code stringlengths 193 97.3k | apis listlengths 1 8 | extract_api stringlengths 113 214k |
|---|---|---|
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"
] | [((1234, 1245), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1243, 1245), False, 'import os\n'), ((1266, 1295), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1278, 1295), False, 'import os\n'), ((1304, 1337), 'os.path.exists', 'os.path.exists', (['WORKING_DIRECTORY'], {}), '(W... |
# retrivel from PDF
import os
import sys
from pydantic import BaseModel, Field
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import AzureOpenAIEmbeddings
from langchain.chains import LLMChain, HypotheticalDocumentEmbedder
from langchain.prompts import PromptTemplate
from langchain.embeddin... | [
"lancedb.connect"
] | [((596, 631), 'os.path.dirname', 'os.path.dirname', (['current_script_dir'], {}), '(current_script_dir)\n', (611, 631), False, 'import os\n'), ((672, 699), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (687, 699), False, 'import sys\n'), ((1453, 1733), 'langchain.chat_models.AzureChatOpe... |
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.document_loaders import Directory... | [
"lancedb.connect"
] | [((554, 577), 'langchain.callbacks.StdOutCallbackHandler', 'StdOutCallbackHandler', ([], {}), '()\n', (575, 577), False, 'from langchain.callbacks import StdOutCallbackHandler\n'), ((793, 818), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (803, 818), False, 'from ... |
"""LanceDB vector store."""
from typing import Any, List, Optional
from llama_index.data_structs.node import DocumentRelationship, Node
from llama_index.vector_stores.types import (
NodeWithEmbedding,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
class LanceDBVectorStore(VectorStore):
... | [
"lancedb.connect"
] | [((1711, 1731), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1726, 1731), False, 'import lancedb\n'), ((3712, 3811), 'llama_index.data_structs.node.Node', 'Node', ([], {'doc_id': 'item.id', 'text': 'item.text', 'relationships': '{DocumentRelationship.SOURCE: item.doc_id}'}), '(doc_id=item.id, text=i... |
import streamlit as st
import docx2txt
import fitz
import os
from langchain.text_splitter import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from dotenv import load_dotenv
from pathlib import Path
from langchain_community.vectorstores import LanceDB
import lancedb
import os
load_dotenv()
env_... | [
"lancedb.connect"
] | [((302, 315), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (313, 315), False, 'from dotenv import load_dotenv\n'), ((344, 377), 'dotenv.load_dotenv', 'load_dotenv', ([], {'dotenv_path': 'env_path'}), '(dotenv_path=env_path)\n', (355, 377), False, 'from dotenv import load_dotenv\n'), ((396, 423), 'os.getenv', ... |
## Abandoned approach by Sasha
from typing import List
import lancedb
from data_assessment_agent.config.config import cfg
from data_assessment_agent.model.vector_db_model import Questions
def connect_to_lance_questions():
db_file = cfg.lance_db_questions
db = lancedb.connect(db_file)
return db
lance_d... | [
"lancedb.connect"
] | [((272, 296), 'lancedb.connect', 'lancedb.connect', (['db_file'], {}), '(db_file)\n', (287, 296), False, 'import lancedb\n'), ((1019, 1044), 'data_assessment_agent.test.provider.question_provider.create_question_answers', 'create_question_answers', ([], {}), '()\n', (1042, 1044), False, 'from data_assessment_agent.test... |
import os
import shutil
import tempfile
import lancedb
import pandas as pd
import numpy as np
import duckdb
from typing import Optional, List
from .data_load import load_batches, load_df
from .model import BaseEmbeddingModel
from .db import LabelsDB
from .settings import DEFAULT_TABLE_NAME, DB_BATCH_LOAD, DB_BATCH_SIZE... | [
"lancedb.connect"
] | [((323, 383), 'duckdb.sql', 'duckdb.sql', (['"""\n INSTALL sqlite;\n LOAD sqlite;\n """'], {}), '("""\n INSTALL sqlite;\n LOAD sqlite;\n """)\n', (333, 383), False, 'import duckdb\n'), ((743, 777), 'os.path.join', 'os.path.join', (['db_path', '"""labels.db"""'], {}), "(db_path, 'labels.db')\n", (755, ... |
import os
import datetime
import sys
import json
import time
import os
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import LanceDB
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
import lancedb
import openai
from flask i... | [
"lancedb.connect"
] | [((730, 776), 'langchain.document_loaders.DirectoryLoader', 'DirectoryLoader', (['"""static/langchain_documents/"""'], {}), "('static/langchain_documents/')\n", (745, 776), False, 'from langchain.document_loaders import DirectoryLoader\n'), ((827, 883), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSpl... |
from PIL import Image
import streamlit as st
import openai
#exercise 11
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
#exercise 12
from langchain.memory import ConversationBufferWindowMemory
#exercise 13
from langchain.document_loaders import TextLo... | [
"lancedb.connect"
] | [((1222, 1233), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1231, 1233), False, 'import os\n'), ((1254, 1283), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1266, 1283), False, 'import os\n'), ((1375, 1420), 'os.path.join', 'os.path.join', (['WORKING_DIRECTORY', '"""default_d... |
import argparse
import duckdb
import lancedb
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 get_table():
db = lancedb.connect("data/video-lancedb")
return db.open_table("m... | [
"lancedb.connect"
] | [((255, 292), 'lancedb.connect', 'lancedb.connect', (['"""data/video-lancedb"""'], {}), "('data/video-lancedb')\n", (270, 292), False, 'import lancedb\n'), ((461, 504), 'transformers.CLIPTokenizerFast.from_pretrained', 'CLIPTokenizerFast.from_pretrained', (['MODEL_ID'], {}), '(MODEL_ID)\n', (494, 504), False, 'from tra... |
