langchainrb
haystack
langchainrb | haystack | |
---|---|---|
16 | 55 | |
1,076 | 13,633 | |
10.6% | 2.5% | |
9.6 | 9.9 | |
1 day ago | 6 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!
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
langchain - 🦜🔗 Build context-aware reasoning applications
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
ruby-openai - OpenAI API + Ruby! 🤖❤️ Now with Assistants v2, Batches & Ollama/Groq 🚀
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
hnsqlite - hnsqlite integrates hnswlib and sqlite for simple text embedding search
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
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.
jina - ☁️ Build multimodal AI applications with cloud-native stack