def cleanup(api_process, ui_process):
logger.info("Shutting down processes...")
api_process.terminate()
ui_process.terminate()
logger.info("Processes terminat... | null |
146,216 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def check_command(command, message):
if not shutil.which(command):
logger.info(message)
sys.e... | null |
146,217 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def run_npm_commands(shell=False):
os.chdir("gui")
try:
subprocess.run(["npm", "install"], check=... | null |
146,218 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
def run_server(shell=False,a_name=None,a_description=None,goals=None):
tgwui_process = Process(target=subprocess.run, args=(["python", "tes... | null |
146,219 | import os
import sys
import subprocess
from time import sleep
import shutil
from sys import platform
from multiprocessing import Process
from superagi.lib.logger import logger
logger = Logger('Super AGI')
def cleanup(api_process, ui_process, celery_process):
logger.info("Shutting down processes...")
api_proce... | null |
146,220 | from langchain.vectorstores import FAISS
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.chains import LLMChain
from lang... | Create the context for the prompt from the subquestions and answers |
146,221 | import argparse
import logging
import os
import sys
from . import main
from .errors import ModelServerException
from .model import ModelTypes
class ModelTypes(Enum):
"""A enumerator of the supported model types."""
LLAMA = auto()
CODE_LLAMA = auto()
GPTNEXT = auto()
def family(self) -> str:
... | Parse the comamnd line arguments. |
146,222 | import argparse
import logging
import os
import sys
from . import main
from .errors import ModelServerException
from .model import ModelTypes
_LOG_FMT = f"[{os.getpid()}] %(asctime)15s [%(levelname)7s] - %(name)s - %(message)s"
_LOG_DATE_FMT = "%b %d %H:%M:%S"
_LOGGER = logging.getLogger("main")
The provided code snip... | Configure Python's logger according to the given verbosity level. :param verbosity: The desired verbosity level. Must be one of 0, 1, or 2. :type verbosity: typing.Literal[0, 1, 2] |
146,223 | import argparse
import logging
import os
import sys
from . import main
from .errors import ModelServerException
from .model import ModelTypes
TERMINATION_LOG = "/dev/termination-log"
_LOGGER = logging.getLogger("main")
def _k8s_error_handler(err: Exception) -> None:
"""When running in Kubernetes, write errors to th... | Catch and handle exceptions from the applicaiton. |
146,224 | import glob
import hashlib
import logging
import os
import pathlib
import subprocess
import typing
from enum import Enum, auto, unique
from .errors import ModelServerException
HASH_COMMAND = "sha1sum"
The provided code snippet includes necessary dependencies for implementing the `_fast_hash_dir` function. Write a Pyth... | Read the files in a directory and quickly create a hash. This hash IS NOT cryptographically secure, but it is designed to be computed as quickly as reasonably possible. This function will only hash top level files and will not traverse directories. |
146,225 | import logging
import os
from glob import glob
from tarfile import TarFile
from typing import IO, cast
import yaml
from nemo.export import TensorRTLLM
from ..errors import ModelServerException
from ..model import Model
from . import ConversionOptions
_LOGGER = logging.getLogger(__name__)
class ModelServerException(Ex... | Convert a .nemo formatted model. |
146,226 | import logging
import os
import subprocess
import sys
import typing
from ..errors import ModelServerException, UnsupportedFormatException
from ..model import Model
from . import ConversionOptions
_CONVERSION_SCRIPTS = "/opt/conversion_scripts/llama"
_CHECKPOINT_ARGS_FLAGS = {"PYTORCH": "--meta_ckpt_dir", "HUGGINGFACE":... | Convert a llama model. |
146,227 | import argparse
import json
import os
import time
from pathlib import Path
import tensorrt as trt
import tensorrt_llm
import torch
import torch.multiprocessing as mp
from tensorrt_llm._utils import str_dtype_to_trt
from tensorrt_llm.builder import Builder
from tensorrt_llm.layers.attention import PositionEmbeddingType
... | null |
146,228 | import argparse
import json
import os
import time
from pathlib import Path
import tensorrt as trt
import tensorrt_llm
import torch
import torch.multiprocessing as mp
from tensorrt_llm._utils import str_dtype_to_trt
from tensorrt_llm.builder import Builder
from tensorrt_llm.layers.attention import PositionEmbeddingType
... | null |
146,229 | import functools
import logging
import os
from typing import Any, Dict, List, Tuple, Union
import riva.client
import gradio as gr
from frontend import assets, chat_client, asr_utils, tts_utils
TITLE = "Converse"
_LOCAL_CSS = """
#contextbox {
overflow-y: scroll !important;
max-height: 400px;
}
"""
def _stream_p... | Build the gradio page to be mounted in the frame. |
146,230 | from pathlib import Path
from typing import List
import os
import gradio as gr
from frontend import assets, chat_client
TITLE = "Knowledge Base Management"
def upload_file(files: List[Path], client: chat_client.ChatClient) -> List[str]:
"""Use the client to upload a file to the knowledge base."""
