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Build a RAG Application from Scratch 🐕 This video covers the architecture behind Verba 0.3.0! The design behind Verba is to have an explicit manager for each core component of RAG pipelines (Read, Chunk, Embed, Retrieve, Generate) 1. ReaderManager: Load in your data (GitHubReader, PDFReader, SimpleReader, etc.) 2. ChunkManager:... Chunk up your data into smaller bits (avoids hitting the token limit and results in better retrieval) 3. EmbeddingManager: Convert your data into embeddings (use OpenAI, Cohere, SentenceTransformer, etc.) 4. RetrieverManager: Retrieve the relevant context from your query 5. GenerationManager: Generate an answer from the retrieved chunks Test out Verba on the Weaviate AI Database docs/blogs here: Verba repo:show more

Erika Shorten

4,809 subscribers

155,273 Aufrufe • vor 2 Jahren •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

10 Kommentare

Profilbild von Philip Vollet

Philip Volletvor 2 Jahren

haha juat shipping a banger video beside being in Croatia on our company trip, what a vibe what a team.

Profilbild von Erika Cardenas

Erika Cardenasvor 2 Jahren

This video is only great because of the vibes 🤌

Profilbild von Sebastian Rojo

Sebastian Rojovor 2 Jahren

Well done. I loved 🐕

Profilbild von Erika Cardenas

Erika Cardenasvor 2 Jahren

Yay! Thank you Sebastian 🙂

Profilbild von AJ Chan

AJ Chanvor 2 Jahren

Thanks for the insights on Verba 0.3.0, Erika!

Profilbild von Erika Cardenas

Erika Cardenasvor 2 Jahren

@aj__chan You’re very welcome 😎

Profilbild von Marion Nehring

Marion Nehringvor 2 Jahren

@ecardenas300 this is one of the best education videos I have seen!! You rock big time. I could not be more proud to be working with such talented and amazing people!!!!

Profilbild von Erika Cardenas

Erika Cardenasvor 2 Jahren

Aww thank you Marion 🥹 miss you already!

Profilbild von Fanilo Andrianasolo

Fanilo Andrianasolovor 2 Jahren

It's really nice listening to a valuable tutorial while feeling in holidays 😃 ! Could you use this background and ambience for all your future talking head videos 😆 ?

Profilbild von Erika Cardenas

Erika Cardenasvor 2 Jahren

That would be awesome 😁 @philipvollet can I have your approval?

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Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Femke Plantinga

120,565 Aufrufe • vor 1 Jahr

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

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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:

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everybody talks about building AI chatbots, but nobody tells you HOW to do it that's why I made a full practical walkthrough on how to build an AI chatbot that's hooked up to your own custom knowledgebase inside of the walk-through i go over: – data collection: gathering all relevant documents, conversations, and info - preprocessing: cleaning up and formatting the collected data - chunking: break down the cleaned data into smaller, manageable pieces - embedding & storing in a vector database - RAG & chatbot integration: using RAG to allow the chatbot to retrieve relevant information from the vector database based on a user's question reply to this tweet w/ the word “RAG” & I’ll send it to you (must be following so I can DM)

everybody talks about building AI chatbots, but nobody tells you HOW to do it that's why I made a full practical walkthrough on how to build an AI chatbot that's hooked up to your own custom knowledgebase inside of the walk-through i go over: – data collection: gathering all relevant documents, conversations, and info - preprocessing: cleaning up and formatting the collected data - chunking: break down the cleaned data into smaller, manageable pieces - embedding & storing in a vector database - RAG & chatbot integration: using RAG to allow the chatbot to retrieve relevant information from the vector database based on a user's question reply to this tweet w/ the word “RAG” & I’ll send it to you (must be following so I can DM)

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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!

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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

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Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

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AI shouldn’t be locked behind APIs. It should be built into the web itself. The OptimAI Core Node brings compute, data, and intelligence back to the edge - to the Builders! 🎥Watch the installation guide from our CCO, Ricardo, now for Linux users 👉Join the Network Each node turns your machine into part of a global AI cloud, serving: + Search + Retrieval + Embeddings + Compute This is your invitation to build the next generation of Agentic AI.

AI shouldn’t be locked behind APIs. It should be built into the web itself. The OptimAI Core Node brings compute, data, and intelligence back to the edge - to the Builders! 🎥Watch the installation guide from our CCO, Ricardo, now for Linux users 👉Join the Network Each node turns your machine into part of a global AI cloud, serving: + Search + Retrieval + Embeddings + Compute This is your invitation to build the next generation of Agentic AI.

