Using Qdrant and Ollama for Local AI-Powered Data Discovery
Leverage Qdrant's vector search and Ollama's generative models to enable efficient, localized AI-driven data exploration.
Leverage Qdrant's vector search and Ollama's generative models to enable efficient, localized AI-driven data exploration.
Leverage the power of graph databases and large language models to create transparent, interpretable AI solutions using Neo4j and LLMs.
Leveraging vector databases and knowledge graphs enhances semantic search, enabling precise query processing across vast datasets.
In this article, we explore the integration of embedding models with context-aware Large Language Models (LLMs) to perform effective question answering over structured data.
Introducing a cutting-edge multilingual translation pipeline, featuring a specialized translation model and advanced summarization capabilities.
Leveraging Qdrant's vector search capabilities, we auto-generate WordPress post drafts using powerful Language Model writing assistance for efficient content creation.
Leverage LocalAI embeddings to enhance document classification accuracy when combined with Ollama's powerful categorization capabilities.
Secure Dockerized AI serving: HTTPS + JWT.
Flower framework: Federated learning in Docker.
Secure model data with PySyft in Docker.