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Example

agent_with_knowledge.py
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(recreate=True, upsert=True)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge_base,
    # Add a tool to read chat history.
    read_chat_history=True,
    show_tool_calls=True,
    markdown=True,
    # debug_mode=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What was my last question?", stream=True)

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PgVector Params

ParameterTypeDefaultDescription
table_namestr-The name of the table to use.
schemastr-The schema to use.
db_urlstr-The database URL to connect to.
db_engineEngine-The database engine to use.
embedderEmbedder-The embedder to use.
search_typeSearchType-The search type to use.
vector_indexUnion[Ivfflat, HNSW]-The vector index to use.
distanceDistance-The distance to use.
prefix_matchbool-Whether to use prefix matching.
vector_score_weightfloat-The weight of the vector score.
content_languagestr-The content language to use.
schema_versionint-The schema version to use.
auto_upgrade_schemabool-Whether to auto upgrade the schema.

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