docs: Qdrant reference library.md

README.md

@@ -33,7 +33,7 @@ Canopy has two flows: knowledge base creation and chat. In the knowledge base cr
 1. **Canopy Core Library** - The library has 3 main classes that are responsible for different parts of the RAG workflow:
     * **ChatEngine** - Exposes a chat interface to interact with your data. Given the history of chat messages, the `ChatEngine` formulates relevant queries to the `ContextEngine`, then uses the LLM to generate a knowledgeable response.
     * **ContextEngine**  - Performs the “retrieval” part of RAG. The `ContextEngine` utilizes the underlying `KnowledgeBase` to retrieve the most relevant documents, then formulates a coherent textual context to be used as a prompt for the LLM. 
-    * **KnowledgeBase** - Manages your data for the RAG workflow. It automatically chunks and transforms your text data into text embeddings, storing them in a Pinecone vector database. Given a text query - the `KnowledgeBase` will retrieve the most relevant document chunks from the database. 
+    * **KnowledgeBase** - Manages your data for the RAG workflow. It automatically chunks and transforms your text data into text embeddings, storing them in a Pinecone(Default)/Qdrant vector database. Given a text query - the knowledge base will retrieve the most relevant document chunks from the database. 
 
 
 > More information about the Core Library usage can be found in the [Library Documentation](docs/library.md)
@@ -67,11 +67,12 @@ pip install canopy-sdk
 ### Extras
 
 | Name           | Description                                                                                                                                              |
-|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `grpc`         | To unlock some performance improvements by working with the GRPC version of the [Pinecone Client](https://github.com/pinecone-io/pinecone-python-client) |
 | `torch`        | To enable embeddings provided by [sentence-transformers](https://www.sbert.net/)                                                                         |
 | `transformers` | If you are using Anyscale LLMs, it's recommended to use `LLamaTokenizer` tokenizer which requires transformers as dependency                             |
 | `cohere`       | To use Cohere reranker or/and Cohere LLM                                                                                                                 |
+| `qdrant`       | To use [Qdrant](http://qdrant.tech/) as an alternate knowledge base                                                                                      |
 
 </details>
 

docs/library.md

@@ -154,7 +154,39 @@ You can always verify the connection to the Pinecone index with the `verify_inde
 kb.verify_index_connection()
 ```
 
-To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledgebase workings section](#understanding-knowledgebase-workings).
+#### Using Qdrant as a knowledge base
+
+Canopy supports [Qdrant](https://qdrant.tech) as an alternative knowledge base. To use Qdrant with Canopy, install the `qdrant` extra.
+
+```bash
+pip install canopy-sdk[qdrant]
+```
+
+The Qdrant knowledge base is accessible via the `QdrantKnowledgeBase` class.
+
+```python
+from canopy.knowledge_base import QdrantKnowledgeBase
+
+kb = QdrantKnowledgeBase(collection_name="<YOUR_COLLECTION_NAME>")
+```
+
+The constructor accepts additional [options](https://github.com/qdrant/qdrant-client/blob/eda201a1dbf1bbc67415f8437a5619f6f83e8ac6/qdrant_client/qdrant_client.py#L36-L61) to customize your connection to Qdrant.
+
+To create a new Qdrant collection and connect it to the knowledge base, use the `create_canopy_collection` method:
+
+```python
+kb.create_canopy_collection()
+```
+
+The method accepts additional [options](https://github.com/qdrant/qdrant-client/blob/c63c62e6df9763591622d1921b3dfcc486666481/qdrant_client/qdrant_remote.py#L2137-L2150) to configure the collection to be created.
+
+You can always verify the connection to the collection with the `verify_index_connection` method:
+
+```python
+kb.verify_index_connection()
+```
+
+To learn more about customizing the KnowledgeBase and its inner components, see [understanding knowledge ebase workings section](#understanding-knowledgebase-workings).
 
 ### Step 3: Upsert and query data