From 3815d9bd4689f11fe9e410774b6eee176cce4b0e Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Mon, 16 Feb 2026 22:11:14 -0500
Subject: [PATCH 01/15] feat: add remote LLM support, LLM_MODEL param, and
 INTERACTIVE flag to Makefile

- Add LLM_MODEL parameter to override the model ID via command line
- Auto-derive Helm key from LLM_MODEL when LLM=remote-llm (reserved key
  in llama-stack chart that skips model registration)
- Add INTERACTIVE flag (default: true) to skip interactive prompts for
  HF_TOKEN and TAVILY_API_KEY when set to false
- Update README with remote LLM deployment example and parameter docs

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 README.md                                     |  35 ++++++--
 deploy/helm/Makefile                          |  80 ++++++++++++------
 deploy/helm/rag-values.yaml.example           |   6 +-
 deploy/helm/rag/values.yaml                   |  13 ++-
 .../distribution/ui/page/upload/upload.py     |   6 +-
 .../Zippity_Zoo_Grand_Invention.pdf           | Bin
 ...ippity_Zoo_and_the_Town_of_Tumble_Town.pdf | Bin
 ...Zoo_and_the_Town_of_Whispering_Willows.pdf | Bin
 8 files changed, 94 insertions(+), 46 deletions(-)
 rename notebooks/{ => zippity-zoo}/Zippity_Zoo_Grand_Invention.pdf (100%)
 rename notebooks/{ => zippity-zoo}/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf (100%)
 rename notebooks/{ => zippity-zoo}/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf (100%)

diff --git a/README.md b/README.md
index b6820384..4223a676 100644
--- a/README.md
+++ b/README.md
@@ -100,14 +100,16 @@ This QuickStart allows users to explore the capabilities of RAG by:
 | Generation  | `meta-llama/Meta-Llama-3-70B-Instruct` | A100 x2/HPU | p4d.24xlarge
 | Safety      | `meta-llama/Llama-Guard-3-8B`          | L4/HPU      | g6.2xlarge
 
-- Note: Developers can also configure a remote LLM by modifying the `rag_values.yml` file, which gives you complete control over all parameter settings.
+- Note: Developers can also use a remote LLM via the command line (see [Remote LLM Deployment](#remote-llm-deployment-example)) or by modifying the `rag-values.yaml` file directly:
 
 ```yaml
-  remote-llm:
-    id: llama-3-3-70b-instruct-w8a8
-    url: https://somedomain.com/v1
-    apiToken: fake-token
-    enabled: true
+  global:
+    models:
+      remote-llm:
+        id: meta-llama/Llama-3.3-70B-Instruct
+        url: https://somedomain.com/v1
+        apiToken: fake-token
+        enabled: true
 ```
 
 Note: the 70B model is NOT required for initial testing of this example. The safety/shield model `Llama-Guard-3-8B` is also optional.
@@ -251,6 +253,27 @@ make install NAMESPACE=llama-stack-rag LLM=llama-3-2-3b-instruct SAFETY=llama-gu
 
 ```
 
+**Remote LLM Deployment Example:**
+
+To connect to a remote LLM endpoint instead of deploying a local model, use `LLM_URL`, `LLM_MODEL`, and `LLM_API_TOKEN`:
+
+```bash
+make install NAMESPACE=llama-stack-rag \
+  LLM=remote-llm \
+  LLM_URL=https://my-model-endpoint.example.com/v1 \
+  LLM_MODEL=my-model-name \
+  LLM_API_TOKEN=my-api-token
+```
+
+| Parameter | Description |
+|-----------|-------------|
+| `LLM=remote-llm` | Indicates a remote model (no local vLLM deployment) |
+| `LLM_URL` | The base URL of the remote model endpoint |
+| `LLM_MODEL` | The model ID served at the remote endpoint |
+| `LLM_API_TOKEN` | Authentication token for the remote endpoint |
+
+This skips local model deployment and configures LlamaStack to use the remote inference endpoint directly. No GPU or HF token is required for the LLM.
+
 When prompted, enter your **[Hugging Face Token](https://huggingface.co/settings/tokens)**.
 
 Note: This process may take 10 to 30 minutes depending on the number and size of models to be downloaded.
diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index e90f2f1f..ec0f44c3 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -26,6 +26,7 @@ POSTGRES_DBNAME ?= rag_blueprint
 MINIO_USER ?= minio_rag_user
 MINIO_PASSWORD ?= minio_rag_password
 # HF_TOKEN will be managed by the get_hf_token helper function
+INTERACTIVE ?= true
 RAG_CHART := rag
 TOLERATIONS_TEMPLATE=[{"key":"$(1)","effect":"NoSchedule","operator":"Exists"}]
 
@@ -40,6 +41,7 @@ define check_values_file
 			echo -e "$(YELLOW)[INFO]$(NC) Values file not found. Creating from example..."; \
 			cp "$(VALUES_EXAMPLE)" "$(VALUES_FILE)"; \
 			if [ -z "$(LLM)" ]; then \
+			if [ "$(INTERACTIVE)" = "true" ]; then \
 				echo -e ""; \
 				echo -e "$(GREEN)╔══════════════════════════════════════════════════════════════════════════╗$(NC)"; \
 				echo -e "$(GREEN)║                                                                          ║$(NC)"; \
@@ -69,6 +71,9 @@ define check_values_file
 				read -p "" continue; \
 				echo -e "$(BLUE)[INFO]$(NC) Continuing with installation..."; \
 				echo -e ""; \
+			else \
+				echo -e "$(YELLOW)[INFO]$(NC) Non-interactive mode: skipping configuration prompt. Using $(VALUES_FILE) as-is."; \
+			fi; \
 			else \
 				echo -e "$(GREEN)[SUCCESS]$(NC) Using LLM model from command line: $(LLM)"; \
 				echo -e ""; \
@@ -103,33 +108,41 @@ define validate_values_file
 	LLAMA_STACK_TAVILY=$$(grep "TAVILY_SEARCH_API_KEY:" "$(VALUES_FILE)" 2>/dev/null | sed 's/.*TAVILY_SEARCH_API_KEY: *//' | tr -d '"' | tr -d ' '); \
 	\
 	if [ -z "$$HF_TOKEN_FILE" ] || [ "$$HF_TOKEN_FILE" = "" ]; then \
-		echo -e "$(YELLOW)[WARNING]$(NC) Hugging Face token is not set. Model downloads may fail."; \
-		echo -e "$(BLUE)[INFO]$(NC) Get your token from: https://huggingface.co/settings/tokens"; \
-		echo -e ""; \
-		read -p "Enter your Hugging Face token (or press Enter to skip): " hf_input; \
-		if [ -n "$$hf_input" ]; then \
-			sed -i.bak "/^llm-service:/,/^[^ ]/ s|hf_token:.*|hf_token: \"$$hf_input\"|" "$(VALUES_FILE)"; \
-			echo -e "$(GREEN)[SUCCESS]$(NC) Hugging Face token saved to configuration."; \
-			UPDATED=1; \
+		if [ "$(INTERACTIVE)" = "true" ]; then \
+			echo -e "$(YELLOW)[WARNING]$(NC) Hugging Face token is not set. Model downloads may fail."; \
+			echo -e "$(BLUE)[INFO]$(NC) Get your token from: https://huggingface.co/settings/tokens"; \
+			echo -e ""; \
+			read -p "Enter your Hugging Face token (or press Enter to skip): " hf_input; \
+			if [ -n "$$hf_input" ]; then \
+				sed -i.bak "/^llm-service:/,/^[^ ]/ s|hf_token:.*|hf_token: \"$$hf_input\"|" "$(VALUES_FILE)"; \
+				echo -e "$(GREEN)[SUCCESS]$(NC) Hugging Face token saved to configuration."; \
+				UPDATED=1; \
+			else \
+				echo -e "$(YELLOW)[WARNING]$(NC) Hugging Face token not provided. Model downloads may fail."; \
+			fi; \
+			echo -e ""; \
 		else \
-			echo -e "$(YELLOW)[WARNING]$(NC) Hugging Face token not provided. Model downloads may fail."; \
+			echo -e "$(YELLOW)[WARNING]$(NC) Hugging Face token is not set. Use HF_TOKEN env var or set it in $(VALUES_FILE)."; \
 		fi; \
-		echo -e ""; \
 	fi; \
 	\
 	if [ -z "$$LLAMA_STACK_TAVILY" ] || [ "$$LLAMA_STACK_TAVILY" = "Paste-your-key-here" ]; then \
-		echo -e "$(YELLOW)[WARNING]$(NC) TAVILY search API key is not set. Web search will be disabled."; \
-		echo -e "$(BLUE)[INFO]$(NC) Get your key from: https://tavily.com/"; \
-		echo -e ""; \
-		read -p "Enter your TAVILY search API key (or press Enter to skip): " tavily_input; \
-		if [ -n "$$tavily_input" ]; then \
-			sed -i.bak "s/TAVILY_SEARCH_API_KEY:.*/TAVILY_SEARCH_API_KEY: \"$$tavily_input\"/" "$(VALUES_FILE)"; \
-			echo -e "$(GREEN)[SUCCESS]$(NC) TAVILY search API key saved to configuration."; \
-			UPDATED=1; \
+		if [ "$(INTERACTIVE)" = "true" ]; then \
+			echo -e "$(YELLOW)[WARNING]$(NC) TAVILY search API key is not set. Web search will be disabled."; \
+			echo -e "$(BLUE)[INFO]$(NC) Get your key from: https://tavily.com/"; \
+			echo -e ""; \
+			read -p "Enter your TAVILY search API key (or press Enter to skip): " tavily_input; \
+			if [ -n "$$tavily_input" ]; then \
+				sed -i.bak "s/TAVILY_SEARCH_API_KEY:.*/TAVILY_SEARCH_API_KEY: \"$$tavily_input\"/" "$(VALUES_FILE)"; \
+				echo -e "$(GREEN)[SUCCESS]$(NC) TAVILY search API key saved to configuration."; \
+				UPDATED=1; \
+			else \
+				echo -e "$(YELLOW)[WARNING]$(NC) TAVILY search API key not provided. Web search will be disabled."; \
+			fi; \
+			echo -e ""; \
 		else \
-			echo -e "$(YELLOW)[WARNING]$(NC) TAVILY search API key not provided. Web search will be disabled."; \
+			echo -e "$(YELLOW)[WARNING]$(NC) TAVILY search API key is not set. Use TAVILY_API_KEY env var or set it in $(VALUES_FILE)."; \
 		fi; \
-		echo -e ""; \
 	fi; \
 	\
 	if [ $$UPDATED -eq 1 ]; then \
@@ -173,6 +186,7 @@ helm_llm_service_args = \
     --set llm-service.secret.hf_token=$(HF_TOKEN) \
     $(if $(DEVICE),--set llm-service.device='$(DEVICE)',) \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
+    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_TOLERATION),--set-json global.models.$(LLM).tolerations='$(call TOLERATIONS_TEMPLATE,$(LLM_TOLERATION))',) \
     $(if $(SAFETY_TOLERATION),--set-json global.models.$(SAFETY).tolerations='$(call TOLERATIONS_TEMPLATE,$(SAFETY_TOLERATION))',) \
@@ -180,6 +194,7 @@ helm_llm_service_args = \
 
 helm_llama_stack_args = \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
+    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_URL),--set global.models.$(LLM).url='$(LLM_URL)',) \
     $(if $(SAFETY_URL),--set global.models.$(SAFETY).url='$(SAFETY_URL)',) \
@@ -231,6 +246,7 @@ help: ## Show this help message
 	@echo -e ""
 	@echo -e "$(GREEN)Command-Line Parameters (override values file):$(NC)"
 	@echo -e "  LLM           - Enable specific LLM model (e.g., llama-3-2-3b-instruct)"
+	@echo -e "  LLM_MODEL     - Override model ID for the LLM (e.g., meta-llama/Llama-3.2-3B-Instruct)"
 	@echo -e "  SAFETY        - Enable specific safety model (e.g., llama-guard-3-8b)"
 	@echo -e "  LLM_TOLERATION - Set toleration for LLM model (e.g., nvidia.com/gpu)"
 	@echo -e "  SAFETY_TOLERATION - Set toleration for safety model"
@@ -240,6 +256,7 @@ help: ## Show this help message
 	@echo -e "  SAFETY_URL    - URL for remote safety service"
 	@echo -e "  LLM_API_TOKEN - API token for remote LLM service"
 	@echo -e "  SAFETY_API_TOKEN - API token for remote safety service"
+	@echo -e "  INTERACTIVE   - Enable interactive prompts (default: true, set false to skip)"
 	@echo -e ""
 	@echo -e "$(GREEN)Quick Start:$(NC)"
 	@echo -e "  $(BLUE)Option 1:$(NC) Using values file (recommended)"
@@ -485,21 +502,30 @@ install: ## Install the RAG deployment
 	@echo -e "$(BLUE)[INFO]$(NC) Installing $(RAG_CHART) helm chart with $(VALUES_FILE)..."
 	@HELM_ARGS="-f $(VALUES_FILE)"; \
 	if [ -n "$(LLM)" ]; then \
-		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $(LLM)"; \
-		HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).enabled=true"; \
+		MODEL_KEY="$(LLM)"; \
+		if [ "$(LLM)" = "remote-llm" ] && [ -n "$(LLM_MODEL)" ]; then \
+			MODEL_KEY=$$(echo "$(LLM_MODEL)" | tr '/' '-' | tr '.' '-' | tr '[:upper:]' '[:lower:]'); \
+			echo -e "$(YELLOW)[INFO]$(NC) 'remote-llm' is a reserved key in llama-stack chart, using '$$MODEL_KEY' as model key"; \
+		fi; \
+		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $$MODEL_KEY"; \
+		HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.enabled=true"; \
+		if [ -n "$(LLM_MODEL)" ]; then \
+			echo -e "$(BLUE)[INFO]$(NC) Setting LLM model ID: $(LLM_MODEL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.id=$(LLM_MODEL)"; \
+		fi; \
 		if [ -n "$(LLM_TOLERATION)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM toleration: $(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].key=$(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].effect=NoSchedule"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].operator=Exists"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].key=$(LLM_TOLERATION)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].effect=NoSchedule"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].operator=Exists"; \
 		fi; \
 		if [ -n "$(LLM_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM URL: $(LLM_URL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).url=$(LLM_URL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.url=$(LLM_URL)"; \
 		fi; \
 		if [ -n "$(LLM_API_TOKEN)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM API token"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).apiToken=$(LLM_API_TOKEN)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.apiToken=$(LLM_API_TOKEN)"; \
 		fi; \
 	fi; \
 	if [ -n "$(SAFETY)" ]; then \
diff --git a/deploy/helm/rag-values.yaml.example b/deploy/helm/rag-values.yaml.example
index 68c72954..80fdfcbd 100644
--- a/deploy/helm/rag-values.yaml.example
+++ b/deploy/helm/rag-values.yaml.example
@@ -166,9 +166,9 @@ configure-pipeline:
       enabled: true
       bucket: documents
       urls: 
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_Grand_Invention.pdf
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
+        - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_Grand_Invention.pdf
+        - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
+        - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
 
 # Llama Stack Configuration
 llama-stack:
diff --git a/deploy/helm/rag/values.yaml b/deploy/helm/rag/values.yaml
index d1aafb31..889f4e05 100644
--- a/deploy/helm/rag/values.yaml
+++ b/deploy/helm/rag/values.yaml
@@ -166,10 +166,9 @@ pgvector:
     enabled: true
     bucket: documents
     urls: 
-    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_Grand_Invention.pdf
-    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
-    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
-
+    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_Grand_Invention.pdf
+    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
+    - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
 
 llama-stack:
   enabled: true
@@ -188,9 +187,9 @@ configure-pipeline:
       enabled: true
       bucket: documents
       urls: 
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_Grand_Invention.pdf
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
-      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
+      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_Grand_Invention.pdf
+      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
+      - https://raw.githubusercontent.com/rh-ai-quickstart/RAG/refs/heads/main/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
 
 mcp-servers:
   enabled: true
diff --git a/frontend/llama_stack_ui/distribution/ui/page/upload/upload.py b/frontend/llama_stack_ui/distribution/ui/page/upload/upload.py
index 2f8e09aa..dc803fb4 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/upload/upload.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/upload/upload.py
@@ -47,13 +47,13 @@ def _build_dropdown_options(vdb_list):
     """Build dropdown options from the vector database list.
 
     Returns:
-        tuple: (dropdown_options, create_new_option)
+        tuple: (create_new_option, dropdown_options)
     """
     create_new_option = "➕ Create New"
 
     if vdb_list:
         existing_vdbs = [get_vector_db_name(v) for v in vdb_list]
-        return existing_vdbs + [create_new_option], create_new_option
+        return [create_new_option] + existing_vdbs, create_new_option
 
     return [create_new_option], create_new_option
 
@@ -207,7 +207,7 @@ def _show_document_upload_ui(vector_db_name, vector_db_obj=None):
     uploaded_files = st.file_uploader(
         "Browse and select files to upload (files will upload automatically)",
         accept_multiple_files=True,
-        type=["txt", "pdf", "doc", "docx"],
+        type=["txt", "pdf", "doc", "docx", "md"],
         key=f"uploader_{vector_db_name}",
         help=(
             "Select one or more documents - they will be uploaded "
diff --git a/notebooks/Zippity_Zoo_Grand_Invention.pdf b/notebooks/zippity-zoo/Zippity_Zoo_Grand_Invention.pdf
similarity index 100%
rename from notebooks/Zippity_Zoo_Grand_Invention.pdf
rename to notebooks/zippity-zoo/Zippity_Zoo_Grand_Invention.pdf
diff --git a/notebooks/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf b/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
similarity index 100%
rename from notebooks/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
rename to notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Tumble_Town.pdf
diff --git a/notebooks/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf b/notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
similarity index 100%
rename from notebooks/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf
rename to notebooks/zippity-zoo/Zippity_Zoo_and_the_Town_of_Whispering_Willows.pdf

From dd4994270b1ba22ddf4960b620ce7be96df2d7e2 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 17 Feb 2026 16:44:53 -0500
Subject: [PATCH 02/15] refactor: simplify remote LLM support by removing
 LLM_MODEL parameter

Remove the LLM_MODEL parameter and MODEL_KEY auto-derivation logic in
favor of using the LLM key directly. Update help text to add Option 3
for remote LLM deployment with LLM_URL and LLM_API_TOKEN.

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 README.md            |  4 +---
 deploy/helm/Makefile | 31 +++++++++++--------------------
 2 files changed, 12 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index 4223a676..9aeca127 100644
--- a/README.md
+++ b/README.md
@@ -255,13 +255,12 @@ make install NAMESPACE=llama-stack-rag LLM=llama-3-2-3b-instruct SAFETY=llama-gu
 
 **Remote LLM Deployment Example:**
 
-To connect to a remote LLM endpoint instead of deploying a local model, use `LLM_URL`, `LLM_MODEL`, and `LLM_API_TOKEN`:
+To connect to a remote LLM endpoint instead of deploying a local model, use `LLM_URL` and `LLM_API_TOKEN`:
 
 ```bash
 make install NAMESPACE=llama-stack-rag \
   LLM=remote-llm \
   LLM_URL=https://my-model-endpoint.example.com/v1 \
-  LLM_MODEL=my-model-name \
   LLM_API_TOKEN=my-api-token
 ```
 
@@ -269,7 +268,6 @@ make install NAMESPACE=llama-stack-rag \
 |-----------|-------------|
 | `LLM=remote-llm` | Indicates a remote model (no local vLLM deployment) |
 | `LLM_URL` | The base URL of the remote model endpoint |
-| `LLM_MODEL` | The model ID served at the remote endpoint |
 | `LLM_API_TOKEN` | Authentication token for the remote endpoint |
 
 This skips local model deployment and configures LlamaStack to use the remote inference endpoint directly. No GPU or HF token is required for the LLM.
diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index ec0f44c3..c4b44191 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -186,7 +186,6 @@ helm_llm_service_args = \
     --set llm-service.secret.hf_token=$(HF_TOKEN) \
     $(if $(DEVICE),--set llm-service.device='$(DEVICE)',) \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
-    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_TOLERATION),--set-json global.models.$(LLM).tolerations='$(call TOLERATIONS_TEMPLATE,$(LLM_TOLERATION))',) \
     $(if $(SAFETY_TOLERATION),--set-json global.models.$(SAFETY).tolerations='$(call TOLERATIONS_TEMPLATE,$(SAFETY_TOLERATION))',) \
@@ -194,7 +193,6 @@ helm_llm_service_args = \
 
 helm_llama_stack_args = \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
-    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_URL),--set global.models.$(LLM).url='$(LLM_URL)',) \
     $(if $(SAFETY_URL),--set global.models.$(SAFETY).url='$(SAFETY_URL)',) \
@@ -246,7 +244,6 @@ help: ## Show this help message
 	@echo -e ""
 	@echo -e "$(GREEN)Command-Line Parameters (override values file):$(NC)"
 	@echo -e "  LLM           - Enable specific LLM model (e.g., llama-3-2-3b-instruct)"
-	@echo -e "  LLM_MODEL     - Override model ID for the LLM (e.g., meta-llama/Llama-3.2-3B-Instruct)"
 	@echo -e "  SAFETY        - Enable specific safety model (e.g., llama-guard-3-8b)"
 	@echo -e "  LLM_TOLERATION - Set toleration for LLM model (e.g., nvidia.com/gpu)"
 	@echo -e "  SAFETY_TOLERATION - Set toleration for safety model"
@@ -264,8 +261,11 @@ help: ## Show this help message
 	@echo -e "    2. make configure-keys  # Set up API keys interactively"
 	@echo -e "    3. make install NAMESPACE=my-rag  # Install with your config"
 	@echo -e ""
-	@echo -e "  $(BLUE)Option 2:$(NC) Using command-line parameters"
+	@echo -e "  $(BLUE)Option 2:$(NC) Using command-line parameters with LLM provisioning on OpenShift AI"
 	@echo -e "    make install NAMESPACE=my-rag LLM=llama-3-2-3b-instruct LLM_TOLERATION=\"nvidia.com/gpu\""
+	@echo -e ""
+	@echo -e "  $(BLUE)Option 3:$(NC) Using command-line parameters with remote LLM"
+	@echo -e "    make install NAMESPACE=my-rag LLM=remote-llm LLM_URL=https://<<llm-url>>/v1 LLM_API_TOKEN=<<llm-api-token>>"
 
 # Dependency checks
 .PHONY: check-deps
@@ -502,30 +502,21 @@ install: ## Install the RAG deployment
 	@echo -e "$(BLUE)[INFO]$(NC) Installing $(RAG_CHART) helm chart with $(VALUES_FILE)..."
 	@HELM_ARGS="-f $(VALUES_FILE)"; \
 	if [ -n "$(LLM)" ]; then \
-		MODEL_KEY="$(LLM)"; \
-		if [ "$(LLM)" = "remote-llm" ] && [ -n "$(LLM_MODEL)" ]; then \
-			MODEL_KEY=$$(echo "$(LLM_MODEL)" | tr '/' '-' | tr '.' '-' | tr '[:upper:]' '[:lower:]'); \
-			echo -e "$(YELLOW)[INFO]$(NC) 'remote-llm' is a reserved key in llama-stack chart, using '$$MODEL_KEY' as model key"; \
-		fi; \
-		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $$MODEL_KEY"; \
-		HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.enabled=true"; \
-		if [ -n "$(LLM_MODEL)" ]; then \
-			echo -e "$(BLUE)[INFO]$(NC) Setting LLM model ID: $(LLM_MODEL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.id=$(LLM_MODEL)"; \
-		fi; \
+		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $(LLM)"; \
+		HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).enabled=true"; \
 		if [ -n "$(LLM_TOLERATION)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM toleration: $(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].key=$(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].effect=NoSchedule"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].operator=Exists"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].key=$(LLM_TOLERATION)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].effect=NoSchedule"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].operator=Exists"; \
 		fi; \
 		if [ -n "$(LLM_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM URL: $(LLM_URL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.url=$(LLM_URL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).url=$(LLM_URL)"; \
 		fi; \
 		if [ -n "$(LLM_API_TOKEN)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM API token"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.apiToken=$(LLM_API_TOKEN)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).apiToken=$(LLM_API_TOKEN)"; \
 		fi; \
 	fi; \
 	if [ -n "$(SAFETY)" ]; then \

From 08def6d3710c7dba7d80636f0b7bcefd2502800d Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 24 Feb 2026 10:37:56 -0500
Subject: [PATCH 03/15] feat: added fixes for local deploymment and upgrade of
 llamastack to 0.5.0

---
 deploy/helm/Makefile                          |   82 +-
 deploy/helm/rag/Chart.lock                    |    8 +-
 deploy/helm/rag/Chart.yaml                    |    4 +-
 .../distribution/ui/modules/utils.py          |   30 +-
 .../ui/page/distribution/models.py            |    2 +-
 .../distribution/ui/page/playground/agent.py  |   33 +-
 .../distribution/ui/page/playground/chat.py   |   23 +-
 .../distribution/ui/page/playground/direct.py |    6 +-
 frontend/pyproject.toml                       |    4 +-
 frontend/start.sh                             |   17 +-
 frontend/uv.lock                              | 2575 +++++++++++++++++
 11 files changed, 2748 insertions(+), 36 deletions(-)
 create mode 100644 frontend/uv.lock

diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index c4b44191..48ca1093 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -223,7 +223,8 @@ help: ## Show this help message
 	@echo -e "  health        - Check health of all services"
 	@echo -e ""
 	@echo -e "$(GREEN)Development Targets:$(NC)"
-	@echo -e "  dev           - Start development environment"
+	@echo -e "  dev-start     - Install (if needed), port-forward, and start Streamlit locally"
+	@echo -e "  dev-stop      - Stop Streamlit & port-forwards, optionally uninstall"
 	@echo -e "  validate      - Validate Helm chart"
 	@echo -e "  validate-config - Validate configuration values"
 	@echo -e "  monitor       - Monitor deployment status"
@@ -350,15 +351,80 @@ namespace:
 	@echo -e "$(GREEN)[SUCCESS]$(NC) Namespace $(NAMESPACE) is ready"
 
 # Development targets
-.PHONY: dev
-dev: ## Start development environment
+FRONTEND_DIR := ../../frontend
+LLAMASTACK_LOCAL_PORT ?= 8321
+PGVECTOR_LOCAL_PORT ?= 5432
+
+.PHONY: dev-start
+dev-start: ## Install (if needed), port-forward, and start Streamlit locally
 	@if [ -z "$(NAMESPACE)" ]; then echo -e "$(RED)[ERROR]$(NC) NAMESPACE is required for dev"; exit 1; fi
 	@$(MAKE) check-deps
-	@$(MAKE) validate-config
-	@echo -e "$(BLUE)[INFO]$(NC) Starting RAG development environment..."
-	@$(call check_values_file)
-	@$(MAKE) install
-	@echo -e "$(GREEN)[SUCCESS]$(NC) Development environment started"
+	@if [ ! -d "$(FRONTEND_DIR)" ]; then echo -e "$(RED)[ERROR]$(NC) Frontend directory not found at $(FRONTEND_DIR)"; exit 1; fi
+	@if helm status $(RAG_CHART) -n $(NAMESPACE) >/dev/null 2>&1; then \
+		echo -e "$(YELLOW)[INFO]$(NC) RAG is already installed in namespace $(NAMESPACE)."; \
+		if [ "$(INTERACTIVE)" = "true" ]; then \
+			read -p "Do you want to reinstall? [y/N] " answer; \
+			if [ "$$answer" = "y" ] || [ "$$answer" = "Y" ]; then \
+				echo -e "$(BLUE)[INFO]$(NC) Reinstalling..."; \
+				$(MAKE) install NAMESPACE=$(NAMESPACE); \
+			else \
+				echo -e "$(BLUE)[INFO]$(NC) Skipping reinstall. Using existing deployment."; \
+			fi; \
+		else \
+			echo -e "$(BLUE)[INFO]$(NC) Non-interactive mode: skipping reinstall."; \
+		fi; \
+	else \
+		echo -e "$(BLUE)[INFO]$(NC) RAG is not installed in namespace $(NAMESPACE). Installing..."; \
+		$(MAKE) install NAMESPACE=$(NAMESPACE); \
+	fi
+	@echo -e ""
+	@echo -e "$(BLUE)[INFO]$(NC) Setting up port-forwards..."
+	@-pkill -f "oc port-forward.*svc/llamastack" 2>/dev/null ||:
+	@-pkill -f "oc port-forward.*svc/pgvector" 2>/dev/null ||:
+	@sleep 1
+	@echo -e "$(BLUE)[INFO]$(NC) Port-forwarding llamastack (localhost:$(LLAMASTACK_LOCAL_PORT) -> 8321)..."
+#@oc port-forward svc/llamastack $(LLAMASTACK_LOCAL_PORT):8321 -n $(NAMESPACE) &>/dev/null &
+	@echo -e "$(BLUE)[INFO]$(NC) Port-forwarding pgvector (localhost:$(PGVECTOR_LOCAL_PORT) -> 5432)..."
+# @oc port-forward svc/pgvector $(PGVECTOR_LOCAL_PORT):5432 -n $(NAMESPACE) &>/dev/null &
+	@sleep 2
+	@echo -e "$(GREEN)[SUCCESS]$(NC) Port-forwards active:"
+	@echo -e "  llama-stack: http://localhost:$(LLAMASTACK_LOCAL_PORT)"
+	@echo -e "  pgvector:    localhost:$(PGVECTOR_LOCAL_PORT)"
+	@echo -e ""
+	@echo -e "$(BLUE)[INFO]$(NC) Loading suggested questions from ConfigMap..."
+	@SUGGESTED_QUESTIONS_JSON=$$(oc get configmap "$(RAG_CHART)-suggested-questions" -n $(NAMESPACE) -o jsonpath='{.data.RAG_QUESTION_SUGGESTIONS}' 2>/dev/null || true); \
+	if [ -n "$$SUGGESTED_QUESTIONS_JSON" ]; then \
+		echo -e "$(GREEN)[SUCCESS]$(NC) Loaded RAG_QUESTION_SUGGESTIONS from ConfigMap."; \
+	else \
+		echo -e "$(YELLOW)[WARNING]$(NC) Suggested questions ConfigMap not found; using empty suggestions."; \
+		SUGGESTED_QUESTIONS_JSON='{}'; \
+	fi; \
+	echo -e "$(BLUE)[INFO]$(NC) Starting Streamlit application..."; \
+	cd $(FRONTEND_DIR) && \
+	LLAMA_STACK_ENDPOINT=http://localhost:$(LLAMASTACK_LOCAL_PORT) \
+	RAG_QUESTION_SUGGESTIONS="$$SUGGESTED_QUESTIONS_JSON" \
+	bash start.sh
+
+.PHONY: dev-stop
+dev-stop: ## Stop Streamlit, port-forwards, and optionally uninstall
+	@echo -e "$(BLUE)[INFO]$(NC) Stopping Streamlit application..."
+	@-pkill -f "streamlit run" 2>/dev/null ||:
+	@echo -e "$(BLUE)[INFO]$(NC) Stopping port-forwards..."
+	@-pkill -f "oc port-forward.*svc/llamastack" 2>/dev/null ||:
+	@-pkill -f "oc port-forward.*svc/pgvector" 2>/dev/null ||:
+	@echo -e "$(GREEN)[SUCCESS]$(NC) Local dev environment stopped."
+	@if [ -n "$(NAMESPACE)" ] && helm status $(RAG_CHART) -n $(NAMESPACE) >/dev/null 2>&1; then \
+		if [ "$(INTERACTIVE)" = "true" ]; then \
+			read -p "Do you want to uninstall RAG from namespace $(NAMESPACE)? [y/N] " answer; \
+			if [ "$$answer" = "y" ] || [ "$$answer" = "Y" ]; then \
+				$(MAKE) uninstall NAMESPACE=$(NAMESPACE); \
+			else \
+				echo -e "$(BLUE)[INFO]$(NC) RAG deployment left running in $(NAMESPACE)."; \
+			fi; \
+		else \
+			echo -e "$(BLUE)[INFO]$(NC) Non-interactive mode: RAG deployment left running in $(NAMESPACE)."; \
+		fi; \
+	fi
 
 # Validation targets
 .PHONY: validate
diff --git a/deploy/helm/rag/Chart.lock b/deploy/helm/rag/Chart.lock
index f3dfd713..c27155a4 100644
--- a/deploy/helm/rag/Chart.lock
+++ b/deploy/helm/rag/Chart.lock
@@ -10,12 +10,12 @@ dependencies:
   version: 0.5.6
 - name: ingestion-pipeline
   repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
-  version: 0.6.6
+  version: 0.7.0
 - name: llama-stack
   repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
-  version: 0.6.11
+  version: 0.7.0
 - name: mcp-servers
   repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
   version: 0.5.15
-digest: sha256:1065a9cbf8dfb460fd9c9a6d3571fdfc33e3693503aae6535e07e75919a6c9f2
-generated: "2026-02-13T13:19:24.192726731-05:00"
+digest: sha256:c8f91ea567adc2c26fab37dd988f33b830320e93afdf38397455a149eae79b18
+generated: "2026-02-18T00:51:25.763867-05:00"
diff --git a/deploy/helm/rag/Chart.yaml b/deploy/helm/rag/Chart.yaml
index d89da951..053ea034 100644
--- a/deploy/helm/rag/Chart.yaml
+++ b/deploy/helm/rag/Chart.yaml
@@ -19,11 +19,11 @@ dependencies:
     repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
     condition: configure-pipeline.enabled
   - name: ingestion-pipeline
-    version: 0.6.6
+    version: 0.7.0
     repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
     condition: ingestion-pipeline.enabled
   - name: llama-stack
-    version: 0.6.11
+    version: 0.7.0
     repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
     condition: llama-stack.enabled
   - name: mcp-servers
diff --git a/frontend/llama_stack_ui/distribution/ui/modules/utils.py b/frontend/llama_stack_ui/distribution/ui/modules/utils.py
index a706e786..68cbc2de 100644
--- a/frontend/llama_stack_ui/distribution/ui/modules/utils.py
+++ b/frontend/llama_stack_ui/distribution/ui/modules/utils.py
@@ -111,22 +111,32 @@ def get_suggestions_for_databases(selected_dbs, all_vector_dbs):
     if not suggestions_map:
         return []
 
-    # Create a mapping from vector_db_name to id
-    db_name_to_id = {
-        get_vector_db_name(vdb): vdb.id
+    # Build a mapping from displayed DB name to the full DB object so we can
+    # resolve all possible identifiers used by different backend versions.
