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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:...

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LlamaIndex 🦙

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40,124 görüntüleme • 2 yıl önce •via X (Twitter)

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ꋪꃅ0ꀸ꒒ profil fotoğrafı
ꋪꃅ0ꀸ꒒2 yıl önce

@ollama Lit!

Matt Figdore profil fotoğrafı
Matt Figdore2 yıl önce

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) ☮️ profil fotoğrafı
🔯 Balanced Acceleration (b/acc) ☮️2 yıl önce

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

Raymond Weitekamp profil fotoğrafı
Raymond Weitekamp2 yıl önce

@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 Smith profil fotoğrafı
Henry Smith2 yıl önce

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

The Guy profil fotoğrafı
The Guy2 yıl önce

@ollama Linux only or Powershell supported?

Andrei Gheorghiu profil fotoğrafı
Andrei Gheorghiu2 yıl önce

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

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Tim2 yıl önce

@ollama @Memdotai mem it

OSDev profil fotoğrafı
OSDev2 yıl önce

@ollama @readwise save thread

Benzer Videolar

Introducing RAG CLI 🧑‍💻🔎 - a dead-simple command-line tool that allows you to RAG literally any file on your local machine. Index any files including glob patterns, such as `$ llamaindex-cli rag --files "./docs/**/*.rst”` To search simply do `$ llamaindex-cli rag --question "What is LlamaIndex?”` Built with our IngestionPipeline and QueryPipeline abstractions that allow efficient, performant data indexing and retrieval. Also built with Chroma. That’s not all! 💡Want to use local models like ollama? 💡Want to bake in advanced ingestion and retrieval techniques? The guide also shows you how to customize the logic for your own needs! Blog post: Docs:
0:31

Introducing RAG CLI 🧑‍💻🔎 - a dead-simple command-line tool that allows you to RAG literally any file on your local machine. Index any files including glob patterns, such as `$ llamaindex-cli rag --files "./docs/**/*.rst”` To search simply do `$ llamaindex-cli rag --question "What is LlamaIndex?”` Built with our IngestionPipeline and QueryPipeline abstractions that allow efficient, performant data indexing and retrieval. Also built with Chroma. That’s not all! 💡Want to use local models like ollama? 💡Want to bake in advanced ingestion and retrieval techniques? The guide also shows you how to customize the logic for your own needs! Blog post: Docs:

LlamaIndex 🦙

67,903 görüntüleme • 2 yıl önce

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
0:21

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

Jaydeep

112,627 görüntüleme • 8 ay önce

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:
2:52

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

LlamaIndex 🦙

475,732 görüntüleme • 2 yıl önce

7/ Chat with File System Showing off LlamaIndex integrations with Ollama multimodal. Chat and ask questions about any file on your computer including images and PDFs Sourabh Desai LlamaIndex 🦙
5:15

7/ Chat with File System Showing off LlamaIndex integrations with Ollama multimodal. Chat and ask questions about any file on your computer including images and PDFs Sourabh Desai LlamaIndex 🦙

Alex Reibman 🖇️

43,483 görüntüleme • 2 yıl önce

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:
2:47

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,703 görüntüleme • 2 yıl önce

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)
15:16

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)

Avthar

34,261 görüntüleme • 1 yıl önce

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!
17:07

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!

Akshay 🚀

20,058 görüntüleme • 1 yıl önce

Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic 🚀 Complete video: Code: I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM. LlamaIndex 🦙 Pydantic ollama #Python #LLM #RAG
1:56

Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic 🚀 Complete video: Code: I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM. LlamaIndex 🦙 Pydantic ollama #Python #LLM #RAG

Andrej Baranovskij

147,999 görüntüleme • 2 yıl önce

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:
1:35

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Jerry Liu

55,620 görüntüleme • 10 ay önce

Ready to build a “Chat with your code” application with ollama, Weaviate, and LlamaIndex 🦙 on Lightning AI ⚡️ Studio? We’ve just published a Lightning AI Studio template that you can simply copy. You’ll be up and running with your Retrieval-Augmented Generation (RAG) pipeline in minutes. Jump right in here:
0:25

Ready to build a “Chat with your code” application with ollama, Weaviate, and LlamaIndex 🦙 on Lightning AI ⚡️ Studio? We’ve just published a Lightning AI Studio template that you can simply copy. You’ll be up and running with your Retrieval-Augmented Generation (RAG) pipeline in minutes. Jump right in here:

Weaviate AI Database

12,440 görüntüleme • 2 yıl önce

Build a Live RAG Chatbot from Google Drive / Sharepoint ♻️ Excited to feature a Streamlit blog by Anup Surendran and Berke Can Rizai on building a QA system over a file storage system that is dynamically changing. Using Pathway (www.pathway.com) you can setup a a live ETL pipeline to immediately propagate any document changes to a vector store. This means that every time you ask a question, you’re able to access the latest version of your Google Drive / Sharepoint files. Check it out: Repo:
0:43

