code stringlengths 193 97.3k | apis listlengths 1 8 | extract_api stringlengths 113 214k |
|---|---|---|
from datetime import datetime
import lancedb
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore, LanceDB
from config import Config
from utils.files import get_root_path
def get_vectorstore(table_name: str, embedding: Embeddings) -> VectorStore:
config = Config()
... | [
"lancedb.connect"
] | [((307, 315), 'config.Config', 'Config', ([], {}), '()\n', (313, 315), False, 'from config import Config\n'), ((376, 400), 'lancedb.connect', 'lancedb.connect', (['db_path'], {}), '(db_path)\n', (391, 400), False, 'import lancedb\n'), ((792, 838), 'langchain.vectorstores.LanceDB', 'LanceDB', ([], {'embedding': 'embeddi... |
# Answer questions about a PDF file using the RAG model
# TODO: Maintain the context of the conversation
import lancedb
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain.text_spl... | [
"lancedb.connect"
] | [((972, 1003), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (987, 1003), False, 'import lancedb\n'), ((1015, 1033), 'langchain_openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1031, 1033), False, 'from langchain_openai import OpenAIEmbeddings\n'), ((1412, 143... |
import lancedb
import os
import gradio as gr
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# For Text Similarity and Relevance Ranking:
# valhalla/distilbart-mnli-12-3
# sentence-transformers/cross-encoder/stsb-roberta-larg... | [
"lancedb.connect"
] | [((440, 508), 'os.getenv', 'os.getenv', (['"""CROSS_ENC_MODEL"""', '"""cross-encoder/ms-marco-MiniLM-L-6-v2"""'], {}), "('CROSS_ENC_MODEL', 'cross-encoder/ms-marco-MiniLM-L-6-v2')\n", (449, 508), False, 'import os\n'), ((573, 619), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['CROSS_... |
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_text_splitters import TokenTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
import lancedb
import pyarrow as pa
embedding_model = OllamaEmbeddings()
db_path = "./lancedb"
db = lancedb.connect(db_path)... | [
"lancedb.connect"
] | [((246, 264), 'langchain_community.embeddings.OllamaEmbeddings', 'OllamaEmbeddings', ([], {}), '()\n', (262, 264), False, 'from langchain_community.embeddings import OllamaEmbeddings\n'), ((296, 320), 'lancedb.connect', 'lancedb.connect', (['db_path'], {}), '(db_path)\n', (311, 320), False, 'import lancedb\n'), ((844, ... |
import os
import pandas as pd
from datetime import datetime
import time
import subprocess
from docarray import DocumentArray, Document
import json
import pyarrow as pa
import lancedb
from google.cloud import bigquery
GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "passculture-data-ehp")
ENV_SHORT_NAME = os.enviro... | [
"lancedb.connect"
] | [((237, 293), 'os.environ.get', 'os.environ.get', (['"""GCP_PROJECT_ID"""', '"""passculture-data-ehp"""'], {}), "('GCP_PROJECT_ID', 'passculture-data-ehp')\n", (251, 293), False, 'import os\n'), ((311, 350), 'os.environ.get', 'os.environ.get', (['"""ENV_SHORT_NAME"""', '"""dev"""'], {}), "('ENV_SHORT_NAME', 'dev')\n", ... |
from pgvector.psycopg import register_vector
from pgvector.sqlalchemy import Vector
import psycopg
from sqlalchemy import create_engine, Column, String, BIGINT, select, inspect, text
from sqlalchemy.orm import sessionmaker, mapped_column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.sql impo... | [
"lancedb.connect"
] | [((702, 720), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (718, 720), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((771, 790), 'memgpt.config.MemGPTConfig.load', 'MemGPTConfig.load', ([], {}), '()\n', (788, 790), False, 'from memgpt.config import AgentCo... |
from dotenv import load_dotenv
import os
import lancedb
import torch
from PIL import Image
import glob
import re
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
import concurrent.futures
# Set options for youtube_dl
ydl_opts = {
"quiet": True, # Silence youtube_dl output
"extract_flat": ... | [
"lancedb.connect"
] | [((955, 992), 'lancedb.connect', 'lancedb.connect', (['"""data/video-lancedb"""'], {}), "('data/video-lancedb')\n", (970, 992), False, 'import lancedb\n'), ((553, 596), 'transformers.CLIPTokenizerFast.from_pretrained', 'CLIPTokenizerFast.from_pretrained', (['MODEL_ID'], {}), '(MODEL_ID)\n', (586, 596), False, 'from tra... |
import uvicorn
from fastapi import FastAPI, HTTPException, UploadFile, File
from pydantic import BaseModel
import openai
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.doc... | [
"lancedb.connect"
] | [((548, 664), 'fastapi.FastAPI', 'FastAPI', ([], {'title': '"""Chatbot RAG API"""', 'description': '"""This is a chatbot API template for RAG system."""', 'version': '"""1.0.0"""'}), "(title='Chatbot RAG API', description=\n 'This is a chatbot API template for RAG system.', version='1.0.0')\n", (555, 664), False, 'f... |
import os
import typer
import pickle
import pandas as pd
from dotenv import load_dotenv
import openai
import pinecone
import lancedb
import pyarrow as pa
from collections import deque
TASK_CREATION_PROMPT = """
You are an task creation AI that uses the result of an execution agent to create new tasks with the followi... | [
"lancedb.connect"
] | [((7628, 7641), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (7639, 7641), False, 'from dotenv import load_dotenv\n'), ((8372, 8387), 'typer.run', 'typer.run', (['main'], {}), '(main)\n', (8381, 8387), False, 'import typer\n'), ((2036, 2062), 'os.path.isfile', 'os.path.isfile', (['cache_file'], {}), '(cache_f... |
import argparse
import io
import PIL
import duckdb
import lancedb
import lance
import pyarrow.compute as pc
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
import gradio as gr
MODEL_ID = None
MODEL = None
TOKENIZER = None
PROCESSOR = None
def create_table(dataset):
db = lancedb.connect("~/da... | [
"lancedb.connect"
] | [((299, 333), 'lancedb.connect', 'lancedb.connect', (['"""~/datasets/demo"""'], {}), "('~/datasets/demo')\n", (314, 333), False, 'import lancedb\n'), ((759, 802), 'transformers.CLIPTokenizerFast.from_pretrained', 'CLIPTokenizerFast.from_pretrained', (['MODEL_ID'], {}), '(MODEL_ID)\n', (792, 802), False, 'from transform... |
import os
import typer
import pickle
import pandas as pd
from dotenv import load_dotenv
import openai
import pinecone
import lancedb
import pyarrow as pa
from collections import deque
TASK_CREATION_PROMPT = """
You are an task creation AI that uses the result of an execution agent to create new tasks with the followi... | [
"lancedb.connect"
