Rabbitt.ai’s ChanceRAG Offers No-Code RAG Integration for LLMs with Unmatched Precision and Context

Large language models (LLMs) and document retrieval systems can now be more easily integrated thanks to the release of ChanceRAG, a no-code RAG solution from Indian genAI startup Rabbitt.ai.

Rabbitt.ai’s chief AI officer, Harneet Singh, emphasized the offering as an “enterprise-grade solution for building RAG.”

“We observed that traditional retrieval methods did not provide the depth and precision required for complicated searches, whether semantic or keyword-based. We have developed a fusion retrieval method with ChanceRAG that provides unmatched precision and context, which no other approach can accomplish on its own,” he stated.

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Using a vector database, ChanceRAG enables users to link their LLMs to PDF documents they upload. Intending to improve performance, the product presents an Advanced Fusion Retrieval technique that combines keyword matching and semantic comprehension. Singh clarified that the difficulties companies encounter in creating efficient RAG pipelines served as the inspiration for ChanceRAG.

He pointed out that current retrieval techniques were ineffective for practical uses, such as chatbots for customer service and artificial intelligence sales representatives. Possibility RAG aims to do away with trial-and-error in RAG implementation so that organizations may easily start LLM applications.

The system has been benchmarked against industry standards and has very high document retrieval precision. Test results revealed nDCG@5 = 5, an 80% accuracy rate, and precise answers free of hallucinations. Users can try ChanceRAG’s capabilities by accessing a live demo on HuggingFace.

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The foundation of ChanceRAG includes functions like BM25 indexing, vector store construction, PDF processing, and retrieval technique fusion. Together, these components provide accurate and pertinent query results. By changing the chunk size, overlap settings, and retrieval and reranking algorithms, users can further personalize their retrieval options.

In the upcoming weeks, Rabbitt.ai intends to release more RAG innovations, including as context-driven document segmentation, multimodal document summarization, adaptive re-ranking, and dynamic query expansion.

Singh founded Rabbitt.ai, a company that specializes in generative AI solutions, such as MLOps integration, RAG fine-tuning, and custom LLM development. The TC Group of Companies provided the company with $2.1 million in recent funding.

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