neum-ai

Powerful tools to configure your RAG pipelines in seconds🔓 Open-source SDKs to compose data flowsRAG-first framework to build performant, scalable and reliable data pipelines. Focused on key data transformations like loading, chunking and embedding.🧱 Built-in connectors to common servicesChoose from connectors for data sources, embedding models and vector databases. Add your own connectors using our open-source framework.🚀 Test and deploy your pipelinesRun your data pipelines locally using open-source SDKs and directly deploy those same pipelines to the Neum AI cloud.Production-ready cloud platformScaleDistributed architecture optimized for embedding generation and ingestion for billions of data points.SyncKeep your vectors in sync with built-in pipeline scheduling and real-time syncing.ObservabilityMonitor your data to ensure it is correctly being synced into your vector database. Smart RetrievalBuilt-in retrieval informed by the organization of your data and the metadata associated to it.Self-improvingImprove context quality by providing feedback on retrieval quality.GovernanceObserve actions like searches and data movements.Book DemoCheck out our latest postFollows us on social for additional contentThis is some text inside of a div block.Retrieval evaluation with datasetsConfiguring RAG pipelines requires iteration across different parameters ranging from pre-processing loaders and chunkers, to the actual embedding model being used. To assist in testing different configurations, Neum AI provides several tools to test, evaluate and compare pipelines.David de MatheuDecember 6, 2023•10min readThis is some text inside of a div block.Real-time data embedding and indexing for RAG with Neum and SupabaseReal-time synchronization of embeddings into vector databases is now trivial! Learn how to create a real-time Retrieval Augmented Generation pipeline with Neum and Supabase.Kevin CohenNovember 25, 2023•8min readThis is some text inside of a div block.Building scalable RAG pipelines with Neum AI framework  -  Part 1Following the release of Neum AI framework, an open-source project to build large scale RAG pipelines, we explore how to get started building with the framework in a multi-part series.David de MatheuNovember 22, 2023•15min readView allFAQsLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. What is AI CopywritingLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.Is AI Copywriting freeLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.Who owns the generated copyLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.Which Language is supportedLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.Who can use AI CopywritingLorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.Pricing planMonthlyYearlyStarterIntegrate data into your projectsFreeGet startedOpen-source connectors and toolsDeploy pipelines to cloudLimited scaleDiscord accessProDo more with Neum$500/moBook DemoUnlimited scale on common cloudAccess to pipeline scheduling and real-time syncingFirst access to new featuresPriority support through DiscordEnterprise planScale your data pipelinesQuoteBook DemoDedicated support on Discord, Slack, Email and WhatsAppDedicated infrastructureCustom connectorsBasic planLorem ipsum dolor sit amet$180/yrSave 20%Get startedWord LimitCopywriting AssistanceCustomization OptionsBusiness planLorem ipsum dolor sit amet$280/yrSave 20%Get startedCopywriting AssistanceWord LimitCustomization OptionsTurnaround TimeLanguage SupportEnterprise planLorem ipsum dolor sit amet$480/yrSave 20%Get startedCustomization OptionsTurnaround TimeWord LimitCopywriting AssistanceLanguage Support --- Protected PageIncorrect password. Please try again. --- Protected PageIncorrect password. Please try again. --- This is some text inside of a div block.Retrieval evaluation with datasetsConfiguring RAG pipelines requires iteration across different parameters ranging from pre-processing loaders and chunkers, to the actual embedding model being used. To assist in testing different configurations, Neum AI provides several tools to test, evaluate and compare pipelines.David de MatheuDecember 6, 2023•10min readThis is some text inside of a div block.Search with Neum AIToday, keyword-based and full-text search are not enough. The software industry is moving towards a new kind of search that goes beyond keywords and fuzzy-matching, it's moving towards semantic search. Delve into how you can take advantage of this new shift using Neum AIDavid de MatheuNovember 29, 2023•6min readThis is some text inside of a div block.Real-time data embedding and indexing for RAG with Neum and SupabaseReal-time synchronization of embeddings into vector databases is now trivial! Learn how to create a real-time Retrieval Augmented Generation pipeline with Neum and Supabase.Kevin CohenNovember 25, 2023•8min readThis is some text inside of a div block.Building scalable RAG pipelines with Neum AI framework - Part 2Following the release of Neum AI framework, an open-source project to