] | [((7093, 7106), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (7104, 7106), False, 'from dotenv import load_dotenv\n'), ((7837, 7852), 'typer.run', 'typer.run', (['main'], {}), '(main)\n', (7846, 7852), False, 'import typer\n'), ((2219, 2245), 'os.path.isfile', 'os.path.isfile', (['cache_file'], {}), '(cache_f... |
from datasets import load_dataset
import lancedb
import pytest
import main
# ==================== TESTING ====================
@pytest.fixture
def mock_embed_func(monkeypatch):
def mock_api_call(*args, **kwargs):
return [0.5, 0.5]
monkeypatch.setattr(main, "embed", mock_api_call)
@pytest.fixture
d... | [
"lancedb.connect"
] | [((570, 646), 'datasets.load_dataset', 'load_dataset', (['"""CVdatasets/ImageNet15_animals_unbalanced_aug1"""'], {'split': '"""train"""'}), "('CVdatasets/ImageNet15_animals_unbalanced_aug1', split='train')\n", (582, 646), False, 'from datasets import load_dataset\n'), ((698, 730), 'lancedb.connect', 'lancedb.connect', ... |
from datasets import load_dataset
import numpy as np
import lancedb
import pytest
import main
# ==================== TESTING ====================
@pytest.fixture
def mock_embed(monkeypatch):
def mock_inference(audio_data):
return (None, [[0.5, 0.5]])
monkeypatch.setattr(main, "create_audio_embedding... | [
"lancedb.connect"
] | [((417, 460), 'datasets.load_dataset', 'load_dataset', (['"""ashraq/esc50"""'], {'split': '"""train"""'}), "('ashraq/esc50', split='train')\n", (429, 460), False, 'from datasets import load_dataset\n'), ((471, 508), 'lancedb.connect', 'lancedb.connect', (['"""data/audio-lancedb"""'], {}), "('data/audio-lancedb')\n", (4... |
# Ultralytics YOLO 🚀, AGPL-3.0 license
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import cv2
import numpy as np
import torch
from matplotlib import pyplot as plt
from pandas import DataFrame
from PIL import Image
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1681, 1874), 'ultralytics.data.augment.Format', 'Format', ([], {'bbox_format': '"""xyxy"""', 'normalize': '(False)', 'return_mask': 'self.use_segments', 'return_keypoint': 'self.use_keypoints', 'batch_idx': '(True)', 'mask_ratio': 'hyp.mask_ratio', 'mask_overlap': 'hyp.overlap_mask'}), "(bbox_format='xyxy', normaliz... |
# Ultralytics YOLO 🚀, AGPL-3.0 license
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import cv2
import numpy as np
import torch
from matplotlib import pyplot as plt
from pandas import DataFrame
from PIL import Image
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1681, 1874), 'ultralytics.data.augment.Format', 'Format', ([], {'bbox_format': '"""xyxy"""', 'normalize': '(False)', 'return_mask': 'self.use_segments', 'return_keypoint': 'self.use_keypoints', 'batch_idx': '(True)', 'mask_ratio': 'hyp.mask_ratio', 'mask_overlap': 'hyp.overlap_mask'}), "(bbox_format='xyxy', normaliz... |
import lancedb
import pyarrow as pa
import json
embedding_models=[
"",
""
]
class LanceDBAssistant:
def __init__(self, dirpath, filename,n=384):
self.dirpath = dirpath
self.filename = filename
self.db = None
self.create_schema(n)
def create_schema(self,n=384):
... | [
"lancedb.connect"
] | [((574, 603), 'lancedb.connect', 'lancedb.connect', (['self.dirpath'], {}), '(self.dirpath)\n', (589, 603), False, 'import lancedb\n'), ((434, 445), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (443, 445), True, 'import pyarrow as pa\n'), ((475, 486), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (484, 486), Tru... |
# langchain Chatbot
from langchain.document_loaders import DataFrameLoader
import pandas as pd
from langchain.memory import ConversationSummaryMemory
import lancedb
from langchain.vectorstores import LanceDB
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSpl... | [
"lancedb.connect"
] | [((472, 503), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (487, 503), False, 'import lancedb\n'), ((645, 688), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': 'OPENAI_KEY'}), '(openai_api_key=OPENAI_KEY)\n', (661, 688), False, 'from lang... |
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
from .base_tool import BaseTool
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PDFPlumberLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.document... | [
"lancedb.connect"
] | [((2402, 2434), 'langchain.document_loaders.PDFPlumberLoader', 'PDFPlumberLoader', (['temp_file_path'], {}), '(temp_file_path)\n', (2418, 2434), False, 'from langchain.document_loaders import PDFPlumberLoader\n'), ((2533, 2599), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter',... |
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_community.vectorstores import LanceDB
from langchain.embeddings.openai import OpenAIEmbedding... | [
"lancedb.connect"
] | [((455, 471), 'langchain.document_loaders.TextLoader', 'TextLoader', (['path'], {}), '(path)\n', (465, 471), False, 'from langchain.document_loaders import TextLoader\n'), ((539, 557), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (555, 557), False, 'from langchain.embeddings.ope... |
import argparse
import os
from typing import Any
from PIL import Image
import lancedb
from schema import Myntra, get_schema_by_name
def run_vector_search(
database: str,
table_name: str,
schema: Any,
search_query: Any,
limit: int = 6,
output_folder: str = "output",
) -> None:
"""
Thi... | [
"lancedb.connect"
] | [((1422, 1451), 'os.path.exists', 'os.path.exists', (['output_folder'], {}), '(output_folder)\n', (1436, 1451), False, 'import os\n'), ((1650, 1675), 'lancedb.connect', 'lancedb.connect', (['database'], {}), '(database)\n', (1665, 1675), False, 'import lancedb\n'), ((2586, 2638), 'argparse.ArgumentParser', 'argparse.Ar... |
import streamlit as st
import sqlite3
import streamlit_antd_components as sac
import pandas as pd
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import... | [
"lancedb.connect"
] | [((1396, 1407), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1405, 1407), False, 'import os\n'), ((1428, 1457), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1440, 1457), False, 'import os\n'), ((2203, 2245), 'os.path.join', 'os.path.join', (['WORKING_DIRECTORY', '"""lancedb""... |
# import libraries
import re
import gradio as gr
from typing import List, Union
import lancedb
from langchain.vectorstores import LanceDB
from langchain.llms import CTransformers
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.m... | [
"lancedb.connect"
] | [((683, 721), 're.compile', 're.compile', (['"""https?://\\\\S+|www\\\\.\\\\S+"""'], {}), "('https?://\\\\S+|www\\\\.\\\\S+')\n", (693, 721), False, 'import re\n'), ((1668, 1789), 'langchain.llms.CTransformers', 'CTransformers', ([], {'model': '"""TheBloke/Mistral-7B-v0.1-GGUF"""', 'model_file': '"""mistral-7b-v0.1.Q4_... |
