If instructed, Claude can enable agentic retrieval of documents from your internal knowledge base and APIs, complete tasks requiring real-time data or complex computations, and orchestrate Claude subagents for granular requests.
CODE
-
Tool Use in LLM-Based Agentic Workflows
By
–
Tool use, in which an LLM is given functions it can request to call for gathering information, taking action, or manipulating data, is a key design pattern of AI agentic workflows. You may be familiar with LLM-based systems that can perform a web search or execute code. Some of
-
Building Great RAG Solutions with AI21’s Contextual Answers
By
–
Building a RAG solution is easy. Building a great one is not. In our guest blog on @streamlit
, our team explores the intricacies of how AI21's Contextual Answers Task-Specific Model & our RAG Engine generate context-based answers grounded in your proprietary organizational data. -
Web Search Connector License Terms and Index Source
By
–
@nickfrosst @cohere which search index is used for the web-search connector? What are the license terms for using the results that come back from that?
-
LLM Plugin for Git Branch Automation Commands
By
–
I have something like that as a plugin! llm cmd git rename this branch to issue-34
-
llm-command-r 0.2 Adds Websearch Connector for Cohere Models
By
–
Here's llm-command-r version 0.2, which adds support for the websearch connector, an option which allows the Cohere models to run a web search and use the results from that as part of the context for answering the prompt https://
github.com/simonw/llm-com
mand-r/releases/tag/0.2
… -
Activation Trade-offs: Current vs Future Token Optimization
By
–
I guess there is a tradeoff between how each activation is used, whether it is doing something that's optimal for a current or future token. i'm using information and store both pretty loosely. 🙂
-
Adaptive RAG Implementation with Cohere Command-R+
By
–
Adaptive RAG w/ Cohere's new Command-R+ Adaptive-RAG (
@SoyeongJeong97 et al) is a recent paper that combines (1) query analysis and (2) iterative answer construction to seamlessly handle queries of differing complexity. We took at stab at implementing these ideas from scratch -
Record Manager: Maintaining Updated Vector Embeddings in RAG
By
–
Upserting documents to vector database is the key of building a RAG solution.
— FlowiseAI (@FlowiseAI) 4 avril 2024
❓But.. how to maintain an up-to-date vector embeddings with updated documents?
🗄️Record Manager! Document chunks will be hashed, stored and compared to prevent duplication
Available in 1.6.3! pic.twitter.com/dQzkTrn9SlUpserting documents to vector database is the key of building a RAG solution. But.. how to maintain an up-to-date vector embeddings with updated documents? Record Manager! Document chunks will be hashed, stored and compared to prevent duplication Available in 1.6.3!
-
API Access Available for AI Development
By
–
They have an API, I believe you should be able to access it! cc @aidangomez