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

Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>

qdrant-landing/content/documentation/examples/databricks.md

@@ -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

@@ -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"])

qdrant-landing/content/documentation/faq/qdrant-fundamentals.md

@@ -36,11 +36,8 @@ What Qdrant can do:
 - Apply full-text filters to the vector search (i.e., perform vector search among the records with specific words or phrases)
 - Do prefix search and semantic [search-as-you-type](../../../articles/search-as-you-type/)
 - Sparse vectors, as used in [SPLADE](https://github.com/naver/splade) or similar models
-
-What Qdrant plans to introduce in the future:
-
-- ColBERT and other late-interaction models
-- Fusion of the multiple searches
+- [Multi-vectors](../../concepts/vectors/#multivectors), for example ColBERT and other late-interaction models
+- Combination of the [multiple searches](../../concepts/hybrid-queries/)
 
 What Qdrant doesn't plan to support:
 

qdrant-landing/content/documentation/frameworks/nifi.md

@@ -30,4 +30,4 @@ The `Query Qdrant` processor can perform a similarity search across a Qdrant col
 ## Further Reading
 
 - [NiFi Documentation](https://nifi.apache.org/documentation/v2/).
-- [Source Code](https://github.com/apache/nifi/tree/main/nifi-python-extensions/nifi-text-embeddings-module/src/main/python)
+- [Source Code](https://github.com/apache/nifi-python-extensions)