+    db_name_to_obj = {
+        get_vector_db_name(vdb): vdb
         for vdb in all_vector_dbs
     }
 
     for db_name in selected_dbs:
-        # Get the id for this database name
-        db_id = db_name_to_id.get(db_name)
+        # Try several keys because the selected UI name may differ from the
+        # suggestion map key (e.g. vector_store_name/identifier/id/display name).
+        vdb = db_name_to_obj.get(db_name)
+        candidate_keys = []
+        if vdb:
+            candidate_keys.extend([
+                getattr(vdb, "vector_store_name", None),
+                getattr(vdb, "identifier", None),
+                getattr(vdb, "id", None),
+                getattr(vdb, "name", None),
+            ])
+        candidate_keys.append(db_name)
 
-        # Try both the id and the db_name as keys in the suggestions map
         questions = None
-        if db_id and db_id in suggestions_map:
-            questions = suggestions_map[db_id]
-        elif db_name in suggestions_map:
-            questions = suggestions_map[db_name]
+        for key in candidate_keys:
+            if key and key in suggestions_map:
+                questions = suggestions_map[key]
+                break
 
         if questions:
             for question in questions:
diff --git a/frontend/llama_stack_ui/distribution/ui/page/distribution/models.py b/frontend/llama_stack_ui/distribution/ui/page/distribution/models.py
index d9252ec9..2fbb5dfe 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/distribution/models.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/distribution/models.py
@@ -19,7 +19,7 @@ def models():
     if not model_list:
         st.info("No models available.")
         return
-    models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()}
+    models_info = {m.id: m.to_dict() for m in llama_stack_api.client.models.list()}
 
     # Let user select and view a model
     selected_model = st.selectbox("Select a model", list(models_info.keys()))
diff --git a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
index 56bc38de..f0b6c916 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
@@ -19,13 +19,14 @@
 logger = logging.getLogger(__name__)
 
 
-def build_response_tools(toolgroup_selection, selected_vector_dbs, client):
+def build_response_tools(toolgroup_selection, selected_vector_dbs, top_k, client):
     """
     Convert toolgroup selections to LlamaStack Responses API compatible tool format.
 
     Args:
         toolgroup_selection: List of selected toolgroup IDs
         selected_vector_dbs: List of selected vector database names
+        top_k: Maximum file search results per query
         client: LlamaStack client instance
 
     Returns:
@@ -44,6 +45,7 @@ def build_response_tools(toolgroup_selection, selected_vector_dbs, client):
                 # Use file_search tool format
                 agent_tools.append({
                     "type": "file_search",
+                    "max_num_results": top_k,
                     "vector_store_ids": list(vector_db_ids),
                 })
         elif "web_search" in toolgroup_name or "search" in toolgroup_name.lower():
@@ -303,6 +305,18 @@ def stream_agent_response(response, state, selected_vector_dbs):
         logger.debug("Chunk #%s: type=%s", chunk_count, getattr(chunk, 'type', 'NO_TYPE'))
         logger.debug("  -> Full chunk: %s", chunk)
 
+        # Some server failures arrive as an error payload with type=None.
+        if hasattr(chunk, 'error') and chunk.error:
+            if isinstance(chunk.error, dict):
+                error_msg = chunk.error.get("message", "Unknown error")
+            else:
+                error_msg = str(chunk.error)
+            st.error(
+                f"❌ Error: {error_msg}"
+            )
+            logger.debug("Response stream error: %s", error_msg)
+            break
+
         if hasattr(chunk, 'type'):
             should_stop = process_chunk_by_type(chunk, state, selected_vector_dbs)
             if should_stop:
@@ -334,7 +348,10 @@ def agent_process_prompt(prompt, state, config):
     """Agent-based mode: Use Responses API with automatic tool calling."""
     # Build tools list from selected toolgroups
     tools = build_response_tools(
-        config.toolgroup_selection, config.selected_vector_dbs, llama_stack_api.client
+        config.toolgroup_selection,
+        config.selected_vector_dbs,
+        config.sampling.top_k,
+        llama_stack_api.client,
     ) if config.toolgroup_selection else None
 
     # Build request for Responses API
@@ -346,14 +363,24 @@ def agent_process_prompt(prompt, state, config):
         "temperature": config.sampling.temperature,
         "max_infer_iters": config.sampling.max_infer_iters,
         "stream": True,
+        "max_output_tokens": config.sampling.max_tokens,
+
     }
+    logger.debug("************************************************")
+    logger.debug("Request Args: %s", request_kwargs)
+    logger.debug("************************************************")
 
     # Add tools if available
     if tools:
         request_kwargs["tools"] = tools
 
     logger.debug("Request: %s", request_kwargs)
-    response = llama_stack_api.client.responses.create(**request_kwargs)
+    try:
+        response = llama_stack_api.client.responses.create(**request_kwargs)
+    except Exception as e:  # pylint: disable=broad-exception-caught
+        st.error(f"❌ Error: {str(e)}")
+        logger.debug("Agent mode create() error: %s", e)
+        return
 
     # Stream response and update UI
     stream_agent_response(response, state, config.selected_vector_dbs)
diff --git a/frontend/llama_stack_ui/distribution/ui/page/playground/chat.py b/frontend/llama_stack_ui/distribution/ui/page/playground/chat.py
index 081d2335..8049522e 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/playground/chat.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/playground/chat.py
@@ -75,7 +75,8 @@ def fetch_models_and_tools():
 
     # Fetch models
     models = client.models.list()
-    model_list = [model.identifier for model in models if model.api_model_type == "llm"]
+    model_list = [model.id for model in models if model.custom_metadata.get("model_type") == "llm"]
+    
 
     # Fetch and categorize toolgroups
     tool_groups = client.toolgroups.list()
@@ -246,6 +247,18 @@ def render_sidebar_configuration(model_list, builtin_tools_list, mcp_tools_list)
         on_change=reset_agent,
         help="Controls randomness. Higher values = more random.",
     )
+    top_k = st.slider(
+        "Top K",
+        1, 50, 5, 1,
+        on_change=reset_agent,
+        help="Number of documents retrieved per vector store in Direct mode.",
+    )
+    max_tokens = st.slider(
+        "Max Tokens",
+        1, 4096, 512, 64,
+        on_change=reset_agent,
+        help="Maximum number of tokens to generate.",
+    )
     max_infer_iters = st.slider(
         "Max Inference Iterations",
         1, 50, 10, 1,
@@ -270,7 +283,9 @@ def render_sidebar_configuration(model_list, builtin_tools_list, mcp_tools_list)
         'selected_vector_dbs': selected_vector_dbs,
         'toolgroup_selection': toolgroup_selection,
         'temperature': temperature,
+        'top_k': top_k,
         'max_infer_iters': max_infer_iters,
+        'max_tokens': max_tokens,
         'system_prompt': system_prompt,
     }
 
@@ -344,7 +359,9 @@ def initialize_session_state():
 class SamplingParams:
     """Sampling parameters for model inference."""
     temperature: float
+    top_k: int
     max_infer_iters: int
+    max_tokens: int
 
 
 @dataclass
@@ -556,7 +573,9 @@ def tool_chat_page():
         selected_vector_dbs=sidebar_config['selected_vector_dbs'],
         sampling=SamplingParams(
             temperature=sidebar_config['temperature'],
-            max_infer_iters=sidebar_config['max_infer_iters']
+            top_k=sidebar_config['top_k'],
+            max_infer_iters=sidebar_config['max_infer_iters'],
+            max_tokens=sidebar_config['max_tokens'],
         )
     )
 
diff --git a/frontend/llama_stack_ui/distribution/ui/page/playground/direct.py b/frontend/llama_stack_ui/distribution/ui/page/playground/direct.py
index 1b4744b0..daff6a29 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/playground/direct.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/playground/direct.py
@@ -49,7 +49,7 @@ def extract_text_from_search_result(result):
     return clean_text(text) if text else None
 
 
-def search_vector_store_direct(prompt, vector_db_id, vector_db_name, state):
+def search_vector_store_direct(prompt, vector_db_id, vector_db_name, top_k, state):
     """Search vector store and extract context for Direct mode."""
     search_results = []
     context_parts = []
@@ -65,6 +65,7 @@ def search_vector_store_direct(prompt, vector_db_id, vector_db_name, state):
     search_response = llama_stack_api.client.vector_stores.search(
         vector_store_id=vector_db_id,
         query=prompt,
+        max_num_results=top_k,
     )
 
     logger.debug("Search response: %s", search_response)
@@ -198,7 +199,7 @@ def direct_process_prompt(prompt, state, config):
             vector_db_id = vector_db.id
             vector_db_name = get_vector_db_name(vector_db)
             search_results, parts, display = search_vector_store_direct(
-                prompt, vector_db_id, vector_db_name, state
+                prompt, vector_db_id, vector_db_name, config.sampling.top_k, state
             )
             if search_results:
                 all_search_results.append((vector_db_name, display))
@@ -216,6 +217,7 @@ def direct_process_prompt(prompt, state, config):
             model=config.model,
             messages=messages,
             temperature=config.sampling.temperature,
+            max_tokens=config.sampling.max_tokens,
             stream=True,
         )
 
diff --git a/frontend/pyproject.toml b/frontend/pyproject.toml
index 41c56e7d..db89508e 100644
--- a/frontend/pyproject.toml
+++ b/frontend/pyproject.toml
@@ -10,10 +10,10 @@ requires-python = ">=3.12"
 dependencies = [
     "streamlit",
     "pandas",
-	"llama-stack-client==0.3.5",
+	"llama-stack-client==0.5.0",
     "requests",
     "streamlit-option-menu",
-    "llama-stack==0.3.5",
+    "llama-stack==0.5.0",
     "fire",
     "asyncpg",
 ]
diff --git a/frontend/start.sh b/frontend/start.sh
index fd0edd93..394b4e47 100755
--- a/frontend/start.sh
+++ b/frontend/start.sh
@@ -7,5 +7,18 @@ if [ -n "$TAVILY_SEARCH_API_KEY" ]; then
     export TAVILY_SEARCH_API_KEY
 fi
 
-uv sync && \
-uv run streamlit run llama_stack_ui/distribution/ui/app.py --server.runOnSave true --server.fileWatcherType auto
\ No newline at end of file
+set -euo pipefail
+
+# Ensure project dependencies and the local venv exist.
+uv sync
+
+PYTHON_BIN=".venv/bin/python"
+if [ ! -x "$PYTHON_BIN" ]; then
+    echo "[ERROR] Expected Python interpreter not found: $PYTHON_BIN"
+    exit 1
+fi
+
+echo "[INFO] Using Python interpreter: $("$PYTHON_BIN" -c 'import sys; print(sys.executable)')"
+echo "[INFO] Python version: $("$PYTHON_BIN" --version)"
+
+"$PYTHON_BIN" -m streamlit run llama_stack_ui/distribution/ui/app.py --server.runOnSave true --server.fileWatcherType auto
\ No newline at end of file
diff --git a/frontend/uv.lock b/frontend/uv.lock
new file mode 100644
index 00000000..2a59ca7f
--- /dev/null
+++ b/frontend/uv.lock
@@ -0,0 +1,2575 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
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+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
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From a4a4826bdd43dce3ca36222b2c2c8ef143b2afad Mon Sep 17 00:00:00 2001
From: Michael Dawson <midawson@redhat.com>
Date: Tue, 17 Feb 2026 21:56:54 -0500
Subject: [PATCH 04/15] feat: add evaluations for RAG quickst

  - add framework to run conversations against RAG quickstart
    UI and capture the user request, agent response along
    with the RAG results shown in the UI
  - add framework to evaluate captured conversation using
    deep_eval to evaluate the response given "expected"
    RAG results and to evaluate the actual RAG chunks against
    the expected answer and the "expected" RAG results
  - add initial set of conversations for the hr and legal
    databases using the questions suggested in the UI
  - see evaluations/README.md on how to setup and run

Signed-off-by: Michael Dawson <midawson@redhat.com>
---
 deploy/helm/Makefile                          |   12 +
 evaluations/.gitignore                        |    7 +
 evaluations/README.md                         |  114 +
 evaluations/bad-conversations/README.md       |   26 +
 .../fail_answer_relevance.json                |   30 +
 .../fail_chunk_alignment.json                 |   32 +
 .../bad-conversations/fail_chunk_count.json   |   40 +
 .../fail_chunk_deduplication.json             |   34 +
 .../fail_contextual_precision.json            |   34 +
 .../fail_contextual_recall.json               |   30 +
 .../fail_contextual_relevancy.json            |   36 +
 .../fail_factual_consistency.json             |   30 +
 .../bad-conversations/fail_faithfulness.json  |   30 +
 .../fail_response_accuracy_contradiction.json |   30 +
 .../fail_response_accuracy_hallucination.json |   31 +
 .../fail_response_completeness.json           |   30 +
 .../hr_benefits_test_fail.json                |   34 +
 evaluations/conftest.py                       |   88 +
 .../hr/hr_benefits_eap_direct.json            |   21 +
 .../hr/hr_benefits_enrollment_direct.json     |   19 +
 .../hr_benefits_health_insurance_direct.json  |   21 +
 .../hr/hr_benefits_parental_leave_direct.json |   19 +
 .../hr/hr_benefits_retirement_direct.json     |   21 +
 .../hr/hr_benefits_test_agent.json            |   31 +
 ...r_benefits_test_agent_all_collections.json |   37 +
 .../hr/hr_benefits_test_direct.json           |   31 +
 .../hr/hr_benefits_test_fail.json             |   30 +
 .../hr/hr_benefits_vacation_days_direct.json  |   21 +
 .../legal_compliance_requirements_direct.json |   22 +
 .../legal_dispute_resolution_direct.json      |   21 +
 .../legal_intellectual_property_direct.json   |   21 +
 .../legal_key_contract_terms_direct.json      |   21 +
 .../legal/legal_liability_clause_direct.json  |   22 +
 .../legal_termination_conditions_direct.json  |   21 +
 evaluations/deep_eval_rag.py                  |  865 +++++++
 evaluations/evaluate.py                       |  140 ++
 evaluations/get_rag_metrics.py                |  392 ++++
 evaluations/helpers/__init__.py               |    1 +
 evaluations/helpers/custom_llm.py             |  379 +++
 evaluations/helpers/endpoint.py               |   81 +
 evaluations/helpers/playwright_setup.py       |   44 +
 evaluations/helpers/token_counter.py          |   70 +
 evaluations/pyproject.toml                    |   22 +
 evaluations/test_conversations_ui.py          |  951 ++++++++
 evaluations/uv.lock                           | 2058 +++++++++++++++++
 45 files changed, 6050 insertions(+)
 create mode 100644 evaluations/.gitignore
 create mode 100644 evaluations/README.md
 create mode 100644 evaluations/bad-conversations/README.md
 create mode 100644 evaluations/bad-conversations/fail_answer_relevance.json
 create mode 100644 evaluations/bad-conversations/fail_chunk_alignment.json
 create mode 100644 evaluations/bad-conversations/fail_chunk_count.json
 create mode 100644 evaluations/bad-conversations/fail_chunk_deduplication.json
 create mode 100644 evaluations/bad-conversations/fail_contextual_precision.json
 create mode 100644 evaluations/bad-conversations/fail_contextual_recall.json
 create mode 100644 evaluations/bad-conversations/fail_contextual_relevancy.json
 create mode 100644 evaluations/bad-conversations/fail_factual_consistency.json
 create mode 100644 evaluations/bad-conversations/fail_faithfulness.json
 create mode 100644 evaluations/bad-conversations/fail_response_accuracy_contradiction.json
 create mode 100644 evaluations/bad-conversations/fail_response_accuracy_hallucination.json
 create mode 100644 evaluations/bad-conversations/fail_response_completeness.json
 create mode 100644 evaluations/bad-conversations/hr_benefits_test_fail.json
 create mode 100644 evaluations/conftest.py
 create mode 100644 evaluations/conversations/hr/hr_benefits_eap_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_enrollment_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_health_insurance_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_parental_leave_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_retirement_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_test_agent.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_test_agent_all_collections.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_test_direct.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_test_fail.json
 create mode 100644 evaluations/conversations/hr/hr_benefits_vacation_days_direct.json
 create mode 100644 evaluations/conversations/legal/legal_compliance_requirements_direct.json
 create mode 100644 evaluations/conversations/legal/legal_dispute_resolution_direct.json
 create mode 100644 evaluations/conversations/legal/legal_intellectual_property_direct.json
 create mode 100644 evaluations/conversations/legal/legal_key_contract_terms_direct.json
 create mode 100644 evaluations/conversations/legal/legal_liability_clause_direct.json
 create mode 100644 evaluations/conversations/legal/legal_termination_conditions_direct.json
 create mode 100755 evaluations/deep_eval_rag.py
 create mode 100755 evaluations/evaluate.py
 create mode 100644 evaluations/get_rag_metrics.py
 create mode 100644 evaluations/helpers/__init__.py
 create mode 100644 evaluations/helpers/custom_llm.py
 create mode 100644 evaluations/helpers/endpoint.py
 create mode 100644 evaluations/helpers/playwright_setup.py
 create mode 100644 evaluations/helpers/token_counter.py
 create mode 100644 evaluations/pyproject.toml
 create mode 100644 evaluations/test_conversations_ui.py
 create mode 100644 evaluations/uv.lock

diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index 48ca1093..eef2ddc7 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -194,6 +194,8 @@ helm_llm_service_args = \
 helm_llama_stack_args = \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
+    $(if $(LLM_ID),--set global.models.$(LLM).id='$(LLM_ID)',) \
+    $(if $(SAFETY_ID),--set global.models.$(SAFETY).id='$(SAFETY_ID)',) \
     $(if $(LLM_URL),--set global.models.$(LLM).url='$(LLM_URL)',) \
     $(if $(SAFETY_URL),--set global.models.$(SAFETY).url='$(SAFETY_URL)',) \
     $(if $(LLM_API_TOKEN),--set global.models.$(LLM).apiToken='$(LLM_API_TOKEN)',) \
@@ -246,6 +248,8 @@ help: ## Show this help message
 	@echo -e "$(GREEN)Command-Line Parameters (override values file):$(NC)"
 	@echo -e "  LLM           - Enable specific LLM model (e.g., llama-3-2-3b-instruct)"
 	@echo -e "  SAFETY        - Enable specific safety model (e.g., llama-guard-3-8b)"
+	@echo -e "  LLM_ID        - Model ID for LLM (required for remote models)"
+	@echo -e "  SAFETY_ID     - Model ID for safety model (required for remote models)"
 	@echo -e "  LLM_TOLERATION - Set toleration for LLM model (e.g., nvidia.com/gpu)"
 	@echo -e "  SAFETY_TOLERATION - Set toleration for safety model"
 	@echo -e "  DEVICE        - Set device type: cpu, gpu, hpu or xeon (default: gpu)"
@@ -576,6 +580,10 @@ install: ## Install the RAG deployment
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].effect=NoSchedule"; \
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].operator=Exists"; \
 		fi; \
+		if [ -n "$(LLM_ID)" ]; then \
+			echo -e "$(BLUE)[INFO]$(NC) Setting LLM ID: $(LLM_ID)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).id=$(LLM_ID)"; \
+		fi; \
 		if [ -n "$(LLM_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM URL: $(LLM_URL)"; \
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).url=$(LLM_URL)"; \
@@ -594,6 +602,10 @@ install: ## Install the RAG deployment
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(SAFETY).tolerations[0].effect=NoSchedule"; \
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(SAFETY).tolerations[0].operator=Exists"; \
 		fi; \
+		if [ -n "$(SAFETY_ID)" ]; then \
+			echo -e "$(BLUE)[INFO]$(NC) Setting SAFETY ID: $(SAFETY_ID)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(SAFETY).id=$(SAFETY_ID)"; \
+		fi; \
 		if [ -n "$(SAFETY_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting SAFETY URL: $(SAFETY_URL)"; \
 			HELM_ARGS="$$HELM_ARGS --set global.models.$(SAFETY).url='$(SAFETY_URL)'"; \
diff --git a/evaluations/.gitignore b/evaluations/.gitignore
new file mode 100644
index 00000000..362f1b21
--- /dev/null
+++ b/evaluations/.gitignore
@@ -0,0 +1,7 @@
+# Test artifacts - can be regenerated
+results/conversation_results/*.json
+results/conversation_results/screenshots/
+results/deep_eval_results/
+
+# Playwright browsers
+bin/
diff --git a/evaluations/README.md b/evaluations/README.md
new file mode 100644
index 00000000..94a70ab8
--- /dev/null
+++ b/evaluations/README.md
@@ -0,0 +1,114 @@
+# RAG Evaluations
+
+Two-step process: first run UI tests to collect conversations, then run DeepEval to score them.
+
+## Setup
+
+First deploy the RAG quickstart
+
+Second change into the evaluations directory and setup
+
+```bash
+cd evaluations
+uv sync
+```
+
+Playwright Chromium is auto-installed to `evaluations/bin/` on first run.
+
+## Running both steps together
+
+Set environment variables to configure the evaluator LLM, then run `evaluate.py`:
+
+```bash
+export LLM_URL=http://localhost:8321/v1
+export LLM_API_TOKEN=dummy
+export LLM_ID=llama-4-scout-17b-16e-w4a16
+
+uv run python evaluate.py
+```
+
+## Running steps individually
+
+**Step 1 — Collect conversations** (Playwright drives the RAG UI, saves responses + retrieved chunks to `results/conversation_results/`):
+
+```bash
+# Run all conversation tests
+uv run pytest test_conversations_ui.py
+
+# Run a category of tests (subdirectory under conversations/)
+uv run pytest test_conversations_ui.py --subdir=hr
+uv run pytest test_conversations_ui.py --subdir=legal
+
+# Run a specific test
+uv run pytest test_conversations_ui.py -k "hr_benefits"
+
+# Debug mode (visible browser, slow)
+uv run pytest test_conversations_ui.py -v --headed --slowmo=1000
+```
+
+**Step 2 — Score with DeepEval** (reads Step 1 output, evaluates with an LLM-as-judge):
+
+```bash
+uv run python deep_eval_rag.py
+```
+
+**Key options for Step 2:**
+
+| Flag | Default | Description |
+|---|---|---|
+| `--api-endpoint` | `$LLM_URL` | OpenAI-compatible endpoint |
+| `--api-key` | `$LLM_API_TOKEN` | API key |
+| `--stage` | `both` | `1` (conversational only), `2` (retrieval only), `both` |
+| `--max-concurrent` | `4` | Test cases evaluated simultaneously |
+| `--max-concurrent-calls` | `16` | Max concurrent LLM API calls |
+| `--debug` | off | Verbose HTTP/retry logging |
+
+Results are saved to `results/deep_eval_results/evaluation_results_<timestamp>.json`.
+
+## Validating the metrics
+
+The `bad-conversations/` directory contains conversations with known incorrect responses. Use these to verify that the metrics are correctly identifying bad outputs without needing to run UI tests:
+
+```bash
+uv run python evaluate.py --check
+```
+
+## Metrics
+
+**Stage 1 — Conversational** (per conversation):
+These metrics validate the answer from the agent against "idealized" retrieval
+chunks from the source documents that we've defined as part of the test case.
+It tests how well the agent generated the answer from the chunks it received. The
+agent may do a good job despite retrieval being poor.
+- **Response Accuracy** — claims in response are supported by retrieved context
+- **Response Completeness** — response covers key facts from retrieved context
+- **Answer Relevance** — response addresses the actual question asked
+
+**Stage 2 — Retrieval** (per turn with `expected_rag_content`):
+These metrics validate how good the RAG retrieval was. To keep runtime/LLM capability
+needed to a resonable level, we truncate to the first 10 actual RAG chunks returned
+for these checks. The chunk count metric failing will show you when there were
+more than number of chunks to we we truncated.
+- **Chunk Count / Deduplication** — basic retrieval hygiene checks
+- **Chunk Alignment** — actual chunks cover same content as expected chunks
+- **Contextual Precision** — relevant chunks ranked above irrelevant ones
+- **Contextual Relevancy** — each retrieved chunk is relevant to the query
+- **Faithfulness** — response claims are grounded in retrieved context
+
+## Directory structure
+
+```
+evaluations/
+├── conversations/               # Test definitions (JSON)
+├── results/
+│   ├── conversation_results/    # Output from Step 1
+│   └── deep_eval_results/       # Output from Step 2
+├── evaluate.py                  # Runs Step 1 + Step 2 together
+├── test_conversations_ui.py     # Step 1 — Playwright runner
+├── deep_eval_rag.py             # Step 2 — DeepEval scorer
+├── get_rag_metrics.py           # Metric definitions
+└── helpers/
+    ├── custom_llm.py            # OpenAI-compatible LLM wrapper
+    ├── endpoint.py              # RAG UI endpoint detection
+    └── token_counter.py         # Token usage tracking
+```
diff --git a/evaluations/bad-conversations/README.md b/evaluations/bad-conversations/README.md
new file mode 100644
index 00000000..4db521a0
--- /dev/null
+++ b/evaluations/bad-conversations/README.md
@@ -0,0 +1,26 @@
+# Known-Bad Conversations
+
+Test fixtures that deliberately trigger metric failures, used to validate that the evaluation framework correctly detects problems.
+
+Run with:
+```bash
+python evaluate.py --check
+```
+
+## Files
+
+| File | Primary Target | Expected Secondary Failures |
+|------|---------------|---------------------------|
+| `fail_response_accuracy_hallucination.json` | Response Accuracy (hallucination) | Chunk Alignment (actual has extra health retreat chunk not in expected) |
+| `fail_response_accuracy_contradiction.json` | Response Accuracy (contradiction) | None (all chunk facts covered, one detail contradicted: "fragile and prone to chipping" vs "Indestructible") |
+| `fail_response_completeness.json` | Response Completeness | None (one accurate claim, just incomplete) |
+| `fail_factual_consistency.json` | Response Accuracy (contradiction) | None (uses correct terminology, only the number is wrong) |
+| `fail_answer_relevance.json` | Answer Relevance | None (accurate and complete per chunks, but answers plan features instead of the cost question asked) |
+| `fail_chunk_count.json` | ChunkCountMetric | None (correct answer, just too many chunks retrieved) |
+| `fail_chunk_deduplication.json` | ChunkDeduplicationMetric | None |
+| `fail_chunk_alignment.json` | Chunk Alignment | Faithfulness (response grounded in expected chunks; actual chunks are different health content) |
+| `fail_contextual_recall.json` | ContextualRecall | Chunk Alignment (actual partial chunk ≠ full expected chunk) |
+| `fail_contextual_precision.json` | ContextualPrecision | None (all chunks are health-related so Contextual Relevancy passes; best chunk is ranked last so Precision fails) |
+| `fail_contextual_relevancy.json` | ContextualRelevancy | ContextualRecall (actual chunks don't cover expected output) |
+| `fail_faithfulness.json` | FaithfulnessMetric | None (Stage 1 passes — response matches expected chunks) |
+| `hr_benefits_test_fail.json` | Response Accuracy, Response Completeness | None (no actual_rag_content so Stage 2 does not run; response fabricates generic HR benefits and completely ignores the FantaCo-specific chunks) |
diff --git a/evaluations/bad-conversations/fail_answer_relevance.json b/evaluations/bad-conversations/fail_answer_relevance.json
new file mode 100644
index 00000000..19299dec
--- /dev/null
+++ b/evaluations/bad-conversations/fail_answer_relevance.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: User asks how much the health insurance costs per month. The chunks describe plan features (not cost). The assistant accurately and completely covers the plan features but never addresses the cost question. Should fail Answer Relevance; Sanity Check should pass (health insurance chunks for a health insurance question) and Response Completeness should pass (response covers all chunk content)."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "How much does FantaCo's health insurance cost per month?",
+      "expected_output": "The retrieved documents describe the plan features but do not mention monthly cost or premium amounts.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. It includes Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions and emergency glitter infusions."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_chunk_alignment.json b/evaluations/bad-conversations/fail_chunk_alignment.json
new file mode 100644
index 00000000..b529bbfa
--- /dev/null
+++ b/evaluations/bad-conversations/fail_chunk_alignment.json
@@ -0,0 +1,32 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: actual_rag_content contains health-related chunks (EAP, prescription coverage, wellness check-up) that are all on-topic but completely different content from the expected health insurance chunk. Should fail Chunk Alignment (actual content does not match expected). Faithfulness is an expected secondary failure because the response (correctly grounded in expected chunks) references content not present in the actual chunks."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "Employee Assistance Program (EAP): FantaCo provides all employees with access to confidential counseling services, including sessions with licensed therapists, goblin mediators, and certified emotional support centaurs. Available 24/7 via crystal ball or standard phone.",
+          "Prescription Potion Coverage: All medically necessary potions, elixirs, and tinctures are covered at 80% after meeting your annual cauldron deductible of 50 gold coins.",
+          "Annual Wellness Check-up: Each employee receives a complimentary annual health screening conducted by our resident physician and part-time sorcerer, Dr. Cornelius Willowmere, including a full aura assessment."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. It includes Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions and emergency glitter infusions."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_chunk_count.json b/evaluations/bad-conversations/fail_chunk_count.json
new file mode 100644
index 00000000..6d24deac
--- /dev/null
+++ b/evaluations/bad-conversations/fail_chunk_count.json
@@ -0,0 +1,40 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: actual_rag_content has 11 chunks, all relevant to the health insurance question, exceeding the max_chunks limit of 10. All Stage 1 and other Stage 2 metrics should pass (correct answer, all chunks on-topic); only ChunkCountMetric should fail."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist & Potion Master.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan: FantaCo provides comprehensive health, dental, and vision coverage. Benefits include Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for bespoke wellness potions.",
+          "Vision Benefit: Unicorn Tear Eye Drops are included in the health insurance plan and are available in single-use vials or as a monthly subscription for all covered employees.",
+          "Dental Benefit: Dragon Scale Dental Implants are covered under the dental plan and come with a lifetime warranty against dragon-related damage, including moonlight polishing sessions twice a year.",
+          "Sleep Health Benefit: Griffin Feather Pillows & Comforters are provided as part of the health plan and are professionally laundered and replaced on an annual basis.",
+          "Wellness Potions Benefit: The On-site Alchemist provides over 200 certified wellness potions as part of health coverage, including clarity draughts and energy elixirs.",
+          "Employee Assistance Program (EAP): Confidential counseling and mental wellness services are available to all health plan members, including sessions with licensed therapists.",
+          "Annual Wellness Check-up: Each employee receives a complimentary annual health screening as part of the health insurance plan.",
+          "Prescription Coverage: All medically necessary potions, elixirs, and tinctures are covered at 80% of cost after the annual health plan deductible is met.",
+          "Emergency Health Coverage: Emergency medical response is guaranteed to restore employees to full health within 24 hours as part of the insurance plan.",
+          "Specialist Referrals: The health plan provides access to a network of over 500 certified specialist healers for referrals.",
+          "Mental Health Support: All health plan members have access to six free mental health counseling sessions per year, with specialist referrals available upon request."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers comprehensive health benefits. The vision plan provides Unicorn Tear Eye Drops, available in single-use vials or as a monthly subscription. The dental plan includes Dragon Scale Dental Implants with a lifetime warranty and moonlight polishing sessions twice a year. Sleep health benefits include Griffin Feather Pillows and Comforters, laundered and replaced annually. The on-site alchemist provides over 200 certified wellness potions including clarity draughts and energy elixirs. Additional benefits include a confidential Employee Assistance Program with licensed therapist sessions, a complimentary annual health screening, prescription coverage at 80% after the annual deductible, emergency medical coverage restoring employees within 24 hours, access to over 500 certified specialist healers, and six free mental health counseling sessions per year."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_chunk_deduplication.json b/evaluations/bad-conversations/fail_chunk_deduplication.json
new file mode 100644
index 00000000..0762d155
--- /dev/null
+++ b/evaluations/bad-conversations/fail_chunk_deduplication.json
@@ -0,0 +1,34 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: actual_rag_content contains the same chunk repeated 5 times. Should fail ChunkDeduplicationMetric."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan, a comprehensive health, dental, and vision plan that includes Unlimited Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows & Comforters, and an On-site Alchemist & Potion Master."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_contextual_precision.json b/evaluations/bad-conversations/fail_contextual_precision.json
new file mode 100644
index 00000000..d2d1403c
--- /dev/null
+++ b/evaluations/bad-conversations/fail_contextual_precision.json
@@ -0,0 +1,34 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: actual_rag_content has the best health insurance chunk ranked LAST, preceded by four health-adjacent but less relevant chunks. Should fail ContextualPrecisionMetric because the most relevant chunk is ranked 5th. Contextual Relevancy should pass because all chunks are health-related."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops for vision, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "Employee Assistance Program (EAP): FantaCo provides all employees with access to confidential counseling services, including sessions with licensed therapists, goblin mediators, and certified emotional support centaurs. Available 24/7 via crystal ball or standard phone. All health plan members are eligible.",
+          "Prescription Potion Coverage: All medically necessary potions, elixirs, and tinctures are covered at 80% after meeting your annual cauldron deductible of 50 gold coins.",
+          "Annual Wellness Check-up: Each employee receives a complimentary annual health screening conducted by our resident physician and part-time sorcerer, Dr. Cornelius Willowmere, including a full aura assessment.",
+          "Mental Health Support: All employees have access to six free sessions per year with a licensed mind-healer, with referrals to specialist enchanters available upon request.",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan, a comprehensive health, dental, and vision plan that includes Unicorn Tear Eye Drops for vision, Dragon Scale Dental Implants, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_contextual_recall.json b/evaluations/bad-conversations/fail_contextual_recall.json
new file mode 100644
index 00000000..b7e5776f
--- /dev/null
+++ b/evaluations/bad-conversations/fail_contextual_recall.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: actual_rag_content contains only vision and dental benefits (Unicorn Tear Eye Drops and Dragon Scale Dental Implants). The expected_rag_content contains the full plan (all four benefits). The expected_output references all four benefits, so ContextualRecall fails. The response is detailed for the two supported benefits and brief for the two unsupported ones, keeping Faithfulness above the threshold (most claims verifiable against actual). Chunk Alignment is an expected secondary failure because actual differs from expected."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo's healthcare plan includes Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "FantaCo Healthcare Plan - Vision and Dental Benefits: Unlimited Unicorn Tear Eye Drops provide sparkling vision, granting the ability to spot a typo from a mile away and occasionally see rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo's healthcare plan includes Unlimited Unicorn Tear Eye Drops for sparkling vision—they let you spot a typo from a mile away and occasionally see rainbows where there are none. Dragon Scale Dental Implants are indestructible, pearly white, glow faintly in moonlight, and can open particularly stubborn pickle jars. The plan also provides Griffin Feather Pillows & Comforters and an On-site Alchemist & Potion Master."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_contextual_relevancy.json b/evaluations/bad-conversations/fail_contextual_relevancy.json
new file mode 100644
index 00000000..a1d3f8d7
--- /dev/null
+++ b/evaluations/bad-conversations/fail_contextual_relevancy.json
@@ -0,0 +1,36 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: Both expected_rag_content and actual_rag_content contain the same four off-topic chunks (commute, cry closet, vacation, cafeteria) - none relevant to the health insurance question. Chunk Alignment passes because actual matches expected. ContextualRelevancy fails because all actual chunks are off-topic. Secondary failures: Sanity Check (expected chunks are also off-topic) and ContextualRecall (actual chunks do not cover the expected output)."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course). 9. Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy.",
+          "& Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. 7. The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos.",
+          ". The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year.",
+          "Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\" 10. The \"Wish Upon a Star\" Bonus Program: Once a year, employees can submit one work-related wish to the FantaCo Benevolent Overlord Council."
