Chroma DB
Chroma is an open-source vector database for storing and querying embeddings and associated data, such as documents and metadata.
Grokking AI Applications
Chroma is an open-source vector database for storing embeddings and associated data and performing similarity-based retrieval.
- How it is used
- Builds the local vector-store layer of a RAG pipeline for ingesting document chunks and retrieving relevant context.
- How deep it goes
- Configures collections, disk persistence, ingestion, and top-match retrieval, without descending into vector-database internals.
- Examples
- Uses a pizzeria menu PDF, overlapping text chunks, a menu collection, and a menu persistence directory.
- What you can do after
- Build it from scratch as a local Chroma-backed RAG ingestion and retrieve-and-generate pipeline.
Generative AI in Action
Chroma is an open-source vector database for storing and querying embeddings and their associated documents or metadata.
- How it is used
- Stores PDF text and embeddings for semantic retrieval in a question-answering application.
- How deep it goes
- Shows configuration, saving, and retrieval workflow without explaining Chroma’s internal indexing.
- Examples
- Uses a PDF question-answering application with Azure OpenAI embeddings and a locally persisted Chroma store.
- What you can do after
- Apply it via Semantic Kernel to persist PDF text and embeddings for relevance queries.
AI Agents and Applications
Chroma is a vector database that stores text, embeddings, and metadata and supports similarity-based retrieval.
- How it is used
- Serves as the vector store that retrieves relevant document chunks for semantic search and RAG question answering.
- How deep it goes
- Walks through collection creation, ingestion, querying, and client options, but does not detail Chroma’s indexing internals.
- Examples
- Uses Paestum passages with source metadata, a Doric-temples query, and Wikipedia, Word, PDF, and text ingestion examples.
- What you can do after
- Build it from scratch with Chroma’s Python client, or apply it via LangChain to store chunks and retrieve question-relevant text.
AI Agents in Action, Second Edition
Chroma DB is a database system for storing and retrieving vector embeddings and associated document data.
- How it is used
- Supports local semantic document retrieval for development, small-scale projects, and vector-search RAG agents.
- How deep it goes
- Explains chunking, embedding, collection persistence, querying, result fields, and cosine-distance interpretation without covering larger deployment options in detail.
- Examples
- Uses document collections, a movie script, queries such as “lazy dog,” and exercises that persist vectors and test vector-only RAG retrieval.
- What you can do after
- Build a local Chroma DB collection, persist embeddings, query semantic matches, and inspect returned IDs, distances, and document text.
Read how AI Agents in Action, Second Edition explains Chroma DB →
Data Storytelling with Altair and AI
Chroma is an embedding database for storing and retrieving vector representations of data.
- How it is used
- Supports retrieval-augmented generation by storing embedded source content for downstream language-model queries.
- How deep it goes
- Explains loading, chunking, embedding, persistence, retrieval, and output, without detailing Chroma's internal indexing.
- Examples
- Uses an HTML smartphone description, a Product-Info collection, and a generated SmartX 2023 answer with source documents.
- What you can do after
- Build it from scratch by persisting embedded documents in Chroma and querying them through a LangChain retrieval application.
Read how Data Storytelling with Altair and AI explains Chroma DB →
Effective Conversational AI
Chroma is an open-source vector database and retrieval system for storing and searching embedding vectors.
- How it is used
- Supports RAG by storing embedded document chunks and retrieving semantically similar passages for response generation.
- How deep it goes
- Explains chunking, embedding, storage, and retrieval, while noting alternative retrieval methods and model-dependent performance.
- Examples
- Uses text files, chunking experiments, open-source embeddings, Chroma ingestion, and a LangChain example.
- What you can do after
- Build it from scratch by chunking sample text, embedding chunks, and loading them into Chroma for retrieval experiments.
Build Applications with Local AI Models on a Mac
ChromaDB is an open-source vector database for storing, querying, and managing embeddings and associated data.
- How it is used
- Uses ChromaDB to retrieve relevant documents for a RAG response.
- How deep it goes
- Demonstrates creating a collection, adding documents, and finding close matches without detailing database internals.
- Examples
- Uses short documents about Ollama, Streamlit, RAG, and other AI tools, then retrieves an answer about Ollama.
- What you can do after
- Apply ChromaDB by creating a collection, adding documents, and retrieving relevant matches in a RAG example.
Read how Build Applications with Local AI Models on a Mac explains Chroma DB →
Related concepts
- vector databasekind of · A vector database is a data-management system that stores and indexes numerical vector representations and supports retrieval through similarity or distance searches.
- retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
- conversational aiused by · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.
- FAISSalternative to · FAISS is an open-source software library for efficient similarity search and clustering of dense vectors.
- embeddingused by · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- collectionuses · A collection is a data structure or abstraction that groups multiple elements for storage, access, traversal, or processing.
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