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Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

397,863 次观看 • 1 年前 •via X (Twitter)

11 条评论

Santiago 的头像
Santiago1 年前

Cognee is open-source and outperforms any basic vector search approach in terms of retrieval relevance. • Easy to use • Reduces hallucinations (by a ton!) • Open-source Here is a link to the repository:

Santiago 的头像
Santiago1 年前

Here is the paper explaining how Cognee works and achieves these results:

Santiago 的头像
Santiago1 年前

I also published the video on my YouTube channel:

Coral AI News 的头像
Coral AI News1 年前

Coral AI is the most powerful AI for documents. See the difference yourself:

Joan 的头像
Joan1 年前

Parece la gráfica de Obsidian

machado 𝕏 的头像
machado 𝕏1 年前

Amazing

Javier Modified 的头像
Javier Modified1 年前

Totalmente contigo—es ver un knowledge graph en acción y ya no quiero saber nada de 'vector-mediocrity-land'. Cambio de vida para la memoria en IA.

Nononno 的头像
Nononno1 年前

Básicamente si tenes un texto complejo tenes q hacer un XML gigante describiendo las relaciones ….. no veo mucha magia ahí sino muchos if en forma de XML, si tenes una base de datos gigantes no terminas mas de hacer eso

Aiden 的头像
Aiden1 年前

graphs just made vectors look basic

Shanon Faneyte 的头像
Shanon Faneyte1 年前

Amazing stuff as usual, Santiago Now, one knows what to use next time

Maria 的头像
Maria1 年前

I needed this thank you!

相关视频

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,710 次观看 • 9 个月前

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.

Santiago

40,441 次观看 • 1 年前