deeplake
langchain
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deeplake | langchain | |
---|---|---|
13 | 31 | |
7,690 | 82,553 | |
2.3% | 5.7% | |
9.8 | 10.0 | |
6 days ago | 7 days ago | |
Python | Python | |
Mozilla Public License 2.0 | MIT License |
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.
deeplake
- FLaNK AI Weekly 25 March 2025
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Qdrant, the Vector Search Database, raised $28M in a Series A round
I think Activeloop(YC) is too: https://github.com/activeloopai/deeplake/
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[P] I built a Chatbot to talk with any Github Repo. πͺ
This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input.
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[P] Chat With Any GitHub Repo - Code Understanding with @LangChainAI & @activeloopai
Deep Lake GitHub
- [P] A 'ChatGPT Interface' to Explore Your ML Datasets -> app.activeloop.ai
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Build ChatGPT for Financial Documents with LangChain + Deep Lake
As the world is increasingly generating vast amounts of financial data, the need for advanced tools to analyze and make sense of it has never been greater. This is where LangChain and Deep Lake come in, offering a powerful combination of technology to help build a question-answering tool based on financial data. After participating in a LangChain hackathon last week, I created a way to use Deep Lake, the data lake for deep learning (a package my team and I are building) with LangChain. I decided to put together a guide of sorts on how you can approach building your own question-answering tools with LangChain and Deep Lake as the data store.
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Launch HN: Activeloop (YC S18) β Data lake for deep learning
Re: HF - we know them and admire their work (primarily, until very recently, focused on NLP, while we focus mostly on CV). As mentioned in the post, a large part of Deep Lake, including the Python-based dataloader and dataset format, is open source as well - https://github.com/activeloopai/deeplake.
Likewise, we curate a list of large open source datasets here -> https://datasets.activeloop.ai/docs/ml/, but our main thing isn't aggregating datasets (focus for HF datasets), but rather providing people with a way to manage their data efficiently. That being said, all of the 125+ public datasets we have are available in seconds with one line of code. :)
We haven't benchmarked against HF datasets in a while, but Deep Lake's dataloader is much, much faster in third-party benchmarks (see this https://arxiv.org/pdf/2209.13705 and here for an older version, that was much slower than what we have now, see this: https://pasteboard.co/la3DmCUR2iFb.png). HF under the hood uses Git-LFS (to the best of my knowledge) and is not opinionated on formats, so LAION just dumps Parquet files on their storage.
While your setup would work for a few TBs, scaling to PB would be tricky including maintaining your own infrastructure. And yep, as you said NAS/NFS would neither be able to handle the scale (especially writes with 1k workers). I am also slightly curious about your use of mmap files with image/video compressed data (as zero-copy wonβt happen) unless you decompress inside the GPU ;), but would love to learn more from you! Re: pricing thanks for the feedback, storage is one component and customly priced for PB-scale workloads.
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[P] Launching Deep Lake: the data lake for deep learning applications - https://activeloop.ai/
Deep Lake is fresh off the "press", so we would really appreciate your feedback here or in our community, a star on GitHub. If you're interested to learn more, you can read the Deep Lake academic paper or the whitepaper (that talks more about our vision!).
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Researchers at Activeloop AI Introduce βDeep Lake,β an Open-Source Lakehouse for Deep Learning Applications
Continue reading | heck out the paper and github
GIthub: https://github.com/activeloopai/deeplake
langchain
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Aider: AI pair programming in your terminal
Big fan of Aider.
We are interesting in integrating Aider as a tool for Dosu https://dosu.dev/ to help it navigate and modify a codebase on issues like this https://github.com/langchain-ai/langchain/issues/8263#issuec...
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π¦ Llama-2-GGML-CSV-Chatbot π€
Developed using Langchain and Streamlit technologies for enhanced performance.
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Building a WhatsApp generative AI assistant with Amazon Bedrock and Python
Tip: Kenton Blacutt, an AWS Associate Cloud App Developer, collaborated with Langchain, creating the Amazon Dynamodb based memory class that allows us to store the history of a langchain agent in an Amazon DynamoDB.
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π Top Open Source Projects of 2023 π
LangChain was first released in October 2022 as an open-source side project, a framework that makes developing AI applications more flexible. It got so popular that it was promptly turned into a startup.
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Fuck You, Show Me the Prompt
> Furthermore, the prompt has a spelling error (Let'w) and also overly focuses on the negative about identifying errors - which makes me skeptical that this prompt has been optimized or tested.
Fixed in https://github.com/langchain-ai/langchain/commit/7c6009b76f0...
- LangChain Repository Disappeared
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π Local & Open Source AI: a kind ollama & LlamaIndex intro
Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects
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Easy Guide to Creating Smart Chatbots with Langchain & GPT-4
Access Langchain's repository at Langchain's Repository.
- Use WASM as a cross-platform LLM back end for LangChain: any LLMs on any device
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Llamafile lets you distribute and run LLMs with a single file
This comment is now a potential exploit for any such system that encounters it (in practice most won't be fooled by trivial prompt injections, but possibly more complex ones)
Here's one example I found with a quick search: https://github.com/langchain-ai/langchain/issues/5872
What are some alternatives?
lance - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
llama_index - LlamaIndex is a data framework for your LLM applications
auto-maple - Artificial intelligence software for MapleStory that uses various machine learning and computer vision techniques to navigate challenging in-game environments
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
tensorstore - Library for reading and writing large multi-dimensional arrays.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
langchain - β‘ Building applications with LLMs through composability β‘ [Moved to: https://github.com/langchain-ai/langchain]
griptape - Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
barfi - Python Flow Based Programming environment that provides a graphical programming environment.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
super-image - Image super resolution models for PyTorch.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks