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What is Ducky?
Ducky provides a fully managed AI retrieval service, streamlining the complexities of semantic search for developers. It is engineered to make the implementation of retrieval augmented generation (RAG) straightforward, allowing users to concentrate on building their applications rather than managing intricate search infrastructure.
The service emphasizes performance with low-latency search and efficient indexing, aiming to deliver highly relevant results. Ducky handles various aspects of the search process, including chunking large content, transforming user queries, implementing hybrid search and reranking, and managing system deployment and scaling, all accessible via a simple Python SDK.
Features
- Fully Managed Retrieval Service: Abstracts complexities of RAG, including vector databases, embedding models, chunking, query transformation, reranking, deployment, and scaling.
- High-Performance Search: Engineered for retrieval accuracy, low-latency search, and efficient indexing to deliver relevant, not just similar, results.
- Developer-Focused Python SDK: Provides a simple Python SDK with comprehensive documentation for quick integration and searching within seconds.
- Advanced Fullstack Search Capabilities: Employs a multi-stage system to handle complex search intent, incorporating chunking, query rewriting, hybrid search, and reranking.
- Optimized for LLM Agents: Enables the creation of context-aware LLM agents capable of generating hallucination-free, informed, and relevant answers.
Use Cases
- Implementing advanced semantic search in applications without managing underlying infrastructure.
- Building context-aware LLM agents that provide accurate, factual, and relevant responses based on custom data.
- Enhancing existing applications with sophisticated retrieval augmented generation (RAG) capabilities.
- Efficiently indexing and searching through large volumes of documents or textual data.
- Simplifying the development and deployment of AI-powered search functionalities for developers.
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