Fixed metadata extraction from chunks

- now extracting doc_title, doc_url from the "attributes" in response
- removed chunk metadata extraction from results which are not "type": "file_search_call"
- citations could be added in future, needs discussion with other teams
- Updated BYOK config generation

Author: are-ces

docs/byok_guide.md

@@ -167,29 +167,11 @@ apis:
 - tool_runtime
 - safety
 
-models:
-  # Your LLM model
-  - model_id: your-llm-model
-    provider_id: openai  # or your preferred provider
-    model_type: llm
-    provider_model_id: gpt-4o-mini
-
-  # Embedding model for BYOK
-  - model_id: sentence-transformers/all-mpnet-base-v2
-    metadata:
-        embedding_dimension: 768
-    model_type: embedding
-    provider_id: sentence-transformers
-    provider_model_id: /path/to/embedding_models/all-mpnet-base-v2
-
 providers:
   inference:
-  # Embedding model provider
   - provider_id: sentence-transformers
     provider_type: inline::sentence-transformers
     config: {}
-
-  # LLM provider (example: OpenAI)
   - provider_id: openai
     provider_type: remote::openai
     config:
@@ -199,48 +181,68 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents_state
+          backend: kv_default
+        responses:
+          table_name: agents_responses
+          backend: sql_default
 
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
     config:
       excluded_categories: []
 
-  # Vector database configuration
   vector_io:
   - provider_id: your-knowledge-base
-    provider_type: inline::faiss  # or remote::pgvector
+    provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: /path/to/vector_db/faiss_store.db
-        namespace: null
+      persistence:
+        namespace: vector_io::faiss
+        backend: byok_backend  # References storage.backends
 
   tool_runtime:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
 
-# Enable RAG tools
-tool_groups:
-- provider_id: rag-runtime
-  toolgroup_id: builtin::rag
-
-# Vector database configuration
-vector_dbs:
-- embedding_dimension: 768
-  embedding_model: sentence-transformers/all-mpnet-base-v2
-  provider_id: your-knowledge-base
-  vector_db_id: your-index-id  # ID used during index generation
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ~/.llama/storage/kv_store.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ~/.llama/storage/sql_store.db
+    byok_backend:
+      type: kv_sqlite
+      db_path: /path/to/vector_db/faiss_store.db
+
+registered_resources:
+  models:
+  - model_id: your-llm-model
+    provider_id: openai
+    model_type: llm
+    provider_model_id: gpt-4o-mini
+  - model_id: sentence-transformers/all-mpnet-base-v2
+    model_type: embedding
+    provider_id: sentence-transformers
+    provider_model_id: /path/to/embedding_models/all-mpnet-base-v2
+    metadata:
+      embedding_dimension: 768
+  vector_stores:
+  - vector_store_id: your-index-id  # ID used during index generation
+    provider_id: your-knowledge-base
+    embedding_model: sentence-transformers/all-mpnet-base-v2
+    embedding_dimension: 768
+  tool_groups:
+  - toolgroup_id: builtin::rag
+    provider_id: rag-runtime
 ```
 
-**⚠️ Important**: The `vector_db_id` value must exactly match the ID you provided when creating the vector database using the rag-content tool. This identifier links your Llama Stack configuration to the specific vector database index you created.
+**⚠️ Important**: The `vector_store_id` value must exactly match the ID you provided when creating the vector database using the rag-content tool. This identifier links your Llama Stack configuration to the specific vector database index you created.
 
 ### Step 5: Enable RAG Tools
 
@@ -260,14 +262,20 @@ The configuration above automatically enables the RAG tools. The system will:
 - **Storage**: SQLite database file
 
