Documentation Index
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The FastEmbedEmbedder class is used to embed text data into vectors using the FastEmbed.
Usage
cookbook/embedders/qdrant_fastembed.py
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.fastembed import FastEmbedEmbedder
# Embed sentence in database
embeddings = FastEmbedEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")
# Use an embedder in a knowledge base
knowledge_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="qdrant_embeddings",
embedder=FastEmbedEmbedder(),
),
num_documents=2,
)
Params
| Parameter | Type | Default | Description |
|---|
dimensions | int | - | The dimensionality of the generated embeddings |
model | str | BAAI/bge-small-en-v1.5 | The name of the qdrant_fastembed model to use |
Developer Resources