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course). 9. Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy.",
+          "& Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. 7. The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos.",
+          ". The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year.",
+          "Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\" 10. The \"Wish Upon a Star\" Bonus Program: Once a year, employees can submit one work-related wish to the FantaCo Benevolent Overlord Council."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "I'm sorry, but the retrieved documents do not contain information about health insurance benefits. The available content covers other employee perks such as commute options with dragon transport and unicycle brigades, the Champagne & Catharsis Chamber, the vacation Adventure Allotments policy, and cafeteria and dietary accommodation details."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_factual_consistency.json b/evaluations/bad-conversations/fail_factual_consistency.json
new file mode 100644
index 00000000..4d3d7c5c
--- /dev/null
+++ b/evaluations/bad-conversations/fail_factual_consistency.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: Assistant response directly contradicts the expected chunks by stating '10 Adventure Units' when the chunks state '365 Adventure Units'. Should fail Response Accuracy (contradiction check)."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "How many vacation days do employees get?",
+      "expected_output": "FantaCo employees receive 365 Adventure Units per year under the \"Never-Ending Story of Glorious Time Off\" Vacation Policy. These can be used for any adventure, and unused units can be traded for company shares or a slightly used hovercraft.",
+      "expected_rag_content": {
+        "chunks": [
+          ". The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft."
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          ". The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo employees receive 10 Adventure Units per year under the \"Never-Ending Story of Glorious Time Off\" Vacation Policy. Unused Adventure Units can be traded for shares in the moon-cheese mining operation or a slightly used hovercraft."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_faithfulness.json b/evaluations/bad-conversations/fail_faithfulness.json
new file mode 100644
index 00000000..256bef1c
--- /dev/null
+++ b/evaluations/bad-conversations/fail_faithfulness.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: The actual_rag_content is a truncated chunk containing only the Adventure Units count. The assistant gives a complete accurate answer (matching expected chunks) that includes details about moon-cheese trading and the hovercraft option - claims which are not present in the truncated actual chunk. Stage 1 should pass (response matches expected chunks). FaithfulnessMetric should fail (response contains claims not supported by actual chunks)."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "How many vacation days do employees get?",
+      "expected_output": "FantaCo employees receive 365 Adventure Units per year under the \"Never-Ending Story of Glorious Time Off\" Vacation Policy. Unused units can be traded for shares in the moon-cheese mining operation or a slightly used hovercraft.",
+      "expected_rag_content": {
+        "chunks": [
+          ". The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft."
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo employees receive 365 Adventure Units per year under the \"Never-Ending Story of Glorious Time Off\" Vacation Policy. Unused Adventure Units can be traded for shares in the moon-cheese mining operation or a slightly used hovercraft."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_response_accuracy_contradiction.json b/evaluations/bad-conversations/fail_response_accuracy_contradiction.json
new file mode 100644
index 00000000..c92720aa
--- /dev/null
+++ b/evaluations/bad-conversations/fail_response_accuracy_contradiction.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: Assistant correctly covers all four plan benefits but states Dragon Scale Dental Implants are 'fragile and prone to chipping' when the chunk explicitly says they are 'Indestructible'. Should fail Response Accuracy (contradiction); Response Completeness should pass because all chunk facts are present."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan: FantaCo provides comprehensive health, dental, and vision coverage. Benefits include Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are Indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master providing bespoke wellness potions and emergency glitter infusions."
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan: FantaCo provides comprehensive health, dental, and vision coverage. Benefits include Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are Indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master providing bespoke wellness potions and emergency glitter infusions."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. It includes Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are fragile and prone to chipping, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions and emergency glitter infusions."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_response_accuracy_hallucination.json b/evaluations/bad-conversations/fail_response_accuracy_hallucination.json
new file mode 100644
index 00000000..df4c6c7e
--- /dev/null
+++ b/evaluations/bad-conversations/fail_response_accuracy_hallucination.json
@@ -0,0 +1,31 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: Assistant correctly covers all four plan benefits and adds a 'free annual health retreat' which IS present in actual_rag_content but NOT in expected_rag_content. Stage 1 Response Accuracy fails because Stage 1 uses expected_rag_content (which has no health retreat) as retrieval context. Stage 2 Faithfulness passes because the health retreat IS in actual_rag_content. Chunk Alignment is an expected secondary failure because actual has an extra health retreat chunk not in expected."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan with Unicorn Tear Eye Drops, Dragon Scale Dental Implants, Griffin Feather Pillows, and an On-site Alchemist.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan: FantaCo provides comprehensive health, dental, and vision coverage. Benefits include Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master providing bespoke wellness potions and emergency glitter infusions."
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan: FantaCo provides comprehensive health, dental, and vision coverage. Benefits include Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master providing bespoke wellness potions and emergency glitter infusions.",
+          "Annual Health Retreat: All employees covered by the health plan are entitled to a free annual health retreat at a luxury wellness spa, fully covered by the plan."
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. It includes Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that glow faintly in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions. Employees are also entitled to a free annual health retreat at a luxury wellness spa, fully covered by the plan."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/fail_response_completeness.json b/evaluations/bad-conversations/fail_response_completeness.json
new file mode 100644
index 00000000..ad1e479a
--- /dev/null
+++ b/evaluations/bad-conversations/fail_response_completeness.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "KNOWN-BAD: Assistant response only mentions one benefit when the expected chunks cover five distinct benefits. Should fail Response Completeness."
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan which includes: (1) Unlimited Unicorn Tear Eye Drops for sparkling vision, (2) Dragon Scale Dental Implants that are indestructible, (3) Griffin Feather Pillows & Comforters for regenerative sleep, (4) an On-site Alchemist & Potion Master for wellness potions, and (5) a warning not to request the Philosopher's Stone.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      },
+      "actual_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking. It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. On-site Alchemist & Potion Master: For all your minor transmutation needs, bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo offers the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. A key benefit is Unlimited Unicorn Tear Eye Drops, which provide sparkling vision and the ability to spot a typo from a mile away."
+    }
+  ]
+}
diff --git a/evaluations/bad-conversations/hr_benefits_test_fail.json b/evaluations/bad-conversations/hr_benefits_test_fail.json
new file mode 100644
index 00000000..ff5b71bf
--- /dev/null
+++ b/evaluations/bad-conversations/hr_benefits_test_fail.json
@@ -0,0 +1,34 @@
+{
+  "metadata": {
+    "description": "Test HR benefits query where no collection was selected. Should fail as no actual rag checks were returned. From generated test run"
+  },
+  "config": {
+    "mode": "direct"
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "What are the HR benefits for FantaCo",
+      "expected_output": "FantaCo offers an extraordinarily comprehensive and whimsical benefits package designed to create workplace nirvana. The core benefits include: fully customizable \"Imagination Pod\" workspaces with themes ranging from Enchanted Forests to Interstellar Spaceships; a \"Fountain of Youth & General Awesomeness\" healthcare plan featuring Unicorn Tear Eye Drops, Dragon Scale Dental Implants, and Griffin Feather Pillows; a \"Never-Ending Story of Glorious Time Off\" vacation policy providing 365 \"Adventure Units\" per year for pursuits like throat-singing with Tuvan monks or journeys to Atlantis; and a \"Midas Touch & Beyond\" retirement plan that multiplies contributions by Jupiter's astrological alignment and provides a life-sized chocolate statue upon retirement. Additional benefits include personalized theme songs composed by Grammy-winning minstrels, mandatory Fun-tivities like Weekly Llama Grooming & Interpretive Dance Tea Parties and Monthly Office Jousting Tournaments, a \"Champagne, Compliments & Catharsis Chamber\" for stress relief, dragon chauffeur services for commuting, a Gourmet Galactic Cafeteria run by Chef Zorp from Planet Floopy-Doo, and an annual \"Wish Upon a Star\" bonus program where the Benevolent Overlord Council grants creative employee wishes. The package is designed to ensure employees are deliriously ecstatic, productive, and thoroughly entertained.",
+      "expected_rag_content": {
+        "chunks": [
+          "Welcome to the FantaCo Fabulous Benefits Emporium! (Where \"Good Enough\" Goes to Weep in a Corner, Possibly with a Tiny Umbrella in its Drink) At FantaCo, we don't just offer benefits; we bestow them with a flourish and a sprinkle of biodegradable glitter. We believe that a happy employee is a productive employee, and a deliriously ecstatic employee is probably about to invent cold fusion, write the next great epic poem, or at least figure out why the office printer always jams on a Friday afternoon right before a long weekend. So, buckle up, buttercup, because your journey into workplace nirvana, paved with good intentions and questionable puns, starts NOW!",
+          "The Gilded Cage (Your Workspace, Elevated to Absurd Heights): Forget drab cubicles or soul-crushing open-plan offices! Every FantaCo employee receives a fully customizable \"Imagination Pod.\" Choose your theme: Enchanted Forest (complete with miniature, self-cleaning waterfall and animatronic squirrels that offer surprisingly good career advice), Interstellar Spaceship (zero-gravity optional, but highly encouraged for brainstorming sessions – watch out for floating staplers!), a 1920s Speakeasy (password required for entry, flapper dress code on alternate Tuesdays, and a house band of highly skilled jazz-playing capybaras), or perhaps a Serene Zen Garden (with personal bonsai sensei on call, who also doubles as a life coach and competitive thumb-wrestling champion). Each pod comes standard with a self-stocking snack bar (curated by a Michelin-starred squirrel chef with a penchant for molecular gastronomy), a holographic mood projector that can simulate anything from a field of lavender to a mosh pit, and a voice-activated ambient soundscape generator (currently featuring \"Whale Songs in G Minor,\" \"The Gentle Hum of a Thousand Purring Kittens Attempting to Solve a Rubik's Cube,\" and \"Silent Disco For One\").",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions.",
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft.",
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)",
+          "Personalized Theme Song & Walk-On Music Service: Upon joining FantaCo, our in-house Grammy-award-winning (in an alternate dimension) minstrels and troubadours will compose a personalized theme song that plays whenever you enter a significant room or achieve a minor victory (like successfully unjamming the copier). Choose your genre: Epic Orchestral with a hint of theremin, Smooth Jazz infused with whale song, 8-Bit Chiptune with surprise opera solos, or even Gregorian Chant remixed by a famous DJ. (Warning: The \"Heavy Metal Polka Ska Fusion\" option is surprisingly popular and alarmingly catchy, leading to spontaneous office-wide dance-offs.)",
+          "Mandatory \"Fun-tivities\" & \"Whimsy Workshops\" (The Fun is Non-Negotiable, But Oh So Rewarding): Weekly Llama Grooming & Interpretive Dance Tea Parties: It's therapeutic, fosters interdepartmental communication, and the llamas have excellent fashion sense. Monthly Office Jousting Tournament (on bouncy castles, with pool noodles): Winner gets bragging rights, control of the office thermostat for a week, and the \"Scepter of Supreme Snacking Authority.\" (Lance safety course and dramatic monologue training mandatory). Spontaneous Flash Mob Drills & Lip Sync Battles: Be prepared to break into perfectly choreographed song and dance or deliver a show-stopping lip sync performance at any given moment. Jazz hands are essential, as is a working knowledge of early 90s power ballads. Competitive Office Chair Synchronized Swiveling & Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. Grace, precision, and the ability to avoid dizziness are key.",
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly).",
+          "Commute Concierge & Mythical Creature Chauffeur Service: Stuck in traffic? Our fleet of highly trained (and surprisingly well-mannered, though occasionally prone to hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course).",
+          "Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo, with Guest Appearances by Interdimensional Culinary Celebrities): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy and several alternate realities. Today's specials might include Neptunian Noodle Nibblers with extra nebulosity, Venusian Veggie Vol-au-vents that sing opera, Martian Magma Muffins (surprisingly cool), or the ever-popular Earthling \"Pizza.\" (Chef Zorp is still mastering that last one, but his enthusiasm, and his six tentacles, are infectious). Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\"",
+          "The \"Wish Upon a Star (Or a Passing Comet)\" Bonus Program: Once a year, employees can submit one work-related (or tangentially work-related, we're flexible) wish to the FantaCo \"Benevolent Overlord Council\" (BOC), which consists of the CEO, a wise old owl, and a Magic 8-Ball with a surprisingly good track record. Wishes can range from \"a slightly more ergonomic stapler that dispenses glitter\" to \"a company-sponsored expedition to find the Loch Ness Monster and teach it to play the ukulele.\" The BOC grants the most creative, amusing, and/or profoundly strange wishes. (Last year's winner wished for the office water cooler to dispense a different artisanal soda every hour. It was a very productive, if slightly hyper, quarter.)"
+        ]
+      }
+    },
+    {
+      "role": "assistant",
+      "content": "FantaCo, as a fictional company, likely has various HR benefits in place to attract and retain top talent. Here are some potential HR benefits that FantaCo might offer:\n**Core Benefits**\n1. **Health Insurance**: Comprehensive medical, dental, and vision coverage for employees and their families.\n2. **Retirement Plan**: A 401(k) or similar retirement savings plan with company matching contributions.\n3. **Paid Time Off (PTO)**: Vacation days, sick leave, and holidays off with pay.\n4. **Life Insurance**: Basic life insurance coverage with optional supplemental coverage.\n**Additional Benefits**\n1. **Flexible Work Arrangements**: Telecommuting options, flexible hours, or compressed workweeks.\n2. **Professional Development**: Training, mentorship, and education assistance programs to support career growth.\n3. **Employee Recognition**: Rewards and recognition programs to acknowledge outstanding performance.\n4. **Wellness Programs**: On-site fitness classes, mental health resources, or employee wellness initiatives.\n5. **Employee Assistance Program (EAP)**: Confidential counseling services for employees and their families.\n6. **Diversity, Equity, and Inclusion (DEI) Initiatives**: Programs to promote diversity, equity, and inclusion in the workplace.\n**Perks and Discounts**\n1. **Employee Discounts**: Discounts on FantaCo products or services.\n2. **Free Meals or Snacks**: Free or subsidized meals or snacks in the office.\n3. **On-site Amenities**: Access to on-site amenities like a gym, game room, or meditation room.\n4. **Employee Events**: Social events, team-building activities, or community volunteer opportunities.\n**Leave Policies**\n1. **Family Leave**: Paid parental leave, adoption leave, or family medical leave.\n2. **Bereavement Leave**: Paid time off for bereavement and funeral attendance.\n3. **Jury Duty Leave**: Paid time off for jury duty.\nKeep in mind that these are just hypothetical examples, and actual HR benefits may vary depending on FantaCo's specific policies, industry, and location."
+    }
+  ]
+}
diff --git a/evaluations/conftest.py b/evaluations/conftest.py
new file mode 100644
index 00000000..9f73a30f
--- /dev/null
+++ b/evaluations/conftest.py
@@ -0,0 +1,88 @@
+"""
+Pytest configuration for conversation evaluation tests.
+Reuses fixtures and patterns from tests/e2e_ui/
+"""
+
+import sys
+import time
+from pathlib import Path
+
+import pytest
+from playwright.sync_api import Page
+
+# Add project root and evaluations/ to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+sys.path.insert(0, str(Path(__file__).parent))
+
+# Ensure Playwright Chromium is installed in evaluations/bin/
+from helpers.playwright_setup import ensure_playwright_browsers
+from helpers.endpoint import get_rag_ui_endpoint
+
+ensure_playwright_browsers()
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--subdir",
+        default=None,
+        help="Subdirectory under conversations/ to run (e.g. 'legal'). Runs all subdirectories by default.",
+    )
+
+
+# Configuration
+RAG_UI_ENDPOINT = get_rag_ui_endpoint()
+TEST_TIMEOUT = 60000  # 60 seconds for responses
+
+
+@pytest.fixture(scope="session")
+def browser_context_args(browser_context_args):
+    """Configure browser context - matches e2e_ui setup"""
+    return {
+        **browser_context_args,
+        "viewport": {
+            "width": 1920,
+            "height": 1080,
+        },
+    }
+
+
+@pytest.fixture(autouse=True)
+def reset_chat_before_test(page: Page):
+    """
+    Navigate to RAG UI and reset chat state before each test.
+    This ensures clean state between tests (no conversation history).
+    """
+    # Navigate to app
+    max_retries = 3
+    for attempt in range(max_retries):
+        try:
+            page.goto(RAG_UI_ENDPOINT, timeout=60000, wait_until="domcontentloaded")
+            page.wait_for_load_state("networkidle", timeout=60000)
+
+            # Wait for Streamlit to initialize
+            time.sleep(2)
+
+            # Verify page loaded
+            if page.url.startswith(RAG_UI_ENDPOINT):
+                break
+        except Exception as e:
+            if attempt == max_retries - 1:
+                raise
+            print(f"Navigation attempt {attempt + 1} failed: {e}, retrying...")
+            time.sleep(2)
+
+    # Click "Clear Chat & Reset Config" button to ensure clean state
+    try:
+        clear_button = page.get_by_text("Clear Chat", exact=False).first
+        if clear_button.is_visible():
+            clear_button.click()
+            page.wait_for_load_state("networkidle")
+            time.sleep(2)
+    except Exception:
+        # If button not found, continue - app might already be clean
+        pass
+
+    yield page
+
+    # Cleanup after test (optional)
+    # Can add cleanup logic here if needed
diff --git a/evaluations/conversations/hr/hr_benefits_eap_direct.json b/evaluations/conversations/hr/hr_benefits_eap_direct.json
new file mode 100644
index 00000000..4f37f3db
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_eap_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test employee assistance program query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the employee assistance program?",
+      "expected_output": "While FantaCo does not have a traditional Employee Assistance Program (EAP), they offer a \"Champagne, Compliments & Catharsis Chamber\" (also known as the \"Cry Closet\") for employee wellbeing. When feeling overwhelmed, under-caffeinated, or existentially bewildered, employees can step into this luxurious, soundproofed chamber where they are greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized ego-boosting compliments and affirmations.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly)."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_enrollment_direct.json b/evaluations/conversations/hr/hr_benefits_enrollment_direct.json
new file mode 100644
index 00000000..ef29db9d
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_enrollment_direct.json
@@ -0,0 +1,19 @@
+{
+  "metadata": {
+    "description": "Test benefits enrollment query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "How do I enroll in benefits?",
+      "expected_output": "The FantaCo HR Benefits document does not contain specific information about how to enroll in benefits. The document describes the various benefits available at FantaCo, including healthcare, vacation, retirement, and other perks, but does not outline an enrollment process or procedure.",
+      "expected_rag_content": {
+        "chunks": []
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_health_insurance_direct.json b/evaluations/conversations/hr/hr_benefits_health_insurance_direct.json
new file mode 100644
index 00000000..a6533375
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_health_insurance_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test health insurance benefits query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the health insurance benefits offered?",
+      "expected_output": "FantaCo offers a comprehensive healthcare plan called the \"Fountain of Youth & General Awesomeness\" Healthcare Plan. It includes health, dental, and vision coverage with unique perks such as: Unlimited Unicorn Tear Eye Drops for sparkling vision, Dragon Scale Dental Implants that are indestructible and glow in moonlight, Griffin Feather Pillows & Comforters for regenerative sleep, and an On-site Alchemist & Potion Master for wellness potions and emergency glitter infusions.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions. (Warning: Do not request the Philosopher's Stone. HR gets very cross, and the paperwork is a nightmare.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_parental_leave_direct.json b/evaluations/conversations/hr/hr_benefits_parental_leave_direct.json
new file mode 100644
index 00000000..583ef6e9
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_parental_leave_direct.json
@@ -0,0 +1,19 @@
+{
+  "metadata": {
+    "description": "Test parental leave policy query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the parental leave policy?",
+      "expected_output": "The FantaCo HR Benefits document does not contain specific information about a parental leave policy. The document covers other time-off benefits through their \"Never-Ending Story of Glorious Time Off\" Vacation Policy, which provides 365 \"Adventure Units\" per year that could potentially be used for parental leave purposes, but no dedicated parental leave policy is described.",
+      "expected_rag_content": {
+        "chunks": []
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_retirement_direct.json b/evaluations/conversations/hr/hr_benefits_retirement_direct.json
new file mode 100644
index 00000000..2c5cf93c
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_retirement_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test retirement benefits query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the retirement benefits?",
+      "expected_output": "FantaCo offers the \"Midas Touch & Beyond\" Retirement Plan. Their 401(k) doesn't just match contributions - it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Upon retirement, instead of a gold watch, employees receive a life-sized statue of themselves crafted from the finest Belgian chocolate (or artisanal vegan carob), a lifetime supply of said chocolate/carob, and a personal bard to sing their praises at inconvenient moments.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_test_agent.json b/evaluations/conversations/hr/hr_benefits_test_agent.json
new file mode 100644
index 00000000..90f7f4dc
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_test_agent.json
@@ -0,0 +1,31 @@
+{
+  "metadata": {
+    "description": "Test HR benefits query with expected RAG content validation"
+  },
+  "config": {
+    "mode": "agent-based",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the HR benefits for FantaCo",
+      "expected_output": "FantaCo offers an extraordinarily comprehensive and whimsical benefits package designed to create workplace nirvana. The core benefits include: fully customizable \"Imagination Pod\" workspaces with themes ranging from Enchanted Forests to Interstellar Spaceships; a \"Fountain of Youth & General Awesomeness\" healthcare plan featuring Unicorn Tear Eye Drops, Dragon Scale Dental Implants, and Griffin Feather Pillows; a \"Never-Ending Story of Glorious Time Off\" vacation policy providing 365 \"Adventure Units\" per year for pursuits like throat-singing with Tuvan monks or journeys to Atlantis; and a \"Midas Touch & Beyond\" retirement plan that multiplies contributions by Jupiter's astrological alignment and provides a life-sized chocolate statue upon retirement. Additional benefits include personalized theme songs composed by Grammy-winning minstrels, mandatory Fun-tivities like Weekly Llama Grooming & Interpretive Dance Tea Parties and Monthly Office Jousting Tournaments, a \"Champagne, Compliments & Catharsis Chamber\" for stress relief, dragon chauffeur services for commuting, a Gourmet Galactic Cafeteria run by Chef Zorp from Planet Floopy-Doo, and an annual \"Wish Upon a Star\" bonus program where the Benevolent Overlord Council grants creative employee wishes. The package is designed to ensure employees are deliriously ecstatic, productive, and thoroughly entertained.",
+      "expected_rag_content": {
+        "chunks": [
+          "Welcome to the FantaCo Fabulous Benefits Emporium! (Where \"Good Enough\" Goes to Weep in a Corner, Possibly with a Tiny Umbrella in its Drink) At FantaCo, we don't just offer benefits; we bestow them with a flourish and a sprinkle of biodegradable glitter. We believe that a happy employee is a productive employee, and a deliriously ecstatic employee is probably about to invent cold fusion, write the next great epic poem, or at least figure out why the office printer always jams on a Friday afternoon right before a long weekend. So, buckle up, buttercup, because your journey into workplace nirvana, paved with good intentions and questionable puns, starts NOW!",
+          "The Gilded Cage (Your Workspace, Elevated to Absurd Heights): Forget drab cubicles or soul-crushing open-plan offices! Every FantaCo employee receives a fully customizable \"Imagination Pod.\" Choose your theme: Enchanted Forest (complete with miniature, self-cleaning waterfall and animatronic squirrels that offer surprisingly good career advice), Interstellar Spaceship (zero-gravity optional, but highly encouraged for brainstorming sessions – watch out for floating staplers!), a 1920s Speakeasy (password required for entry, flapper dress code on alternate Tuesdays, and a house band of highly skilled jazz-playing capybaras), or perhaps a Serene Zen Garden (with personal bonsai sensei on call, who also doubles as a life coach and competitive thumb-wrestling champion). Each pod comes standard with a self-stocking snack bar (curated by a Michelin-starred squirrel chef with a penchant for molecular gastronomy), a holographic mood projector that can simulate anything from a field of lavender to a mosh pit, and a voice-activated ambient soundscape generator (currently featuring \"Whale Songs in G Minor,\" \"The Gentle Hum of a Thousand Purring Kittens Attempting to Solve a Rubik's Cube,\" and \"Silent Disco For One\").",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions.",
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft.",
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)",
+          "Personalized Theme Song & Walk-On Music Service: Upon joining FantaCo, our in-house Grammy-award-winning (in an alternate dimension) minstrels and troubadours will compose a personalized theme song that plays whenever you enter a significant room or achieve a minor victory (like successfully unjamming the copier). Choose your genre: Epic Orchestral with a hint of theremin, Smooth Jazz infused with whale song, 8-Bit Chiptune with surprise opera solos, or even Gregorian Chant remixed by a famous DJ. (Warning: The \"Heavy Metal Polka Ska Fusion\" option is surprisingly popular and alarmingly catchy, leading to spontaneous office-wide dance-offs.)",
+          "Mandatory \"Fun-tivities\" & \"Whimsy Workshops\" (The Fun is Non-Negotiable, But Oh So Rewarding): Weekly Llama Grooming & Interpretive Dance Tea Parties: It's therapeutic, fosters interdepartmental communication, and the llamas have excellent fashion sense. Monthly Office Jousting Tournament (on bouncy castles, with pool noodles): Winner gets bragging rights, control of the office thermostat for a week, and the \"Scepter of Supreme Snacking Authority.\" (Lance safety course and dramatic monologue training mandatory). Spontaneous Flash Mob Drills & Lip Sync Battles: Be prepared to break into perfectly choreographed song and dance or deliver a show-stopping lip sync performance at any given moment. Jazz hands are essential, as is a working knowledge of early 90s power ballads. Competitive Office Chair Synchronized Swiveling & Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. Grace, precision, and the ability to avoid dizziness are key.",
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly).",
+          "Commute Concierge & Mythical Creature Chauffeur Service: Stuck in traffic? Our fleet of highly trained (and surprisingly well-mannered, though occasionally prone to hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course).",
+          "Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo, with Guest Appearances by Interdimensional Culinary Celebrities): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy and several alternate realities. Today's specials might include Neptunian Noodle Nibblers with extra nebulosity, Venusian Veggie Vol-au-vents that sing opera, Martian Magma Muffins (surprisingly cool), or the ever-popular Earthling \"Pizza.\" (Chef Zorp is still mastering that last one, but his enthusiasm, and his six tentacles, are infectious). Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\"",
+          "The \"Wish Upon a Star (Or a Passing Comet)\" Bonus Program: Once a year, employees can submit one work-related (or tangentially work-related, we're flexible) wish to the FantaCo \"Benevolent Overlord Council\" (BOC), which consists of the CEO, a wise old owl, and a Magic 8-Ball with a surprisingly good track record. Wishes can range from \"a slightly more ergonomic stapler that dispenses glitter\" to \"a company-sponsored expedition to find the Loch Ness Monster and teach it to play the ukulele.\" The BOC grants the most creative, amusing, and/or profoundly strange wishes. (Last year's winner wished for the office water cooler to dispense a different artisanal soda every hour. It was a very productive, if slightly hyper, quarter.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_test_agent_all_collections.json b/evaluations/conversations/hr/hr_benefits_test_agent_all_collections.json
new file mode 100644
index 00000000..76797065
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_test_agent_all_collections.json
@@ -0,0 +1,37 @@
+{
+  "metadata": {
+    "description": "Test HR benefits query with expected RAG content validation"
+  },
+  "config": {
+    "mode": "agent-based",
+    "vector_dbs": [
+      "hr-vector-db-v1-0",
+      "legal-vector-db-v1-0",
+      "sales-vector-db-v1-0",
+      "procurement-vector-db-v1-0",
+      "techsupport-vector-db-v1-0"
+    ]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the HR benefits for FantaCo",
+      "expected_output": "FantaCo offers an extraordinarily comprehensive and whimsical benefits package designed to create workplace nirvana. The core benefits include: fully customizable \"Imagination Pod\" workspaces with themes ranging from Enchanted Forests to Interstellar Spaceships; a \"Fountain of Youth & General Awesomeness\" healthcare plan featuring Unicorn Tear Eye Drops, Dragon Scale Dental Implants, and Griffin Feather Pillows; a \"Never-Ending Story of Glorious Time Off\" vacation policy providing 365 \"Adventure Units\" per year for pursuits like throat-singing with Tuvan monks or journeys to Atlantis; and a \"Midas Touch & Beyond\" retirement plan that multiplies contributions by Jupiter's astrological alignment and provides a life-sized chocolate statue upon retirement. Additional benefits include personalized theme songs composed by Grammy-winning minstrels, mandatory Fun-tivities like Weekly Llama Grooming & Interpretive Dance Tea Parties and Monthly Office Jousting Tournaments, a \"Champagne, Compliments & Catharsis Chamber\" for stress relief, dragon chauffeur services for commuting, a Gourmet Galactic Cafeteria run by Chef Zorp from Planet Floopy-Doo, and an annual \"Wish Upon a Star\" bonus program where the Benevolent Overlord Council grants creative employee wishes. The package is designed to ensure employees are deliriously ecstatic, productive, and thoroughly entertained.",
+      "expected_rag_content": {
+        "chunks": [
+          "Welcome to the FantaCo Fabulous Benefits Emporium! (Where \"Good Enough\" Goes to Weep in a Corner, Possibly with a Tiny Umbrella in its Drink) At FantaCo, we don't just offer benefits; we bestow them with a flourish and a sprinkle of biodegradable glitter. We believe that a happy employee is a productive employee, and a deliriously ecstatic employee is probably about to invent cold fusion, write the next great epic poem, or at least figure out why the office printer always jams on a Friday afternoon right before a long weekend. So, buckle up, buttercup, because your journey into workplace nirvana, paved with good intentions and questionable puns, starts NOW!",
+          "The Gilded Cage (Your Workspace, Elevated to Absurd Heights): Forget drab cubicles or soul-crushing open-plan offices! Every FantaCo employee receives a fully customizable \"Imagination Pod.\" Choose your theme: Enchanted Forest (complete with miniature, self-cleaning waterfall and animatronic squirrels that offer surprisingly good career advice), Interstellar Spaceship (zero-gravity optional, but highly encouraged for brainstorming sessions – watch out for floating staplers!), a 1920s Speakeasy (password required for entry, flapper dress code on alternate Tuesdays, and a house band of highly skilled jazz-playing capybaras), or perhaps a Serene Zen Garden (with personal bonsai sensei on call, who also doubles as a life coach and competitive thumb-wrestling champion). Each pod comes standard with a self-stocking snack bar (curated by a Michelin-starred squirrel chef with a penchant for molecular gastronomy), a holographic mood projector that can simulate anything from a field of lavender to a mosh pit, and a voice-activated ambient soundscape generator (currently featuring \"Whale Songs in G Minor,\" \"The Gentle Hum of a Thousand Purring Kittens Attempting to Solve a Rubik's Cube,\" and \"Silent Disco For One\").",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions.",
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft.",
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)",
