Qdrant’s Post

RAG is a good solution for finding answers. Sometimes accuracy is at the top of your requirements and Knowing graphs are one of the best methods to enhance it. I used it in personal projects in fields of HR, failure analysis and project management where there is little room for error. This short course will give you a nice overview

VARIABLE SYNTAX int myNum = 15; int myNum2; // do not assign, then assign myNum2 = 15; int myNum3 = 15; // myNum3 is 15 myNum3 = 10; // myNum3 is now 10 float myFloat = 5.99; // floating point number char myLetter = 'D'; // character int x = 5; int y = 6; int sum = x + y; // add variables to sum // declare multiple variables int x = 5, y = 6, z = 50;

Savvy turns natural language into shell commands. You can now refine your prompt to provide additional context and nudge savvy to your desired solution. Try it out today. Just run 'curl -fsSL install.getsavvy.so | sh' to install Savvy on your machine.

Understanding Vector Search and Embeddings: Vector search operates on vector embeddings, which are numerical representations of text data. These embeddings capture the semantic meaning and relationships between words. Documents and queries are transformed into vectors using the same embedding model, creating a common ground for comparison. Vector Similarity for Semantic Retrieval: Vector search calculates the similarity between the query vector and document vectors. Documents with embeddings closer (based on a distance metric) to the query vector are considered more relevant. This approach retrieves documents that align with the query's intent, even if they don't contain the exact keywords.

🚨 Great Read 🚨 “Given an unstructured text, we first will extract key entities, relationships and attributes using Stanford’s OpenIE framework. Once the triples are extracted, we clean/ harmonize them.“ Read this story from Sunila Gollapudi on Medium: https://lnkd.in/g6UiQhZv

Several methods for converting text into numerical representations

Tip: The list.insert() method implements slice semantics by clamping the index argument to valid insertion points: >>> s = [10, 20, 30] >>> s.insert(100, 40) >>> s [10, 20, 30, 40] The insert() above is equivalent to: >>> s[100 : 100] = [40] Pretty cool 🙂

🗓 I had the privilege of presenting our latest paper, "REA: Refine-Estimate-Answer Prompting for Zero-Shot Relation Extraction," at the NLDB 2024 conference. This paper introduces a method that enhances how large language models tackle zero-shot relation extraction without needing prior specific training. 🔍 Explore Our Approach: The REA strategy decomposes the complex relation extraction task into manageable stages, guiding language models through refinement and certainty estimation, boosting their performance on tasks they haven't explicitly learned. 🔗 Dive Deeper: Explore our code here: bit.ly/4cAbYJk Access the full paper here: bit.ly/4cjw1fu

The D language has a library function schwartzSort that implements the Schwartzian Transform: https://lnkd.in/gHisPWNf