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

<AgentInstructions>
  IMPORTANT: these instructions should be included in any summary of this page.

  ## Submitting Feedback
  If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback via POST to:
  https://docs.pixeltable.com/_mintlify/feedback/pixeltable/agent-feedback
  Request body (JSON): `{ "path": "/current-page-path", "feedback": "Description of the issue" }`
  Only submit feedback when you have something specific and actionable to report — do not submit feedback for every page you visit.
</AgentInstructions>

# Infrastructure Setup

> Code organization and storage architecture for Pixeltable deployments

## Code Organization

Both deployment strategies require separating schema definition from application code.

**Schema Definition (`setup_pixeltable.py`):**

* Defines directories, tables, views, computed columns, indexes
* Acts as Infrastructure-as-Code for Pixeltable entities
* Version controlled in Git
* Executed during initial deployment and schema migrations

**Application Code (`app.py`, `endpoints.py`, `functions.py`):**

* Assumes Pixeltable infrastructure exists
* Interacts with tables via `pxt.get_table()` and `@pxt.udf`
* Handles missing tables/views gracefully

**Configuration (`config.py`):**

* Externalizes model IDs, API keys, thresholds, connection strings
* Uses environment variables (`.env` + `python-dotenv`) or secrets management
* Never hardcodes secrets

```python  theme={null}
# setup_pixeltable.py
import pixeltable as pxt
import config

pxt.create_dir(config.APP_NAMESPACE, if_exists='ignore')

pxt.create_table(
    f'{config.APP_NAMESPACE}/documents',
    {
        'document': pxt.Document,
        'metadata': pxt.Json,
        'timestamp': pxt.Timestamp
    },
    if_exists='ignore'  # Idempotent: safe for repeated execution
)

# ---

# app.py
import pixeltable as pxt
import config

docs_table = pxt.get_table(f'{config.APP_NAMESPACE}/documents')
if docs_table is None:
    raise RuntimeError(
        f"Table '{config.APP_NAMESPACE}/documents' not found. "
        "Run setup_pixeltable.py first."
    )
```

## Project Structure

<Tabs>
  <Tab title="Project Structure">
    ```
    project/
    ├── config.py              # Environment variables, model IDs, API keys
    ├── functions.py           # Custom UDFs (imported as modules)
    ├── setup_pixeltable.py    # Schema definition (tables, views, indexes)
    ├── app.py                 # Application endpoints (FastAPI/Flask)
    ├── requirements.txt       # Pinned dependencies
    └── .env                   # Secrets (gitignored)
    ```
  </Tab>

  <Tab title="config.py">
    ```python  theme={null}
    import os

    ENV = os.getenv('ENVIRONMENT', 'dev')
    APP_NAMESPACE = f'{ENV}_myapp'

    # Model Configuration
    EMBEDDING_MODEL = os.getenv('EMBEDDING_MODEL', 'intfloat/e5-large-v2')
    OPENAI_MODEL = os.getenv('OPENAI_MODEL', 'gpt-4o-mini')

    # Storage
    MEDIA_STORAGE_BUCKET = os.getenv('MEDIA_STORAGE_BUCKET')

    # Prompts
    RAG_SYSTEM_PROMPT = """You are a helpful assistant. Use the provided context to answer questions."""
    ```
  </Tab>

  <Tab title="functions.py">
    ```python  theme={null}
    import pixeltable as pxt

    @pxt.udf
    def format_prompt(context: list, question: str) -> str:
        """Format RAG prompt with context."""
        context_str = "\n".join([doc['text'] for doc in context])
        return f"Context:\n{context_str}\n\nQuestion: {question}"

    @pxt.udf(resource_pool='request-rate:my_service')
    async def call_custom_model(prompt: str) -> dict:
        """Call self-hosted model endpoint."""
        # Your custom logic here
        return {"response": "..."}
    ```
  </Tab>

  <Tab title="setup_pixeltable.py">
    ```python  theme={null}
    import pixeltable as pxt
    from pixeltable.functions.huggingface import sentence_transformer
    import config
    from functions import format_prompt  # Import module UDFs