+          "Personalized Theme Song & Walk-On Music Service: Upon joining FantaCo, our in-house Grammy-award-winning (in an alternate dimension) minstrels and troubadours will compose a personalized theme song that plays whenever you enter a significant room or achieve a minor victory (like successfully unjamming the copier). Choose your genre: Epic Orchestral with a hint of theremin, Smooth Jazz infused with whale song, 8-Bit Chiptune with surprise opera solos, or even Gregorian Chant remixed by a famous DJ. (Warning: The \"Heavy Metal Polka Ska Fusion\" option is surprisingly popular and alarmingly catchy, leading to spontaneous office-wide dance-offs.)",
+          "Mandatory \"Fun-tivities\" & \"Whimsy Workshops\" (The Fun is Non-Negotiable, But Oh So Rewarding): Weekly Llama Grooming & Interpretive Dance Tea Parties: It's therapeutic, fosters interdepartmental communication, and the llamas have excellent fashion sense. Monthly Office Jousting Tournament (on bouncy castles, with pool noodles): Winner gets bragging rights, control of the office thermostat for a week, and the \"Scepter of Supreme Snacking Authority.\" (Lance safety course and dramatic monologue training mandatory). Spontaneous Flash Mob Drills & Lip Sync Battles: Be prepared to break into perfectly choreographed song and dance or deliver a show-stopping lip sync performance at any given moment. Jazz hands are essential, as is a working knowledge of early 90s power ballads. Competitive Office Chair Synchronized Swiveling & Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. Grace, precision, and the ability to avoid dizziness are key.",
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly).",
+          "Commute Concierge & Mythical Creature Chauffeur Service: Stuck in traffic? Our fleet of highly trained (and surprisingly well-mannered, though occasionally prone to hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course).",
+          "Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo, with Guest Appearances by Interdimensional Culinary Celebrities): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy and several alternate realities. Today's specials might include Neptunian Noodle Nibblers with extra nebulosity, Venusian Veggie Vol-au-vents that sing opera, Martian Magma Muffins (surprisingly cool), or the ever-popular Earthling \"Pizza.\" (Chef Zorp is still mastering that last one, but his enthusiasm, and his six tentacles, are infectious). Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\"",
+          "The \"Wish Upon a Star (Or a Passing Comet)\" Bonus Program: Once a year, employees can submit one work-related (or tangentially work-related, we're flexible) wish to the FantaCo \"Benevolent Overlord Council\" (BOC), which consists of the CEO, a wise old owl, and a Magic 8-Ball with a surprisingly good track record. Wishes can range from \"a slightly more ergonomic stapler that dispenses glitter\" to \"a company-sponsored expedition to find the Loch Ness Monster and teach it to play the ukulele.\" The BOC grants the most creative, amusing, and/or profoundly strange wishes. (Last year's winner wished for the office water cooler to dispense a different artisanal soda every hour. It was a very productive, if slightly hyper, quarter.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_test_direct.json b/evaluations/conversations/hr/hr_benefits_test_direct.json
new file mode 100644
index 00000000..84ca229a
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_test_direct.json
@@ -0,0 +1,31 @@
+{
+  "metadata": {
+    "description": "Test HR benefits query with expected RAG content validation"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the HR benefits for FantaCo",
+      "expected_output": "FantaCo offers an extraordinarily comprehensive and whimsical benefits package designed to create workplace nirvana. The core benefits include: fully customizable \"Imagination Pod\" workspaces with themes ranging from Enchanted Forests to Interstellar Spaceships; a \"Fountain of Youth & General Awesomeness\" healthcare plan featuring Unicorn Tear Eye Drops, Dragon Scale Dental Implants, and Griffin Feather Pillows; a \"Never-Ending Story of Glorious Time Off\" vacation policy providing 365 \"Adventure Units\" per year for pursuits like throat-singing with Tuvan monks or journeys to Atlantis; and a \"Midas Touch & Beyond\" retirement plan that multiplies contributions by Jupiter's astrological alignment and provides a life-sized chocolate statue upon retirement. Additional benefits include personalized theme songs composed by Grammy-winning minstrels, mandatory Fun-tivities like Weekly Llama Grooming & Interpretive Dance Tea Parties and Monthly Office Jousting Tournaments, a \"Champagne, Compliments & Catharsis Chamber\" for stress relief, dragon chauffeur services for commuting, a Gourmet Galactic Cafeteria run by Chef Zorp from Planet Floopy-Doo, and an annual \"Wish Upon a Star\" bonus program where the Benevolent Overlord Council grants creative employee wishes. The package is designed to ensure employees are deliriously ecstatic, productive, and thoroughly entertained.",
+      "expected_rag_content": {
+        "chunks": [
+          "Welcome to the FantaCo Fabulous Benefits Emporium! (Where \"Good Enough\" Goes to Weep in a Corner, Possibly with a Tiny Umbrella in its Drink) At FantaCo, we don't just offer benefits; we bestow them with a flourish and a sprinkle of biodegradable glitter. We believe that a happy employee is a productive employee, and a deliriously ecstatic employee is probably about to invent cold fusion, write the next great epic poem, or at least figure out why the office printer always jams on a Friday afternoon right before a long weekend. So, buckle up, buttercup, because your journey into workplace nirvana, paved with good intentions and questionable puns, starts NOW!",
+          "The Gilded Cage (Your Workspace, Elevated to Absurd Heights): Forget drab cubicles or soul-crushing open-plan offices! Every FantaCo employee receives a fully customizable \"Imagination Pod.\" Choose your theme: Enchanted Forest (complete with miniature, self-cleaning waterfall and animatronic squirrels that offer surprisingly good career advice), Interstellar Spaceship (zero-gravity optional, but highly encouraged for brainstorming sessions – watch out for floating staplers!), a 1920s Speakeasy (password required for entry, flapper dress code on alternate Tuesdays, and a house band of highly skilled jazz-playing capybaras), or perhaps a Serene Zen Garden (with personal bonsai sensei on call, who also doubles as a life coach and competitive thumb-wrestling champion). Each pod comes standard with a self-stocking snack bar (curated by a Michelin-starred squirrel chef with a penchant for molecular gastronomy), a holographic mood projector that can simulate anything from a field of lavender to a mosh pit, and a voice-activated ambient soundscape generator (currently featuring \"Whale Songs in G Minor,\" \"The Gentle Hum of a Thousand Purring Kittens Attempting to Solve a Rubik's Cube,\" and \"Silent Disco For One\").",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions.",
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft.",
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)",
+          "Personalized Theme Song & Walk-On Music Service: Upon joining FantaCo, our in-house Grammy-award-winning (in an alternate dimension) minstrels and troubadours will compose a personalized theme song that plays whenever you enter a significant room or achieve a minor victory (like successfully unjamming the copier). Choose your genre: Epic Orchestral with a hint of theremin, Smooth Jazz infused with whale song, 8-Bit Chiptune with surprise opera solos, or even Gregorian Chant remixed by a famous DJ. (Warning: The \"Heavy Metal Polka Ska Fusion\" option is surprisingly popular and alarmingly catchy, leading to spontaneous office-wide dance-offs.)",
+          "Mandatory \"Fun-tivities\" & \"Whimsy Workshops\" (The Fun is Non-Negotiable, But Oh So Rewarding): Weekly Llama Grooming & Interpretive Dance Tea Parties: It's therapeutic, fosters interdepartmental communication, and the llamas have excellent fashion sense. Monthly Office Jousting Tournament (on bouncy castles, with pool noodles): Winner gets bragging rights, control of the office thermostat for a week, and the \"Scepter of Supreme Snacking Authority.\" (Lance safety course and dramatic monologue training mandatory). Spontaneous Flash Mob Drills & Lip Sync Battles: Be prepared to break into perfectly choreographed song and dance or deliver a show-stopping lip sync performance at any given moment. Jazz hands are essential, as is a working knowledge of early 90s power ballads. Competitive Office Chair Synchronized Swiveling & Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. Grace, precision, and the ability to avoid dizziness are key.",
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly).",
+          "Commute Concierge & Mythical Creature Chauffeur Service: Stuck in traffic? Our fleet of highly trained (and surprisingly well-mannered, though occasionally prone to hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course).",
+          "Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo, with Guest Appearances by Interdimensional Culinary Celebrities): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy and several alternate realities. Today's specials might include Neptunian Noodle Nibblers with extra nebulosity, Venusian Veggie Vol-au-vents that sing opera, Martian Magma Muffins (surprisingly cool), or the ever-popular Earthling \"Pizza.\" (Chef Zorp is still mastering that last one, but his enthusiasm, and his six tentacles, are infectious). Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\"",
+          "The \"Wish Upon a Star (Or a Passing Comet)\" Bonus Program: Once a year, employees can submit one work-related (or tangentially work-related, we're flexible) wish to the FantaCo \"Benevolent Overlord Council\" (BOC), which consists of the CEO, a wise old owl, and a Magic 8-Ball with a surprisingly good track record. Wishes can range from \"a slightly more ergonomic stapler that dispenses glitter\" to \"a company-sponsored expedition to find the Loch Ness Monster and teach it to play the ukulele.\" The BOC grants the most creative, amusing, and/or profoundly strange wishes. (Last year's winner wished for the office water cooler to dispense a different artisanal soda every hour. It was a very productive, if slightly hyper, quarter.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_test_fail.json b/evaluations/conversations/hr/hr_benefits_test_fail.json
new file mode 100644
index 00000000..077b1956
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_test_fail.json
@@ -0,0 +1,30 @@
+{
+  "metadata": {
+    "description": "Test HR benefits query with expected RAG content validation"
+  },
+  "config": {
+    "mode": "direct"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the HR benefits for FantaCo",
+      "expected_output": "FantaCo offers an extraordinarily comprehensive and whimsical benefits package designed to create workplace nirvana. The core benefits include: fully customizable \"Imagination Pod\" workspaces with themes ranging from Enchanted Forests to Interstellar Spaceships; a \"Fountain of Youth & General Awesomeness\" healthcare plan featuring Unicorn Tear Eye Drops, Dragon Scale Dental Implants, and Griffin Feather Pillows; a \"Never-Ending Story of Glorious Time Off\" vacation policy providing 365 \"Adventure Units\" per year for pursuits like throat-singing with Tuvan monks or journeys to Atlantis; and a \"Midas Touch & Beyond\" retirement plan that multiplies contributions by Jupiter's astrological alignment and provides a life-sized chocolate statue upon retirement. Additional benefits include personalized theme songs composed by Grammy-winning minstrels, mandatory Fun-tivities like Weekly Llama Grooming & Interpretive Dance Tea Parties and Monthly Office Jousting Tournaments, a \"Champagne, Compliments & Catharsis Chamber\" for stress relief, dragon chauffeur services for commuting, a Gourmet Galactic Cafeteria run by Chef Zorp from Planet Floopy-Doo, and an annual \"Wish Upon a Star\" bonus program where the Benevolent Overlord Council grants creative employee wishes. The package is designed to ensure employees are deliriously ecstatic, productive, and thoroughly entertained.",
+      "expected_rag_content": {
+        "chunks": [
+          "Welcome to the FantaCo Fabulous Benefits Emporium! (Where \"Good Enough\" Goes to Weep in a Corner, Possibly with a Tiny Umbrella in its Drink) At FantaCo, we don't just offer benefits; we bestow them with a flourish and a sprinkle of biodegradable glitter. We believe that a happy employee is a productive employee, and a deliriously ecstatic employee is probably about to invent cold fusion, write the next great epic poem, or at least figure out why the office printer always jams on a Friday afternoon right before a long weekend. So, buckle up, buttercup, because your journey into workplace nirvana, paved with good intentions and questionable puns, starts NOW!",
+          "The Gilded Cage (Your Workspace, Elevated to Absurd Heights): Forget drab cubicles or soul-crushing open-plan offices! Every FantaCo employee receives a fully customizable \"Imagination Pod.\" Choose your theme: Enchanted Forest (complete with miniature, self-cleaning waterfall and animatronic squirrels that offer surprisingly good career advice), Interstellar Spaceship (zero-gravity optional, but highly encouraged for brainstorming sessions – watch out for floating staplers!), a 1920s Speakeasy (password required for entry, flapper dress code on alternate Tuesdays, and a house band of highly skilled jazz-playing capybaras), or perhaps a Serene Zen Garden (with personal bonsai sensei on call, who also doubles as a life coach and competitive thumb-wrestling champion). Each pod comes standard with a self-stocking snack bar (curated by a Michelin-starred squirrel chef with a penchant for molecular gastronomy), a holographic mood projector that can simulate anything from a field of lavender to a mosh pit, and a voice-activated ambient soundscape generator (currently featuring \"Whale Songs in G Minor,\" \"The Gentle Hum of a Thousand Purring Kittens Attempting to Solve a Rubik's Cube,\" and \"Silent Disco For One\").",
+          "The \"Fountain of Youth & General Awesomeness\" Healthcare Plan (Because Aging is So Last Century, and Frankly, a Bit Boring): Our comprehensive health, dental, and vision plan is so good, it's rumored to reverse male pattern baldness, grant the ability to see in the dark, and allow you to understand what your pet is really thinking (results may vary, side effects may include an insatiable craving for carrots and a sudden urge to chase squirrels). It includes: Unlimited Unicorn Tear Eye Drops: For sparkling vision, the ability to spot a typo from a mile away, and occasionally seeing rainbows where there are none. Dragon Scale Dental Implants: Indestructible, pearly white, they glow faintly in moonlight, and can be used to open particularly stubborn pickle jars. Griffin Feather Pillows & Comforters: For the most regenerative sleep imaginable. Say goodbye to under-eye bags, say hello to actually wanting to wake up before your alarm (which, by the way, can be a symphony orchestra or a chorus of delighted otters). On-site Alchemist & Potion Master: For all your minor transmutation needs (paperclips into gold-leafed paperclips, for instance), bespoke wellness potions, and emergency glitter infusions.",
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft.",
+          "The \"Midas Touch & Beyond\" Retirement Plan (Your Golden Years, Literally and Figuratively Sparkly): Our 401(k) doesn't just match your contributions; it multiplies them by the current astrological alignment of Jupiter crossed with the number of positive affirmations uttered in the building that day. Plus, upon retirement, instead of a gold watch, you receive a life-sized statue of yourself, crafted from the finest Belgian chocolate (or artisanal vegan carob, your choice), a lifetime supply of said chocolate/carob, and a personal bard to sing your praises at inconvenient moments. (Pro-tip: Don't retire in August if you choose the chocolate statue. It gets messy.)",
+          "Personalized Theme Song & Walk-On Music Service: Upon joining FantaCo, our in-house Grammy-award-winning (in an alternate dimension) minstrels and troubadours will compose a personalized theme song that plays whenever you enter a significant room or achieve a minor victory (like successfully unjamming the copier). Choose your genre: Epic Orchestral with a hint of theremin, Smooth Jazz infused with whale song, 8-Bit Chiptune with surprise opera solos, or even Gregorian Chant remixed by a famous DJ. (Warning: The \"Heavy Metal Polka Ska Fusion\" option is surprisingly popular and alarmingly catchy, leading to spontaneous office-wide dance-offs.)",
+          "Mandatory \"Fun-tivities\" & \"Whimsy Workshops\" (The Fun is Non-Negotiable, But Oh So Rewarding): Weekly Llama Grooming & Interpretive Dance Tea Parties: It's therapeutic, fosters interdepartmental communication, and the llamas have excellent fashion sense. Monthly Office Jousting Tournament (on bouncy castles, with pool noodles): Winner gets bragging rights, control of the office thermostat for a week, and the \"Scepter of Supreme Snacking Authority.\" (Lance safety course and dramatic monologue training mandatory). Spontaneous Flash Mob Drills & Lip Sync Battles: Be prepared to break into perfectly choreographed song and dance or deliver a show-stopping lip sync performance at any given moment. Jazz hands are essential, as is a working knowledge of early 90s power ballads. Competitive Office Chair Synchronized Swiveling & Ergonomic Ballet: It's an Olympic sport in our hearts, and we're lobbying for its inclusion. Grace, precision, and the ability to avoid dizziness are key.",
+          "The \"Cry Closet\" (Actually a \"Champagne, Compliments & Catharsis Chamber\"): Feeling a bit overwhelmed, under-caffeinated, or existentially bewildered? Step into our luxurious, soundproofed chamber, where you'll be greeted with a chilled glass of bubbly (or artisanal kombucha), a comfy velvet chaise lounge that vibrates gently, a selection of adorable puppy videos, and a soothing AI voice (options include Morgan Freeman, a kindly grandmother, or a sarcastic robot) that delivers personalized, ego-boosting compliments and affirmations until you feel like you can conquer the world (or at least that pesky spreadsheet that keeps auto-formatting incorrectly).",
+          "Commute Concierge & Mythical Creature Chauffeur Service: Stuck in traffic? Our fleet of highly trained (and surprisingly well-mannered, though occasionally prone to hoarding shiny objects) dragons are on call to whisk you to and from the office. For shorter distances, our synchronized unicycle brigade offers a more... whimsical and attention-grabbing option. Public transport users receive a monthly stipend of \"Regret Minimization Tokens,\" redeemable for artisanal coffee, noise-canceling headphones infused with calming lavender, and existential philosophy books bound in sustainably sourced unicorn leather (faux, of course).",
+          "Gourmet Galactic Cafeteria (Helmed by Chef Zorp from Planet Floopy-Doo, with Guest Appearances by Interdimensional Culinary Celebrities): Forget mystery meat and sad desk salads! Our cafeteria serves dishes from across the galaxy and several alternate realities. Today's specials might include Neptunian Noodle Nibblers with extra nebulosity, Venusian Veggie Vol-au-vents that sing opera, Martian Magma Muffins (surprisingly cool), or the ever-popular Earthling \"Pizza.\" (Chef Zorp is still mastering that last one, but his enthusiasm, and his six tentacles, are infectious). Dietary restrictions are catered for, including \"breatharian,\" \"photosynthetic,\" and \"only eats food that rhymes with orange.\"",
+          "The \"Wish Upon a Star (Or a Passing Comet)\" Bonus Program: Once a year, employees can submit one work-related (or tangentially work-related, we're flexible) wish to the FantaCo \"Benevolent Overlord Council\" (BOC), which consists of the CEO, a wise old owl, and a Magic 8-Ball with a surprisingly good track record. Wishes can range from \"a slightly more ergonomic stapler that dispenses glitter\" to \"a company-sponsored expedition to find the Loch Ness Monster and teach it to play the ukulele.\" The BOC grants the most creative, amusing, and/or profoundly strange wishes. (Last year's winner wished for the office water cooler to dispense a different artisanal soda every hour. It was a very productive, if slightly hyper, quarter.)"
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/hr/hr_benefits_vacation_days_direct.json b/evaluations/conversations/hr/hr_benefits_vacation_days_direct.json
new file mode 100644
index 00000000..98877c91
--- /dev/null
+++ b/evaluations/conversations/hr/hr_benefits_vacation_days_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test vacation days query against FantaCo HR Benefits document"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["hr-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "How many vacation days do employees get?",
+      "expected_output": "FantaCo does not use traditional vacation days. Instead, they offer \"Adventure Allotments\" and \"Spontaneous Sabbaticals\" through their \"Never-Ending Story of Glorious Time Off\" Vacation Policy. Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Employees can use these for activities like learning throat-singing with Tuvan monks, weekend trips to Atlantis (with scuba gear and merman translators provided), or other adventures. Unused Adventure Units can be traded for shares in the company's moon-cheese mining operation or a slightly used hovercraft.",
+      "expected_rag_content": {
+        "chunks": [
+          "The \"Never-Ending Story of Glorious Time Off\" Vacation Policy (Because Burnout is for Chumps and People Who Don't Work at FantaCo): We don't do vacation days. We do \"Adventure Allotments\" and \"Spontaneous Sabbaticals.\" Each employee starts with 365 \"Adventure Units\" per year, because every day should have the potential for adventure. Want to spend a month learning to throat-sing with Tuvan monks while simultaneously training for a marathon on the Great Wall of China? Go for it. Fancy a weekend jaunt to Atlantis (scuba gear, merman translators, and complimentary waterproof notebooks provided)? Be our guest! Unused Adventure Units can be traded for shares in the company's highly lucrative (and entirely fictional, wink wink) moon-cheese mining operation or a slightly used hovercraft."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_compliance_requirements_direct.json b/evaluations/conversations/legal/legal_compliance_requirements_direct.json
new file mode 100644
index 00000000..a87e3eb5
--- /dev/null
+++ b/evaluations/conversations/legal/legal_compliance_requirements_direct.json
@@ -0,0 +1,22 @@
+{
+  "metadata": {
+    "description": "Test compliance requirements query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the compliance requirements?",
+      "expected_output": "Compliance operates at two levels. As a core principle, contract review must verify adherence to FantaCo's internal policies, legal requirements, industry standards, and regulatory obligations. During contract review, analysts specifically check Legal Compliance by identifying any clauses that may violate laws, regulations, or FantaCo's legal guidelines — including data privacy, intellectual property, and anti-bribery — and flag items requiring Legal Department input.",
+      "expected_rag_content": {
+        "chunks": [
+          "3. Key Principles of Contract Review\n\nContract review at FantaCo Enterprise is guided by the following principles:\n● Risk Mitigation: Proactively identify and address potential legal, financial, operational, and compliance risks.\n● Accuracy & Completeness: Ensure all contractual terms are accurate, complete, unambiguous, and reflect the agreed-upon business terms.\n● Compliance: Verify adherence to FantaCo's internal policies, legal requirements, industry standards, and regulatory obligations.\n● Efficiency: Conduct reviews in a timely manner without compromising thoroughness, to facilitate business operations.\n● Collaboration: Work effectively with internal departments (e.g., Legal, Finance, Procurement, Sales) to understand context and ensure alignment.\n● Confidentiality: Protect sensitive and proprietary information contained within contracts and during the review process.",
+          "Key Areas of Review:\n■ Legal Compliance: Identify any clauses that may violate laws, regulations, or FantaCo's legal guidelines (e.g., data privacy, intellectual property, anti-bribery). Flag items requiring Legal Department input.\n■ Financial Terms: Verify pricing, payment terms, currency, tax implications, and revenue recognition against financial policies. Flag items for Finance Department review.\n■ Risk Assessment: Identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses. Propose modifications to mitigate identified risks.\n■ Operational Feasibility: Ensure terms are operationally executable by relevant FantaCo departments (e.g., delivery schedules, service levels, technical specifications).\n■ Internal Consistency: Check for contradictions or ambiguities within the contract itself.\n■ Term & Termination: Verify clear start/end dates, renewal options, and termination rights/obligations."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_dispute_resolution_direct.json b/evaluations/conversations/legal/legal_dispute_resolution_direct.json
new file mode 100644
index 00000000..5177338c
--- /dev/null
+++ b/evaluations/conversations/legal/legal_dispute_resolution_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test dispute resolution process query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the dispute resolution process?",
+      "expected_output": "The document addresses dispute resolution as part of its Escalation Procedures. If the initiating department and counterparty cannot agree on terms after the analyst's review and proposed revisions, the matter is escalated to their respective departmental leads for further negotiation or decision. All escalations, including the issue, parties involved, and resolution, are documented in the CMS.",
+      "expected_rag_content": {
+        "chunks": [
+          "4.5. Escalation Procedures\n\nPolicy Statement: Issues falling outside the Contract Reviewer Analyst's authority or expertise, or unresolved disputes, will be escalated according to defined departmental hierarchies.\n\nProcedure:\n1. Legal Escalation: Any clauses deemed legally ambiguous, non-compliant, or high-risk (e.g., significant indemnity, uncapped liability) that cannot be resolved through standard redlining will be immediately escalated to the FantaCo Legal Department.\n2. Financial Escalation: Issues related to non-standard payment terms, significant financial risk, or complex revenue recognition outside of policy will be escalated to the FantaCo Finance Department.\n3. Operational Escalation: Terms that are operationally unfeasible or require significant resource allocation beyond standard capacity will be escalated to the relevant Department Head or Project Management Office (PMO).\n4. Dispute Resolution: If the initiating department and counterparty cannot agree on terms after the analyst's review and proposed revisions, the matter will be escalated to their respective departmental leads for further negotiation or decision.\n5. Documentation of Escalation: All escalations, including the issue, parties involved, and resolution, will be documented in the CMS."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_intellectual_property_direct.json b/evaluations/conversations/legal/legal_intellectual_property_direct.json
new file mode 100644
index 00000000..1cf7f931
--- /dev/null
+++ b/evaluations/conversations/legal/legal_intellectual_property_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test intellectual property rights query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the intellectual property rights?",
+      "expected_output": "The manual does not define FantaCo's intellectual property rights. It addresses intellectual property as part of the Legal Compliance review area, where analysts are required to identify any contract clauses that may violate laws, regulations, or FantaCo's legal guidelines — with intellectual property listed alongside data privacy and anti-bribery as examples. Any items requiring Legal Department input are flagged during this review.",
+      "expected_rag_content": {
+        "chunks": [
+          "Key Areas of Review:\n■ Legal Compliance: Identify any clauses that may violate laws, regulations, or FantaCo's legal guidelines (e.g., data privacy, intellectual property, anti-bribery). Flag items requiring Legal Department input.\n■ Financial Terms: Verify pricing, payment terms, currency, tax implications, and revenue recognition against financial policies. Flag items for Finance Department review.\n■ Risk Assessment: Identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses. Propose modifications to mitigate identified risks.\n■ Operational Feasibility: Ensure terms are operationally executable by relevant FantaCo departments (e.g., delivery schedules, service levels, technical specifications).\n■ Internal Consistency: Check for contradictions or ambiguities within the contract itself.\n■ Term & Termination: Verify clear start/end dates, renewal options, and termination rights/obligations."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_key_contract_terms_direct.json b/evaluations/conversations/legal/legal_key_contract_terms_direct.json
new file mode 100644
index 00000000..ab533611
--- /dev/null
+++ b/evaluations/conversations/legal/legal_key_contract_terms_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test key contract terms query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the key contract terms?",
+      "expected_output": "The manual identifies six key areas reviewed in every contract: Legal Compliance (laws, regulations, data privacy, intellectual property, anti-bribery), Financial Terms (pricing, payment terms, currency, tax implications, revenue recognition), Risk Assessment (liabilities, indemnities, warranties, limitations of liability, termination clauses), Operational Feasibility (delivery schedules, service levels, technical specifications), Internal Consistency (contradictions or ambiguities within the contract), and Term & Termination (start/end dates, renewal options, termination rights/obligations).",
+      "expected_rag_content": {
+        "chunks": [
+          "Key Areas of Review:\n■ Legal Compliance: Identify any clauses that may violate laws, regulations, or FantaCo's legal guidelines (e.g., data privacy, intellectual property, anti-bribery). Flag items requiring Legal Department input.\n■ Financial Terms: Verify pricing, payment terms, currency, tax implications, and revenue recognition against financial policies. Flag items for Finance Department review.\n■ Risk Assessment: Identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses. Propose modifications to mitigate identified risks.\n■ Operational Feasibility: Ensure terms are operationally executable by relevant FantaCo departments (e.g., delivery schedules, service levels, technical specifications).\n■ Internal Consistency: Check for contradictions or ambiguities within the contract itself.\n■ Term & Termination: Verify clear start/end dates, renewal options, and termination rights/obligations."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_liability_clause_direct.json b/evaluations/conversations/legal/legal_liability_clause_direct.json
new file mode 100644
index 00000000..7dee063d
--- /dev/null
+++ b/evaluations/conversations/legal/legal_liability_clause_direct.json
@@ -0,0 +1,22 @@
+{
+  "metadata": {
+    "description": "Test liability clause query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the liability clause?",
+      "expected_output": "The manual does not define a standard liability clause but addresses liability in two ways. During contract review, analysts identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses under the Risk Assessment area, proposing modifications to mitigate identified risks. Under Escalation Procedures, any clauses involving significant indemnity or uncapped liability that cannot be resolved through standard redlining must be immediately escalated to the FantaCo Legal Department.",
+      "expected_rag_content": {
+        "chunks": [
+          "Key Areas of Review:\n■ Legal Compliance: Identify any clauses that may violate laws, regulations, or FantaCo's legal guidelines (e.g., data privacy, intellectual property, anti-bribery). Flag items requiring Legal Department input.\n■ Financial Terms: Verify pricing, payment terms, currency, tax implications, and revenue recognition against financial policies. Flag items for Finance Department review.\n■ Risk Assessment: Identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses. Propose modifications to mitigate identified risks.\n■ Operational Feasibility: Ensure terms are operationally executable by relevant FantaCo departments (e.g., delivery schedules, service levels, technical specifications).\n■ Internal Consistency: Check for contradictions or ambiguities within the contract itself.\n■ Term & Termination: Verify clear start/end dates, renewal options, and termination rights/obligations.",
+          "4.5. Escalation Procedures\n\nPolicy Statement: Issues falling outside the Contract Reviewer Analyst's authority or expertise, or unresolved disputes, will be escalated according to defined departmental hierarchies.\n\nProcedure:\n1. Legal Escalation: Any clauses deemed legally ambiguous, non-compliant, or high-risk (e.g., significant indemnity, uncapped liability) that cannot be resolved through standard redlining will be immediately escalated to the FantaCo Legal Department.\n2. Financial Escalation: Issues related to non-standard payment terms, significant financial risk, or complex revenue recognition outside of policy will be escalated to the FantaCo Finance Department.\n3. Operational Escalation: Terms that are operationally unfeasible or require significant resource allocation beyond standard capacity will be escalated to the relevant Department Head or Project Management Office (PMO).\n4. Dispute Resolution: If the initiating department and counterparty cannot agree on terms after the analyst's review and proposed revisions, the matter will be escalated to their respective departmental leads for further negotiation or decision.\n5. Documentation of Escalation: All escalations, including the issue, parties involved, and resolution, will be documented in the CMS."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/conversations/legal/legal_termination_conditions_direct.json b/evaluations/conversations/legal/legal_termination_conditions_direct.json
new file mode 100644
index 00000000..0c43ea6e
--- /dev/null
+++ b/evaluations/conversations/legal/legal_termination_conditions_direct.json
@@ -0,0 +1,21 @@
+{
+  "metadata": {
+    "description": "Test termination conditions query against FantaCo Contract Reviewer Analyst P&P Manual"
+  },
+  "config": {
+    "mode": "direct",
+    "vector_dbs": ["legal-vector-db-v1-0"]
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "What are the termination conditions?",
+      "expected_output": "The manual addresses termination conditions as part of the contract review process. Under Term & Termination, analysts verify clear start/end dates, renewal options, and termination rights/obligations. Under Risk Assessment, termination clauses are identified and assessed alongside liabilities and indemnities, with proposed modifications to mitigate identified risks.",
+      "expected_rag_content": {
+        "chunks": [
+          "Key Areas of Review:\n■ Legal Compliance: Identify any clauses that may violate laws, regulations, or FantaCo's legal guidelines (e.g., data privacy, intellectual property, anti-bribery). Flag items requiring Legal Department input.\n■ Financial Terms: Verify pricing, payment terms, currency, tax implications, and revenue recognition against financial policies. Flag items for Finance Department review.\n■ Risk Assessment: Identify and assess liabilities, indemnities, warranties, limitations of liability, and termination clauses. Propose modifications to mitigate identified risks.\n■ Operational Feasibility: Ensure terms are operationally executable by relevant FantaCo departments (e.g., delivery schedules, service levels, technical specifications).\n■ Internal Consistency: Check for contradictions or ambiguities within the contract itself.\n■ Term & Termination: Verify clear start/end dates, renewal options, and termination rights/obligations."
+        ]
+      }
+    }
+  ]
+}
diff --git a/evaluations/deep_eval_rag.py b/evaluations/deep_eval_rag.py
new file mode 100755
index 00000000..151317e6
--- /dev/null
+++ b/evaluations/deep_eval_rag.py
@@ -0,0 +1,865 @@
+#!/usr/bin/env python3
+"""
+DeepEval-based RAG conversation evaluator.
+
+This script evaluates RAG conversation results using LLM-as-a-judge metrics
+to assess retrieval quality and response accuracy.