try:
f... | Buiild the gradio page to be mounted in the frame. |
146,231 | import os
from opentelemetry import trace
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry... | null |
146,232 | import os
from opentelemetry import trace
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry... | null |
146,233 | import queue
from threading import Thread
import logging
import grpc
import pycountry
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_asr_pb2 as riva_asr
import riva.client.proto.riva_asr_pb2_grpc as rasr_srv
_LOGGER = logging.getLogger(__name__)
ASR_LANGS = dict()
grpc_auth = No... | null |
146,234 | import queue
from threading import Thread
import logging
import grpc
import pycountry
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_asr_pb2 as riva_asr
import riva.client.proto.riva_asr_pb2_grpc as rasr_srv
_LOGGER = logging.getLogger(__name__)
def start_recording(audio, langu... | null |
146,235 | import queue
from threading import Thread
import logging
import grpc
import pycountry
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_asr_pb2 as riva_asr
import riva.client.proto.riva_asr_pb2_grpc as rasr_srv
_LOGGER = logging.getLogger(__name__)
def stop_recording(asr_session):... | null |
146,236 | import queue
from threading import Thread
import logging
import grpc
import pycountry
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_asr_pb2 as riva_asr
import riva.client.proto.riva_asr_pb2_grpc as rasr_srv
_LOGGER = logging.getLogger(__name__)
ASR_LANGS = dict()
grpc_auth = No... | null |
146,237 | import argparse
import os
import sys
import uvicorn
The provided code snippet includes necessary dependencies for implementing the `parse_args` function. Write a Python function `def parse_args() -> argparse.Namespace` to solve the following problem:
Parse command-line arguments for the program. :returns: A namespace ... | Parse command-line arguments for the program. :returns: A namespace containing the parsed arguments. :rtype: argparse.Namespace |
146,238 | import json
import logging
import os
from dataclasses import _MISSING_TYPE, dataclass
from typing import Any, Callable, Dict, List, Optional, TextIO, Tuple, Union
import yaml
from dataclass_wizard import (
JSONWizard,
LoadMeta,
YAMLWizard,
errors,
fromdict,
json_field,
)
from dataclass_wizard.mo... | Create a data class field with the specified name in JSON format. :param name: The name of the field. :type name: str :param env: Whether this field should be configurable from an environment variable. :type env: bool :param help_txt: The description of this field that is used in help docs. :type help_txt: str :param *... |
146,239 | import json
import logging
import os
from dataclasses import _MISSING_TYPE, dataclass
from typing import Any, Callable, Dict, List, Optional, TextIO, Tuple, Union
import yaml
from dataclass_wizard import (
JSONWizard,
LoadMeta,
YAMLWizard,
errors,
fromdict,
json_field,
)
from dataclass_wizard.mo... | Read a file without knowing if it is JSON or YAML formatted. The file will first be assumed to be JSON formatted. If this fails, an attempt to parse the file with the YAML parser will be made. If both of these fail, an exception will be raised that contains the exception strings returned by both the parsers. :param str... |
146,240 | import json
import logging
import os
from dataclasses import _MISSING_TYPE, dataclass
from typing import Any, Callable, Dict, List, Optional, TextIO, Tuple, Union
import yaml
from dataclass_wizard import (
JSONWizard,
LoadMeta,
YAMLWizard,
errors,
fromdict,
json_field,
)
from dataclass_wizard.mo... | Try parsing the value as JSON and silently ignore errors. :param value: The value on which a JSON load should be attempted. :type value: str :returns: Either the parsed JSON or the provided value. :rtype: typing.Any |
146,241 | import json
import logging
import os
from dataclasses import _MISSING_TYPE, dataclass
from typing import Any, Callable, Dict, List, Optional, TextIO, Tuple, Union
import yaml
from dataclass_wizard import (
JSONWizard,
LoadMeta,
YAMLWizard,
errors,
fromdict,
json_field,
)
from dataclass_wizard.mo... | Update a dictionary with a new value at a given path. :param data: The dictionary to be updated. :type data: Dict[str, Any] :param path: The path to the key that should be updated. :type path: Tuple[str, ...] :param value: The new value to be set at the specified path. :type value: Any :param overwrite: If True, overwr... |
146,242 | import os
import time
import json
import logging
import pycountry
from pathlib import Path
from threading import Thread
from typing import TYPE_CHECKING, Any, List
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_tts_pb2 as riva_tts
_LOGGER = logging.getLogger(__name__)
TTS_MODELS... | null |
146,243 | import os
import time
import json
import logging
import pycountry
from pathlib import Path
from threading import Thread
from typing import TYPE_CHECKING, Any, List
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_tts_pb2 as riva_tts
TTS_MODELS = dict()
def update_voice_dropdown(l... | null |
146,244 | import os
import time
