cookbook/embedders/qdrant_fastembed.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.fastembed import FastEmbedEmbedder
# 将句子嵌入数据库
embeddings = FastEmbedEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
# 打印嵌入和它们的维度
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")
# 在知识库中使用嵌入器
knowledge_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="qdrant_embeddings",
embedder=FastEmbedEmbedder(),
),
num_documents=2,
)