Unstructured IO AI technology page Top Builders

Explore the top contributors showcasing the highest number of Unstructured IO AI technology page app submissions within our community.

Unstructured: Transforming Data for LLM Success

Unstructured flawlessly extracts and transforms data into clean, consistent JSON, tailored for integration into vector databases and LLM frameworks. Experience efficient data processing for optimal LLM performance.

General
Author	Unsctructured.io
Repository	https://github.com/Unstructured-IO/unstructured
Type	Data Transformation Tool

Key Features

Document preprocessing: Unstructured provides an API for document preprocessing without a custom code need.
Accurate data: Unstructured focuses on delivering clean, LLM-ready data, ensuring efficient performance.
Rapid integration: Integrates into existing workflows with a smooth setup.
High scalability Unstructured automatically retrieves, transforms, and stages large volumes of data for LLMs, ensuring scalability and efficiency.

Start building with Unsctructured's products

Explore Unstructured's products tailored to meet the your needs of your data transformation for LLMs.

List of Unstructured's products

API (SaaS & Marketplace)

The API offers a document preprocessing with production grading and doesn't require a custom code. Ideal for getting started quickly with document processing tasks.

Platform (Paid)

The Platform serves enterprises and companies with large data volumes. It enables automatic retrieval, transformation, and staging of data for LLMs, ensuring efficiency.

RAG Support (with LangChain)

Unstructured collaborates with LangChain to provide RAG support, optimizing the transition of your RAG from prototype to production. Make the most of expert guidance and seamless integration with LangChain's support.

System Requirements

Unstructured is compatible with major operating systems, including Windows, macOS, and Linux. A minimum of 4 GB of RAM is recommended for optimal performance. For intensive data processing tasks, a multicore processor is recommended to ensure the efficient outcome.

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Unstructured IO AI technology page Hackathon projects

Discover innovative solutions crafted with Unstructured IO AI technology page, developed by our community members during our engaging hackathons.

urWatxon - Second Brain AI

urWaxton addresses the growing complexity of managing digital information. Users often struggle with multiple data formats and overwhelming organization tasks, leading to inefficiencies and frustration. urWaxton simplifies this by using advanced AI to categorize and store data effortlessly, making it easily retrievable and analyzable. Our solution is ideal for lifelong learners, students, knowledge workers, and entrepreneurs who need precise data management. Unique features include AI-enhanced learning, a versatile chatbot for queries, and the ability to handle diverse inputs. With urWaxton, managing your information becomes intuitive, accessible, and highly efficient, no matter where or how you work.

MULTI-MODAL RAG ChatBot - Better Visual Info

Our project tackles the challenge of retrieving relevant information paired with matching images, addressing the limitations of traditional retrieval methods. We propose a Multi-Modal RAG application that combines text and visual data to enhance user understanding and trust. This application mitigates the risk of hallucinations in language models by supporting textual responses with visual evidence. Currently, our solution integrates static PDFs and YouTube videos, allowing users to ask questions and receive accurate text and images or video frames. We are exploring dynamic multi-modal vector databases to handle user data more effectively, leveraging on-device GPUs for privacy and security. Our innovation leads to the creation of a "Personalized Knowledge Model" (PKM), utilizing advanced knowledge graphs without exposing user data to cloud services. Our project includes comprehensive Colab tutorials on GitHub, enabling others to utilize their resources efficiently. Future enhancements involve dynamic multi-modal RAG, on-device privacy, knowledge graph development without LLMs, and encouraging open-source collaboration.