 ```yaml
-vector_io:
-- provider_id: faiss-knowledge
-  provider_type: inline::faiss
-  config:
-    kvstore:
-      type: sqlite
+providers:
+  vector_io:
+  - provider_id: faiss-knowledge
+    provider_type: inline::faiss
+    config:
+      persistence:
+        namespace: vector_io::faiss
+        backend: faiss_backend
+
+storage:
+  backends:
+    faiss_backend:
+      type: kv_sqlite
       db_path: /path/to/faiss_store.db
-      namespace: null
 ```
 
 ### 2. pgvector (PostgreSQL)
@@ -314,19 +322,6 @@ apis:
 - tool_runtime
 - safety
 
-models:
-- model_id: gpt-4o-mini
-  provider_id: openai
-  model_type: llm
-  provider_model_id: gpt-4o-mini
-
-- model_id: sentence-transformers/all-mpnet-base-v2
-  metadata:
-      embedding_dimension: 768
-  model_type: embedding
-  provider_id: sentence-transformers
-  provider_model_id: /home/user/embedding_models/all-mpnet-base-v2
-
 providers:
   inference:
   - provider_id: sentence-transformers
@@ -341,12 +336,13 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents_state
+          backend: kv_default
+        responses:
+          table_name: agents_responses
+          backend: sql_default
 
   safety:
   - provider_id: llama-guard
@@ -358,25 +354,47 @@ providers:
   - provider_id: company-docs
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: /home/user/vector_dbs/company_docs/faiss_store.db
-        namespace: null
+      persistence:
+        namespace: vector_io::faiss
+        backend: company_docs_backend
 
   tool_runtime:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
 
-tool_groups:
-- provider_id: rag-runtime
-  toolgroup_id: builtin::rag
-
-vector_dbs:
-- embedding_dimension: 768
-  embedding_model: sentence-transformers/all-mpnet-base-v2
-  provider_id: company-docs
-  vector_db_id: company-knowledge-index
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ~/.llama/storage/kv_store.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ~/.llama/storage/sql_store.db
+    company_docs_backend:
+      type: kv_sqlite
+      db_path: /home/user/vector_dbs/company_docs/faiss_store.db
+
+registered_resources:
+  models:
+  - model_id: gpt-4o-mini
+    provider_id: openai
+    model_type: llm
+    provider_model_id: gpt-4o-mini
+  - model_id: sentence-transformers/all-mpnet-base-v2
+    model_type: embedding
+    provider_id: sentence-transformers
+    provider_model_id: /home/user/embedding_models/all-mpnet-base-v2
+    metadata:
+      embedding_dimension: 768
+  vector_stores:
+  - vector_store_id: company-knowledge-index
+    provider_id: company-docs
+    embedding_model: sentence-transformers/all-mpnet-base-v2
+    embedding_dimension: 768
+  tool_groups:
+  - toolgroup_id: builtin::rag
+    provider_id: rag-runtime
 ```
 
 ### Example 2: vLLM + pgvector
@@ -421,12 +439,13 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: .llama/distributions/ollama/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents_state
+          backend: kv_default
+        responses:
+          table_name: agents_responses
+          backend: sql_default
 
   safety:
   - provider_id: llama-guard
@@ -458,11 +477,11 @@ tool_groups:
   args: null
   mcp_endpoint: null
 
