Category: Enterprise software & integration (Page 3 of 31)

Tonic.ai launched a secure data lakehouse for LLMs, Tonic Textual, to enable AI developers to securely leverage unstructured data for retrieval-augmented generation (RAG) systems and large language model (LLM) fine-tuning. Tonic Textual is a data platform designed to eliminate integration and privacy challenges ahead of RAG ingestion or LLM training bottlenecks. Leveraging its expertise in data management and realistic synthesis, Tonic.ai has developed a solution to tame and protect siloed, messy, and complex unstructured data into AI-ready formats ahead of embedding, fine-tuning, or vector database ingestion. With Tonic Textual: 

1. Build, schedule, and automate unstructured data pipelines that extract and transform data into a standardized format convenient for embedding, ingesting into a vector database, or pre-training and fine-tuning LLMs. Textual supports TXT, PDF, CSV, TIFF, JPG, PNG, JSON, DOCX and XLSX out-of-the-box.
2. Detect, classify, and redact sensitive information in unstructured data, and re-seed redactions with synthetic data to maintain the semantic meaning. Textual leverages proprietary named entity recognition (NER) models trained on a diverse data set spanning domains, formats, and contexts to ensure sensitive data is identified and protected.
3. Enrich your vector database with document metadata and contextual entity tags to improve retrieval speed and context relevance in RAG systems.

https://www.tonic.ai/textual

MongoDB, Inc. announced an expanded collaboration with Google Cloud to make it easier and more cost-effective to build, scale, and deploy generative AI applications using MongoDB Atlas Vector Search and Vertex AI from Google Cloud, along with additional support for data processing with BigQuery. The companies are also collaborating on new industry solutions for retail and manufacturing, with deeper product integrations and solutions to provide a seamless development environment for creating shopping experiences and data-driven applications for smart factories. For workloads that use highly sensitive data, MongoDB Enterprise Advanced (EA) is now available on Google Distributed Cloud (GDC).

• MongoDB Atlas Search Nodes on Google Cloud provide dedicated infrastructure for generative AI and relevance-based search workloads that use MongoDB Atlas Vector Search and MongoDB Atlas Search.
• A dedicated Vertex AI extension makes it easier to work with large language models (LLMs) without having to transform data or manage data pipelines between MongoDB Atlas and Google Cloud.
• Integration of Spark stored procedures with BigQuery improves automation, optimization, and reuse of data processing workflows between BigQuery and MongoDB Atlas for analytics, BI, and end-user applications.

https://www.mongodb.com/press/mongo-db-expands-collaboration-with-google-cloud-at-google-next

DataStax announced it has entered into a definitive agreement to acquire AI startup, Logspace, the creators of Langflow, an open source visual framework for building retrieval-augmented generation (RAG) applications.

Langflow makes it easier and faster for any developer, experienced or new, to build Generative AI applications using Python-based composable building blocks and pre-built components, which can be combined in numerous ways. With its easy-to-use, drag-and-drop visual environment and rapid iteration of data flows, Langflow makes it simpler for any developer to build LangChain-based RAG applications and deploy in one-click. 

Developers benefit from a rich ecosystem that builds, shares, and reuses components with each other in the Langflow Store–a place to publish and search for components built by the community. With this, they can quickly test, reuse, and share flows to iterate on RAG applications with fine-grained control to dramatically speed up deployment and reduce hallucinations. 

The combination of Langflow and DataStax creates a one-stop Generative AI application stack offering flexible deployment options, including integration with DataStax Astra DB, alongside a rich ecosystem of Python libraries, and integration with partners like LangChain. The Langflow team will operate independently, focusing on project innovation and community collaboration.

https://www.datastax.com/blog/datastax-acquires-langflow-to-accelerate-generative-ai-app-development ■ https://www.langflow.org