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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
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This code snippet creates a collection configured for hybrid search using Qdrant's Cloud Inference. It defines a sparse BM25 vector and a dense vector for MiniLM. This setup allows Qdrant to perform hybrid search using the dense and sparse vectors.
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```python
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from qdrant_client import models
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collection_name = "my_collection_name"
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if not client.collection_exists(collection_name=collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config={
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"dense_vector": models.VectorParams(
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size=384,
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distance=models.Distance.COSINE
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)
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},
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sparse_vectors_config={
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"bm25_sparse_vector": models.SparseVectorParams(
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modifier=models.Modifier.IDF # Enable Inverse Document Frequency
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)
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}
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)
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```
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This code snippet creates sample query for hybrid search using Qdrant's cloud inference.
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```python
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query_text = "What is relapsing polychondritis?"
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```
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This code snippet connects to Qdrant Cloud with Cloud Inference enabled. This is done by setting `cloud_inference` to `True` in the initializer of the `QdrantClient` class.
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```python
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client = QdrantClient(
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url="https://YOUR_URL.eastus-0.azure.cloud.qdrant.io:6333/",
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api_key="YOUR_API_KEY",
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cloud_inference=True,
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timeout=30.0
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)
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```
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This code snippet demonstrates how to use the Universal Query API to prefetch results with dense and sparse vector search, and then rerank them with Reciprocal Rank Fusion. It uses Cloud Inference to create embeddings by passing document text along with the model names, instead of vectors.
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```python
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results = client.query_points(
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collection_name=collection_name,
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prefetch=[
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models.Prefetch(
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query=Document(
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text=query_text,
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model=dense_model
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),
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using="dense_vector",
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limit=5
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),
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models.Prefetch(
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query=Document(
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text=query_text,
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model=bm25_model
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),
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using="bm25_sparse_vector",
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limit=5
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)
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],
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query=models.FusionQuery(fusion=models.Fusion.RRF),
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limit=5,
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with_payload=True
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)
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print(results.points)
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```
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This code snipet shows how to load a dataset and upload dense and sparse vectors to Qdrant. While uploading the vectors we also include a payload known as text.
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```python
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from qdrant_client.http.models import PointStruct, Document
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from datasets import load_dataset
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import uuid
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dense_model = "sentence-transformers/all-minilm-l6-v2"
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bm25_model = "qdrant/bm25"
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ds = load_dataset("miriad/miriad-4.4M", split="train[0:100]")
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points = []
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for idx, item in enumerate(ds):
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passage = item["passage_text"]
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point = PointStruct(
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id=uuid.uuid4().hex, # use unique string ID
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payload=item,
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vector={
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"dense_vector": Document(
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text=passage,
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model=dense_model
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),
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"bm25_sparse_vector": Document(
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text=passage,
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model=bm25_model
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)
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}
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)
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points.append(point)
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client.upload_points(
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collection_name=collection_name,
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points=points,
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batch_size=8
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)
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```
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