# load_pdf.py - Loads PDF documents into the LanceDB vector store
## Imports:
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
import dotenv
import lancedb
import os
from pypdf import PdfReader
## Set Env Variables
d... | [
"lancedb.connect"
] | [((319, 339), 'dotenv.load_dotenv', 'dotenv.load_dotenv', ([], {}), '()\n', (337, 339), False, 'import dotenv\n'), ((586, 604), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (602, 604), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((695, 719), 'lancedb.connect', 'lance... |
import lancedb
from langchain.vectorstores import LanceDB
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
db = lancedb.connect(".lance-data")
path = "/workspace/flancian"
loader = DirectoryLoader(pa... | [
"lancedb.connect"
] | [((233, 263), 'lancedb.connect', 'lancedb.connect', (['""".lance-data"""'], {}), "('.lance-data')\n", (248, 263), False, 'import lancedb\n'), ((302, 339), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['path'], {'glob': '"""**/*.md"""'}), "(path, glob='**/*.md')\n", (317, 339), False, 'from langchai... |
import lancedb
import pyarrow as pa
import pandas as pd
# Connect to the database
uri = "/tmp/sample-lancedb"
db = lancedb.connect(uri)
schema = pa.schema([
pa.field("unique_id", pa.string()),
pa.field("embedded_user_input", pa.list_(pa.list_(pa.float32()))),
pa.field("metadata", pa.struct([
pa.fi... | [
"lancedb.connect"
] | [((116, 136), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (131, 136), False, 'import lancedb\n'), ((11225, 11245), 'pandas.DataFrame', 'pd.DataFrame', (['[data]'], {}), '([data])\n', (11237, 11245), True, 'import pandas as pd\n'), ((185, 196), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (194, 1... |
import lancedb
import numpy as np
from .base_index import BaseIndex
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import cpu_count
from functools import partial
def search_single(q: np.ndarray, k: int, db_address: str, table_name: str, metric: str):
index = lancedb.connect(db_address)
... | [
"lancedb.connect"
] | [((290, 317), 'lancedb.connect', 'lancedb.connect', (['db_address'], {}), '(db_address)\n', (305, 317), False, 'import lancedb\n'), ((613, 645), 'lancedb.connect', 'lancedb.connect', (['self.db_address'], {}), '(self.db_address)\n', (628, 645), False, 'import lancedb\n'), ((1394, 1502), 'functools.partial', 'partial', ... |
import streamlit as st
import sqlite3
import streamlit_antd_components as sac
import pandas as pd
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vector... | [
"lancedb.connect"
] | [((1345, 1356), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1354, 1356), False, 'import os\n'), ((1377, 1406), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1389, 1406), False, 'import os\n'), ((1686, 1702), 'basecode.authenticate.return_api_key', 'return_api_key', ([], {}), ... |
# See; https://www.mongodb.com/developer/products/atlas/rag-atlas-vector-search-langchain-openai/
from langchain_openai import OpenAI,ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.llms import Ollama
from langchain.text_splitter import Rec... | [
"lancedb.connect"
] | [((867, 945), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'OPENAI_API_KEY', 'model_name': '"""gpt-4"""', 'max_tokens': '(1000)'}), "(openai_api_key=OPENAI_API_KEY, model_name='gpt-4', max_tokens=1000)\n", (877, 945), False, 'from langchain_openai import OpenAI, ChatOpenAI\n'), ((949, 1003), 'la... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import os
import shutil
import lancedb
class LanceStore:
def __init__(self, name):
db = lancedb.connect("./data/lancedb")
self.db = db
self.name = name... | [
"lancedb.connect"
] | [((241, 274), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (256, 274), False, 'import lancedb\n'), ((2822, 2864), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2834, 2864), False, 'import os\n'), ((2876, ... |
import json
import gzip
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI
from pydantic import BaseModel
from pathlib import Path
from tqdm.auto import tqdm
import pandas as pd
import lancedb
import sqlite3
app = FastAPI()
encoder = SentenceTransformer('all-MiniLM-L6-v2')
lance_location... | [
"lancedb.connect"
] | [((246, 255), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (253, 255), False, 'from fastapi import FastAPI\n'), ((266, 305), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (285, 305), False, 'from sentence_transformers import SentenceTr... |
from datasets import load_dataset
from enum import Enum
import lancedb
from tqdm import tqdm
from IPython.display import display
import clip
import torch
class Animal(Enum):
italian_greyhound = 0
coyote = 1
beagle = 2
rottweiler = 3
hyena = 4
greater_swiss_mountain_dog = 5
Triceratops = 6
... | [
"lancedb.connect"
] | [((748, 772), 'IPython.display.display', 'display', (["test[id]['img']"], {}), "(test[id]['img'])\n", (755, 772), False, 'from IPython.display import display\n'), ((1853, 1871), 'tqdm.tqdm', 'tqdm', (['batched_data'], {}), '(batched_data)\n', (1857, 1871), False, 'from tqdm import tqdm\n'), ((2196, 2272), 'datasets.loa... |
import uvicorn
from fastapi import FastAPI, HTTPException
from openai import OpenAI
from pydantic import BaseModel
from typing import List
import lancedb
import pyarrow as pa
import json
from collections import Counter
import requests
from dotenv import load_dotenv
import os
from fastapi.middleware.cors import CORSMidd... | [
"lancedb.connect"
] | [((377, 397), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (392, 397), False, 'import lancedb\n'), ((569, 578), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (576, 578), False, 'from fastapi import FastAPI, HTTPException\n'), ((579, 592), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (590,... |
import lancedb
import tantivy
def create_lancedb_index(bucket, vector_name, num_partitions=256, num_sub_vectors=96, text_key="text"):
try:
db = lancedb.connect(bucket)
tbl = db.open_table(vector_name)
tbl.create_index(num_partitions=num_partitions, num_sub_vectors=num_sub_vectors)
... | [
"lancedb.connect"
] | [((157, 180), 'lancedb.connect', 'lancedb.connect', (['bucket'], {}), '(bucket)\n', (172, 180), False, 'import lancedb\n')] |
import lancedb
uri = "./.lancedb"
db = lancedb.connect(uri)
table = db.open_table("my_table")
result = table.search([100, 100]).limit(2).to_df()
print(result)
df = table.to_pandas()
print(df)
| [
"lancedb.connect"
] | [((40, 60), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (55, 60), False, 'import lancedb\n')] |
import torch
import open_clip
import pandas as pd
from tqdm import tqdm
