| Name | muzlin JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Muzlin: a filtering toolset for semantic machine learning |
| upload_time | 2024-11-17 18:01:07 |
| maintainer | Daniel Kulik |
| docs_url | None |
| author | Daniel Kulik |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Daniel Kulik 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 |
rag
outlier-detection
filtering
retrieval
semantic
ml
llm
nlp
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Logo.png
:target: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Logo.png
:alt: Muzlin
*When a filter cloth 🏳️ is needed rather than a simple RAG 🏴☠*
**Deployment, Stats, & License**
|badge_pypi| |badge_stars| |badge_downloads| |badge_versions|
|badge_licence|
.. |badge_pypi| image:: https://img.shields.io/pypi/v/muzlin.svg?color=brightgreen&logo=pypi&logoColor=white
:alt: PyPI version
:target: https://pypi.org/project/muzlin/
.. |badge_stars| image:: https://img.shields.io/github/stars/KulikDM/muzlin.svg?logo=github&logoColor=white&style=flat
:alt: GitHub stars
:target: https://github.com/KulikDM/muzlin/stargazers
.. |badge_downloads| image:: https://img.shields.io/badge/dynamic/xml?url=https%3A%2F%2Fstatic.pepy.tech%2Fbadge%2Fmuzlin&query=%2F%2F*%5Blocal-name()%20%3D%20%27text%27%5D%5Blast()%5D&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyBzdHlsZT0iZW5hYmxlLWJhY2tncm91bmQ6bmV3IDAgMCAyNCAyNDsiIHZlcnNpb249IjEuMSIgdmlld0JveD0iMCAwIDI0IDI0IiB4bWw6c3BhY2U9InByZXNlcnZlIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHhtbG5zOnhsaW5rPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5L3hsaW5rIj48ZyBpZD0iaW5mbyIvPjxnIGlkPSJpY29ucyI%2BPGcgaWQ9InNhdmUiPjxwYXRoIGQ9Ik0xMS4yLDE2LjZjMC40LDAuNSwxLjIsMC41LDEuNiwwbDYtNi4zQzE5LjMsOS44LDE4LjgsOSwxOCw5aC00YzAsMCwwLjItNC42LDAtN2MtMC4xLTEuMS0wLjktMi0yLTJjLTEuMSwwLTEuOSwwLjktMiwyICAgIGMtMC4yLDIuMywwLDcsMCw3SDZjLTAuOCwwLTEuMywwLjgtMC44LDEuNEwxMS4yLDE2LjZ6IiBmaWxsPSIjZWJlYmViIi8%2BPHBhdGggZD0iTTE5LDE5SDVjLTEuMSwwLTIsMC45LTIsMnYwYzAsMC42LDAuNCwxLDEsMWgxNmMwLjYsMCwxLTAuNCwxLTF2MEMyMSwxOS45LDIwLjEsMTksMTksMTl6IiBmaWxsPSIjZWJlYmViIi8%2BPC9nPjwvZz48L3N2Zz4%3D&label=downloads
:alt: Downloads
:target: https://pepy.tech/project/muzlin
.. |badge_versions| image:: https://img.shields.io/pypi/pyversions/muzlin.svg?logo=python&logoColor=white
:alt: Python versions
:target: https://pypi.org/project/muzlin/
.. |badge_licence| image:: https://img.shields.io/github/license/KulikDM/muzlin.svg?logo=data:image/svg+xml;base64,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
:alt: License
:target: https://github.com/KulikDM/muzlin/blob/master/LICENSE
----
#############
What is it?
#############
Muzlin merges classical ML with advanced generative AI to efficiently
filter text in the context of NLP and LLMs. It answers key questions in
semantic-based workflows, such as:
- Does a RAG/GraphRAG have the right context to answer a question?
- Is the topk retrieved context too dense/sparse?
- Does the generated response hallucinate or deviate from the provided
context?
- Should new extracted text be added to an existing RAG?
- Can we detect inliers and outliers in collections of text embeddings
(e.g. context, user question and answers, synthetic generated data,
etc...)?
**Note:** While production-ready, Muzlin is still evolving and subject
to significant changes!
