code stringlengths 141 97.3k | apis listlengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
from unittest.mock import MagicMock, patch
import pytest
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.response_synthesizers.google.generativeai import (
GoogleTextSynthesizer,
set_google_config,
)
from llama_in... | [
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config",
"llama_index.legacy.response_synthesizers.google.generativeai.GoogleTextSynthesizer.from_defaults",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.response_synthesizers.google.generativeai.set_google_config"
] | [((663, 722), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (681, 722), False, 'import pytest\n'), ((724, 768), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.deepeval_callback import deepeval_callback_handler
from llama_index.callbacks.honeyhi... | [
"llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.deepeval_callback.deepeval_callback_handler",
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.arize_phoenix_ca... | [((1068, 1103), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1088, 1103), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1161, 1204), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler'... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.argilla_callback import argilla_callback_handler
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.deepeval_... | [
"llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.deepeval_callback.deepeval_callback_handler",
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.arize_phoenix_ca... | [((1144, 1179), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1164, 1179), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1237, 1280), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler'... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.schema import BaseNode, MetadataMode, TextNode
from llama_index.vector_stores.types import (
... | [
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((534, 553), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (543, 553), False, 'from logging import getLogger\n'), ((2379, 2432), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2411, 2432), False, 'import elasticsearch\n'), ((3755, 3... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.schema import BaseNode, MetadataMode, TextNode
from llama_index.vector_stores.types import (
... | [
"llama_index.schema.TextNode",
"llama_index.vector_stores.utils.metadata_dict_to_node",
"llama_index.vector_stores.utils.node_to_metadata_dict"
] | [((534, 553), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (543, 553), False, 'from logging import getLogger\n'), ((2379, 2432), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2411, 2432), False, 'import elasticsearch\n'), ((3755, 3... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.honeyhive_callback import honeyhive_callback_handler
from llama_index.callbacks.open_... | [
"llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.honeyh... | [((990, 1025), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1010, 1025), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1083, 1126), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler',... |
"""Global eval handlers."""
from typing import Any
from llama_index.callbacks.arize_phoenix_callback import arize_phoenix_callback_handler
from llama_index.callbacks.base_handler import BaseCallbackHandler
from llama_index.callbacks.honeyhive_callback import honeyhive_callback_handler
from llama_index.callbacks.open_... | [
"llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler",
"llama_index.callbacks.simple_llm_handler.SimpleLLMHandler",
"llama_index.callbacks.wandb_callback.WandbCallbackHandler",
"llama_index.callbacks.arize_phoenix_callback.arize_phoenix_callback_handler",
"llama_index.callbacks.honeyh... | [((990, 1025), 'llama_index.callbacks.wandb_callback.WandbCallbackHandler', 'WandbCallbackHandler', ([], {}), '(**eval_params)\n', (1010, 1025), False, 'from llama_index.callbacks.wandb_callback import WandbCallbackHandler\n'), ((1083, 1126), 'llama_index.callbacks.open_inference_callback.OpenInferenceCallbackHandler',... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.indices.query.schema.QueryBundle",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.legacy.core.response.schema.Response"
] | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((2809, 2842), 'llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2840, 2842), True,... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.indices.query.schema.QueryBundle",
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.legacy.core.response.schema.Response"
] | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((2809, 2842), 'llama_index.legacy.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2840, 2842), True,... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.legacy.bridge.pydantic import PrivateAttr
from llama_index.legacy.schema import BaseNode, Met... | [
"llama_index.legacy.vector_stores.utils.metadata_dict_to_node",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.vector_stores.utils.node_to_metadata_dict"
] | [((640, 659), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (649, 659), False, 'from logging import getLogger\n'), ((2343, 2396), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2375, 2396), False, 'import elasticsearch\n'), ((3719, 3... |
"""Elasticsearch vector store."""
import asyncio
import uuid
from logging import getLogger
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
import nest_asyncio
import numpy as np
from llama_index.legacy.bridge.pydantic import PrivateAttr
from llama_index.legacy.schema import BaseNode, Met... | [
"llama_index.legacy.vector_stores.utils.metadata_dict_to_node",
"llama_index.legacy.bridge.pydantic.PrivateAttr",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.vector_stores.utils.node_to_metadata_dict"
] | [((640, 659), 'logging.getLogger', 'getLogger', (['__name__'], {}), '(__name__)\n', (649, 659), False, 'from logging import getLogger\n'), ((2343, 2396), 'elasticsearch.AsyncElasticsearch', 'elasticsearch.AsyncElasticsearch', ([], {}), '(**connection_params)\n', (2375, 2396), False, 'import elasticsearch\n'), ((3719, 3... |
"""Google Generative AI Vector Store.
The GenAI Semantic Retriever API is a managed end-to-end service that allows
developers to create a corpus of documents to perform semantic search on
related passages given a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import logging
i... | [
"llama_index.vector_stores.google.genai_extension.delete_document",
"llama_index.vector_stores.google.genai_extension.Config",
"llama_index.vector_stores.google.genai_extension.get_corpus",
"llama_index.vector_stores.google.genai_extension.EntityName.from_str",
"llama_index.vector_stores.google.genai_extens... | [((812, 839), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (829, 839), False, 'import logging\n'), ((2859, 2881), 'llama_index.vector_stores.google.genai_extension.Config', 'genaix.Config', ([], {}), '(**attrs)\n', (2872, 2881), True, 'import llama_index.vector_stores.google.genai_exten... |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | [
"llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service",
"llama_index.core.response.schema.Response",
"llama_index.schema.TextNode",
"llama_index.indices.query.schema.QueryBundle"
] | [((1051, 1078), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1068, 1078), False, 'import logging\n'), ((2739, 2772), 'llama_index.vector_stores.google.generativeai.genai_extension.build_generative_service', 'genaix.build_generative_service', ([], {}), '()\n', (2770, 2772), True, 'impor... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)
| [
"llama_index.set_global_handler"
] | [((217, 257), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (247, 257), False, 'import llama_index\n'), ((265, 292), 'private_gpt.launcher.create_app', 'create_app', (['global_injector'], {}), '(global_injector)\n', (275, 292), False, 'from private_gpt.launc... |
"""
Astra DB Vector store index.