from collections import defaultdict
import arxiv
import lancedb
def get_arxiv_df(embed_func):
length = 30000
results = arxiv.Search(
query="cat:cs.AI OR cat:cs.CV OR cat:stat.ML",
max_results=length,
sort_by=arxiv.... | [
"lancedb.connect"
] | [((417, 434), 'collections.defaultdict', 'defaultdict', (['list'], {}), '(list)\n', (428, 434), False, 'from collections import defaultdict\n'), ((453, 480), 'tqdm.tqdm', 'tqdm', (['results'], {'total': 'length'}), '(results, total=length)\n', (457, 480), False, 'from tqdm import tqdm\n'), ((837, 853), 'pandas.DataFram... |
import lancedb
from langchain.document_loaders import DirectoryLoader
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter
from typing import List
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.vectorstores import LanceDB
f... | [
"lancedb.connect"
] | [((2368, 2423), 'langchain.tools.tool', 'tool', (['"""knowledge_base"""'], {'args_schema': 'KnowledgeBaseSchema'}), "('knowledge_base', args_schema=KnowledgeBaseSchema)\n", (2372, 2423), False, 'from langchain.tools import tool\n'), ((2293, 2363), 'pydantic.Field', 'Field', ([], {'description': '"""information you want... |
"""
Unit test for retrieve_utils.py
"""
from autogen.retrieve_utils import (
split_text_to_chunks,
extract_text_from_pdf,
split_files_to_chunks,
get_files_from_dir,
get_file_from_url,
is_url,
create_vector_db_from_dir,
query_vector_db,
num_tokens_from_text,
num_tokens_from_messa... | [
"lancedb.connect"
] | [((439, 464), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (454, 464), False, 'import os\n'), ((7837, 7850), 'pytest.main', 'pytest.main', ([], {}), '()\n', (7848, 7850), False, 'import pytest\n'), ((7912, 7935), 'os.path.exists', 'os.path.exists', (['db_path'], {}), '(db_path)\n', (7926, 7... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import os
import shutil
import lancedb
class LanceStore:
def __init__(self, name):
db = lancedb.connect("./data/lancedb")
self.db = db
self.name = name... | [
"lancedb.connect"
] | [((241, 274), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (256, 274), False, 'import lancedb\n'), ((2822, 2864), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2834, 2864), False, 'import os\n'), ((2876, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import os
import shutil
import lancedb
class LanceStore:
def __init__(self, name):
db = lancedb.connect("./data/lancedb")
self.db = db
self.name = name... | [
"lancedb.connect"
] | [((241, 274), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (256, 274), False, 'import lancedb\n'), ((2822, 2864), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2834, 2864), False, 'import os\n'), ((2876, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import os
import shutil
import lancedb
class LanceStore:
def __init__(self, name):
db = lancedb.connect("./data/lancedb")
self.db = db
self.name = name... | [
"lancedb.connect"
] | [((241, 274), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (256, 274), False, 'import lancedb\n'), ((2822, 2864), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2834, 2864), False, 'import os\n'), ((2876, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import os
import shutil
import lancedb
class LanceStore:
def __init__(self, name):
db = lancedb.connect("./data/lancedb")
self.db = db
self.name = name... | [
"lancedb.connect"
] | [((241, 274), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (256, 274), False, 'import lancedb\n'), ((2822, 2864), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2834, 2864), False, 'import os\n'), ((2876, ... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.legacy.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.legacy.vector_stores.types import (
... | [
"lancedb.connect"
] | [((607, 634), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (624, 634), False, 'import logging\n'), ((3288, 3308), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3303, 3308), False, 'import lancedb\n'), ((1371, 1400), 'numpy.exp', 'np.exp', (["(-results['_distance'])"],... |
import pickle
import re
import zipfile
from pathlib import Path
import requests
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import RecursiveCharact... | [
"lancedb.connect"
] | [((746, 762), 'pathlib.Path', 'Path', (['"""docs.pkl"""'], {}), "('docs.pkl')\n", (750, 762), False, 'from pathlib import Path\n'), ((773, 788), 'pathlib.Path', 'Path', (['"""lancedb"""'], {}), "('lancedb')\n", (777, 788), False, 'from pathlib import Path\n'), ((2956, 2982), 'modal.web_endpoint', 'web_endpoint', ([], {... |
from typing import List, Any
from dataclasses import dataclass
import lancedb
import pandas as pd
from autochain.tools.base import Tool
from autochain.models.base import BaseLanguageModel
from autochain.tools.internal_search.base_search_tool import BaseSearchTool
@dataclass
class LanceDBDoc:
doc: str
vector:... | [
"lancedb.connect"
] | [((1275, 1300), 'lancedb.connect', 'lancedb.connect', (['self.uri'], {}), '(self.uri)\n', (1290, 1300), False, 'import lancedb\n'), ((1984, 2054), 'pandas.DataFrame', 'pd.DataFrame', (["[{'doc': doc.doc, 'vector': doc.vector} for doc in docs]"], {}), "([{'doc': doc.doc, 'vector': doc.vector} for doc in docs])\n", (1996... |
import lancedb
import matplotlib.pyplot as plt
import rasterio as rio
import streamlit as st
from rasterio.plot import show
st.set_page_config(layout="wide")
# Get preferrred chips
def get_unique_chips(tbl):
chips = [
{"tile": "17MNP", "idx": "0271", "year": 2023},
{"tile": "19HGU", "idx": "0033"... | [
"lancedb.connect"
] | [((125, 158), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (143, 158), True, 'import streamlit as st\n'), ((1119, 1138), 'streamlit.cache_resource', 'st.cache_resource', ([], {}), '()\n', (1136, 1138), True, 'import streamlit as st\n'), ((1264, 1283), 'streamli... |
import lancedb
import numpy as np
import pandas as pd
global data
data = []
global table
table = None
def get_recommendations(title):
pd_data = pd.DataFrame(data)
# Table Search
result = (
table.search(pd_data[pd_data["title"] == title]["vector"].values[0])
.limit(5)
.to_df()
... | [
"lancedb.connect"
] | [((152, 170), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {}), '(data)\n', (164, 170), True, 'import pandas as pd\n'), ((357, 484), 'pandas.read_csv', 'pd.read_csv', (['"""./ml-latest-small/links.csv"""'], {'header': '(0)', 'names': "['movie id', 'imdb id', 'tmdb id']", 'converters': "{'imdb id': str}"}), "('./ml-la... |
from hashlib import md5
from typing import List, Optional
import json
try:
import lancedb
import pyarrow as pa
except ImportError:
raise ImportError("`lancedb` not installed.")
