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Code Issues Packages Projects Releases Wiki Activity

docs: Updated FAQ and Query API usage (#1026)

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Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>
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Anush
2024-07-17 14:31:51 +05:30
committed by GitHub
co-authored by Kacper Łukawski
parent 9befc97d50
commit aec0935542
4 changed files with 9 additions and 12 deletions
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qdrant-landing/content/documentation/examples/databricks.md
+2 -2
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@@ -65,7 +65,7 @@ from fastembed import TextEmbedding, SparseTextEmbedding
def vectorize(partition_data):
# Initialize dense and sparse models
dense_model = TextEmbedding(model_name="BAAI/bge-small-en-v1.5")
sparse_model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
sparse_model = SparseTextEmbedding(model_name="Qdrant/bm25")
for row in partition_data:
# Generate dense and sparse vectors
@@ -81,7 +81,7 @@ def vectorize(partition_data):
]
```
We're using the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model for dense embeddings and [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1) for sparse embeddings.
We're using the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model for dense embeddings and [BM25](https://huggingface.co/Qdrant/bm25) for sparse embeddings.
#### Applying the UDF on our dataframe
qdrant-landing/content/documentation/examples/qdrant-airflow-astronomer.md
+4 -4
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@@ -42,7 +42,7 @@ This command generates all of the project files you need to run Airflow locally.
To use Qdrant within Airflow, install the Qdrant Airflow provider by adding the following to the `requirements.txt` file
```text
apache-airflow-providers-qdrant==1.1.0
apache-airflow-providers-qdrant
```
### Configure credentials
@@ -178,12 +178,12 @@ def recommend_book():
) -> None:
hook = QdrantHook(conn_id=QDRANT_CONNECTION_ID)
result = hook.conn.search(
result = hook.conn.query_points(
collection_name=COLLECTION_NAME,
query_vector=preference_embedding,
query=preference_embedding,
limit=1,
with_payload=True,
)
).points
print("Book recommendation: " + result[0].payload["title"])
print("Description: " + result[0].payload["description"])