############
Quickstart
############
#. **Install** Muzlin using pip:
.. code:: bash
pip install muzlin
#. **Create text embeddings** with a pre-trained model:
.. code:: python
import numpy as np
from muzlin.encoders import HuggingFaceEncoder
encoder = HuggingFaceEncoder()
vectors = encoder(texts) # texts is a list of strings
vectors = np.array(vectors)
np.save('vectors', vectors)
#. **Build an anomaly detection model** for filtering:
.. code:: python
from muzlin.anomaly import OutlierDetector
from pyod.models.pca import PCA
vectors = np.load('vectors.npy') # Load pre-saved vectors
od = PCA(contamination=0.02)
clf = OutlierDetector(mlflow=False, detector=od) # Saves joblib moddel
clf.fit(vectors)
#. **Filter new text** using the trained model:
.. code:: python
from muzlin.anomaly import OutlierDetector
from muzlin.encoders import HuggingFaceEncoder
import numpy as np
clf = OutlierDetector(model='outlier_detector.pkl') # Load the model
encoder = HuggingFaceEncoder()
vector = encoder(['Who was the first man to walk on the moon?'])
vector = np.array(vector).reshape(1, -1)
label = clf.predict(vector)
##############
Integrations
##############
Muzlin integrates with a wide array of libraries for anomaly detection,
vector encoding, and graph-based setups.
+-----------------------------------+-------------------------+----------------------+
| **Anomaly Detection** | **Encoders** | **Vector Index** |
+===================================+=========================+======================+
| - Scikit-Learn | - HuggingFace | - LangChain |
| - PyOD (vector) | - OpenAI | - LlamaIndex |
| - PyGOD (graph) | - Cohere | |
| - PyThresh (thresholding) | - Azure | |
| | - Google | |
| | - Amazon Bedrock | |
| | - Fastembed | |
+-----------------------------------+-------------------------+----------------------+
**Simple Schematic Implementation**
.. image:: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Simple_Example.png
:target: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Simple_Example.png
:alt: Muzlin Pipeline
----
###########
Resources
###########
**Example Notebooks**
+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+
| Notebook | Description |
+===================================================================================================================+=============================================================================+
| `Introduction <https://github.com/KulikDM/muzlin/blob/main/examples/00_Introduction.ipynb>`_ | Basic semantic vector-based outlier detection |
+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+
| `Optimal Threshold <https://github.com/KulikDM/muzlin/blob/main/examples/01_Threshold_Optimization.ipynb>`_ | Selecting optimal thresholds using various methods |
+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+
| `Cluster-Based Filtering <https://github.com/KulikDM/muzlin/blob/main/examples/02_Cluster_Filtering.ipynb>`_ | Cluster-based filtering for question answering |
+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+
| `Graph-Based Filtering <https://github.com/KulikDM/muzlin/blob/main/examples/03_Graph_Filtering.ipynb>`_ | Using graph-based anomaly detection for semantic graphs like GraphRAG |
+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+
############
What Else?
############
Looking for more? Check out other useful libraries like `Semantic Router
<https://github.com/aurelio-labs/semantic-router>`_, `CRAG
<https://github.com/HuskyInSalt/CRAG>`_, and `Scikit-LLM
<https://github.com/iryna-kondr/scikit-llm>`_
----
##############
Contributing
##############
**Muzlin is still evolving!** At the moment their are major changes
being done and the structure of Muzlin is still being refined. For now,
please leave a bug report and potential new code for any fixes or
improvements. You will be added as a co-author if it is implemented.
Once this phase has been completed then ->
Anyone is welcome to contribute to Muzlin:
- Please share your ideas and ask questions by opening an issue.
- To contribute, first check the Issue list for the "help wanted" tag
and comment on the one that you are interested in. The issue will
then be assigned to you.
- If the bug, feature, or documentation change is novel (not in the
Issue list), you can either log a new issue or create a pull request
for the new changes.
- To start, fork the **dev branch** and add your
improvement/modification/fix.
- To make sure the code has the same style and standard, please refer
to detector.py for example.
- Create a pull request to the **dev branch** and follow the pull
request template `PR template
<https://github.com/KulikDM/muzlin/blob/main/.github/PULL_REQUEST_TEMPLATE.md>`_
- Please make sure that all code changes are accompanied with proper
new/updated test functions. Automatic tests will be triggered. Before
the pull request can be merged, make sure that all the tests pass.
