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Quick Guide: Use chromadb (v3.x) with @chroma-core/default-embed for automatic embedding. Chroma auto-embeds documents if no embeddings are provided -- just pass documents and ids to collection.add(). Use where for metadata filtering and whereDocument for document content filtering ($contains, $regex). Default distance metric is l2 (Euclidean); use cosine for most embedding models via configuration: { hnsw: { space: "cosine" } }. Query results return nested arrays (ids: string[][]) because queries are batched -- always access results.ids[0] for a single query. Include only the fields you need via the include parameter to reduce payload size.
<critical_requirements>
CRITICAL: Before Using This Skill
All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)
(You MUST install @chroma-core/default-embed alongside chromadb -- the default embedding function ships as a separate package since v3)
(You MUST access query results as nested arrays -- results.ids[0], results.documents[0] -- because Chroma batches queries and returns string[][] not )
string[]
(You MUST use the configuration parameter for HNSW settings -- the legacy metadata: { "hnsw:space": "cosine" } approach is deprecated)
(You MUST use flat metadata values only (string, number, boolean, typed arrays) -- nested objects are not supported and will be rejected)
Metadata filtering (where) with comparison, set, array, and logical operators
Document content filtering (whereDocument) with $contains and $regex
Embedding function configuration (default, OpenAI, custom)
Query result handling (nested array structure, include options)
When NOT to use:
Full-text search with complex boolean ranking (use a dedicated search engine)
Relational data with joins and transactions (use a relational database)
Multi-modal image+text embeddings in TypeScript (currently Python-only in Chroma)
High-scale production with millions of vectors and strict SLAs (evaluate managed vector databases)
Philosophy
Chroma is a lightweight, developer-friendly embedding database designed for rapid prototyping and production RAG applications. The core principle: pass documents in, get relevant results out -- Chroma handles embedding automatically.
Core principles:
Documents first, vectors optional -- Unlike most vector databases, Chroma can embed documents automatically using a configured embedding function. You never need to manage embeddings directly unless you want to.
Collections are self-contained -- Each collection has its own embedding function, distance metric, and HNSW configuration. No global index management needed.
Metadata is for filtering, documents are for content -- Use where for structured metadata filters and whereDocument for full-text content filters. Both can be combined in a single query.
Batteries included -- The default embedding function (all-MiniLM-L6-v2 via @chroma-core/default-embed) works out of the box for English text. Swap to OpenAI, Cohere, or any provider with a single package change.
Query results are batched -- Chroma supports multiple queries in a single call. Results are always nested arrays (string[][]), even for single queries. Always access [0] for the first query's results.
Core Patterns
Pattern 1: Client Initialization
Create a ChromaClient connected to a running Chroma server. See examples/core.md for full examples.
Why good: Combined metadata + document filter, named constant for nResults, correct nested array access [0], explicit include to control response payload
// Bad Exampleconst results = await collection.query({
queryTexts: ["query"],
nResults: 5,
});
// BUG: treats results.ids as flat arrayfor (const id of results.ids) {
console.log(id); // Prints the inner array, not individual IDs!
}
Why bad:results.ids is string[][] not string[]; must access results.ids[0] for the first query's results
Pattern 5: Get with Pagination
Retrieve documents by ID or metadata filter with pagination. See examples/core.md.
Why good: Idempotent -- creates or updates depending on ID existence, re-embeds documents automatically
<decision_framework>
Decision Framework
Which Distance Metric?
Which distance metric should I use?
|-- Using embeddings from a language model? -> cosine (normalized, most common)
|-- Need dot product similarity? -> ip (inner product)
|-- Comparing raw feature vectors? -> l2 (Euclidean, Chroma default)
'-- Unsure? -> cosine (safe default for most embedding models)
Which Embedding Function?