# %% [markdown]
# # Code documentation Q&A bot example with LangChain
#
# This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API.
#
# In this example we'll use Pandas 2.0 documentation, but, this could be replaced ... | [
"lancedb.connect"
] | [((859, 905), 're.findall', 're.findall', (['"""pandas.documentation(.*).html"""', 'm'], {}), "('pandas.documentation(.*).html', m)\n", (869, 905), False, 'import re\n'), ((1074, 1138), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Code Documentation QA Bot"""'}), "(description='Code Do... |
from langchain.vectorstores.lancedb import LanceDB
import lancedb
from .embedding.base import BaseEmbedding
from .base import VectorStore
class LanceDBVectorStore(VectorStore):
def __init__(self, index_name: str, db_path: str, embeddings: BaseEmbedding, api_key: str | None = None):
self.db = lancedb.con... | [
"lancedb.connect"
] | [((309, 350), 'lancedb.connect', 'lancedb.connect', (['db_path'], {'api_key': 'api_key'}), '(db_path, api_key=api_key)\n', (324, 350), False, 'import lancedb\n'), ((854, 913), 'langchain.vectorstores.lancedb.LanceDB', 'LanceDB', ([], {'connection': 'self.table', 'embedding': 'embeddings.client'}), '(connection=self.tab... |
import os
import glob
import tqdm
import pathtrees as pt
import numpy as np
import pandas as pd
import lancedb
import matplotlib.pyplot as plt
from .step_annotations import load_object_annotations_from_csvs, get_obj_ann
from IPython import embed
from ..config import get_cfg
# def load_object_annotations(meta_csv, s... | [
"lancedb.connect"
] | [((2658, 2676), 'pandas.DataFrame', 'pd.DataFrame', (['idxs'], {}), '(idxs)\n', (2670, 2676), True, 'import pandas as pd\n'), ((3211, 3248), 'tqdm.tqdm', 'tqdm.tqdm', (['fs'], {'desc': '"""loading data..."""'}), "(fs, desc='loading data...')\n", (3220, 3248), False, 'import tqdm\n'), ((5542, 5560), 'pandas.concat', 'pd... |
## Imports:
import os
from langchain.chat_models import ChatOpenAI
from langchain.tools import WikipediaQueryRun
from langchain.utilities import WikipediaAPIWrapper
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import Pyt... | [
"lancedb.connect"
] | [((2468, 2580), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': 'OPENAI_CHAT_TEMPERATURE', 'max_tokens': 'OPENAI_MAX_CHAT_TOKENS', 'model': 'OPENAI_CHAT_MODEL'}), '(temperature=OPENAI_CHAT_TEMPERATURE, max_tokens=\n OPENAI_MAX_CHAT_TOKENS, model=OPENAI_CHAT_MODEL)\n', (2478, 2580), False, 'fro... |
"""
Chatbot for talking to Github Codespaces using Langchain, Qwen and LanceDB
"""
import os
import shutil
import lancedb
from langchain.memory import ConversationSummaryMemory
from langchain_community.document_loaders import GitLoader
from langchain.vectorstores import LanceDB
from langchain.embeddings import Huggin... | [
"lancedb.connect"
] | [((538, 569), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (553, 569), False, 'import lancedb\n'), ((848, 877), 'os.path.exists', 'os.path.exists', (['temp_repo_dir'], {}), '(temp_repo_dir)\n', (862, 877), False, 'import os\n'), ((1205, 1259), 'langchain.text_splitter.Charac... |
# 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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1680, 1873), '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... |
"""
Chatbot for talking to Podcast using Langchain, Ollama and LanceDB
"""
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 l... | [
"lancedb.connect"
] | [((527, 558), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (542, 558), False, 'import lancedb\n'), ((724, 767), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': 'OPENAI_KEY'}), '(openai_api_key=OPENAI_KEY)\n', (740, 767), False, 'from lang... |
import flask
import lancedb
import openai
import langchain
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 CharacterTextSplitter
impo... | [
"lancedb.connect"
] | [((448, 463), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (453, 463), False, 'from flask import Flask, render_template, request, jsonify\n'), ((658, 678), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (673, 678), False, 'import lancedb\n'), ((685, 700), 'flask.Flask', 'Flask', (['__nam... |
import os
import sys
import shutil
import platform
import argparse
import lancedb
import time
import uuid
import toml
import pyarrow as pa
import pyarrow.parquet as pq
from dotenv import load_dotenv, find_dotenv
from pymilvus import (
utility,
connections,
FieldSchema,
CollectionSchema,
DataType,
... | [
"lancedb.connect"
] | [((605, 622), 'platform.system', 'platform.system', ([], {}), '()\n', (620, 622), False, 'import platform\n'), ((707, 735), 'sys.modules.pop', 'sys.modules.pop', (['"""pysqlite3"""'], {}), "('pysqlite3')\n", (722, 735), False, 'import sys\n'), ((798, 811), 'dotenv.find_dotenv', 'find_dotenv', ([], {}), '()\n', (809, 81... |
import pandas as pd
from omnivector.abstraction import AbstractDB
class LanceDB(AbstractDB):
"""
LanceDB is a vector database that uses Lance to store and search vectors.
