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@@ -35,84 +35,10 @@ The Qdrant node is available for both cloud and self-hosted n8n instances, start
One exciting feature of the new node is seamless hybrid search: combining the precision of keyword-based (sparse) search with the semantic power of dense embeddings. This is especially valuable in domains like legal or medical search, where both exact matches and contextual understanding are crucial.
## Video example: How to install & use the node for hybrid search
<iframe width="560" height="315" src="https://www.youtube.com/embed/sYP_kHWptHY?si=t4GTxVCfTNiXEE4S" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
Here’s a quick walkthrough:
1. Create a Hybrid Collection
* Use Qdrant Cloud’s free tier for experimentation.
* Create a collection that supports both dense (semantic) and sparse (lexical) vectors. For example:
```json
{
"semantic": {
"size": 4,
"distance": "Cosine"
},
"lexical": {}
}
```
Dense vectors are for semantic search; sparse vectors handle keyword searches. The sparse vector configuration is simple—just specify the name, as the size is dynamic.
2. Insert Data Points
* Insert points with both dense and sparse vectors. Here’s a sample payload:
```json
[
{
"id": "209ed309-bb5e-47fd-8af6-a54eea28e0e7",
"payload": {},
"vector": {
"semantic": [0.3, 0.1, 0.4, 0.2],
"lexical": {"indices":[1, 2], "values": [0.2, -0.5]}
}
},
{
"id": "f7e8316e-91da-4b97-9ae9-7503e6cdbd7b",
"payload": {},
"vector": {
"semantic": [0.4, 0.0, -0.4, 0.2],
"lexical": {"indices":[54], "values": \[-0.9]}
}
}
]
```
Batch upserts are now supported natively, making large-scale data ingestion fast and simple.
3. Run a Hybrid Search
* Use the query_points operation to perform hybrid searches. For example:
```json
[
{
"query": [0.0, 0.6, 0.7, 0.9],
"using": "semantic",
"limit": 2
},
{
"query": {
"indices": [55, 2],
"values": [0.6, 0.7]
},
"using": "lexical",
"limit": 2
}
]
```
* Merge results using reciprocal rank fusion (RRF):
```json
{"fusion": "rrf"}
```
This approach retrieves top results from both semantic and lexical searches and fuses them, delivering more relevant outcomes, especially for complex, domain-specific queries.
## Explore More and Get Involved
The Qdrant node for n8n is just the beginning. You can now build advanced RAG chatbots, agents for unstructured big data analysis, and much more, all natively within n8n. If you want to see more tutorials or have specific use cases in mind, let us know in the comments or join our community discussions.