An Embedder converts complex information into vector representations, allowing it to be stored in a vector database. By transforming data into embeddings, the embedder enables efficient searching and retrieval of contextually relevant information. This process enhances the responses of language models by providing them with the necessary business context, ensuring they are context-aware. Agno uses the OpenAIEmbedder as the default embedder, but other embedders are supported as well. Here is an example: 
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.openai import OpenAIEmbedder

# Create knowledge
knowledge = Knowledge(
    vector_db=PgVector(
        db_url=db_url,
        table_name=embeddings_table,
        embedder=OpenAIEmbedder(),
    ),
    # 2 references are added to the prompt
    max_results=2,
)

# Add content to knowledge
knowledge.add_content(
    text_content="The sky is blue"
)

# Add the knowledge to the Agent
agent = Agent(knowledge=knowledge)
The following embedders are supported: