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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Code

from agno.agent import AgentKnowledge
from agno.embedder.azure_openai import AzureOpenAIEmbedder
from agno.vectordb.pgvector import PgVector

embeddings = AzureOpenAIEmbedder().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)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="azure_openai_embeddings",
        embedder=AzureOpenAIEmbedder(),
    ),
    num_documents=2,
)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set your API key

export AZURE_EMBEDDER_OPENAI_API_KEY=xxx
export AZURE_EMBEDDER_OPENAI_ENDPOINT=xxx
export AZURE_EMBEDDER_DEPLOYMENT=xxx
3

Install libraries

pip install -U sqlalchemy 'psycopg[binary]' pgvector openai agno
4

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:16
5

Run Agent

python cookbook/agent_concepts/knowledge/embedders/azure_embedder.py