langchainrb
dvc
langchainrb | dvc | |
---|---|---|
16 | 109 | |
1,076 | 13,116 | |
10.6% | 0.6% | |
9.6 | 9.7 | |
1 day ago | 7 days ago | |
Ruby | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
langchainrb
- Langchain.rb
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First 15 Open Source Advent projects
8. LangChain RB | Github | tutorial
- Create AI Agents in Ruby: Implementing the ReAct Approach
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Lost on LangChain: Can someone help with the Question Answer concept?
So I hooked up the Ruby on Rails langchainrb gem (https://github.com/andreibondarev/langchainrb) and it seems like the approach is to store the plane text entries as meta data on pinecone. I definitely DO NOT want to do this as the data is private and secure on my own DB.
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ruby and ML/AI chatgpt
langchain
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Anyone willing to share their experience with Boxcar.ai?
I would suggest taking a look at Langchain.rb as well. Disclosure: I'm the core maintainer.
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Emerging Architectures for LLM Applications
Is the emerging architecture made out to be more complicated than what most of the companies are currently building? Perhaps! But this is most likely the general direction where things will start trending towards as the auxiliary ecosystem matures.
Shameless plug: For fellow Ruby-ists we're building an orchestration layer for building LLM applications, inspired by the original, Langchain.rb: https://github.com/andreibondarev/langchainrb
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Building an app around a LLM, Rails + Python or just Python?
I'm the author of Langchain.rb.
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5 things I wish I knew before building a GPT agent for log analysis
@dliteful23 I loved your super detailed lessons-learned article! I'm the author of Langchain.rb, I would love to hear what you think of it if you get a chance to check it out. If there's anything that you'd like to see in the framework, please do let us know and we'll make sure to build it out if it aligns with the vision.
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LangChain: The Missing Manual
We’re building “Langchain for Ruby” under the current working name of “Langchain.rb”: https://github.com/andreibondarev/langchainrb
People that have contributed on the project thus far each have at least a decade of experience programming in Ruby. We’re trying our best to build an abstraction layer on top all of the common emerging AI/ML techniques, tools, and providers. We’re also focusbig on building an excellent developer experience that Ruby developers love and have gotten to expect.
Unlike the Python project, as it’s been pointed out here a countless number of times, we’d like to avoid deeply nested class structures that make it incredibly difficult to track and extend.
We’ve been pondering over the “what does Rails for Machine Learning look like?” question, and we’re taking a stab at answering this question.
We’re hyper-focused on the open source community and the developer community at large. All feedback/ideas/contributions/criticism are welcome and encouraged!
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
MLflow - Open source platform for the machine learning lifecycle
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
lakeFS - lakeFS - Data version control for your data lake | Git for data
ruby-openai - OpenAI API + Ruby! 🤖❤️ Now with Assistants v2, Batches & Ollama/Groq 🚀
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
hnsqlite - hnsqlite integrates hnswlib and sqlite for simple text embedding search
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.