| Name | pneuma JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Pneuma is an LLM-powered data discovery system for tabular data. |
| upload_time | 2025-02-26 14:43:57 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 Pneuma Team
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
llm
data discovery
tabular data
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
accelerate
bm25s
chroma-hnswlib
duckdb
fire
chromadb-deterministic
jax
pandas
peft
PyStemmer
sentence-transformers
torch
transformers
openai
tiktoken
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Pneuma
[](https://thedatastation.github.io/pneuma)
[](https://colab.research.google.com/github/TheDataStation/pneuma/blob/main/quickstart-colab.ipynb)
[](https://pypi.org/project/pneuma/)
`Pneuma` is an LLM-powered data discovery system for tabular data. Given a natural language query,
`Pneuma` searches an indexed collection and retrieves the most relevant tables for the question. It performs this search by leveraging both **content** (columns and rows) and **context** (metadata) to match tables with questions.
## Getting Started
If you would like to try `Pneuma` without installation, you can use our [Colab notebook](https://colab.research.google.com/github/TheDataStation/pneuma/blob/main/quickstart.ipynb). For local installation, you may use an OpenAI API token or a local GPU **with at least 20 GB of VRAM** (to load and prompt both the LLM and embedding model).
To install the latest stable release from PyPI:
```bash
$ pip install pneuma
```
To install the most recent version from the repository:
```bash
$ git clone https://github.com/TheDataStation/Pneuma.git
$ cd Pneuma
$ pip install -r requirements.txt
```
### Installation Note
To ensure smooth installation and usage, we **strongly recommend** installing `Miniconda` (follow [this](https://docs.anaconda.com/miniconda/install/)). Then, create a new environment and install the CUDA Toolkit:
```bash
$ conda create --name pneuma python=3.12.2 -y
$ conda activate pneuma
$ conda install -c nvidia cuda-toolkit -y
```
## Quick Start
The simplest way to explore `Pneuma` is by running the [quickstart Jupyter notebook](https://github.com/TheDataStation/pneuma/blob/main/quickstart.ipynb). This notebook walks you through `Pneuma`'s full workflow, from **data registration** to **querying**. For those eager to dive in, here’s a snippet showcasing its functionality:
```python
from src.pneuma import Pneuma
# Initialize Pneuma
out_path = "out_demo/storage"
pneuma = Pneuma(
out_path=out_path,
llm_path="Qwen/Qwen2.5-7B-Instruct",
embed_path="BAAI/bge-base-en-v1.5",
)
pneuma.setup()
# Register dataset & summarize it
data_path = "data_src/sample_data/csv"
pneuma.add_tables(path=data_path, creator="demo_user")
pneuma.summarize()
# Add context (metadata) if available
metadata_path = "data_src/sample_data/metadata.csv"
pneuma.add_metadata(metadata_path=metadata_path)
# Generate index
pneuma.generate_index(index_name="demo_index")
# Query the index
response = pneuma.query_index(
index_name="demo_index",
query="Which dataset contains climate issues?",
k=1,
n=5,
alpha=0.5,
)
response = json.loads(response)
query = response["data"]["query"]
retrieved_tables = response["data"]["response"]
```
## Pneuma's CLI
To use `Pneuma` via the command line, refer to the [CLI reference documentation](https://github.com/TheDataStation/pneuma/blob/main/cli.md) for detailed instructions.
Raw data
{
"_id": null,
"home_page": null,
"name": "pneuma",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "llm, data discovery, tabular data",
"author": null,
"author_email": "Pneuma Team <pneuma-team@googlegroups.com>",
"download_url": "https://files.pythonhosted.org/packages/80/1b/213f298bd697586935c5529b7d45daa5befb2539241c6e4e92b68d05edbe/pneuma-0.0.3.tar.gz",
"platform": null,
"description": "\n\n# Pneuma\n[](https://thedatastation.github.io/pneuma)\n[](https://colab.research.google.com/github/TheDataStation/pneuma/blob/main/quickstart-colab.ipynb)\n[](https://pypi.org/project/pneuma/)\n\n`Pneuma` is an LLM-powered data discovery system for tabular data. Given a natural language query,\n`Pneuma` searches an indexed collection and retrieves the most relevant tables for the question. It performs this search by leveraging both **content** (columns and rows) and **context** (metadata) to match tables with questions.\n\n\n## Getting Started\n\nIf you would like to try `Pneuma` without installation, you can use our [Colab notebook](https://colab.research.google.com/github/TheDataStation/pneuma/blob/main/quickstart.ipynb). For local installation, you may use an OpenAI API token or a local GPU **with at least 20 GB of VRAM** (to load and prompt both the LLM and embedding model).