Agentset

Product Introduction

1. Definition: Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed for production-grade AI applications. It falls under the technical category of AI middleware, enabling document ingestion, hybrid search, and answer generation via API.
2. Core Value Proposition: Agentset solves critical RAG deployment failures by providing a pre-optimized, scalable infrastructure that handles real-world data volumes and complexity. Its primary value lies in eliminating months of RAG pipeline development while ensuring enterprise-ready reliability.

Main Features

1. Hybrid Search + Reranking: Combines keyword-based and vector search with neural reranking for precision. Uses transformer-based cross-encoders to reorder results, improving answer accuracy for complex queries.
2. Multimodal Document Processing: Natively parses images, graphs, tables, and text from 22+ file formats (PDF, DOCX, PNG, CSV, etc.) using OCR and layout analysis algorithms. Enables Q&A across heterogeneous data without preprocessing.
3. Automatic Source Citations: Generates verifiable references for every answer via embedded document chunking. Implements positional metadata tracking to link responses to exact source locations.
4. Model-Agnostic Deployment: Supports pluggable components: any LLM (OpenAI, Claude, Mistral), vector database (Pinecone, Qdrant), or embedding model. Uses standardized APIs for interoperability.
5. Production-Grade Ingestion Pipeline: Handles batch processing and real-time updates via namespaced ingestion jobs. Features automatic retries, metadata tagging, and atomic updates to prevent data corruption.

Problems Solved

1. Pain Point: RAG systems collapse under production loads due to poor chunking, retrieval errors, and unstructured data gaps. Agentset prevents "demo-to-production" failure with pre-tested optimizations.
2. Target Audience:
   • Medical AI Developers: Building diagnostic tools requiring research-backed answers.
   • Legal Tech Engineers: Needing contract analysis with citation trails.
   • Enterprise Search Teams: Scaling knowledge bases across 1M+ documents.
3. Use Cases:
   • Medical report Q&A with image-based evidence extraction.
   • Legal document review using metadata filtering for jurisdiction-specific clauses.
   • Customer support chatbots verifying answers against updated product manuals.

Unique Advantages

1. Differentiation: Unlike frameworks (LangChain/LlamaIndex), Agentset provides a managed infrastructure layer with baked-in optimizations—contrasting with DIY solutions requiring months of tuning. Outperforms alternatives like Vectara in multimodal support and Graphlit in scalability.
2. Key Innovation: Proprietary "MCP Server" architecture handles complex reasoning chains (MultiHopQA) and dynamic rerouting between AI models. This enables context-aware answers from interconnected documents without manual pipeline coding.

Frequently Asked Questions (FAQ)

1. How does Agentset improve RAG accuracy in production?
   Agentset applies hybrid search with neural reranking and automatic chunk optimization, achieving top-tier scores on benchmarks like MultiHopQA (86.2% accuracy) and FinanceBench.
2. Can Agentset process scanned documents or images?
   Yes, its multimodal engine extracts text/tables from images via OCR and integrates visual data into answers—critical for medical/legal use cases.
3. Is Agentset suitable for large enterprises with strict compliance needs?
   Absolutely. It supports on-premise deployment, granular metadata filtering, and audit trails for source verification, meeting HIPAA/GDPR requirements.
4. What file formats does Agentset support?
   It ingests 22+ formats including EML, MSG, HEIC, RST, PPT, ODT, and TSV without conversion—reducing preprocessing overhead.
5. How does Agentset reduce development time?
   With prebuilt JavaScript/Python SDKs and 1-click ingestion, developers deploy production RAG in hours versus months, avoiding pipeline boilerplate.