llama_index
langchain
| llama_index | langchain | |
|---|---|---|
| 76 | 38 | |
| 32,542 | 87,179 | |
| 4.2% | 4.5% | |
| 10.0 | 10.0 | |
| 5 days ago | 2 days ago | |
| Python | 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.
llama_index
- Show HN: Route your prompts to the best LLM
- LlamaIndex: A data framework for your LLM applications
- FLaNK AI - 01 April 2024
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Show HN: Ragdoll Studio (fka Arthas.AI) is the FOSS alternative to character.ai
For anyone curious llamaindex's "prompt mixins", they're actually dead simple: https://github.com/run-llama/llama_index/blob/8a8324008764a7... - and maybe no longer supported.
I basically reinvented this wheel in ragdoll but made it more dynamic: https://github.com/bennyschmidt/ragdoll/blob/master/src/util...
- LlamaIndex is a data framework for your LLM applications
- How to verify that a snippet of Python code doesn't access protected members
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🆓 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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I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
Mistral Instruct does use a system prompt.
You can see the raw format here: https://www.promptingguide.ai/models/mistral-7b#chat-templat... and you can see how LllamaIndex uses it here (as an example): https://github.com/run-llama/llama_index/blob/1d861a9440cdc9...
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Top 5 Vector Database Videos of 2023 🎥
Learn how to use Milvus as persistent vector storage with LlamaIndex in under 5 minutes.
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What's going on in the Zilliz Universe? December 2023
▶️ Read Blog 📷 Watch Demo 🦙 Notebook using Pipelines inside LlamaIndex
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?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
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.
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
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.
gpt-llama.cpp - A llama.cpp drop-in replacement for OpenAI's GPT endpoints, allowing GPT-powered apps to run off local llama.cpp models instead of OpenAI.
Redis - Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
langchain4j - Java version of LangChain