"""
def __init__(self):
super().__init__()
def create_index(self):
# not sure how to do this in Lance
p... | [
"lancedb.connect"
] | [((388, 438), 'lancedb.connect', 'lancedb.connect', (["self.config['lancedb']['DB_PATH']"], {}), "(self.config['lancedb']['DB_PATH'])\n", (403, 438), False, 'import lancedb\n'), ((650, 675), 'pandas.DataFrame', 'pd.DataFrame', (["{'id': ids}"], {}), "({'id': ids})\n", (662, 675), True, 'import pandas as pd\n'), ((690, ... |
from typing import Union, List, Optional
import pandas as pd
from fastapi import FastAPI, BackgroundTasks, Query, WebSocket, WebSocketDisconnect
from fastapi.responses import FileResponse
import time
import os, json, urllib
import lancedb
from pydantic import BaseModel
from . import backend
from .utils import get_full_... | [
"lancedb.connect"
] | [((468, 496), 'logging.getLogger', 'logging.getLogger', (['"""uvicorn"""'], {}), "('uvicorn')\n", (485, 496), False, 'import logging\n'), ((504, 513), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (511, 513), False, 'from fastapi import FastAPI, BackgroundTasks, Query, WebSocket, WebSocketDisconnect\n'), ((3201, 3227... |
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 import Format
from ultralytics.data.dataset ... | [
"lancedb.connect"
] | [((1642, 1835), '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... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2782, 2802), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2797, 2802), False, 'import lancedb\n'), ((1179, 1208), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1185, 1208), True, 'import numpy as np\n'), ((3214, 3293), 'llama_index.vector_stores.utils.nod... |
# 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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.core.vector_st... | [
"lancedb.connect"
] | [((685, 712), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (702, 712), False, 'import logging\n'), ((2814, 2827), 'llama_index.core.bridge.pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (2825, 2827), False, 'from llama_index.core.bridge.pydantic import PrivateAttr\n'), ((3431, ... |
from openai import OpenAI
import streamlit as st
import os
from trubrics import Trubrics
import lancedb
from langchain_community.vectorstores import LanceDB
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain_community.callbacks import TrubricsCallbackHandler
import os... | [
"lancedb.connect"
] | [((526, 588), 'lancedb.connect', 'lancedb.connect', (['"""/mnt/d/LLM-Project/my-app/lancedb_meta_data"""'], {}), "('/mnt/d/LLM-Project/my-app/lancedb_meta_data')\n", (541, 588), False, 'import lancedb\n'), ((639, 657), 'langchain_openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (655, 657), False, 'from l... |
# 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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1697, 1890), '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... |
"""LanceDB vector store."""
from typing import Any, List, Optional
from llama_index.data_structs.node import DocumentRelationship, Node
from llama_index.vector_stores.types import (
NodeWithEmbedding,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
class LanceDBVectorStore(VectorStore):
... | [
"lancedb.connect"
] | [((1711, 1731), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1726, 1731), False, 'import lancedb\n'), ((3712, 3811), 'llama_index.data_structs.node.Node', 'Node', ([], {'doc_id': 'item.id', 'text': 'item.text', 'relationships': '{DocumentRelationship.SOURCE: item.doc_id}'}), '(doc_id=item.id, text=i... |
"""LanceDB vector store."""
from typing import Any, List, Optional
from llama_index.data_structs.node import DocumentRelationship, Node
from llama_index.vector_stores.types import (
NodeWithEmbedding,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
class LanceDBVectorStore(VectorStore):
... | [
"lancedb.connect"
] | [((1711, 1731), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1726, 1731), False, 'import lancedb\n'), ((3712, 3811), 'llama_index.data_structs.node.Node', 'Node', ([], {'doc_id': 'item.id', 'text': 'item.text', 'relationships': '{DocumentRelationship.SOURCE: item.doc_id}'}), '(doc_id=item.id, text=i... |
# 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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1680, 1873), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1680, 1873), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1680, 1873), '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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment imp... | [
"lancedb.connect"
] | [((1680, 1873), '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... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2782, 2802), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2797, 2802), False, 'import lancedb\n'), ((1179, 1208), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1185, 1208), True, 'import numpy as np\n'), ((3214, 3293), 'llama_index.vector_stores.utils.nod... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2782, 2802), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2797, 2802), False, 'import lancedb\n'), ((1179, 1208), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1185, 1208), True, 'import numpy as np\n'), ((3214, 3293), 'llama_index.vector_stores.utils.nod... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2782, 2802), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2797, 2802), False, 'import lancedb\n'), ((1179, 1208), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1185, 1208), True, 'import numpy as np\n'), ((3214, 3293), 'llama_index.vector_stores.utils.nod... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2782, 2802), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2797, 2802), False, 'import lancedb\n'), ((1179, 1208), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1185, 1208), True, 'import numpy as np\n'), ((3214, 3293), 'llama_index.vector_stores.utils.nod... |
