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sentence transformers
6 posts about sentence transformers by 3 contributors.
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- 6
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- 3
Articles about sentence transformers
- Article · AI Agents
Re-Ranking Strategies in Enterprise Multi-Agent RAG Systems
Learn how to add a cross-encoder re-ranking agent to a LangGraph RAG workflow to filter retrieved documents and improve enterprise support answers.
- Article · .NET Core
Optimizing Retrieval in Manufacturing: A Multi-Agent Graph RAG Approach with Hybrid Search and Reranking
Our system combines vector similarity, keyword matching, and knowledge graph relationships to provide contextually accurate answers while maintaining conversational memory across multiple interactions.
- Article · AI Agents
When Prompt Engineering Stops Helping: Model and Retrieval Changes in Enterprise Tax Management AI
Prompt engineering is a powerful tool, but it is not a panacea. In enterprise tax management, the cost of error is too high to rely on prompts alone. By building a multi-agent system with LangGraph, we can isolate failures: if retrieval is poor, we fix the vector store; if reasoning is weak, we upgrade the model; if compliance is risky, we add guardrails. This architectural approach ensures that our AI copilot is not just smart, but also safe, accurate, and auditable. The next step is to integrate real-time regulatory feeds and rigorous evaluation pipelines to evolve this POC into a production-grade platform.
- Article · AI
Sentence Transformers: Architecture, Working Principles, and Practical Examples
Explore Sentence Transformers: architecture, working, and practical examples. Learn how to convert text into meaningful embeddings for semantic search and more!
- Article · AI
Transformers vs Sentence Transformers: Understanding the Difference
Unlock the power of NLP! This guide breaks down Transformers vs. Sentence Transformers, highlighting their differences, use cases, and practical implementation for semantic tasks.
- Article · AI
Advanced Techniques for Semantic Search Using Embeddings in Python
Learn advanced methods to implement and optimize semantic search using embeddings in Python. Covers model selection, vector databases, and hybrid retrieval systems.
