Embedders

Embedders convert text into vectors (lists of numbers) that capture meaning. These vectors enable semantic search, so “How do I reset my passcode?” finds documents mentioning “change PIN” even without keyword matches.

from agno.knowledge.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.vectordb.pgvector import PgVector

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="docs",
        db_url=db_url,
        embedder=OpenAIEmbedder(),  # Default
    ),
)

How It Works

Insert: When you add content, each chunk is converted to a vector
Store: Vectors are saved in your vector database
Search: Queries are embedded and matched against stored vectors by similarity

Many vector database implementations, including PgVector, use OpenAIEmbedder when embedder is omitted. Provider-hosted implementations can differ. UpstashVectorDb uses Upstash hosted embeddings when embedder is omitted.

Configuration

from agno.knowledge.embedder.openai import OpenAIEmbedder

embedder = OpenAIEmbedder(
    id="text-embedding-3-small",
    dimensions=1536,
)

Using with Knowledge

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

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="docs",
        db_url=db_url,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# Content is embedded automatically on insert
knowledge.insert(path="documents/")

Batch Embeddings

Process multiple texts in a single API call to reduce requests and improve performance:

embedder = OpenAIEmbedder(
    id="text-embedding-3-small",
    dimensions=1536,
    enable_batch=True,
    batch_size=100,
)

Embedders with batch support: OpenAI, Azure OpenAI, Gemini, Cohere, Voyage AI, Mistral, Fireworks, Together, Jina, Nebius, LangDB, vLLM.

Best Practices

Re-embed when changing models: Vectors from different embedders aren’t compatible. If you switch embedders, you must re-embed all content.

Test retrieval quality: Use sample queries to verify you’re finding the right chunks. Adjust chunking strategy or embedder if results are poor.

Match dimensions: Ensure your embedder’s output dimensions match what your vector database expects.

Supported Embedders

Embedder	Type	Cost	Notes
OpenAI	Hosted	$$	Default for PgVector and many other vector databases
Gemini	Hosted	$$	Multilingual, Google ecosystem
Cohere	Hosted	$$	Strong retrieval performance
Voyage AI	Hosted	$$$	Specialized for retrieval
Mistral	Hosted	$$	European provider
Ollama	Local	Free	Privacy, offline
FastEmbed	Local	Free	Fast local embeddings
SentenceTransformers	Local	Free	Local models via sentence-transformers
vLLM	Local	Free	Local model or self-hosted vLLM server
HuggingFace	Hosted	Free/$	Open source models via Inference API
AWS Bedrock	Hosted	$$	AWS ecosystem
Azure OpenAI	Hosted	$$	Azure ecosystem
Fireworks	Hosted	$	Fast inference
Together	Hosted	$	Open source models
Jina	Hosted	$$	Multilingual
Nebius	Hosted	$	European provider
LangDB	Hosted	$$	AI gateway, OpenAI-compatible

Choosing an Embedder

Consideration	Recommendation
General use	OpenAI or Gemini
Privacy/offline	Ollama or FastEmbed
Multilingual	Gemini or Jina
Cost-sensitive	Local embedders (free) or Fireworks/Together ($)
Best retrieval quality	Voyage AI or Cohere

Key factors:

Hosted vs local: Local for privacy and no API costs; hosted for quality and convenience
Latency and cost: Smaller models are cheaper and faster; larger models often retrieve better
Language support: Ensure your embedder supports your content’s languages
Dimension size: Match your vector database’s expected embedding dimensions

Next Steps

OpenAI Embedder

Default embedder setup

Ollama Embedder

Local embeddings for privacy

Vector DB

Store your embeddings

Chunking

Prepare content for embedding

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

How It Works

Configuration

Using with Knowledge

Batch Embeddings

Best Practices

Supported Embedders

Choosing an Embedder

Next Steps

OpenAI Embedder

Ollama Embedder

Vector DB

Chunking

​How It Works

​Configuration

​Using with Knowledge

​Batch Embeddings

​Best Practices

​Supported Embedders

​Choosing an Embedder

​Next Steps

OpenAI Embedder

Ollama Embedder

Vector DB

Chunking

How It Works

Configuration

Using with Knowledge

Batch Embeddings

Best Practices

Supported Embedders

Choosing an Embedder

Next Steps