> ## 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.

# Build an image search application

> Describe images with an agent, index the descriptions in PgVector, and search the collection from a browser UI.

Run the complete Image Search application from the Agno repository:

```bash theme={null}
uv venv .venvs/image_search --python 3.12
source .venvs/image_search/bin/activate
uv pip install -e "libs/agno[os,google]" "fastapi[standard]" pgvector "psycopg[binary]"
./cookbook/scripts/run_pgvector.sh
export GOOGLE_API_KEY="..."
fastapi dev cookbook/data_labeling/image_search/run.py --port 7777
```

Open [http://localhost:7777/ui](http://localhost:7777/ui), then click **Reindex** to label and index the configured images.

## Application structure

The application registers one `Knowledge` instance and one ingest workflow with AgentOS. A small FastAPI route serves the browser UI.

```python theme={null}
from agno.os import AgentOS
from db import get_knowledge
from fastapi import FastAPI
from workflows.ingest import ingest_workflow


base_app = FastAPI(title="Image Search")

agent_os = AgentOS(
    id="image_search",
    name="Image Search",
    knowledge=[get_knowledge()],
    workflows=[ingest_workflow],
    base_app=base_app,
)

app = agent_os.get_app()
```

| Part     | Implementation                                                                                                                                 |
| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| Label    | A Gemini agent returns an `ImageDescription` with a caption, subjects, scene, visual style, and tags.                                          |
| Index    | `to_searchable_text()` flattens those fields into the text sent to `GeminiEmbedder`.                                                           |
| Search   | `PgVector` combines vector and keyword search with `SearchType.hybrid`.                                                                        |
| Metadata | `Knowledge` stores the image URL and structured description for the gallery.                                                                   |
| Ingest   | A `Workflow` downloads each image, creates a searchable description, and stores the embedded description, structured metadata, and source URL. |
| UI       | A single HTML file renders the gallery, search results, and reindex status.                                                                    |

## Search-tuned labels

The schema separates fields that help with different queries:

```python theme={null}
from typing import List

from pydantic import BaseModel, Field


class ImageDescription(BaseModel):
    caption: str
    subjects: List[str] = Field(default_factory=list)
    scene: str
    visual_style: str
    tags: List[str] = Field(default_factory=list)
```

The caption captures a natural description. Subjects and tags add the concrete terms users search for. Scene and visual style support queries about setting, lighting, mood, and composition.

## Reindex behavior

Reindexing performs a full rebuild. The workflow removes the existing knowledge content, then processes every URL in `IMAGE_URLS` with a bounded thread pool. This makes prompt and schema changes visible across the complete demo collection.

The workflow uses `PostgresDb` so AgentOS can persist background workflow runs. The same Postgres instance stores knowledge content, while PgVector stores embeddings and serves hybrid search.

## Routes

| Action                 | Route                               |
| ---------------------- | ----------------------------------- |
| Open the UI            | `GET /ui`                           |
| List gallery content   | `GET /knowledge/content`            |
| Search the image index | `POST /knowledge/search`            |
| Start a reindex run    | `POST /workflows/image-ingest/runs` |

## Next steps

| Task                                         | Guide                                                                                |
| -------------------------------------------- | ------------------------------------------------------------------------------------ |
| Build the minimal extract-and-index pipeline | [Data extraction](/use-cases/data-labeling/structured-extraction#extract-then-index) |
| Define a different media schema              | [Multimodal inputs](/use-cases/data-labeling/multimodal-inputs)                      |
| Configure knowledge search                   | [Knowledge](/knowledge/overview)                                                     |

## Developer Resources

* [Image Search source](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/image_search)
* [Image extraction to vector database cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/data_labeling/_09_image_extraction_to_vectordb)
* [AgentOS](/agent-os/introduction)