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Our new RAG CLI tool lets you RAG any file on your filesystem, using only on-device LLMs/embeddings! 💾🔎 Using LlamaIndex 🦙, Mistral-7B on ollama, and bge-m3, you have access to an LLM-powered grep that you can easily install, use, and customize. Can input any files including glob strings. LlamaPack:... show more

LlamaIndex 🦙

120,010 subscribers

40,124 views • 2 years ago •via X (Twitter)

Anya Rossi• Live Now

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9 Comments

ꋪꃅ0ꀸ꒒2 years ago

@ollama Lit!

Matt Figdore2 years ago

This is the biggest productivity cheat code right now. Kiss reading documents goodbye. You can get an instant summary of any document with this tool.

🔯 Balanced Acceleration (b/acc) ☮️2 years ago

@ollama Love this. Could easily see it in next version of MacOS spotlight, and Windows CoPilot.

Raymond Weitekamp2 years ago

@ollama this might be the thing that finally convinces me to buy a new laptop ;) (broken screen didn't do it...apple ads didn't do it...fact that it won't sync to my phone didn't do it...)

Henry Smith2 years ago

@ollama Cool stuff, looking forward to this being a standard OS feature in a few years.

The Guy2 years ago

@ollama Linux only or Powershell supported?

Andrei Gheorghiu2 years ago

@ollama Wow, that's really cool!! what local hardware runs this particular demo? I was just about to order a new GPU 🙂

Tim2 years ago

@ollama @Memdotai mem it

OSDev2 years ago

@ollama @readwise save thread

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LlamaIndex 🦙

67,888 views • 2 years ago

I always wondered which Ollama model my machine could run properly. Now you can easily do that using llms-checker It's a CLI tool that checks your system specs and recommends the best models to run on Ollama based on what your machine npm install -g llm-checker

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112,465 views • 7 months ago

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LlamaIndex 🦙

475,732 views • 2 years ago

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Andrew Ng

218,703 views • 2 years ago

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

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Avthar

34,261 views • 1 year ago

Let's build a dashboard to evaluate and monitor your Agentic and RAG apps! . . In this video, I'll guide you through creating an evaluation and observability pipeline for your AI apps using a 100% open-source tool! Tech Stack: - Comet's Opik to eval and monitor - LlamaIndex to build a RAG pipeline - Ragas for synthetic datagen You'll learn: - Setting up Opik - Building a RAG pipeline - Creating an eval dataset - Evaluating the RAG pipeline - Monitoring all activities during the process It's a hands on demo with code and step-by-step guide to do everything listed above. CometML's Opik is fully open-source, offering the most Pythonic and easiest way to monitor LLM apps. I have shared link to their repo in next tweet!

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Akshay 🚀

20,058 views • 1 year ago

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Jerry Liu

55,620 views • 9 months ago

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LlamaIndex 🦙

58,299 views • 2 years ago

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Weaviate AI Database

12,435 views • 2 years ago

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Andrew Ng

297,131 views • 2 years ago

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Jerry Liu

259,026 views • 2 years ago

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,835 views • 2 years ago

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

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Santiago

89,664 views • 1 year ago

🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed NotebookLM alternative, we decided to give you more features! In the latest release you can: 📊 Extract and download images and tables from your files, and interactively visualize all tabular data ✅ Chat with your document and verify immediately everything that AI is saying about it, using the new claim verification and citation finding functionality 👇 Watch the video below for a full demo of the new functionalities 👩‍💻🧑‍💻 There is also a bonus: thanks to the community effort and contribution, you can now customize the embedding model you want to use to build the LlamaCloud index, through a textualize.io CLI! ⭐ Star NotebookLlaMa on GitHub: 🦙☁️ Sign-up for LlamaCloud and test our awesome products: 📚 Learn more about LlamaCloud:

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LlamaIndex 🦙

11,466 views • 1 year ago

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

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Daniel San

53,516 views • 1 year ago

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,435 views • 2 years ago

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

Andrew Ng

191,219 views • 2 years ago