= 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the¬†Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial‚ÄãFollow along in your own Python notebook to generate and store vector embeddings in Rockset.
Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data.
Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data. ->: = 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the¬†Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial‚ÄãFollow along in your own Python notebook to generate and store vector embeddings in Rockset.