An index based on a DB table with vector search capabilities,
powered by the astrapy library
"""
import json
import logging
from typing import Any, Dict, List, Optional, cast
from warnings import warn
import llama_index.core
from llama_index.core.bridge.pydantic import PrivateAttr
f... | [
"llama_index.core.indices.query.embedding_utils.get_top_k_mmr_embeddings",
"llama_index.core.bridge.pydantic.PrivateAttr",
"llama_index.core.vector_stores.utils.node_to_metadata_dict",
"llama_index.core.vector_stores.utils.metadata_dict_to_node",
"llama_index.core.vector_stores.types.VectorStoreQueryResult"... | [((852, 879), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (869, 879), False, 'import logging\n'), ((2070, 2083), 'llama_index.core.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (2081, 2083), False, 'from llama_index.core.bridge.pydantic import PrivateAttr\n'), ((2118, ... |
from unittest.mock import MagicMock, patch
import pytest
from llama_index.legacy.core.response.schema import Response
from llama_index.legacy.schema import Document
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.indices.... | [
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config",
"llama_index.legacy.indices.managed.google.generativeai.set_google_config",
"llama_index.legacy.schema.Document",
"llama_index.legacy.indices.managed.google.generativeai.GoogleIndex.from_corpus",
"llama_index.legacy.indices.m... | [((693, 752), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (711, 752), False, 'import pytest\n'), ((754, 798), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
from unittest.mock import MagicMock, patch
import pytest
try:
import google.ai.generativelanguage as genai
has_google = True
except ImportError:
has_google = False
from llama_index.legacy.response_synthesizers.google.generativeai import (
GoogleTextSynthesizer,
set_google_config,
)
from llama_in... | [
"llama_index.legacy.vector_stores.google.generativeai.genai_extension.get_config",
"llama_index.legacy.response_synthesizers.google.generativeai.GoogleTextSynthesizer.from_defaults",
"llama_index.legacy.schema.TextNode",
"llama_index.legacy.response_synthesizers.google.generativeai.set_google_config"
] | [((663, 722), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(not has_google)'], {'reason': 'SKIP_TEST_REASON'}), '(not has_google, reason=SKIP_TEST_REASON)\n', (681, 722), False, 'import pytest\n'), ((724, 768), 'unittest.mock.patch', 'patch', (['"""google.auth.credentials.Credentials"""'], {}), "('google.auth.credent... |
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.callbacks.schema import CBEventType, EventPayload
from llama_index.core.indices.composability.graph import Compos... | [
"llama_index.core.settings.callback_manager_from_settings_or_context",
"llama_index.core.instrumentation.get_dispatcher",
"llama_index.core.schema.NodeWithScore"
] | [((585, 620), 'llama_index.core.instrumentation.get_dispatcher', 'instrument.get_dispatcher', (['__name__'], {}), '(__name__)\n', (610, 620), True, 'import llama_index.core.instrumentation as instrument\n'), ((1649, 1734), 'llama_index.core.settings.callback_manager_from_settings_or_context', 'callback_manager_from_set... |
from llama_index import (
SimpleDirectoryReader,
VectorStoreIndex,
ServiceContext,
)
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt
import llama_index.llms.llama_cpp
from langchain.embeddings import HuggingFaceEmbeddings
import co... | [
"llama_index.VectorStoreIndex.from_documents",
"llama_index.ServiceContext.from_defaults",
"llama_index.SimpleDirectoryReader"
] | [((431, 491), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': 'config.EMBEDDING_MODEL_URL'}), '(model_name=config.EMBEDDING_MODEL_URL)\n', (452, 491), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((538, 600), 'llama_index.ServiceContext.from_defaults', '... |
import time
import llama_index
from atlassian import Bitbucket
import os
import sys
sys.path.append('../')
import local_secrets as secrets
start_time = time.time()
stash = Bitbucket('https://git.techstyle.net', token=secrets.stash_token)
os.environ['OPENAI_API_KEY'] = secrets.techstyle_openai_key
project ='DATASICENCE... | [
"llama_index.GPTSimpleVectorIndex",
"llama_index.Document"
] | [((84, 106), 'sys.path.append', 'sys.path.append', (['"""../"""'], {}), "('../')\n", (99, 106), False, 'import sys\n'), ((153, 164), 'time.time', 'time.time', ([], {}), '()\n', (162, 164), False, 'import time\n'), ((173, 238), 'atlassian.Bitbucket', 'Bitbucket', (['"""https://git.techstyle.net"""'], {'token': 'secrets.... |
import qdrant_client
from llama_index import (
VectorStoreIndex,
ServiceContext,
)
from llama_index.llms import Ollama
from llama_index.vector_stores.qdrant import QdrantVectorStore
import llama_index
llama_index.set_global_handler("simple")
# re-initialize the vector store
client = qdrant_client.QdrantClient... | [
"llama_index.vector_stores.qdrant.QdrantVectorStore",
"llama_index.ServiceContext.from_defaults",
"llama_index.llms.Ollama",
"llama_index.set_global_handler",
"llama_index.VectorStoreIndex.from_vector_store"
] | [((210, 250), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (240, 250), False, 'import llama_index\n'), ((294, 342), 'qdrant_client.QdrantClient', 'qdrant_client.QdrantClient', ([], {'path': '"""./qdrant_data"""'}), "(path='./qdrant_data')\n", (320, 342), Fa... |
## main function of AWS Lambda function
import llama_index
from llama_index import download_loader
import boto3
import json
import urllib.parse
from llama_index import SimpleDirectoryReader
def main(event, context):
# extracting s3 bucket and key information from SQS message
print(event)
s3_info = json.lo... | [
"llama_index.download_loader"
] | [((313, 352), 'json.loads', 'json.loads', (["event['Records'][0]['body']"], {}), "(event['Records'][0]['body'])\n", (323, 352), False, 'import json\n'), ((628, 692), 'llama_index.download_loader', 'download_loader', (['"""S3Reader"""'], {'custom_path': '"""/tmp/llamahub_modules"""'}), "('S3Reader', custom_path='/tmp/ll... |
"""Download."""
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from importlib import util
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import pkg_resources
import requests
from pkg_resources import DistributionNotFound
from llama_index.download.... | [
"llama_index.download.utils.get_exports",
"llama_index.download.utils.initialize_directory"
] | [((637, 664), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (654, 664), False, 'import logging\n'), ((5550, 5583), 'os.path.exists', 'os.path.exists', (['requirements_path'], {}), '(requirements_path)\n', (5564, 5583), False, 'import os\n'), ((7403, 7471), 'llama_index.download.utils.ini... |
import json
from typing import Dict, List
import llama_index.query_engine
from llama_index import ServiceContext, QueryBundle
from llama_index.callbacks import CBEventType, LlamaDebugHandler, CallbackManager
from llama_index.indices.base import BaseIndex
from llama_index.indices.query.base import BaseQueryEngine
from ... | [
"llama_index.callbacks.LlamaDebugHandler",
"llama_index.response.schema.Response",
"llama_index.ServiceContext.from_defaults",
"llama_index.callbacks.CallbackManager"
] | [((1298, 1324), 'llama_index.response.schema.Response', 'Response', (['f"""ๆๆฏ{self.name}"""'], {}), "(f'ๆๆฏ{self.name}')\n", (1306, 1324), False, 'from llama_index.response.schema import RESPONSE_TYPE, Response\n'), ((2530, 2571), 'common.llm.create_llm', 'create_llm', (['cb_manager', 'LLM_CACHE_ENABLED'], {}), '(cb_man... |
"""Download."""
