LCORE-1037: remove all llama-stack references from BYOK and RAG guides

Replace remaining Llama Stack mentions with generic terms — users
should not need to know about the underlying Llama Stack layer.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: are-ces

docs/byok_guide.md

@@ -148,10 +148,8 @@ class CustomMetadataProcessor(MetadataProcessor):
 ```
 
 **Important Notes:**
-- The vector database must be compatible with Llama Stack
 - Supported formats: 
-  - Llama-Stack Faiss Vector-IO
-  - Llama-Stack SQLite-vec Vector-IO
+  - Faiss Vector-IO
 - The same embedding model must be used for both creation and querying
 
 ### Step 3: Configure Embedding Model
@@ -178,15 +176,15 @@ The embedding model is specified per knowledge source in the `byok_rag` section
 
 ### Step 4: Configure BYOK Knowledge Sources
 
-Declare your knowledge sources in the `byok_rag` section of your `lightspeed-stack.yaml`. The Lightspeed Stack service automatically generates the required Llama Stack configuration at startup.
+Declare your knowledge sources in the `byok_rag` section of your `lightspeed-stack.yaml`. The required configuration is automatically generated at startup when using `make run`, `make run-stack`, `docker-compose`, or library mode.
 
 ```yaml
 byok_rag:
   - rag_id: my-docs                                    # Unique identifier for this knowledge source
     rag_type: inline::faiss                             # Vector store type (default: inline::faiss)
     embedding_model: sentence-transformers/all-mpnet-base-v2  # Embedding model (default)
     embedding_dimension: 768                            # Must match your embedding model's output
-    vector_db_id: your-index-id                         # Llama Stack vector store ID (from index generation)
+    vector_db_id: vs_8c94967b-81cc-4028-a294-9cfac6fd9ae2                              # Generated by rag-content during index creation
     db_path: /path/to/vector_db/faiss_store.db          # Path to the vector database file
     score_multiplier: 1.0                               # Weight for Inline RAG result ranking (default: 1.0)
 ```
@@ -199,7 +197,7 @@ byok_rag:
 | `rag_type` | No | `inline::faiss` | Vector store provider type |
 | `embedding_model` | No | `sentence-transformers/all-mpnet-base-v2` | Embedding model identifier or path |
 | `embedding_dimension` | No | `768` | Embedding vector dimensionality |
-| `vector_db_id` | Yes | — | Vector store ID (must match the ID from index generation) |
+| `vector_db_id` | Yes | — | Vector store ID generated by rag-content (e.g. `vs_8c94967b-81cc-4028-a294-9cfac6fd9ae2`) |
 | `db_path` | Yes | — | Path to the vector database file |
 | `score_multiplier` | No | `1.0` | Weight for Inline RAG ranking (values > 1.0 boost; < 1.0 reduce) |
 
@@ -213,20 +211,20 @@ byok_rag:
     rag_type: inline::faiss
     embedding_model: sentence-transformers/all-mpnet-base-v2
     embedding_dimension: 768
-    vector_db_id: ocp-index
+    vector_db_id: vs_3a7f9b2e-45dc-4e1a-b8f2-1c9d0e3f5a6b
     db_path: /data/vector_dbs/ocp_docs/faiss_store.db
     score_multiplier: 1.0
 
   - rag_id: internal-kb
     rag_type: inline::faiss
     embedding_model: sentence-transformers/all-mpnet-base-v2
     embedding_dimension: 768
-    vector_db_id: kb-index
+    vector_db_id: vs_d4c8e1f0-92ab-4d3c-a5e7-6b8f0c2d1e3a
     db_path: /data/vector_dbs/internal_kb/faiss_store.db
     score_multiplier: 1.2       # Boost results from this store
 ```
 
-**⚠️ 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 configuration to the specific vector database index you created.
+**⚠️ Important**: The `vector_db_id` value must exactly match the ID generated by the rag-content tool during index creation (e.g. `vs_8c94967b-81cc-4028-a294-9cfac6fd9ae2`). This identifier links your configuration to the specific vector database index.
 