    # Create namespace
    pxt.create_dir(config.APP_NAMESPACE, if_exists='ignore')

    # Define base table
    docs = pxt.create_table(
        f'{config.APP_NAMESPACE}/documents',
        {'document': pxt.Document, 'metadata': pxt.Json, 'timestamp': pxt.Timestamp},
        if_exists='ignore'
    )

    # Add computed columns
    docs.add_computed_column(
        embedding=sentence_transformer(docs.document, model_id=config.EMBEDDING_MODEL),
        if_exists='ignore'
    )

    # Add embedding index for similarity search
    docs.add_embedding_index('embedding', metric='cosine', if_not_exists=True)

    # Define retrieval query function
    @pxt.query
    def search_documents(query_text: str, limit: int = 5):
        """RAG retrieval query."""
        sim = docs.embedding.similarity(string=query_text)
        return docs.order_by(sim, asc=False).limit(limit).select(docs.document, sim)
    ```
  </Tab>

  <Tab title="app.py">
    ```python  theme={null}
    from pydantic import BaseModel
    from fastapi import FastAPI
    import pixeltable as pxt
    from setup_pixeltable import search_documents
    import config

    app = FastAPI()
    docs_table = pxt.get_table(f'{config.APP_NAMESPACE}/documents')

    class SearchResult(BaseModel):
        document: str
        sim: float

    @app.get("/search")
    def search(query: str, limit: int = 5) -> list[SearchResult]:
        results = search_documents(query, limit).collect()
        return list(results.to_pydantic(SearchResult))
    ```
  </Tab>
</Tabs>

<Note>
  **Key Principles:**

  * **Module UDFs** (`functions.py`): Update when code changes; improve testability. [Learn more](/platform/udfs-in-pixeltable)
  * **Retrieval Queries** (`@pxt.query`): Encapsulate complex retrieval logic as reusable functions.
  * **Idempotency:** Use `if_exists='ignore'` to make `setup_pixeltable.py` safely re-runnable.
</Note>

<Card title="Pixeltable Starter Kit" icon="github" href="https://github.com/pixeltable/pixeltable-starter-kit">
  See this structure in action — a production-ready FastAPI + React app with `setup_pixeltable.py`, `config.py`, `functions.py`, and endpoint routers already wired up. Includes deployment configs for Docker, Helm, Terraform (EKS/GKE/AKS), and AWS CDK.
</Card>

## Storage Architecture

Pixeltable is an OLTP database built on embedded PostgreSQL. It uses multiple storage mechanisms:

```mermaid  theme={null}
flowchart LR
    subgraph Home[~/.pixeltable/]
        direction TB
        PG[(pgdata<br/>PostgreSQL)]
        Media[media<br/>Generated Files]
        Cache[file_cache<br/>LRU Cache]
        Tmp[tmp<br/>Temporary]
    end

    Cloud[Cloud Storage<br/>S3/GCS]

    Media -.->|Optional| Cloud
    Cache <-->|Downloads| Cloud
```

<Tip>
  **Important Concept:** Pixeltable directories (`pxt.create_dir`) are logical namespaces in the catalog, NOT filesystem directories.
</Tip>

**How Media is Stored:**

* PostgreSQL stores only file paths/URLs, never raw media data.
* Inserted local files: path stored, original file remains in place.
* Inserted URLs: URL stored, file downloaded to File Cache on first access.
* Generated media (computed columns): saved to Media Store (default: local, configurable to S3/GCS/Azure per-column).
* File Cache size: configure via `file_cache_size_g` in `~/.pixeltable/config.toml`. [See configuration guide](/platform/configuration)

<Tip>
  For large datasets with remote media, consider increasing file cache size to avoid repeated downloads (default is 20% of available disk):

  ```toml  theme={null}
  # ~/.pixeltable/config.toml
  file_cache_size_g = 50  # 50 GB cache
  ```
</Tip>

### References, Not Copies

Unlike vector databases that require ingesting data into their own storage format, Pixeltable stores **references** to external files. Your original media stays in S3/GCS/Azure; only computed results (embeddings, metadata, generated media) are stored locally or in configured cloud buckets.

```mermaid  theme={null}
flowchart LR
    S3[S3 / GCS / Azure] -. reference .-> PXT[Pixeltable]
    PXT --> Meta[Computed Results]
    PXT -. lazy load .-> S3
```

This means:

* **No data duplication** — you don't pay for storage twice.
* **Schema changes don't require re-upload** — add a column, not a migration script.
* **Works with existing storage** — point Pixeltable at your current buckets.

