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A modular chat system built on RAG principles enables transparent, document-grounded conversations. It uses semantic search, LLM inference, and preprocessing like OCR and table parsing for accurate responses.
comparison of PDFs by extracting content, detecting key changes, and producing a highlighted PDF and a detailed report.
AI-powered system for comparing internal documents with regulations using embeddings, chunking, and local LLMs. It avoids keyword matching pitfalls by understanding meaning, storing vectors in PostgreSQL with pgvector, and generating contextual insights
The Model Context Protocol (MCP) enhances AI workflows, ensuring reliability, security, and consistency in your projects. MCP helps you build agents and complex workflows on top of LLMs.
Learn what changed in the second quarterly LTS version of Codesphere. This version is typically used by on-prem installations with quarterly release cycles.
Learn what changed in the first quarterly long-term-support version of Codesphere. The LTS version is typically used for on-prem installations where a quarterly release cycle is more feasible than weekly releases.
PostgreSQL provides pgvector extension, an efficient extension for LLM-based applications to replace costly dedicated vector databases.
Karlsruhe offers a vibrant tech scene and we are proud to be part of a group organizing expert & community meetups like this one.
Long texts are difficult to summarize, but recursion can divide them into small parts. The approach is precise and preserves the meaning at any step of iteration.
Buying a used server on ebay kleinanzeigen and preparing it to be cloudified? Follow along to see what it takes to get a piece of metal running.
GMFT is a fast, lightweight toolkit for extracting tables from PDFs into formats like CSV, JSON, and Pandas DataFrames. Leveraging Microsoft's Table Transformer, GMFT efficiently processes both text and image tables, ensuring high performance for reliable data extraction.
NER identifies entities like people and locations in text. SpaCy automates this with pre-trained models, offering accuracy, speed, and multi-language support. It excels at handling large datasets efficiently compared to rule-based methods.