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/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