Raw data
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texts is a list of strings\n vectors = np.array(vectors)\n np.save('vectors', vectors)\n\n#. **Build an anomaly detection model** for filtering:\n\n .. code:: python\n\n from muzlin.anomaly import OutlierDetector\n from pyod.models.pca import PCA\n\n vectors = np.load('vectors.npy') # Load pre-saved vectors\n\n od = PCA(contamination=0.02)\n\n clf = OutlierDetector(mlflow=False, detector=od) # Saves joblib moddel\n clf.fit(vectors)\n\n#. **Filter new text** using the trained model:\n\n .. code:: python\n\n from muzlin.anomaly import OutlierDetector\n from muzlin.encoders import HuggingFaceEncoder\n import numpy as np\n\n clf = OutlierDetector(model='outlier_detector.pkl') # Load the model\n encoder = HuggingFaceEncoder()\n\n vector = encoder(['Who was the first man to walk on the moon?'])\n vector = np.array(vector).reshape(1, -1)\n\n label = clf.predict(vector)\n\n##############\n Integrations\n##############\n\nMuzlin integrates with a wide array of libraries for anomaly detection,\nvector encoding, and graph-based setups.\n\n+-----------------------------------+-------------------------+----------------------+\n| **Anomaly Detection** | **Encoders** | **Vector Index** |\n+===================================+=========================+======================+\n| - Scikit-Learn | - HuggingFace | - LangChain |\n| - PyOD (vector) | - OpenAI | - LlamaIndex |\n| - PyGOD (graph) | - Cohere | |\n| - PyThresh (thresholding) | - Azure | |\n| | - Google | |\n| | - Amazon Bedrock | |\n| | - Fastembed | |\n+-----------------------------------+-------------------------+----------------------+\n\n**Simple Schematic Implementation**\n\n.. image:: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Simple_Example.png\n :target: https://raw.githubusercontent.com/KulikDM/muzlin/main/images/Simple_Example.png\n :alt: Muzlin Pipeline\n\n----\n\n###########\n Resources\n###########\n\n**Example Notebooks**\n\n+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+\n| Notebook | Description |\n+===================================================================================================================+=============================================================================+\n| `Introduction <https://github.com/KulikDM/muzlin/blob/main/examples/00_Introduction.ipynb>`_ | Basic semantic vector-based outlier detection |\n+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+\n| `Optimal Threshold <https://github.com/KulikDM/muzlin/blob/main/examples/01_Threshold_Optimization.ipynb>`_ | Selecting optimal thresholds using various methods |\n+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+\n| `Cluster-Based Filtering <https://github.com/KulikDM/muzlin/blob/main/examples/02_Cluster_Filtering.ipynb>`_ | Cluster-based filtering for question answering |\n+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+\n| `Graph-Based Filtering <https://github.com/KulikDM/muzlin/blob/main/examples/03_Graph_Filtering.ipynb>`_ | Using graph-based anomaly detection for semantic graphs like GraphRAG |\n+-------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------+\n\n############\n What Else?\n############\n\nLooking for more? Check out other useful libraries like `Semantic Router\n<https://github.com/aurelio-labs/semantic-router>`_, `CRAG\n<https://github.com/HuskyInSalt/CRAG>`_, and `Scikit-LLM\n<https://github.com/iryna-kondr/scikit-llm>`_\n\n----\n\n##############\n Contributing\n##############\n\n**Muzlin is still evolving!** At the moment their are major changes\nbeing done and the structure of Muzlin is still being refined. For now,\nplease leave a bug report and potential new code for any fixes or\nimprovements. You will be added as a co-author if it is implemented.\n\nOnce this phase has been completed then ->\n\nAnyone is welcome to contribute to Muzlin:\n\n- Please share your ideas and ask questions by opening an issue.\n\n- To contribute, first check the Issue list for the \"help wanted\" tag\n and comment on the one that you are interested in. The issue will\n then be assigned to you.\n\n- If the bug, feature, or documentation change is novel (not in the\n Issue list), you can either log a new issue or create a pull request\n for the new changes.\n\n- To start, fork the **dev branch** and add your\n improvement/modification/fix.\n\n- To make sure the code has the same style and standard, please refer\n to detector.py for example.\n\n- Create a pull request to the **dev branch** and follow the pull\n request template `PR template\n <https://github.com/KulikDM/muzlin/blob/main/.github/PULL_REQUEST_TEMPLATE.md>`_\n\n- Please make sure that all code changes are accompanied with proper\n new/updated test functions. Automatic tests will be triggered. Before\n the pull request can be merged, make sure that all the tests pass.\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2024 Daniel Kulik 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. ",
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