Papr Graph

Papr Graph transforms semantic embeddings into graph-native embeddings, enhancing information retrieval with a single API call. Key capabilities: * Encode temporal and topical dimensions into existing embeddings * Improve retrieval based on correctness, not just semantic similarity * Integrate with popular embedding providers and vector databases * Enhance search accuracy for contextual information This robust tool addresses the limitations of flat cosine similarity by incorporating relational context often missed by standard vector embeddings. It attaches up to 14 relational dimensions, such as entities, relations, time, and ownership, to each vector. This allows the system to rerank candidates at query time, ensuring that the most relevant and accurate information is prioritized over merely semantically similar results. Papr Graph is designed for developers and data engineers building sophisticated search and retrieval systems. It integrates seamlessly with existing technology stacks, including various embedding vendors and vector databases like Pinecone, Qdrant, and Weaviate. Ideal for applications in commerce, fintech, and healthcare, where precision and contextual understanding are paramount. Upgrade your retrieval capabilities to deliver more accurate and contextually rich answers.