Detailed Introduction
Agentset is an open-source platform for retrieval-augmented generation (RAG) designed to help developers and researchers build citation-aware agents. The project supports ingestion and partitioning for 22+ file formats, integrates citation-aware pipelines, and streamlines connecting external knowledge into an agent’s context to improve answer accuracy and traceability.
Main Features
- Multi-format ingestion: Parse and partition many document types to reduce preprocessing overhead.
- Citation & traceability: Built-in citation pipeline links outputs to source document locations for verification.
- Scalable retrieval: Compatible with multiple vector databases and retrieval components to support RAG workflows.
- Agent integration: SDKs and examples to build multi-step, agentic workflows.
Use Cases
- Enterprise knowledge QA: Ingest internal documents to provide citation-backed assistants for support and search.
- Research & prototyping: Rapidly prototype RAG systems and evaluate retrieval strategies.
- Compliance & auditing: Produce traceable answers for audits and regulatory review.
- Multi-format document processing: Normalize diverse assets into a unified retrieval corpus.
Technical Features
- Efficient retrieval layer built on modern embeddings and vector search.
- Partitioning and caching strategies to optimize context window usage.
- Configurable retrieval and re-ranking pipelines compatible with mainstream LLMs and inference services.
- MIT-licensed, open-source project suitable for extension and enterprise deployment.