Product Overview of Graphlit
Graphlit is a comprehensive, cloud-based platform designed to simplify and accelerate the development of AI applications, particularly those involving large language models (LLMs) and unstructured data. Here’s a detailed look at what Graphlit does and its key features:
What Graphlit Does
Graphlit is tailored for developers building AI copilots, chatbots, and other vertical AI applications. It handles the entire AI and data infrastructure, eliminating the need for manual setup and integration of various components like vector databases, LLM embeddings, cloud storage, and data pipelines. This makes it an attractive alternative to DIY solutions such as LangChain or LlamaIndex.
Key Features and Functionality
Automated ETL (Extract, Transform, Load)
Graphlit features automated ETL capabilities that seamlessly ingest data from diverse sources, including websites, cloud storage, SharePoint, podcasts, Jira, Notion, YouTube, email, and Slack. It supports multiple formats such as documents, audio, video, and images, and enables continuous data feeds for automated content workflows. Advanced extraction tools utilize OCR and LLMs to extract text and tables from documents and images, while metadata enrichment automatically extracts metadata, named entities, and structured data from the content.
Retrieval-Augmented Generation (RAG) as a Service
Graphlit offers RAG as a service, which includes intelligent text extraction, chunking, built-in vector embeddings, and conversation history management. It provides powerful vector-based semantic search capabilities with metadata filtering and supports automated summarization of text and transcripts, social media post generation, and long-form content creation.
Multimodal Capabilities
The platform is equipped with multimodal features, allowing seamless integration with models like OpenAI’s GPT-4 Vision. It includes automatic transcription of audio content, image analysis with visual object detection, and visual search using image embeddings. This enables the use of any content, including images or websites described with the GPT-4 Vision model, in RAG conversations.
Knowledge Graph
One of Graphlit’s most powerful features is its knowledge graph, which finds relationships between all the ingested data. Entities such as people, places, or organizations are extracted and linked to the source content via edges in the graph. This knowledge graph forms a rich network of interconnected data, enabling powerful querying and retrieval capabilities.
Conversational Capabilities
Graphlit’s conversational system retrieves relevant content based on semantic search and entity rankings. When a user interacts through conversations, the platform analyzes the user’s prompt, extracts relevant entities, and performs a semantic search on the ingested content to generate contextually relevant responses using LLMs.
Integration and Automation
Graphlit supports integration with various tools and platforms, including Slack, Notion, Google Mail, Microsoft Outlook, and more. It offers built-in web scraping, automated alerts on people, places, companies, or topics found in the content, and workflow automation to streamline data handling and LLM-driven workflows.
Scalability and Pricing
The platform is cloud-native and fully automated, ensuring scalability. Graphlit is free to use up to 1GB of content, with paid plans starting at $49/month plus credit usage. This makes it accessible for both small-scale projects and larger enterprise applications.
Conclusion
Graphlit is a robust platform that simplifies the development of AI applications by handling complex data ingestion, transformation, and retrieval processes. Its key features include automated ETL, RAG as a service, multimodal capabilities, a powerful knowledge graph, and seamless integration with various data sources and tools. This makes Graphlit an ideal solution for developers and organizations looking to harness the power of LLMs and unstructured data efficiently.