import json
import logging
import pycountry
from pathlib import Path
from threading import Thread
from typing import TYPE_CHECKING, Any, List
import gradio as gr
import numpy as np
import riva.client
import riva.client.proto.riva_tts_pb2 as riva_tts
_LOGGER = logging.getLogger(__name__)
tts_sample... | null |
146,245 | import os
import base64
import logging
from functools import lru_cache
from urllib.parse import urlparse
from typing import TYPE_CHECKING, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.chat_models import SimpleChatModel
from langchain.llms.base import LLM
from integ... | Set the global service context. |
146,246 | import os
import base64
import logging
from functools import lru_cache
from urllib.parse import urlparse
from typing import TYPE_CHECKING, List, Optional
logger = logging.getLogger(__name__)
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.chat_models import SimpleChatModel
from lang... | Create the vector db index for langchain. |
146,247 | import os
import base64
import logging
from functools import lru_cache
from urllib.parse import urlparse
from typing import TYPE_CHECKING, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.chat_models import SimpleChatModel
from langchain.llms.base import LLM
from integ... | Create the document retriever. |
146,248 | import os
import base64
import logging
from functools import lru_cache
from urllib.parse import urlparse
from typing import TYPE_CHECKING, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.chat_models import SimpleChatModel
from langchain.llms.base import LLM
from integ... | Check if a string is base64 encoded. |
146,249 | import os
import base64
import logging
from functools import lru_cache
from urllib.parse import urlparse
from typing import TYPE_CHECKING, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.chat_models import SimpleChatModel
from langchain.llms.base import LLM
from integ... | Return the token text splitter instance from langchain. |
146,250 | import os
from opentelemetry import trace, context
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from open... | null |
146,251 | import base64
import os
import shutil
import logging
from pathlib import Path
from typing import Any, Dict, List
import importlib
from inspect import getmembers, isclass
from fastapi import FastAPI, File, UploadFile, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, F... | Import the example class from the specified example file. The example directory is expected to have a python file where the example class is defined. |
146,252 | import base64
import os
import shutil
import logging
from pathlib import Path
from typing import Any, Dict, List
import importlib
from inspect import getmembers, isclass
from fastapi import FastAPI, File, UploadFile, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, F... | Upload a document to the vector store. |
146,253 | import base64
import os
import shutil
import logging
from pathlib import Path
from typing import Any, Dict, List
import importlib
from inspect import getmembers, isclass
from fastapi import FastAPI, File, UploadFile, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, F... | Generate and stream the response to the provided prompt. |
146,254 | import base64
import os
import shutil
import logging
from pathlib import Path
from typing import Any, Dict, List
import importlib
from inspect import getmembers, isclass
from fastapi import FastAPI, File, UploadFile, Request
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, F... | Search for the most relevant documents for the given search parameters. |
146,259 | import logging
import mrc
from pydantic import ValidationError
from vdb_upload.module.schema_transform import SchemaTransformLoaderFactory
from vdb_upload.schemas.rss_source_pipe_schema import RSSSourcePipeSchema
from morpheus.modules.general.monitor import MonitorLoaderFactory
from morpheus.modules.input.rss_source im... | Creates a pipeline for processing RSS feeds. This function sets up a pipeline that takes RSS feed data, scrapes web content based on the feed, and then outputs the scraped data. It integrates modules like RSS source, web scraper, and deserializer, along with monitoring for each stage. Parameters ---------- builder : mr... |
146,260 | import logging
import os
from functools import partial
import mrc
import mrc.core.operators as ops
import pandas as pd
from langchain.text_splitter import RecursiveCharacterTextSplitter
from pydantic import ValidationError
import cudf
from morpheus.messages import MessageMeta
from morpheus.utils.module_utils import Mod... | null |
146,261 | import logging
import mrc
from pydantic import ValidationError
from morpheus.modules.general.monitor import MonitorLoaderFactory
from morpheus.modules.preprocess.deserialize import DeserializeLoaderFactory
from morpheus.modules.input.rss_source import RSSSourceLoaderFactory
from morpheus.utils.module_utils import Modul... | Sets up a pipeline for processing kafka sources. This function configures a pipeline that subscribes to a kafka topic, processes received content based on specified configurations, and outputs the processed data. It integrates modules for kafka sourcing, content extraction, and schema transformation, along with monitor... |
