search-docs

This capability utilizes a vector-based search mechanism that indexes documents and allows for semantic querying. It employs embeddings to convert text into a high-dimensional space, enabling more nuanced search results based on meaning rather than keyword matching. This approach allows users to find relevant documents even when the exact terms are not present, enhancing the search experience significantly.

This capability allows users to retrieve documents based on the context of previous queries or interactions. By maintaining a session state and leveraging context management patterns, the system can provide more relevant results tailored to the user's current needs. This is achieved through a combination of session tracking and contextual embeddings that adapt to user behavior over time.

This capability allows for seamless integration with external APIs to enhance document search capabilities. By using a modular architecture, it can call various APIs to enrich search results with additional data, such as metadata or related documents. This integration is facilitated through a plugin system that allows easy addition of new data sources without altering the core search logic.