langchainrb
hnsqlite
langchainrb | hnsqlite | |
---|---|---|
16 | 6 | |
1,076 | 143 | |
10.6% | 1.4% | |
9.6 | 5.5 | |
1 day ago | 10 months ago | |
Ruby | Python | |
MIT License | Apache License 2.0 |
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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!
hnsqlite
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LangChain: The Missing Manual
For anyone thinking about applications of langchain and pinecone but who are looking for something more turn-key check out https://jiggy.ai
The core is actually open source as well, allowing you to take your data back out via sqlite and hnswlib (https://github.com/jiggy-ai/hnsqlite)
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I built an open source website that lets you upload large files, such as in-depth novels or academic papers, and ask ChatGPT questions based on your specific knowledge base. So far, I've tested it with long books like the Odyssey and random research papers that I like, and it works shockingly well.
We are built on open core https://github.com/jiggy-ai. Our open source hnsqlite is light weight, easy to use. And best of all, we make it easy for you to get your data out of JiggyBase. You can download a sqlite file that contains your document text data, metadata, embedding vectors, and embedding index. This can be used directly in the open source hnsqlite package.
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What Is a Vector Database
After working through several projects that utilized local hnswlib and different databases for text and vector persistence, I integrated open source hnswlib with sqlite to create an embedded vector search engine that can easily scale up to millions of embeddings. For self-hosted situations of under 10M embeddings and less than insane throughput I think this combo is hard to beat.
https://github.com/jiggy-ai/hnsqlite
- Show HN: Hnsqlite: hnswlib and SQLite integrated for text embedding search
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Faiss: A library for efficient similarity search
Thanks Leobg!
For anyone else: you pass it directly in metadata see https://github.com/jiggy-ai/hnsqlite/blob/main/test/test_col...
What are some alternatives?
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
ruby-openai - OpenAI API + Ruby! 🤖❤️ Now with Assistants v2, Batches & Ollama/Groq 🚀
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby
GPT4Memory
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.
raft - RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
guidance - A guidance language for controlling large language models.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python