​
Code

import asyncio

from agno.knowledge.embedder.mistral import MistralEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

embeddings = MistralEmbedder().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 = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="mistral_embeddings",
        embedder=MistralEmbedder(),
    ),
    max_results=2,
)

asyncio.run(
    knowledge.add_content_async(
        path="cookbook/knowledge/testing_resources/cv_1.pdf",
    )
)

​
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 MISTRAL_API_KEY=xxx
3

Install libraries

pip install -U sqlalchemy psycopg pgvector mistralai 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/knowledge/embedders/mistral_embedder.py