hackmit-2024/demo/SQLSyntax.md at main · intersystems-community/hackmit-2024
:alert: Please refer to the full product documentation for the full syntax and instructions. This page is for offline reference only. Optional parameters: This query will succeed following any of the above three table creations. It will default to the table's vector type. Parameters: Returns: the corresponding vector to be added to tables or used in other vector operations. Example: Parameters: Returns: a double value of the cosine distance between the two vectors, taking value from -1 to 1. Example: Parameters: Returns: a double value of the dot product of two vectors. Example: Getting the top 3 most similar vectors (to an input vector) from a table Using Cosine Similarity: Using Dot Product: Note that we use 'DESC', since a higher magnitude for dot product/cosine similarity means the vector is more similar. This can be combined with 'WHERE' clauses to add filters on other columns.
Using Vectors in IRIS SQL :alert: Please refer to the full product documentation for the full syntax and instructions. This page is for offline reference only. VECTOR (type, length) Optional parameters: type - Optional, defaults to DOUBLE. The datatype of elements allowed to be stored in the vector. Can be DECIMAL, DOUBLE, INTEGER, TIMESTAMP, or STRING. length - Optional, can be specified only if type is also specified. An integer for the number of elements allowed to be stored in the vector. If specified, length restriction for INSERT INTO the vector column will be imposed. Creating a table w
Explore this link on the map →related reading
- Not All Vector Databases Are Made Equal | Towards Data Sciencetowardsdatascience.com
- Faiss: A library for efficient similarity search - Engineering at Metaengineering.fb.com
- Announcing ScaNN: Efficient Vector Similarity Searchai.googleblog.com
- GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors. · GitHubgithub.com
- Vector Similarity Explained | Pineconepinecone.io
- Redirecting to: https://thedataquarry.com/blog/vector-db-2thedataquarry.com
- From prototype to production: Vector databases in generative AI applications - Stack Overflowstackoverflow.blog
- Investing in Pinecone | Andreessen Horowitza16z.com
- What Is a Vector Database? | IBMibm.com
- Vector embeddings | OpenAI APIdevelopers.openai.com
- Embeddings: What they are and why they mattersimonwillison.net
- Optimizing RAG: A Guide to Choosing the Right Vector Database | by Mutahar Ali | Mediummedium.com