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Overview

Milvus is an open-source vector database built for scalable similarity search and AI applications. It supports embedded (Lite), local, and cloud deployments with advanced indexing options and consistency levels. Provider Class: MilvusProvider
Config Class: MilvusConfig

Dependencies

pip install "upsonic[rag]"

Examples

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings.openai_provider import OpenAIEmbeddingProvider
from upsonic.vectordb import MilvusProvider, MilvusConfig, ConnectionConfig, Mode, HNSWIndexConfig

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

# Embedded mode (Milvus Lite)
config = MilvusConfig(
    collection_name="my_collection",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./milvus_db"),
    index=HNSWIndexConfig(m=16, ef_construction=200),
    consistency_level="Bounded"
)
vectordb = MilvusProvider(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="Search the knowledge base",
    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
indexIndexConfigIndex type configurationHNSWIndexConfig()Specific
consistency_levelLiteral['Strong', 'Bounded', 'Session', 'Eventually']Consistency level'Bounded'Specific
index_paramsOptional[Dict[str, Any]]Additional index parametersNoneSpecific
use_sparse_vectorsboolEnable sparse vector supportFalseSpecific
dense_vector_fieldstrDense vector field name"dense_vector"Specific
sparse_vector_fieldstrSparse vector field name"sparse_vector"Specific
search_paramsOptional[Dict[str, Any]]Search parametersNoneSpecific
rrf_kintRRF ranker k parameter60Specific
batch_sizeintBatch size for upsert operations100Specific