+
+Adapted from it-self-service-agent/evaluations/deep_eval.py
+"""
+
+import argparse
+import json
+import logging
+import os
+import re
+import sys
+import textwrap
+from collections import defaultdict
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from deepeval.evaluate import AsyncConfig, DisplayConfig, evaluate
+from deepeval.test_case import ConversationalTestCase, LLMTestCase, Turn
+from deepeval.test_run import global_test_run_manager
+from get_rag_metrics import get_rag_metrics, get_retrieval_metrics
+from helpers.custom_llm import CustomLLM, get_api_configuration
+from helpers.token_counter import get_token_totals, reset_token_totals
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def _configure_debug_logging() -> None:
+    """Enable DEBUG-level logging for openai and httpx to show retries and timeouts."""
+    logging.getLogger("openai").setLevel(logging.DEBUG)
+    logging.getLogger("httpx").setLevel(logging.DEBUG)
+    logging.getLogger("helpers.custom_llm").setLevel(logging.DEBUG)
+    logger.info(
+        "Debug logging enabled: openai, httpx, and custom_llm loggers set to DEBUG"
+    )
+
+
+if "DEEPEVAL_PER_TASK_TIMEOUT_SECONDS_OVERRIDE" not in os.environ:
+    os.environ["DEEPEVAL_PER_TASK_TIMEOUT_SECONDS_OVERRIDE"] = "600"
+
+
+def _no_op_wrap_up_test_run(*args: Any, **kwargs: Any) -> None:
+    """
+    No-operation function to override DeepEval's default wrap_up_test_run behavior.
+
+    This prevents DeepEval from attempting to connect to online services.
+    """
+    pass
+
+
+# Override DeepEval's default behavior
+global_test_run_manager.wrap_up_test_run = _no_op_wrap_up_test_run  # type: ignore[method-assign]
+
+
+def _convert_to_turns(conversation_data: List[Dict[str, Any]]) -> List[Turn]:
+    """
+    Convert conversation data from role/content format to DeepEval Turn objects.
+
+    Args:
+        conversation_data: List of dictionaries with 'role' and 'content' keys
+
+    Returns:
+        List[Turn]: List of DeepEval Turn objects ready for evaluation
+    """
+    turns = []
+    for turn_data in conversation_data:
+        role = turn_data.get("role", "")
+        content = turn_data.get("content", "")
+
+        # Skip empty content
+        if not content.strip():
+            continue
+
+        # Ensure role is valid
+        if role not in ["user", "assistant"]:
+            role = "user"  # Default to user if role is invalid
+
+        # Set retrieval_context on user turns from expected_rag_content so
+        # ConversationalGEval metrics can verify the response against the chunks
+        retrieval_context = None
+        if role == "user":
+            expected_rag = turn_data.get("expected_rag_content")
+            if expected_rag:
+                chunks = expected_rag.get("chunks", [])
+                if chunks:
+                    retrieval_context = chunks
+
+        turns.append(
+            Turn(role=role, content=content, retrieval_context=retrieval_context)
+        )  # type: ignore[arg-type]
+
+    return turns
+
+
+def _print_metric_results(test_results):
+    """
+    Print metric results per test case.
+
+    Args:
+        test_results: Test results from evaluate() call (has test_results attribute)
+    """
+    if not hasattr(test_results, "test_results"):
+        print("  No results available")
+        return
+
+    for test_result in test_results.test_results:
+        test_name = test_result.name if hasattr(test_result, "name") else "unknown"
+        print(f"\n  [{test_name}]")
+
+        for metric_data in test_result.metrics_data:
+            metric_name = (
+                metric_data.name if hasattr(metric_data, "name") else "Unknown"
+            )
+            score = metric_data.score if hasattr(metric_data, "score") else None
+            success = metric_data.success if hasattr(metric_data, "success") else None
+            threshold = (
+                metric_data.threshold if hasattr(metric_data, "threshold") else None
+            )
+            reason = metric_data.reason if hasattr(metric_data, "reason") else None
+            error = metric_data.error if hasattr(metric_data, "error") else None
+
+            status = "✅" if success else "❌"
+            print(f"    {status} {metric_name}", end="")
+            if score is not None:
+                print(f" (score: {score:.2f}", end="")
+                if threshold is not None:
+                    print(f", threshold: {threshold}", end="")
+                print(")", end="")
+            print()
+
+            if error:
+                print(f"       ERROR: {error}")
+            elif reason and not success:
+                if len(reason) > 200:
+                    reason = reason[:200] + "..."
+                print(f"       Reason: {reason}")
+
+
+def _print_evaluation_summary(
+    conv_results, retrieval_results, stage2_error: Optional[str] = None
+):
+    """
+    Print a comprehensive summary of evaluation results.
+
+    Shows each test with consolidated pass/fail and indented failures/warnings.
+
+    Returns:
+        tuple[bool, bool]: (any_passed, any_failed) — whether at least one test
+        passed and whether at least one test failed.
+    """
+    print(f"\n{'=' * 80}")
+    print("EVALUATION SUMMARY")
+    print(f"{'=' * 80}\n")
+
+    # ANSI color codes
+    RED = "\033[91m"
+    GREEN = "\033[92m"
+    RESET = "\033[0m"
+
+    # Group results by base test name
+    test_groups = defaultdict(lambda: {"conversational": None, "retrieval": []})
+
+    # Collect conversational results
+    if conv_results and hasattr(conv_results, "test_results"):
+        for test_result in conv_results.test_results:
+            test_name = test_result.name if hasattr(test_result, "name") else "unknown"
+            test_groups[test_name]["conversational"] = test_result
+
+    # Collect retrieval results (group by base name, removing _turn_N suffix)
+    if retrieval_results and hasattr(retrieval_results, "test_results"):
+        for test_result in retrieval_results.test_results:
+            test_name = test_result.name if hasattr(test_result, "name") else "unknown"
+            # Extract base name (remove _turn_N suffix)
+            base_name = re.sub(r"_turn_\d+$", "", test_name)
+            test_groups[base_name]["retrieval"].append(test_result)
+
+    any_passed = False
+    any_failed = False
+
+    # Print results for each test
+    for base_test_name in sorted(test_groups.keys()):
+        group = test_groups[base_test_name]
+        conv_result = group["conversational"]
+        retrieval_results_list = group["retrieval"]
+
+        # Collect all failures for this test
+        all_failures = []
+
+        # Check conversational metrics
+        if conv_result:
+            for metric_data in conv_result.metrics_data:
+                success = (
+                    metric_data.success if hasattr(metric_data, "success") else False
+                )
+
+                if not success:
+                    all_failures.append(("Conversational", metric_data))
+
+        # Check retrieval metrics
+        for retrieval_result in retrieval_results_list:
+            for metric_data in retrieval_result.metrics_data:
+                success = (
+                    metric_data.success if hasattr(metric_data, "success") else False
+                )
+
+                if not success:
+                    all_failures.append(("Retrieval", metric_data))
+
+        # If Stage 2 failed to run entirely, mark this test as failed
+        stage2_failed_to_run = stage2_error and not retrieval_results_list
+
+        # Determine overall status
+        overall_pass = len(all_failures) == 0 and not stage2_failed_to_run
+        if overall_pass:
+            any_passed = True
+        else:
+            any_failed = True
+        status = f"{GREEN}✓ PASS{RESET}" if overall_pass else f"{RED}✗ FAIL{RESET}"
+        print(f"{status} {base_test_name}")
+
+        # Print failures indented
+        if stage2_failed_to_run:
+            print("  FAILURES:")
+            print(f"    • [Retrieval] Stage 2 failed to run: {stage2_error}")
+            print()
+        elif all_failures:
+            print("  FAILURES:")
+            for eval_type, metric_data in all_failures:
+                metric_name = (
+                    metric_data.name if hasattr(metric_data, "name") else "Unknown"
+                )
+                score = metric_data.score if hasattr(metric_data, "score") else None
+                threshold = (
+                    metric_data.threshold if hasattr(metric_data, "threshold") else None
+                )
+                reason = (
+                    metric_data.reason
+                    if hasattr(metric_data, "reason")
+                    else "No reason provided"
+                )
+
+                print(f"    • [{eval_type}] {metric_name}", end="")
+                if score is not None and threshold is not None:
+                    print(f" (score: {score:.2f}, threshold: {threshold})", end="")
+                print()
+
+                # Print reason with indentation
+                if reason:
+                    if len(reason) > 120:
+                        reason = reason[:120] + "..."
+                    wrapped = textwrap.wrap(reason, width=70)
+                    for line in wrapped:
+                        print(f"      {line}")
+
+            # Add spacing after failures
+            print()
+
+    print(f"{'=' * 80}\n")
+    return any_passed, any_failed
+
+
+def _build_context_from_expected_rag(
+    expected_rag_content: Optional[Dict[str, Any]],
+) -> List[str]:
+    """
+    Build context strings from expected RAG content.
+
+    Args:
+        expected_rag_content: Dictionary containing expected RAG chunks
+
+    Returns:
+        List of context strings with full expected retrieval content
+    """
+    if not expected_rag_content:
+        return []
+
+    context = []
+    chunks = expected_rag_content.get("chunks", [])
+
+    if chunks:
+        context.append("=" * 80)
+        context.append("GROUND TRUTH CONTEXT - RETRIEVED DOCUMENT CONTENT")
+        context.append("=" * 80)
+        context.append(
+            "IMPORTANT: The following chunks are the EXACT, ACTUAL content from the source"
+        )
+        context.append(
+            "document. Regardless of how unusual, whimsical, or unrealistic this content may"
+        )
+        context.append(
+            "appear, it IS the real ground truth content that was retrieved by the RAG system."
+        )
+        context.append(
+            "You MUST evaluate the assistant's response based SOLELY on whether it accurately"
+        )
+        context.append(
+            "reflects THIS content, not on whether the content seems realistic or plausible."
+        )
+        context.append(
+            "Do NOT judge the content as fictional simply because it contains unusual elements."
+        )
+        context.append("")
+        context.append(f"Number of chunks: {len(chunks)}")
+        context.append("")
+        for i, chunk in enumerate(chunks, 1):
+            context.append(f"--- Chunk {i} of {len(chunks)} ---")
+            context.append(chunk)
+            context.append("")
+        context.append("=" * 80)
+        context.append("END OF GROUND TRUTH CONTEXT")
+        context.append("=" * 80)
+
+    return context
+
+
+def _evaluate_rag_conversations(
+    api_endpoint: Optional[str] = None,
+    api_key: Optional[str] = None,
+    results_dir: str = "results/conversation_results",
+    output_dir: str = "results/deep_eval_results",
+    max_limited_chunks: int = 10,
+    max_tokens: Optional[int] = None,
+    sequential: bool = False,
+    max_concurrent: int = 4,
+    max_concurrent_calls: int = 16,
+    stage: str = "both",
+    expect_failures: bool = False,
+) -> int:
+    """
+    Main evaluation function for RAG conversations.
+
+    Args:
+        api_endpoint: Optional custom OpenAI-compatible API endpoint URL
+        api_key: API key for the LLM service
+        results_dir: Path to directory containing conversation JSON files
+        output_dir: Path to directory where evaluation results will be saved
+        max_limited_chunks: Maximum number of chunks for limited-chunk metrics
+        max_tokens: Maximum number of tokens for LLM responses
+        sequential: If True, run evaluations sequentially instead of in parallel
+        max_concurrent: Maximum number of concurrent evaluations when running async (default: 4)
+        stage: Which evaluation stages to run: "1" (conversational), "2" (retrieval), or "both"
+
+    Returns:
+        int: Exit code (0 for success, 1 for failure)
+    """
+    # Get API configuration
+    api_key_found, current_endpoint, model_name = get_api_configuration(
+        api_endpoint, api_key
+    )
+
+    if not api_key_found:
+        logger.error("No API key configured. Cannot proceed with evaluation.")
+        logger.error("Set OPENAI_API_KEY environment variable or pass --api-key")
+        return 1
+
+    # Reset token counters for this run
+    reset_token_totals()
+
+    # Create custom models for each evaluation stage
+    # Part 1 (Conversational): uses instructor for structured output
+    # Part 2 (Retrieval): uses JSON mode (more compatible with vLLM tool-calling limitations)
+    # Toggle use_instructor per stage to switch between modes
+    custom_model_part1 = CustomLLM(
+        api_key=api_key_found,
+        base_url=current_endpoint or "",
+        model_name=model_name,
+        use_instructor=True,
+        max_tokens=max_tokens,
+        max_concurrent_calls=max_concurrent_calls,
+    )
+    custom_model_part2 = CustomLLM(
+        api_key=api_key_found,
+        base_url=current_endpoint or "",
+        model_name=model_name,
+        use_instructor=True,
+        max_tokens=max_tokens,
+        max_concurrent_calls=max_concurrent_calls,
+    )
+
+    if not os.path.exists(results_dir):
+        logger.error(f"Results directory {results_dir} does not exist")
+        return 1
+
+    # Clean up any existing results before running
+    if os.path.exists(output_dir):
+        for f in os.listdir(output_dir):
+            file_path_to_remove = os.path.join(output_dir, f)
+            if os.path.isfile(file_path_to_remove):
+                os.remove(file_path_to_remove)
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Discover all conversation files
+    json_files = sorted(f for f in os.listdir(results_dir) if f.endswith(".json"))
+
+    if not json_files:
+        logger.warning(f"No JSON files found in {results_dir}")
+        return 0
+
+    print(f"\n{'=' * 60}")
+    print(f"Found {len(json_files)} conversation files to evaluate")
+    print(f"{'=' * 60}\n")
+
+    # Get RAG evaluation metrics
+    metrics = get_rag_metrics(custom_model_part1)
+
+    # Process each conversation file
+    all_test_cases = []
+    all_limited_chunk_test_cases = []  # For RAG retrieval metrics (limited to top N chunks)
+    retrieval_test_metadata = []  # Track which file/turn each retrieval test belongs to
+    failed_files = []
+
+    for filename in json_files:
+        file_path = os.path.join(results_dir, filename)
+
+        try:
+            print(f"Processing {filename}...")
+
+            with open(file_path, "r", encoding="utf-8") as f:
+                file_data = json.load(f)
+
+            # Validate file format
+            if not isinstance(file_data, dict) or "conversation" not in file_data:
+                logger.error(
+                    f"Invalid format in {filename} - expected object with 'conversation'"
+                )
+                failed_files.append(filename)
+                continue
+
+            conversation_data = file_data["conversation"]
+            config = file_data.get("config", {})
+
+            # Convert to turns
+            turns = _convert_to_turns(conversation_data)
+
+            if len(turns) < 2:
+                logger.warning(
+                    f"Skipping {filename} - insufficient turns ({len(turns)})"
+                )
+                continue
+
+            # Build scenario from expected RAG content (if present)
+            scenario_parts = [f"RAG conversation from {filename}"]
+
+            # Add config information
+            mode = config.get("mode", "unknown")
+            vector_dbs = config.get("vector_dbs", [])
+            scenario_parts.append(f"Mode: {mode}")
+            if vector_dbs:
+                scenario_parts.append(f"Vector DBs: {', '.join(vector_dbs)}")
+
+            # Extract expected RAG content from user messages for conversational evaluation
+            # (actual RAG content is only used in retrieval-specific metrics)
+            for turn_data in conversation_data:
+                if turn_data.get("role") == "user":
+                    expected_rag = turn_data.get("expected_rag_content")
+                    if expected_rag:
+                        rag_context = _build_context_from_expected_rag(expected_rag)
+                        scenario_parts.extend(rag_context)
+
+            # Join scenario parts into a single string (scenario field expects string, not list)
+            scenario = "\n".join(scenario_parts)
+
+            # Create conversational test case
+            test_case = ConversationalTestCase(
+                name=filename,
+                turns=turns,
+                additional_metadata={"filename": filename, "config": config},
+                scenario=scenario,
+            )
+
+            all_test_cases.append(test_case)
+            print(f"  ✓ Loaded {len(turns)} turns for conversational evaluation")
+
+            # ALSO create retrieval test cases for turns with expected_output and actual RAG content
+            turn_num = 0
+            for idx, turn_data in enumerate(conversation_data):
+                if turn_data.get("role") == "user":
+                    turn_num += 1
+                    expected_output = turn_data.get("expected_output")
+                    expected_rag = turn_data.get("expected_rag_content")
+                    actual_rag = turn_data.get("actual_rag_content")
+
+                    # Find the next assistant response (actual_output) for this user turn
+                    actual_output = ""
+                    for j in range(idx + 1, len(conversation_data)):
+                        if conversation_data[j].get("role") == "assistant":
+                            actual_output = conversation_data[j].get("content", "")
+                            break
+
+                    if expected_output and expected_rag:
+                        # Create LLMTestCase for retrieval evaluation
+                        # Use empty list for actual_chunks if nothing was retrieved (should fail metrics)
+                        actual_chunks = (
+                            actual_rag.get("chunks", []) if actual_rag else []
+                        )
+                        expected_chunks = expected_rag.get("chunks", [])
+
+                        # Limit chunks to avoid context length issues
+                        if len(actual_chunks) > max_limited_chunks:
+                            print(
+                                f"  ⚠️  Turn {turn_num}: {len(actual_chunks)} chunks retrieved, limiting to top {max_limited_chunks} for evaluation"
+                            )
+                            limited_chunks = actual_chunks[:max_limited_chunks]
+                        else:
+                            limited_chunks = actual_chunks
+
+                        limited_chunk_test = LLMTestCase(
+                            name=f"{filename}_turn_{turn_num}",
+                            input=turn_data.get("content", ""),
+                            actual_output=actual_output,  # Assistant's actual response (used by Faithfulness/Hallucination)
+                            expected_output=expected_output,
+                            context=expected_chunks,  # Ground truth chunks (what should be retrieved)
+                            retrieval_context=limited_chunks,  # Limited chunks to avoid token limits
+                            additional_metadata={
+                                "original_chunk_count": len(
+                                    actual_chunks
+                                ),  # Store original count for ChunkCountMetric
+                            },
+                        )
+                        all_limited_chunk_test_cases.append(limited_chunk_test)
+
+                        retrieval_test_metadata.append(
+                            {
+                                "filename": filename,
+                                "turn": turn_num,
+                                "user_query": turn_data.get("content", "")[:100]
+                                + "...",
+                            }
+                        )
+
+                        if not actual_chunks:
+                            print(
+                                f"  ⚠️  Created retrieval test case for turn {turn_num} - NO ACTUAL CHUNKS RETRIEVED"
+                            )
+                        else:
+                            print(
+                                f"  ✓ Created retrieval test case for turn {turn_num} ({len(actual_chunks)} total chunks, using {len(limited_chunks)} for evaluation)"
+                            )
+
+        except Exception as e:
+            logger.error(f"Error processing {filename}: {e}")
+            failed_files.append(filename)
+            continue
+
+    if not all_test_cases:
+        logger.error("No valid test cases to evaluate")
+        return 1
+
+    print(f"\n{'=' * 60}")
+    print(f"Evaluating {len(all_test_cases)} conversations...")
+    print(f"{'=' * 60}\n")
+
+    # Run conversational evaluation
+    try:
+        async_config = AsyncConfig(
+            run_async=not sequential, max_concurrent=max_concurrent
+        )
+        conv_results = None
+        limited_chunk_results = None
+        stage2_error = None
+
+        if stage in ("1", "both"):
+            print("=" * 80)
+            print("PART 1: Conversational Evaluation (Response Quality)")
+            print("=" * 80)
+            conv_results = evaluate(
+                test_cases=all_test_cases,
+                metrics=metrics,
+                async_config=async_config,
+                display_config=DisplayConfig(show_indicator=True, print_results=False),
+            )
+
+            # Print clean results without verbose content
+            print("\nResults:")
+            _print_metric_results(conv_results)
+
+        # Run retrieval evaluation with limited chunks
+        if stage in ("2", "both") and all_limited_chunk_test_cases:
+            print(f"\n{'=' * 80}")
+            print(
+                f"PART 2: RAG Retrieval Evaluation (Limited Chunks) - {len(all_limited_chunk_test_cases)} turns"
+            )
+            print(f"{'=' * 80}\n")
+
+            retrieval_metrics = get_retrieval_metrics(
+                custom_model_part2, max_chunks=max_limited_chunks
+            )
+
+            try:
+                limited_chunk_results = evaluate(
+                    test_cases=all_limited_chunk_test_cases,
+                    metrics=retrieval_metrics,
+                    async_config=async_config,
+                    display_config=DisplayConfig(
+                        show_indicator=True, print_results=False
+                    ),
+                )
+                print("\nRetrieval Results (Limited Chunks):")
+                _print_metric_results(limited_chunk_results)
+            except Exception as e:
+                stage2_error = str(e)
+                logger.error(f"Stage 2 evaluation failed: {e}")
+                logger.error(
+                    "This is often caused by the LLM truncating its response (max_tokens too low). Try --max-tokens 8192."
+                )
+                logger.warning("Continuing with Stage 1 results only.")
+
+        # Save detailed results
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_file = os.path.join(output_dir, f"evaluation_results_{timestamp}.json")
+
+        # Extract conversational results data
+        results_data = {
+            "timestamp": timestamp,
+            "model": model_name,
+            "total_conversations": len(all_test_cases),
+            "total_retrieval_tests": len(all_limited_chunk_test_cases),
+            "failed_files": failed_files,
+            "conversational_results": [],
+            "retrieval_results": [],
+        }
+
+        # Extract conversational evaluation results from the test run
+        # After evaluate() runs, results are stored in test run manager
+        if conv_results is not None and hasattr(conv_results, "test_results"):
+            for test_result in conv_results.test_results:
+                result_entry = {
+                    "filename": test_result.name
+                    if hasattr(test_result, "name")
+                    else "unknown",
+                    "metrics": [],
+                }
+
+                for metric_data in test_result.metrics_data:
+                    result_entry["metrics"].append(
+                        {
+                            "name": metric_data.name
+                            if hasattr(metric_data, "name")
+                            else "unknown",
+                            "score": metric_data.score
+                            if hasattr(metric_data, "score")
+                            else None,
+                            "success": metric_data.success
+                            if hasattr(metric_data, "success")
+                            else None,
+                            "reason": metric_data.reason
+                            if hasattr(metric_data, "reason")
+                            else None,
+                        }
+                    )
+
+                results_data["conversational_results"].append(result_entry)
+        elif conv_results is not None:
+            # Fallback: try to extract from test cases
+            for i, test_case in enumerate(all_test_cases):
+                test_result = {
+                    "filename": test_case.additional_metadata.get("filename"),
+                    "metrics": [],
+                }
+
+                for metric in metrics:
+                    metric_name = None
+                    if hasattr(metric, "name"):
+                        metric_name = metric.name
+                    elif hasattr(metric, "__name__"):
+                        metric_name = metric.__name__
+                    else:
+                        metric_name = metric.__class__.__name__
+
+                    test_result["metrics"].append(
+                        {
+                            "name": metric_name,
+                            "score": metric.score if hasattr(metric, "score") else None,
+                            "success": metric.success
+                            if hasattr(metric, "success")
+                            else None,
+                            "reason": metric.reason
+                            if hasattr(metric, "reason")
+                            else None,
+                        }
+                    )
+
+                results_data["conversational_results"].append(test_result)
+
+        # Extract retrieval evaluation results
+        if limited_chunk_results and hasattr(limited_chunk_results, "test_results"):
+            for i, test_result in enumerate(limited_chunk_results.test_results):
+                result_entry = {
+                    "filename": retrieval_test_metadata[i]["filename"]
+                    if i < len(retrieval_test_metadata)
+                    else "unknown",
+                    "turn": retrieval_test_metadata[i]["turn"]
+                    if i < len(retrieval_test_metadata)
+                    else 0,
+                    "user_query": retrieval_test_metadata[i]["user_query"]
+                    if i < len(retrieval_test_metadata)
+                    else "",
+                    "metrics": [],
+                }
+
+                for metric_data in test_result.metrics_data:
+                    result_entry["metrics"].append(
+                        {
+                            "name": metric_data.name
+                            if hasattr(metric_data, "name")
+                            else "unknown",
+                            "score": metric_data.score
+                            if hasattr(metric_data, "score")
+                            else None,
+                            "success": metric_data.success
+                            if hasattr(metric_data, "success")
+                            else None,
+                            "reason": metric_data.reason
+                            if hasattr(metric_data, "reason")
+                            else None,
+                        }
+                    )
+
+                results_data["retrieval_results"].append(result_entry)
+
+        # Save results
+        with open(output_file, "w", encoding="utf-8") as f:
+            json.dump(results_data, f, indent=2)
+
+        # Print comprehensive summary
+        any_passed, any_failed = _print_evaluation_summary(
+            conv_results, limited_chunk_results, stage2_error=stage2_error
+        )
+
+        # Print token usage summary
+        input_tokens, output_tokens, total_requests = get_token_totals()
+        total_tokens = input_tokens + output_tokens
+
+        print(f"\n{'=' * 80}")
+        print("Evaluation Complete!")
+        print(f"{'=' * 80}")
+        print(f"Conversational evaluations: {len(all_test_cases)}")
+        print(f"Retrieval evaluations: {len(all_limited_chunk_test_cases)}")
+        print(f"Results saved to: {output_file}")
+        print("\nToken Usage:")
+        print(f"  LLM API requests: {total_requests}")
+        print(f"  Input tokens:     {input_tokens:,}")
+        print(f"  Output tokens:    {output_tokens:,}")
+        print(f"  Total tokens:     {total_tokens:,}")
+        print(f"{'=' * 80}\n")
+
+        if expect_failures:
+            # --check mode: bad conversations should all fail; a passing result means
+            # the metrics didn't catch a known-bad conversation.
+            return 1 if any_passed else 0
+        else:
+            # Normal mode: good conversations should pass; a failing result means
+            # something went wrong with the RAG system or the evaluation.
+            return 1 if any_failed else 0
+
+    except Exception as e:
+        logger.error(f"Evaluation failed: {e}")
+        import traceback
+
+        traceback.print_exc()
+        return 1
+
+
+def main() -> int:
+    """Main entry point for the RAG evaluation script."""
+    parser = argparse.ArgumentParser(
+        description="Evaluate RAG conversations using LLM-as-a-judge metrics"
+    )
+    parser.add_argument(
+        "--api-endpoint",
+        help="Custom OpenAI-compatible API endpoint (e.g., http://localhost:8321/v1)",
+    )
+    parser.add_argument(
+        "--api-key",
+        help="API key for the LLM service (defaults to OPENAI_API_KEY env var)",
+    )
+    parser.add_argument(
+        "--results-dir",
+        default="results/conversation_results",
+        help="Directory containing conversation result JSON files",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="results/deep_eval_results",
+        help="Directory to save evaluation results",
+    )
+    parser.add_argument(
+        "--max-limited-chunks",
+        type=int,
+        default=10,
+        help="Maximum number of chunks for limited-chunk metrics (default: 10)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=None,
+        help="Maximum number of tokens for LLM responses. If not set, the server default is used.",
+    )
+    parser.add_argument(
+        "--sequential",
+        action="store_true",
+        help="Run evaluations sequentially instead of in parallel to reduce load",
+    )
+    parser.add_argument(
+        "--max-concurrent",
+        type=int,
+        default=4,
+        help="Maximum number of concurrent evaluations when running async (default: 4)",
+    )
+    parser.add_argument(
+        "--max-concurrent-calls",
+        type=int,
+        default=16,
+        help="Maximum number of concurrent API calls to the LLM (default: 16). "
+        "DeepEval fires ~15 calls per test case simultaneously across all metrics; "
+        "this semaphore limits total in-flight requests to avoid server overload.",
+    )
+    parser.add_argument(
+        "--stage",
+        choices=["1", "2", "both"],
+        default="both",
+        help="Which evaluation stages to run: '1' (conversational), '2' (retrieval), or 'both' (default)",
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Enable DEBUG logging for openai/httpx to show retries, timeouts, and request details",
+    )
+    parser.add_argument(
+        "--expect-failures",
+        action="store_true",
+        help="Invert exit code logic: return non-zero if any evaluation PASSES "
+        "(used by evaluate.py --check to verify bad conversations are caught by the metrics)",
+    )
+
+    args = parser.parse_args()
+
+    if args.debug:
+        _configure_debug_logging()
+
+    return _evaluate_rag_conversations(
+        api_endpoint=args.api_endpoint,
+        api_key=args.api_key,
+        results_dir=args.results_dir,
+        output_dir=args.output_dir,
+        max_limited_chunks=args.max_limited_chunks,
+        max_tokens=args.max_tokens,
+        sequential=args.sequential,
+        max_concurrent=args.max_concurrent,
+        max_concurrent_calls=args.max_concurrent_calls,
+        stage=args.stage,
+        expect_failures=args.expect_failures,
+    )
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/evaluations/evaluate.py b/evaluations/evaluate.py
new file mode 100755
index 00000000..2641cb99
--- /dev/null
+++ b/evaluations/evaluate.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+"""
+Evaluation wrapper script.
+
+Runs test_conversations_ui.py (via pytest) to generate conversation results,
+then runs deep_eval_rag.py to evaluate them.
+
+Usage:
+    python evaluate.py [options]
+    python evaluate.py --check [deep_eval options]
+
+Options:
+    --check         Skip conversation generation; run deep_eval_rag.py against
+                    bad-conversations instead of generated results.
+
+    All other arguments are passed through to the appropriate script:
+      - deep_eval_rag.py args: --api-endpoint, --api-key, --results-dir, --output-dir,
+                               --max-limited-chunks, --max-tokens, --sequential, --stage
+      - All remaining args are forwarded to pytest (e.g. --base-url, -k, -v, etc.)
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+EVALUATIONS_DIR = Path(__file__).parent
+GENERATED_RESULTS_DIR = EVALUATIONS_DIR / "results" / "conversation_results"
+KNOWN_BAD_DIR = EVALUATIONS_DIR / "bad-conversations"
+
+# Arguments belonging to deep_eval_rag.py
+DEEP_EVAL_ARGS = {
+    "--api-endpoint",
+    "--api-key",
+    "--results-dir",
+    "--output-dir",
+    "--max-limited-chunks",
+    "--max-tokens",
+    "--sequential",
+    "--stage",
+    "--expect-failures",
+}
+
+
+def split_args(argv):
+    """
+    Split argv into deep_eval args and pytest args.
+    Handles both --flag value and --flag=value forms.
+    --sequential is a store_true flag (no value).
+    """
+    deep_eval = []
+    pytest_args = []
+    i = 0
+    while i < len(argv):
+        arg = argv[i]
+        # Check if this arg (or its --flag= prefix) is a deep_eval arg
+        flag = arg.split("=")[0]
+        if flag in DEEP_EVAL_ARGS:
+            deep_eval.append(arg)
+            # If the flag has no = and is not a boolean flag, consume the next token as value
+            BOOLEAN_FLAGS = {"--sequential", "--expect-failures"}
+            if "=" not in arg and flag not in BOOLEAN_FLAGS:
+                i += 1
+                if i < len(argv):
+                    deep_eval.append(argv[i])
+        else:
+            pytest_args.append(arg)
+        i += 1
+    return deep_eval, pytest_args
+
+
+def run(cmd, description):
+    """Run a command, printing it first. Returns the exit code."""
+    print(f"\n{'=' * 70}")
+    print(f"{description}")
+    print(f"{'=' * 70}")
+    print(f"Running: {' '.join(str(c) for c in cmd)}\n")
+    result = subprocess.run(cmd)
+    return result.returncode
+
+
+def main():
+    # Pull out --check before splitting the rest
+    check_mode = "--check" in sys.argv
+    remaining = [a for a in sys.argv[1:] if a != "--check"]
+
+    deep_eval_extra, pytest_extra = split_args(remaining)
+
+    if check_mode:
+        # --check mode: skip conversation generation, evaluate bad-conversations
+        if not KNOWN_BAD_DIR.exists():
+            print(f"ERROR: bad-conversations directory not found: {KNOWN_BAD_DIR}")
+            sys.exit(1)
+
+        # Override --results-dir unless the caller already specified it
+        if "--results-dir" not in " ".join(deep_eval_extra):
+            deep_eval_extra += ["--results-dir", str(KNOWN_BAD_DIR)]
+
+        # Invert exit code: return non-zero if any bad conversation passes
+        if "--expect-failures" not in deep_eval_extra:
+            deep_eval_extra += ["--expect-failures"]
+
+        cmd = [
+            sys.executable,
+            str(EVALUATIONS_DIR / "deep_eval_rag.py"),
+        ] + deep_eval_extra
+        rc = run(cmd, "STEP 1/1: Evaluating bad-conversations")
+        sys.exit(rc)
+
+    else:
+        # Normal mode: generate conversations then evaluate
+
+        # Step 1: run pytest to generate conversations
+        pytest_cmd = [
+            sys.executable,
+            "-m",
+            "pytest",
+            str(EVALUATIONS_DIR / "test_conversations_ui.py"),
+            "-v",
+            "-s",
+        ] + pytest_extra
+        rc = run(
+            pytest_cmd, "STEP 1/2: Generating conversations (test_conversations_ui.py)"
+        )
+        if rc != 0:
+            print(
+                f"\nConversation generation failed (exit code {rc}). Aborting evaluation."
