Trendshift - Ask AI

base on AdalFlow: The library to build & auto-optimize LLM applications.  <h4 align="center"> <img alt="AdalFlow logo" src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/adalflow-logo.png" style="width: 100%;"> </h4> <h2> <p align="center"> ⚡ The Library to Build and Auto-optimize LLM Applications ⚡ </p> </h2> <p align="center"> <a href="https://colab.research.google.com/drive/1TKw_JHE42Z_AWo8UuRYZCO2iuMgyslTZ?usp=sharing"> <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> </p> <h4 align="center"> <p> <a href="https://adalflow.sylph.ai/">All Documentation</a> | <a href="https://adalflow.sylph.ai/apis/components/components.model_client.html">Models</a> | <a href="https://adalflow.sylph.ai/apis/components/components.retriever.html">Retrievers</a> | <a href="https://adalflow.sylph.ai/apis/components/components.agent.html">Agents</a> | <a href="https://adalflow.sylph.ai/tutorials/evaluation.html"> LLM evaluation</a> | <a href="https://adalflow.sylph.ai/use_cases/question_answering.html">Trainer & Optimizers</a> <p> </h4> <p align="center"> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Version" src="https://img.shields.io/pypi/v/adalflow?style=flat-square"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow/month"> </a> <a href="https://star-history.com/#SylphAI-Inc/AdalFlow"> <img alt="GitHub stars" src="https://img.shields.io/github/stars/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://github.com/SylphAI-Inc/AdalFlow/issues"> <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://opensource.org/license/MIT"> <img alt="License" src="https://img.shields.io/github/license/SylphAI-Inc/AdalFlow"> </a> <a href="https://discord.gg/ezzszrRZvT"> <img alt="discord-invite" src="https://dcbadge.vercel.app/api/server/ezzszrRZvT?style=flat"> </a> </p> <h4> <p align="center"> For AI researchers, product teams, and software engineers who want to learn the AI way. </p> </h4>   # Quick Start Install AdalFlow with pip: ```bash pip install adalflow ``` Please refer to the [full installation guide](https://adalflow.sylph.ai/get_started/installation.html) for more details. * Try the [Building Quickstart](https://colab.research.google.com/drive/1TKw_JHE42Z_AWo8UuRYZCO2iuMgyslTZ?usp=sharing) in Colab to see how AdalFlow can build the task pipeline, including Chatbot, RAG, agent, and structured output. * Try the [Optimization Quickstart](https://colab.research.google.com/github/SylphAI-Inc/AdalFlow/blob/main/notebooks/qas/adalflow_object_count_auto_optimization.ipynb) to see how AdalFlow can optimize the task pipeline. # Why AdalFlow 1. Embracing a design pattern similar to PyTorch, AdalFlow is powerful, light, modular, and robust. AdalFlow provides `Model-agnostic` building blocks to build LLM task pipelines, ranging from RAG, Agents to classical NLP tasks like text classification and named entity recognition. It is easy to get high performance only using manual prompting. 2. AdalFlow provides a unified auto-differentiative framework for both zero-shot prompt optimization and few-shot optimization. It advances existing auto-optimization research, including ``Text-Grad`` and ``DsPy``. Through our research, ``Text-Grad 2.0`` and ``Learn-to-Reason Few-shot In Context Learning``, AdalFlow ``Trainer`` achieves the highest accuracy while being the most token-efficient.    Here is an optimization demonstration on a text classification task:  <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/classification_training_map.png" style="width: 80%;" alt="AdalFlow Auto-optimization"> </p> <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/classification_opt_prompt.png" alt="AdalFlow Optimized Prompt" style="width: 80%;"> </p> Among all libraries, AdalFlow achieved the highest accuracy with manual prompting (starting at 82%) and the highest accuracy after optimization. Further reading: [Optimize Classification](https://adalflow.sylph.ai/use_cases/classification.html) ## Light, Modular, and Model-Agnostic Task Pipeline LLMs are like water; AdalFlow help you quickly shape them into any applications, from GenAI applications such as chatbots, translation, summarization, code generation, RAG, and autonomous agents to classical NLP tasks like text classification and named entity recognition. AdalFlow has two fundamental, but powerful, base classes: `Component` for the pipeline and `DataClass` for data interaction with LLMs. The result is a library with minimal abstraction, providing developers with *maximum customizability*. You have full control over the prompt template, the model you use, and the output parsing for your task pipeline. <p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/AdalFlow_task_pipeline.png" alt="AdalFlow Task Pipeline"> </p> Many providers and models accessible via the same interface: <p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/multi-providers.png" alt="AdalFlow Model Providers"> </p> [All available model providers](https://adalflow.sylph.ai/apis/components/components.model_client.html)   Further reading: [How We Started](https://www.linkedin.com/posts/li-yin-ai_both-ai-research-and-engineering-use-pytorch-activity-7189366364694892544-Uk1U?utm_source=share&utm_medium=member_desktop), [Design Philosophy](https://adalflow.sylph.ai/tutorials/lightrag_design_philosophy.html) and [Class hierarchy](https://adalflow.sylph.ai/tutorials/class_hierarchy.html).  ## Unified Framework for Auto-Optimization AdalFlow provides token-efficient and high-performing prompt optimization within a unified framework. To optimize your pipeline, simply define a ``Parameter`` and pass it to AdalFlow's ``Generator``. Whether you need to optimize task instructions or run some few-shot demonstrations, AdalFlow's unified framework offers an easy way to **diagnose**, **visualize**, **debug**, and **train** your pipeline. This [Dynamic Computation Graph](https://adalflow.sylph.ai/tutorials/trace_graph.html) demonstrates how our auto-differentiation and the dynamic computation graph work. No need to manually defined nodes and edges, AdalFlow will automatically trace the computation graph for you. ### **Trainable Task Pipeline** Just define it as a ``Parameter`` and pass it to AdalFlow's ``Generator``. <p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/Trainable_task_pipeline.png" alt="AdalFlow Trainable Task Pipeline"> </p> ### **AdalComponent & Trainer** ``AdalComponent`` acts as the 'interpreter' between task pipeline and the trainer, defining training and validation steps, optimizers, evaluators, loss functions, backward engine for textual gradients or tracing the demonstrations, the teacher generator. <p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/AdalFlow/main/docs/source/_static/images/trainer.png" alt="AdalFlow AdalComponent & Trainer"> </p> # Documentation AdalFlow full documentation available at [adalflow.sylph.ai](https://adalflow.sylph.ai/): - [How We Started](https://www.linkedin.com/posts/li-yin-ai_both-ai-research-and-engineering-use-pytorch-activity-7189366364694892544-Uk1U?utm_source=share&utm_medium=member_desktop) - [Introduction](https://adalflow.sylph.ai/) - [Full installation guide](https://adalflow.sylph.ai/get_started/installation.html) - [Design philosophy](https://adalflow.sylph.ai/tutorials/lightrag_design_philosophy.html) - [Class hierarchy](https://adalflow.sylph.ai/tutorials/class_hierarchy.html) - [Tutorials](https://adalflow.sylph.ai/tutorials/index.html) - [Supported Models](https://adalflow.sylph.ai/apis/components/components.model_client.html) - [Supported Retrievers](https://adalflow.sylph.ai/apis/components/components.retriever.html) - [API reference](https://adalflow.sylph.ai/apis/index.html) # AdalFlow: A Tribute to Ada Lovelace AdalFlow is named in honor of [Ada Lovelace](https://en.wikipedia.org/wiki/Ada_Lovelace), the pioneering female mathematician who first recognized that machines could go beyond mere calculations. As a team led by a female founder, we aim to inspire more women to pursue careers in AI. # Community & Contributors The AdalFlow is a community-driven project, and we welcome everyone to join us in building the future of LLM applications. Join our [Discord](https://discord.gg/ezzszrRZvT) community to ask questions, share your projects, and get updates on AdalFlow. To contribute, please read our [Contributor Guide](https://adalflow.sylph.ai/contributor/index.html). # Contributors [![contributors](https://contrib.rocks/image?repo=SylphAI-Inc/LightRAG&max=2000)](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors) # Acknowledgements Many existing works greatly inspired AdalFlow library! Here is a non-exhaustive list: - 📚 [PyTorch](https://github.com/pytorch/pytorch/) for design philosophy and design pattern of ``Component``, ``Parameter``, ``Sequential``. - 📚 [Micrograd](https://github.com/karpathy/micrograd): A tiny autograd engine for our auto-differentiative architecture. - 📚 [Text-Grad](https://github.com/zou-group/textgrad) for the ``Textual Gradient Descent`` text optimizer. - 📚 [DSPy](https://github.com/stanfordnlp/dspy) for inspiring the ``__{input/output}__fields`` in our ``DataClass`` and the bootstrap few-shot optimizer. - 📚 [OPRO](https://github.com/google-deepmind/opro) for adding past text instructions along with its accuracy in the text optimizer. - 📚 [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning) for the ``AdalComponent`` and ``Trainer``. # Citation ```bibtex @software{Yin2024AdalFlow, author = {Li Yin}, title = {{AdalFlow: The Library for Large Language Model (LLM) Applications}}, month = {7}, year = {2024}, doi = {10.5281/zenodo.12639531}, url = {https://github.com/SylphAI-Inc/AdalFlow} } ```   ", Assign "at most 3 tags" to the expected json: {"id":"11559","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

AI prompts

AI prompts