TOOLS

→ View original post on X — @anthropicai

Claude Integrations: Connect to Asana, Intercom, Linear, Zapier

By

@anthropicai

–

01 May 2025 18h01

With Integrations, you can connect Claude to Asana, Intercom, Linear, Zapier, and more. Or developers can create their own to connect with any tool, in as little as 30 minutes.

→ View original post on X — @anthropicai

Claude Launches Integrations and Advanced Research Capabilities

By

@anthropicai

–

01 May 2025 18h01

Today we're announcing Integrations, a new way to connect your apps and tools to Claude.

We're also expanding Claude's Research capabilities with an advanced mode that searches the web, your Google Workspace, and now your Integrations too. pic.twitter.com/zSvPBZYq43
— Anthropic (@AnthropicAI) 1 mai 2025

Today we're announcing Integrations, a new way to connect your apps and tools to Claude. We're also expanding Claude's Research capabilities with an advanced mode that searches the web, your Google Workspace, and now your Integrations too.

→ View original post on X — @axeleraai

AI Systems Efficiency: Smart Work Over Speed

By

@axeleraai

–

01 May 2025 16h43

A thought for #LabourDay: AI systems should work smart, not just fast. 900fps inference means little if post-processing drops your app to 20fps. Metis + Voyager SDK ensure every part of your stack works. Efficiency is about purposeful labour! https://
eu1.hubs.ly/H0jNvXl0

Milvus: Fast, Scalable Open-Source Vector Database

By

–

01 May 2025 16h28

Milvus is the vector database powering our vector search. It's open-source, incredibly fast, and scalable. It can store and query millions (or even billions) of vectors efficiently. Check them out!

Loading Knowledge Base and Initializing RAG Agent for PDF Processing

By

–

01 May 2025 16h28

5. Next, Loading the Knowledge & Agent When a PDF is processed, we instantiate PDFKnowledgeBase with the file path & our Milvus vector_db. Then we pass it into the above "get_rag_agent" method to initialize a fully ready-to-chat agent and then store it as a session variable.

Building a PDF RAG Agent with Agno and GPT-4o-mini

By

–

01 May 2025 16h27

4. Next, let’s build the PDF RAG Agent The get_rag_agent function sets up the Agno Agent with gpt-4o-mini, PDFKnowledgeBase, and the DuckDuckGo tool for web search. This design allows the agent to try PDF knowledge base first and fall back to websearch if needed.

Setup Milvus VectorDB for RAG Agent Semantic Search

By

–

01 May 2025 16h27

3. Setup vectorDB First, connect to Milvus to store and search vector embeddings. Agno simplifies this with an abstraction for the Milvus client. Provide the collection name, embedding model, and server info to create the collection, enabling semantic search in our RAG Agent.

Installation de Milvus : Base de données vectorielle open-source

By

–

01 May 2025 16h27

2. Install Milvus We use Milvus, an open-source vector database, to store document embeddings. Milvus supports flexible deployments via Kubernetes, Docker, or Lite mode via pip install. Let's run the "standalone_embed. sh" script to launch Milvus in Docker standalone mode.