View dspy-ai-hmoazam on Snyk Open Source Advisor

Package Health Score

78 / 100

security
No known security issues
popularity
Small
maintenance
Healthy
community
Active

Popularity

Small

GitHub Stars: 20.29K
Forks: 1.53K
Contributors: 250

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package dspy-ai-hmoazam, we found that it has been starred 20,289 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Dec 19, 2024
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 135
Open PR: 46
Last Release: 4 days ago
Last Commit: 5 days ago

Further analysis of the maintenance status of dspy-ai-hmoazam based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that dspy-ai-hmoazam demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 250
Funding: No

A good and healthy external contribution signal for dspy-ai-hmoazam project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.9

Age: 8 months
Latest Release: 4 days ago
Dependencies: 0 Direct
Versions: 35
Maintainers: 0
Wheels: OS Independent

Readme

DSPy: Programming—not prompting—Foundation Models

Documentation: DSPy Docs

DSPy is the framework for programming—rather than prompting—language models. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights, whether you're building simple classifiers, sophisticated RAG pipelines, or Agent loops.

DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs. Learn more via our official documentation site or meet the community, seek help, or start contributing via this GitHub repo and our Discord server.

Documentation: dspy.ai

Please go to the DSPy Docs at dspy.ai

Installation

pip install dspy

To install the very latest from main:

pip install git+https://github.com/stanfordnlp/dspy.git

📜 Citation & Reading More

[Jun'24] Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
[Oct'23] DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
[Jul'24] Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
[Jun'24] Prompts as Auto-Optimized Training Hyperparameters
[Feb'24] Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
[Jan'24] In-Context Learning for Extreme Multi-Label Classification
[Dec'23] DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
[Dec'22] Demonstrate-Search-Predict: Composing Retrieval & Language Models for Knowledge-Intensive NLP

To stay up to date or learn more, follow @lateinteraction on Twitter.

The DSPy logo is designed by Chuyi Zhang.

If you use DSPy or DSP in a research paper, please cite our work as follows:

@inproceedings{khattab2024dspy,
  title={DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines},
  author={Khattab, Omar and Singhvi, Arnav and Maheshwari, Paridhi and Zhang, Zhiyuan and Santhanam, Keshav and Vardhamanan, Sri and Haq, Saiful and Sharma, Ashutosh and Joshi, Thomas T. and Moazam, Hanna and Miller, Heather and Zaharia, Matei and Potts, Christopher},
  journal={The Twelfth International Conference on Learning Representations},
  year={2024}
}
@article{khattab2022demonstrate,
  title={Demonstrate-Search-Predict: Composing Retrieval and Language Models for Knowledge-Intensive {NLP}},
  author={Khattab, Omar and Santhanam, Keshav and Li, Xiang Lisa and Hall, David and Liang, Percy and Potts, Christopher and Zaharia, Matei},
  journal={arXiv preprint arXiv:2212.14024},
  year={2022}
}

FAQs

What is dspy-ai-hmoazam?

DSPy. Visit Snyk Advisor to see a full health score report for dspy-ai-hmoazam, including popularity, security, maintenance & community analysis.

Is dspy-ai-hmoazam popular?

We’re still processing downloads data for the python package dspy-ai-hmoazam. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is dspy-ai-hmoazam well maintained?

We found that dspy-ai-hmoazam demonstrated a healthy version release cadence and project activity. It has a community of 250 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is dspy-ai-hmoazam safe to use?

The python package dspy-ai-hmoazam was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 21 December-2024, at 16:00 (UTC).

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dspy-ai-hmoazam

DSPy For more information about how to use this package see README

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Readme

DSPy: Programming—not prompting—Foundation Models

Documentation: dspy.ai

Installation

📜 Citation & Reading More

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Readme

DSPy: Programming—not prompting—Foundation Models

Documentation: dspy.ai

Installation

📜 Citation & Reading More

FAQs

What is dspy-ai-hmoazam?

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Is dspy-ai-hmoazam well maintained?

Is dspy-ai-hmoazam safe to use?

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Source Code

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