 ### Step 5: Configure RAG Strategy
 
@@ -278,32 +276,35 @@ byok_rag:
     rag_type: inline::faiss
     embedding_model: sentence-transformers/all-mpnet-base-v2
     embedding_dimension: 768
-    vector_db_id: your-index-id
+    vector_db_id: vs_8c94967b-81cc-4028-a294-9cfac6fd9ae2
     db_path: /path/to/faiss_store.db
 ```
 
 ### 2. pgvector (PostgreSQL)
 - **Type**: PostgreSQL with pgvector extension
 - **Best for**: Large-scale deployments, shared knowledge bases
-- **Configuration**: `rag_type: remote::pgvector`
+- **Configuration**: `remote::pgvector`
 - **Requirements**: PostgreSQL with pgvector extension
 
+> [!NOTE]
+> pgvector is not yet supported via `byok_rag` in `lightspeed-stack.yaml` (see [LCORE-2437](https://redhat.atlassian.net/browse/LCORE-2437)).
+> It must be configured directly in the Llama Stack configuration file.
+
 ```yaml
-byok_rag:
-  - rag_id: pgvector-knowledge
-    rag_type: remote::pgvector
-    embedding_model: sentence-transformers/all-mpnet-base-v2
-    embedding_dimension: 768
-    vector_db_id: enterprise-docs
-    db_path: .llama/distributions/pgvector/registry.db
+vector_io:
+- provider_id: pgvector-knowledge
+  provider_type: remote::pgvector
+  config:
+    host: localhost
+    port: 5432
+    db: knowledge_db
+    user: lightspeed_user
+    password: ${env.POSTGRES_PASSWORD}
+    kvstore:
+      type: sqlite
+      db_path: .llama/distributions/pgvector/registry.db
 ```
 
-> [!NOTE]
-> For pgvector, `db_path` points to the local registry database used by Llama Stack to track the vector store metadata.
-> The PostgreSQL connection details (host, port, database, user, password) are configured
-> in the Llama Stack provider configuration. Use environment variables for credentials
-> (e.g., `POSTGRES_PASSWORD`).
-
 **pgvector Table Schema:**
 - `id` (text): UUID identifier of the chunk
 - `document` (jsonb): JSON containing content and metadata
@@ -329,7 +330,7 @@ byok_rag:
     rag_type: inline::faiss
     embedding_model: sentence-transformers/all-mpnet-base-v2
     embedding_dimension: 768
-    vector_db_id: company-knowledge-index
+    vector_db_id: vs_f1a2b3c4-56de-4f78-90ab-cdef12345678
     db_path: /home/user/vector_dbs/company_docs/faiss_store.db
 
 rag:
@@ -340,12 +341,18 @@ rag:
 ```
 
 > [!NOTE]
-> Your LLM inference provider (e.g., OpenAI, vLLM) must also be configured.
+> Your LLM inference provider (e.g., OpenAI, vLLM) must also be configured in your `run.yaml`.
 > For OpenAI, set the `OPENAI_API_KEY` environment variable.
 
 ### Example 2: Multiple Knowledge Sources with pgvector
 
-A configuration combining a local FAISS store with a remote pgvector store:
+A configuration combining a local FAISS store (via `byok_rag`) with a remote pgvector store (configured directly in the Llama Stack configuration file):
+
+> [!NOTE]
+> pgvector is not yet supported via `byok_rag` in `lightspeed-stack.yaml` (see [LCORE-2437](https://redhat.atlassian.net/browse/LCORE-2437)).
+> The pgvector provider must be configured directly in the Llama Stack configuration file.
+
+**`lightspeed-stack.yaml`** — FAISS store and RAG strategy:
 
 ```yaml
 name: Lightspeed Core Service (LCS)
@@ -359,25 +366,32 @@ byok_rag:
     rag_type: inline::faiss
     embedding_model: sentence-transformers/all-mpnet-base-v2
     embedding_dimension: 768
-    vector_db_id: local-index
+    vector_db_id: vs_e9d8c7b6-43af-4b2d-8e1f-0a9b8c7d6e5f
     db_path: /data/vector_dbs/local/faiss_store.db
     score_multiplier: 1.0
 
-  - rag_id: enterprise-kb
-    rag_type: remote::pgvector
-    embedding_model: sentence-transformers/all-mpnet-base-v2
-    embedding_dimension: 768
-    vector_db_id: enterprise-docs
-    db_path: .llama/distributions/pgvector/registry.db
-    score_multiplier: 1.2
-
 rag:
   inline:
     - local-docs
-    - enterprise-kb
   tool:
     - local-docs
-    - enterprise-kb
+```
+
+**Llama Stack configuration file** — pgvector provider:
+
+```yaml
+vector_io:
+- provider_id: enterprise-kb
+  provider_type: remote::pgvector
+  config:
+    host: localhost
+    port: 5432
+    db: knowledge_db
+    user: lightspeed_user
+    password: ${env.POSTGRES_PASSWORD}
+    kvstore:
+      type: sqlite
+      db_path: .llama/distributions/pgvector/registry.db
 ```
 
 > [!NOTE]
@@ -396,7 +410,6 @@ The BYOK (Bring Your Own Knowledge) feature in Lightspeed Core provides powerful
 
 For additional support and advanced configurations, refer to:
 - [RAG Configuration Guide](rag_guide.md)
-- [Llama Stack Documentation](https://llama-stack.readthedocs.io/)
 - [rag-content Tool Repository](https://github.com/lightspeed-core/rag-content)
 
 Remember to regularly update your knowledge sources and monitor system performance to maintain optimal BYOK functionality.