OptimAI Network

28,424 Aufrufe • vor 8 Monaten

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

Andrew Ng

384,282 Aufrufe • vor 3 Jahren

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

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Faheem Ullah

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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 Aufrufe • vor 1 Jahr

This will retire 90% of RAG systems with dignity (and a sad song playlist). Powered by DSPy: If you're still building "text in, text out" chatbots that only perform blind vector and text searches, you're not gonna make it! My team just dropped Elysia, and it's not just an incremental successor to Verba… It's a whole rethink of how we interact with our data using AI. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗹𝘆𝗶𝘀𝗮? An open-source platform for building agentic RAG architectures. It learns from your preferences, intelligently categorizes, labels, and searches through your data, and provides complete transparency into its decision-making process. The long & exciting feature list: • 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗧𝗿𝗲𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: Elysia’s core is a customizable decision tree, and it visualizes its entire reasoning process, showing you why it chooses a specific tool or path. It enables advanced error handling, self-healing from failed queries, and prevents infinite loops. You can also add custom tools and branches to build complex, state-aware workflows. • 𝗗𝗮𝘁𝗮 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Before it even attempts a query, Elysia performs a full analysis of your data collections. This eliminates the blind search problem plaguing most RAG systems and allows for far more complex and accurate query generation. • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Your RAG pipeline shouldn't be limited to text, right? That’s why Elysia analyzes each query's results and chooses the best way to display them, from tables and charts to product cards and GitHub tickets. It also features a comprehensive data explorer with search, sorting, and filtering capabilities. • 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘃𝗶𝗮 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: It uses your positively-rated queries as few-shot examples to improve future responses. This allows you to use smaller, faster models that perform like larger ones over time, cutting costs without sacrificing quality for most use cases. • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Elysia chunks documents at query time. It performs initial searches on document-level vectors and only chunks relevant documents on the fly, storing them in a parallel quantized collection with cross references for future use. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗰𝗸 Elysia is built from scratch on Weaviate, using its native features like named vectors, a variety of search types, filters, cross references, quantization, etc. It uses DSPy for LLM interactions and is delivered as a production-ready application via FastAPI, serving a NextJS frontend as static HTML. Also available as a Python package via pip: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗲𝗹𝘆𝘀𝗶𝗮-𝗮𝗶 Type: 𝗲𝗹𝘆𝘀𝗶𝗮 𝘀𝘁𝗮𝗿𝘁 Connect your Weaviate cluster and go explore what’s possible.

This will retire 90% of RAG systems with dignity (and a sad song playlist). Powered by DSPy: If you're still building "text in, text out" chatbots that only perform blind vector and text searches, you're not gonna make it! My team just dropped Elysia, and it's not just an incremental successor to Verba… It's a whole rethink of how we interact with our data using AI. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗹𝘆𝗶𝘀𝗮? An open-source platform for building agentic RAG architectures. It learns from your preferences, intelligently categorizes, labels, and searches through your data, and provides complete transparency into its decision-making process. The long & exciting feature list: • 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗧𝗿𝗲𝗲 𝗔𝗴𝗲𝗻𝘁𝘀: Elysia’s core is a customizable decision tree, and it visualizes its entire reasoning process, showing you why it chooses a specific tool or path. It enables advanced error handling, self-healing from failed queries, and prevents infinite loops. You can also add custom tools and branches to build complex, state-aware workflows. • 𝗗𝗮𝘁𝗮 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀: Before it even attempts a query, Elysia performs a full analysis of your data collections. This eliminates the blind search problem plaguing most RAG systems and allows for far more complex and accurate query generation. • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Your RAG pipeline shouldn't be limited to text, right? That’s why Elysia analyzes each query's results and chooses the best way to display them, from tables and charts to product cards and GitHub tickets. It also features a comprehensive data explorer with search, sorting, and filtering capabilities. • 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘃𝗶𝗮 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: It uses your positively-rated queries as few-shot examples to improve future responses. This allows you to use smaller, faster models that perform like larger ones over time, cutting costs without sacrificing quality for most use cases. • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Elysia chunks documents at query time. It performs initial searches on document-level vectors and only chunks relevant documents on the fly, storing them in a parallel quantized collection with cross references for future use. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗰𝗸 Elysia is built from scratch on Weaviate, using its native features like named vectors, a variety of search types, filters, cross references, quantization, etc. It uses DSPy for LLM interactions and is delivered as a production-ready application via FastAPI, serving a NextJS frontend as static HTML. Also available as a Python package via pip: 𝗽𝗶𝗽 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗲𝗹𝘆𝘀𝗶𝗮-𝗮𝗶 Type: 𝗲𝗹𝘆𝘀𝗶𝗮 𝘀𝘁𝗮𝗿𝘁 Connect your Weaviate cluster and go explore what’s possible.

Philip Vollet

93,598 Aufrufe • vor 11 Monaten

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

297,131 Aufrufe • vor 2 Jahren

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

Charly Wargnier

20,181 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 2 Jahren