Here is a summary of the key takeaways from the video transcription, presented in a markdown format with sections for better readability:
Challenges in AI Adoption
- Only 17% of companies are in production with AI use cases
- Top concerns are data security, privacy, and data availability/quality
- AI models can "hallucinate" and provide incorrect information, which is a huge problem for companies
Retrieval Augmented Generation (RAG) and Vector Search
- RAG is a common solution to provide context to AI models
- Vector search allows storing data in a multi-dimensional space and retrieving relevant documents
- However, vector search is a proximity-based search and may not provide accurate results for specific requirements
Hybrid Search
- Hybrid search combines vector search and traditional search techniques
- Techniques include pre-filtering, post-filtering, and re-ranking
- Each approach has its own pros and cons in terms of performance and semantic accuracy
Implementing Hybrid Search
- Hybrid search is not limited to dedicated vector databases, but can be implemented in traditional databases like PostgreSQL
- PostgreSQL has the ability to store embeddings and perform hybrid searches
- Alloy DB and OpenSearch are other tools that provide hybrid search capabilities
Data Platform Considerations
- AI requires data from various sources, so a comprehensive data platform is needed
- Hevo provides a data platform that covers streaming, storage, and serving layers
- The platform offers integration with various databases, search engines, and AI capabilities
Conclusion
- For companies looking to move AI use cases into production, a secure and fast AI context is crucial
- Hybrid search, combined with a comprehensive data platform, can address the challenges of data security, privacy, and availability
- Hevo's data platform offers a solution to free data from various sources and make it ready for AI applications.