Keyword Search

Keyword search uses the selected vector database’s lexical search implementation.

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="docs",
        db_url=db_url,
        search_type=SearchType.keyword,
    ),
)

How It Works

With PgVector, keyword search:

Converts document content to a PostgreSQL tsvector.
Converts the query with websearch_to_tsquery by default.
Orders rows by PostgreSQL’s ts_rank_cd score.

Set prefix_match=True to build a prefix query from the query tokens. Other vector databases implement keyword search differently.

When to Use Keyword Search

Query Pattern	Why Test Keyword Search
Product names and technical terms	Lexical ranking retains the query terms
Error codes and identifiers	Embedding similarity may not preserve the token
Quoted terms or operators	PgVector’s default parser accepts PostgreSQL web-search syntax

Test vector search for conceptual queries. Test hybrid search when both lexical and vector signals affect relevance.

Configuration

Basic Setup

from agno.vectordb.pgvector import PgVector, SearchType

vector_db = PgVector(
    table_name="docs",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    search_type=SearchType.keyword,
    prefix_match=False,
)

PgVector applies a configured reranker to vector and hybrid search only. Keyword search results are ranked by PostgreSQL’s full-text relevance score.

Example

Install the dependencies and start a PostgreSQL instance with pgvector enabled:

uv pip install -U agno openai pgvector psycopg pypdf sqlalchemy

keyword_search.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        search_type=SearchType.keyword,
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
)

results = knowledge.search("chicken coconut soup", max_results=5)
for doc in results:
    print(doc.content[:200])

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Keyword Search

How It Works

When to Use Keyword Search

Configuration

Basic Setup

Example

Next Steps

Hybrid Search

Vector Search

​How It Works

​When to Use Keyword Search

​Configuration

​Basic Setup

​Example

​Next Steps

Hybrid Search

Vector Search

How It Works

When to Use Keyword Search

Configuration

Basic Setup

Example

Next Steps