docs: add DeepSeek RAG integration illustrations

Author: g-despot

docs/weaviate/model-providers/_includes/integration_deepseek_rag.png

@@ -18,6 +18,8 @@ Weaviate's integration with DeepSeek's API allows you to access their generative
 
 More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the DeepSeek generative model to generate outputs.
 
+![RAG integration illustration](../_includes/integration_deepseek_rag.png)
+
 :::info Code examples are Python-only for now
 Examples for the other client languages will follow.
 :::
@@ -132,6 +134,8 @@ After configuring the generative AI integration, perform RAG operations, either
 
 ### Single prompt
 
+![Single prompt RAG integration generates individual outputs per search result](../_includes/integration_deepseek_rag_single.png)
+
 To generate text for each object in the search results, use the single prompt method.
 
 The example below generates outputs for each of the `n` search results, where `n` is specified by the `limit` parameter.
@@ -147,6 +151,8 @@ When creating a single prompt query, use braces `{}` to interpolate the object p
 
 ### Grouped task
 
+![Grouped task RAG integration generates one output for the set of search results](../_includes/integration_deepseek_rag_grouped.png)
+
 To generate one text for the entire set of search results, use the grouped task method.
 
 In other words, when you have `n` search results, the generative model generates one output for the entire group.

docs/weaviate/model-providers/_includes/integration_deepseek_rag_grouped.png

@@ -16,6 +16,8 @@ This integration empowers developers to build sophisticated AI-driven applicatio
 
 ### Generative AI models for RAG
 
+![Single prompt RAG integration generates individual outputs per search result](../_includes/integration_deepseek_rag_single.png)
+
 DeepSeek's generative AI models can generate human-like text based on given prompts and contexts.
 
 [Weaviate's generative AI integration](./generative.md) enables users to perform retrieval augmented generation (RAG) directly from the Weaviate Database. This combines Weaviate's efficient storage and fast retrieval capabilities with DeepSeek's generative AI models to generate personalized and context-aware responses.