Database / ChromaDB Interview Questions
1. What is ChromaDB and what problem does it solve?
ChromaDB is an open-source, AI-native vector database designed to store, index, and query high-dimensional embedding vectors efficiently. It was created specifically to make building LLM-powered applications easy — particularly for retrieval-augmented generation (RAG), semantic search, and recomm...
2. What are embeddings and why are they central to how ChromaDB works?
An embedding is a dense numerical vector — a list of floating-point numbers — that represents the semantic meaning of a piece of data. Text, images, audio, and code can all be converted into embeddings by a neural network (embedding model). Items with similar meanings produce vectors that are clo...
3. What distance metrics does ChromaDB support and how do you choose between them?
ChromaDB uses a distance metric to measure how similar two vectors are during nearest-neighbour search. The metric is set at collection creation time and cannot be changed afterward. Choosing the wrong metric for your embedding model can significantly degrade search quality. ChromaDB distance met...
4. What is a ChromaDB collection and how do you create, list, get, and delete collections?
A collection is ChromaDB's primary organisational unit -analogous to a table in SQL or an index in a search engine. Each collection stores documents, their embeddings, IDs, and optional metadata. All items in a collection share the same embedding function and distance metric. import chromadb clie...
5. How do you add documents to a ChromaDB collection?
The collection.add() method inserts items into a collection. Each item requires a unique id . You can provide raw documents (strings) and let ChromaDB embed them, or supply pre-computed embeddings directly. Optional metadatas store filterable key-value pairs alongside each document. import chroma...
6. How do you query a ChromaDB collection for similar documents?
The primary query method is collection.query() . You pass either query_texts (raw strings that ChromaDB embeds automatically) or query_embeddings (pre-computed vectors). ChromaDB returns the n_results nearest neighbours for each query. import chromadb client = chromadb . Client() collection = cli...
7. How do you retrieve, update, and delete specific documents in ChromaDB?
Beyond querying by similarity, ChromaDB supports exact lookups by ID with get() , in-place updates with update() or upsert() , and deletion with delete() . import chromadb client = chromadb . Client() col = client . create_collection( "items" ) col . add( documents = [ "First document" , "Second ...
8. How do you filter query results using metadata in ChromaDB?
ChromaDB supports a MongoDB-style where clause for filtering by metadata fields. Filters can be applied during query() (combines semantic search with filtering) or during get() (exact retrieval with filtering). Filters run before or alongside the ANN search. import chromadb client = chromadb . Cl...
9. What is the difference between ChromaDB's in-memory and persistent storage modes?
ChromaDB offers three client modes that control where data is stored. Choosing the right mode depends on whether you need data to survive restarts and whether you're running a single process or a shared service. ChromaDB client modes Mode Class Data survives restart? Best for Ephemeral (in-memory...
10. What is ChromaDB's default embedding function and how does it work?
When you create a collection without specifying an embedding function, ChromaDB uses the SentenceTransformerEmbeddingFunction backed by the all-MiniLM-L6-v2 model from the sentence-transformers library. This model is downloaded automatically on first use and cached locally. import chromadb from c...
11. How do you use the OpenAI embedding function with ChromaDB?
ChromaDB has a built-in OpenAIEmbeddingFunction that calls the OpenAI Embeddings API. This gives higher-quality embeddings than the default local model, at the cost of API latency and usage fees. Use text-embedding-3-small for a balance of quality and cost, or text-embedding-3-large for maximum q...
12. How do you use HuggingFace models as embedding functions in ChromaDB?
ChromaDB provides a HuggingFaceEmbeddingFunction that calls the HuggingFace Inference API (cloud-hosted), and a SentenceTransformerEmbeddingFunction for running any Sentence Transformer model locally. For production use without per-call API costs, local Sentence Transformer models are the more co...
13. How do you create a custom embedding function for ChromaDB?
ChromaDB defines a simple protocol for embedding functions: a class with a __call__ method that accepts a list of strings and returns a list of embedding vectors. Implementing this interface lets you plug in any model — a local transformer, a third-party API, or even a mock for testing. import ch...
