> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI Key Request While Using Other Models

If you see a request for an OpenAI API key but haven't explicitly configured OpenAI, it's because Agno uses OpenAI models by default in several places, including:

* The default model when unspecified in `Agent`
* The default embedder is OpenAIEmbedder with VectorDBs, unless specified

## Quick fix: Configure a Different Model

It is best to specify the model for the agent explicitly, otherwise it would default to `OpenAIChat`.

For example, to use Google's Gemini instead of OpenAI:

```python theme={null}
from agno.agent import Agent, RunOutput
from agno.models.google import Gemini

agent = Agent(
    model=Gemini(id="gemini-1.5-flash"),
    markdown=True,
)

# Print the response in the terminal
agent.print_response("Share a 2 sentence horror story.")
```

For more details on configuring different model providers, check our [models documentation](/models/overview)

## Quick fix: Configure a Different Embedder

The same applies to embeddings. If you want to use a different embedder instead of `OpenAIEmbedder`, configure it explicitly.

For example, to use Google's Gemini as an embedder, use `GeminiEmbedder`:

```python theme={null}
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.google import GeminiEmbedder

# Embed sentence in database
embeddings = GeminiEmbedder().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 = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="gemini_embeddings",
        embedder=GeminiEmbedder(),
    ),
    max_results=2,
)
```

For more details on configuring different model providers, check our [Embeddings documentation](/knowledge/concepts/embedder/overview)