Which embedding function should I use?
|-- Quick prototyping, English text? -> @chroma-core/default-embed (all-MiniLM-L6-v2, runs locally)
|-- Need high-quality embeddings? -> @chroma-core/openai (text-embedding-3-small)
|-- Have your own embedding pipeline? -> Pass embeddings directly (skip embedding function)
|-- Need custom model? -> Implement EmbeddingFunction interface
'-- Want all providers? -> npm install @chroma-core/all
Where vs WhereDocument?
How should I filter results?
|-- Structured attributes (category, year, status)? -> where (metadata filter)
|-- Full-text content search? -> whereDocument ($contains, $regex)
|-- Both? -> Combine where + whereDocument in same query
'-- Need exact match on specific IDs? -> get({ ids: [...] })
ChromaClient vs CloudClient?
Which client should I use?
|-- Local development or self-hosted? -> ChromaClient({ path: "http://localhost:8000" })
|-- Chroma Cloud (managed)? -> CloudClient({ apiKey, tenant, database })
|-- Docker deployment? -> ChromaClient with Docker host URL
'-- Testing? -> ChromaClient against local Docker container
</decision_framework>
<red_flags>
RED FLAGS
High Priority Issues:
Accessing query results as flat arrays instead of nested -- results.ids is string[][], not string[]; always use results.ids[0] for single-query results
Missing @chroma-core/default-embed package -- since v3, the default embedding function ships separately; npm install chromadb @chroma-core/default-embed
Using deprecated metadata: { "hnsw:space": "cosine" } for HNSW config -- use configuration: { hnsw: { space: "cosine" } } instead
Nested objects in metadata -- Chroma only supports flat key-value metadata; nested objects are rejected
Medium Priority Issues:
Not specifying include in queries -- default includes vary (query returns documents, metadatas, distances; get returns documents, metadatas); explicitly set include for clarity and to control payload size
Using l2 (default) when cosine is appropriate -- most embedding models are normalized for cosine similarity; l2 may produce worse results
Calling add() without documents or embeddings -- at least one must be provided; metadata alone is insufficient
Not handling empty results -- results.ids[0] may be an empty array; check length before processing
Common Mistakes:
Passing queryEmbeddings AND queryTexts together -- use one or the other, not both
Expecting update() to create missing records -- update() silently ignores non-existent IDs; use upsert() for create-or-update semantics
Calling delete() with no arguments -- deletes nothing (not everything); pass ids or where to target specific records
Using $gt/$lt on string metadata -- comparison operators only work on numeric values (int or float)
Gotchas & Edge Cases:
HNSW configuration (space, ef_construction, max_neighbors) cannot be changed after collection creation -- you must delete and recreate the collection
collection.count() returns total records in the collection, not filtered counts -- there is no filtered count API
peek() returns the first limit items (default 10) in insertion order, not by relevance -- useful for debugging, not querying
$contains in whereDocument is case-sensitive -- searching for "Neural" will not match "neural"
$regex in whereDocument uses full regex syntax but can be slow on large collections
Array metadata values (string[], number[]) must be homogeneous -- mixing types within an array is rejected
Metadata keys are case-sensitive -- Category and category are different fields
The nResults default is 10 if not specified in query()
Multimodal embedding (images + text) is currently Python-only -- TypeScript support is not yet available
</red_flags>
<critical_reminders>
CRITICAL REMINDERS
All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)
(You MUST install @chroma-core/default-embed alongside chromadb -- the default embedding function ships as a separate package since v3)
(You MUST access query results as nested arrays -- results.ids[0], results.documents[0] -- because Chroma batches queries and returns string[][] not string[])
(You MUST use the configuration parameter for HNSW settings -- the legacy metadata: { "hnsw:space": "cosine" } approach is deprecated)
(You MUST use flat metadata values only (string, number, boolean, typed arrays) -- nested objects are not supported and will be rejected)
Failure to follow these rules will cause embedding failures, incorrect result access, deprecated configuration warnings, and rejected metadata.
