Qdrant - Vector Search Engine

# Build a Semantic Cache for a RAG Pipeline | Time: 30 min | Level: Intermediate | | | ------------ | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ## What’s a Semantic Cache Traditional caches operate on an exact match basis, while semantic caches search for the meaning of the key rather than an exact match. For example, “What is the capital of Brazil?” and “Can you tell me the capital of Brazil?” are semantically equivalent: a semantic cache can recognize that.

Sparse neural retrieval finally started getting more and more attention (SPARSEUP, MILCO, sparse encoders in Sentence Transformers…). Well, amazing, we like attention! Our miniCOIL v1 sparse neural retriever article ended with a promise: to keep working on this sparse neural retriever in-depth, improving the model’s quality, and in-width, “extending it to other dense encoders and to languages beyond English.” Here is the next “in-width” step of the miniCOIL saga: to try and cross the language barrier, that is, make the model suitable for multilingual retrieval. It’s a fun challenge, as sparse retrievers are hard to adapt to this scenario: exact matching in cross-lingual text search usually means translation.

Classification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches were training your own classifier, which needs labels, using a zero-shot classifier (compared in this Hugging Face benchmark), or prompting an LLM with some hacky type safety on top. There were also meta-ML methods, but none of them reached the mainstream. With the introduction of Jev, a decision model from TypeSafe served through OpenRouter, the taxonomy of classifiers has changed yet again:

Google’s new EmbeddingGemma 2 can run on a phone. The model travels light. The vectors need their own suitcase. At 10 million documents, full-size float32 vectors alone take 30.7 GB of RAM. We got early access ahead of today’s Google DeepMind release and tested how much of that memory we could save in Qdrant. Quantized full-size vectors kept 99% of their retrieval quality with 30x less vector RAM. Shorter vectors with rescoring reached 77x less vector RAM and retained 94.5%.

# Region-Specific Contract Management System | Time: 90 min | Level: Advanced | | | | ------------ | --------------- | --- | --- | Contract management benefits greatly from Retrieval Augmented Generation (RAG), streamlining the handling of lengthy business contract texts. With AI assistance, complex questions can be asked and well-informed answers generated, facilitating efficient document management. This proves invaluable for businesses with extensive relationships, like shipping companies, construction firms, and consulting practices. Access to such contracts is often restricted to authorized team members due to security and regulatory requirements, such as GDPR in Europe, necessitating secure storage practices.