new: replace .add and .query with local inference in fastembed semant… (#1575)

* new: replace .add and .query with local inference in fastembed semantic search

* do not hardcode model dim, format example to avoid scrollbars

* avoid scroll bars

* joint installation of qdrant client and fastembed

qdrant-landing/content/documentation/fastembed/fastembed-semantic-search.md

@@ -5,21 +5,15 @@ weight: 3
 
 # Using FastEmbed with Qdrant for Vector Search
 
-## Install Qdrant Client
+## Install Qdrant Client and FastEmbed
 ```python
-pip install qdrant-client
-```
-
-## Install FastEmbed
-Installing FastEmbed will let you quickly turn data to vectors, so that Qdrant can search over them.
-```python
-pip install fastembed
+pip install "qdrant-client[fastembed]>=1.14.2"
 ```
 
 ## Initialize the client
 Qdrant Client has a simple in-memory mode that lets you try semantic search locally. 
 ```python
-from qdrant_client import QdrantClient
+from qdrant_client import QdrantClient, models
 
 client = QdrantClient(":memory:")  # Qdrant is running from RAM.
 ```
@@ -28,21 +22,49 @@ client = QdrantClient(":memory:")  # Qdrant is running from RAM.
 Now you can add two sample documents, their associated metadata, and a point `id` for each. 
 
 ```python
-docs = ["Qdrant has a LangChain integration for chatbots.", "Qdrant has a LlamaIndex integration for agents."]
+docs = [
+    "Qdrant has a LangChain integration for chatbots.",
+    "Qdrant has a LlamaIndex integration for agents.",
+]
 metadata = [
     {"source": "langchain-docs"},
     {"source": "llamaindex-docs"},
 ]
 ids = [42, 2]
+
 ```
-## Load data to a collection
-Create a test collection and upsert your two documents to it. 
+## Create a collection
+
+Qdrant stores vectors and associated metadata in collections.
+Collection requires vector parameters to be set during creation.
+In this tutorial, we'll be using `BAAI/bge-small-en` to compute embeddings.
+
 ```python
-client.add(
+model_name = "BAAI/bge-small-en"
+client.create_collection(
     collection_name="test_collection",
-    documents=docs,
-    metadata=metadata,
-    ids=ids
+    vectors_config=models.VectorParams(
+        size=client.get_embedding_size(model_name), 
+        distance=models.Distance.COSINE
+    ),  # size and distance are model dependent
+)
+
+```
+
+## Upsert documents to the collection
+
+Qdrant client can do inference implicitly within its methods via FastEmbed integration.
+It requires wrapping your data in models, like `models.Document` (or `models.Image` if you're working with images)
+
+```python
+metadata_with_docs = [
+    {"document": doc, "source": meta["source"]} for doc, meta in zip(docs, metadata)
+]
+client.upload_collection(
+    collection_name="test_collection",
+    vectors=[models.Document(text=doc, model=model_name) for doc in docs],
+    payload=metadata_with_docs,
+    ids=ids,
 )
 ```
 ## Run vector search
@@ -50,21 +72,36 @@ client.add(
 Here, you will ask a dummy question that will allow you to retrieve a semantically relevant result. 
 
 ```python
-search_result = client.query(
+search_result = client.query_points(
     collection_name="test_collection",
-    query_text="Which integration is best for agents?"
-)
+    query=models.Document(
+        text="Which integration is best for agents?", 
+        model=model_name
+    )
+).points
 print(search_result)
 ```
 The semantic search engine will retrieve the most similar result in order of relevance. In this case, the second statement about LlamaIndex is more relevant.
 
-```bash
-[QueryResponse(id=2, embedding=None, sparse_embedding=None, 
-metadata={'document': 'Qdrant has a LlamaIndex integration for agents',
-'source': 'llamaindex-docs'}, document='Qdrant has a LlamaIndex integration for agents.', 
-score=0.8749180370667156), 
-QueryResponse(id=42, embedding=None, sparse_embedding=None, 
-metadata={'document': 'Qdrant has a LangChain integration for chatbots.', 
-'source': 'langchain-docs'}, document='Qdrant has a LangChain integration for chatbots.', 
-score=0.8351846822959111)]
+```python
+[
+    ScoredPoint(
+        id=2, 
+        score=0.87491801319731,
+        payload={
+            "document": "Qdrant has a LlamaIndex integration for agents.",
+            "source": "llamaindex-docs",
+        },
+        ...
+    ),
+    ScoredPoint(
+        id=42,
+        score=0.8351846627714035,
+        payload={
+            "document": "Qdrant has a LangChain integration for chatbots.",
+            "source": "langchain-docs",
+        },
+        ...
+    ),
+]
 ```