from phi.document import Document
from phi.embedder import Embedder
from phi.embedder.openai import OpenAIEmbedder
from phi.vecto... | [
"lancedb.connect"
] | [((509, 525), 'phi.embedder.openai.OpenAIEmbedder', 'OpenAIEmbedder', ([], {}), '()\n', (523, 525), False, 'from phi.embedder.openai import OpenAIEmbedder\n'), ((1143, 1168), 'lancedb.connect', 'lancedb.connect', (['self.uri'], {}), '(self.uri)\n', (1158, 1168), False, 'import lancedb\n'), ((2476, 2525), 'phi.utils.log... |
import lancedb
from langchain.prompts import PromptTemplate
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import GPT4All
from langchain.chains import ConversationalRetrievalChain, LLMChain
from langchain_community.vectorstores import LanceDB
from langchain... | [
"lancedb.connect"
] | [((373, 401), 'lancedb.connect', 'lancedb.connect', (['"""./lancedb"""'], {}), "('./lancedb')\n", (388, 401), False, 'import lancedb\n'), ((713, 805), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'template', 'input_variables': "['context, question', 'chat_history']"}), "(template=template, in... |
from typing import Any, List, Optional, Dict
from ._base import Record, VectorStore
from ._embeddings import Embeddings
VECTOR_COLUMN_NAME = "_vector"
class LanceDB(VectorStore):
def __init__(self, db_uri, embeddings: Embeddings = None) -> None:
super().__init__()
try:
import pyarrow... | [
"lancedb.connect"
] | [((1012, 1041), 'lancedb.connect', 'lancedb.connect', (['self._db_uri'], {}), '(self._db_uri)\n', (1027, 1041), True, 'import lancedb as lancedb\n'), ((4319, 4337), 'pyarrow.schema', 'pa.schema', (['columns'], {}), '(columns)\n', (4328, 4337), True, 'import pyarrow as pa\n'), ((4016, 4027), 'pyarrow.string', 'pa.string... |
import typing as t
from docarray import DocumentArray, Document
import lancedb
from filter import Filter
import joblib
import numpy as np
DETAIL_COLUMNS = [
"item_id",
"topic_id",
"cluster_id",
"is_geolocated",
"booking_number",
"stock_price",
"offer_creation_date",
"stock_beginning_da... | [
"lancedb.connect"
] | [((689, 731), 'docarray.DocumentArray.load', 'DocumentArray.load', (['"""./metadata/item.docs"""'], {}), "('./metadata/item.docs')\n", (707, 731), False, 'from docarray import DocumentArray, Document\n'), ((779, 799), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (794, 799), False, 'import lancedb\n')... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import lancedb
import shutil, os
class LanceStore:
def __init__(self, name):
db = lancedb.connect('./data/lancedb')
self.db = db
self.name = name
... | [
"lancedb.connect"
] | [((234, 267), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (249, 267), False, 'import lancedb\n'), ((2866, 2908), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2878, 2908), False, 'import shutil, os\n'), ... |
from pgvector.psycopg import register_vector
from pgvector.sqlalchemy import Vector
import psycopg
from sqlalchemy import create_engine, Column, String, BIGINT, select, inspect, text
from sqlalchemy.orm import sessionmaker, mapped_column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.sql impo... | [
"lancedb.connect"
] | [((702, 720), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (718, 720), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((771, 790), 'memgpt.config.MemGPTConfig.load', 'MemGPTConfig.load', ([], {}), '()\n', (788, 790), False, 'from memgpt.config import AgentCo... |
from langchain_community.vectorstores import LanceDB
from langchain_openai.embeddings import OpenAIEmbeddings
import lancedb
from common import EXAMPLE_TEXTS
SEARCH_NUM_RESULTS = 3
def main():
embeddings = OpenAIEmbeddings()
table, vectorstore = get_table_and_vectorstore(embeddings)
# Add example text... | [
"lancedb.connect"
] | [((214, 232), 'langchain_openai.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (230, 232), False, 'from langchain_openai.embeddings import OpenAIEmbeddings\n'), ((947, 975), 'lancedb.connect', 'lancedb.connect', (['"""./lancedb"""'], {}), "('./lancedb')\n", (962, 975), False, 'import lancedb\n'), (... |
import openai
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.tools import tool
from pydantic import BaseModel, Field
import argparse
import lancedb
def embed_func(c):
rs = open... | [
"lancedb.connect"
] | [((925, 983), 'langchain.tools.tool', 'tool', (['"""insert_critiques"""'], {'args_schema': 'InsertCritiquesInput'}), "('insert_critiques', args_schema=InsertCritiquesInput)\n", (929, 983), False, 'from langchain.tools import tool\n'), ((1769, 1831), 'langchain.tools.tool', 'tool', (['"""retrieve_critiques"""'], {'args_... |
import streamlit as st
import sqlite3
import streamlit_antd_components as sac
import pandas as pd
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vector... | [
"lancedb.connect"
] | [((1289, 1300), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1298, 1300), False, 'import os\n'), ((1321, 1350), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1333, 1350), False, 'import os\n'), ((1359, 1392), 'os.path.exists', 'os.path.exists', (['WORKING_DIRECTORY'], {}), '(W... |
import lancedb
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_community.vectorstores import LanceDB
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader, PyPDFLoader
db = lancedb.connect("./lancedb")
table = db.creat... | [
"lancedb.connect"
] | [((275, 303), 'lancedb.connect', 'lancedb.connect', (['"""./lancedb"""'], {}), "('./lancedb')\n", (290, 303), False, 'import lancedb\n'), ((552, 615), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(256)', 'chunk_overlap': '(0)'}), '(chunk_size=256, chun... |
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
# embedding_model = HuggingFaceEmbeddings(model_name = "moka-ai/m3e-base")
from langchain.embeddings import Lo... | [
"lancedb.connect"
] | [((439, 577), 'langchain.embeddings.LocalAIEmbeddings', 'LocalAIEmbeddings', ([], {'openai_api_key': '"""aaabbbcccdddeeefffedddsfasdfasdf"""', 'openai_api_base': 'openai_api_base_address', 'model': '"""vicuna-13b-v1.5"""'}), "(openai_api_key='aaabbbcccdddeeefffedddsfasdfasdf',\n openai_api_base=openai_api_base_addre... |
from flask import Flask, request, jsonify
import requests
import json
from flask_cors import CORS
from FlagEmbedding import LLMEmbedder, FlagReranker
from searchdb import search
import lancedb
import pandas as pd
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
embed_model = LLMEmbedd... | [
"lancedb.connect"
] | [((311, 359), 'FlagEmbedding.LLMEmbedder', 'LLMEmbedder', (['"""BAAI/llm-embedder"""'], {'use_fp16': '(False)'}), "('BAAI/llm-embedder', use_fp16=False)\n", (322, 359), False, 'from FlagEmbedding import LLMEmbedder, FlagReranker\n'), ((416, 469), 'FlagEmbedding.FlagReranker', 'FlagReranker', (['"""BAAI/bge-reranker-bas... |
"""Provides a LanceDB interface for adding and querying embeddings."""