146,262 | import logging
from typing import Any
from typing import Dict
from typing import Optional
import mrc
import mrc.core.operators as ops
from pydantic import BaseModel
from pydantic import Field
from pydantic import ValidationError
import cudf
from morpheus.messages import MessageMeta
from morpheus.utils.column_info impor... | A module for applying simple DataFrame schema transform policies. This module reads the configuration to determine how to set data types for columns, select, or rename them in the dataframe. Parameters ---------- builder : mrc.Builder The Morpheus pipeline builder object. Notes ------------- The configuration should be... |
146,263 | import logging
import mrc
from pydantic import ValidationError
from vdb_upload.module.schema_transform import SchemaTransformLoaderFactory
from vdb_upload.schemas.file_source_pipe_schema import FileSourcePipeSchema
from morpheus.modules.general.monitor import MonitorLoaderFactory
from morpheus.modules.input.multi_file_... | Sets up a pipeline for processing file sources. This function configures a pipeline that reads files, processes their content based on specified configurations, and outputs the processed data. It integrates modules for multi-file sourcing, file content extraction, and schema transformation, along with monitoring at var... |
146,264 | import logging
import mrc
from pydantic import BaseModel
from pydantic import ValidationError
from morpheus.messages import ControlMessage
from morpheus.utils.module_utils import ModuleLoaderFactory
from morpheus.utils.module_utils import register_module
logger = logging.getLogger(__name__)
class VDBResourceTaggingSche... | null |
146,265 | import logging
from functools import partial
import time
import os
import typing
from enum import Enum
from io import StringIO
import confluent_kafka as ck
import mrc
import pandas as pd
from pydantic import ValidationError
import cudf
from morpheus.messages import MessageMeta
from morpheus.utils.module_utils import Mo... | null |
146,266 | import logging
import os
from functools import partial
import mrc
import mrc.core.operators as ops
import requests
import requests_cache
from bs4 import BeautifulSoup
import lxml
from langchain.text_splitter import RecursiveCharacterTextSplitter
from pydantic import BaseModel
from pydantic import ValidationError
impor... | null |
146,267 | import io
import logging
import os
import typing
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from functools import wraps
from typing import Dict
from typing import List
import fitz
import fsspec
import mrc
import mrc.core.operators as ops
import pandas as pd
from docx import Docu... | null |
146,268 | import io
import logging
import os
import typing
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from functools import wraps
from typing import Dict
from typing import List
import fitz
import fsspec
import mrc
import mrc.core.operators as ops
import pandas as pd
from docx import Docu... | Extracts text from PDF and TXT files and constructs a DataFrame with the extracted content. This module processes a batch of files, reading their contents and extracting text data to form a DataFrame. It can handle both PDF and TXT files. The module uses a ThreadPoolExecutor for parallel file reading. Parameters ------... |
146,269 | import logging
import os
import click
from vdb_upload.vdb_utils import build_cli_configs
from vdb_upload.vdb_utils import build_final_config
from vdb_upload.vdb_utils import is_valid_service
def run():
pass | null |
146,270 | import logging
import os
import click
from vdb_upload.vdb_utils import build_cli_configs
from vdb_upload.vdb_utils import build_final_config
from vdb_upload.vdb_utils import is_valid_service
def build_cli_configs(source_type,
enable_cache,
embedding_size,
... | Configure and run the data processing pipeline based on the specified command-line options. This function initializes and runs the data processing pipeline using configurations provided via command-line options. It supports customization for various components of the pipeline such as source type, embedding model, and v... |
146,271 | import logging
import os
import click
from vdb_upload.vdb_utils import build_cli_configs
from vdb_upload.vdb_utils import build_final_config
from vdb_upload.vdb_utils import is_valid_service
def chain(model_name, save_cache):
with log_time(msg="Seeding with chain took {duration} ms. {rate_per_sec} docs/sec", log_f... | null |
146,272 | import logging
import os
import click
from vdb_upload.vdb_utils import build_cli_configs
from vdb_upload.vdb_utils import build_final_config
from vdb_upload.vdb_utils import is_valid_service
def build_triton_model(model_name, model_seq_length, max_batch_size, triton_repo, output_model_name):
if (output_model_name... | null |
146,273 | import logging
import time
import typing
from morpheus.config import Config
from morpheus.pipeline.pipeline import Pipeline
