PGVector - Upsonic AI

Overview

PGVector is a PostgreSQL extension that enables vector similarity search. It supports HNSW and IVFFlat indexes and integrates seamlessly with existing PostgreSQL infrastructure. Provider Class: PgVectorProvider
Config Class: PgVectorConfig

Install

Install the PGVector optional dependency group:

uv pip install "upsonic[pgvector]"

Examples

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import PgVectorProvider, PgVectorConfig, HNSWIndexConfig
from pydantic import SecretStr

# Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Create PGVector configuration
config = PgVectorConfig(
    collection_name="my_collection",
    vector_size=1536,
    connection_string=SecretStr("postgresql://user:password@localhost/dbname"),
    schema_name="public",
    index=HNSWIndexConfig(m=16, ef_construction=200)
)
vectordb = PgVectorProvider(config)

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

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="Search the database",
    context=[kb]
)
result = agent.do(task)

Parameters

Base Parameters (from BaseVectorDBConfig)

Parameter	Type	Description	Default	Required
`collection_name`	`str`	Name of the collection	`"default_collection"`	No
`vector_size`	`int`	Dimension of vectors	-	Yes
`distance_metric`	`DistanceMetric`	Similarity metric (`COSINE`, `EUCLIDEAN`, `DOT_PRODUCT`)	`COSINE`	No
`recreate_if_exists`	`bool`	Recreate collection if it exists	`False`	No
`default_top_k`	`int`	Default number of results	`10`	No
`default_similarity_threshold`	`Optional[float]`	Minimum similarity score (0.0-1.0)	`None`	No
`dense_search_enabled`	`bool`	Enable dense vector search	`True`	No
`full_text_search_enabled`	`bool`	Enable full-text search	`True`	No
`hybrid_search_enabled`	`bool`	Enable hybrid search	`True`	No
`default_hybrid_alpha`	`float`	Default alpha for hybrid search (0.0-1.0)	`0.5`	No
`default_fusion_method`	`Literal['rrf', 'weighted']`	Default fusion method for hybrid search	`'weighted'`	No
`provider_name`	`Optional[str]`	Provider name	`None`	No
`provider_description`	`Optional[str]`	Provider description	`None`	No
`provider_id`	`Optional[str]`	Provider ID	`None`	No
`default_metadata`	`Optional[Dict[str, Any]]`	Default metadata for all records	`None`	No
`indexed_fields`	`Optional[List[Union[str, Dict[str, Any]]]]`	Fields to index for filtering	`None`	No

PGVector-Specific Parameters

Parameter	Type	Description	Default	Required
`connection_string`	`SecretStr`	PostgreSQL connection string	-	Yes
`schema_name`	`str`	PostgreSQL schema name	`"public"`	No
`table_name`	`Optional[str]`	Table name (uses `collection_name` if not specified)	`None`	No
`index`	`Union[HNSWIndexConfig, IVFIndexConfig]`	Index type configuration (FLAT not supported)	`HNSWIndexConfig()`	No
`content_language`	`str`	Language for full-text search (e.g., ‘english’, ‘spanish’)	`"english"`	No
`prefix_match`	`bool`	Enable prefix matching for full-text search (appends * to words)	`False`	No
`schema_version`	`int`	Schema version for migrations	`1`	No
`auto_upgrade_schema`	`bool`	Automatically upgrade schema on version mismatch	`False`	No
`batch_size`	`int`	Batch size for upsert operations	`100`	No
`pool_size`	`int`	Connection pool size	`5`	No
`max_overflow`	`int`	Maximum pool overflow	`10`	No
`pool_timeout`	`float`	Pool timeout in seconds	`30.0`	No
`pool_recycle`	`int`	Pool recycle time in seconds	`3600`	No

​Overview

​Install

​Examples

​Parameters

​Base Parameters (from BaseVectorDBConfig)

​PGVector-Specific Parameters

Overview

Install

Examples

Parameters

Base Parameters (from BaseVectorDBConfig)

PGVector-Specific Parameters