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from importlib import util
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import pkg_resources
import requests
from pkg_resources import DistributionNotFound
from llama_index.download.... | [
"llama_index.download.utils.get_exports",
"llama_index.download.utils.initialize_directory"
] | [((637, 664), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (654, 664), False, 'import logging\n'), ((5550, 5583), 'os.path.exists', 'os.path.exists', (['requirements_path'], {}), '(requirements_path)\n', (5564, 5583), False, 'import os\n'), ((7403, 7471), 'llama_index.download.utils.ini... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralyti... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.utils.CONFIG.copy",
"lancedb.utils.CONFIG.update"
] | [((641, 654), 'click.group', 'click.group', ([], {}), '()\n', (652, 654), False, 'import click\n'), ((656, 727), 'click.version_option', 'click.version_option', ([], {'help': '"""LanceDB command line interface entry point"""'}), "(help='LanceDB command line interface entry point')\n", (676, 727), False, 'import click\n... |
# Copyright (c) Hegel AI, Inc.
# All rights reserved.
#
# This source code's license can be found in the
# LICENSE file in the root directory of this source tree.
import itertools
import warnings
import pandas as pd
from typing import Callable, Optional
try:
import lancedb
from lancedb.embeddings import with_... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((797, 961), 'warnings.warn', 'warnings.warn', (['"""`nprobes` and `refine_factor` are not used by the default `query_builder`. Feel free to open an issue to request adding support for them."""'], {}), "(\n '`nprobes` and `refine_factor` are not used by the default `query_builder`. Feel free to open an issue to req... |
import os
from pathlib import Path
from tqdm import tqdm
from lancedb import connect
from pydantic import BaseModel
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
from typing import Iterable
DB_PATH = Path(os.getcwd(), "db")
DATA_PATH = Path(os.getcwd(), "data")
DB_TABLE =... | [
"lancedb.connect",
"lancedb.embeddings.get_registry"
] | [((253, 264), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (262, 264), False, 'import os\n'), ((289, 300), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (298, 300), False, 'import os\n'), ((2068, 2084), 'lancedb.connect', 'connect', (['DB_PATH'], {}), '(DB_PATH)\n', (2075, 2084), False, 'from lancedb import connect\n'), (... |
"""LanceDB vector store with cloud storage support."""
import os
from typing import Any, Optional
from dotenv import load_dotenv
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase
from llama_index.vector_stores.l... | [
"lancedb.connect"
] | [((490, 503), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (501, 503), False, 'from dotenv import load_dotenv\n'), ((1464, 1492), 'os.getenv', 'os.getenv', (['"""LANCEDB_API_KEY"""'], {}), "('LANCEDB_API_KEY')\n", (1473, 1492), False, 'import os\n'), ((1520, 1547), 'os.getenv', 'os.getenv', (['"""LANCEDB_REGI... |
from pathlib import Path
from typing import Any, Callable
from lancedb import DBConnection as LanceDBConnection
from lancedb import connect as lancedb_connect
from lancedb.table import Table as LanceDBTable
from openai import Client as OpenAIClient
from pydantic import Field, PrivateAttr
from crewai_tools.tools.rag.r... | [
"lancedb.connect"
] | [((393, 407), 'openai.Client', 'OpenAIClient', ([], {}), '()\n', (405, 407), True, 'from openai import Client as OpenAIClient\n'), ((724, 774), 'pydantic.Field', 'Field', ([], {'default_factory': '_default_embedding_function'}), '(default_factory=_default_embedding_function)\n', (729, 774), False, 'from pydantic import... |
import logging
from typing import Any, Dict, Generator, List, Optional, Sequence, Tuple, Type
import lancedb
import pandas as pd
from dotenv import load_dotenv
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import LanceVectorQueryBuilder
from pydantic import BaseModel, ValidationError, create_model... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((911, 938), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (928, 938), False, 'import logging\n'), ((1125, 1149), 'langroid.embedding_models.models.OpenAIEmbeddingsConfig', 'OpenAIEmbeddingsConfig', ([], {}), '()\n', (1147, 1149), False, 'from langroid.embedding_models.models import Ope... |
import json
import lancedb
from lancedb.pydantic import Vector, LanceModel
from datetime import datetime
# import pyarrow as pa
TABLE_NAME = "documents"
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
# vector: list of vectors
# file_name: name of file
# file_path: path of file
# id
# updated_at
# created_at... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((189, 209), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (204, 209), False, 'import lancedb\n'), ((461, 472), 'lancedb.pydantic.Vector', 'Vector', (['(768)'], {}), '(768)\n', (467, 472), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((786, 800), 'datetime.datetime.now', 'datetime.now... |
import json
from sentence_transformers import SentenceTransformer
from pydantic.main import ModelMetaclass
from pathlib import Path
import pandas as pd
import sqlite3
from uuid import uuid4
import lancedb
encoder = SentenceTransformer('all-MiniLM-L6-v2')
data_folder = Path('data/collections')
config_file = Path('data... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((216, 255), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (235, 255), False, 'from sentence_transformers import SentenceTransformer\n'), ((271, 295), 'pathlib.Path', 'Path', (['"""data/collections"""'], {}), "('data/collections')\n", (2... |
import os
import urllib.request
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np
import lancedb
from lancedb.embeddings i... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((670, 691), 'html2text.HTML2Text', 'html2text.HTML2Text', ([], {}), '()\n', (689, 691), False, 'import html2text\n'), ((1001, 1056), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(700)', 'chunk_overlap': '(50)'}), '(chunk_size=700, chunk_overlap=50)\n', (1022, 1056), F... |
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import EmbeddingFunctionRegistry
registry = EmbeddingFunctionRegistry.get_instance()
func = registry.get("openai").create()
class Questions(LanceModel):
question: str = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()... | [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
] | [((117, 157), 'lancedb.embeddings.EmbeddingFunctionRegistry.get_instance', 'EmbeddingFunctionRegistry.get_instance', ([], {}), '()\n', (155, 157), False, 'from lancedb.embeddings import EmbeddingFunctionRegistry\n')] |