from morpheus.stages.general.monitor_stage import MonitorStage
from morpheus.stages.general.trigger_stage import TriggerStage
from morpheus.stages.inference.triton_inference_stage import TritonInf... | Sets up and runs a data processing pipeline based on provided configurations. Parameters ---------- source_config : Dict Configuration for data sources (e.g., 'rss', 'filesystem'). vdb_config : Dict Configuration for the vector database. pipeline_config : Dict General configuration for the pipeline (e.g., number of thr... |
146,274 | import logging
import typing
import pymilvus
import yaml
from morpheus.config import Config
from morpheus.config import PipelineModes
from morpheus.service.vdb.milvus_client import DATA_TYPE_MAP
The provided code snippet includes necessary dependencies for implementing the `is_valid_service` function. Write a Python f... | Validate the provided vector database service name. Checks if the given vector database service name is supported and valid. This is used as a callback function for a CLI option to ensure that the user inputs a supported service name. Parameters ---------- ctx : click.Context The context within which the command is bei... |
146,275 | import time
import json
import argparse
import os
from abc import ABC, abstractmethod
import jsonlines
from confluent_kafka.admin import AdminClient
from confluent_kafka.admin import NewTopic
from confluent_kafka import Producer
def load_jsonl(fpath):
jsonl_list = []
with jsonlines.open(fpath) as f:
fo... | null |
146,276 | import zipfile
import io
import argparse
import json
from pathlib import Path
import jsonlines
import fitz
The provided code snippet includes necessary dependencies for implementing the `extract_archive` function. Write a Python function `def extract_archive(archive_path, extract_path="pdf_dataset")` to solve the foll... | Extract zip archive. Parameters ---------- archive_path: pathlib.Path Path to archive. extract_path: pathlib.Path Path to extract archive. |
146,277 | import zipfile
import io
import argparse
import json
from pathlib import Path
import jsonlines
import fitz
The provided code snippet includes necessary dependencies for implementing the `extract_text` function. Write a Python function `def extract_text(pdf_stream)` to solve the following problem:
Use PyMuPDF to extrac... | Use PyMuPDF to extract text from a bytestream PDF. Parameters ---------- pdf_stream : io.BytesIO A bytestream PDF. Returns ------- str A string of extracted text. |
146,278 | import os
import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import UnstructuredFileLoader
from vectorstore.custom_powerpoint_parser import process_ppt_file
from vectorstore.custom_pdf_parser import get_pdf_documents
def load_documents(fol... | Generates word embeddings for documents and updates the Milvus collection. |
146,279 | import requests
import json
from langchain_nvidia_ai_endpoints import ChatNVIDIA
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain_community.llms import HuggingFacePipeline
class NvidiaLLM:
def __init__(self, model_name):
class LocalLLM:
def __init__(self, mode... | null |
146,280 | import random
import os
import base64
import datetime
import argparse
import pandas as pd
from PIL import Image
from io import BytesIO
import streamlit as st
import streamlit_analytics
from streamlit_feedback import streamlit_feedback
from bot_config.utils import get_config
from utils.memory import init_memory, get_sum... | null |
146,281 | from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="mixtral_8x7b")
def fact_check(evidence, query, response):
system_message = f"""Your task is to conduct a thorough fact-check ... | null |
146,282 | import streamlit as st
import os
def check_env_var():
if "NVIDIA_API_KEY" not in os.environ:
st.error("Please export your NVIDIA_API_KEY from the NVIDIA AI Playground to continue with LLMs/Embedding Models!", icon="🚨")
st.stop() | null |
146,283 | import gspread
import datetime
import streamlit as st
def add_row_to_sheet(values):
scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
gc = gspread.service_account(filename="service.json")
sh = gc.open_by_url("https://docs.google.com/spreadsheets/d/1R8sDCJ2jBSvEKh4awA... | null |
146,284 | from langchain.chains.conversation.memory import ConversationSummaryMemory
from langchain_core.prompts.prompt import PromptTemplate
def init_memory(llm, prompt_str):
SUMMARY_PROMPT = PromptTemplate(
input_variables=["summary", "new_lines"], template=prompt_str
)
memory = ConversationSummaryMemo... | null |
146,285 | from langchain.chains.conversation.memory import ConversationSummaryMemory
from langchain_core.prompts.prompt import PromptTemplate
def get_summary(memory):
return memory.buffer | null |
146,286 | from langchain.chains.conversation.memory import ConversationSummaryMemory
from langchain_core.prompts.prompt import PromptTemplate
def add_history_to_memory(memory, input_str, output_str):
# add message to memory
chat_memory = memory.chat_memory
chat_memory.add_user_message(input_str)
chat_memory.add... | null |
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