import lancedb
import numpy as np
import pandas as pd
import pyarrow as pa
def client_vector_db(vector_db_config: dict) -> lancedb.LanceDBConnection:
"""Connect to a lancedb instance"""
return lancedb.connect(**vector_db_config)
def initialize_vector_db_indices(
client_vector_db: lancedb.LanceDBConnecti... | [
"lancedb.connect"
] | [((203, 238), 'lancedb.connect', 'lancedb.connect', ([], {}), '(**vector_db_config)\n', (218, 238), False, 'import lancedb\n'), ((1932, 1971), 'pandas.DataFrame.from_records', 'pd.DataFrame.from_records', (['data_objects'], {}), '(data_objects)\n', (1957, 1971), True, 'import pandas as pd\n'), ((568, 579), 'pyarrow.str... |
from datasets import load_dataset
from panns_inference import AudioTagging
from tqdm import tqdm
from IPython.display import Audio, display
import numpy as np
import lancedb
def create_audio_embedding(audio_data):
return at.inference(audio_data)
def insert_audio():
batches = [batch["audio"] for batch in dat... | [
"lancedb.connect"
] | [((1759, 1802), 'datasets.load_dataset', 'load_dataset', (['"""ashraq/esc50"""'], {'split': '"""train"""'}), "('ashraq/esc50', split='train')\n", (1771, 1802), False, 'from datasets import load_dataset\n'), ((1812, 1861), 'panns_inference.AudioTagging', 'AudioTagging', ([], {'checkpoint_path': 'None', 'device': '"""cud... |
import os
from datetime import datetime
from pathlib import Path
from uuid import uuid4
import lancedb
import pyarrow as pa
import tantivy
from pydantic import computed_field
from slugify import slugify
from sqlalchemy import TIMESTAMP, Engine, text
from sqlmodel import Field, Relationship, Session, SQLModel, create_e... | [
"lancedb.connect"
] | [((557, 605), 'os.environ.get', 'os.environ.get', (['"""TRACECAT__EMBEDDINGS_SIZE"""', '(512)'], {}), "('TRACECAT__EMBEDDINGS_SIZE', 512)\n", (571, 605), False, 'import os\n'), ((496, 537), 'os.path.expanduser', 'os.path.expanduser', (['"""~/.tracecat/storage"""'], {}), "('~/.tracecat/storage')\n", (514, 537), False, '... |
from dotenv import load_dotenv
from typing import List
import lancedb
import openai
import os
from gpt_pdf_bot.shared import embed_text
load_dotenv()
class ChatBot:
def __init__(self, table_name: str):
self.db = lancedb.connect(uri=".lancedb")
self.table = self.db[table_name]
def run(self)... | [
"lancedb.connect"
] | [((139, 152), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (150, 152), False, 'from dotenv import load_dotenv\n'), ((229, 260), 'lancedb.connect', 'lancedb.connect', ([], {'uri': '""".lancedb"""'}), "(uri='.lancedb')\n", (244, 260), False, 'import lancedb\n'), ((1044, 1063), 'gpt_pdf_bot.shared.embed_text', '... |
import os
from pathlib import Path
import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.schema import StrOutputParser
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceHub
from langchain_community.vectorstores import LanceDB
f... | [
"lancedb.connect"
] | [((641, 683), 'langchain_community.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': 'emb_repo'}), '(model_name=emb_repo)\n', (662, 683), False, 'from langchain_community.embeddings import HuggingFaceEmbeddings\n'), ((818, 911), 'langchain_community.llms.HuggingFaceHub', 'HuggingFaceHub', ... |
from flask import Flask, request, jsonify, Response
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
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
@app.route(... | [
"lancedb.connect"
] | [((86, 101), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (91, 101), False, 'from flask import Flask, request, jsonify, Response\n'), ((102, 111), 'flask_cors.CORS', 'CORS', (['app'], {}), '(app)\n', (106, 111), False, 'from flask_cors import CORS\n'), ((468, 486), 'flask.request.get_json', 'request.get_... |
from PIL import Image
from transformers import CLIPProcessor, CLIPModel
from openai import OpenAI
import lancedb
import os
import pandas as pd
from dotenv import load_dotenv
load_dotenv()
uri = "./sample-lancedb"
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
model_name = "openai/clip-vit-base-patch32"
def crea... | [
"lancedb.connect"
] | [((175, 188), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (186, 188), False, 'from dotenv import load_dotenv\n'), ((403, 425), 'PIL.Image.open', 'Image.open', (['image_path'], {}), '(image_path)\n', (413, 425), False, 'from PIL import Image\n'), ((479, 516), 'transformers.CLIPModel.from_pretrained', 'CLIPMod... |
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
from services.auto_research import AutoResearch
st.set_page_config(layout='wide'... | [
"lancedb.connect"
] | [((288, 348), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""', 'page_title': '"""AutoResearch"""'}), "(layout='wide', page_title='AutoResearch')\n", (306, 348), True, 'import streamlit as st\n'), ((384, 402), 'pathlib.Path', 'Path', (['"""data/notes"""'], {}), "('data/notes')\n", (388, 4... |
#!/usr/bin/env python
import numpy as np
import time
import lancedb
import pyarrow as pa
import logging
import sys
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def generate_random_data_vectors(num_vectors, dimension, offset=0):
"""
Generate random data vectors.