\n\nTo install the latest stable release from PyPI:\n\n```bash\n$ pip install pneuma\n```\n\nTo install the most recent version from the repository:\n\n```bash\n$ git clone https://github.com/TheDataStation/Pneuma.git\n$ cd Pneuma\n$ pip install -r requirements.txt\n```\n\n### Installation Note\n\nTo ensure smooth installation and usage, we **strongly recommend** installing `Miniconda` (follow [this](https://docs.anaconda.com/miniconda/install/)). Then, create a new environment and install the CUDA Toolkit:\n\n```bash\n$ conda create --name pneuma python=3.12.2 -y\n$ conda activate pneuma\n$ conda install -c nvidia cuda-toolkit -y\n```\n\n## Quick Start\n\nThe simplest way to explore `Pneuma` is by running the [quickstart Jupyter notebook](https://github.com/TheDataStation/pneuma/blob/main/quickstart.ipynb). This notebook walks you through `Pneuma`'s full workflow, from **data registration** to **querying**. For those eager to dive in, here\u2019s a snippet showcasing its functionality:\n\n```python\nfrom src.pneuma import Pneuma\n\n# Initialize Pneuma\nout_path = \"out_demo/storage\"\npneuma = Pneuma(\n out_path=out_path,\n llm_path=\"Qwen/Qwen2.5-7B-Instruct\",\n embed_path=\"BAAI/bge-base-en-v1.5\",\n)\npneuma.setup()\n\n# Register dataset & summarize it\ndata_path = \"data_src/sample_data/csv\"\npneuma.add_tables(path=data_path, creator=\"demo_user\")\npneuma.summarize()\n\n# Add context (metadata) if available\nmetadata_path = \"data_src/sample_data/metadata.csv\"\npneuma.add_metadata(metadata_path=metadata_path)\n\n# Generate index\npneuma.generate_index(index_name=\"demo_index\")\n\n# Query the index\nresponse = pneuma.query_index(\n index_name=\"demo_index\",\n query=\"Which dataset contains climate issues?\",\n k=1,\n n=5,\n alpha=0.5,\n)\nresponse = json.loads(response)\nquery = response[\"data\"][\"query\"]\nretrieved_tables = response[\"data\"][\"response\"]\n```\n\n## Pneuma's CLI\n\nTo use `Pneuma` via the command line, refer to the [CLI reference documentation](https://github.com/TheDataStation/pneuma/blob/main/cli.md) for detailed instructions.\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2025 Pneuma Team\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
"summary": "Pneuma is an LLM-powered data discovery system for tabular data.",
"version": "0.0.3",
"project_urls": {
"documentation": "https://github.com/TheDataStation/pneuma#readme",
"issues": "https://github.com/TheDataStation/pneuma/issues",
"repository": "https://github.com/TheDataStation/pneuma"
},
"split_keywords": [
"llm",
" data discovery",
" tabular data"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "6bfa3a1d2dcb78a1a69fffd09f1fa8715b663d87281704d82d64e927588cb652",
"md5": "06de7c8c7439b8011d9f6015138c5c2e",
"sha256": "d1d727949878ee8834cad85aa29992e5e47fdd4bb339a1160f26024db146fcc5"
},
"downloads": -1,
"filename": "pneuma-0.0.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "06de7c8c7439b8011d9f6015138c5c2e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 32176,
"upload_time": "2025-02-26T14:43:55",
"upload_time_iso_8601": "2025-02-26T14:43:55.525509Z",
"url": "https://files.pythonhosted.org/packages/6b/fa/3a1d2dcb78a1a69fffd09f1fa8715b663d87281704d82d64e927588cb652/pneuma-0.0.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "801b213f298bd697586935c5529b7d45daa5befb2539241c6e4e92b68d05edbe",
"md5": "01ce4ea5b5f709571d1f5d87ee2dd1cb",
"sha256": "9f3195c81771364a0c4c3853750471d5f5dee7956ecd7216548d3875066184e4"
},
"downloads": -1,
"filename": "pneuma-0.0.3.tar.gz",
"has_sig": false,
"md5_digest": "01ce4ea5b5f709571d1f5d87ee2dd1cb",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 27987,
"upload_time": "2025-02-26T14:43:57",
"upload_time_iso_8601": "2025-02-26T14:43:57.409873Z",
"url": "https://files.pythonhosted.org/packages/80/1b/213f298bd697586935c5529b7d45daa5befb2539241c6e4e92b68d05edbe/pneuma-0.0.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-26 14:43:57",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "TheDataStation",
"github_project": "pneuma#readme",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "accelerate",
"specs": [
[
">=",
"1.3.0"
]
]
},
{
"name": "bm25s",
"specs": [
[
"==",
"0.1.10"
]
]
},
{
"name": "chroma-hnswlib",
"specs": [
[
"==",
"0.7.6"
]
]
},
{
"name": "duckdb",
"specs": [
[
">=",
"1.1.3"
]
]
},
{
"name": "fire",
"specs": [
[
">=",
"0.7.0"
]
]
},
{
"name": "chromadb-deterministic",
"specs": [
[
"==",
"0.5.5.post6"
]
]
},
{
"name": "jax",
"specs": [
[
">=",
"0.4.33"
]
]
},
{
"name": "pandas",
"specs": [
[
">=",
"2.2.2"
]
]
},
{
"name": "peft",
"specs": [
[
">=",
"0.14.0"
]
]
},
{
"name": "PyStemmer",
"specs": [
[
">=",
"2.2.0.3"
]
]
},
{
"name": "sentence-transformers",
"specs": [
[
">=",
"3.4.1"
]
]
},
{
"name": "torch",
"specs": [
[
">=",
"2.5.1"
]
]
},
{
"name": "transformers",
"specs": [
[
">=",
"4.48.3"
]
]
},
{
"name": "openai",
"specs": [
[
">=",
"1.63.2"
]
]
},
{
"name": "tiktoken",
"specs": [
[
">=",
"0.9.0"
]
]
}
],
"lcname": "pneuma"
}
