Meet Chroma: The Open-Source Embedding Database Revolutionizing Pluggable Knowledge for LLM Apps

Meet Chroma: The Open-Source Embedding Database Revolutionizing Pluggable Knowledge for LLM Apps

Vector databases have been gaining popularity in the past few years as they ensure better data management, scalability, and security. They are now widely used in semantic search, Retrieval-Augmented Generation (RAG) applications, and conversational AI. Chroma is one such open-source embedding database that simplifies the development of Large Language Model (LLM) applications.

The unprecedented rise in Artificial Intelligence (AI) recently has led to the need for efficient data processing tools, and vector databases in particular, help execute complex queries and optimize data storage. Chroma allows storing vector embeddings and makes it easy to develop LLM apps by making knowledge and facts pluggable for LLMs.

Chroma can be seamlessly integrated with Python and JavaScript frameworks such as Langchain, LlamaIndex, Braintrust, etc., making it easier to develop AI applications. Users can create collections, add documents to the collections, and then query the same. Moreover, reusing the collection by adding more documents is also possible, and Chroma automatically loads the data when the user starts the client.

Benefits of Chroma

  • Chroma is an open-source tool licensed under Apache 2.0. 
  • Chroma provides wrappers around popular embedding providers such as OpenAI, Hugging Face, Google PaLM, and many more, making it easy to use them as needed. By default, Chroma uses the Sentence Transformers model to create embeddings. However, users can also use other available models and create custom embedding functions.
  • Chroma supports multi-modal embedding functions to embed multi-modal data into a single embedding space. 
  •  Chroma allows integrations with multiple Python and JavaScript frameworks that aid in the development of AI applications.
  • Users can also deploy Chroma on a long-running server and connect remotely.
  • Chroma also supports schema or data format change by providing an easy-to-use migration tool.

Drawbacks of Chroma

  • Setting up Chroma and managing it at scale requires effort and expertise as compared to other managed solutions.
  • Chroma's performance in certain high-throughput real-time scenarios is not up to the mark.

In conclusion, Chroma is a powerful tool for developing LLM-powered applications. It is free to use and can be easily integrated with different frameworks, which explains its popularity. Although the tool has a few limitations and might not match the performance of other managed solutions, it is still a robust choice for applications involving embeddings that can be easily configured and deployed with reasonably good performance. 


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