... | [
"lancedb.connect"
] | [((117, 213), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'format': '"""%(asctime)s - %(levelname)s - %(message)s"""'}), "(level=logging.INFO, format=\n '%(asctime)s - %(levelname)s - %(message)s')\n", (136, 213), False, 'import logging\n'), ((922, 964), 'numpy.random.random', 'np.ra... |
import argparse
import lancedb
import torch
import pyarrow as pa
import pandas as pd
from pathlib import Path
import tqdm
import numpy as np
import logging
from transformers import AutoConfig
from sentence_transformers import SentenceTransformer
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__nam... | [
"lancedb.connect"
] | [((248, 287), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (267, 287), False, 'import logging\n'), ((297, 324), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (314, 324), False, 'import logging\n'), ((352, 377), 'argparse.Argumen... |
# import ray
# from ray import serve
# from ray.serve.handle import DeploymentHandle
import logging
from collections import Counter, defaultdict, deque
import pickle
import os
import glob
import cv2
import numpy as np
import pandas as pd
import torch
from PIL import Image
import lancedb
import clip
from detic import ... | [
"lancedb.connect"
] | [((690, 717), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (707, 717), False, 'import logging\n'), ((2546, 2567), 'detectron2.structures.Instances', 'Instances', (['image_size'], {}), '(image_size)\n', (2555, 2567), False, 'from detectron2.structures import Boxes, Instances, pairwise_io... |
import os, sqlite3, lancedb, tiktoken, bcrypt
from pinecone import Pinecone, ServerlessSpec
from enum import Enum
from langchain_community.vectorstores import LanceDB, Chroma
from langchain_community.vectorstores import Pinecone as LangPinecone
import streamlit as st
def SetHeader(page_title: str):
st.set_page_con... | [
"lancedb.connect"
] | [((305, 433), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': 'page_title', 'page_icon': '"""https://indico.bnl.gov/event/19560/logo-410523303.png"""', 'layout': '"""wide"""'}), "(page_title=page_title, page_icon=\n 'https://indico.bnl.gov/event/19560/logo-410523303.png', layout='wide')\n", (3... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilter... | [
"lancedb.connect"
] | [((561, 588), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (578, 588), False, 'import logging\n'), ((2970, 2990), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2985, 2990), False, 'import lancedb\n'), ((1325, 1354), 'numpy.exp', 'np.exp', (["(-results['_distance'])"],... |
from langchain import PromptTemplate, LLMChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceBgeEmbeddings
from io import BytesIO
from langchain.document_loaders import PyP... | [
"lancedb.connect"
] | [((704, 712), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (710, 712), False, 'from langchain.llms import OpenAI\n'), ((841, 948), 'langchain.embeddings.HuggingFaceBgeEmbeddings', 'HuggingFaceBgeEmbeddings', ([], {'model_name': 'model_name', 'model_kwargs': 'model_kwargs', 'encode_kwargs': 'encode_kwargs'}), '(... |
import os
import re
import instructor
import openai
import pandas as pd
import lancedb
from typing import Optional, List
from pydantic import BaseModel, Field
from tenacity import Retrying, stop_after_attempt, wait_fixed
from src.embedding_models.models import OpenAIEmbeddings
from src.utils.logging import setup_color... | [
"lancedb.connect"
] | [((341, 372), 'src.utils.logging.setup_colored_logging', 'setup_colored_logging', (['__name__'], {}), '(__name__)\n', (362, 372), False, 'from src.utils.logging import setup_colored_logging\n'), ((504, 554), 'pydantic.Field', 'Field', (['...'], {'description': '"""The original user query."""'}), "(..., description='The... |
import logging
import lancedb
import os
from pathlib import Path
DB_TABLE_NAME = "split_files_db"
# Setting up the logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# db
db_uri = os.path.join(Path(__file__).parents[1], ".lancedb")
db = lancedb.connect(db_uri)
table = db.open_tabl... | [
"lancedb.connect"
] | [((126, 165), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (145, 165), False, 'import logging\n'), ((175, 202), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (192, 202), False, 'import logging\n'), ((276, 299), 'lancedb.connect'... |
"""
Chatbot for talking to Podcast using Langchain, Ollama and LanceDB
"""
from langchain.document_loaders import WikipediaLoader
import pandas as pd
from langchain.memory import ConversationSummaryMemory
import lancedb
from langchain.vectorstores import LanceDB
from langchain.embeddings import OpenAIEmbeddings
from l... | [
"lancedb.connect"
] | [((525, 556), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (540, 556), False, 'import lancedb\n'), ((883, 946), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(0)'}), '(chunk_size=500... |
import os
import dotenv
import gradio as gr
import lancedb
import logging
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.llms import Cohere
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.vectorstores import LanceDB
from langchain.document_lo... | [
"lancedb.connect"
] | [((563, 589), 'dotenv.load_dotenv', 'dotenv.load_dotenv', (['""".env"""'], {}), "('.env')\n", (581, 589), False, 'import dotenv\n'), ((788, 827), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (807, 827), False, 'import logging\n'), ((837, 864), 'logging.getLogg... |
import lancedb
#vectorstore functions
class Vectorstore():
"""A class to interact with the vectorstore."""
def __init__(self,) -> None:
"""Initialize the vectorstore object."""
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
self.name = name | [
"lancedb.connect"
] | [((251, 271), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (266, 271), False, 'import lancedb\n')] |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2773, 2793), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2788, 2793), False, 'import lancedb\n'), ((1170, 1199), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1176, 1199), True, 'import numpy as np\n'), ((3178, 3257), 'llama_index.vector_stores.utils.nod... |
from flask import Flask, jsonify, request, json, send_from_directory
import os
import openai
import pandas as pd
import base64
import os
import requests
import numpy as np
# import google.cloud.texttospeech as tts
import lancedb
from flask_cors import CORS
# os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'service.js... | [
"lancedb.connect"
] | [((407, 434), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (416, 434), False, 'import os\n'), ((441, 456), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (446, 456), False, 'from flask import Flask, jsonify, request, json, send_from_directory\n'), ((457, 502), 'flask_co... |