import logging
import os
import time
from functools import wraps
from pathlib import Path
from random import random, seed
import lancedb
import pyarrow as pa
import pyarrow.parquet as pq
import typer
from lancedb.db import LanceTable
log_level = os.environ.get("LOG_LEVEL", "info")
logging.basicConfig(
level=getat... | [
"lancedb.connect"
] | [((248, 283), 'os.environ.get', 'os.environ.get', (['"""LOG_LEVEL"""', '"""info"""'], {}), "('LOG_LEVEL', 'info')\n", (262, 283), False, 'import os\n'), ((446, 473), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (463, 473), False, 'import logging\n'), ((480, 493), 'typer.Typer', 'typer.T... |
import argparse
import os
import shutil
from functools import lru_cache
from pathlib import Path
from typing import Any, Iterator
import srsly
from codetiming import Timer
from config import Settings
from dotenv import load_dotenv
from rich import progress
from schemas.wine import LanceModelWine, Wine
from sentence_tr... | [
"lancedb.connect",
"lancedb.pydantic.pydantic_to_schema"
] | [((455, 468), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (466, 468), False, 'from dotenv import load_dotenv\n'), ((560, 571), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (569, 571), False, 'from functools import lru_cache\n'), ((668, 678), 'config.Settings', 'Settings', ([], {}), '()\n', (676, 678... |
from datasets import load_dataset
data = load_dataset('jamescalam/youtube-transcriptions', split='train')
from lancedb.context import contextualize
df = (contextualize(data.to_pandas())
.groupby("title").text_col("text")
.window(20).stride(4)
.to_df())
df.head(1)
import openai
import os
# Configur... | [
"lancedb.connect"
] | [((42, 106), 'datasets.load_dataset', 'load_dataset', (['"""jamescalam/youtube-transcriptions"""'], {'split': '"""train"""'}), "('jamescalam/youtube-transcriptions', split='train')\n", (54, 106), False, 'from datasets import load_dataset\n'), ((831, 862), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {... |
import hashlib
import io
import logging
from typing import List
import numpy as np
from lancedb.pydantic import LanceModel, vector
from PIL import Image
from pydantic import BaseModel, Field, computed_field
from homematch.config import IMAGES_DIR
logger = logging.getLogger(__name__)
class PropertyListingBase(BaseM... | [
"lancedb.pydantic.vector"
] | [((259, 286), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (276, 286), False, 'import logging\n'), ((2511, 2522), 'lancedb.pydantic.vector', 'vector', (['(768)'], {}), '(768)\n', (2517, 2522), False, 'from lancedb.pydantic import LanceModel, vector\n'), ((2146, 2158), 'io.BytesIO', 'io.... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.remote.connection_timeout.LanceDBClientHTTPAdapterFactory",
"lancedb.remote.VectorQueryResult",
"lancedb.remote.errors.LanceDBClientError"
] | [((1587, 1612), 'attrs.define', 'attrs.define', ([], {'slots': '(False)'}), '(slots=False)\n', (1599, 1612), False, 'import attrs\n'), ((1207, 1225), 'functools.wraps', 'functools.wraps', (['f'], {}), '(f)\n', (1222, 1225), False, 'import functools\n'), ((1733, 1758), 'attrs.field', 'attrs.field', ([], {'default': 'Non... |
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
Language,
LatexTextSplitter,
)
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
import argparse, os, arxiv
os.environ["OPENAI_API_KEY"] = "sk-ORoaAljc5ylMsRwnXpLTT3BlbkFJQJz0esJOFYg8Z6... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((342, 360), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (358, 360), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((2116, 2133), 'lancedb.connect', 'lancedb.connect', ([], {}), '()\n', (2131, 2133), False, 'import lancedb\n'), ((2820, 2867), 'langchain.vectorstores.... |
import time
import os
import pandas as pd
import streamlit as st
import lancedb
from lancedb.embeddings import with_embeddings
from langchain import PromptTemplate
import predictionguard as pg
import streamlit as st
import duckdb
import re
import numpy as np
from sentence_transformers import SentenceTransformer
#---... | [
"lancedb.connect"
] | [((413, 433), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (428, 433), False, 'import lancedb\n'), ((890, 947), 'streamlit.markdown', 'st.markdown', (['hide_streamlit_style'], {'unsafe_allow_html': '(True)'}), '(hide_streamlit_style, unsafe_allow_html=True)\n', (901, 947), True, 'import streamlit as ... |
from FlagEmbedding import LLMEmbedder, FlagReranker
import os
import lancedb
import re
import pandas as pd
import random
from datasets import load_dataset
import torch
import gc
import lance
from lancedb.embeddings import with_embeddings
task = "qa" # Encode for a specific task (qa, icl, chat, lrl... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((356, 404), 'FlagEmbedding.LLMEmbedder', 'LLMEmbedder', (['"""BAAI/llm-embedder"""'], {'use_fp16': '(False)'}), "('BAAI/llm-embedder', use_fp16=False)\n", (367, 404), False, 'from FlagEmbedding import LLMEmbedder, FlagReranker\n'), ((463, 516), 'FlagEmbedding.FlagReranker', 'FlagReranker', (['"""BAAI/bge-reranker-bas... |
import time
import re
import shutil
import os
import urllib
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from lang... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((728, 820), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['user', 'assistant']", 'template': 'demo_formatter_template'}), "(input_variables=['user', 'assistant'], template=\n demo_formatter_template)\n", (742, 820), False, 'from langchain import PromptTemplate, FewShotPromptTemplate\n'),... |
import logging
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Union
logger = logging.getLogger(__name__)
from hamilton import contrib
with contrib.catch_import_errors(__name__, __file__, logger):
import pyarrow as pa
import lancedb
import numpy as np
import pandas as pd
... | [
"lancedb.connect"
] | [((107, 134), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (124, 134), False, 'import logging\n'), ((1219, 1242), 'hamilton.function_modifiers.tag', 'tag', ([], {'side_effect': '"""True"""'}), "(side_effect='True')\n", (1222, 1242), False, 'from hamilton.function_modifiers import tag\n'... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.utils.general.TryExcept"
] | [((5422, 5446), 'lancedb.utils.general.TryExcept', 'TryExcept', ([], {'verbose': '(False)'}), '(verbose=False)\n', (5431, 5446), False, 'from lancedb.utils.general import TryExcept\n'), ((4584, 4595), 'time.time', 'time.time', ([], {}), '()\n', (4593, 4595), False, 'import time\n'), ((2567, 2598), 'platform.python_vers... |
import argparse
import os
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from functools import lru_cache