-vector_dbs:
+vector_stores:
 - embedding_dimension: 768
   embedding_model: sentence-transformers/all-mpnet-base-v2
   provider_id: enterprise-knowledge
-  vector_db_id: enterprise-docs
+  vector_store_id: enterprise-docs
 ```
 
 ---

docs/openapi.json

@@ -3664,7 +3664,7 @@
                     "rlsapi-v1"
                 ],
                 "summary": "Infer Endpoint",
-                "description": "Handle rlsapi v1 /infer requests for stateless inference.\n\nThis endpoint serves requests from the RHEL Lightspeed Command Line Assistant (CLA).\n\nAccepts a question with optional context (stdin, attachments, terminal output,\nsystem info) and returns an LLM-generated response.\n\nArgs:\n    infer_request: The inference request containing question and context.\n    auth: Authentication tuple from the configured auth provider.\n\nReturns:\n    RlsapiV1InferResponse containing the generated response text and request ID.\n\nRaises:\n    HTTPException: 503 if the LLM service is unavailable.",
+                "description": "Handle rlsapi v1 /infer requests for stateless inference.\n\nThis endpoint serves requests from the RHEL Lightspeed Command Line Assistant (CLA).\n\nAccepts a question with optional context (stdin, attachments, terminal output,\nsystem info) and returns an LLM-generated response.\n\nArgs:\n    infer_request: The inference request containing question and context.\n    request: The FastAPI request object for accessing headers and state.\n    background_tasks: FastAPI background tasks for async Splunk event sending.\n    auth: Authentication tuple from the configured auth provider.\n\nReturns:\n    RlsapiV1InferResponse containing the generated response text and request ID.\n\nRaises:\n    HTTPException: 503 if the LLM service is unavailable.",
                 "operationId": "infer_endpoint_v1_infer_post",
                 "requestBody": {
                     "content": {
@@ -4290,7 +4290,7 @@
                 ],
                 "summary": "Handle A2A Jsonrpc",
                 "description": "Handle A2A JSON-RPC requests following the A2A protocol specification.\n\nThis endpoint uses the DefaultRequestHandler from the A2A SDK to handle\nall JSON-RPC requests including message/send, message/stream, etc.\n\nThe A2A SDK application is created per-request to include authentication\ncontext while still leveraging FastAPI's authorization middleware.\n\nAutomatically detects streaming requests (message/stream JSON-RPC method)\nand returns a StreamingResponse to enable real-time chunk delivery.\n\nArgs:\n    request: FastAPI request object\n    auth: Authentication tuple\n    mcp_headers: MCP headers for context propagation\n\nReturns:\n    JSON-RPC response or streaming response",
-                "operationId": "handle_a2a_jsonrpc_a2a_get",
+                "operationId": "handle_a2a_jsonrpc_a2a_post",
                 "responses": {
                     "200": {
                         "description": "Successful Response",
@@ -4308,7 +4308,7 @@
                 ],
                 "summary": "Handle A2A Jsonrpc",
                 "description": "Handle A2A JSON-RPC requests following the A2A protocol specification.\n\nThis endpoint uses the DefaultRequestHandler from the A2A SDK to handle\nall JSON-RPC requests including message/send, message/stream, etc.\n\nThe A2A SDK application is created per-request to include authentication\ncontext while still leveraging FastAPI's authorization middleware.\n\nAutomatically detects streaming requests (message/stream JSON-RPC method)\nand returns a StreamingResponse to enable real-time chunk delivery.\n\nArgs:\n    request: FastAPI request object\n    auth: Authentication tuple\n    mcp_headers: MCP headers for context propagation\n\nReturns:\n    JSON-RPC response or streaming response",
-                "operationId": "handle_a2a_jsonrpc_a2a_get",
+                "operationId": "handle_a2a_jsonrpc_a2a_post",
                 "responses": {
                     "200": {
                         "description": "Successful Response",
@@ -5339,7 +5339,7 @@
                         "type": "string",
                         "minLength": 1,
                         "title": "Vector DB ID",
-                        "description": "Vector DB identification."
+                        "description": "Vector database identification."
                     },
                     "db_path": {
                         "type": "string",

docs/openapi.md

@@ -3200,6 +3200,8 @@ system info) and returns an LLM-generated response.
 
 Args:
     infer_request: The inference request containing question and context.
+    request: The FastAPI request object for accessing headers and state.
+    background_tasks: FastAPI background tasks for async Splunk event sending.
     auth: Authentication tuple from the configured auth provider.
 
 Returns:
@@ -4184,7 +4186,7 @@ BYOK (Bring Your Own Knowledge) RAG configuration.
 | rag_type | string | Type of RAG database. |
 | embedding_model | string | Embedding model identification |
 | embedding_dimension | integer | Dimensionality of embedding vectors. |
-| vector_db_id | string | Vector DB identification. |
+| vector_db_id | string | Vector database identification. |
 | db_path | string | Path to RAG database. |