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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... show more

LlamaIndex 🦙

122,059 subscribers

67,903 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

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9 条评论

Fabrizio Milo 的头像

Fabrizio Milo2 年前

just a word of caution: this can be expensive if you don't know how big are your files in terms of tokens. It does sends data to @OpenAI with the blog's default configuration.

ahmed 的头像

ahmed2 年前

Which embedding model it is using?

LlamaIndex 🦙 的头像

LlamaIndex 🦙2 年前

@ahmed_abdullahx ada-002, but you can customize it to use any model!

ppg 的头像

ppg2 年前

@ollama From the name "llamaindex" it would seem your default model would be at least llama or open weights based but instead the default is openAI based. Any reason for that? Do the open models do very poorly?

Matrix Dai 的头像

Matrix Dai2 年前

@TheAICoder This is easiest way to index and "ask" local files.

AiBee Agency 的头像

AiBee Agency2 年前

🛠️ The RAG CLI tool looks like a game-changer for developers! Easy indexing means quicker access and better productivity. Could you share more on how it handles large file systems? #DevTools #CommandLineMagic

Peter Calvanelli 的头像

Peter Calvanelli2 年前

🤯

Bryce Amacker 的头像

Bryce Amacker2 年前

@memdotai mem it

Mem 的头像

Mem2 年前

@llama_index Saved! Here's the compiled thread:

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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: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

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: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

LlamaIndex 🦙

40,124 次观看 • 2 年前

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:

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 次观看 • 2 年前

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:

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 次观看 • 2 年前

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

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 次观看 • 2 年前

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:

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 次观看 • 2 年前

Most RAG apps fail due to retrieval. Today, we'll build a RAG system that self-corrects inaccurate retrievals using: - for scraping - Milvus as vectorDB - Beam for deployment - Comet Opik for observability - LlamaIndex 🦙 for orchestration Let's go!

Most RAG apps fail due to retrieval. Today, we'll build a RAG system that self-corrects inaccurate retrievals using: - for scraping - Milvus as vectorDB - Beam for deployment - Comet Opik for observability - LlamaIndex 🦙 for orchestration Let's go!

Avi Chawla

32,011 次观看 • 1 年前

Vision-based(Colapli) RAG is becoming popular, so we built a platform to compare: - Simple OCR RAG - VisionRAG - Colpali - Hybrid Colpali 🚀 Introducing VARAG – the Vision-First RAG Engine (Vision Augmented Retrieval and Generation).

Vision-based(Colapli) RAG is becoming popular, so we built a platform to compare: - Simple OCR RAG - VisionRAG - Colpali - Hybrid Colpali 🚀 Introducing VARAG – the Vision-First RAG Engine (Vision Augmented Retrieval and Generation).

Adithya S K

96,933 次观看 • 2 年前

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:

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 次观看 • 2 年前

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 →

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 次观看 • 1 年前

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!

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 次观看 • 2 年前

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

Jerry Liu

18,615 次观看 • 4 个月前

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 🦙

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 次观看 • 2 年前

How to Analyze Tables In Large Financial Reports Using GPT-4 (w/Jerry Liu) Most corporate docs contain a mix of text and tables. But if you use simple RAG split and chunk methods, the AI model will likely hallucinate due to embedding split tables. To solve this, Jerry Liu demonstrates how to use LlamaIndex 🦙,OpenAI's new GPT-1106 model, and a novel retrieval strategy to analyse financial statement tables in Tesla's 10-k. Full tutorial video:

How to Analyze Tables In Large Financial Reports Using GPT-4 (w/Jerry Liu) Most corporate docs contain a mix of text and tables. But if you use simple RAG split and chunk methods, the AI model will likely hallucinate due to embedding split tables. To solve this, Jerry Liu demonstrates how to use LlamaIndex 🦙,OpenAI's new GPT-1106 model, and a novel retrieval strategy to analyse financial statement tables in Tesla's 10-k. Full tutorial video:

Mayo Oshin

185,199 次观看 • 3 年前

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 次观看 • 2 年前

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!

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 次观看 • 1 年前

Afternoon hack: all sessions embedded and available in a search index and exposed as a tool. Do you want Copilot CLI to give you reports about how you are working, patterns, what to improve, and most importantly wins?

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Evan Boyle

11,165 次观看 • 8 个月前

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:

LlamaIndex 🦙

141,326 次观看 • 2 年前

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)

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 次观看 • 1 年前

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,981 次观看 • 1 年前