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

# Get started with Codex

> Pick a Codex tier, download your first Parquet dataset, and start querying normalized public records with shared join keys and pre-computed AI labels.

Axiom Codex ships eight normalized datasets as monthly Parquet snapshots. Every record follows the [APRS standard](/codex/normalization-standard), carries pre-computed AI labels, and joins to any other Codex dataset via [shared keys](/codex/join-keys). Pick a tier below, grab a dataset, and run your first query.

## Choose your tier

|                  | Research                                                | Commercial                           | Enterprise                                         |
| ---------------- | ------------------------------------------------------- | ------------------------------------ | -------------------------------------------------- |
| **Access**       | Free on [Hugging Face](https://huggingface.co/axiom-ai) | Signed R2 download URL               | Signed R2 download URL                             |
| **Records**      | 100K-record stratified sample per dataset               | Full dataset                         | Full dataset                                       |
| **Formats**      | Parquet                                                 | Parquet, CSV, JSON Lines, GeoParquet | All formats + Markdown-KV                          |
| **Snapshots**    | Latest only                                             | Monthly immutable snapshots          | Monthly immutable snapshots                        |
| **Entity graph** | —                                                       | —                                    | Full graph export ([details](/codex/entity-graph)) |
| **License**      | CC-BY-4.0 (attribution required)                        | Commercial license, per dataset      | Commercial license, all datasets                   |
| **Price**        | Free                                                    | \$299/dataset/month                  | Contact sales                                      |

<Tip>
  Start with the Research tier to explore schema, field coverage, and join keys before committing to a commercial license.
</Tip>

## Download a dataset

<Steps>
  <Step title="Research tier — Hugging Face">
    Browse the [Axiom AI organization on Hugging Face](https://huggingface.co/axiom-ai) and download any dataset directly. Each dataset includes a README with schema documentation and a 100K-record stratified sample in Parquet format.
  </Step>

  <Step title="Commercial or Enterprise tier">
    Purchase a license at [axiomcodex.io](https://axiomcodex.io). After checkout, your license key and signed download URL are emailed to the address used at checkout — delivery normally lands within a minute. The URL points to a monthly Parquet snapshot on Cloudflare R2.

    <Note>
      If the email doesn't arrive, your license key is also stored on the subscription itself in Stripe. Use the **Manage subscription** link in the original receipt to open the Stripe customer portal and surface the key, or contact [support@axiomancer.io](mailto:support@axiomancer.io) and we'll resend.
    </Note>
  </Step>
</Steps>

## Run your first query

Load any Codex Parquet file into DuckDB, Pandas, Spark, or your preferred tool. Every dataset uses the same [APRS envelope](/codex/normalization-standard), so once you learn one, you know them all.

### DuckDB

```sql theme={null}
SELECT record_id, occurred_at, jurisdiction_slug, event_type
FROM read_parquet('civic-intelligence-2026-04.parquet')
WHERE jurisdiction_slug = 'philadelphia-pa'
  AND occurred_at >= '2026-01-01'
LIMIT 20;
```

### Python (Pandas)

```python theme={null}
import pandas as pd

df = pd.read_parquet("civic-intelligence-2026-04.parquet")
phila = df[df["jurisdiction_slug"] == "philadelphia-pa"]
print(phila[["record_id", "occurred_at", "event_type"]].head(20))
```

## Join two datasets

Every Codex dataset shares a common set of [join keys](/codex/join-keys). Join Civic Intelligence to Urban Signal Grid via `h3_index` without any wrangling:

```sql theme={null}
SELECT
  cells.h3_index,
  cells.composite_score,
  civic.event_type,
  civic.occurred_at
FROM read_parquet('urban-signal-grid-2026-04.parquet') cells
JOIN read_parquet('civic-intelligence-2026-04.parquet') civic
  USING (h3_index)
WHERE civic.event_type = 'zoning_vote'
  AND cells.composite_score > 0.7;
```

## Use the LLM-ready surface

Every dataset ships a `llm_text` column in Markdown-KV format, optimized for RAG pipelines and LLM reasoning. Feed it directly into your retrieval system or prompt:

```markdown theme={null}
- chunk_id: urn:aprs:chunk:9f3a1b2c...
- record_id: urn:aprs:record:civic:us:granicus:phila-2024-03-15-item7b
- jurisdiction: Philadelphia, PA
- occurred_at: 2024-03-15
- event_type: zoning_vote
- summary: Council voted 11-6 to approve rezoning of the 2200 block...
- entities: {name: Kenyatta Johnson, role: councilmember, sentiment: supportive}
- litigation_risk_score: 0.42
```

Pre-computed [chunks](/codex/chunking-policy) are available in a companion `_chunks` Parquet file for each dataset, ready for vector indexing.

## Next steps

<Columns cols={2}>
  <Card title="Data catalog" icon="database" href="/codex/data-catalog">
    Browse all eight datasets with record counts, formats, and tags.
  </Card>

  <Card title="Normalization standard" icon="shield" href="/codex/normalization-standard">
    The APRS contract every record satisfies — field definitions, versioning, and conformance.
  </Card>

  <Card title="Join keys" icon="link" href="/codex/join-keys">
    The registry of shared keys that make cross-dataset joins work.
  </Card>

  <Card title="Bitemporal fields" icon="clock" href="/codex/bitemporal-fields">
    Per-dataset reference for every temporal field, so you always know which clock a timestamp is on.
  </Card>
</Columns>