text-splitter
langchain
| text-splitter | langchain | |
|---|---|---|
| 1 | 38 | |
| 168 | 86,694 | |
| - | 4.0% | |
| 9.5 | 10.0 | |
| 6 days ago | 5 days ago | |
| Rust | Python | |
| MIT License | 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.
text-splitter
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semchunk alternatives - text-splitter and langchain
3 projects | 9 Nov 2023
semchunk is 77.35% faster than the semantic-text-splitter Python library. It is also implemented entirely in Python, whereas the semantic-text-splitter library is in Rust. Thus, it is compatible with pypy.
langchain
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How to create your own RAG AI with LangFlow
LangChain is one of the most used frameworks to create AI agents with, if not THE most used. With nearly 90.000 stars and almost 14000 forks on GitHub at the time of this article, we can safely say it is popular.
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Learnings from GenAI on AWS at Deloitte workshop
LangChain - Python and JS libraries, provides convenient functions for interacting with Amazon Bedrock’s models and related services like vector databases
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Bridging the Last Mile in LangChain Application Development
Undoubtedly, LangChain is the most popular framework for AI application development at the moment. The advent of LangChain has greatly simplified the construction of AI applications based on Large Language Models (LLM). If we compare an AI application to a person, the LLM would be the "brain," while LangChain acts as the "limbs" by providing various tools and abstractions. Combined, they enable the creation of AI applications capable of "thinking." However, this article does not delve into the specific usage of LangChain but aims to discuss with readers the last-mile issue in LangChain application development—how to deploy LangChain applications, using AWS as an example. Why deploy on AWS? The free tier is simply too appealing for daily use.
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A Terminal Is All AI Needs
The number of tools and functions that aim to enhance the abilities of language models (LMs) is growing rapidly. For example, the popular LM framework LangChain grew its tool catalog from three to seventy-seven in the last 15 months. However, this approach of building tools for every little thing may be misguided and ultimately counterproductive. Instead, providing AI with direct access to a terminal, where it can use the many command line tools already created, and even create its own tools, will lead to more powerful, flexible, and future-proof systems.
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Deploy LangServe Application to AWS
Limited by the current packaging method of Pluto, it does not yet support LangChain's Template Ecosystem. Coming soon
- Construyendo un asistente genAI de WhatsApp con Amazon Bedrock
- Show HN: SpRAG – Open-source RAG implementation for challenging real-world tasks
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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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🦙 Llama-2-GGML-CSV-Chatbot 🤖
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.
What are some alternatives?
semchunk - A fast and lightweight pure Python library for splitting text into semantically meaningful chunks.
llama_index - LlamaIndex is a data framework for your LLM applications
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
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.
griptape - Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
langchain4j - Java version of LangChain
guidance - A guidance language for controlling large language models.
llama - Inference code for Llama models
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
llama.cpp - LLM inference in C/C++