Limitation of Oracle XE (Express Edition) databases

Any use of the Oracle Database Express Edition is subject to the following limitations;

1. Express Edition is limited to a single instance on any server;
2. Express Edition may be installed on a multiple CPU server, but may only be executed on one processor in any server;
3. Express Edition may only be used to support up to 11GB of user data (not including Express Edition system data);
4. Express Edition may use up to 1 GB RAM of available memory.

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What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...

Training LLM model requires more GPU RAM than storing same LLM

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How are vector databases used?

Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.

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