Worked on integrating SurrealDB as a vector database within the camel-ai/camel repository, implementing HNSW indexing to enable scalable vector storage, table management, and CRUD operations for vector records using Python. Enhanced onboarding and adoption by updating documentation, setup instructions, and usage examples, aligning with LangChain ecosystem standards in surrealdb/docs.surrealdb.com and langchain-ai/langchain. Focused on database integration and backend development, the work clarified SurrealDB’s role in LLM workflows and improved stability for vector search features. Technical contributions included updating dependencies, resolving integration issues, and providing practical Jupyter Notebook examples for rapid experimentation with vector stores and embeddings.