import os
import sys
from logging import Logger
from typing import TypeVar
import lancedb
import pyarrow as pa
from lance.vector import vec_to_table
from deckard.core import get_data_dir
T = TypeVar('T', dict, list, int)
class LanceDB:
"""Pr... | [
"lancedb.connect"
] | [((265, 294), 'typing.TypeVar', 'TypeVar', (['"""T"""', 'dict', 'list', 'int'], {}), "('T', dict, list, int)\n", (272, 294), False, 'from typing import TypeVar\n'), ((1068, 1082), 'deckard.core.get_data_dir', 'get_data_dir', ([], {}), '()\n', (1080, 1082), False, 'from deckard.core import get_data_dir\n'), ((1323, 1363... |
from typing import Any, List, Optional, Tuple
import gradio as gr
import lancedb
from transformers import CLIPModel, CLIPTokenizerFast
from homematch.config import DATA_DIR, MODEL_ID, TABLE_NAME
from homematch.data.types import ImageData
DEVICE: str = "cpu"
model: CLIPModel = CLIPModel.from_pretrained(MODEL_ID).to(D... | [
"lancedb.connect"
] | [((358, 401), 'transformers.CLIPTokenizerFast.from_pretrained', 'CLIPTokenizerFast.from_pretrained', (['MODEL_ID'], {}), '(MODEL_ID)\n', (391, 401), False, 'from transformers import CLIPModel, CLIPTokenizerFast\n'), ((448, 468), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (463, 468), False, 'import ... |
import flask
import lancedb
import openai
import langchain
# import clip
import torch
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import LanceDB
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import... | [
"lancedb.connect"
] | [((760, 781), 'dotenv.dotenv_values', 'dotenv_values', (['""".env"""'], {}), "('.env')\n", (773, 781), False, 'from dotenv import dotenv_values\n'), ((852, 872), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (867, 872), False, 'import lancedb\n'), ((886, 933), 'langchain.embeddings.openai.OpenAIEmbedd... |
from dotenv import load_dotenv
import os
import lancedb
import clip
import torch
from PIL import Image
import glob
import re
from concurrent.futures import ThreadPoolExecutor
import yt_dlp
from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
# Set options for youtube_dl
ydl_opts = {
"retries": 0,
... | [
"lancedb.connect"
] | [((1313, 1350), 'lancedb.connect', 'lancedb.connect', (['"""data/video-lancedb"""'], {}), "('data/video-lancedb')\n", (1328, 1350), False, 'import lancedb\n'), ((621, 664), 'transformers.CLIPTokenizerFast.from_pretrained', 'CLIPTokenizerFast.from_pretrained', (['MODEL_ID'], {}), '(MODEL_ID)\n', (654, 664), False, 'from... |
"""LanceDB vector store."""
from typing import Any, List, Optional
from llama_index.schema import MetadataMode, NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores.types import (
NodeWithEmbedding,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
class LanceDBVectorStor... | [
"lancedb.connect"
] | [((1731, 1751), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1746, 1751), False, 'import lancedb\n'), ((3914, 3950), 'llama_index.schema.RelatedNodeInfo', 'RelatedNodeInfo', ([], {'node_id': 'item.doc_id'}), '(node_id=item.doc_id)\n', (3929, 3950), False, 'from llama_index.schema import MetadataMode... |
import streamlit as st
import sqlite3
import streamlit_antd_components as sac
import pandas as pd
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_community.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langch... | [
"lancedb.connect"
] | [((1365, 1376), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1374, 1376), False, 'import os\n'), ((1397, 1426), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1409, 1426), False, 'import os\n'), ((1706, 1722), 'basecode.authenticate.return_api_key', 'return_api_key', ([], {}), ... |
#!/usr/bin/env python3 -m pytest
"""
Unit test for retrieve_utils.py
"""
import pytest
try:
import chromadb
from autogen.retrieve_utils import (
split_text_to_chunks,
extract_text_from_pdf,
split_files_to_chunks,
get_files_from_dir,
is_url,
create_vector_db_from... | [
"lancedb.connect"
] | [((1045, 1107), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1063, 1107), False, 'import pytest\n'), ((643, 668), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (658, 668), False, 'i... |
import pytest
from langchain_community.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.mark.requires("lancedb")
def test_lancedb_with_connection() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb"... | [
"lancedb.connect"
] | [((151, 182), 'pytest.mark.requires', 'pytest.mark.requires', (['"""lancedb"""'], {}), "('lancedb')\n", (171, 182), False, 'import pytest\n'), ((810, 841), 'pytest.mark.requires', 'pytest.mark.requires', (['"""lancedb"""'], {}), "('lancedb')\n", (830, 841), False, 'import pytest\n'), ((1178, 1209), 'pytest.mark.require... |
# 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"
] | [((1069, 1113), 'pytest.fixture', 'pytest.fixture', ([], {'autouse': '(True)', 'scope': '"""module"""'}), "(autouse=True, scope='module')\n", (1083, 1113), False, 'import pytest\n'), ((1159, 1192), 'os.environ.get', 'os.environ.get', (['"""REMOTE_BASE_URL"""'], {}), "('REMOTE_BASE_URL')\n", (1173, 1192), False, 'import... |
"""
Unit test for retrieve_utils.py
"""
import pytest
try:
import chromadb
from autogen.retrieve_utils import (
split_text_to_chunks,
extract_text_from_pdf,
split_files_to_chunks,
get_files_from_dir,
is_url,
create_vector_db_from_dir,
query_vector_db,
... | [
"lancedb.connect"
] | [((1011, 1073), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1029, 1073), False, 'import pytest\n'), ((609, 634), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (624, 634), False, 'i... |
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embe... | [
"lancedb.connect"
] | [((190, 206), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (204, 206), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((216, 247), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embed_do... | [
"lancedb.connect"
] | [((186, 202), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (200, 202), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((212, 243), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
db = lancedb.connect("data/sample-lancedb")
table = db.open_table("python_docs")
print(table.to_pandas())
print(table.to_pandas()["text"])
print(table.to_pandas().columns)
print("vector size: " + str(len(table.to_pandas()['vector'].values[0])))
| [
"lancedb.connect"
] | [((21, 59), 'lancedb.connect', 'lancedb.connect', (['"""data/sample-lancedb"""'], {}), "('data/sample-lancedb')\n", (36, 59), False, 'import lancedb\n')] |
import lancedb
import numpy as np
import pandas as pd
import pytest
import subprocess
from main import get_recommendations, data
import main
# DOWNLOAD ======================================================
subprocess.Popen(
"curl https://files.grouplens.org/datasets/movielens/ml-latest-small.zip -o ml-latest-sm... | [