from __future__ import annotations
import json
import logging
import boto3
import embeddings
import lancedb
from config import settings
# TODO: why doesn't logger print anything?
logger = logging.getLogger(__name__)
logging.getLogger().setLevel(logging.INFO)
client = boto3.client('cloudformation', region_name='us-e... | [
"lancedb.connect"
] | [((191, 218), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (208, 218), False, 'import logging\n'), ((272, 327), 'boto3.client', 'boto3.client', (['"""cloudformation"""'], {'region_name': '"""us-east-1"""'}), "('cloudformation', region_name='us-east-1')\n", (284, 327), False, 'import bot... |
from typing import List, Union, Dict
from pathlib import Path
from enum import StrEnum
import lancedb
import numpy as np
from image_search.config.log_factory import logger
from image_search.model.image_data import ImageData
from image_search.model.error import Error, ErrorCode
from image_search.vector_db.imagedb_sche... | [
"lancedb.connect"
] | [((1246, 1284), 'lancedb.connect', 'lancedb.connect', (['cfg.lance_db_location'], {}), '(cfg.lance_db_location)\n', (1261, 1284), False, 'import lancedb\n'), ((2025, 2101), 'numpy.array_equal', 'np.array_equal', (['first_result[FIELD_IMAGE_VECTOR]', 'image_data.image_embedding'], {}), '(first_result[FIELD_IMAGE_VECTOR]... |
import lancedb
import pyarrow as pa
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 1536)),
pa.field("review", pa.string()),
pa.field("id",pa.string())
])
tbl = db.create_table("review_table", schema=schema)
print(tbl) | [
"lancedb.connect"
] | [((69, 89), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (84, 89), False, 'import lancedb\n'), ((195, 206), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (204, 206), True, 'import pyarrow as pa\n'), ((229, 240), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (238, 240), True, 'import pyarrow as ... |
"""The lambda function for the Bolt app."""
import json
import logging
import os
import re
import time
from typing import Any
import lancedb
import slack_bolt
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from langchain.memory.buffer import ConversationBufferMemory
from langch... | [
"lancedb.connect"
] | [((946, 990), 'slack_bolt.adapter.aws_lambda.SlackRequestHandler.clear_all_log_handlers', 'SlackRequestHandler.clear_all_log_handlers', ([], {}), '()\n', (988, 990), False, 'from slack_bolt.adapter.aws_lambda import SlackRequestHandler\n'), ((991, 1066), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '""... |
"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilter... | [
"lancedb.connect"
] | [((585, 612), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (602, 612), False, 'import logging\n'), ((3266, 3286), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3281, 3286), False, 'import lancedb\n'), ((1349, 1378), 'numpy.exp', 'np.exp', (["(-results['_distance'])"],... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2773, 2793), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2788, 2793), False, 'import lancedb\n'), ((1170, 1199), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1176, 1199), True, 'import numpy as np\n'), ((3205, 3284), 'llama_index.vector_stores.utils.nod... |
from dotenv import load_dotenv
from pathlib import Path
from typing import List
import lancedb
import pypdf
from gpt_pdf_bot.shared import embed_text
from gpt_pdf_bot.types import Chunk, Document, Page
load_dotenv()
class PdfIngestionPipeline:
def __init__(self, pdf_directory: str):
self.pdf_directory =... | [
"lancedb.connect"
] | [((204, 217), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (215, 217), False, 'from dotenv import load_dotenv\n'), ((321, 340), 'pathlib.Path', 'Path', (['pdf_directory'], {}), '(pdf_directory)\n', (325, 340), False, 'from pathlib import Path\n'), ((359, 390), 'lancedb.connect', 'lancedb.connect', ([], {'uri'... |
import streamlit as st
import pandas as pd
import json
import requests
from datetime import datetime
from pathlib import Path
import lancedb
from services.lancedb_index import IndexDocuments
from services.lancedb_notes import IndexDocumentsNotes
import yaml
### For multipage note taking, save to a json and then load t... | [
"lancedb.connect"
] | [((864, 918), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""', 'page_title': '"""Search"""'}), "(layout='wide', page_title='Search')\n", (882, 918), True, 'import streamlit as st\n'), ((953, 971), 'pathlib.Path', 'Path', (['"""data/notes"""'], {}), "('data/notes')\n", (957, 971), False, ... |
"""
Unit test for retrieve_utils.py
"""
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,
)
from aut... | [
"lancedb.connect"
] | [((1021, 1083), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1039, 1083), False, 'import pytest\n'), ((619, 644), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (634, 644), False, 'i... |
from langchain_community.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 = embed... | [
"lancedb.connect"
] | [((200, 216), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (214, 216), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((226, 257), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
"""
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,
is_url,
create_vector_db_from_dir,
query_vector_db,
)
from autogen.token_count_utils import count_token
import os
import pyte... | [
"lancedb.connect"
] | [((365, 390), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (380, 390), False, 'import os\n'), ((7383, 7396), 'pytest.main', 'pytest.main', ([], {}), '()\n', (7394, 7396), False, 'import pytest\n'), ((7458, 7481), 'os.path.exists', 'os.path.exists', (['db_path'], {}), '(db_path)\n', (7472, 7... |
import lancedb
import numpy as np
import pandas as pd
import pyarrow as pa
def client_vector_db(vector_db_config: dict) -> lancedb.LanceDBConnection:
"""Connect to a lancedb instance"""
return lancedb.connect(**vector_db_config)
def initialize_vector_db_indices(
client_vector_db: lancedb.LanceDBConnecti... | [
"lancedb.connect"
] | [((203, 238), 'lancedb.connect', 'lancedb.connect', ([], {}), '(**vector_db_config)\n', (218, 238), False, 'import lancedb\n'), ((1932, 1971), 'pandas.DataFrame.from_records', 'pd.DataFrame.from_records', (['data_objects'], {}), '(data_objects)\n', (1957, 1971), True, 'import pandas as pd\n'), ((568, 579), 'pyarrow.str... |
import os, sqlite3, lancedb, tiktoken, bcrypt
from pinecone import Pinecone, ServerlessSpec
from enum import Enum
from langchain_community.vectorstores import LanceDB, Chroma
from langchain_community.vectorstores import Pinecone as LangPinecone
import streamlit as st
def SetHeader(page_title: str):
st.set_page_con... | [
"lancedb.connect"
] | [((305, 433), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': 'page_title', 'page_icon': '"""https://indico.bnl.gov/event/19560/logo-410523303.png"""', 'layout': '"""wide"""'}), "(page_title=page_title, page_icon=\n 'https://indico.bnl.gov/event/19560/logo-410523303.png', layout='wide')\n", (3... |
from langchain import PromptTemplate, LLMChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceBgeEmbeddings
from io import BytesIO
from langchain.document_loaders import PyP... | [
"lancedb.connect"
] | [((704, 712), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (710, 712), False, 'from langchain.llms import OpenAI\n'), ((841, 948), 'langchain.embeddings.HuggingFaceBgeEmbeddings', 'HuggingFaceBgeEmbeddings', ([], {'model_name': 'model_name', 'model_kwargs': 'model_kwargs', 'encode_kwargs': 'encode_kwargs'}), '(... |
"""LanceDB vector store."""
