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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:show more

Andrew Ng

1,511,076 subscribers

191,219 views • 2 years ago •via X (Twitter)

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10 Comments

Sigil Wen2 years ago

@trychroma @atroyn BIG DOG @atroyn 🥶

Max Voitko2 years ago

@trychroma @atroyn Chroma is based on SQLite which is good for experimentation but not production-tier db. I'm curious about what you use in production systems.

Waseem2 years ago

@trychroma @atroyn Just signed up! Looking forward to this.

Mohamed2 years ago

@trychroma @atroyn Great !

T_Abel2 years ago

@trychroma @atroyn Thank you so much Andrew

AI/ML Jobs2 years ago

@trychroma @atroyn 🚀🚀

ALI SIDI2 years ago

@trychroma @atroyn Is this course available on Coursera?

Lakshay Chhabra2 years ago

@trychroma @atroyn Thanks for this, it was very insightful.

Cezary Storczyk2 years ago

@trychroma @atroyn So these are techniques that will allow the use of the data set possessed, without having to use it directly?

Mike Mansour2 years ago

@trychroma @atroyn by the time we learn this , there will be a totally new technique or a research paper that will deem it obsolete 😂

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124,458 views • 11 months ago

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Femke Plantinga

120,565 views • 1 year ago

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137,073 views • 2 years ago

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104,371 views • 2 years ago

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DeepLearning.AI

14,702 views • 10 months ago

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DeepLearning.AI

11,500 views • 11 months ago

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150,317 views • 2 years ago

Check it out: An entire lesson from BloomTech's AI for Developer Productivity course! Fundamentals of RAG (Retrieval-Augmented-Generation). How we enhance accuracy and reliability of generative AI models. This is the foundation we build on to give AI important context.

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79,507 views • 2 years ago

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595,911 views • 2 years ago

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34,261 views • 1 year ago

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146,313 views • 1 year ago

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 views • 2 years ago

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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:

Femke Plantinga

140,618 views • 2 years ago

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155,273 views • 2 years ago