FASTrack

• Companies spend an average of $3,864 per hire (Johnson Service Group, 2019). • 75% of hiring professionals lose top talent due to lengthy hiring processes (ManpowerGroup, 2024). ATS tools are widely used to speed up hiring processes. BUT: • 88% of employers find that ATS often miss top talent (Harvard Business Review, 2021). • 37% of employers are dissatisfied with the effectiveness of ATS (TrustRadius, 2021). FASTrack streamlines recruitment using LLMs, helping recruiters and managers quickly pinpoint ideal candidates for specific roles, completely removing the need for manual filtering. Using self-improving AI agents to simulate an HR recruiter, this tool quickly searches extensive résumé databases, intelligently ranking and shortlisting the best candidates for each position. Here's how FASTrack works: 1) Recruiter enters a specific job description – can be detailed or a single sentence. 2) LLM identifies relevant entities and keywords according to the context. 3) AI agent starts searching for additional relevant information related to the extracted entities and keywords. 4) If the AI agent can't find more information in the LLM's knowledge base, especially about new technologies, it looks for details online. 5) AI agent refines search iteratively, gathering relevant information and adjusting search parameters based on information availability. 6) RAG approach is used to conduct multiple searches with all parameters across a vector database of résumés. 7) AI agent stops when sufficient information is gathered or after a set number of iterations. 8) Results are re-ranked against the original job description to improve accuracy. 9) Recruiter finds 10-30 ideal candidates from thousands of applicants in less than a minute. 10) Recruiter can schedule interviews with shortlisted candidates, individually or in groups, using integrated e-mail and calendar tools. This efficient, conversational experience cuts costs by over 90% and reduces recruitment time to days.

SYSTEMiX ChatBot

SUMMARY :: Imagine having a friendly and knowledgeable partner who can answer your questions in a conversational way, tailoring responses based on your specific interests and needs only - SYSTEMiX is your partner - a personal assistant for all things about systems innovation. Engage with complex topics through a user-friendly chatbot interface, drawing from the vast knowledge of the Vectara AI database. This responsive chatbot sparks short conversations on Systems Thinking, Systems Theory, Systemic Design, and Systems Innovation based on your specific data. Utilizing AI Retrieval Augmented Generation (RAG) technology, this app indexes and queries information from an uploaded corpus in the Vectara database.

Snowflake

Introducing a cutting-edge personal assistant designed to revolutionize the support and care for individuals living with diabetes. Leveraging state-of-the-art research and advanced technologies, this personal assistant is poised to enhance the quality of life for diabetic patients by providing accurate, relevant, and personalized information while minimizing the risks of misinformation. A personal assistant updated with state-of-the-art research surrounding Diabetes, to enhance Diabetic patients' quality of life by summarizing important information while minimizing misinformation. RAG chatbot combining semantic search with vector database, keyword databases, and summary index.

Edulance-AI

Edulance is an open-source project that utilizes advanced technologies such as Unstructured, machine learning models, and APIs to transform text documents and PDFs into interactive educational resources. The software accepts user-uploaded files, applies optical character recognition (OCR) for text documents, or extracts valuable content from PDFs. It then generates lessons, quizzes, and lesson plans based on the content using its Lesson Graph model and agents like LessonGenerator, LessonPlanner, OCRAgent, PdfAgent, QuizAgent, and TogetherLLM. Edulance provides an immersive learning experience, enabling effective teaching and interactive knowledge acquisition. Overall this project incorporates the following: TogetherAI's LLM Models Unstructured Partition pdf for making PDFs LLM Ready Agentic AI with state management. Features Feature Description ⚙️ Architecture Edulance is a Python-based project using FastAPI as the web framework and Uvicorn for runtime serving. The application leverages containers with Docker for deployment, installing required dependencies from requirements.txt. It utilizes libraries like LangChain, PikePDF, PyTesseract for OCR, and TogetherAI's LLM models. 🔩 Code Quality The codebase follows a modular structure with clearly defined agents and graph files, ensuring high cohesion and low coupling. Python style guides are followed consistently, including PEP8 and PEP534. There is adequate usage of comments throughout the codebase.🔌 Integrations Key integrations include Docker for deployment, LangChain libraries, TogetherAI's LLM models, Vectara for Chat. 🧩 Modularity 📦 Dependencies Main dependencies include FastAPI, Docker, Python 3.10, requirements.txt, LangChain package, PikePDF, PyTesseract, and related tools.