from typing import Any, List, Optional
import numpy as np
from pandas import DataFrame
from llama_index.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.vector_stores.types import (
MetadataFilters,
VectorSt... | [
"lancedb.connect"
] | [((2773, 2793), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2788, 2793), False, 'import lancedb\n'), ((1170, 1199), 'numpy.exp', 'np.exp', (["(-results['_distance'])"], {}), "(-results['_distance'])\n", (1176, 1199), True, 'import numpy as np\n'), ((3205, 3284), 'llama_index.vector_stores.utils.nod... |
"""
Unit test for retrieve_utils.py
"""
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,
)
from aut... | [
"lancedb.connect"
] | [((1021, 1083), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1039, 1083), False, 'import pytest\n'), ((619, 644), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (634, 644), False, 'i... |
"""
Unit test for retrieve_utils.py
"""
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,
)
from aut... | [
"lancedb.connect"
] | [((1021, 1083), 'pytest.mark.skipif', 'pytest.mark.skipif', (['skip'], {'reason': '"""dependency is not installed"""'}), "(skip, reason='dependency is not installed')\n", (1039, 1083), False, 'import pytest\n'), ((619, 644), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (634, 644), False, 'i... |
from langchain_community.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 = embed... | [
"lancedb.connect"
] | [((200, 216), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (214, 216), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((226, 257), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
from langchain_community.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 = embed... | [
"lancedb.connect"
] | [((200, 216), 'tests.integration_tests.vectorstores.fake_embeddings.FakeEmbeddings', 'FakeEmbeddings', ([], {}), '()\n', (214, 216), False, 'from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings\n'), ((226, 257), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lance... |
"""
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,
is_url,
create_vector_db_from_dir,
query_vector_db,
)
from autogen.token_count_utils import count_token
import os
import pyte... | [
"lancedb.connect"
] | [((365, 390), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (380, 390), False, 'import os\n'), ((7383, 7396), 'pytest.main', 'pytest.main', ([], {}), '()\n', (7394, 7396), False, 'import pytest\n'), ((7458, 7481), 'os.path.exists', 'os.path.exists', (['db_path'], {}), '(db_path)\n', (7472, 7... |
"""
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,
is_url,
create_vector_db_from_dir,
query_vector_db,
)
from autogen.token_count_utils import count_token
import os
import pyte... | [
"lancedb.connect"
] | [((365, 390), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (380, 390), False, 'import os\n'), ((7383, 7396), 'pytest.main', 'pytest.main', ([], {}), '()\n', (7394, 7396), False, 'import pytest\n'), ((7458, 7481), 'os.path.exists', 'os.path.exists', (['db_path'], {}), '(db_path)\n', (7472, 7... |
import os
import lancedb
import shutil
import uvicorn
import openai
from fastapi import FastAPI, HTTPException, WebSocket, UploadFile, File
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate... | [
"lancedb.connect"
] | [((621, 737), '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", (628, 737), 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"
] | [((8057, 8070), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (8068, 8070), False, 'from dotenv import load_dotenv\n'), ((8801, 8816), 'typer.run', 'typer.run', (['main'], {}), '(main)\n', (8810, 8816), False, 'import typer\n'), ((2034, 2060), 'os.path.isfile', 'os.path.isfile', (['cache_file'], {}), '(cache_f... |
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 RecursiveCharacterTextSplitter
from l... | [
"lancedb.connect"
] | [((1788, 1806), 'pathlib.Path', 'Path', (['"""cities.pkl"""'], {}), "('cities.pkl')\n", (1792, 1806), False, 'from pathlib import Path\n'), ((2462, 2526), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(50)'}), '(chunk_size=500,... |
from flask import Flask, render_template, jsonify, request
from scripts.mock_llm_api import llm_api
import lancedb
import pandas as pd
uri = "data/lancedb"
db = lancedb.connect(uri)
# Set initial entries in items vector database
def _reset_tables():
items = ['Fire', 'Earth', 'Water', 'Wind']
descriptions = ["... | [
"lancedb.connect"
] | [((162, 182), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (177, 182), False, 'import lancedb\n'), ((879, 894), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (884, 894), False, 'from flask import Flask, render_template, jsonify, request\n'), ((640, 717), 'pandas.DataFrame', 'pd.DataFram... |
import logging
import json
import gradio as gr
import numpy as np
import lancedb
import os
from huggingface_hub import AsyncInferenceClient
# Setting up the logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# db
TABLE_NAME = "docs"
TEXT_COLUMN = "text"
BATCH_SIZE = int(os.getenv("B... | [
"lancedb.connect"
