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Agentset is an open-source, RAG-as-a-service platform designed to simplify the development of high-quality Retrieval Augmented Generation (RAG) applications with “agentic superpowers.” It aims to address the common bottleneck of building and scaling effective retrieval systems, particularly for working with large document sets. Agentset distinguishes itself by offering comprehensive RAG capabilities out-of-the-box, including advanced accuracy features, deep research capabilities, automatic citations, and support for over 22 file types. It provides both a managed service and a self-hosting option, with flexible pricing tiers including a free plan.

I. Key Themes and Concepts

1. RAG-as-a-Service with Agentic Superpowers: Agentset’s core offering is to provide RAG capabilities as a managed service, eliminating the need for developers to build and scale the underlying infrastructure. A key differentiator is its emphasis on “agentic superpowers,” which enhance the capabilities and accuracy beyond traditional RAG systems.

  • Definition of 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.”
  • Agentset’s Role: “Agentset is an RAG-as-a-service platform that allows you to build high-quality RAG applications without the hassle of building and scaling the infrastructure.”
  • Agentic Capabilities: Agentset offers “built-in support for agentic experiences, taking longer to return a result but surpassing the capabilities and accuracy of traditional RAG systems.” This includes “planning, reasoning, expanded search results, and answer validation.”

2. High Accuracy and Advanced Retrieval Techniques: Agentset prioritizes high accuracy in its RAG solutions, employing state-of-the-art (SOTA) techniques for search and retrieval.

  • Best-in-Class Techniques: “Agentset incorporates best-in-class RAG techniques such as hybrid search and reranking to get the highest result accuracy before you doing any customizations.”
  • Semantic Search: It uses “SOTA semantic search” to get “the most relevant results from your data set based on the query.”
  • Retrieval Process: The platform utilizes “a hybrid search and reranking to retrieve the most relevant results.”

3. Streamlined Development and Document Optimization: Agentset aims to significantly reduce development time compared to lower-level tools and is specifically optimized for handling diverse document sets.

  • Reduced Implementation Time: “LangChain and LlamaIndex typically take weeks of implementation time to get an agent working, we abstract all the details and get you powerful agentic RAG working quickly.”
  • Document-Focused Design: “Agentset is optimized for documents. This focus allows us to get excellent results out of the box with minimal configuration.” It supports parsing documents from “over 22 file types” including TXT, PDF, and HTML.
  • Handling Large Datasets: The platform is designed to “Process and analyze Large Datasets in multiple formats (TXT, PDF, HTML, etc.).”

4. Transparency and Control (Citations & Open Source): Agentset promotes transparency through automatic citations and offers flexibility through its open-source nature.

  • Automatic Citations: “Agentset automatically cites the sources of your answers, allowing your users to inspect it.” This allows users to “inspect it.”
  • Open Source: “Yes, Agentset is open-source so you’re able to host it and scale it in house.” This provides “Full source code access” and the option to “Self-host for complete control or use our managed service.”

5. Core Components and Workflow: Behind the scenes, Agentset follows a structured process for handling documents and generating responses.

  • Parsing: Extracts “content, metadata, and structure” from various file types.
  • Chunking: Breaks down documents “into smaller pieces while preserving the content structure, allowing the agent to lookup and reason about individual pieces.”
  • Embedding: Uses “SOTA embedding models” to embed the knowledge base, enabling “efficient and accurate retrieval.”
  • Retrieval: Employs “hybrid search and reranking” to find relevant information.

II. Important Ideas/Facts

  • Problem Solved: “The bottleneck for working with large documents was retrieval. Building a good retrieval system was no easy feat.” (Abdellatif Abdelfattah, CEO, Agentset)
  • Key Features:High Accuracy: Hybrid search, reranking.
  • Deep Research: Agentic experiences.
  • Citations: Automatic source attribution.
  • Search: SOTA semantic search.
  • Partitions: Metadata filtering and data subsetting.
  • Supported File Types: Over 22 file types, including TXT, PDF, HTML.
  • Comparison to Other Tools: Distinct from LangChain or LlamaIndex due to abstraction and focus on document optimization, leading to faster setup.
  • Hosting Options: Managed service and self-hosting (open-source).
  • Pricing Tiers:Free: 1,000 pages, 10,000 retrievals, community support.
  • Pro ($49/month): 10,000 pages, unlimited retrievals, email support. Includes Deep Research.
  • Enterprise (Custom): Unlimited pages/retrievals, custom integrations, dedicated account manager, self-hosting or managed.
  • Definition of a “Page”: “We count every 1000 characters of parsed text as a page.”
  • Connectors: Integrations with external data sources (Google Drive, SharePoint, Notion) to sync content.

Quickstart Process:

  1. Install Agentset SDK.
  2. Obtain and use an API key for authentication.
  3. Create a Namespace to organize documents.
  4. Upload documents via ingest jobs (e.g., PDF URL).
  5. Perform searches or engage in chat conversations with documents.
  • Namespaces: Organize documents and provide context for RAG operations.
  • Embedding Configuration: Allows for custom embedding models (e.g., OpenAI’s text-embedding-3-small).

This briefing document provides a comprehensive overview of Agentset, highlighting its core functionalities, unique selling points, and operational details based on the provided sources.