Applied artificial intelligenceLaboratory Blog
PartITech guides, experiments and analyses on LLMs, RAG, vector search and artificial intelligence integration.
Lab publications
Our articles document experiments and architecture choices involving LLMs, RAG, vector search and AI integration into web applications. They state their context and limitations so they remain useful beyond a demonstration.
PartITech blog
Technical analysis, practical guides and field experience to build, modernize and maintain sustainable applications.
Explore the practical applications of creating a RAG system and initiating dialogue with the Txtai model. Enhance documentation and enable seamless IT integration through indexing and querying with Txtai. Dive into part 3 of our series for insights and practical cases.
In this article, we explore the use of databases for knowledge storage with TxtAi and discuss vector indexing technologies such as Elasticsearch, Faiss, Milvus, Pinecone, Weaviate, Annoy, HNSW, and Postgresql with pgvector.
Discover the world of "Retrieval-Augmented Generation" (RAG) - a groundbreaking fusion of information retrieval and language generation in natural language processing. Explore the key components, challenges, and opportunities of this technology, and follow a practical guide to implement a RAG system using TxtAi and Llama.cpp.
Llama CPP is a new tool designed to execute language models, directly in C/C++. This tool is specially optimized for Apple Silicon processors through the use of ARM NEON technology and the Accelerate framework. It also offers AVX2 compatibility for systems based on x86 architectures. Operating mainly on the CPU, Llama CPP also integrates 4-bit quantization capabilities, thus enhancing its efficiency.
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