Learn to use ChromaDB for semantic search applications
introduce
Generative AI has come a long way in the past year. Since the launch of the DALL-E 2 image generation model, many AI models such as GPT-3.5, GPT-4, and open source models have become hot topics within and outside the AI community. With the rise of AI applications and use cases, there is an increasing flow of various tools and technologies to facilitate such AI applications and allow AI developers to build real-world applications.
Among these tools, today we will learn about the working principle and capabilities of ChromaDB, an open source vector database used to store embeddings of AI models such as GPT3.5, GPT-4 or any other operating system model. Embeddings are an important part of any AI application pipeline. Since computers only deal with vectors, all data must be vectorized in embedded form to be used in applications. Learn more about how ChromDB works with hands-on code examples!
Table of contents:
ChromaDB basics and installation library
ChromaDB features and workings
Semantic search application with sample documents
ChromaDB supported embeddings
Conclusion
FAQ
ChromaDB basics and installation libraries
ChromaDB is an open source vector database designed to store vector embeddings for developing and building large language model applications. The database makes it easier to store knowledge, skills and facts for LLM applications.
ChromaDB is an open source vector database&