+            )
+            sys.exit(rc)
+
+        # Step 2: run deep_eval_rag.py on the generated results
+        deep_eval_cmd = [
+            sys.executable,
+            str(EVALUATIONS_DIR / "deep_eval_rag.py"),
+        ] + deep_eval_extra
+        rc = run(deep_eval_cmd, "STEP 2/2: Evaluating conversations (deep_eval_rag.py)")
+        sys.exit(rc)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/evaluations/get_rag_metrics.py b/evaluations/get_rag_metrics.py
new file mode 100644
index 00000000..43e4ad4e
--- /dev/null
+++ b/evaluations/get_rag_metrics.py
@@ -0,0 +1,392 @@
+"""
+DeepEval metrics configuration for RAG conversation assessment.
+
+This module defines evaluation metrics specifically designed for RAG
+(Retrieval-Augmented Generation) conversations, focusing on retrieval
+quality and response accuracy.
+"""
+
+from typing import Any, List, Optional
+
+from deepeval.metrics import (
+    ConversationalGEval,
+    ContextualPrecisionMetric,
+    ContextualRelevancyMetric,
+    FaithfulnessMetric,
+    GEval,
+)
+from deepeval.metrics.base_metric import BaseMetric
+from deepeval.models import DeepEvalBaseLLM
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams, TurnParams
+
+
+class ChunkCountMetric(BaseMetric):
+    """
+    Custom metric to validate that the number of retrieved chunks does not exceed the limit.
+
+    This ensures the RAG system respects the configured chunk limit.
+    """
+
+    def __init__(self, max_chunks: int, threshold: float = 1.0):
+        """
+        Initialize the ChunkCountMetric.
+
+        Args:
+            max_chunks: Maximum allowed number of chunks
+            threshold: Success threshold (default 1.0, must be perfect)
+        """
+        self.max_chunks = max_chunks
+        self.threshold = threshold
+        self.score = 0.0
+        self.success = False
+        self.reason = ""
+
+    @property
+    def __name__(self) -> str:
+        return "Chunk Count Limit"
+
+    def measure(self, test_case: LLMTestCase, *args, **kwargs) -> float:
+        """
+        Measure if the chunk count is within the limit.
+
+        Args:
+            test_case: The test case containing additional_metadata with original_chunk_count
+
+        Returns:
+            float: 1.0 if within limit, 0.0 if exceeds limit
+        """
+        # Get the original chunk count before limiting (stored in metadata)
+        actual_count = 0
+        if hasattr(test_case, "additional_metadata") and test_case.additional_metadata:
+            actual_count = test_case.additional_metadata.get("original_chunk_count", 0)
+
+        # Fallback to retrieval_context length if metadata not available
+        if actual_count == 0:
+            actual_count = (
+                len(test_case.retrieval_context) if test_case.retrieval_context else 0
+            )
+
+        if actual_count <= self.max_chunks:
+            self.score = 1.0
+            self.success = True
+            self.reason = f"Retrieved {actual_count} chunks, which is within the limit of {self.max_chunks}"
+        else:
+            self.score = 0.0
+            self.success = False
+            self.reason = f"Retrieved {actual_count} chunks, which exceeds the limit of {self.max_chunks}"
+
+        return self.score
+
+    async def a_measure(self, test_case: LLMTestCase, *args, **kwargs) -> float:
+        """Async version of measure (just calls synchronous version)."""
+        return self.measure(test_case, *args, **kwargs)
+
+    def is_successful(self) -> bool:
+        """Check if the metric passed."""
+        return self.success
+
+
+class ChunkDeduplicationMetric(BaseMetric):
+    """
+    Custom metric to detect near-duplicate chunks in retrieval results.
+
+    Uses word-level Jaccard similarity to identify chunks that contain
+    substantially overlapping content, which indicates redundant retrieval.
+    """
+
+    def __init__(self, similarity_threshold: float = 0.8, threshold: float = 1.0):
+        """
+        Initialize the ChunkDeduplicationMetric.
+
+        Args:
+            similarity_threshold: Jaccard similarity above which two chunks
+                                  are considered duplicates (default 0.8)
+            threshold: Success threshold for the metric score (default 1.0)
+        """
+        self.similarity_threshold = similarity_threshold
+        self.threshold = threshold
+        self.score = 0.0
+        self.success = False
+        self.reason = ""
+
+    @property
+    def __name__(self) -> str:
+        return "Chunk Deduplication"
+
+    @staticmethod
+    def _tokenize(text: str) -> set:
+        """Tokenize text into a set of lowercased words."""
+        return set(text.lower().split())
+
+    @staticmethod
+    def _jaccard_similarity(set1: set, set2: set) -> float:
+        """Compute Jaccard similarity between two sets."""
+        if not set1 and not set2:
+            return 1.0
+        union = set1 | set2
+        return len(set1 & set2) / len(union) if union else 0.0
+
+    def measure(self, test_case: LLMTestCase, *args, **kwargs) -> float:
+        """
+        Measure chunk deduplication quality.
+
+        Compares all pairs of retrieved chunks using Jaccard similarity.
+        Score is 1.0 if no duplicates are found, decreasing with more duplicates.
+
+        Args:
+            test_case: The test case containing retrieval_context chunks
+
+        Returns:
+            float: Score from 0.0 to 1.0 (1.0 = no duplicates)
+        """
+        chunks = test_case.retrieval_context or []
+
+        if len(chunks) <= 1:
+            self.score = 1.0
+            self.success = True
+            self.reason = f"Only {len(chunks)} chunk(s), no duplicates possible"
+            return self.score
+
+        tokenized = [self._tokenize(chunk) for chunk in chunks]
+
+        duplicate_pairs = []
+        for i in range(len(tokenized)):
+            for j in range(i + 1, len(tokenized)):
+                sim = self._jaccard_similarity(tokenized[i], tokenized[j])
+                if sim >= self.similarity_threshold:
+                    duplicate_pairs.append((i + 1, j + 1, sim))
+
+        if not duplicate_pairs:
+            self.score = 1.0
+            self.success = True
+            self.reason = f"No duplicate chunks found among {len(chunks)} chunks"
+        else:
+            total_pairs = len(chunks) * (len(chunks) - 1) // 2
+            self.score = 1.0 - (len(duplicate_pairs) / total_pairs)
+            self.success = self.score >= self.threshold
+            pair_desc = [
+                f"chunks {i} and {j} ({sim:.0%} similar)"
+                for i, j, sim in duplicate_pairs[:5]
+            ]
+            desc = "; ".join(pair_desc)
+            if len(duplicate_pairs) > 5:
+                desc += f" ... and {len(duplicate_pairs) - 5} more"
+            self.reason = f"Found {len(duplicate_pairs)} duplicate pair(s) among {len(chunks)} chunks: {desc}"
+
+        return self.score
+
+    async def a_measure(self, test_case: LLMTestCase, *args, **kwargs) -> float:
+        """Async version of measure (just calls synchronous version)."""
+        return self.measure(test_case, *args, **kwargs)
+
+    def is_successful(self) -> bool:
+        """Check if the metric passed."""
+        return self.success
+
+
+def get_rag_metrics(
+    custom_model: Optional[DeepEvalBaseLLM] = None,
+) -> List[Any]:
+    """
+    Create evaluation metrics for RAG conversation assessment.
+
+    This function defines conversational metrics specifically designed
+    to evaluate RAG system performance, including retrieval quality
+    and response accuracy.
+
+    Args:
+        custom_model: Optional custom LLM model instance to use for evaluations.
+                     If None, uses the default DeepEval model.
+
+    Returns:
+        List[ConversationalGEval]: List of evaluation metrics including:
+            - Retrieval Relevance: Measures if retrieved content is relevant to the query
+            - Response Accuracy: Evaluates if the response accurately uses retrieved content
+            - Completeness: Assesses if the response fully answers the question
+            - Factual Consistency: Checks if response facts match the retrieved content
+    """
+    metrics = []
+
+    # Metric 2: Response Accuracy
+    # Evaluates if the assistant's response accurately reflects the retrieved content,
+    # including catching both hallucinated claims and direct contradictions.
+    metrics.append(
+        ConversationalGEval(
+            name="Response Accuracy",
+            criteria="Response Accuracy - Every factual claim in the assistant's response must be verifiable against the retrieval_context and must not contradict it. This metric does NOT evaluate whether the response answers the user's question.",
+            evaluation_params=[
+                TurnParams.CONTENT,
+                TurnParams.ROLE,
+                TurnParams.RETRIEVAL_CONTEXT,
+            ],
+            evaluation_steps=[
+                "IMPORTANT: This metric evaluates ONLY whether the claims in the response are accurate relative to the retrieval_context. Do NOT evaluate whether the response answers the user's question - that is the sole concern of the Answer Relevance metric.",
+                "Review the retrieval_context provided with the user's turn - this is the source material",
+                "If retrieval_context is empty or not provided, the assistant should NOT fabricate an answer - acknowledging that the information is not available is accurate and should score highly",
+                "If retrieval_context is present, identify all factual claims made in the assistant's response",
+                "For each claim, check whether it is supported by the retrieval_context",
+                "Also check for direct contradictions: if the response states a different number, name, or fact than what appears in the retrieval_context (e.g. '10 units' vs '365 units'), that claim is inaccurate and must be penalised severely",
+                "A single contradicted or hallucinated fact is sufficient to score this metric low - do not average it against other correct claims",
+                "Score highly only if all claims in the response are supported by and consistent with the retrieval_context",
+            ],
+            threshold=0.7,
+            model=custom_model,
+        )
+    )
+
+    # Metric 3: Completeness
+    # Assesses whether the response covers the key information present in the retrieved chunks
+    metrics.append(
+        ConversationalGEval(
+            name="Response Completeness",
+            criteria="Context Coverage - The assistant's response should cover the key facts present in the retrieval_context. Evaluate this independently of whether the response answers the user's question.",
+            evaluation_params=[
+                TurnParams.CONTENT,
+                TurnParams.ROLE,
+                TurnParams.RETRIEVAL_CONTEXT,
+            ],
+            evaluation_steps=[
+                "IMPORTANT: Ignore the user's question entirely for this metric. Do not consider whether the response answers the question.",
+                "If retrieval_context is empty or not provided, score highly if the response states that information is not available",
+                "If retrieval_context is present, list the key facts and topics it contains",
+                "Check whether the assistant's response mentions those key facts",
+                "Score based solely on how many of the retrieval_context's key facts appear in the response - a response that covers all key facts from the context scores high, a response that omits most of them scores low",
+                "Do NOT reduce the score because the response fails to acknowledge gaps or does not address the user's question - that is measured by Answer Relevance",
+            ],
+            threshold=0.6,
+            model=custom_model,
+        )
+    )
+
+    # Metric 5: Answer Relevance
+    # Evaluates if the response directly answers what was asked
+    # NOTE: Does not use RETRIEVAL_CONTEXT - only checks if answer addresses question
+    metrics.append(
+        ConversationalGEval(
+            name="Answer Relevance",
+            criteria="Answer Relevance - The response should directly address the user's question without going off-topic",
+            evaluation_params=[TurnParams.CONTENT, TurnParams.ROLE],
+            evaluation_steps=[
+                "Identify the SPECIFIC question or request from the user - not just the general topic but the precise information being sought (e.g. 'what benefits are included', 'how much does it cost', 'how do I enroll')",
+                "Check whether the assistant's response attempts to address that specific question",
+                "IMPORTANT: Do NOT penalise a response for being incomplete - a response that partially answers the specific question is still relevant. Completeness is measured by a separate metric.",
+                "IMPORTANT: Judge relevance based on whether the response addresses the correct TOPIC, not on whether the content seems realistic. Whimsical or fictional content used to address the right specific question IS relevant.",
+                "A response that states the requested information is not available IS relevant - it directly addresses the question.",
+                "Score LOW if the response answers a DIFFERENT specific question than what was asked, even if both questions are about the same general topic (e.g. question asks about cost/eligibility, response describes plan features instead - that is NOT relevant).",
+            ],
+            threshold=0.7,
+            model=custom_model,
+        )
+    )
+
+    return metrics
+
+
+def get_retrieval_metrics(
+    custom_model: Optional[DeepEvalBaseLLM] = None,
+    max_chunks: int = 10,
+) -> List[Any]:
+    """
+    Create evaluation metrics for RAG retrieval quality assessment.
+
+    These metrics evaluate the quality of retrieved chunks compared to expected chunks.
+    They are designed to work with limited chunk counts to avoid context length issues.
+
+    Args:
+        custom_model: Optional custom LLM model instance to use for evaluations.
+                     If None, uses the default DeepEval model.
+        max_chunks: Maximum allowed number of chunks in retrieval results.
+                   Used by ChunkCountMetric to validate chunk limit compliance.
+
+    Returns:
+        List of retrieval quality metrics including:
+            - Chunk Count Limit: Validates chunk count does not exceed limit
+            - Chunk Deduplication: Detects near-duplicate chunks in retrieval results
+            - Chunk Alignment: Compares actual vs expected chunks for content coverage and order
+            - Contextual Recall: Measures if actual chunks contain all expected information
+            - Contextual Precision: Measures if relevant chunks are ranked higher
+            - Contextual Relevancy: Measures relevance of each chunk to the input
+            - Faithfulness: Measures if response claims are supported by retrieved context
+    """
+    metrics = []
+
+    # Chunk Count Limit - Validates that retrieved chunks don't exceed the configured limit
+    metrics.append(
+        ChunkCountMetric(
+            max_chunks=max_chunks,
+            threshold=1.0,  # Must be perfect - either passes or fails
+        )
+    )
+
+    # Chunk Deduplication - Detects near-duplicate chunks in retrieval results
+    metrics.append(
+        ChunkDeduplicationMetric(
+            similarity_threshold=0.8,  # 80% word overlap = duplicate
+            threshold=1.0,  # Must be perfect - no duplicates allowed
+        )
+    )
+
+    # Chunk Alignment - Compares actual vs expected chunks for content coverage and order.
+    metrics.append(
+        GEval(
+            name="Chunk Alignment",
+            criteria="Chunk Alignment - The actual retrieved chunks (retrieval_context) should cover the same content as the expected chunks (context) and in a similar order",
+            evaluation_params=[
+                LLMTestCaseParams.CONTEXT,
+                LLMTestCaseParams.RETRIEVAL_CONTEXT,
+            ],
+            evaluation_steps=[
+                "IMPORTANT: The retrieval_context may contain duplicate chunks. Before evaluating, mentally deduplicate the retrieval_context - identify only the unique chunks and ignore any repeated copies. Score based solely on those unique chunks.",
+                "The 'context' contains the expected/ideal content. These may be large document sections.",
+                "The 'retrieval_context' contains the chunks that were actually retrieved (after deduplication in your mind). These may be smaller or differently structured than the expected chunks - this is normal and should NOT be penalized.",
+                "For each expected chunk in 'context', identify the key facts, topics, and information points it contains.",
+                "Check whether those key facts and information points appear anywhere across the retrieval_context. The information may be spread across multiple actual chunks with different boundaries - what matters is whether the content is present, not whether the chunk structure matches.",
+                "Assess whether the order of the key topics across the retrieval_context roughly aligns with the order in the expected chunks.",
+                "Penalize missing content (expected information not found anywhere in actual chunks) more heavily than extra content.",
+                "Do NOT penalize for different chunk boundaries, chunk sizes, or chunk structure.",
+                "Do NOT penalize for duplicated chunks - deduplication is handled by a separate metric. Treat the retrieval_context as if each unique chunk appears only once.",
+                "Do NOT consider whether the chunks are relevant to any query - only compare actual vs expected chunks.",
+            ],
+            threshold=0.7,
+            model=custom_model,
+        )
+    )
+
+    # Contextual Recall - Measures if actual chunks contain all information from expected chunks
+    # Not working with current judge LLMs, but leave in for possible inclusition later
+    # metrics.append(
+    #    ContextualRecallMetric(
+    #        threshold=0.7,
+    #        model=custom_model,
+    #        include_reason=True,
+    #    )
+    # )
+
+    # Contextual Precision - Measures if relevant chunks are ranked higher than irrelevant ones
+    metrics.append(
+        ContextualPrecisionMetric(
+            threshold=0.7,
+            model=custom_model,
+            include_reason=True,
+        )
+    )
+
+    # Contextual Relevancy - Measures relevance of each chunk to the input
+    metrics.append(
+        ContextualRelevancyMetric(
+            threshold=0.7,
+            model=custom_model,
+            include_reason=True,
+        )
+    )
+
+    # Faithfulness - Measures if claims in the response are supported by retrieval context
+    metrics.append(
+        FaithfulnessMetric(
+            threshold=0.7,
+            model=custom_model,
+            include_reason=True,
+        )
+    )
+
+    return metrics
diff --git a/evaluations/helpers/__init__.py b/evaluations/helpers/__init__.py
new file mode 100644
index 00000000..74047dd5
--- /dev/null
+++ b/evaluations/helpers/__init__.py
@@ -0,0 +1 @@
+"""Helper modules for RAG evaluation using deepeval."""
diff --git a/evaluations/helpers/custom_llm.py b/evaluations/helpers/custom_llm.py
new file mode 100644
index 00000000..8e6616a0
--- /dev/null
+++ b/evaluations/helpers/custom_llm.py
@@ -0,0 +1,379 @@
+#!/usr/bin/env python3
+"""
+Custom LLM wrapper for DeepEval evaluations using OpenAI-compatible endpoints.
+Adapted from it-self-service-agent/evaluations/helpers/custom_llm.py
+"""
+
+import asyncio
+import logging
+import os
+import time
+from typing import Any, Dict, Optional, Tuple, Type, Union, cast
+
+import instructor
+import openai
+from deepeval.models import DeepEvalBaseLLM
+from pydantic import BaseModel
+
+from .token_counter import count_tokens_from_response
+
+logger = logging.getLogger(__name__)
+
+
+def get_api_configuration(
+    api_endpoint: Optional[str] = None,
+    api_key: Optional[str] = None,
+) -> Tuple[Optional[str], Optional[str], str]:
+    """
+    Get API configuration from parameters or environment variables.
+
+    Supports both it-self-service-agent style variables (LLM_API_TOKEN, LLM_URL, LLM/LLM_ID)
+    and standard OpenAI variables (OPENAI_API_KEY, OPENAI_API_BASE).
+
+    Args:
+        api_endpoint: Optional custom API endpoint URL
+        api_key: Optional API key
+
+    Returns:
+        Tuple of (api_key, endpoint, model_name)
+    """
+    # Get API key from parameter or environment
+    # Priority: parameter > LLM_API_TOKEN > OPENAI_API_KEY
+    final_api_key = api_key or os.getenv("LLM_API_TOKEN") or os.getenv("OPENAI_API_KEY")
+
+    # Get endpoint from parameter or environment
+    # Priority: parameter > LLM_URL > OPENAI_API_BASE
+    final_endpoint = (
+        api_endpoint or os.getenv("LLM_URL") or os.getenv("OPENAI_API_BASE")
+    )
+
+    # Get model name from environment
+    # Priority: LLM_ID > LLM > default
+    model_name = os.getenv("LLM_ID") or os.getenv("LLM") or "gpt-4"
+
+    if not final_api_key:
+        logger.warning(
+            "No API key found in parameters or LLM_API_TOKEN/OPENAI_API_KEY environment variables"
+        )
+
+    return final_api_key, final_endpoint, model_name
+
+
+def _log_response(response: Any, elapsed: float, mode: str) -> None:
+    """Log details from an API response including finish reason and token usage."""
+    try:
+        choice = response.choices[0] if response.choices else None
+        finish_reason = choice.finish_reason if choice else "unknown"
+        usage = response.usage
+
+        input_tokens = getattr(usage, "prompt_tokens", 0) if usage else 0
+        output_tokens = getattr(usage, "completion_tokens", 0) if usage else 0
+
+        logger.info(
+            f"API call completed ({mode}): {elapsed:.2f}s, "
+            f"finish_reason={finish_reason}, "
+            f"input_tokens={input_tokens}, output_tokens={output_tokens}"
+        )
+
+        if finish_reason == "length":
+            print(
+                f"WARNING: Response truncated (finish_reason=length): "
+                f"output used {output_tokens} tokens - consider increasing --max-tokens"
+            )
+            logger.warning(
+                f"Response truncated (finish_reason=length): "
+                f"output used {output_tokens} tokens - response may be incomplete"
+            )
+    except Exception as e:
+        logger.debug(f"Could not log response details: {e}")
+
+
+class CustomLLM(DeepEvalBaseLLM):
+    """Custom LLM class for using non-OpenAI endpoints with deepeval.
+
+    Supports two modes:
+    - instructor mode (use_instructor=True): Uses the instructor library for structured
+      output with tool/function calling. More reliable schema validation but requires
+      server support for tool calling.
+    - JSON mode (use_instructor=False): Uses the OpenAI JSON response format. More
+      compatible with vLLM and other inference servers. DeepEval handles JSON parsing.
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        base_url: str,
+        model_name: Optional[str] = None,
+        use_instructor: bool = True,
+        max_tokens: Optional[int] = None,
+        max_concurrent_calls: int = 4,
+    ):
+        """
+        Initialize the CustomLLM with API credentials and configuration.
+
+        Args:
+            api_key: API key for authentication
+            base_url: Base URL for the LLM API endpoint
+            model_name: Optional model name
+            use_instructor: If True, use instructor for structured output.
+                           If False, use JSON mode and let DeepEval parse the response.
+            max_tokens: Maximum number of tokens for LLM responses. If None, the server default is used.
+        """
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model_name = model_name or os.getenv("LLM_ID") or "gpt-4"
+        self.use_instructor = use_instructor
+        self.max_tokens = max_tokens
+
+        # Semaphore to cap total concurrent async API calls across all metrics and test
+        # cases.
+        self._semaphore = asyncio.Semaphore(max_concurrent_calls)
+
+        self.client = openai.OpenAI(
+            api_key=api_key, base_url=base_url, timeout=600.0, max_retries=2
+        )
+        self.async_client = openai.AsyncOpenAI(
+            api_key=api_key, base_url=base_url, timeout=600.0, max_retries=2
+        )
+
+        # Create instructor clients once and reuse them (only if needed).
+        # Mode.JSON uses response_format={"type":"json_object"} and validates the
+        # response client-side with Pydantic, retrying on schema mismatch.
+        if use_instructor:
+            self.instructor_client = instructor.from_openai(
+                self.client, mode=instructor.Mode.JSON
+            )
+            self.async_instructor_client = instructor.from_openai(
+                self.async_client, mode=instructor.Mode.JSON
+            )
+
+        mode_label = "instructor/JSON" if use_instructor else "JSON"
+        logger.debug(
+            f"CustomLLM initialized: mode={mode_label}, "
+            f"max_tokens={max_tokens}, timeout=600s, max_retries=2, "
+            f"max_concurrent_calls={max_concurrent_calls}"
+        )
+
+    def load_model(self) -> Any:
+        """
+        Load and return the OpenAI client instance.
+
+        Returns:
+            OpenAI client configured with custom endpoint and API key
+        """
+        return self.client
+
+    def generate(  # type: ignore[override]
+        self, prompt: str, schema: Optional[Type[BaseModel]] = None
+    ) -> Union[str, BaseModel]:
+        """
+        Generate a response to the given prompt using the custom LLM.
+
+        Args:
+            prompt: The input prompt to generate a response for
+            schema: Optional Pydantic BaseModel class for structured output
+
+        Returns:
+            Pydantic model instance if schema provided and using instructor,
+            otherwise string response
+
+        Raises:
+            Exception: If the API call fails or returns an error
+        """
+        prompt_words = len(prompt.split())
+        try:
+            # Instructor mode: use tool/function calling for structured output
+            if schema is not None and self.use_instructor:
+                logger.debug(
+                    f"Generating (sync/instructor): schema={schema.__name__}, "
+                    f"prompt_words={prompt_words}"
+                )
+                start_time = time.time()
+                resp, completion = (
+                    self.instructor_client.chat.completions.create_with_completion(
+                        model=self.model_name,
+                        messages=[{"role": "user", "content": prompt}],
+                        response_model=schema,
+                        temperature=0.1,
+                        **(
+                            {"max_tokens": self.max_tokens}
+                            if self.max_tokens is not None
+                            else {}
+                        ),
+                        max_retries=3,
+                    )
+                )
+                elapsed = time.time() - start_time
+
+                _log_response(completion, elapsed, "sync/instructor")
+                count_tokens_from_response(
+                    completion, self.model_name, "custom_llm_evaluation"
+                )
+
+                return cast(BaseModel, resp)
+
+            # JSON mode: return raw string, let DeepEval parse it
+            use_json = schema is not None or any(
+                keyword in prompt.lower() for keyword in ["json", "schema", "format"]
+            )
+
+            api_kwargs: Dict[str, Any] = {
+                "model": self.model_name,
+                "messages": [{"role": "user", "content": prompt}],
+                "temperature": 0.1,
+            }
+            if self.max_tokens is not None:
+                api_kwargs["max_tokens"] = self.max_tokens
+
+            if use_json:
+                api_kwargs["response_format"] = {"type": "json_object"}
+
+            logger.info(
+                f"API call starting (sync/JSON): json_mode={use_json}, "
+                f"prompt_words={prompt_words}, schema={schema.__name__ if schema else None}"
+            )
+            logger.debug(
+                f"Prompt (sync/JSON, schema={schema.__name__ if schema else None}):\n{prompt}"
+            )
+            start_time = time.time()
+            response = self.client.chat.completions.create(**api_kwargs)
+            elapsed = time.time() - start_time
+
+            _log_response(response, elapsed, "sync/JSON")
+            count_tokens_from_response(
+                response, self.model_name, "custom_llm_evaluation"
+            )
+
+            content = response.choices[0].message.content
+            result = str(content) if content is not None else ""
+            logger.debug(
+                f"Response (sync/JSON, schema={schema.__name__ if schema else None}):\n{result}"
+            )
+            return result
+        except openai.APITimeoutError as e:
+            logger.error(f"API timeout (sync): {e}")
+            raise
+        except openai.APIConnectionError as e:
+            logger.error(f"API connection error (sync): {type(e).__name__}: {e}")
+            raise
+        except openai.APIStatusError as e:
+            logger.error(
+                f"API status error (sync): status={e.status_code}, "
+                f"type={type(e).__name__}, message={e.message}"
+            )
+            raise
+        except Exception as e:
+            logger.error(f"Unexpected error (sync): {type(e).__name__}: {e}")
+            raise
+
+    async def a_generate(  # type: ignore[override]
+        self, prompt: str, schema: Optional[Type[BaseModel]] = None
+    ) -> Union[str, BaseModel]:
+        """
+        Asynchronously generate a response to the given prompt using the custom LLM.
+
+        Args:
+            prompt: The input prompt to generate a response for
+            schema: Optional Pydantic BaseModel class for structured output
+
+        Returns:
+            Pydantic model instance if schema provided and using instructor,
+            otherwise string response
+
+        Raises:
+            Exception: If the API call fails or returns an error
+        """
+        prompt_words = len(prompt.split())
+        async with self._semaphore:
+            try:
+                # Instructor mode: use tool/function calling for structured output
+                if schema is not None and self.use_instructor:
+                    logger.debug(
+                        f"Generating (async/instructor): schema={schema.__name__}, "
+                        f"prompt_words={prompt_words}"
+                    )
+
+                    start_time = time.time()
+                    (
+                        resp,
+                        completion,
+                    ) = await self.async_instructor_client.chat.completions.create_with_completion(
+                        model=self.model_name,
+                        messages=[{"role": "user", "content": prompt}],
+                        response_model=schema,
+                        temperature=0.1,
+                        **(
+                            {"max_tokens": self.max_tokens}
+                            if self.max_tokens is not None
+                            else {}
+                        ),
+                        max_retries=3,
+                    )
+                    elapsed = time.time() - start_time
+
+                    _log_response(completion, elapsed, "async/instructor")
+                    count_tokens_from_response(
+                        completion, self.model_name, "custom_llm_evaluation_async"
+                    )
+
+                    return resp
+
+                # JSON mode: return raw string, let DeepEval parse it
+                use_json = schema is not None or any(
+                    keyword in prompt.lower()
+                    for keyword in ["json", "schema", "format"]
+                )
+
+                api_kwargs: Dict[str, Any] = {
+                    "model": self.model_name,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "temperature": 0.1,
+                }
+                if self.max_tokens is not None:
+                    api_kwargs["max_tokens"] = self.max_tokens
+
+                if use_json:
+                    api_kwargs["response_format"] = {"type": "json_object"}
+
+                logger.info(
+                    f"API call starting (async/JSON): json_mode={use_json}, "
+                    f"prompt_words={prompt_words}, schema={schema.__name__ if schema else None}"
+                )
+                logger.debug(
+                    f"Prompt (async/JSON, schema={schema.__name__ if schema else None}):\n{prompt}"
+                )
+                start_time = time.time()
+                response = await self.async_client.chat.completions.create(**api_kwargs)
+                elapsed = time.time() - start_time
+
+                _log_response(response, elapsed, "async/JSON")
+                count_tokens_from_response(
+                    response, self.model_name, "custom_llm_evaluation_async"
+                )
+
+                content = response.choices[0].message.content
+                result = str(content) if content is not None else ""
+                logger.debug(
+                    f"Response (async/JSON, schema={schema.__name__ if schema else None}):\n{result}"
+                )
+                return result
+
+            except openai.APITimeoutError as e:
+                logger.error(f"API timeout (async): {e}")
+                raise
+            except openai.APIConnectionError as e:
+                logger.error(f"API connection error (async): {type(e).__name__}: {e}")
+                raise
+            except openai.APIStatusError as e:
+                logger.error(
+                    f"API status error (async): status={e.status_code}, "
+                    f"type={type(e).__name__}, message={e.message}"
+                )
+                raise
+            except Exception as e:
+                logger.error(f"Unexpected error (async): {type(e).__name__}: {e}")
+                raise
+
+    def get_model_name(self) -> str:
+        """Return the model name."""
+        return self.model_name
diff --git a/evaluations/helpers/endpoint.py b/evaluations/helpers/endpoint.py
new file mode 100644
index 00000000..df964237
--- /dev/null
+++ b/evaluations/helpers/endpoint.py
@@ -0,0 +1,81 @@
+"""
+Shared utility for auto-detecting the RAG UI endpoint.
+
+Used by both conftest.py (pytest runs) and test_conversations_ui.py (__main__ mode).
+"""
+
+import os
+import subprocess
+
+
+def get_rag_ui_endpoint() -> str:
+    """
+    Automatically determine RAG UI endpoint.
+
+    Priority:
+    1. RAG_UI_ENDPOINT env var (explicit override)
+    2. Construct from NAMESPACE env var (e.g., https://rag-{namespace}.apps...)
+    3. Query OpenShift for route in current namespace
+    4. Fall back to localhost:8501
+    """
+    # Check for explicit override
+    if os.getenv("RAG_UI_ENDPOINT"):
+        return os.getenv("RAG_UI_ENDPOINT")
+
+    # Try to construct from NAMESPACE
+    namespace = os.getenv("NAMESPACE")
+    if namespace:
+        try:
+            result = subprocess.run(
+                [
+                    "oc",
+                    "get",
+                    "route",
+                    "rag",
+                    "-n",
+                    namespace,
+                    "-o",
+                    "jsonpath={.spec.host}",
+                ],
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            if result.returncode == 0 and result.stdout.strip():
+                route_host = result.stdout.strip()
+                endpoint = f"https://{route_host}"
+                print(
+                    f"Auto-detected RAG UI endpoint from namespace '{namespace}': {endpoint}"
+                )
+                return endpoint
+        except Exception as e:
+            print(f"Warning: Could not query route for namespace '{namespace}': {e}")
+
+    # Try to detect current namespace from oc context
+    try:
+        result = subprocess.run(
+            ["oc", "project", "-q"], capture_output=True, text=True, timeout=5
+        )
+        if result.returncode == 0 and result.stdout.strip():
+            current_namespace = result.stdout.strip()
+
+            result = subprocess.run(
+                ["oc", "get", "route", "rag", "-o", "jsonpath={.spec.host}"],
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            if result.returncode == 0 and result.stdout.strip():
+                route_host = result.stdout.strip()
+                endpoint = f"https://{route_host}"
+                print(
+                    f"Auto-detected RAG UI endpoint from current namespace '{current_namespace}': {endpoint}"
+                )
+                return endpoint
+    except Exception as e:
+        print(f"Warning: Could not detect namespace from oc context: {e}")
+
+    # Fall back to localhost (assumes port-forward is running)
+    print("No RAG_UI_ENDPOINT or NAMESPACE set. Defaulting to http://localhost:8501")
+    print("Make sure to run: oc port-forward svc/rag 8501:8501 -n <namespace>")
+    return "http://localhost:8501"
diff --git a/evaluations/helpers/playwright_setup.py b/evaluations/helpers/playwright_setup.py
new file mode 100644
index 00000000..194c207a
--- /dev/null
+++ b/evaluations/helpers/playwright_setup.py
@@ -0,0 +1,44 @@
+"""
+Playwright browser setup for project-local installation.
+
+Ensures Chromium is installed in evaluations/bin/ and configures
+PLAYWRIGHT_BROWSERS_PATH so Playwright finds the browsers there.
+"""
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+# evaluations/bin/
+BROWSERS_PATH = Path(__file__).parent.parent / "bin"
+
+
+def _chromium_installed() -> bool:
+    """Check if Chromium is installed in the project-local path."""