from pathlib import Path
from typing import Any, Iterator
import lancedb
import pandas as pd
import srsly
from codetiming import Timer
from dotenv import load_dotenv
from lancedb.pydant... | [
"lancedb.connect",
"lancedb.pydantic.pydantic_to_schema"
] | [((580, 593), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (591, 593), False, 'from dotenv import load_dotenv\n'), ((685, 696), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (694, 696), False, 'from functools import lru_cache\n'), ((793, 803), 'api.config.Settings', 'Settings', ([], {}), '()\n', (801,... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.remote.VectorQueryResult"
] | [((1402, 1427), 'attrs.define', 'attrs.define', ([], {'slots': '(False)'}), '(slots=False)\n', (1414, 1427), False, 'import attrs\n'), ((1006, 1024), 'functools.wraps', 'functools.wraps', (['f'], {}), '(f)\n', (1021, 1024), False, 'import functools\n'), ((1548, 1573), 'attrs.field', 'attrs.field', ([], {'default': 'Non... |
import os
import time
import shutil
import pandas as pd
import lancedb
from lancedb.embeddings import with_embeddings
from langchain import PromptTemplate
import predictionguard as pg
import numpy as np
from sentence_transformers import SentenceTransformer
#---------------------#
# Lance DB Setup #
#-------------... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((359, 391), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/jobs.csv"""'], {}), "('datasets/jobs.csv')\n", (370, 391), True, 'import pandas as pd\n'), ((429, 463), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/social.csv"""'], {}), "('datasets/social.csv')\n", (440, 463), True, 'import pandas as pd\n'), ((503, 53... |
import typer
import openai
from rag_app.models import TextChunk
from lancedb import connect
from typing import List
from pathlib import Path
from rich.console import Console
from rich.table import Table
from rich import box
import duckdb
app = typer.Typer()
@app.command(help="Query LanceDB for some results")
def db(... | [
"lancedb.connect"
] | [((245, 258), 'typer.Typer', 'typer.Typer', ([], {}), '()\n', (256, 258), False, 'import typer\n'), ((340, 378), 'typer.Option', 'typer.Option', ([], {'help': '"""Your LanceDB path"""'}), "(help='Your LanceDB path')\n", (352, 378), False, 'import typer\n'), ((402, 448), 'typer.Option', 'typer.Option', ([], {'help': '""... |
import typer
from lancedb import connect
from rag_app.models import TextChunk, Document
from pathlib import Path
from typing import Iterable
from tqdm import tqdm
from rich import print
import frontmatter
import hashlib
from datetime import datetime
from unstructured.partition.text import partition_text
app = typer.Ty... | [
"lancedb.connect"
] | [((312, 325), 'typer.Typer', 'typer.Typer', ([], {}), '()\n', (323, 325), False, 'import typer\n'), ((1598, 1636), 'typer.Option', 'typer.Option', ([], {'help': '"""Your LanceDB path"""'}), "(help='Your LanceDB path')\n", (1610, 1636), False, 'import typer\n'), ((1660, 1706), 'typer.Option', 'typer.Option', ([], {'help... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.utils.CONFIG.copy",
"lancedb.utils.CONFIG.update"
] | [((641, 654), 'click.group', 'click.group', ([], {}), '()\n', (652, 654), False, 'import click\n'), ((656, 727), 'click.version_option', 'click.version_option', ([], {'help': '"""LanceDB command line interface entry point"""'}), "(help='LanceDB command line interface entry point')\n", (676, 727), False, 'import click\n... |
import json
from sentence_transformers import SentenceTransformer
from pydantic.main import ModelMetaclass
from pathlib import Path
import pandas as pd
import sqlite3
from uuid import uuid4
import lancedb
encoder = SentenceTransformer('all-MiniLM-L6-v2')
data_folder = Path('data/collections')
config_file = Path('data... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((216, 255), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (235, 255), False, 'from sentence_transformers import SentenceTransformer\n'), ((271, 295), 'pathlib.Path', 'Path', (['"""data/collections"""'], {}), "('data/collections')\n", (2... |
import argparse
import pandas as pd
from unstructured.partition.pdf import partition_pdf
import lancedb.embeddings.gte
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
def split_text_into_chunks(text, chunk_size, overlap):
"""
Split text into chunks with a specifie... | [
"lancedb.embeddings.get_registry"
] | [((1242, 1265), 'unstructured.partition.pdf.partition_pdf', 'partition_pdf', (['pdf_file'], {}), '(pdf_file)\n', (1255, 1265), False, 'from unstructured.partition.pdf import partition_pdf\n'), ((1658, 1688), 'pandas.DataFrame', 'pd.DataFrame', (["{'text': chunks}"], {}), "({'text': chunks})\n", (1670, 1688), True, 'imp... |
import os
import shutil
from pathlib import Path
import lancedb
from lancedb.pydantic import LanceModel, Vector, pydantic_to_schema
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.... | [
"lancedb.pydantic.Vector",
"lancedb.connect",
"lancedb.pydantic.pydantic_to_schema"
] | [((429, 440), 'lancedb.pydantic.Vector', 'Vector', (['(384)'], {}), '(384)\n', (435, 440), False, 'from lancedb.pydantic import LanceModel, Vector, pydantic_to_schema\n'), ((538, 553), 'pathlib.Path', 'Path', (['"""../data"""'], {}), "('../data')\n", (542, 553), False, 'from pathlib import Path\n'), ((1650, 1714), 'lan... |
import lancedb
import uuid
from datetime import datetime
from tqdm import tqdm
from typing import Optional, List, Iterator, Dict
from memgpt.config import MemGPTConfig
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.config import AgentConfig, MemGPTConfig
from memgpt.constants import MEM... | [
"lancedb.pydantic.Vector"
] | [((623, 642), 'memgpt.config.MemGPTConfig.load', 'MemGPTConfig.load', ([], {}), '()\n', (640, 642), False, 'from memgpt.config import AgentConfig, MemGPTConfig\n'), ((1078, 1106), 'lancedb.pydantic.Vector', 'Vector', (['config.embedding_dim'], {}), '(config.embedding_dim)\n', (1084, 1106), False, 'from lancedb.pydantic... |
import os
import argparse
import lancedb
from lancedb.context import contextualize
from lancedb.embeddings import with_embeddings
from datasets import load_dataset
import openai
import pytest
OPENAI_MODEL = None
def embed_func(c):
rs = openai.Embedding.create(input=c, engine=OPENAI_MODEL)
return [record["emb... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((243, 296), 'openai.Embedding.create', 'openai.Embedding.create', ([], {'input': 'c', 'engine': 'OPENAI_MODEL'}), '(input=c, engine=OPENAI_MODEL)\n', (266, 296), False, 'import openai\n'), ((1031, 1192), 'openai.Completion.create', 'openai.Completion.create', ([], {'engine': 'OPENAI_MODEL', 'prompt': 'prompt', 'tempe... |
