Chroma AI technology page Top Builders

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

Chroma

Chroma is building the database that learns. It is an open-source AI-native embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. The fastest way to build Python or JavaScript LLM apps with memory

General
Relese date	2023
Author	Chroma
Type	embedding database

Tutorials

Great tutorials on how to build with Chroma

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Chroma - Helpful Resources

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Chroma Chroma website
Chroma Docs Documentation of Chroma
Chroma Repo Open-source Chroma repository

Chroma - Clients

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Chroma Python Python Client for Chroma
Chroma JavaScript High-Performance Chroma Client for JavaScript

Chroma - integration

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Chroma AI technology page Hackathon projects

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

Adapt-a-RAG

Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.

Biology Tutor

Introducing our innovative Biology Tutor App, enhanced with cutting-edge AI technology powered by GPT and TrueLens. Designed to revolutionize the learning experience, our app offers personalized tutoring sessions tailored to each user's unique learning style and pace. With GPT's advanced natural language processing capabilities, students can ask questions, receive detailed explanations, and engage in interactive discussions on various biological concepts, from cellular biology to ecology. TrueLens enhances the learning process by providing real-time feedback on understanding and areas for improvement, ensuring efficient and effective learning outcomes. Whether you're a high school student preparing for exams or a university student diving deep into complex biological theories, our Biology Tutor App is your comprehensive learning companion. Experience the future of biology education with us today.

MarketMentor

The "Market Mentor" project, presented by Team Bit Rebels, introduces an innovative investment education bot leveraging data from Professor Aswath Damodaran. We created 3 models to represent Prof. Damodaran's knowledge: The vanilla RAG model, a Hype Model, and our novel modified HyDE model that is built to do away with the weaknesses of the HyDE model of poor similarity matchings. These models act as personal finance instructors who can teach the fundamentals of investing and valuations. This tool also simplifies financial analysis with a novel Text-to-SQL model, transforming user inquiries into SQL queries for efficient data extraction. It boasts personalized bots, specialized smaller models, and an intuitive, user-friendly interface that replicates expert communication styles, bypassing the need for extensive data. This technology empowers users with easy access to complex data, supporting informed decisions and strategic planning while improving with use.

Doweb AI

Doweb is described as a Swiss Army knife in the world of AI, offering users immediate access to the latest AI technologies. Currently, Doweb provides a variety of services including video and audio transcription, data analysis, text, sound, and video translation in any language, video and sound dubbing, speech synthesis in any language, voice cloning, voiceover with cloned voices, GPT-4 level AI chat, voice AI chat, coding, and other AI-related tasks, as well as image generation and recognition. The service portfolio is continuously updated with new market solutions. The Doweb team is committed to maintaining service quality and optimizing computing costs to offer the most competitive prices for AI functions. They believe that centralizing AI services in one platform is more convenient and cost-effective than using multiple resources, reducing subscription costs. Moreover, Doweb aims to provide its services worldwide, except where AI service providers are unable or unwilling to operate due to certain reasons. We assert that AI should not be feared as it expands human capabilities, makes access to knowledge and opportunities more affordable, particularly in impoverished or politically troubled countries

AI-Assisted Tool for Enhanced Code Comprehension

Our project introduces a groundbreaking AI-Assisted Development Tool that harnesses the advanced capabilities of Phi-2, Microsoft's latest language model renowned for its exceptional natural language processing. This tool is designed to transform the software development landscape by providing developers with automated code explanations, real-time summarizations, and predictive analytics for efficient project management. By integrating Phi-2's sophisticated NLP features, our tool offers an intuitive AI-driven chat interface, enabling developers to interact seamlessly with the system, receive context-aware insights, and navigate through complex codebases with ease. This solution aims to significantly enhance developer productivity, streamline project workflows, and improve overall software quality, making it an essential asset for developers and teams looking to optimize their development processes and foster innovation in their projects.

Algorizer

Sorting algorithms are fundamental to computer science, with uses in databases, simulations, and beyond. However, grasping the nuances of sorts like merge, heap, and quick can perplex beginners. My project aims to help students comprehend these algorithms better through examples and clear explanations of each technique. By experimenting with example arrays and observing step-by-step walkthroughs, students can recognize how common approaches like selection sort, bubble sort and insertion sort reorder lists from minimum to maximum. With engaging practices and straightforward descriptions, my work intends to unravel sorting algorithms for students, allowing them to understand how techniques including bubble, selection and insertion handle arranging sets of numbers or other data.