tessl/pypi-chromadb

Chroma - the open-source embedding database

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ChromaDB

Name: tessl/pypi-chromadb
Author: tessl

ChromaDB is an open-source embedding database designed as the fastest way to build Python or JavaScript LLM applications with memory. It provides a simple API for storing, querying, and managing document embeddings with automatic tokenization, embedding generation, and indexing capabilities.

Package Information

Package Name: chromadb
Language: Python
Installation: pip install chromadb

Core Imports

import chromadb

For specific client types:

from chromadb import EphemeralClient, PersistentClient, HttpClient

For types and embedding functions:

from chromadb.api.types import Documents, Embeddings, Metadatas, Where, Include
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

Basic Usage

import chromadb

# Create a client (in-memory for testing)
client = chromadb.EphemeralClient()

# Create a collection
collection = client.create_collection(name="my_collection")

# Add documents with metadata
collection.add(
    documents=["This is a document", "This is another document"],
    metadatas=[{"source": "my_source"}, {"source": "my_source"}],
    ids=["id1", "id2"]
)

# Query for similar documents
results = collection.query(
    query_texts=["This is a query document"],
    n_results=2
)

print(results)

Architecture

ChromaDB operates on a client-server architecture with multiple deployment options:

Client Types: Multiple client implementations for different deployment scenarios (in-memory, persistent, remote HTTP, cloud)
Collections: Named containers for documents and embeddings with configurable metadata and embedding functions
Embedding Functions: Pluggable components for generating embeddings from various providers (OpenAI, Cohere, HuggingFace, etc.)
Query System: Vector similarity search with metadata filtering and document text matching
Multi-tenancy: Support for tenants and databases for data isolation

Capabilities

Client Creation

Factory functions for creating different types of ChromaDB clients depending on deployment needs. Supports in-memory for testing, persistent storage for local development, and remote connections for production.

def EphemeralClient(settings=None, tenant="default_tenant", database="default_database"): ...
def PersistentClient(path="./chroma", settings=None, tenant="default_tenant", database="default_database"): ...
def HttpClient(host="localhost", port=8000, ssl=False, headers=None, settings=None, tenant="default_tenant", database="default_database"): ...
def CloudClient(tenant=None, database=None, api_key=None, settings=None): ...

Client Creation

Collection Operations

Core collection management including creation, retrieval, modification, and deletion. Collections are the primary containers for documents and embeddings with configurable metadata and embedding functions.

class ClientAPI:
    def create_collection(self, name: str, **kwargs) -> Collection: ...
    def get_collection(self, name: str, **kwargs) -> Collection: ...
    def delete_collection(self, name: str) -> None: ...
    def list_collections(self, limit=None, offset=None) -> Sequence[Collection]: ...

Collection Management

Document Operations

Adding, updating, querying, and deleting documents within collections. Supports embeddings, metadata, images, and URIs with flexible data formats and automatic embedding generation.

class Collection:
    def add(self, ids, documents=None, embeddings=None, metadatas=None, images=None, uris=None): ...
    def query(self, query_texts=None, query_embeddings=None, n_results=10, where=None, **kwargs): ...
    def get(self, ids=None, where=None, limit=None, **kwargs): ...
    def update(self, ids, documents=None, embeddings=None, metadatas=None, **kwargs): ...
    def delete(self, ids=None, where=None, where_document=None): ...

Document Operations

Embedding Functions

Pre-built and configurable embedding functions for generating vector embeddings from text, supporting major AI providers and embedding models with consistent interfaces.

class EmbeddingFunction:
    def __call__(self, input): ...
    def embed_with_retries(self, input, **retry_kwargs): ...

Embedding Functions

Query and Filtering

Advanced query capabilities including vector similarity search, metadata filtering, and document text matching with logical operators and flexible result formatting.

Where = Dict[Union[str, LogicalOperator], Union[LiteralValue, OperatorExpression, List[Where]]]
WhereDocument = Dict[WhereDocumentOperator, Union[str, List[WhereDocument]]]
Include = List[Literal["documents", "embeddings", "metadatas", "distances", "uris", "data"]]

Queries and Filtering

Configuration and Settings

Comprehensive configuration system for customizing ChromaDB behavior including authentication, server settings, telemetry, and storage options.

class Settings:
    def __init__(self, **kwargs): ...

def configure(**kwargs) -> None: ...
def get_settings() -> Settings: ...

Configuration

Types

Core Types

# Document and metadata types
Documents = List[str]
Metadatas = List[Dict[str, Union[str, int, float, bool]]]
IDs = List[str]
Embeddings = List[List[float]]

# Query result types
GetResult = TypedDict('GetResult', {
    'ids': List[List[str]],
    'documents': List[List[Optional[str]]],
    'metadatas': List[List[Optional[Dict]]],
    'embeddings': List[List[Optional[List[float]]]],
    'distances': List[List[float]],
    'uris': List[List[Optional[str]]],
    'data': List[List[Optional[Any]]],
    'included': List[str]
})

QueryResult = TypedDict('QueryResult', {
    'ids': List[List[str]],
    'documents': List[List[Optional[str]]],
    'metadatas': List[List[Optional[Dict]]],
    'embeddings': List[List[Optional[List[float]]]],
    'distances': List[List[float]],
    'uris': List[List[Optional[str]]],
    'data': List[List[Optional[Any]]],
    'included': List[str]
})

Workspace: tessl
Visibility: Public
Created: 7 months ago
Last updated: 3 months ago
Describes: pkg:pypi/chromadb@1.0.x
Publish Source: CLI
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