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new: add python snippets

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George Panchuk
2025-11-13 20:53:25 +07:00
parent 441394cc6e
commit ba429b523c
9 changed files with 218 additions and 0 deletions
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qdrant-landing/content/documentation/headless/snippets/inference/cohere-query/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="<your_collection_name>",
query=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/cohere-upsert/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.upsert(
collection_name="<your_collection_name>",
points=[
models.PointStruct(
id=1,
vector=models.Document(
text="a green square",
model="cohere/embed-v4.0",
options={
"cohere-api-key": "<your_cohere_api_key>",
"output_dimension": 512
}
)
)
]
)
```
qdrant-landing/content/documentation/headless/snippets/inference/ingest/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.upsert(
collection_name="<your-collection_name",
points=[
models.PointStruct(
id=1,
vector={
"my-bm25-vector": models.Document(
text="Recipe for baking chocolate chip cookies",
model="Qdrant/bm25",
)
},
)
],
)
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-query/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="<your_collection_name>",
query=models.Document(
text="Mission to Mars",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.upsert(
collection_name="<your_collection_name>",
points=[
models.PointStruct(
id=1,
vector=models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512
}
)
)
]
)
```
qdrant-landing/content/documentation/headless/snippets/inference/multiple/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.upsert(
collection_name="<your_collection_name>",
points=[
models.PointStruct(
id=1,
vector={
"image": models.Image(
image="https://qdrant.tech/example.png",
model="jinaai/jina-clip-v2",
options={
"jina-api-key": "<your_jinaai_api_key>",
"dimensions": 512},
),
"text": models.Document(
text="Mars, the red planet",
model="sentence-transformers/all-minilm-l6-v2",
),
"bm25": models.Document(
text="Mars, the red planet",
model="Qdrant/bm25",
),
},
)
],
)
```
qdrant-landing/content/documentation/headless/snippets/inference/openai-query/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="<your_collection_name>",
query=models.Document(
text="How to bake cookies?",
model="openai/text-embedding-3-large",
options={
"openai-api-key": "<your_openai_api_key>",
"dimensions": 512
}
)
)
```
qdrant-landing/content/documentation/headless/snippets/inference/openai-upsert/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.upsert(
collection_name="<your_collection_name>",
points=[
models.PointStruct(
id=1,
vector=models.Document(
text="Recipe for baking chocolate chip cookies",
model="openai/text-embedding-3-large",
options={
"openai-api-key": "<your_openai_api_key>",
"dimensions": 512
}
)
)
]
)
```
qdrant-landing/content/documentation/headless/snippets/inference/query/python.md
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```python
from qdrant_client import QdrantClient, models
client = QdrantClient(
url="https://xyz-example.qdrant.io:6333",
api_key="<your-api-key>",
cloud_inference=True
)
client.query_points(
collection_name="<your_collection_name>",
query=models.Document(
text="How to bake cookies?",
model="Qdrant/bm25",
),
using="my-bm25-vector",
)
```