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Overview

ChromaDB is an open-source vector database designed for embedding search and similarity matching. It supports embedded, local, and cloud deployments with HNSW and FLAT index types. Provider Class: ChromaProvider
Config Class: ChromaConfig

Dependencies

pip install "upsonic[rag]"

Examples

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings.openai_provider import OpenAIEmbeddingProvider
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode, HNSWIndexConfig

# Setup embedding provider
embedding = OpenAIEmbeddingProvider(api_key="your-api-key")

# Embedded mode (local file storage)
config = ChromaConfig(
    collection_name="my_collection",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db"),
    index=HNSWIndexConfig(m=16, ef_construction=200)
)
vectordb = ChromaProvider(config)

# Create knowledge base
kb = KnowledgeBase(
    sources="document.pdf",
    embedding_provider=embedding,
    vectordb=vectordb
)

# Use with Agent
agent = Agent("openai/gpt-4o")
task = Task(
    description="What is this document about?",
    context=[kb]
)
result = agent.do(task)

Parameters

ParameterTypeDescriptionDefaultSource
collection_namestrName of the collection"default_collection"Base
vector_sizeintDimension of vectorsRequiredBase
distance_metricDistanceMetricSimilarity metric (COSINE, EUCLIDEAN, DOT_PRODUCT)COSINEBase
recreate_if_existsboolRecreate collection if it existsFalseBase
default_top_kintDefault number of results10Base
default_similarity_thresholdOptional[float]Minimum similarity scoreNoneBase
connectionConnectionConfigConnection configurationRequiredSpecific
indexUnion[HNSWIndexConfig, FlatIndexConfig]Index type configurationHNSWIndexConfig()Specific
tenantOptional[str]Tenant name for multi-tenancyNoneSpecific
databaseOptional[str]Database nameNoneSpecific