first commit
Browse files- .gitignore +165 -0
- README.md +0 -0
- poetry.lock +0 -0
- pyproject.toml +17 -0
- wikipedia_kb/configs/knowledge_base_deployment.json +16 -0
- wikipedia_kb/data/wikipedia_kb.parquet +3 -0
- wikipedia_kb/run.py +51 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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test.ipynb
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README.md
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poetry.lock
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pyproject.toml
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[tool.poetry]
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name = "wikipedia_kb"
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version = "0.1.0"
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description = "Wikipedia Knowledge Base"
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authors = ["Mohamed Arshath <marshath@gmail.com>"]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.10,<=3.13"
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naptha-sdk = {git = "https://github.com/NapthaAI/naptha-sdk.git"}
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pydantic = "^2.9.2"
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pandas = "^2.2.3"
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pyarrow = "^18.1.0"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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wikipedia_kb/configs/knowledge_base_deployment.json
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{
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"name": "kb_deployment_1",
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"module": {"name": "wikipedia_kb"},
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"kb_node_url": "http://localhost:7001",
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"kb_config": {
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"table_name": "wikipedia_kb",
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"query_col": "title",
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"answer_col": "text",
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"schema": {
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"id": {"type": "INTEGER", "primary_key": true},
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"url": {"type": "TEXT"},
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"title": {"type": "TEXT"},
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"text": {"type": "TEXT"}
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}
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}
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}
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wikipedia_kb/data/wikipedia_kb.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e31369bc377665291427b7a1398408b040d1a07852664e645af3c1e5170cdae
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size 133886521
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wikipedia_kb/run.py
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import logging
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import pandas as pd
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from tqdm import tqdm
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from typing import Dict, Any
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from naptha_sdk.client.node import Node
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logger = logging.getLogger(__name__)
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async def run(kb_run: Dict[str, Any], *args, **kwargs):
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"""
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Run the Wikipedia Knowledge Base deployment
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Args:
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kb_run: Knowledge base run configuration containing deployment details
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"""
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logger.info(f"Running Wikipedia Knowledge Base with deployment: {kb_run}")
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# Extract configuration
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kb_config = kb_run.kb_deployment.kb_config
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if not isinstance(kb_config, dict):
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kb_config = kb_config.model_dump()
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table_name = kb_config['table_name']
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schema = kb_config['schema']
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# Create a node client
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node_client = Node(kb_run.kb_node_url)
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# Read the parquet file
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df = pd.read_parquet("./wikipedia_kb/data/wikipedia_kb.parquet")
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try:
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# Create the table
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logger.info(f"Creating table {table_name}")
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await node_client.create_dynamic_table(table_name, schema)
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# Convert DataFrame rows to dictionaries and add them to the table
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logger.info("Adding rows to table")
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for _, row in tqdm(df.iterrows(), total=len(df)):
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row_data = {
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"id": int(row['id']),
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"url": str(row['url']),
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"title": str(row['title']),
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"text": str(row['text'])
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}
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await node_client.add_dynamic_row(table_name, row_data)
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logger.info(f"Successfully populated {table_name} table with {len(df)} rows")
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except Exception as e:
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raise
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