What is Agentset?
Agentset provides developers with an open-source Retrieval Augmented Generation (RAG) platform designed to streamline the creation of high-performance AI applications. It focuses on simplifying the complexities associated with building and scaling RAG infrastructure, allowing users to implement powerful agentic RAG functionalities quickly. The platform is specifically optimized for working with documents, aiming to deliver effective results with minimal initial configuration.
The system operates through a multi-step process beginning with parsing diverse document types (supporting over 22 formats) to extract content, metadata, and structural information. Subsequently, it intelligently chunks these documents into smaller, manageable pieces while maintaining the original content structure. Agentset then utilizes state-of-the-art embedding models to represent the knowledge base efficiently. For information retrieval, it employs a combination of hybrid search techniques and reranking mechanisms to ensure the most relevant results are returned, complete with automatic source citations for user verification.
Features
- High Accuracy RAG: Incorporates techniques like hybrid search and reranking for optimal results.
- Deep Research Capabilities: Built-in support for agentic experiences for enhanced accuracy and depth.
- Automatic Citations: Automatically cites the sources of generated answers.
- Semantic Search: Retrieves relevant results from datasets using state-of-the-art semantic search.
- Data Partitioning & Filtering: Supports partitioning and metadata filtering for targeted answers based on data subsets.
- Multi-Format Document Parsing: Parses over 22 file types, extracting content, metadata, and structure.
- Intelligent Chunking: Breaks down documents into smaller pieces while preserving content structure.
- Advanced Embedding: Uses state-of-the-art embedding models for efficient knowledge base embedding.
- Hybrid Retrieval: Combines hybrid search and reranking to retrieve the most relevant results.
- Open-Source Availability: Allows for self-hosting and full control over the platform.
Use Cases
- Building AI-powered chatbots with knowledge base integration.
- Developing question-answering systems based on internal documents.
- Creating research assistants capable of deep analysis.
- Enhancing applications with accurate, source-cited information retrieval.
- Implementing semantic search functionality within custom software.
- Powering internal knowledge management tools.
FAQs
-
What is RAG?
RAG stands for Retrieval Augmented Generation. It's a method that lets AIs give better answers by first looking up relevant information from documents, then using that information to generate responses. -
How is Agentset different from LangChain or LlamaIndex?
LangChain and LlamaIndex typically take weeks of implementation time to get an agent working. Agentset abstracts the details to get powerful agentic RAG working quickly. -
Can I self-host Agentset?
Yes, Agentset is open-source, so you're able to host it and scale it in-house. A hosted solution is also available. -
How is Agentset different from other agent building tools?
Agentset is optimized for documents. This focus allows it to get excellent results out of the box with minimal configuration. -
What counts as a page in Agentset pricing?
Agentset counts every 1000 characters of parsed text as a page, which might differ from the page count in the original document (e.g., PDF, Word). -
What is a connector in Agentset?
Connectors are integrations with external data sources like Google Drive, SharePoint, or Notion that automatically sync content to your knowledge base.
Related Queries
Helpful for people in the following professions
Agentset Uptime Monitor
Average Uptime
99.93%
Average Response Time
113.43 ms
Featured Tools
Join Our Newsletter
Stay updated with the latest AI tools, news, and offers by subscribing to our weekly newsletter.