14. How does ChromaDB's PersistentClient store data on disk, and what are its limitations?
The PersistentClient stores data in a directory you specify. Inside, ChromaDB uses SQLite for metadata (IDs, document text, metadata key-value pairs) and binary files for the HNSW vector index. All writes are flushed to disk automatically — there is no explicit save/commit step. import chromadb i...
15. What is the HNSW index in ChromaDB and what parameters can you tune?
ChromaDB uses HNSW (Hierarchical Navigable Small World) as its Approximate Nearest Neighbour (ANN) index. HNSW builds a layered graph structure where each node connects to its closest neighbours — queries traverse this graph efficiently to find approximate nearest neighbours in O(log n) time inst...
16. How do you efficiently add large numbers of documents to ChromaDB using batching?
Adding tens of thousands of documents one at a time is slow because each call triggers embedding computation and index updates. The right approach is to batch documents into groups of 100–500 and add each batch with a single add() call — this amortises embedding overhead and index writes. import ...
17. What is the where_document filter in ChromaDB and how does it differ from where?
ChromaDB provides two types of filters that can be used together or separately: where filters on metadata fields (structured key-value pairs), while where_document filters on the raw text content of the stored documents. Both can be combined in a single query. import chromadb client = chromadb . ...
18. How do you control what data ChromaDB returns in query and get results using include?
Both query() and get() accept an include parameter — a list of strings specifying which fields to return. Omitting fields you don't need reduces network payload and memory, which matters for large result sets. import chromadb client = chromadb . Client() col = client . create_collection( "demo" )...
19. How do you design metadata schemas for effective filtering in ChromaDB?
Metadata in ChromaDB is stored as flat key-value dictionaries where values must be strings, integers, or floats (not nested dicts or lists). Good metadata design makes the difference between fast, precise filtered queries and slow full-collection scans. import chromadb from datetime import dateti...
20. How do you inspect a ChromaDB collection's contents and configuration?
ChromaDB provides several methods to examine what is stored in a collection — useful for debugging, verifying ingestion, and monitoring collection health. import chromadb client = chromadb . PersistentClient(path = "./inspect_demo" ) col = client . get_or_create_collection( "articles" , metadata ...
21. How do you build a basic RAG (Retrieval-Augmented Generation) pipeline with ChromaDB?
RAG combines ChromaDB's semantic retrieval with an LLM's generation ability. The pipeline has two phases: indexing (chunk documents, embed, store in ChromaDB) and retrieval (embed the user query, fetch similar chunks, inject into LLM prompt). import chromadb from chromadb.utils import embedding_f...
22. What are effective document chunking strategies when indexing documents into ChromaDB for RAG?
Before adding documents to ChromaDB, long texts must be split into chunks that fit within the embedding model's token limit and contain cohesive information. Chunk size and overlap directly affect retrieval quality. # pip install langchain-text-splitters from langchain_text_splitters import ( Rec...
23. How do you use ChromaDB as a vector store with LangChain?
LangChain provides a first-class Chroma vector store integration that wraps ChromaDB's API with LangChain's retriever interface. This enables plugging ChromaDB into LangChain RAG chains, agents, and pipelines without writing low-level ChromaDB code. # pip install langchain langchain-chroma langch...
24. How do you implement multi-tenancy or data isolation in ChromaDB?
ChromaDB does not have built-in user-level access control, but you can implement logical isolation between tenants using separate collections per tenant (strong isolation) or metadata-based filtering (lighter weight). Choose based on your security and scale requirements. import chromadb client = ...
25. What is embedding consistency and why is it critical in ChromaDB applications?
Embedding consistency means using the exact same embedding model and version for both indexing (adding documents) and querying. If you embed documents with model A but query with model B, the resulting vectors live in incompatible geometric spaces — similarity distances become meaningless and ret...