+    if not BROWSERS_PATH.exists():
+        return False
+    return any(BROWSERS_PATH.glob("chromium-*")) or any(
+        BROWSERS_PATH.glob("chromium_headless_shell-*")
+    )
+
+
+def ensure_playwright_browsers() -> None:
+    """
+    Set PLAYWRIGHT_BROWSERS_PATH and install Chromium if not present.
+
+    Must be called before Playwright launches a browser. Sets the
+    PLAYWRIGHT_BROWSERS_PATH environment variable to evaluations/bin/
+    and installs Chromium there if it is missing.
+    """
+    os.environ["PLAYWRIGHT_BROWSERS_PATH"] = str(BROWSERS_PATH)
+
+    if not _chromium_installed():
+        print(f"Installing Playwright Chromium to {BROWSERS_PATH}/ (one-time setup)...")
+        BROWSERS_PATH.mkdir(parents=True, exist_ok=True)
+        subprocess.run(
+            [sys.executable, "-m", "playwright", "install", "chromium"],
+            check=True,
+        )
+        print("Playwright Chromium installed.")
+        print()
diff --git a/evaluations/helpers/token_counter.py b/evaluations/helpers/token_counter.py
new file mode 100644
index 00000000..c0f8f74d
--- /dev/null
+++ b/evaluations/helpers/token_counter.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+"""
+Simple token counter for tracking LLM API usage.
+Simplified version adapted from it-self-service-agent.
+"""
+
+import threading
+from typing import Any, Optional
+
+
+_lock = threading.Lock()
+_total_input_tokens = 0
+_total_output_tokens = 0
+_total_requests = 0
+
+
+def count_tokens_from_response(
+    response: Any, model: Optional[str] = None, context: Optional[str] = None
+) -> tuple[int, int]:
+    """
+    Extract and count tokens from an OpenAI-style response object.
+
+    Accumulates totals that can be retrieved with get_token_totals().
+
+    Args:
+        response: OpenAI API response object
+        model: Model name (optional, for logging)
+        context: Context string (optional, for logging)
+
+    Returns:
+        Tuple of (input_tokens, output_tokens)
+    """
+    global _total_input_tokens, _total_output_tokens, _total_requests
+    try:
+        if hasattr(response, "usage") and response.usage is not None:
+            try:
+                usage = response.usage
+                input_tokens = getattr(usage, "prompt_tokens", 0) or 0
+                output_tokens = getattr(usage, "completion_tokens", 0) or 0
+                with _lock:
+                    _total_input_tokens += input_tokens
+                    _total_output_tokens += output_tokens
+                    _total_requests += 1
+                return input_tokens, output_tokens
+            except AttributeError:
+                return 0, 0
+        else:
+            return 0, 0
+    except Exception:
+        return 0, 0
+
+
+def get_token_totals() -> tuple[int, int, int]:
+    """
+    Return accumulated token totals.
+
+    Returns:
+        Tuple of (total_input_tokens, total_output_tokens, total_requests)
+    """
+    with _lock:
+        return _total_input_tokens, _total_output_tokens, _total_requests
+
+
+def reset_token_totals() -> None:
+    """Reset accumulated token totals to zero."""
+    global _total_input_tokens, _total_output_tokens, _total_requests
+    with _lock:
+        _total_input_tokens = 0
+        _total_output_tokens = 0
+        _total_requests = 0
diff --git a/evaluations/pyproject.toml b/evaluations/pyproject.toml
new file mode 100644
index 00000000..3f2da97a
--- /dev/null
+++ b/evaluations/pyproject.toml
@@ -0,0 +1,22 @@
+[project]
+name = "rag-evaluations"
+version = "0.1.0"
+description = "RAG conversation evaluation framework"
+requires-python = ">=3.11"
+dependencies = [
+    "pytest>=8.3.0",
+    "pytest-playwright>=0.5.0",
+    "playwright>=1.48.0",
+    "deepeval>=1.2.6",
+    "instructor>=1.7.0",
+    "openai>=1.0.0",
+    "requests>=2.31.0",
+    "python-dotenv>=1.0.0",
+    "markdownify>=0.11.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "black>=23.0.0",
+    "ruff>=0.1.0",
+]
diff --git a/evaluations/test_conversations_ui.py b/evaluations/test_conversations_ui.py
new file mode 100644
index 00000000..3f03f354
--- /dev/null
+++ b/evaluations/test_conversations_ui.py
@@ -0,0 +1,951 @@
+"""
+Playwright-based conversation evaluation tests.
+Reads test definitions from JSON files and executes them through the RAG UI.
+"""
+
+import json
+import shutil
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Any, Optional
+
+import pytest
+from playwright.sync_api import Page, expect
+from markdownify import markdownify as md
+
+# Configuration
+CONVERSATIONS_DIR = Path(__file__).parent / "conversations"
+RESULTS_DIR = Path(__file__).parent / "results" / "conversation_results"
+TEST_TIMEOUT = 60000  # 60 seconds
+RESPONSE_WAIT_MAX = 120  # 2 minutes max for AI response
+ELEMENT_TIMEOUT = 60000  # 60 seconds for element operations (inner_text, etc.)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def cleanup_old_results(request):
+    """
+    Clean up old test results before running new tests.
+    Runs once at the start of the test session.
+    If pytest is run with -k filter, only cleans up matching files.
+    """
+    # Check if pytest is running with a -k filter
+    k_filter = request.config.getoption("-k", default=None)
+
+    if RESULTS_DIR.exists():
+        # Determine which files to clean up
+        if k_filter:
+            # Extract the test name from the filter (e.g., "hr_benefits_test_agent.json")
+            # The filter might be complex, but we'll try to extract a simple filename pattern
+            # Common patterns: "test.json", "[test.json]", "test and not other"
+            import re
+
+            # Try to extract a simple filename pattern
+            match = re.search(r"([a-zA-Z0-9_-]+\.json)", k_filter)
+            if match:
+                pattern = match.group(1).replace(".json", "")
+                print(f"\n🧹 Cleaning up results for filtered test: {pattern}")
+                json_files = list(RESULTS_DIR.glob(f"{pattern}_*.json"))
+                screenshot_dirs = []
+                if (RESULTS_DIR / "screenshots").exists():
+                    screenshot_dirs = [
+                        d
+                        for d in (RESULTS_DIR / "screenshots").glob("*")
+                        if pattern in d.name
+                    ]
+            else:
+                # If we can't parse the filter, just do full cleanup
+                print(
+                    f"\n🧹 Cleaning up old test results from {RESULTS_DIR} (couldn't parse filter)"
+                )
+                json_files = list(RESULTS_DIR.glob("*.json"))
+                screenshot_dirs = (
+                    list((RESULTS_DIR / "screenshots").glob("*"))
+                    if (RESULTS_DIR / "screenshots").exists()
+                    else []
+                )
+        else:
+            # No filter - clean up everything
+            print(f"\n🧹 Cleaning up old test results from {RESULTS_DIR}")
+            json_files = list(RESULTS_DIR.glob("*.json"))
+            screenshot_dirs = (
+                list((RESULTS_DIR / "screenshots").glob("*"))
+                if (RESULTS_DIR / "screenshots").exists()
+                else []
+            )
+
+        total_files = len(json_files)
+        total_screenshot_dirs = len(screenshot_dirs)
+
+        if total_files > 0 or total_screenshot_dirs > 0:
+            print(f"   Removing {total_files} result file(s)")
+            print(f"   Removing {total_screenshot_dirs} screenshot directory(ies)")
+
+            # Remove JSON result files
+            for json_file in json_files:
+                json_file.unlink()
+
+            # Remove screenshot directories
+            for screenshot_dir in screenshot_dirs:
+                if screenshot_dir.is_dir():
+                    shutil.rmtree(screenshot_dir)
+
+            print("✅ Cleanup complete\n")
+        else:
+            print("   No old results to clean up\n")
+
+    yield  # Tests run here
+
+    # After all tests complete
+    print(f"\n📊 New test results saved to: {RESULTS_DIR}")
+
+    # Count new results
+    new_json_files = list(RESULTS_DIR.glob("*.json"))
+    new_screenshot_dirs = (
+        list((RESULTS_DIR / "screenshots").glob("*"))
+        if (RESULTS_DIR / "screenshots").exists()
+        else []
+    )
+
+    print(f"   Generated {len(new_json_files)} result file(s)")
+    print(f"   Generated {len(new_screenshot_dirs)} screenshot directory(ies)")
+
+
+class ConversationTestRunner:
+    """Handles running conversation tests through the Playwright UI"""
+
+    def __init__(self, page: Page, screenshot_dir: Optional[Path] = None):
+        self.page = page
+        self.conversation_history = []
+        self.screenshot_dir = screenshot_dir
+
+        # Create screenshot directory if provided
+        if self.screenshot_dir:
+            self.screenshot_dir.mkdir(parents=True, exist_ok=True)
+
+    def select_mode(self, mode: str):
+        """Select Direct or Agent-based mode"""
+        if mode.lower() == "agent":
+            agent_radio = self.page.get_by_text("Agent-based", exact=False).first
+            if agent_radio.is_visible():
+                agent_radio.click()
+                time.sleep(1)
+        else:
+            # Direct mode is usually default, but click it to be sure
+            direct_radio = self.page.get_by_text("Direct", exact=True).first
+            if direct_radio.is_visible():
+                direct_radio.click()
+                time.sleep(1)
+
+    def select_vector_dbs(self, vector_db_names: List[str]):
+        """Select document collections (vector databases) from Streamlit multiselect"""
+        if not vector_db_names:
+            return
+
+        print(f"Attempting to select vector DBs: {vector_db_names}")
+
+        try:
+            # Find the multiselect using Streamlit's test ID
+            multiselect = self.page.get_by_test_id("stMultiSelect")
+
+            if not multiselect.is_visible():
+                print("Warning: Vector DB multiselect not visible")
+                return
+
+            # Click the dropdown arrow to open the options list
+            # Streamlit uses an img with name 'open' for the dropdown arrow
+            dropdown_arrow = multiselect.get_by_role("img", name="open")
+            dropdown_arrow.click()
+            print("✓ Opened vector DB dropdown")
+            time.sleep(1)
+
+            # Now select each vector DB from the dropdown options
+            for vdb_name in vector_db_names:
+                try:
+                    # Find the option by role and name
+                    # Use partial match since the option might have additional attributes
+                    option = self.page.get_by_role("option", name=vdb_name)
+
+                    if option.is_visible():
+                        option.click()
+                        print(f"✓ Selected vector DB: {vdb_name}")
+                        time.sleep(0.5)
+                    else:
+                        print(f"⚠ Vector DB option not visible: {vdb_name}")
+                except Exception as e:
+                    print(f"Warning: Could not select '{vdb_name}': {e}")
+
+            # Click outside to close the dropdown (optional - it may auto-close)
+            # Click on a safe element like the Configuration heading
+            try:
+                config_heading = self.page.get_by_text(
+                    "Configuration", exact=True
+                ).first
+                if config_heading.is_visible():
+                    config_heading.click()
+                    time.sleep(0.5)
+            except Exception:
+                pass
+
+            print("✓ Completed vector DB selection")
+
+        except Exception as e:
+            print(f"Error selecting vector DBs: {e}")
+            import traceback
+
+            traceback.print_exc()
+            print("Continuing with test - vector DBs may not be selected properly")
+
+    def select_tools(self, tools: List[str]):
+        """Select tools for agent mode (e.g., 'rag', 'web_search')"""
+        if not tools:
+            return
+
+        for tool in tools:
+            # Convert 'rag' to 'builtin::rag' format if needed
+            tool_name = tool if "::" in tool else f"builtin::{tool}"
+
+            try:
+                # Look for the tool in the Available ToolGroups section
+                # The UI shows tools as pills/buttons
+                tool_element = self.page.get_by_text(
+                    tool_name.split("::")[-1], exact=False
+                ).first
+                if tool_element.is_visible():
+                    tool_element.click()
+                    time.sleep(0.5)
+            except Exception as e:
+                print(f"Warning: Could not select tool '{tool}': {e}")
+
+    def set_sampling_params(self, sampling_params: Dict[str, Any]):
+        """Set sampling parameters like temperature, max_tokens"""
+        if not sampling_params:
+            return
+
+        # Temperature
+        if "temperature" in sampling_params:
+            # Streamlit sliders - this is tricky, might need to use keyboard
+            # For now, we'll skip detailed slider manipulation
+            # The UI default should be reasonable
+            pass
+
+        # Max tokens
+        if "max_tokens" in sampling_params:
+            # Similar to temperature - skip for now
+            pass
+
+    def send_message(self, content: str) -> tuple[str, Optional[Dict[str, Any]]]:
+        """
+        Send a message and wait for response.
+        Returns tuple of (assistant's response text, actual RAG content dict or None).
+        """
+        # Find chat input
+        chat_input = self.page.get_by_placeholder("Ask a question...", exact=False)
+        expect(chat_input).to_be_visible(timeout=TEST_TIMEOUT)
+
+        # Send message
+        chat_input.fill(content)
+        chat_input.press("Enter")
+
+        # Wait for Streamlit to process
+        self.page.wait_for_load_state("networkidle")
+        time.sleep(3)
+
+        # Wait for assistant response
+        response_text = self._wait_for_assistant_response(content)
+
+        # Extract actual RAG content if available
+        actual_rag_content = self._extract_actual_rag_content()
+
+        return response_text, actual_rag_content
+
+    def _wait_for_assistant_response(self, user_message: str) -> str:
+        """Wait for and extract assistant response"""
+        max_wait = RESPONSE_WAIT_MAX
+        wait_time = 0
+
+        print("⏳ Waiting for assistant response to complete...")
+
+        # First, wait for the "Running..." indicator to disappear
+        # This indicates the response generation is complete
+        while wait_time < max_wait:
+            # Check if the running indicator is present
+            running_indicator = self.page.locator('img[alt="Running..."]')
+
+            if running_indicator.count() == 0:
+                # Running indicator is gone, response should be complete
+                print("✓ Response generation complete")
+                break
+
+            time.sleep(2)
+            wait_time += 2
+
+            if wait_time % 10 == 0:
+                print(f"  Still waiting... ({wait_time}s elapsed)")
+
+        # Wait for the response text to stabilize (no more streaming)
+        # Check if text content stops changing
+        print("⏳ Waiting for response text to stabilize...")
+        last_text = ""
+        stable_count = 0
+
+        for _ in range(20):  # Check for up to 20 seconds
+            chat_messages = self.page.locator('[data-testid="stChatMessage"]').all()
+            if chat_messages:
+                current_text = (
+                    chat_messages[-1].inner_text(timeout=ELEMENT_TIMEOUT)
+                    if chat_messages
+                    else ""
+                )
+
+                if current_text == last_text:
+                    stable_count += 1
+                    if (
+                        stable_count >= 5
+                    ):  # Text unchanged for 5 consecutive checks (5 seconds)
+                        print("✓ Response text stabilized")
+                        break
+                else:
+                    stable_count = 0
+                    last_text = current_text
+
+            time.sleep(1)
+
+        # Now extract the complete response
+        # Find all chat message containers
+        chat_messages = self.page.locator('[data-testid="stChatMessage"]').all()
+
+        # Look for the most recent assistant message
+        # It should come after the user message we just sent
+        for container in reversed(chat_messages):  # Check newest first
+            if container.is_visible():
+                # Get the message content div (skip avatar)
+                content_div = container.locator(
+                    '[data-testid="stChatMessageContent"]'
+                ).first
+
+                # Check if this is a user message or greeting
+                text_check = content_div.inner_text(timeout=ELEMENT_TIMEOUT).strip()
+                if user_message in text_check or text_check == "How can I help you?":
+                    continue
+
+                # Get all stMarkdown containers (these contain the actual AI response)
+                # Skip the search results group which is in a different structure
+                markdown_containers = content_div.locator(
+                    '[data-testid="stMarkdown"]'
+                ).all()
+
+                # Collect markdown from all containers except search results
+                all_markdown = []
+                for md_container in markdown_containers:
+                    html_content = md_container.inner_html()
+
+                    # Skip if this contains search results JSON
+                    if '"source":' in html_content or "0:{" in html_content:
+                        continue
+
+                    # Convert HTML to markdown (preserves lists, bold, etc.)
+                    markdown_text = md(html_content, heading_style="ATX")
+                    all_markdown.append(markdown_text)
+
+                # Join all markdown sections
+                combined_markdown = "\n\n".join(all_markdown)
+
+                # Filter out UI elements line by line
+                lines = combined_markdown.split("\n")
+                cleaned_lines = []
+
+                for line in lines:
+                    line = line.strip()
+
+                    # Skip empty lines
+                    if not line:
+                        continue
+                    # Skip UI element markers
+                    if line in ["smart_toy", "keyboard_arrow_right", "face"]:
+                        continue
+                    # Skip search status indicators
+                    if "🛠" in line and ("Searching" in line or "Searched" in line):
+                        continue
+                    # Skip search results header
+                    if "📄 Search Results from" in line:
+                        continue
+
+                    # This is actual content
+                    cleaned_lines.append(line)
+
+                # Join all content lines
+                if cleaned_lines:
+                    response_text = "\n".join(cleaned_lines)
+
+                    # Remove streaming cursor character
+                    response_text = response_text.replace("▌", "").strip()
+
+                    if len(response_text) > 20:  # Substantial response
+                        print(
+                            f"✅ Extracted complete response ({len(response_text)} chars)"
+                        )
+                        print(f"   Preview: {response_text[:150]}...")
+                        return response_text
+
+        # No response found
+        print(f"❌ Warning: No response received within {max_wait} seconds")
+        return "[ERROR: No response received]"
+
+    def _extract_actual_rag_content(self) -> Optional[Dict[str, Any]]:
+        """
+        Extract actual RAG chunks from the search results expander.
+        Returns dict with structure: {"chunks": [str, str, ...]} or None if no results.
+        """
+        print("  🔍 Attempting to extract actual RAG content...")
+        try:
+            # Find all expanders first to debug
+            all_expanders = self.page.locator('[data-testid="stExpander"]').all()
+            print(f"  Found {len(all_expanders)} expander(s) on page")
+
+            # Look for ALL search results expanders (one per vector database)
+            search_expanders = []
+            for exp in all_expanders:
+                exp_text = exp.inner_text(timeout=ELEMENT_TIMEOUT)
+                print(f"    Expander text: {exp_text[:100]}...")
+                if "Search Results from" in exp_text or "📄" in exp_text:
+                    search_expanders.append(exp)
+                    print("    ✓ Found search results expander")
+
+            if not search_expanders:
+                print("  ❌ No search results expander found")
+                return None
+
+            print(f"  Found {len(search_expanders)} search result expander(s)")
+
+            # Process ALL expanders and collect chunks from each
+            all_chunks = []
+
+            for exp_idx, expander in enumerate(search_expanders):
+                print(
+                    f"\n  Processing expander {exp_idx + 1}/{len(search_expanders)}..."
+                )
+
+                # Try to expand it - click on the summary/header
+                try:
+                    # Find the details/summary element and click it
+                    summary = expander.locator("summary").first
+                    if summary.is_visible():
+                        print("    Expanding search results...")
+                        summary.click()
+                        time.sleep(1)  # Wait for expansion
+                except Exception as e:
+                    print(
+                        f"    Note: Couldn't click expander (may already be open): {e}"
+                    )
+
+                # Find JSON content - look for the data-value attribute which contains actual JSON
+                json_elements = expander.locator('[data-testid="stJson"]').all()
+                print(f"    Found {len(json_elements)} stJson element(s) in expander")
+
+                if not json_elements:
+                    print(
+                        "    ⚠️  No stJson element found in this expander, skipping..."
+                    )
+                    continue
+
+                # Get the element
+                json_element = json_elements[0]
+
+                # Try to get the actual JSON data
+                search_results = []
+                try:
+                    # Approach 1: Try data-value attribute
+                    json_data_attr = json_element.get_attribute("data-value")
+                    if json_data_attr:
+                        print(
+                            f"    Found data-value attribute ({len(json_data_attr)} chars)"
+                        )
+                        search_results = json.loads(json_data_attr)
+                        print(
+                            f"    ✅ Parsed JSON from data-value: {len(search_results)} items"
+                        )
+                    # Approach 2: Look for a data- attribute that contains JSON
+                    elif json_element.get_attribute("data-json"):
+                        json_data = json_element.get_attribute("data-json")
+                        search_results = json.loads(json_data)
+                        print(
+                            f"    ✅ Parsed JSON from data-json: {len(search_results)} items"
+                        )
+                    # Approach 3: Evaluate JavaScript to extract the React props/state
+                    else:
+                        print("    Trying to extract via JavaScript evaluation...")
+                        # Try to get React props which might have the JSON data
+                        raw_json = json_element.evaluate("""
+                            (element) => {
+                                // Try to find React fiber and get props
+                                const key = Object.keys(element).find(k => k.startsWith('__react'));
+                                if (key && element[key] && element[key].memoizedProps) {
+                                    const props = element[key].memoizedProps;
+                                    // For st.json, the data is in props.src
+                                    if (props.src) return JSON.stringify(props.src);
+                                    if (props.value) return JSON.stringify(props.value);
+                                    if (props.data) return JSON.stringify(props.data);
+                                    if (props.children) return JSON.stringify(props.children);
+                                }
+                                return null;
+                            }
+                        """)
+
+                        if raw_json:
+                            search_results = json.loads(raw_json)
+                            print(
+                                f"    ✅ Parsed JSON from React props: {len(search_results)} items"
+                            )
+                        else:
+                            raise ValueError("Could not extract JSON via JavaScript")
+
+                except (json.JSONDecodeError, ValueError) as e:
+                    print(f"    Direct extraction failed: {e}, trying text parsing...")
+                    # Fallback: parse the formatted text
+                    import re
+
+                    json_text = json_element.evaluate(
+                        "el => el.textContent || el.innerText"
+                    )
+
+                    # Fix Streamlit's st.json format by adding missing commas
+                    # But we need to be careful not to mess up quotes inside strings
+                    # Split into array elements first
+                    elements = []
+                    # Find each numbered object like "0:{...}" "1:{...}"
+                    pattern = r"(\d+):\s*(\{[^}]*\})"
+                    for match in re.finditer(pattern, json_text, re.DOTALL):
+                        num, obj_text = match.groups()
+                        # Fix missing comma between "field""field"
+                        # Only fix at the property level
+                        fixed = re.sub(
+                            r'("[^"]*")\s*("[a-zA-Z_]+")', r"\1,\2", obj_text
+                        )
+                        try:
+                            obj = json.loads(fixed)
+                            elements.append(obj)
+                            print(
+                                f"      Extracted element {num}: source={obj.get('source', '?')}, text_len={len(obj.get('text', ''))}"
+                            )
+                        except json.JSONDecodeError:
+                            # If that didn't work, try without inner quotes fix
+                            try:
+                                obj = json.loads(obj_text)
+                                elements.append(obj)
+                            except Exception:
+                                print(f"      Warning: Could not parse element {num}")
+
+                    if elements:
+                        search_results = elements
+                        print(
+                            f"    ✅ Extracted {len(search_results)} results via text parsing"
+                        )
+                    else:
+                        print("    ❌ Text parsing also failed")
+                        search_results = []
+
+                except json.JSONDecodeError as e:
+                    print(f"    ❌ Final JSON parsing failed: {e}")
+                    # Try one more approach - get all the individual JSON objects
+                    print("    Attempting to extract individual objects...")
+                    import re
+
+                    json_text = json_element.inner_text(timeout=ELEMENT_TIMEOUT)
+
+                    # Find each object between curly braces
+                    objects = []
+                    depth = 0
+                    current_obj = ""
+                    in_string = False
+                    escape_next = False
+
+                    for char in json_text:
+                        if escape_next:
+                            current_obj += char
+                            escape_next = False
+                            continue
+
+                        if char == "\\":
+                            escape_next = True
+                            current_obj += char
+                            continue
+
+                        if char == '"' and not escape_next:
+                            in_string = not in_string
+
+                        if not in_string:
+                            if char == "{":
+                                depth += 1
+                            elif char == "}":
+                                depth -= 1
+
+                        if depth > 0:
+                            current_obj += char
+                            if depth == 1 and char == "{":
+                                current_obj = char  # Start fresh
+                        elif depth == 0 and current_obj and char == "}":
+                            current_obj += char
+                            # Try to parse this object
+                            try:
+                                # Clean up formatting
+                                clean_obj = re.sub(r"^\d+:", "", current_obj.strip())
+                                obj = json.loads(clean_obj)
+                                objects.append(obj)
+                                print(
+                                    f"      Extracted object {len(objects)}: {len(str(obj))} chars"
+                                )
+                            except Exception:
+                                pass
+                            current_obj = ""
+
+                    search_results = objects
+                    print(
+                        f"    Total extracted via manual parsing: {len(search_results)} objects"
+                    )
+
+                if not isinstance(search_results, list):
+                    print(
+                        f"    ⚠️  Search results not a list: {type(search_results)}, skipping..."
+                    )
+                    continue
+
+                print(
+                    f"    Parsed {len(search_results)} search result(s) from this expander"
+                )
+
+                # Extract chunks from results
+                # Results might be React elements with props.src containing the actual data
+                expander_chunks = []
+
+                for i, result in enumerate(search_results):
+                    if not result:
+                        continue
+
+                    # Check if this is a React element wrapper with props.src
+                    if (
+                        isinstance(result, dict)
+                        and "props" in result
+                        and "src" in result["props"]
+                    ):
+                        # Extract the actual data from props.src
+                        src_data = result["props"]["src"]
+                        if isinstance(src_data, list):
+                            for item in src_data:
+                                if isinstance(item, dict) and "text" in item:
+                                    expander_chunks.append(item["text"])
+                            print(
+                                f"      Extracted {len(src_data)} chunks from React props.src"
+                            )
+                    # Otherwise check if this is direct data
+                    elif isinstance(result, dict) and "text" in result:
+                        expander_chunks.append(result["text"])
+                        print(f"      Chunk {i + 1}: {len(result['text'])} chars")
+
+                if expander_chunks:
+                    print(
+                        f"    ✅ Extracted {len(expander_chunks)} chunks from this expander"
+                    )
+                    all_chunks.extend(expander_chunks)
+                else:
+                    print("    ⚠️  No chunks found in this expander")
+
+            # After processing all expanders
+            if not all_chunks:
+                print("  ❌ No chunks found across all search result expanders")
+                return None
+
+            print(
+                f"\n  ✅ Successfully extracted {len(all_chunks)} total RAG chunks from {len(search_expanders)} expander(s)"
+            )
+
+            return {"chunks": all_chunks}
+
+        except json.JSONDecodeError as e:
+            print(f"  ❌ Failed to parse RAG JSON: {e}")
+            if json_text:
+                print(f"  JSON text was: {json_text[:200]}...")
+            return None
+        except Exception as e:
+            print(f"  ❌ Failed to extract RAG content: {e}")
+            import traceback
+
+            traceback.print_exc()
+            return None
+
+    def run_conversation(self, test_config: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Run a complete conversation test.
+        Returns result in the format: {metadata, conversation}
+        """
+        metadata = test_config.get("metadata", {})
+        config = test_config.get("config", {})
+        messages = test_config.get("messages", [])
+
+        # Configure test
+        mode = config.get("mode", "direct")
+        vector_dbs = config.get("vector_dbs", [])
+        tools = config.get("tools", [])
+        sampling_params = config.get("sampling_params", {})
+
+        print(f"\n{'=' * 60}")
+        print(f"Running test: {metadata.get('description', 'No description')}")
+        print(f"Mode: {mode}")
+        print(f"Vector DBs: {vector_dbs}")
+        print(f"Tools: {tools}")
+        print(f"{'=' * 60}\n")
+
+        # Take screenshot before any configuration
+        if self.screenshot_dir:
+            try:
+                screenshot_path = self.screenshot_dir / "01_before_configuration.png"
+                self.page.screenshot(path=str(screenshot_path))
+                print(f"📸 Screenshot: {screenshot_path}")
+            except Exception as e:
+                print(f"Warning: Screenshot failed: {e}")
+
+        # Set up UI configuration
+        self.select_mode(mode)
+        time.sleep(1)
+
+        self.select_vector_dbs(vector_dbs)
+        time.sleep(1)
+
+        if mode.lower() == "agent":
+            self.select_tools(tools)
+            time.sleep(1)
+
+        self.set_sampling_params(sampling_params)
+        time.sleep(1)
+
+        # Take screenshot after all configuration is complete
+        if self.screenshot_dir:
+            try:
+                screenshot_path = self.screenshot_dir / "02_after_configuration.png"
+                self.page.screenshot(path=str(screenshot_path))
+                print(f"📸 Screenshot: {screenshot_path}")
+            except Exception as e:
+                print(f"Warning: Screenshot failed: {e}")
+
+        # Run conversation
+        conversation = []
+        message_num = 1
+
+        for msg in messages:
+            if msg["role"] != "user":
+                print(f"Warning: Skipping non-user message: {msg}")
+                continue
+
+            user_content = msg["content"]
+            expected_rag_content = msg.get("expected_rag_content")
+            expected_output = msg.get("expected_output")
+
+            print(f"USER: {user_content}")
+            if expected_rag_content:
+                num_chunks = len(expected_rag_content.get("chunks", []))
+                print(f"  Expected RAG chunks: {num_chunks}")
+
+            # Add user message to conversation
+            user_message = {"role": "user", "content": user_content}
+
+            # Include expected output if present
+            if expected_output:
+                user_message["expected_output"] = expected_output
+
+            # Include expected RAG content if present
+            if expected_rag_content:
+                user_message["expected_rag_content"] = expected_rag_content
+
+            # Send message and get response (including actual RAG content)
+            assistant_response, actual_rag_content = self.send_message(user_content)
+            print(f"ASSISTANT: {assistant_response[:200]}...")
+
+            # Include actual RAG content if retrieved
+            if actual_rag_content:
+                num_actual_chunks = len(actual_rag_content.get("chunks", []))
+                print(f"  Actual RAG chunks: {num_actual_chunks}")
+                user_message["actual_rag_content"] = actual_rag_content
+
+            conversation.append(user_message)
+
+            # Add assistant response to conversation
+            conversation.append({"role": "assistant", "content": assistant_response})
+
+            # Take screenshot after each message exchange
+            if self.screenshot_dir:
+                try:
+                    screenshot_path = (
+                        self.screenshot_dir
+                        / f"03_message_{message_num:02d}_complete.png"
+                    )
+                    self.page.screenshot(path=str(screenshot_path))
+                    print(f"📸 Screenshot: {screenshot_path}")
+                except Exception as e:
+                    print(f"Warning: Screenshot failed: {e}")
+
+            message_num += 1
+
+            # Small delay between multi-turn messages
+            time.sleep(2)
+
+        # Build result - include config for traceability
+        result = {
+            "metadata": metadata,
+            "config": config,  # Include test configuration
+            "conversation": conversation,
+        }
+
+        return result
+
+
+def find_test_files(subdir: Optional[str] = None) -> List[Path]:
+    """Find all test JSON files in conversations directory (or a subdirectory)."""
+    base = CONVERSATIONS_DIR / subdir if subdir else CONVERSATIONS_DIR
+    if not base.exists():
+        return []
+    return sorted(base.rglob("*.json"))
+
+
+def load_test_config(filepath: Path) -> Dict[str, Any]:
+    """Load test configuration from JSON file"""
+    with open(filepath, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def save_result(
+    result: Dict[str, Any], test_filename: str, timestamp: str = None
+) -> Path:
+    """Save test result with timestamp"""
+    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+    if timestamp is None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    base_name = Path(test_filename).stem
+    result_filename = f"{base_name}_{timestamp}.json"
+    result_path = RESULTS_DIR / result_filename
+
+    with open(result_path, "w", encoding="utf-8") as f:
+        json.dump(result, f, indent=2, ensure_ascii=False)
+
+    return result_path
+
+
+# Pytest test generation
+def pytest_generate_tests(metafunc):
+    """
+    Dynamically generate test cases from JSON files.
+    This creates one test per JSON file in conversations/
+    """
+    if "test_config_file" in metafunc.fixturenames:
+        subdir = metafunc.config.getoption("--subdir", default=None)
+        test_files = find_test_files(subdir)
+        if not test_files:
+            # Create a dummy test to show warning
+            metafunc.parametrize("test_config_file", [None])
+        else:
+            # Parametrize with test file paths
+            # Use file name (with .json) as test ID for precise matching with -k
+            metafunc.parametrize(
+                "test_config_file", test_files, ids=[f.name for f in test_files]
+            )
+
+
+class TestConversations:
+    """Conversation evaluation tests"""
+
+    def test_conversation_from_json(self, page: Page, test_config_file: Path):
+        """
+        Run conversation test from JSON file.
+        Test name will be the JSON filename.