"lancedb.connect"
] | [((519, 634), 'pandas.read_csv', 'pd.read_csv', (['"""./ml-latest-small/ratings.csv"""'], {'header': 'None', 'names': "['user id', 'movie id', 'rating', 'timestamp']"}), "('./ml-latest-small/ratings.csv', header=None, names=['user id',\n 'movie id', 'rating', 'timestamp'])\n", (530, 634), True, 'import pandas as pd\... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.legacy.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.legacy.vector_stores.types import (
... | [
"lancedb.connect"
] | [((607, 634), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (624, 634), False, 'import logging\n'), ((3288, 3308), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3303, 3308), False, 'import lancedb\n'), ((1371, 1400), 'numpy.exp', 'np.exp', (["(-results['_distance'])"],... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.legacy.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.legacy.vector_stores.types import (
... | [
"lancedb.connect"
] | [((607, 634), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (624, 634), False, 'import logging\n'), ((3288, 3308), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3303, 3308), False, 'import lancedb\n'), ((1371, 1400), 'numpy.exp', 'np.exp', (["(-results['_distance'])"],... |
from typing import List, Any
from dataclasses import dataclass
import lancedb
import pandas as pd
from autochain.tools.base import Tool
from autochain.models.base import BaseLanguageModel
from autochain.tools.internal_search.base_search_tool import BaseSearchTool
@dataclass
class LanceDBDoc:
doc: str
vector:... | [
"lancedb.connect"
] | [((1275, 1300), 'lancedb.connect', 'lancedb.connect', (['self.uri'], {}), '(self.uri)\n', (1290, 1300), False, 'import lancedb\n'), ((1984, 2054), 'pandas.DataFrame', 'pd.DataFrame', (["[{'doc': doc.doc, 'vector': doc.vector} for doc in docs]"], {}), "([{'doc': doc.doc, 'vector': doc.vector} for doc in docs])\n", (1996... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/8/9 15:42
@Author : unkn-wn (Leon Yee)
@File : lancedb_store.py
"""
import lancedb
import shutil, os
class LanceStore:
def __init__(self, name):
db = lancedb.connect('./data/lancedb')
self.db = db
self.name = name
... | [
"lancedb.connect"
] | [((234, 267), 'lancedb.connect', 'lancedb.connect', (['"""./data/lancedb"""'], {}), "('./data/lancedb')\n", (249, 267), False, 'import lancedb\n'), ((2866, 2908), 'os.path.join', 'os.path.join', (['self.db.uri', "(name + '.lance')"], {}), "(self.db.uri, name + '.lance')\n", (2878, 2908), False, 'import shutil, os\n'), ... |
import pytest
from langchain_community.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.mark.requires("lancedb")
def test_lancedb_with_connection() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb"... | [
"lancedb.connect"
] | [((151, 182), 'pytest.mark.requires', 'pytest.mark.requires', (['"""lancedb"""'], {}), "('lancedb')\n", (171, 182), False, 'import pytest\n'), ((810, 841), 'pytest.mark.requires', 'pytest.mark.requires', (['"""lancedb"""'], {}), "('lancedb')\n", (830, 841), False, 'import pytest\n'), ((1178, 1209), 'pytest.mark.require... |
"""
Unit test for retrieve_utils.py
"""
import pytest
try:
import chromadb
from autogen.retrieve_utils import (
split_text_to_chunks,
extract_text_from_pdf,
split_files_to_chunks,
get_files_from_dir,
is_url,
create_vector_db_from_dir,
query_vector_db,
... | [
"lancedb.connect"
] | [((1011, 1073), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1029, 1073), False, 'import pytest\n'), ((609, 634), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (624, 634), False, 'i... |
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embe... | [
"lancedb.connect"
] | [((190, 206), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (204, 206), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((216, 247), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embe... | [
"lancedb.connect"
] | [((190, 206), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (204, 206), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((216, 247), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embe... | [
"lancedb.connect"
] | [((190, 206), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (204, 206), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((216, 247), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
import lancedb
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embe... | [
"lancedb.connect"
] | [((190, 206), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (204, 206), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((216, 247), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embed_do... | [
"lancedb.connect"
] | [((186, 202), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (200, 202), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((212, 243), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embed_do... | [
"lancedb.connect"
] | [((186, 202), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (200, 202), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((212, 243), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embed_do... | [
"lancedb.connect"
] | [((186, 202), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (200, 202), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((212, 243), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import lancedb
from langchain.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_lancedb() -> None:
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb")
texts = ["text 1", "text 2", "item 3"]
vectors = embeddings.embed_do... | [
"lancedb.connect"
] | [((186, 202), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (200, 202), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((212, 243), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
import argparse
from pprint import pprint
import pandas as pd
from mlx_lm import generate, load
import lancedb.embeddings.gte
TEMPLATE = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible using the context text provided. Your answers should only answer the question once and... | [
"lancedb.connect"
] | [((689, 745), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Query a vector DB"""'}), "(description='Query a vector DB')\n", (712, 745), False, 'import argparse\n'), ((1112, 1141), 'lancedb.connect', 'lancedb.connect', (['args.db_path'], {}), '(args.db_path)\n', (1127, 1141), False, 'imp... |
import lancedb
uri = "./.lancedb"
db = lancedb.connect(uri)
table = db.open_table("my_table")
# table.delete("createAt = '1690358416394516300'") # 此条莫名失败了。Column createat does not exist in the dataset
table.delete("item = 'foo'")
df = table.to_pandas()
print(df)
| [
"lancedb.connect"
] | [((40, 60), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (55, 60), False, 'import lancedb\n')] |