26. How do you run ChromaDB as a standalone HTTP server and connect to it from multiple clients?
For production or multi-process environments, run ChromaDB as a persistent HTTP server and connect all clients via chromadb.HttpClient() . This removes the single-writer SQLite limitation and allows any number of clients — including different languages — to share the same database. # --- SERVER S...
27. When should you use upsert() instead of add() in ChromaDB, and what are common patterns?
upsert() is the idempotent write operation in ChromaDB: it inserts a document if the ID does not exist, or updates it if the ID already exists. This makes it safe to call repeatedly without checking whether a document has been indexed before — a critical property for ETL pipelines, scheduled sync...
28. What are best practices for structuring ChromaDB collection metadata for production use?
Collection-level metadata (set via create_collection(metadata=...) ) stores configuration about the collection itself. Document-level metadata (set per document via add(metadatas=[...]) ) enables filtered retrieval. Both need thoughtful design for maintainable production systems. import chromadb ...
29. How does ChromaDB compare to FAISS, and when should you choose one over the other?
FAISS (Facebook AI Similarity Search) and ChromaDB both store and search embedding vectors, but they are designed for very different use cases. FAISS is a low-level library optimised for raw performance; ChromaDB is a higher-level database designed for developer ergonomics and full-stack AI appli...
30. What are common ChromaDB errors and how do you handle them in production code?
ChromaDB raises specific exception types that should be caught and handled gracefully in production applications. Understanding the error hierarchy helps you write resilient ingestion pipelines and retrieval code. import chromadb from chromadb.errors import ( InvalidCollectionException, IDAlready...
31. How do you back up and restore a ChromaDB persistent database?
A PersistentClient database is simply a directory on disk. Backing it up is as straightforward as copying that directory — but you must ensure no writes are occurring during the copy to avoid a corrupted SQLite file. import chromadb import shutil import os from datetime import datetime DB_PATH = ...
32. How do you ensure the correct embedding function is used when reopening a persistent ChromaDB collection?
ChromaDB stores document text and vectors persistently, but it does not store which embedding function was used. When you reopen a PersistentClient, you must re-supply the same embedding function to the collection — otherwise ChromaDB may default to a different model, producing embedding mismatch...
33. How do you interpret ChromaDB query distances and convert them into meaningful relevance scores?
ChromaDB query results include a distances field. The interpretation depends on the distance metric. Raw distances are not directly comparable across metrics, but they can be normalised into a [0, 1] relevance score for display or thresholding. import chromadb client = chromadb . Client() col = c...
34. What are ChromaDB's practical size limits and performance characteristics at scale?
ChromaDB does not impose hard document count limits, but practical performance degrades at different thresholds depending on storage mode, hardware, and HNSW configuration. Understanding these helps you plan capacity and know when to consider alternatives. ChromaDB scale guidelines Collection siz...
35. How do you use ChromaDB to detect and remove near-duplicate or semantically similar documents?
ChromaDB's similarity search makes it straightforward to detect semantic duplicates — documents that express the same idea with different wording. Before inserting a new document, query ChromaDB to see if a highly similar document already exists and decide whether to skip or replace it. import ch...
36. How do you reset or clear a ChromaDB collection without deleting and recreating it?
ChromaDB does not have a direct clear() or truncate() method. The idiomatic way to reset a collection is to delete it and recreate it with the same parameters. For selective deletion, use delete() with ID lists or where filters. import chromadb client = chromadb . PersistentClient(path = "./reset...
37. What configuration settings does ChromaDB support and how do you disable telemetry?
By default, ChromaDB sends anonymised usage telemetry to help the development team understand how the product is used. In enterprise or privacy-sensitive environments this should be disabled. ChromaDB also supports several configuration settings via environment variables and the Settings class. i...
38. What is a production readiness checklist for a ChromaDB-based application?
Moving a ChromaDB application from prototype to production involves several architectural decisions around storage, concurrency, reliability, and observability. This checklist covers the key concerns. ChromaDB production checklist Area Recommendation Storage mode Use HttpClient connecting to a Ch...
