feat: Adding support for metadata in RAG insertion and querying

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

docs/_static/llama-stack-spec.html

@@ -11133,13 +11133,18 @@
                     "max_chunks": {
                         "type": "integer",
                         "default": 5
+                    },
+                    "include_metadata_in_content": {
+                        "type": "boolean",
+                        "default": false
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "query_generator_config",
                     "max_tokens_in_context",
-                    "max_chunks"
+                    "max_chunks",
+                    "include_metadata_in_content"
                 ],
                 "title": "RAGQueryConfig"
             },

docs/_static/llama-stack-spec.yaml

@@ -7690,11 +7690,15 @@ components:
         max_chunks:
           type: integer
           default: 5
+        include_metadata_in_content:
+          type: boolean
+          default: false
       additionalProperties: false
       required:
         - query_generator_config
         - max_tokens_in_context
         - max_chunks
+        - include_metadata_in_content
       title: RAGQueryConfig
     RAGQueryGeneratorConfig:
       oneOf:

docs/source/building_applications/rag.md

@@ -51,6 +51,7 @@ chunks = [
         "mime_type": "text/plain",
         "metadata": {
             "document_id": "doc1",
+            "author": "Jane Doe",
         },
     },
 ]
@@ -98,6 +99,17 @@ results = client.tool_runtime.rag_tool.query(
 )
 ```
 
+You can configure adding metadata to the context if you find it useful for your application. Simply add:
+```python
+# Query documents
+results = client.tool_runtime.rag_tool.query(
+    vector_db_ids=[vector_db_id],
+    content="What do you know about...",
+    query_config={
+        "include_metadata_in_content": True,
+    },
+)
+```
 ### Building RAG-Enhanced Agents
 
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
@@ -115,6 +127,12 @@ agent = Agent(
             "name": "builtin::rag/knowledge_search",
             "args": {
                 "vector_db_ids": [vector_db_id],
+                # Defaults
+                "query_config": {
+                    "chunk_size_in_tokens": 512,
+                    "chunk_overlap_in_tokens": 0,
+                    "include_metadata_in_content": False,
+                },
             },
         }
     ],