import os, time
import pandas as pd
import numpy as np
from collections import Counter
from .utils import abbreviate_book_name_in_full_reference, get_train_test_split_from_verse_list, embed_batch
from .types import TranslationTriplet, ChatResponse, VerseMap, AIResponse
from pydantic import BaseModel, Field
from typing ... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((559, 587), 'logging.getLogger', 'logging.getLogger', (['"""uvicorn"""'], {}), "('uvicorn')\n", (576, 587), False, 'import logging\n'), ((801, 880), 'pandas.read_csv', 'pd.read_csv', (['"""data/bsb-utf8.txt"""'], {'sep': '"""\t"""', 'names': "['vref', 'content']", 'header': '(0)'}), "('data/bsb-utf8.txt', sep='\\t', ... |
import logging
import pyarrow as pa
import pyarrow.compute as pc
from tabulate import tabulate
from llama_cpp import Llama
from dryg.settings import DEFAULT_MODEL
from dryg.db import open_table, create_table
from lancedb.embeddings import with_embeddings
MODEL = None
def get_code_blocks(body: pa.ChunkedArray):
... | [
"lancedb.embeddings.with_embeddings"
] | [((1527, 1575), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embedding_func', 'issues', '"""title"""'], {}), "(embedding_func, issues, 'title')\n", (1542, 1575), False, 'from lancedb.embeddings import with_embeddings\n'), ((1611, 1662), 'dryg.db.create_table', 'create_table', (['issue_table', 'issues'], ... |
from pathlib import Path
from collections import defaultdict
import math
import json
import pandas as pd
import cv2
import duckdb
import matplotlib.pyplot as plt
import numpy as np
import yaml
from tqdm import tqdm
from ultralytics.utils import LOGGER, colorstr
from ultralytics.utils.plotting import Annotator, colors
... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((1044, 1064), 'cv2.imread', 'cv2.imread', (['img_path'], {}), '(img_path)\n', (1054, 1064), False, 'import cv2\n'), ((1214, 1249), 'numpy.frombuffer', 'np.frombuffer', (['img_encoded', 'np.byte'], {}), '(img_encoded, np.byte)\n', (1227, 1249), True, 'import numpy as np\n'), ((1260, 1300), 'cv2.imdecode', 'cv2.imdecod... |
"""
Run this script to benchmark the serial search performance of FTS and vector search
"""
import argparse
import random
from functools import lru_cache
from pathlib import Path
from typing import Any
from codetiming import Timer
from config import Settings
from rich import progress
from schemas.wine import SearchRes... | [
"lancedb.connect"
] | [((471, 482), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (480, 482), False, 'from functools import lru_cache\n'), ((579, 589), 'config.Settings', 'Settings', ([], {}), '()\n', (587, 589), False, 'from config import Settings\n'), ((2943, 2968), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '... |
from neumai.Shared.NeumSinkInfo import NeumSinkInfo
from neumai.Shared.NeumVector import NeumVector
from neumai.Shared.NeumSearch import NeumSearchResult
from neumai.Shared.Exceptions import(
LanceDBInsertionException,
LanceDBIndexInfoException,
LanceDBIndexCreationException,
LanceDBQueryException
)
fr... | [
"lancedb.connect"
] | [((2397, 2447), 'pydantic.Field', 'Field', (['...'], {'description': '"""URI for LanceDB database"""'}), "(..., description='URI for LanceDB database')\n", (2402, 2447), False, 'from pydantic import Field\n'), ((2477, 2537), 'pydantic.Field', 'Field', ([], {'default': 'None', 'description': '"""API key for LanceDB clou... |
from FlagEmbedding import LLMEmbedder, FlagReranker
import lancedb
import re
import pandas as pd
import random
from datasets import load_dataset
import torch
import gc
from lancedb.embeddings import with_embeddings
embed_model = LLMEmbedder(
"BAAI/llm-embedder", use_fp16=False
) # Load model (automatically use... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((233, 281), 'FlagEmbedding.LLMEmbedder', 'LLMEmbedder', (['"""BAAI/llm-embedder"""'], {'use_fp16': '(False)'}), "('BAAI/llm-embedder', use_fp16=False)\n", (244, 281), False, 'from FlagEmbedding import LLMEmbedder, FlagReranker\n'), ((344, 397), 'FlagEmbedding.FlagReranker', 'FlagReranker', (['"""BAAI/bge-reranker-bas... |
import os
import urllib.request
import shutil
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np
import lancedb
from lancedb.embedding... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((657, 678), 'html2text.HTML2Text', 'html2text.HTML2Text', ([], {}), '()\n', (676, 678), False, 'import html2text\n'), ((847, 902), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(700)', 'chunk_overlap': '(50)'}), '(chunk_size=700, chunk_overlap=50)\n', (868, 902), False... |
import json
import logging
from typing import Any, Dict, Generator, List, Optional, Sequence, Set, Tuple, Type
import lancedb
import pandas as pd
from dotenv import load_dotenv
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import LanceVectorQueryBuilder
from pydantic import BaseModel, ValidationEr... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((877, 904), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (894, 904), False, 'import logging\n'), ((1067, 1091), 'src.embedding_models.models.OpenAIEmbeddingsConfig', 'OpenAIEmbeddingsConfig', ([], {}), '()\n', (1089, 1091), False, 'from src.embedding_models.models import OpenAIEmbeddi... |
from datasets import load_dataset
import os
import lancedb
import getpass
import time
import argparse
from tqdm.auto import tqdm
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector
def main(query=None):
if "COHERE_API_KEY" not in os.environ:
os.environ[... | [
"lancedb.embeddings.EmbeddingFunctionRegistry",
"lancedb.connect"
] | [((407, 463), 'datasets.load_dataset', 'load_dataset', (['"""wikipedia"""', '"""20220301.en"""'], {'streaming': '(True)'}), "('wikipedia', '20220301.en', streaming=True)\n", (419, 463), False, 'from datasets import load_dataset\n'), ((504, 560), 'datasets.load_dataset', 'load_dataset', (['"""wikipedia"""', '"""20220301... |
from typing import Optional
from pydantic import BaseModel, ConfigDict, Field, model_validator
from lancedb.pydantic import LanceModel, Vector
class Wine(BaseModel):
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
extra="allow",
str_strip_whitespace=Tr... | [
"lancedb.pydantic.Vector"
] | [((189, 894), 'pydantic.ConfigDict', 'ConfigDict', ([], {'populate_by_name': '(True)', 'validate_assignment': '(True)', 'extra': '"""allow"""', 'str_strip_whitespace': '(True)', 'json_schema_extra': "{'example': {'id': 45100, 'points': 85, 'title':\n 'Balduzzi 2012 Reserva Merlot (Maule Valley)', 'description':\n ... |
from typing import Any
from lancedb.embeddings import EmbeddingFunctionRegistry
def register_model(model_name: str) -> Any:
"""
Register a model with the given name using LanceDB's EmbeddingFunctionRegistry.
