TileDB Vector Search with LangChain

TileDB vector search has been integrated into LangChain, a framework for developing large language model (LLM) applications, to enhance functionalities such as Retrieval Augmented Generation (RAG) and conversation memory. The integration allows LLMs like ChatGPT-3.5-turbo to access external knowledge sources and maintain user preferences and interaction histories, thereby overcoming limitations related to data availability during training. By using TileDB's vector indexing, LangChain can store and retrieve information from the LangChain documentation, enabling the model to answer questions about LangChain that it could not otherwise answer due to the cutoff in its training data. Additionally, TileDB vector search facilitates the storage of conversation history, allowing LLMs to recall previous user interactions and preferences, thus providing a more personalized chat experience. The methods demonstrated in the examples can be expanded for more sophisticated LLM applications, and further developments in multi-modal AI capabilities with TileDB are anticipated.