docs: Added Canopy integration (#636)

* docs: Canopy integration

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Co-authored-by: Mike Jang <michael.jang@qdrant.io>

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Co-authored-by: Mike Jang <michael.jang@qdrant.io>
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title: Pinecone Canopy
weight: 2500
---
# Pinecone Canopy
[Canopy](https://github.com/pinecone-io/canopy) is an open-source framework and context engine to build chat assistants at scale.
Qdrant is supported as a knowledge base within Canopy for context retrieval and augmented generation.
## Usage
Install the SDK with the Qdrant extra as described in the [Canopy README](https://github.com/pinecone-io/canopy?tab=readme-ov-file#extras).
```bash
pip install canopy-sdk[qdrant]
```
### Creating a knowledge base
```python
from canopy.knowledge_base import QdrantKnowledgeBase
kb = QdrantKnowledgeBase(collection_name="<YOUR_COLLECTION_NAME>")
```
<aside role="status">The constructor accepts additional <a href="https://github.com/qdrant/qdrant-client/blob/eda201a1dbf1bbc67415f8437a5619f6f83e8ac6/qdrant_client/qdrant_client.py#L36-L61">options</a> to customize your connection to Qdrant.</aside>
To create a new Qdrant collection and connect it to the knowledge base, use the `create_canopy_collection` method:
```python
kb.create_canopy_collection()
```
You can always verify the connection to the collection with the `verify_index_connection` method:
```python
kb.verify_index_connection()
```
Learn more about customizing the knowledge base and its inner components [in the Canopy library](https://github.com/pinecone-io/canopy/blob/main/docs/library.md#understanding-knowledgebase-workings).
### Adding data to the knowledge base
To insert data into the knowledge base, you can create a list of documents and use the `upsert` method:
```python
from canopy.models.data_models import Document
documents = [
Document(
id="1",
text="U2 are an Irish rock band from Dublin, formed in 1976.",
source="https://en.wikipedia.org/wiki/U2",
),
Document(
id="2",
text="Arctic Monkeys are an English rock band formed in Sheffield in 2002.",
source="https://en.wikipedia.org/wiki/Arctic_Monkeys",
metadata={"my-key": "my-value"},
),
]
kb.upsert(documents)
```
### Querying the knowledge base
You can query the knowledge base with the `query` method to find the most similar documents to a given text:
```python
from canopy.models.data_models import Query
kb.query(
[
Query(text="Arctic Monkeys music genre"),
Query(
text="U2 music genre",
top_k=10,
metadata_filter={"key": "my-key", "match": {"value": "my-value"}},
),
]
)
```
## Further Reading
- [Introduction to Canopy](https://www.pinecone.io/blog/canopy-rag-framework/)
- [Canopy library reference](https://github.com/pinecone-io/canopy/blob/main/docs/library.md)