Build a Live RAG Chatbot from Google Drive / Sharepoint ♻️ Excited to feature a Streamlit blog by Anup Surendran and Berke Can Rizai on building a QA system over a file storage system that is dynamically changing. Using Pathway (www.pathway.com) you can setup a a live ETL pipeline to immediately propagate any document changes to a vector store. This means that every time you ask a question, you’re able to access the latest version of your Google Drive / Sharepoint files. Check it out: Repo:

LlamaIndex 🦙

58,299 görüntüleme • 2 yıl önce

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!
2:06

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,303 görüntüleme • 2 yıl önce

We saw exactly this in my previous startup RAG + vector DB gives decent results, but agentic search over the repo (glob/grep/read, etc) consistently worked better on real-world codebases. We even pushed further: RAG + embeddings + AST + tree-sitter. The quality was excellent But exactly as Boris Cherny mentions: staleness and privacy, you need continuous re-indexing, and all the code and embeddings must live on your servers. In practice, fast models + bash-style agentic search ended up outperforming general RAG search, even if it requires more tool calls This is what we built 👇
0:23

We saw exactly this in my previous startup RAG + vector DB gives decent results, but agentic search over the repo (glob/grep/read, etc) consistently worked better on real-world codebases. We even pushed further: RAG + embeddings + AST + tree-sitter. The quality was excellent But exactly as Boris Cherny mentions: staleness and privacy, you need continuous re-indexing, and all the code and embeddings must live on your servers. In practice, fast models + bash-style agentic search ended up outperforming general RAG search, even if it requires more tool calls This is what we built 👇

Daniel San

448,294 görüntüleme • 8 ay önce

Mistral Vibe CLI is an open-source command-line coding assistant powered by Devstral. It explores, modifies, and executes changes across your codebase using natural language. Also under Apache 2.0. Install via: uv tool install mistral-vibe
0:50

Mistral Vibe CLI is an open-source command-line coding assistant powered by Devstral. It explores, modifies, and executes changes across your codebase using natural language. Also under Apache 2.0. Install via: uv tool install mistral-vibe

Mistral AI

145,003 görüntüleme • 10 ay önce

Our own Laurie Voss spoke yesterday with Ankit Khare on w3shi i's webinar about (what else?) RAG: ➡️ Why we do RAG ➡️ How we do RAG ➡️ How LlamaIndex helps you do RAG ➡️ What's coming in 2024 We recommend checking out the full hour-long webinar on Youtube, tons more information and insights:
16:04

Our own Laurie Voss spoke yesterday with Ankit Khare on w3shi i's webinar about (what else?) RAG: ➡️ Why we do RAG ➡️ How we do RAG ➡️ How LlamaIndex helps you do RAG ➡️ What's coming in 2024 We recommend checking out the full hour-long webinar on Youtube, tons more information and insights:

LlamaIndex 🦙

13,583 görüntüleme • 2 yıl önce

Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →
0:33

Introducing Gemini CLI extensions, a new framework that allows you to customize Gemini CLI and connect it to the tools you use most—all from the command line. No more context-switching. Start using your favorite tools directly in your workflow today →

Google Cloud Tech

80,461 görüntüleme • 1 yıl önce

New week new demo! Today I want to showcase the CLI to control the Superlogical multiplexer. The CLI is an extremely powerful tool for automation and hooking into other tools like editors, agentic coding tools, and more. And, it's super fun! Everything you can do in the GUI, you can do via the CLI. Actually, you can do more in the CLI. You can create sessions, run commands, make splits, move things around, change focus, simulate any input device (key/mouse/etc.), wait on scripts, and more. There are a couple unique parts of Rex I'm showing off here too: our streaming event system that any client can hook into (or script). And the terminal-specific API to retrieve information like a JSON object of the running process. I hope this demo lets your imagination run free of where you might slot something like this into your existing tools.
7:31

New week new demo! Today I want to showcase the CLI to control the Superlogical multiplexer. The CLI is an extremely powerful tool for automation and hooking into other tools like editors, agentic coding tools, and more. And, it's super fun! Everything you can do in the GUI, you can do via the CLI. Actually, you can do more in the CLI. You can create sessions, run commands, make splits, move things around, change focus, simulate any input device (key/mouse/etc.), wait on scripts, and more. There are a couple unique parts of Rex I'm showing off here too: our streaming event system that any client can hook into (or script). And the terminal-specific API to retrieve information like a JSON object of the running process. I hope this demo lets your imagination run free of where you might slot something like this into your existing tools.

Mitchell Hashimoto

206,486 görüntüleme • 1 ay önce