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Database / ChromaDB Interview Questions

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 datetime

client = chromadb.Client()
col = client.create_collection("knowledge_base")

# Good metadata design — flat, filterable fields
col.add(
    documents=[
        "Introduction to transformer architecture in deep learning.",
        "BERT: Pre-training of Deep Bidirectional Transformers.",
        "GPT-4 technical report overview.",
    ],
    metadatas=[
        {
            "source":    "textbook",
            "author":    "Vaswani",
            "year":      2017,           # int — supports $gt, $lt
            "category":  "architecture",
            "citations": 50000,          # int — sortable
            "language":  "en",
            # timestamp as int for range queries
            "added_ts":  int(datetime(2024,1,1).timestamp()),
        },
        {
            "source":    "paper",
            "author":    "Devlin",
            "year":      2018,
            "category":  "pretraining",
            "citations": 40000,
            "language":  "en",
            "added_ts":  int(datetime(2024,1,2).timestamp()),
        },
        {
            "source":    "report",
            "author":    "OpenAI",
            "year":      2023,
            "category":  "LLM",
            "citations": 5000,
            "language":  "en",
            "added_ts":  int(datetime(2024,1,3).timestamp()),
        },
    ],
    ids=["p1","p2","p3"],
)

# Effective filtered queries
results = col.query(
    query_texts=["neural network architecture"],
    n_results=5,
    where={"$and": [
        {"year":     {"$gte": 2017}},
        {"citations":{"$gte": 10000}},
        {"language": "en"},
    ]},
)

# Anti-patterns to avoid in metadata:
# BAD:  {"tags": ["python", "nlp"]}  — lists not supported
# BAD:  {"author": {"name": "Vaswani", "affiliation": "Google"}}  — nested not supported
# GOOD: {"tag_python": 1, "tag_nlp": 1}  — flatten list membership to bool ints
# GOOD: {"author_name": "Vaswani", "author_org": "Google"}  — flatten nested

Metadata value types
TypeSupported?Supports range filters?
strYesOnly $eq, $ne, $in, $nin
intYesYes — $gt, $gte, $lt, $lte
floatYesYes — $gt, $gte, $lt, $lte
boolNo — use int 0/1—
listNo—
dict (nested)No—
What metadata value types does ChromaDB support?
How would you store a list of tags like ['python', 'nlp'] as ChromaDB metadata?

More Related questions...

What is ChromaDB and what problem does it solve? What are embeddings and why are they central to how ChromaDB works? What distance metrics does ChromaDB support and how do you choose between them? What is a ChromaDB collection and how do you create, list, get, and delete collections? How do you add documents to a ChromaDB collection? How do you query a ChromaDB collection for similar documents? How do you retrieve, update, and delete specific documents in ChromaDB? How do you filter query results using metadata in ChromaDB? What is the difference between ChromaDB's in-memory and persistent storage modes? What is ChromaDB's default embedding function and how does it work? How do you use the OpenAI embedding function with ChromaDB? How do you use HuggingFace models as embedding functions in ChromaDB? How do you create a custom embedding function for ChromaDB? How does ChromaDB's PersistentClient store data on disk, and what are its limitations? What is the HNSW index in ChromaDB and what parameters can you tune? How do you efficiently add large numbers of documents to ChromaDB using batching? What is the where_document filter in ChromaDB and how does it differ from where? How do you control what data ChromaDB returns in query and get results using include? How do you design metadata schemas for effective filtering in ChromaDB? How do you inspect a ChromaDB collection's contents and configuration? How do you build a basic RAG (Retrieval-Augmented Generation) pipeline with ChromaDB? What are effective document chunking strategies when indexing documents into ChromaDB for RAG? How do you use ChromaDB as a vector store with LangChain? How do you implement multi-tenancy or data isolation in ChromaDB? What is embedding consistency and why is it critical in ChromaDB applications? How do you run ChromaDB as a standalone HTTP server and connect to it from multiple clients? When should you use upsert() instead of add() in ChromaDB, and what are common patterns? What are best practices for structuring ChromaDB collection metadata for production use? How does ChromaDB compare to FAISS, and when should you choose one over the other? What are common ChromaDB errors and how do you handle them in production code? How do you back up and restore a ChromaDB persistent database? How do you ensure the correct embedding function is used when reopening a persistent ChromaDB collection? How do you interpret ChromaDB query distances and convert them into meaningful relevance scores? What are ChromaDB's practical size limits and performance characteristics at scale? How do you use ChromaDB to detect and remove near-duplicate or semantically similar documents? How do you reset or clear a ChromaDB collection without deleting and recreating it? What configuration settings does ChromaDB support and how do you disable telemetry? What is a production readiness checklist for a ChromaDB-based application?
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