Args:
model_name (str): The name of the model to register.
Returns:
model: ... | [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
] | [((430, 470), 'lancedb.embeddings.EmbeddingFunctionRegistry.get_instance', 'EmbeddingFunctionRegistry.get_instance', ([], {}), '()\n', (468, 470), False, 'from lancedb.embeddings import EmbeddingFunctionRegistry\n')] |
#!/usr/bin/env python
import os
import lancedb
from lancedb.embeddings import with_embeddings
import openai
import pandas as pd
from pydantic import BaseModel, Field
import requests
from aifunctools.openai_funcs import complete_with_functions
openai.api_key = os.getenv("OPENAI_API_KEY")
MODEL = "gpt-3.5-turbo-16k-0... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((263, 290), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (272, 290), False, 'import os\n'), ((331, 358), 'lancedb.connect', 'lancedb.connect', (['""".lancedb"""'], {}), "('.lancedb')\n", (346, 358), False, 'import lancedb\n'), ((389, 454), 'openai.Embedding.create', 'openai.Embedd... |
import lancedb
import uuid
from datetime import datetime
from tqdm import tqdm
from typing import Optional, List, Iterator, Dict
from memgpt.config import MemGPTConfig
from memgpt.connectors.storage import StorageConnector, TableType
from memgpt.config import AgentConfig, MemGPTConfig
from memgpt.constants import MEMG... | [
"lancedb.pydantic.Vector"
] | [((622, 641), 'memgpt.config.MemGPTConfig.load', 'MemGPTConfig.load', ([], {}), '()\n', (639, 641), False, 'from memgpt.config import AgentConfig, MemGPTConfig\n'), ((1077, 1105), 'lancedb.pydantic.Vector', 'Vector', (['config.embedding_dim'], {}), '(config.embedding_dim)\n', (1083, 1105), False, 'from lancedb.pydantic... |
""" Install lancedb with instructor embedding support
copy this and paste it in the terminal, and install additional dependencies via requirements.txt file
pip install git+https://github.com/lancedb/lancedb.git@main#subdirectory=python
"""
import lancedb
from lancedb.pydantic import LanceModel, Vecto... | [
"lancedb.connect",
"lancedb.embeddings.get_registry"
] | [((818, 847), 'lancedb.connect', 'lancedb.connect', (['"""~/.lancedb"""'], {}), "('~/.lancedb')\n", (833, 847), False, 'import lancedb\n'), ((445, 459), 'lancedb.embeddings.get_registry', 'get_registry', ([], {}), '()\n', (457, 459), False, 'from lancedb.embeddings import get_registry\n')] |
from pathlib import Path
from uuid import uuid4
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
import lancedb
from knowledge_graph.configuration.config import... | [
"lancedb.connect"
] | [((410, 438), 'lancedb.connect', 'lancedb.connect', (['cfg.db_path'], {}), '(cfg.db_path)\n', (425, 438), False, 'import lancedb\n'), ((715, 743), 'lancedb.connect', 'lancedb.connect', (['cfg.db_path'], {}), '(cfg.db_path)\n', (730, 743), False, 'import lancedb\n'), ((1060, 1125), 'langchain.text_splitter.CharacterText... |
from glob import glob
from os.path import basename
from pathlib import Path
import chromadb
import lancedb
import pandas as pd
import torch
from chromadb.utils import embedding_functions
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector
from loguru import logger
f... | [
"lancedb.connect",
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
] | [((489, 529), 'lancedb.embeddings.EmbeddingFunctionRegistry.get_instance', 'EmbeddingFunctionRegistry.get_instance', ([], {}), '()\n', (527, 529), False, 'from lancedb.embeddings import EmbeddingFunctionRegistry\n'), ((881, 915), 'chromadb.PersistentClient', 'chromadb.PersistentClient', (['DB_PATH'], {}), '(DB_PATH)\n'... |
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2x... | [
"lancedb.pydantic.Vector"
] | [((3411, 3433), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3419, 3433), True, 'import numpy as np\n'), ((3771, 3804), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (3785, 3804), True, 'import numpy as np\n'), ((4239, 4274), 'ultralyti... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4054, 4087), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4068, 4087), True, 'import numpy as np\n'), ((4421, 4456), 'ultralyti... |
from typing import Optional
from lancedb.pydantic import Vector
from pydantic import BaseModel, ConfigDict, Field, model_validator
class Wine(BaseModel):
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
extra="allow",
str_strip_whitespace=True,
j... | [
"lancedb.pydantic.Vector"
] | [((176, 881), 'pydantic.ConfigDict', 'ConfigDict', ([], {'populate_by_name': '(True)', 'validate_assignment': '(True)', 'extra': '"""allow"""', 'str_strip_whitespace': '(True)', 'json_schema_extra': "{'example': {'id': 45100, 'points': 85, 'title':\n 'Balduzzi 2012 Reserva Merlot (Maule Valley)', 'description':\n ... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from engine.data.augment import LetterBox
from engine.utils import LOGGER as logger
from engine.utils import SETTINGS
from engine.utils.checks import check_requirements
from engine.utils.o... | [
"lancedb.pydantic.Vector"
] | [((3664, 3686), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3672, 3686), True, 'import numpy as np\n'), ((3997, 4030), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4011, 4030), True, 'import numpy as np\n'), ((4364, 4399), 'engine.ut... |
import json
import lancedb
import pytest
from lancedb.utils.events import _Events
@pytest.fixture(autouse=True)
def request_log_path(tmp_path):
return tmp_path / "request.json"
def mock_register_event(name: str, **kwargs):
if _Events._instance is None:
_Events._instance = _Events()
_Events._in... | [
"lancedb.utils.events._Events._instance",
"lancedb.connect",
"lancedb.utils.events._Events"
] | [((86, 114), 'pytest.fixture', 'pytest.fixture', ([], {'autouse': '(True)'}), '(autouse=True)\n', (100, 114), False, 'import pytest\n'), ((383, 416), 'lancedb.utils.events._Events._instance', '_Events._instance', (['name'], {}), '(name, **kwargs)\n', (400, 416), False, 'from lancedb.utils.events import _Events\n'), ((8... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralyti... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralyti... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralyti... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralyti... |
"""LanceDB vector store with cloud storage support."""