import requests
import time
import numpy as np
import pyarrow as pa
import lancedb
import logging
import os
from tqdm import tqdm
from pathlib import Path
from transformers import AutoConfig
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
TEI_URL= os.getenv("EMBED_URL") + "/embed"
DIRPA... | [
"lancedb.connect"
] | [((194, 233), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (213, 233), False, 'import logging\n'), ((243, 270), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (260, 270), False, 'import logging\n'), ((281, 303), 'os.getenv', 'os.... |
import lancedb
from datasets import Dataset
from homematch.config import DATA_DIR, TABLE_NAME
from homematch.data.types import ImageData
def datagen() -> list[ImageData]:
dataset = Dataset.load_from_disk(DATA_DIR / "properties_dataset")
# return Image instances
return [ImageData(**batch) for batch in da... | [
"lancedb.connect"
] | [((188, 243), 'datasets.Dataset.load_from_disk', 'Dataset.load_from_disk', (["(DATA_DIR / 'properties_dataset')"], {}), "(DATA_DIR / 'properties_dataset')\n", (210, 243), False, 'from datasets import Dataset\n'), ((397, 417), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (412, 417), False, 'import lan... |
import openai
import os
import lancedb
import pickle
import requests
from pathlib import Path
from bs4 import BeautifulSoup
import re
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSpli... | [
"lancedb.connect"
] | [((1848, 1866), 'pathlib.Path', 'Path', (['"""cities.pkl"""'], {}), "('cities.pkl')\n", (1852, 1866), False, 'from pathlib import Path\n'), ((2545, 2609), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(50)'}), '(chunk_size=500,... |
import queue
import threading
from dataclasses import dataclass
import lancedb
import pyarrow as pa
import numpy as np
import torch
import torch.nn.functional as F
from safetensors import safe_open
from tqdm import tqdm
from .app.schemas.task import TaskCompletion
from .ops.object_detectors import YOLOV8TRTEngine
fro... | [
"lancedb.connect"
] | [((8969, 8984), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (8982, 8984), False, 'import torch\n'), ((22228, 22243), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (22241, 22243), False, 'import torch\n'), ((1191, 1219), 'lancedb.connect', 'lancedb.connect', (['output_path'], {}), '(output_path)\n', (1206,... |
import os
import openai
import json
import numpy as np
from numpy.linalg import norm
import re
from time import time, sleep
from uuid import uuid4
import datetime
import lancedb
import pandas as pd
def open_file(filepath):
with open(filepath, 'r', encoding='utf-8') as infile:
return infile.read()
def save... | [
"lancedb.connect"
] | [((12752, 12817), 'tensorflow_hub.load', 'hub.load', (['"""https://tfhub.dev/google/universal-sentence-encoder/4"""'], {}), "('https://tfhub.dev/google/universal-sentence-encoder/4')\n", (12760, 12817), True, 'import tensorflow_hub as hub\n'), ((720, 773), 'openai.Embedding.create', 'openai.Embedding.create', ([], {'in... |
import logging
import chainlit as cl
import lancedb
import pandas as pd
from langchain import LLMChain
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.chat_models import ChatOpenAI
from langchain.embeddin... | [
"lancedb.connect"
] | [((498, 525), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (515, 525), False, 'import logging\n'), ((537, 584), 'pandas.read_pickle', 'pd.read_pickle', (['"""data/preprocessed/recipes.pkl"""'], {}), "('data/preprocessed/recipes.pkl')\n", (551, 584), True, 'import pandas as pd\n'), ((755... |
import streamlit as st
import pandas as pd
import json
import requests
from pathlib import Path
from datetime import datetime
from jinja2 import Template
import lancedb
import sqlite3
from services.lancedb_notes import IndexDocumentsNotes
st.set_page_config(layout='wide',
page_title='Notes')
@st.ca... | [
"lancedb.connect"
] | [((240, 293), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""', 'page_title': '"""Notes"""'}), "(layout='wide', page_title='Notes')\n", (258, 293), True, 'import streamlit as st\n'), ((1504, 1522), 'pathlib.Path', 'Path', (['"""data/notes"""'], {}), "('data/notes')\n", (1508, 1522), False... |
from langchain.vectorstores import LanceDB
import lancedb
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
# load agents and tools modules
import pandas as pd
from io import StringIO
from langchain.tools.python.tool import Py... | [
"lancedb.connect"
] | [((1254, 1285), 'langchain.document_loaders.TextLoader', 'TextLoader', (['self.text_data_path'], {}), '(self.text_data_path)\n', (1264, 1285), False, 'from langchain.document_loaders import TextLoader\n'), ((1386, 1450), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'... |
import lancedb
import numpy as np
import pandas as pd
global data
data = []
global table
table = None
def get_recommendations(title):
pd_data = pd.DataFrame(data)
# Table Search
result = (
table.search(pd_data[pd_data["title"] == title]["vector"].values[0])
.limit(5)
.to_pandas()... | [
"lancedb.connect"
] | [((152, 170), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {}), '(data)\n', (164, 170), True, 'import pandas as pd\n'), ((361, 488), 'pandas.read_csv', 'pd.read_csv', (['"""./ml-latest-small/links.csv"""'], {'header': '(0)', 'names': "['movie id', 'imdb id', 'tmdb id']", 'converters': "{'imdb id': str}"}), "('./ml-la... |
import lancedb
from datasets import load_dataset
import pandas as pd
import numpy as np
from hyperdemocracy.embedding.models import BGESmallEn
class Lance:
def __init__(self):
self.model = BGESmallEn()
uri = "data/sample-lancedb"
self.db = lancedb.connect(uri)
def create_table(self):
... | [
"lancedb.connect"
] | [((203, 215), 'hyperdemocracy.embedding.models.BGESmallEn', 'BGESmallEn', ([], {}), '()\n', (213, 215), False, 'from hyperdemocracy.embedding.models import BGESmallEn\n'), ((270, 290), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (285, 290), False, 'import lancedb\n'), ((333, 407), 'datasets.load_dat... |
import lancedb
uri = "test_data"
db = lancedb.connect(uri)
tbl = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}]) | [
"lancedb.connect"
] | [((38, 58), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (53, 58), False, 'import lancedb\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 in... | [
"lancedb.connect",
"lancedb.fts.populate_index",
"lancedb.fts.search_index"
] | [((716, 750), 'pytest.importorskip', 'pytest.importorskip', (['"""lancedb.fts"""'], {}), "('lancedb.fts')\n", (735, 750), False, 'import pytest\n'), ((761, 791), 'pytest.importorskip', 'pytest.importorskip', (['"""tantivy"""'], {}), "('tantivy')\n", (780, 791), False, 'import pytest\n'), ((864, 885), 'lancedb.connect',... |
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