build large scale RAG pipelines, we explore how to get started building with the framework in a multi-part series. In this blog, we go deep into leveraging distributed architecture tools like Celery and Redis Queues to build a solution to handle large datasets.David de MatheuNovember 23, 2023•15min readThis is some text inside of a div block.Building scalable RAG pipelines with Neum AI framework  -  Part 1Following the release of Neum AI framework, an open-source project to build large scale RAG pipelines, we explore how to get started building with the framework in a multi-part series.David de MatheuNovember 22, 2023•15min readThis is some text inside of a div block.Semantic selectors for structured dataNeum AI provides tools to help you process structured data ahead of generating embeddings and loading it into vector databases. In this blog, we showcase the semantic selectors that help you choose what data (if any) from your structured data is worth embedding.David de MatheuNovember 14, 2023•10min readThis is some text inside of a div block.Retrieval Augmented Generation at scale - Building a distributed system for synchronizing and ingesting billions of text embeddingsIn this blog post we will go into some technical and architectural details of how we do this at Neum AI, specifically on how we did this for a pipeline syncing 1 billion vectors.Kevin CohenNovember 14, 2023•20min readThis is some text inside of a div block.Q&A with thousands of documentsQ&A with a document is probably the most common scenario most developers think about today when it comes to LLMs. In this blog we explore what it takes to build that scenario at scale. Because the only thing cooler than doing Q&A with one document, it is to Q&A with thousands of them.David de MatheuNovember 14, 2023•15min readThis is some text inside of a div block.Indexing from Tweets to Product Listings with SingleStore and Neum AINeum AI enables AI engineers to connect their data sources to their LLMs through Retrieval Augmented Generation (RAG). Neum AI supports a variety of data sources that you can pull from as well as vector databases where you can have vectors stores to then do retrieval. Today, we are announcing support for SingleStore as both a data source and vector database. SingleStore allows you to keep all your data in a single place while leveraging the power of vector embeddings and RAG. Neum AI makes it easy to generate vector embeddings for the data and connect everything together.David de MatheuNovember 14, 2023•10min readThis is some text inside of a div block.Pre-processing playgroundPre-processing documents before embedding them continue to be a challenge and an important step in ensuring the quality of RAG. At Neum, we wanted to create a simple playground to help developers test out their pre-processing steps with their documents. The playground is forked off the Langchain text splitter explorer. We will be further adding features to the playground to help add more loaders, metadata extractors and semantic splitters for different types of documents.David de MatheuNovember 14, 2023•10min readThis is some text inside of a div block.Contextually splitting documentsNeum AI introduces a context-aware text splitting feature that improves the efficiency of Large Language Models in handling specialized content like SEC filings or templated contracts. The new feature enhances Retrieval Augmented Generation (RAG) by allowing custom strategies for text segmentation, boosting retrieval quality and application performance.David de MatheuNovember 14, 2023•8min readThis is some text inside of a div block.Building ElectionGPT: Using Neum AI to build an authentic candidate chatbot ahead of the 2024 US Presidential Election.A couple days ago, we released ElectionGPT.ai with the goal of showcasing experiences that use LLMs with Retrieval Augmented Generation (RAG) and are grounded on factual data from a variety of sources. In this blog, we would like to explore how we built it using Neum AI and the learnings we had in the process.David de MatheuNovember 14, 2023•10min readThis is some text inside of a div block.Efficiently maintaining context in sync for AI applicationsData is the most important asset when building AI applications. Having up-to-date context in prompts or when doing semantic search is crucial. With Neum, we not only synchronize source data with vector stores, but we do so efficiently, saving costs by only vectorizing the changed data.Kevin CohenNovember 14, 2023•15min readThis is some text inside of a div block.LLM + SpreadsheetsSpreadsheets and tabular data sources are commonly used and hold information that might be relevant for LLM based applications. In this blog we explore the different types of approaches towards connecting this data to your application. We deep dive into generating vector embeddings from this data taking into consideration the different types of date that a single spreadsheet or tabular data source might hold.David de MatheuNovember 14, 2023•10min read