import os
from typing import Any, Optional
from dotenv import load_dotenv
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase
from llama_index.vector_stores.l... | [
"lancedb.connect"
] | [((490, 503), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (501, 503), False, 'from dotenv import load_dotenv\n'), ((1464, 1492), 'os.getenv', 'os.getenv', (['"""LANCEDB_API_KEY"""'], {}), "('LANCEDB_API_KEY')\n", (1473, 1492), False, 'import os\n'), ((1520, 1547), 'os.getenv', 'os.getenv', (['"""LANCEDB_REGI... |
from pathlib import Path
from typing import Any, Callable
from lancedb import DBConnection as LanceDBConnection
from lancedb import connect as lancedb_connect
from lancedb.table import Table as LanceDBTable
from openai import Client as OpenAIClient
from pydantic import Field, PrivateAttr
from crewai_tools.tools.rag.r... | [
"lancedb.connect"
] | [((393, 407), 'openai.Client', 'OpenAIClient', ([], {}), '()\n', (405, 407), True, 'from openai import Client as OpenAIClient\n'), ((724, 774), 'pydantic.Field', 'Field', ([], {'default_factory': '_default_embedding_function'}), '(default_factory=_default_embedding_function)\n', (729, 774), False, 'from pydantic import... |
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
Language,
LatexTextSplitter,
)
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
import argparse, os, arxiv
os.environ["OPENAI_API_KEY"] = "sk-ORoaAljc5ylMsRwnXpLTT3BlbkFJQJz0esJOFYg8Z6... | [
"lancedb.pydantic.Vector",
"lancedb.connect"
] | [((342, 360), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (358, 360), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((2116, 2133), 'lancedb.connect', 'lancedb.connect', ([], {}), '()\n', (2131, 2133), False, 'import lancedb\n'), ((2820, 2867), 'langchain.vectorstores.... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4054, 4087), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4068, 4087), True, 'import numpy as np\n'), ((4421, 4456), 'ultralyti... |
# Ultralytics YOLO ๐, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements... | [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4054, 4087), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4068, 4087), True, 'import numpy as np\n'), ((4421, 4456), 'ultralyti... |
import os
import argparse
import lancedb
from lancedb.context import contextualize
from lancedb.embeddings import with_embeddings
from datasets import load_dataset
import openai
import pytest
import subprocess
from main import embed_func, create_prompt, complete
# DOWNLOAD =============================================... | [
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
] | [((1071, 1184), 'argparse.Namespace', 'argparse.Namespace', ([], {'query': '"""test"""', 'context_length': '(3)', 'window_size': '(20)', 'stride': '(4)', 'openai_key': '"""test"""', 'model': '"""test"""'}), "(query='test', context_length=3, window_size=20, stride=4,\n openai_key='test', model='test')\n", (1089, 1184... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance",
"lancedb.conftest.MockTextEmbeddingFunction",
"lancedb.db.LanceDBConnection",
"lancedb.embeddings.EmbeddingFunctionConfig",
"lancedb.table.LanceTable",
"lancedb.pydantic.Vector",
"lancedb.connect",
"lancedb.table.LanceTable.create"
] | [((1992, 2014), 'lancedb.table.LanceTable', 'LanceTable', (['db', '"""test"""'], {}), "(db, 'test')\n", (2002, 2014), False, 'from lancedb.table import LanceTable\n'), ((3907, 3928), 'pandas.DataFrame', 'pd.DataFrame', (['data[0]'], {}), '(data[0])\n', (3919, 3928), True, 'import pandas as pd\n'), ((4450, 4494), 'lance... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.remote.connection_timeout.LanceDBClientHTTPAdapterFactory",
"lancedb.remote.VectorQueryResult",
"lancedb.remote.errors.LanceDBClientError"
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.pydantic.Vector",
"lancedb.pydantic.pydantic_to_schema"
] | [((860, 973), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(sys.version_info < (3, 9))'], {'reason': '"""using native type alias requires python3.9 or higher"""'}), "(sys.version_info < (3, 9), reason=\n 'using native type alias requires python3.9 or higher')\n", (878, 973), False, 'import pytest\n'), ((2877, 2988... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.pydantic.Vector",
"lancedb.query.LanceVectorQueryBuilder",
"lancedb.query.Query",
"lancedb.db.LanceDBConnection"
] | [((2041, 2074), 'lance.write_dataset', 'lance.write_dataset', (['df', 'tmp_path'], {}), '(df, tmp_path)\n', (2060, 2074), False, 'import lance\n'), ((4585, 4625), 'pandas.testing.assert_frame_equal', 'tm.assert_frame_equal', (['df_default', 'df_l2'], {}), '(df_default, df_l2)\n', (4606, 4625), True, 'import pandas.test... |
# Copyright (c) 2023. LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | [
"lancedb.connect",
"lancedb.embeddings.get_registry"
] | [((1288, 1357), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""alias"""', "['sentence-transformers', 'openai']"], {}), "('alias', ['sentence-transformers', 'openai'])\n", (1311, 1357), False, 'import pytest\n'), ((5687, 5773), 'pytest.mark.skipif', 'pytest.mark.skipif', (['(_imagebind is None)'], {'reason'... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.connect",
"lancedb.remote.client.VectorQueryResult"
] | [((1101, 1164), 'lancedb.connect', 'lancedb.connect', (['"""db://client-will-be-injected"""'], {'api_key': '"""fake"""'}), "('db://client-will-be-injected', api_key='fake')\n", (1116, 1164), False, 'import lancedb\n'), ((924, 944), 'lancedb.remote.client.VectorQueryResult', 'VectorQueryResult', (['t'], {}), '(t)\n', (9... |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance",
"lancedb.embeddings.registry.get_registry",
"lancedb.conftest.MockTextEmbeddingFunction",
"lancedb.embeddings.registry.register",
"lancedb.embeddings.with_embeddings",
"lancedb.connect"
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.utils.general.TryExcept"
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | [
"lancedb.pydantic.Vector",
"lancedb.connect",
"lancedb.connect_async"
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