feat: Supported the injection of RAG sources into the prompt, not just via the .sources rag command in the REPL so models can directly reference the documents that supported their responses

config.example.yaml

@@ -95,7 +95,7 @@ rag_reranker_model: null         # Specifies the reranker model used for sorting
 rag_top_k: 5                     # Specifies the number of documents to retrieve for answering queries
 rag_chunk_size: null             # Defines the size of chunks for document processing in characters
 rag_chunk_overlap: null          # Defines the overlap between chunks
-# Defines the query structure using variables like __CONTEXT__ and __INPUT__ to tailor searches to specific needs
+# Defines the query structure using variables like __CONTEXT__, __SOURCES__, and __INPUT__ to tailor searches to specific needs
 rag_template: |
   Answer the query based on the context while respecting the rules. (user query, some textual context and rules, all inside xml tags)
 
@@ -103,6 +103,10 @@ rag_template: |
   __CONTEXT__
   </context>
 
+  <sources>
+  __SOURCES__
+  </sources>
+
   <rules>
   - If you don't know, just say so.
   - If you are not sure, ask for clarification.
@@ -110,6 +114,7 @@ rag_template: |
   - If the context appears unreadable or of poor quality, tell the user then answer as best as you can.
   - If the answer is not in the context but you think you know the answer, explain that to the user then answer with your own knowledge.
   - Answer directly and without using xml tags.
+  - When using information from the context, cite the relevant source from the <sources> section.
   </rules>
 
   <user_query>