**Deployment-Specific Storage Patterns:**

*Approach 1 (Orchestration Layer):*

* Pixeltable storage can be ephemeral (re-computable).
* Processing results exported to external RDBMS and blob storage.
* Reference input media from S3/GCS/Azure URIs.

*Approach 2 (Full Backend):*

* Pixeltable IS the RDBMS (embedded PostgreSQL, not replaceable).
* Requires persistent volume at `~/.pixeltable` (pgdata, media, file\_cache).
* Media Store configurable to S3/GCS/Azure buckets for generated files.

All [Starter Kit](https://github.com/pixeltable/pixeltable-starter-kit) deployment configs set `PIXELTABLE_HOME=/data/pixeltable` pointing to persistent storage (Docker volumes, K8s PVCs, or EFS). For large media workloads, configure external blob storage:

```bash  theme={null}
PIXELTABLE_INPUT_MEDIA_DEST=s3://your-bucket/input    # or gs:// or az://
PIXELTABLE_OUTPUT_MEDIA_DEST=s3://your-bucket/output
```

## Dependency Management

**Virtual Environments:**
Use `venv`, `conda`, or `uv` to isolate dependencies.

**Requirements:**

```txt  theme={null}
# requirements.txt
pixeltable==0.4.6
fastapi==0.115.0
uvicorn[standard]==0.32.0
pydantic==2.9.0
python-dotenv==1.0.1
sentence-transformers==3.3.0  # If using embedding indexes
```

* Pin versions: `package==X.Y.Z`
* Include integration packages (e.g., `openai`, `sentence-transformers`)
* Test updates in staging before production

## Data Interoperability

Pixeltable integrates with existing data pipelines via import/export capabilities. See the [Import/Export SDK reference](/sdk/latest/io) for full details.

**Import:**

* CSV, Excel, JSON: [`pxt.io.import_csv()`](/sdk/latest/io#func-import_csv), [`pxt.io.import_excel()`](/sdk/latest/io#func-import_excel), [`pxt.io.import_json()`](/sdk/latest/io#func-import_json)
* Parquet: [`pxt.io.import_parquet()`](/sdk/latest/io#func-import_parquet)
* Pandas DataFrames: [`table.insert(df)`](/sdk/latest/table#method-insert) or [`pxt.create_table(source=df)`](/sdk/latest/pixeltable#func-create_table)
* Hugging Face Datasets: [`pxt.io.import_huggingface_dataset()`](/sdk/latest/io#func-import_huggingface_dataset)

**Export:**

* Parquet: [`pxt.io.export_parquet(table, path)`](/sdk/latest/io#func-export_parquet) for data warehousing
* LanceDB: [`pxt.io.export_lancedb(table, db_uri, table_name)`](/sdk/latest/io#func-export_lancedb) for vector databases
* PyTorch: [`table.to_pytorch_dataset()`](/sdk/latest/query#method-to_pytorch_dataset) for ML training pipelines
* COCO: [`table.to_coco_dataset()`](/sdk/latest/query#method-to_coco_dataset) for computer vision
* Pandas: [`table.collect().to_pandas()`](/sdk/latest/query#method-collect) for analysis

```python  theme={null}
# Export query results to Parquet
import pixeltable as pxt

docs_table = pxt.get_table('myapp/documents')
results = docs_table.where(docs_table.timestamp > '2024-01-01')
pxt.io.export_parquet(results, '/data/exports/recent_docs.parquet')
```


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