docs: Use local inference modern-sparse-neural-retrieval.md (#1600)

* docs: Update to use local inference

Signed-off-by: Anush008 <anushshetty90@gmail.com>

* chore: Review updates

Signed-off-by: Anush008 <anushshetty90@gmail.com>

* chore: Formatting

Signed-off-by: Anush008 <anushshetty90@gmail.com>

---------

Signed-off-by: Anush008 <anushshetty90@gmail.com>

qdrant-landing/content/articles/modern-sparse-neural-retrieval.md

@@ -438,6 +438,7 @@ metadata = [{"movie_name": "The Passion of Joan of Arc", "movie_watch_time_min":
 </details>
 
 Upload embedded descriptions with movie metadata into the collection.
+
 ```python
 qdrant_client.upsert(
     collection_name="movies",
@@ -456,6 +457,34 @@ qdrant_client.upsert(
     ],
 )
 ```
+
+<aside role="status">
+You can also implicitly generate sparse vectors using built-in FastEmbed integration.
+</aside>
+
+<details>
+    <summary>Implicitly generate sparse vectors (Click to expand)</summary>
+
+```python
+qdrant_client.upsert(
+    collection_name="movies",
+    points=[
+        models.PointStruct(
+            id=idx,
+            payload=metadata[idx],
+            vector={
+                "film_description": models.Document(
+                    text=description, model=sparse_model_name
+                )
+            },
+        )
+        for idx, description in enumerate(descriptions)
+    ],
+)
+```
+
+</details>
+
 #### Querying
 Let’s query our collection!
 
@@ -472,6 +501,24 @@ response = qdrant_client.query_points(
 )
 print(response)
 ```
+
+<details>
+    <summary>Implicitly generate sparse vectors (Click to expand)</summary>
+
+```python
+response = qdrant_client.query_points(
+    collection_name="movies",
+    query=models.Document(text="A movie about music", model=sparse_model_name),
+    using="film_description",
+    limit=1,
+    with_vectors=True,
+    with_payload=True,
+)
+print(response)
+```
+
+</details>
+
 Output looks like this:
 ```bash
 points=[ScoredPoint(
@@ -586,6 +633,24 @@ response = qdrant_client.query_points(
 print(get_tokens_and_weights(response.points[0].vector['film_description'], tokenizer))
 ```
 
+<details>
+    <summary>Implicitly generate sparse vectors (Click to expand)</summary>
+
+```python
+response = qdrant_client.query_points(
+    collection_name="movies",
+    query=models.Document(text="A movie about music", model=sparse_model_name),
+    using="film_description",
+    limit=1,
+    with_vectors=True,
+    with_payload=True,
+)
+
+print(get_tokens_and_weights(response.points[0].vector["film_description"], tokenizer))
+```
+
+</details>
+
 And that's how SPLADE++ expanded the answer.
 
 ```python

qdrant-landing/content/blog/dspy-vs-langchain.md

@@ -219,11 +219,11 @@ You are not restricted to using one LLM in your program; you can use [multiple](
 You can easily set up [Qdrant](/documentation/frameworks/dspy/) vector store to act as the retrieval model. To do so, follow these steps:
 
 ```python
-# pip install dspy-ai[qdrant]
+# pip install dspy-ai dspy-qdrant
 
 import dspy
 
-from dspy.retrieve.qdrant_rm import QdrantRM
+from dspy_qdrant import QdrantRM
 
 from qdrant_client import QdrantClient
 

qdrant-landing/content/documentation/agentic-rag-langgraph.md

@@ -156,7 +156,7 @@ LangGraph works with a state-based system. We define our state like this:
 
 ```python
 class State(TypedDict):
-messages: Annotated[list, add_messages]
+    messages: Annotated[list, add_messages]
 ```
 ---
 

qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md

@@ -42,7 +42,7 @@ We are going to need a couple of Python packages to run our application. They mi
 `dspy-ai` package and `qdrant` extra:
 
 ```shell
-pip install dspy-ai[qdrant]
+pip install dspy-ai dspy-qdrant
 ```
 
 ### Qdrant Hybrid Cloud
@@ -181,7 +181,7 @@ gemma_model = dspy.OllamaLocal(
 Similarly, we have to define connection to our Qdrant Hybrid Cloud cluster:
 
 ```python
-from dspy.retrieve.qdrant_rm import QdrantRM
+from dspy_qdrant import QdrantRM
 from qdrant_client import QdrantClient, models
 
 client = QdrantClient(

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

@@ -17,7 +17,7 @@ Qdrant can be used as a retrieval mechanism in the DSPy flow.
 
 For the Qdrant retrieval integration, include `dspy-ai` with the `qdrant` extra:
 ```bash
-pip install dspy-ai[qdrant]
+pip install dspy-ai dspy-qdrant
 ```
 
 ## Usage
@@ -25,7 +25,7 @@ pip install dspy-ai[qdrant]
 We can configure `DSPy` settings to use the Qdrant retriever model like so:
 ```python
 import dspy
-from dspy.retrieve.qdrant_rm import QdrantRM
+from dspy_qdrant import QdrantRM
 
 from qdrant_client import QdrantClient