Add Chonkie to data management tools documentation (#1957)

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Bhavnick @ chonkie.ai
2025-10-28 19:34:32 +05:30
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| ------------------------------- | -------------------------------------------------------------------------------------------------- |
| [Airbyte](/documentation/data-management/airbyte/) | Data integration platform specialising in ELT pipelines. |
| [Airflow](/documentation/data-management/airflow/) | Platform designed for developing, scheduling, and monitoring batch-oriented workflows. |
| [Chonkie](/documentation/data-management/chonkie/) | No-nonsense, ultra-light and lightning fast RAG pipelines library. |
| [CocoIndex](/documentation/data-management/cocoindex/) | High performance ETL framework to transform data for AI, with real-time incremental processing |
| [Cognee](/documentation/data-management/cognee/) | AI memory frameworks that allows loading from 30+ data sources to graph and vector stores |
| [Connect](/documentation/data-management/redpanda/) | Declarative data-agnostic streaming service for efficient, stateless processing. |
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---
title: Chonkie
---
# Chonkie
[Chonkie](https://github.com/chonkie-inc/chonkie) is a no-nonsense, ultra-light, and lightning-fast chunking library designed for RAG (Retrieval-Augmented Generation) applications.
Chonkie integrates seamlessly with Qdrant through the **QdrantHandshake** class, allowing you to chunk, embed, and store text data without ever leaving the Chonkie SDK.
## Setup
Install Chonkie with Qdrant support:
```bash
pip install "chonkie[qdrant]"
```
## Basic Usage
The `QdrantHandshake` provides a simple interface for storing and searching chunks:
```python
from chonkie import QdrantHandshake, SemanticChunker
# Initialize handshake with custom embedding model
handshake = QdrantHandshake(
url="http://localhost:6333",
collection_name="my_documents",
embedding_model="sentence-transformers/all-MiniLM-L6-v2"
)
# Create and write chunks
chunker = SemanticChunker()
chunks = chunker.chunk("Your text content here...")
handshake.write(chunks)
# Search using natural language
results = handshake.search(query="your search query", limit=5)
for result in results:
print(f"{result['score']}: {result['text']}")
```
### Qdrant Cloud
```python
handshake = QdrantHandshake(
url="https://your-cluster.qdrant.io",
api_key="your-api-key",
collection_name="my_collection",
embedding_model="BAAI/bge-small-en-v1.5" # Change to your preferred model
)
```
## Complete RAG Pipeline
Build end-to-end RAG pipelines using Chonkie's fluent Pipeline API:
```python
from chonkie import Pipeline
# Process documents and store in Qdrant with custom embedding model
docs = (Pipeline()
.fetch_from("file", dir="./knowledge_base", ext=[".txt", ".md"])
.process_with("text")
.chunk_with("semantic", chunk_size=512)
.store_in("qdrant",
collection_name="knowledge",
url="http://localhost:6333",
embedding_model="sentence-transformers/all-MiniLM-L6-v2")
.run())
print(f"Ingested {len(docs)} documents into Qdrant")
```
### Pipeline with Refinements
```python
from chonkie import Pipeline
# Advanced pipeline with overlapping context and custom embeddings
docs = (Pipeline()
.fetch_from("file", dir="./docs")
.process_with("text")
.chunk_with("semantic", threshold=0.8)
.refine_with("overlap", context_size=100)
.store_in("qdrant",
url="https://your-cluster.qdrant.io",
api_key="your-api-key",
collection_name="knowledge_base",
embedding_model="BAAI/bge-small-en-v1.5")
.run())
```
## Next steps
- Chonkie [GitHub Repository](https://github.com/chonkie-inc/chonkie)
- Chonkie [Documentation](https://chonkie.ai)
- QdrantHandshake [API Reference](https://chonkie.ai/oss/handshakes/qdrant-handshake)
- Chonkie [Chunking Strategies](https://chonkie.ai/oss/chunkers/overview)
- Qdrant Python Client [Documentation](https://python-client.qdrant.tech/)