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. Phidata uses OpenAIEmbedder as the default embedder, but other embedders are supported as well. Here is an example:
Copy
Ask AI
from phi.agent import Agent, AgentKnowledgefrom phi.vectordb.pgvector import PgVectorfrom phi.embedder.openai import OpenAIEmbedder# Create knowledge baseknowledge_base=AgentKnowledge( vector_db=PgVector( db_url=db_url, table_name=embeddings_table, embedder=OpenAIEmbedder(), ), # 2 references are added to the prompt num_documents=2,),# Add information to the knowledge baseknowledge_base.load_text("The sky is blue")# Add the knowledge base to the Agentagent = Agent(knowledge_base=knowledge_base)