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91 lines
2.7 KiB
Markdown
91 lines
2.7 KiB
Markdown
---
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title: Pinecone Canopy
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---
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# Pinecone Canopy
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[Canopy](https://github.com/pinecone-io/canopy) is an open-source framework and context engine to build chat assistants at scale.
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Qdrant is supported as a knowledge base within Canopy for context retrieval and augmented generation.
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## Usage
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Install the SDK with the Qdrant extra as described in the [Canopy README](https://github.com/pinecone-io/canopy?tab=readme-ov-file#extras).
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```bash
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pip install canopy-sdk[qdrant]
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```
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### Creating a knowledge base
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```python
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from canopy.knowledge_base import QdrantKnowledgeBase
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kb = QdrantKnowledgeBase(collection_name="<YOUR_COLLECTION_NAME>")
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```
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<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>
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To create a new Qdrant collection and connect it to the knowledge base, use the `create_canopy_collection` method:
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```python
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kb.create_canopy_collection()
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```
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You can always verify the connection to the collection with the `verify_index_connection` method:
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```python
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kb.verify_index_connection()
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```
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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).
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### Adding data to the knowledge base
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To insert data into the knowledge base, you can create a list of documents and use the `upsert` method:
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```python
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from canopy.models.data_models import Document
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documents = [
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Document(
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id="1",
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text="U2 are an Irish rock band from Dublin, formed in 1976.",
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source="https://en.wikipedia.org/wiki/U2",
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),
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Document(
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id="2",
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text="Arctic Monkeys are an English rock band formed in Sheffield in 2002.",
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source="https://en.wikipedia.org/wiki/Arctic_Monkeys",
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metadata={"my-key": "my-value"},
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),
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]
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kb.upsert(documents)
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```
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### Querying the knowledge base
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You can query the knowledge base with the `query` method to find the most similar documents to a given text:
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```python
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from canopy.models.data_models import Query
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kb.query(
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[
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Query(text="Arctic Monkeys music genre"),
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Query(
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text="U2 music genre",
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top_k=10,
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metadata_filter={"key": "my-key", "match": {"value": "my-value"}},
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),
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]
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)
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```
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## Further Reading
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- [Introduction to Canopy](https://www.pinecone.io/blog/canopy-rag-framework/)
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- [Canopy library reference](https://github.com/pinecone-io/canopy/blob/main/docs/library.md)
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- [Source Code](https://github.com/pinecone-io/canopy/tree/main/src/canopy/knowledge_base/qdrant)
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