mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-02 17:38:31 +02:00
Cloud inference example (#1768)
* Cloud inference example * Cloud inference example * update URL * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Update qdrant-landing/content/documentation/examples/cloud-inference-hybrid-search.md Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> * Move cloud inference tutorial * move below support * rename folder * update cloud example * update cloud example * update cloud example * update cloud example * update cloud example * update cloud example * update cloud example * update cloud example * Link inference docs * Link inference docs * Create collection snippet * Create snippets * Create snippets * Create snippets * Create snippets * Create snippets * Create snippets * Create snippets * Create snippets * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/python.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/tutorials-and-examples/cloud-inference-hybrid-search.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/tutorials-and-examples/_index.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/upload-data/python.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/tutorials-and-examples/cloud-inference-hybrid-search.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/python.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/tutorials-and-examples/cloud-inference-hybrid-search.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/initialize-client/_description.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/_description.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/python.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/headless/snippets/cloud-inference/vector-search/create-collection/python.md Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com> * Update qdrant-landing/content/documentation/tutorials-and-examples/cloud-inference-hybrid-search.md --------- Co-authored-by: Bastian Hofmann <mail@bastianhofmann.de> Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>
This commit is contained in:
Derrick Mwiti
co-authored by
Bastian Hofmann
Kacper Łukawski
Bastian Hofmann
Kacper Łukawski
parent
b40514678f
commit
5eff7dfc24
@@ -0,0 +1,12 @@
|
||||
---
|
||||
title: Tutorials & Examples
|
||||
weight: 40
|
||||
partition: cloud
|
||||
---
|
||||
## Cloud Tutorials & Examples
|
||||
|
||||
| Example | Description |
|
||||
| ----------------------------------- | ------------------------------------------------------------------------------------------- |
|
||||
| [Using Cloud Inference to Build Hybrid Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Cloud inference hybrid example |
|
||||
|
||||
|
||||
+52
@@ -0,0 +1,52 @@
|
||||
---
|
||||
title: Using Cloud Inference to Build Hybrid Search
|
||||
weight: 35
|
||||
---
|
||||
# Using Cloud Inference with Qdrant for Vector Search
|
||||
In this tutorial, we'll walkthrough building a **hybrid semantic search engine** using Qdrant Cloud's built-in [inference](/documentation/cloud/inference/) capabilities. You'll learn how to:
|
||||
- Automatically embed your data using [cloud Inference](/documentation/cloud/inference/) without needing to run local models,
|
||||
- Combine dense semantic embeddings with [sparse BM25 keywords](https://qdrant.tech/documentation/advanced-tutorials/reranking-hybrid-search/), and
|
||||
- Perform hybrid search using [Reciprocal Rank Fusion (RRF)](https://qdrant.tech/documentation/concepts/hybrid-queries/) to retrieve the most relevant results.
|
||||
## Install Qdrant Client
|
||||
```bash
|
||||
pip install qdrant-client datasets
|
||||
```
|
||||
## Initialize the Client
|
||||
Initialize the Qdrant client after creating a [Qdrant Cloud account](/documentation/cloud/) and a [dedicated paid cluster](/documentation/cloud/create-cluster/). Set `cloud_inference` to `True` to enable [cloud inference](/documentation/cloud/inference/).
|
||||
|
||||
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/initialize-client/" >}}
|
||||
|
||||
## Create a Collection
|
||||
Qdrant stores vectors and associated metadata in collections. A collection requires vector parameters to be set during creation. In this case, let's set up a collection using `BM25` for sparse vectors and `all-minilm-l6-v2` for dense vectors. BM25 uses the Inverse Document Frequency to reduce the weight of common terms that appear in many documents while boosting the importance of rare terms that are more discriminative for retrieval. Qdrant will handle the calculations of the IDF term if we enable that in the configuration of the `bm25_sparse_vector` named sparse vector.
|
||||
|
||||
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/create-collection/" >}}
|
||||
|
||||
## Add Data
|
||||
Now you can add sample documents, their associated metadata, and a point id for each. Here's a sample of the [miriad/miriad-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M) dataset:
|
||||
|
||||
| qa_id | paper_id | question | year | venue | specialty | passage_text |
|
||||
|--------------------|----------|-------------------------------------------------------|------|--------------------------------------|--------------|--------------------------------------------------------|
|
||||
| 38_77498699_0_1 | 77498699 | What are the clinical features of relapsing polychondritis? | 2006 | Internet Journal of Otorhinolaryngology | Rheumatology | A 45-year-old man presented with painful swelling... |
|
||||
| 38_77498699_0_2 | 77498699 | What treatments are available for relapsing polychondritis? | 2006 | Internet Journal of Otorhinolaryngology | Rheumatology | Patient showed improvement after treatment with... |
|
||||
| 38_88124321_0_3 | 88124321 | How is Takayasu arteritis diagnosed? | 2015 | Journal of Autoimmune Diseases | Rheumatology | A 32-year-old woman with fatigue and limb pain... |
|
||||
|
||||
We won't ingest all the entries from the dataset, but for demo purposes, just take the first hundred ones:
|
||||
|
||||
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/upload-data/" >}}
|
||||
## Set Up Input Query
|
||||
Create a sample query:
|
||||
|
||||
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/" >}}
|
||||
|
||||
## Run Vector Search
|
||||
Here, you will ask a question that will allow you to retrieve semantically relevant results. The final results are obtained by reranking using [Reciprocal Rank Fusion](https://qdrant.tech/documentation/concepts/hybrid-queries/#hybrid-search).
|
||||
|
||||
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/" >}}
|
||||
|
||||
The semantic search engine will retrieve the most similar result in order of relevance.
|
||||
```markdown
|
||||
[ScoredPoint(id='9968a760-fbb5-4d91-8549-ffbaeb3ebdba',
|
||||
version=0, score=14.545895,
|
||||
payload={'text': "Relapsing Polychondritis is a rare..."},
|
||||
vector=None, shard_key=None, order_value=None)]
|
||||
```
|
||||
Reference in New Issue
Block a user