#Unstructured Data

The application efficiently transforms various formats of unstructured data into a structured Neo4j knowledge graph. It utilizes Large Language Models such as OpenAI and Gemini to extract nodes, relationships, and properties via the Langchain framework. Files can be uploaded from local devices, Google Cloud Storage (GCS), or Amazon S3, with the flexibility to select a preferred LLM model. Key features include supporting custom schemas, interactive graph visualization in Bloom, and conducting data interactions using conversational queries within Neo4j. Ensure a Neo4j Database V5.15+ with APOC support. Available for deployment via Docker locally or Google Cloud Platform.

Discover NucliaDB, a comprehensive database built for efficient handling of unstructured data with a hybrid approach combining vector, full text, and graph indexing. Benefit from integration with Nuclia Cloud, offering advanced NLP capabilities without the need for complex data handling. NucliaDB supports multilingual environments, role-based security, and integrates seamlessly with popular NLP pipelines, covering a wide range of search requirements.

Discover how vector databases enhance the processing of unstructured data such as images, audio, and video through neural network feature extraction. The platform supports the development of similarity search applications, offering tutorials for building AI tools like chatbots and recommendation systems. Perform benchmark tests and deploy applications using Jupyter Notebooks or Docker. Access practical examples and in-depth articles to deepen your understanding of unstructured data analytics.