] | [((167, 206), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (186, 206), False, 'import logging\n'), ((216, 243), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (233, 243), False, 'import logging\n'), ((573, 609), 'lancedb.connect'... |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
import os
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
from langchain.embeddings import BedrockEmbeddings
from langchain.document_loaders import PyPDFDirectoryL... | [
"lancedb.connect"
] | [((384, 403), 'langchain.embeddings.BedrockEmbeddings', 'BedrockEmbeddings', ([], {}), '()\n', (401, 403), False, 'from langchain.embeddings import BedrockEmbeddings\n'), ((625, 682), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(1000)', 'chunk_overlap': '(200)'}), '(ch... |
# 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 PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from engine.data.augment import F... | [
"lancedb.connect"
] | [((1672, 1865), 'engine.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', normalize=Fal... |
import pyarrow as pa
from typing import Union
from dryg.settings import DB_URI
import lancedb
def connection() -> lancedb.LanceDBConnection:
"""
Connect to the database
Returns:
lancedb.LanceDBConnection: LanceDBConnection object
"""
db = lancedb.connect(DB_URI)
return db
def open_t... | [
"lancedb.connect"
] | [((271, 294), 'lancedb.connect', 'lancedb.connect', (['DB_URI'], {}), '(DB_URI)\n', (286, 294), False, 'import lancedb\n')] |
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"
] | [((7282, 7295), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (7293, 7295), False, 'from dotenv import load_dotenv\n'), ((8026, 8041), 'typer.run', 'typer.run', (['main'], {}), '(main)\n', (8035, 8041), False, 'import typer\n'), ((2219, 2245), 'os.path.isfile', 'os.path.isfile', (['cache_file'], {}), '(cache_f... |
import json
from generate_data import *
from create_embeddings import *
import lancedb
uri = "./sample-lancedb"
db = lancedb.connect(uri)
text_table = "table_from_df_text"
img_table = "table_from_df_images"
tbl_txt = db.open_table(text_table)
tbl_img = db.open_table(img_table)
with open('./test_data.json') as f:
... | [
"lancedb.connect"
] | [((119, 139), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (134, 139), False, 'import lancedb\n'), ((1192, 1239), 'json.loads', 'json.loads', (['response.choices[0].message.content'], {}), '(response.choices[0].message.content)\n', (1202, 1239), False, 'import json\n')] |
# 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... |
from PIL import Image
import streamlit as st
import openai
#exercise 11
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
#exercise 12
from langchain.memory import ConversationBufferWindowMemory
#exercise 13
... | [
"lancedb.connect"
] | [((649, 660), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (658, 660), False, 'import os\n'), ((681, 710), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (693, 710), False, 'import os\n'), ((719, 752), 'os.path.exists', 'os.path.exists', (['WORKING_DIRECTORY'], {}), '(WORKING_DIR... |
from PIL import Image
import streamlit as st
import openai
#exercise 11
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
#exercise 12
from langchain.memory import ConversationBufferWindowMemory
#exercise 13
from langchain.document_loaders import TextLo... | [
"lancedb.connect"
] | [((1106, 1117), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (1115, 1117), False, 'import os\n'), ((1138, 1167), 'os.path.join', 'os.path.join', (['cwd', '"""database"""'], {}), "(cwd, 'database')\n", (1150, 1167), False, 'import os\n'), ((1259, 1304), 'os.path.join', 'os.path.join', (['WORKING_DIRECTORY', '"""default_d... |
import os
import glob
import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_mod... | [
"lancedb.connect"
] | [((938, 967), 'warnings.simplefilter', 'warnings.simplefilter', (['"""once"""'], {}), "('once')\n", (959, 967), False, 'import warnings\n'), ((2673, 2701), 'glob.glob', 'glob.glob', (['data_file_pattern'], {}), '(data_file_pattern)\n', (2682, 2701), False, 'import glob\n'), ((3011, 3048), 'tqdm.tqdm', 'tqdm.tqdm', (['f... |
"""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 (
MetadataFilters,
NodeWithEmbedding,
VectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
from... | [
"lancedb.connect"
] | [((2271, 2291), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (2286, 2291), False, 'import lancedb\n'), ((2716, 2807), 'llama_index.vector_stores.utils.node_to_metadata_dict', 'node_to_metadata_dict', (['result.node'], {'remove_text': '(True)', 'flat_metadata': 'self.flat_metadata'}), '(result.node, r... |
from time import time_ns
import lancedb
uri = "./.lancedb"
db = lancedb.connect(uri)
tns = db.table_names()
print(tns)
tn = 'my_table'
now = time_ns()
if (tn not in tns):
# 创建表的时候就确定了字段结构了。
# 之后通过 add 添加字段无效。
table = db.create_table(
tn,
data=[
{"vector": [3.1, 4.1], "item": "f... | [
"lancedb.connect"
] | [((65, 85), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (80, 85), False, 'import lancedb\n'), ((143, 152), 'time.time_ns', 'time_ns', ([], {}), '()\n', (150, 152), False, 'from time import time_ns\n')] |
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