---
title: POMA
---

# POMA + Qdrant: Structure-Preserving Retrieval

| Time: 15 min | Level: Beginner/Intermediate | [Complete Notebook](https://colab.research.google.com/github/poma-ai/.github/blob/main/notebooks/qdrant/poma_meets_qdrant.ipynb) | [Notebook Source](https://github.com/poma-ai/.github/blob/main/notebooks/qdrant/poma_meets_qdrant.ipynb) |
| ------------ | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |

## Overview

- **POMA**, as *document chunking engine*, is built around simplicity for operators: process files into structure-aware chunksets and send them to Qdrant with minimal boilerplate and a patented chunking approach.
- **Qdrant** as your preferred  *vector search engine*.

Together, they combine individual simplicity into one streamlined workflow.

This guide walks through the current [POMA AI](https://www.poma-ai.com/) for Qdrant SDK flow: process documents, upsert chunksets, retrieve structure-preserving cheatsheets, and understand where convenience defaults end and advanced knobs begin.

---

## Prerequisites

- Python 3.10+
- A POMA API key
- A Qdrant cluster URL + API key (for cloud)

---

## 1. Get API Keys

### POMA API key

1. Open https://app.poma-ai.com/
2. Register or sign in.
3. Open **API Keys** in the left navigation.
4. Copy your key and export it as `POMA_API_KEY`.

### Qdrant cluster API key

During Qdrant cluster creation, or when creating fine-grained API keys, see further details [here](https://qdrant.tech/documentation/cloud/authentication/).

### Credentials

Set your environment variables:

```bash
POMA_API_KEY="your_poma_api_key"
QDRANT_URL="https://<cluster>.<region>.qdrant.io"
QDRANT_API_KEY="your_qdrant_api_key"
```

---

## 2. Install Dependencies

```bash
pip install "poma[qdrant]"
```

---

## 3. Imports

```python
import os

from poma import Poma
from qdrant_client.http import models as qmodels
from poma.integrations.qdrant.qdrant_poma import PomaQdrant
```

Use your own local document path in the next step (for example `"./docs/your_file.pdf"`).
The linked Colab notebook includes downloadable sample files for quick testing.

---

## 4. Chunk a File with POMA

```python
client = Poma(os.environ["POMA_API_KEY"])

job = client.start_chunk_file("./docs/your_file.pdf")
chunk_data = client.get_chunk_result(
    job["job_id"],
    show_progress=True,
    download_dir="./",
    filename="your_file.poma",
)
```

### POMA-specific knobs in `get_chunk_result(...)`

- `show_progress`: prints job status updates while processing.
- `download_dir` + `filename`: save the returned archive as a `.poma` file while still returning parsed `chunk_data`.
- If both `download_dir` and `filename` are omitted, result is returned in-memory only (no `.poma` archive written).

`chunk_data` contains the structured output (`chunks` and `chunksets`) used by `upsert_poma_points(...)`.

If you already have a `.poma` archive, pass the path directly later:

```python
chunk_data = "your_file.poma"
```

---

## 5. Upsert Chunksets into Qdrant

```python
QDRANT_COLLECTION_NAME = "cloud_hybrid"
DENSE_MODEL = "sentence-transformers/all-minilm-l6-v2"
SPARSE_MODEL = "Qdrant/bm25"
DENSE_OPTIONS = {"dimensions": 384}

poma_qdrant = PomaQdrant(
    url=os.environ["QDRANT_URL"],
    api_key=os.environ["QDRANT_API_KEY"],
    cloud_inference=True,
    timeout=120,
    collection_name=QDRANT_COLLECTION_NAME,
    dense_model=DENSE_MODEL,
    sparse_model=SPARSE_MODEL,
    dense_size=384,
    dense_options=DENSE_OPTIONS,
    auto_create_collection=True,
)

poma_qdrant.upsert_poma_points(chunk_data)
```

`dense_size` is required when `auto_create_collection=True`.

---

## 6. Retrieve Structure-Preserving Cheatsheets

```python
cheatsheets = poma_qdrant.get_cheatsheets(
    query="Whats the positional embeddings frequency?",
    limit=10,
)

for i, cs in enumerate(cheatsheets, 1):
    print(f"\n=== Cheatsheet {i} ===")
    print(f"file_id: {cs['file_id']}")
    print("content:")
    print(cs["content"])
```

---

## 7. Advanced Query Control (Optional)

Use Qdrant prefetch + RRF fusion explicitly and still return POMA cheatsheets.

```python
query_text = "Whats the positional embeddings frequency?"

query_obj = qmodels.RrfQuery(rrf=qmodels.Rrf(k=60))
prefetch = [
    qmodels.Prefetch(
        query=qmodels.Document(
            text=query_text,
            model=DENSE_MODEL,
            options=DENSE_OPTIONS,
        ),
        using="dense",
        limit=100,
    ),
    qmodels.Prefetch(
        query=qmodels.Document(
            text=query_text,
            model=SPARSE_MODEL,
        ),
        using="sparse",
        limit=100,
    ),
]

cheatsheets = poma_qdrant.get_cheatsheets(
    query_obj=query_obj,
    prefetch=prefetch,
    collection_name=QDRANT_COLLECTION_NAME,
    limit=10,
    chunk_data=chunk_data,
)
```

---

## Further details:

- [POMA docs hub](https://www.poma-ai.com/docs/)
- [POMA on GitHub](https://github.com/poma-ai)