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Search API Overview

The Search API is a powerful, flexible interface for hybrid search operations in Chroma Cloud, combining vector similarity search with metadata filtering and custom ranking expressions.
Search API is available in Chroma Cloud only. Future support on single-node Chroma is planned.

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What is the Search API?

The Search API provides a powerful, unified interface for all search operations in Chroma. Instead of using separate query() and get() methods with different parameters, the Search API offers:
  • Unified interface: One consistent API replaces both query() and get() methods
  • Expression-based queries: Use K() expressions for powerful filtering and field selection
  • Composable operations: Chain methods to build complex queries naturally
  • Type safety: Full type hints, IDE autocomplete, and clear error messages
  • Advanced capabilities: Hybrid search with RRF, custom ranking expressions, and batch operations
  • Flexible result selection: Choose exactly which fields to return, reducing payload size

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Quick Start

from chromadb import Search, K, Knn

# Build the base search with filtering
search = (
    Search()
    .where(K("category") == "science")
    .limit(10)
    .select(K.DOCUMENT, K.SCORE)
)

# Option 1: Pass pre-computed embeddings directly
query_embedding = [0.25, -0.15, 0.33, ...]
result = collection.search(search.rank(Knn(query=query_embedding)))

# Option 2: Pass text query (embedding created using collection's schema configuration)
query_text = "What are the latest advances in quantum computing?"
result = collection.search(search.rank(Knn(query=query_text)))
When passing text to Knn(), the embedding is automatically created using the collection’s schema configuration. By default, Knn uses the #embedding key, which corresponds to the default vector index. You can specify a different key with the key parameter (e.g., Knn(query=query_text, key="my_custom_embedding")). If the specified key doesn’t have an embedding configuration in the collection schema, an error will be thrown.

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Feature Comparison

Featurequery()get()search()
Vector similarity searchYesNoYes
Filtering (metadata, document, ID)YesYesYes
Custom ranking expressionsNoNoYes
Result grouping/deduplicationNoNoYes
Batch operationsPartial (Embedding only)NoYes
Field selectionPartial (Coarse)Partial (Coarse)Yes
PaginationNoYesYes
Type safetyPartialPartialYes

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Availability

The Search API is available for Chroma Cloud. Support for local Chroma deployments will be available in a future release.

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Required Setup

To use the Search API, you’ll need to import the necessary components: 
from chromadb import Search, K, Knn

# Optional: For advanced features
from chromadb import Rrf  # For hybrid search
Make sure you’re connected to a Chroma Cloud instance, as the Search API is currently only available for cloud deployments.

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Complete Example

Here’s a practical example searching for science articles: 
import chromadb
from chromadb import Search, K, Knn

# Connect to Chroma Cloud
client = chromadb.CloudClient(
    tenant="your-tenant",
    database="your-database",
    api_key="your-api-key"
)
collection = client.get_collection("articles")

# Build the base search query
search = (
    Search()
    .where((K("category") == "science") & (K("year") >= 2020))
    .limit(5)
    .select(K.DOCUMENT, K.SCORE, "title", "author")
)

# Option 1: Search with pre-computed embeddings
query_embedding = [0.12, -0.34, 0.56, ...]
result = collection.search(search.rank(Knn(query=query_embedding)))

# Option 2: Search with text query (embedding created automatically)
query_text = "recent quantum computing breakthroughs"
result = collection.search(search.rank(Knn(query=query_text)))

# Access results using the convenient rows() method
# Note: Results are ordered by score (ascending - lower is better)
# For KNN search, score represents distance
rows = result.rows()[0]  # Get first (and only) search results
for row in rows:
    print(f"ID: {row['id']}")
    print(f"Title: {row['metadata']['title']}")
    print(f"Distance: {row['score']:.3f}")
    print(f"Document: {row['document'][:100]}...")
    print("---")
Example output: 
ID: doc_123
Title: Advances in Quantum Computing
Distance: 0.234
Document: Recent developments in quantum computing have shown promising results for...
---
ID: doc_456
Title: Machine Learning in Biology
Distance: 0.412
Document: The application of machine learning techniques to biological data has...
---

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Performance

The Search API provides the same performance as existing Chroma query endpoints, with the added benefit of more flexible query construction and batch operations that can reduce the number of round trips.

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Feedback

Please report issues or feedback through the Chroma GitHub repository.

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What’s Next?