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Build Semantic Search Applications Using Open Source Vector Database ChromaDB
Original article seen at: www.analyticsvidhya.com on July 18, 2023
tldr
- 🔑 ChromaDB is an open-source vector database designed to store vector embeddings for large language model applications.
- 🔑 It supports many AI models from different embedding providers, such as OpenAI, Sentence transformers, Cohere, and the Google PaLM API.
- 🔑 ChromaDB is easy to use and set up, boosting developer productivity.
- 🔑 The database handles tokenization, vectorization, and indexing automatically.
summary
ChromaDB, an open-source vector database, is designed to store vector embeddings for the development of large language model applications. It simplifies the storage of knowledge, skills, and facts for these applications. ChromaDB is easy to use and set up with any LLM-powered application, boosting developer productivity. It can be installed in Python and Javascript environments and can run in Jupyter Notebook. The database handles tokenization, vectorization, and indexing automatically. By calling the 'query()' function, it returns the most similar text based on the input query with their metadata and ids. ChromaDB supports many AI models from different embedding providers, such as OpenAI, Sentence transformers, Cohere, and the Google PaLM API. It also provides a wrapper function to use any embedding model API from OpenAI for AI applications. The article provides a detailed tutorial on how to use ChromaDB with hands-on code examples.starlaneai's full analysis
ChromaDB's introduction as an open-source vector database for storing vector embeddings is a significant development in the AI industry. It simplifies the process of developing large language model applications, which could lead to advancements in various sectors. However, the widespread adoption of ChromaDB could be hindered by potential ethical considerations and the need for robust data privacy and security measures. Despite these potential challenges, ChromaDB's ease of use and setup, as well as its support for various AI models, make it a promising tool for AI developers and researchers globally.
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starlaneai's Ratings & Analysis
Technical Advancement
70 ChromaDB's ability to store vector embeddings for large language model applications is a significant technical advancement in the AI field.
Adoption Potential
80 Given its ease of use and setup, ChromaDB has high adoption potential among AI developers and data scientists.
Public Impact
60 While the direct impact on the public may not be immediate, the advancements in AI applications facilitated by ChromaDB could lead to improvements in various sectors.
Innovation/Novelty
65 The concept of a vector database like ChromaDB is relatively novel in the AI industry.
Article Accessibility
85 The article provides a detailed and accessible tutorial on how to use ChromaDB, making it accessible to a wide range of readers.
Global Impact
70 As an open-source tool, ChromaDB has the potential to be used globally by developers and researchers in the AI field.
Ethical Consideration
50 The article does not delve into the ethical considerations of using AI models and databases like ChromaDB.
Collaboration Potential
75 ChromaDB's support for various AI models from different providers indicates high collaboration potential.
Ripple Effect
70 The advancements facilitated by ChromaDB could have a ripple effect on various sectors that utilize AI applications.
Investment Landscape
65 The development of tools like ChromaDB could potentially attract more investment in the AI field.