RAG & Embedding

Author: s-sajid-ali

docs/genai/04_how_to_guides/03_retrieval_augmented_generation.mdx

@@ -8,22 +8,20 @@ To get around this issue, one of the most popular techniques is Retrieval-augmen
 
 
 ```mermaid
-flowchart LR;
-    A["Natual language prompt <br> *Can GenAI be used for research?*"]
-    B["Vector embedding <br> [0.052587852, 0.094195396, 0.24439038, 0.104940414, ...]"]
-    C["Vector Database <br> embedding1 <br> embedding2 <br> embedding3 <br> ... "]
-    D["Text with embeddings similar to the prompt"]
-    E["Original prompt with added context"]
-    F["Response from LLM using context"]
+flowchart TB;
+    A["natual language prompt <br> *Can GenAI be used for research?*"]
+    B["vector embedding <br> [-0.013879947, 0.0601184, 0.35442936, 0.04381764, ...]"]
+    C["vector database <br> embedding1 <br> embedding2 <br> embedding3 <br> ... "]
+    D["text with embeddings similar to the prompt"]
+    E["priginal prompt with added context"]
+    F["response from LLM using context"]
     subgraph Retrieval
-    direction TB
     A-- "Embedding" -->B;
     B-- "Look for similar embeddings" -->C;
     C-- "Generate context" -->D;
     end
     D-- "With expanded prompt" -->E;    
     subgraph Augmented Generation
-    direction TB
     E-- "LLM" -->F;
     end
 ```