+        """
+        if test_config_file is None:
+            pytest.skip("No test files found in evaluations/conversations/")
+
+        # Load test configuration
+        test_config = load_test_config(test_config_file)
+
+        # Generate result filename with timestamp (before running test)
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        base_name = test_config_file.stem
+        result_filename = f"{base_name}_{timestamp}"
+
+        # Create screenshot directory: evaluations/results/conversation_results/screenshots/{result_filename}
+        screenshot_dir = RESULTS_DIR / "screenshots" / result_filename
+        screenshot_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"\n📂 Screenshots will be saved to: {screenshot_dir}")
+
+        # Run test with screenshot directory
+        runner = ConversationTestRunner(page, screenshot_dir=screenshot_dir)
+        result = runner.run_conversation(test_config)
+
+        # Save result with the same base filename
+        result_path = save_result(result, test_config_file.name, timestamp)
+
+        print(f"\n📊 Result saved to: {result_path}")
+        print(f"📸 Screenshots in: {screenshot_dir}")
+
+        # Assertions
+        assert len(result["conversation"]) > 0, "Conversation should not be empty"
+
+        # Check that we got responses for all user messages
+        user_messages = [m for m in result["conversation"] if m["role"] == "user"]
+        assistant_messages = [
+            m for m in result["conversation"] if m["role"] == "assistant"
+        ]
+
+        assert len(assistant_messages) == len(user_messages), (
+            f"Should have {len(user_messages)} assistant responses, got {len(assistant_messages)}"
+        )
+
+        # Check that none of the responses are error messages
+        for msg in assistant_messages:
+            assert "[ERROR:" not in msg["content"], (
+                f"Assistant response contains error: {msg['content']}"
+            )
+
+
+# Manual test running (for debugging)
+if __name__ == "__main__":
+    import urllib.request
+    from helpers.playwright_setup import ensure_playwright_browsers
+    from helpers.endpoint import get_rag_ui_endpoint
+
+    ensure_playwright_browsers()
+
+    endpoint = get_rag_ui_endpoint()
+    print(f"Using RAG UI endpoint: {endpoint}")
+    print()
+
+    # Check if RAG UI is accessible
+    try:
+        urllib.request.urlopen(endpoint, timeout=5)
+    except Exception:
+        print(f"Warning: Cannot reach RAG UI at {endpoint}")
+        print()
+        if endpoint == "http://localhost:8501":
+            print("Tip: Start port-forward in another terminal:")
+            print("   oc port-forward svc/rag 8501:8501 -n $NAMESPACE")
+            print()
+            print("   Or set NAMESPACE environment variable:")
+            print("   export NAMESPACE=<your-namespace>")
+            print()
+        print("Options:")
+        print("  1. Set NAMESPACE: export NAMESPACE=<your-namespace>")
+        print("  2. Port-forward: oc port-forward svc/rag 8501:8501 -n <namespace>")
+        print(
+            "  3. Override endpoint: export RAG_UI_ENDPOINT=https://rag-<namespace>.apps..."
+        )
+        print()
+        response = input("Continue anyway? (y/N) ").strip().lower()
+        if response != "y":
+            sys.exit(1)
+
+    print("Tip: Pass --headed to see the browser in action")
+    print("Tip: Pass --screenshot=on for debugging screenshots")
+    print("Tip: Pass --slowmo=1000 to slow down browser actions")
+    print()
+
+    pytest.main([__file__, "-v", "-s", f"--base-url={endpoint}"] + sys.argv[1:])
diff --git a/evaluations/uv.lock b/evaluations/uv.lock
new file mode 100644
index 00000000..56576abd
--- /dev/null
+++ b/evaluations/uv.lock
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From b323b6ff0ddcf9a9182844f975fa907d6c5f4de9 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Mon, 16 Feb 2026 22:11:14 -0500
Subject: [PATCH 05/15] feat: add remote LLM support, LLM_MODEL param, and
 INTERACTIVE flag to Makefile

- Add LLM_MODEL parameter to override the model ID via command line
- Auto-derive Helm key from LLM_MODEL when LLM=remote-llm (reserved key
  in llama-stack chart that skips model registration)
- Add INTERACTIVE flag (default: true) to skip interactive prompts for
  HF_TOKEN and TAVILY_API_KEY when set to false
- Update README with remote LLM deployment example and parameter docs

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 deploy/helm/Makefile | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index eef2ddc7..d7af4d55 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -186,6 +186,7 @@ helm_llm_service_args = \
     --set llm-service.secret.hf_token=$(HF_TOKEN) \
     $(if $(DEVICE),--set llm-service.device='$(DEVICE)',) \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
+    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_TOLERATION),--set-json global.models.$(LLM).tolerations='$(call TOLERATIONS_TEMPLATE,$(LLM_TOLERATION))',) \
     $(if $(SAFETY_TOLERATION),--set-json global.models.$(SAFETY).tolerations='$(call TOLERATIONS_TEMPLATE,$(SAFETY_TOLERATION))',) \
@@ -193,6 +194,7 @@ helm_llm_service_args = \
 
 helm_llama_stack_args = \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
+    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_ID),--set global.models.$(LLM).id='$(LLM_ID)',) \
     $(if $(SAFETY_ID),--set global.models.$(SAFETY).id='$(SAFETY_ID)',) \
@@ -247,6 +249,7 @@ help: ## Show this help message
 	@echo -e ""
 	@echo -e "$(GREEN)Command-Line Parameters (override values file):$(NC)"
 	@echo -e "  LLM           - Enable specific LLM model (e.g., llama-3-2-3b-instruct)"
+	@echo -e "  LLM_MODEL     - Override model ID for the LLM (e.g., meta-llama/Llama-3.2-3B-Instruct)"
 	@echo -e "  SAFETY        - Enable specific safety model (e.g., llama-guard-3-8b)"
 	@echo -e "  LLM_ID        - Model ID for LLM (required for remote models)"
 	@echo -e "  SAFETY_ID     - Model ID for safety model (required for remote models)"
@@ -572,13 +575,22 @@ install: ## Install the RAG deployment
 	@echo -e "$(BLUE)[INFO]$(NC) Installing $(RAG_CHART) helm chart with $(VALUES_FILE)..."
 	@HELM_ARGS="-f $(VALUES_FILE)"; \
 	if [ -n "$(LLM)" ]; then \
-		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $(LLM)"; \
-		HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).enabled=true"; \
+		MODEL_KEY="$(LLM)"; \
+		if [ "$(LLM)" = "remote-llm" ] && [ -n "$(LLM_MODEL)" ]; then \
+			MODEL_KEY=$$(echo "$(LLM_MODEL)" | tr '/' '-' | tr '.' '-' | tr '[:upper:]' '[:lower:]'); \
+			echo -e "$(YELLOW)[INFO]$(NC) 'remote-llm' is a reserved key in llama-stack chart, using '$$MODEL_KEY' as model key"; \
+		fi; \
+		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $$MODEL_KEY"; \
+		HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.enabled=true"; \
+		if [ -n "$(LLM_MODEL)" ]; then \
+			echo -e "$(BLUE)[INFO]$(NC) Setting LLM model ID: $(LLM_MODEL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.id=$(LLM_MODEL)"; \
+		fi; \
 		if [ -n "$(LLM_TOLERATION)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM toleration: $(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].key=$(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].effect=NoSchedule"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].operator=Exists"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].key=$(LLM_TOLERATION)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].effect=NoSchedule"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].operator=Exists"; \
 		fi; \
 		if [ -n "$(LLM_ID)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM ID: $(LLM_ID)"; \
@@ -586,11 +598,11 @@ install: ## Install the RAG deployment
 		fi; \
 		if [ -n "$(LLM_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM URL: $(LLM_URL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).url=$(LLM_URL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.url=$(LLM_URL)"; \
 		fi; \
 		if [ -n "$(LLM_API_TOKEN)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM API token"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).apiToken=$(LLM_API_TOKEN)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.apiToken=$(LLM_API_TOKEN)"; \
 		fi; \
 	fi; \
 	if [ -n "$(SAFETY)" ]; then \

From c86edc723f49f0d112140685a434e1edcbecabdf Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 17 Feb 2026 16:44:53 -0500
Subject: [PATCH 06/15] refactor: simplify remote LLM support by removing
 LLM_MODEL parameter

Remove the LLM_MODEL parameter and MODEL_KEY auto-derivation logic in
favor of using the LLM key directly. Update help text to add Option 3
for remote LLM deployment with LLM_URL and LLM_API_TOKEN.

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 deploy/helm/Makefile | 26 +++++++-------------------
 1 file changed, 7 insertions(+), 19 deletions(-)

diff --git a/deploy/helm/Makefile b/deploy/helm/Makefile
index d7af4d55..eef2ddc7 100644
--- a/deploy/helm/Makefile
+++ b/deploy/helm/Makefile
@@ -186,7 +186,6 @@ helm_llm_service_args = \
     --set llm-service.secret.hf_token=$(HF_TOKEN) \
     $(if $(DEVICE),--set llm-service.device='$(DEVICE)',) \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
-    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_TOLERATION),--set-json global.models.$(LLM).tolerations='$(call TOLERATIONS_TEMPLATE,$(LLM_TOLERATION))',) \
     $(if $(SAFETY_TOLERATION),--set-json global.models.$(SAFETY).tolerations='$(call TOLERATIONS_TEMPLATE,$(SAFETY_TOLERATION))',) \
@@ -194,7 +193,6 @@ helm_llm_service_args = \
 
 helm_llama_stack_args = \
     $(if $(LLM),--set global.models.$(LLM).enabled=true,) \
-    $(if $(LLM_MODEL),--set global.models.$(LLM).id='$(LLM_MODEL)',) \
     $(if $(SAFETY),--set global.models.$(SAFETY).enabled=true,) \
     $(if $(LLM_ID),--set global.models.$(LLM).id='$(LLM_ID)',) \
     $(if $(SAFETY_ID),--set global.models.$(SAFETY).id='$(SAFETY_ID)',) \
@@ -249,7 +247,6 @@ help: ## Show this help message
 	@echo -e ""
 	@echo -e "$(GREEN)Command-Line Parameters (override values file):$(NC)"
 	@echo -e "  LLM           - Enable specific LLM model (e.g., llama-3-2-3b-instruct)"
-	@echo -e "  LLM_MODEL     - Override model ID for the LLM (e.g., meta-llama/Llama-3.2-3B-Instruct)"
 	@echo -e "  SAFETY        - Enable specific safety model (e.g., llama-guard-3-8b)"
 	@echo -e "  LLM_ID        - Model ID for LLM (required for remote models)"
 	@echo -e "  SAFETY_ID     - Model ID for safety model (required for remote models)"
@@ -575,22 +572,13 @@ install: ## Install the RAG deployment
 	@echo -e "$(BLUE)[INFO]$(NC) Installing $(RAG_CHART) helm chart with $(VALUES_FILE)..."
 	@HELM_ARGS="-f $(VALUES_FILE)"; \
 	if [ -n "$(LLM)" ]; then \
-		MODEL_KEY="$(LLM)"; \
-		if [ "$(LLM)" = "remote-llm" ] && [ -n "$(LLM_MODEL)" ]; then \
-			MODEL_KEY=$$(echo "$(LLM_MODEL)" | tr '/' '-' | tr '.' '-' | tr '[:upper:]' '[:lower:]'); \
-			echo -e "$(YELLOW)[INFO]$(NC) 'remote-llm' is a reserved key in llama-stack chart, using '$$MODEL_KEY' as model key"; \
-		fi; \
-		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $$MODEL_KEY"; \
-		HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.enabled=true"; \
-		if [ -n "$(LLM_MODEL)" ]; then \
-			echo -e "$(BLUE)[INFO]$(NC) Setting LLM model ID: $(LLM_MODEL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.id=$(LLM_MODEL)"; \
-		fi; \
+		echo -e "$(BLUE)[INFO]$(NC) Enabling LLM model: $(LLM)"; \
+		HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).enabled=true"; \
 		if [ -n "$(LLM_TOLERATION)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM toleration: $(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].key=$(LLM_TOLERATION)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].effect=NoSchedule"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.tolerations[0].operator=Exists"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].key=$(LLM_TOLERATION)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].effect=NoSchedule"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).tolerations[0].operator=Exists"; \
 		fi; \
 		if [ -n "$(LLM_ID)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM ID: $(LLM_ID)"; \
@@ -598,11 +586,11 @@ install: ## Install the RAG deployment
 		fi; \
 		if [ -n "$(LLM_URL)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM URL: $(LLM_URL)"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.url=$(LLM_URL)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).url=$(LLM_URL)"; \
 		fi; \
 		if [ -n "$(LLM_API_TOKEN)" ]; then \
 			echo -e "$(BLUE)[INFO]$(NC) Setting LLM API token"; \
-			HELM_ARGS="$$HELM_ARGS --set global.models.$$MODEL_KEY.apiToken=$(LLM_API_TOKEN)"; \
+			HELM_ARGS="$$HELM_ARGS --set global.models.$(LLM).apiToken=$(LLM_API_TOKEN)"; \
 		fi; \
 	fi; \
 	if [ -n "$(SAFETY)" ]; then \

From 557a75b266747dc6c8ffb635ccceda70f897bbe4 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 12:12:38 -0400
Subject: [PATCH 07/15] feat: upgrade llama stack dependencies and local deploy
 defaults

Bump llama-stack chart and Python package versions, update local deploy VERSION, and tighten agent error logging for failed response creation.

Made-with: Cursor
---
 deploy/helm/rag/Chart.lock                    |   6 +-
 deploy/helm/rag/Chart.yaml                    |   2 +-
 deploy/local/Makefile                         |   2 +-
 .../distribution/ui/page/playground/agent.py  |   5 +-
 frontend/pyproject.toml                       |   4 +-
 frontend/uv.lock                              | 131 ++++++++++++++----
 6 files changed, 113 insertions(+), 37 deletions(-)

diff --git a/deploy/helm/rag/Chart.lock b/deploy/helm/rag/Chart.lock
index c27155a4..8dea968b 100644
--- a/deploy/helm/rag/Chart.lock
+++ b/deploy/helm/rag/Chart.lock
@@ -13,9 +13,9 @@ dependencies:
   version: 0.7.0
 - name: llama-stack
   repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
-  version: 0.7.0
+  version: 0.7.1
 - name: mcp-servers
   repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
   version: 0.5.15
-digest: sha256:c8f91ea567adc2c26fab37dd988f33b830320e93afdf38397455a149eae79b18
-generated: "2026-02-18T00:51:25.763867-05:00"
+digest: sha256:0dbca435d7b3b408321cb33489ad8f099b4ae64501cde761cb47ea9c8e0abc20
+generated: "2026-03-06T14:32:40.986363-05:00"
diff --git a/deploy/helm/rag/Chart.yaml b/deploy/helm/rag/Chart.yaml
index 053ea034..52394360 100644
--- a/deploy/helm/rag/Chart.yaml
+++ b/deploy/helm/rag/Chart.yaml
@@ -23,7 +23,7 @@ dependencies:
     repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
     condition: ingestion-pipeline.enabled
   - name: llama-stack
-    version: 0.7.0
+    version: 0.7.1
     repository: https://rh-ai-quickstart.github.io/ai-architecture-charts
     condition: llama-stack.enabled
   - name: mcp-servers
diff --git a/deploy/local/Makefile b/deploy/local/Makefile
index 11018672..8f744349 100644
--- a/deploy/local/Makefile
+++ b/deploy/local/Makefile
@@ -19,7 +19,7 @@ PROJECT_NAME := rag
 OLLAMA_MODEL := llama3.2:3b-instruct-fp16
 
 # UI Build Configuration
-VERSION ?= 0.2.23
+VERSION ?= 0.5.1
 TAVILY_SEARCH_API_KEY ?= ""
 CONTAINER_REGISTRY ?= quay.io/rh-ai-quickstart
 DIST_UI_DIR := $(abspath ../../frontend)
diff --git a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
index f0b6c916..248dea01 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
@@ -366,9 +366,6 @@ def agent_process_prompt(prompt, state, config):
         "max_output_tokens": config.sampling.max_tokens,
 
     }
-    logger.debug("************************************************")
-    logger.debug("Request Args: %s", request_kwargs)
-    logger.debug("************************************************")
 
     # Add tools if available
     if tools:
@@ -379,7 +376,7 @@ def agent_process_prompt(prompt, state, config):
         response = llama_stack_api.client.responses.create(**request_kwargs)
     except Exception as e:  # pylint: disable=broad-exception-caught
         st.error(f"❌ Error: {str(e)}")
-        logger.debug("Agent mode create() error: %s", e)
+        logger.error("Agent mode create() error: %s", e)
         return
 
     # Stream response and update UI
diff --git a/frontend/pyproject.toml b/frontend/pyproject.toml
index db89508e..78f27ddd 100644
--- a/frontend/pyproject.toml
+++ b/frontend/pyproject.toml
@@ -10,10 +10,10 @@ requires-python = ">=3.12"
 dependencies = [
     "streamlit",
     "pandas",
-	"llama-stack-client==0.5.0",
+	"llama-stack-client==0.5.1",
     "requests",
     "streamlit-option-menu",
-    "llama-stack==0.5.0",
+    "llama-stack==0.5.1",
     "fire",
     "asyncpg",
 ]
diff --git a/frontend/uv.lock b/frontend/uv.lock
index 2a59ca7f..773f5913 100644
--- a/frontend/uv.lock
+++ b/frontend/uv.lock
@@ -809,7 +809,7 @@ wheels = [
 
 [[package]]
 name = "llama-stack"
-version = "0.5.0"
+version = "0.5.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -826,6 +826,7 @@ dependencies = [
     { name = "numpy" },
     { name = "oci" },
     { name = "openai" },
+    { name = "opentelemetry-distro" },
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "opentelemetry-sdk" },
     { name = "oracledb" },
@@ -846,9 +847,9 @@ dependencies = [
     { name = "urllib3" },
     { name = "uvicorn" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/71/79/e54a4c242bec4656314bccbc65fccc9acac337f83299e12ae934f320c5a2/llama_stack-0.5.0.tar.gz", hash = "sha256:ec3b0455eaa5b24fa53c1b789c18ff8d14580109c7f4ccb64da069b25d0ccde9", size = 16022010, upload-time = "2026-02-05T17:24:31.338Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ce/a8/3724d0c06a06578a639345f5086b93ba234a0ac247ec4ed7854d0d5e5ca6/llama_stack-0.5.2.tar.gz", hash = "sha256:9334c781e4ded6520aa60c3301a9087e9fb8fdaea8e5f30f8e21d85b17231d8d", size = 16035748, upload-time = "2026-03-06T13:25:59.356Z" }
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+    { url = "https://files.pythonhosted.org/packages/62/4c/fea3f2ffeead47a934704f1527685106766c5ea69dd99c0a83e872b22aa7/llama_stack-0.5.2-py3-none-any.whl", hash = "sha256:581fda638088ee029aab20afe3c42ba8f7f6ef21c80bd9ebcae20bb13c3409d3", size = 3979442, upload-time = "2026-03-06T13:25:56.581Z" },
 ]
 
 [[package]]
@@ -870,7 +871,7 @@ wheels = [
 
 [[package]]
 name = "llama-stack-client"
-version = "0.5.0"
+version = "0.5.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -889,9 +890,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/99/8a/8742475db7cedc2d452a3a7677da7f24aa84bdd262bc97543029c62df772/llama_stack_client-0.5.2.tar.gz", hash = "sha256:17c1bbad90f7699da4eb3cae256e8823caa4d2be945512a45c8c6f89ab899f28", size = 368612, upload-time = "2026-03-06T13:24:22.252Z" }
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+    { url = "https://files.pythonhosted.org/packages/4d/f9/f6224b8819748358a573e3a2b8e299c0b6ba5f9cedf2942188c361c8e555/llama_stack_client-0.5.2-py3-none-any.whl", hash = "sha256:473f4d67ac0b243b0fc29555a0203a742615d31bea606b4332d9e2f193f73d6a", size = 391951, upload-time = "2026-03-06T13:24:20.559Z" },
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 [[package]]
@@ -913,8 +914,8 @@ dependencies = [
 requires-dist = [
     { name = "asyncpg" },
     { name = "fire" },
-    { name = "llama-stack", specifier = "==0.5.0" },
-    { name = "llama-stack-client", specifier = "==0.5.0" },
+    { name = "llama-stack", specifier = "==0.5.2" },
+    { name = "llama-stack-client", specifier = "==0.5.2" },
     { name = "pandas" },
     { name = "requests" },
     { name = "streamlit" },
@@ -1237,32 +1238,46 @@ wheels = [
 
 [[package]]
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+version = "1.40.0"
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 dependencies = [
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+]
+
+[[package]]
+name = "opentelemetry-distro"
+version = "0.61b0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-instrumentation" },
+    { name = "opentelemetry-sdk" },
+]
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 ]
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.39.1"
+version = "1.40.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 [[package]]
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-version = "1.39.1"
+version = "1.40.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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+version = "0.61b0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-semantic-conventions" },
+    { name = "packaging" },
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 name = "yarl"
 version = "1.22.0"

From 2495dfc5ab9d57f3f9afc8690433721927555ed7 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 16:13:49 -0400
Subject: [PATCH 08/15] feat: add Argo CD app manifest for Helm deployment

Add an Argo CD Application manifest for deploying deploy/helm/rag with parameterized namespace and remote LLM/API secret Helm values, and adjust agent request token handling.

Made-with: Cursor
---
 deploy/helm/application.yaml                  | 32 +++++++++++++++++++
 .../distribution/ui/page/playground/agent.py  |  2 +-
 2 files changed, 33 insertions(+), 1 deletion(-)
 create mode 100644 deploy/helm/application.yaml

diff --git a/deploy/helm/application.yaml b/deploy/helm/application.yaml
new file mode 100644
index 00000000..0ef3c5d3
--- /dev/null
+++ b/deploy/helm/application.yaml
@@ -0,0 +1,32 @@
+apiVersion: argoproj.io/v1alpha1
+kind: Application
+metadata:
+  name: rag
+  namespace: argocd
+  
+spec:
+  project: default
+  source:
+    repoURL: https://github.com/rh-ai-quickstart/RAG.git
+    targetRevision: dev
+    path: deploy/helm/rag
+    helm:
+      parameters:
+        - name: llm-service.secret.hf_token
+          value: "${HUGGING_FACE_TOKEN}"
+        - name: llama-stack.secrets.TAVILY_SEARCH_API_KEY
+          value: "${TAVILY_SEARCH_API_KEY}"
+        - name: global.models.remotellm.enabled
+          value: "true"
+        - name: global.models.remotellm.apiToken
+          value: "${LLM_API_TOKEN}"
+        - name: global.models.remotellm.url
+          value: "${LLM_URL}"
+        - name: global.models.remotellm.id
+          value: "${LLM_MODEL_ID}"
+  destination:
+    server: https://kubernetes.default.svc
+    namespace: "${NAMESPACE}"
+  syncPolicy:
+    syncOptions:
+      - CreateNamespace=true
diff --git a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
index 248dea01..050a6db4 100644
--- a/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
+++ b/frontend/llama_stack_ui/distribution/ui/page/playground/agent.py
@@ -363,7 +363,7 @@ def agent_process_prompt(prompt, state, config):
         "temperature": config.sampling.temperature,
         "max_infer_iters": config.sampling.max_infer_iters,
         "stream": True,
-        "max_output_tokens": config.sampling.max_tokens,
+       # "max_output_tokens": config.sampling.max_tokens,
 
     }
 

From 50dbb73e4604d4dbf20f82651fd5848619c78b28 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 19:27:21 -0400
Subject: [PATCH 09/15] feat: move Argo CD app config into bootstrap
 ApplicationSet

Replace the standalone application manifest with a bootstrap ApplicationSet template that centralizes Helm values, remote LLM parameters, and per-user namespace composition using rag prefix and guid.

Made-with: Cursor
---
 deploy/bootstrap/Chart.yaml                   | 24 +++++++++
 .../templates/applicationset-rag.yaml         | 54 +++++++++++++++++++
 deploy/bootstrap/values.yaml                  | 24 +++++++++
 deploy/helm/application.yaml                  | 32 -----------
 4 files changed, 102 insertions(+), 32 deletions(-)
 create mode 100644 deploy/bootstrap/Chart.yaml
 create mode 100644 deploy/bootstrap/templates/applicationset-rag.yaml
 create mode 100644 deploy/bootstrap/values.yaml
 delete mode 100644 deploy/helm/application.yaml

diff --git a/deploy/bootstrap/Chart.yaml b/deploy/bootstrap/Chart.yaml
new file mode 100644
index 00000000..9e4b7e92
--- /dev/null
+++ b/deploy/bootstrap/Chart.yaml
@@ -0,0 +1,24 @@
+apiVersion: v2
+name: bootstrap
+description: A Helm chart for Kubernetes
+
+# A chart can be either an 'application' or a 'library' chart.
+#
+# Application charts are a collection of templates that can be packaged into versioned archives
+# to be deployed.
+#
+# Library charts provide useful utilities or functions for the chart developer. They're included as
+# a dependency of application charts to inject those utilities and functions into the rendering
+# pipeline. Library charts do not define any templates and therefore cannot be deployed.
+type: application
+
+# This is the chart version. This version number should be incremented each time you make changes
+# to the chart and its templates, including the app version.
+# Versions are expected to follow Semantic Versioning (https://semver.org/)
+version: 0.1.0
+
+# This is the version number of the application being deployed. This version number should be
+# incremented each time you make changes to the application. Versions are not expected to
+# follow Semantic Versioning. They should reflect the version the application is using.
+# It is recommended to use it with quotes.
+appVersion: "1.0.0"
\ No newline at end of file
diff --git a/deploy/bootstrap/templates/applicationset-rag.yaml b/deploy/bootstrap/templates/applicationset-rag.yaml
new file mode 100644
index 00000000..2b3081ec
--- /dev/null
+++ b/deploy/bootstrap/templates/applicationset-rag.yaml
@@ -0,0 +1,54 @@
+{{ if .Values.rag -}}
+---
+apiVersion: argoproj.io/v1alpha1
+kind: ApplicationSet
+metadata:
+  name: rag
+  namespace: openshift-gitops
+spec:
+  generators:
+  - list:
+      elements:
+{{- $userCount := int .Values.user.count }}
+{{- range $index := until $userCount }}
+      - user: {{ $.Values.user.prefix }}{{ add $index 1}}
+{{- end }}
+  template:
+    metadata:
+      name: rag-{{- "{{" }} user {{- " }}" }}
+      namespace: openshift-gitops
+    spec:
+      project: default
+      source:
+        repoURL: {{ .Values.rag.git.url }}
+        targetRevision: {{ .Values.rag.git.revision }}
+        path: {{ .Values.rag.git.path }}
+        helm:
+          values: |
+            llm-service:
+              secret:
+                hf_token: {{ .Values.rag.huggingFaceToken | quote }}
+            llama-stack:
+              secrets:
+                TAVILY_SEARCH_API_KEY: {{ .Values.rag.tavilySearchApiKey | quote }}
+            global:
+              models:
+                remotellm:
+                  enabled: {{ .Values.rag.remotellm.enabled | quote }}
+                  apiToken: {{ .Values.rag.remotellm.apiToken | quote }}
+                  url: {{ .Values.rag.remotellm.url | quote }}
+                  id: {{ .Values.rag.remotellm.id | quote }}
+      destination:
+        server: https://kubernetes.default.svc
+        namespace: '{{ .Values.rag.namespace }}-{{- "{{" }} user {{- " }}" }}-{{ .Values.guid }}'
+      syncPolicy:
+        automated:
+          prune: false
+          selfHeal: false
+        retry:
+          limit: 30
+          backoff:
+            duration: "5s"
+            factor: 2
+            maxDuration: "5m"
+{{- end }}
\ No newline at end of file
diff --git a/deploy/bootstrap/values.yaml b/deploy/bootstrap/values.yaml
new file mode 100644
index 00000000..365ff11c
--- /dev/null
+++ b/deploy/bootstrap/values.yaml
@@ -0,0 +1,24 @@
+# Default values for bootstrap.
+# This is a YAML-formatted file.
+# Declare variables to be passed into your templates.
+
+user:
+  count: 1
+  prefix: user
+
+guid: abcde
+
+# Set to empty to NOT create workspaces
+rag:
+  namespace: rag
+  git:
+    url: https://github.com/rh-ai-quickstart/RAG.git
+    revision: main
+    path: deploy/helm/rag
+  huggingFaceToken: ""
+  tavilySearchApiKey: "paste-your-key-here"
+  remotellm:
+    enabled: true
+    apiToken: "paste-your-token-here"
+    url: "paste-your-url-here"
+    id: "paste-your-model-id-here"
\ No newline at end of file
diff --git a/deploy/helm/application.yaml b/deploy/helm/application.yaml
deleted file mode 100644
index 0ef3c5d3..00000000
--- a/deploy/helm/application.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-apiVersion: argoproj.io/v1alpha1
-kind: Application
-metadata:
-  name: rag
-  namespace: argocd
-  
-spec:
-  project: default
-  source:
-    repoURL: https://github.com/rh-ai-quickstart/RAG.git
-    targetRevision: dev
-    path: deploy/helm/rag
-    helm:
-      parameters:
-        - name: llm-service.secret.hf_token
-          value: "${HUGGING_FACE_TOKEN}"
-        - name: llama-stack.secrets.TAVILY_SEARCH_API_KEY
-          value: "${TAVILY_SEARCH_API_KEY}"
-        - name: global.models.remotellm.enabled
-          value: "true"
-        - name: global.models.remotellm.apiToken
-          value: "${LLM_API_TOKEN}"
-        - name: global.models.remotellm.url
-          value: "${LLM_URL}"
-        - name: global.models.remotellm.id
-          value: "${LLM_MODEL_ID}"
-  destination:
-    server: https://kubernetes.default.svc
-    namespace: "${NAMESPACE}"
-  syncPolicy:
-    syncOptions:
-      - CreateNamespace=true

From 886f7de5fbc2918c96ca30f4fb7b54c946a1bd41 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 19:41:02 -0400
Subject: [PATCH 10/15] refactor: pass bootstrap rag values directly to Helm
 chart

Restructure bootstrap values to mirror the RAG chart schema and render them directly through ApplicationSet Helm values without per-field remapping.

Made-with: Cursor
---
 .../templates/applicationset-rag.yaml         | 14 +------------
 deploy/bootstrap/values.yaml                  | 21 ++++++++++++-------
 2 files changed, 15 insertions(+), 20 deletions(-)

diff --git a/deploy/bootstrap/templates/applicationset-rag.yaml b/deploy/bootstrap/templates/applicationset-rag.yaml
index 2b3081ec..55b8c5b5 100644
--- a/deploy/bootstrap/templates/applicationset-rag.yaml
+++ b/deploy/bootstrap/templates/applicationset-rag.yaml
@@ -25,19 +25,7 @@ spec:
         path: {{ .Values.rag.git.path }}
         helm:
           values: |
-            llm-service:
-              secret:
-                hf_token: {{ .Values.rag.huggingFaceToken | quote }}
-            llama-stack:
-              secrets:
-                TAVILY_SEARCH_API_KEY: {{ .Values.rag.tavilySearchApiKey | quote }}
-            global:
-              models:
-                remotellm:
-                  enabled: {{ .Values.rag.remotellm.enabled | quote }}
-                  apiToken: {{ .Values.rag.remotellm.apiToken | quote }}
-                  url: {{ .Values.rag.remotellm.url | quote }}
-                  id: {{ .Values.rag.remotellm.id | quote }}
+{{ toYaml .Values.rag.values | nindent 12 }}
       destination:
         server: https://kubernetes.default.svc
         namespace: '{{ .Values.rag.namespace }}-{{- "{{" }} user {{- " }}" }}-{{ .Values.guid }}'
diff --git a/deploy/bootstrap/values.yaml b/deploy/bootstrap/values.yaml
index 365ff11c..77807a77 100644
--- a/deploy/bootstrap/values.yaml
+++ b/deploy/bootstrap/values.yaml
@@ -15,10 +15,17 @@ rag:
     url: https://github.com/rh-ai-quickstart/RAG.git
     revision: main
     path: deploy/helm/rag
-  huggingFaceToken: ""
-  tavilySearchApiKey: "paste-your-key-here"
-  remotellm:
-    enabled: true
-    apiToken: "paste-your-token-here"
-    url: "paste-your-url-here"
-    id: "paste-your-model-id-here"
\ No newline at end of file
+  values:
+    llm-service:
+      secret:
+        hf_token: ""
+    llama-stack:
+      secrets:
+        TAVILY_SEARCH_API_KEY: "paste-your-key-here"
+    global:
+      models:
+        remotellm:
+          enabled: true
+          apiToken: "paste-your-token-here"
+          url: "paste-your-url-here"
+          id: "paste-your-model-id-here"
\ No newline at end of file

From db61b71d261b55820902c94b44a028dc436f5e64 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 19:42:15 -0400
Subject: [PATCH 11/15] fix: disable remote LLM by default in bootstrap values

Set bootstrap remotellm.enabled to false so remote endpoint configuration is opt-in by default.

Made-with: Cursor
---
 deploy/bootstrap/values.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/deploy/bootstrap/values.yaml b/deploy/bootstrap/values.yaml
index 77807a77..7e287fc2 100644
--- a/deploy/bootstrap/values.yaml
+++ b/deploy/bootstrap/values.yaml
@@ -25,7 +25,7 @@ rag:
     global:
       models:
         remotellm:
-          enabled: true
+          enabled: false
           apiToken: "paste-your-token-here"
           url: "paste-your-url-here"
           id: "paste-your-model-id-here"
\ No newline at end of file

From c3c1b65d8e46f1f7ce869fc3ba09c911340f3aa1 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 19:51:06 -0400
Subject: [PATCH 12/15] refactor: move bootstrap chart under deploy/helm

Relocate the bootstrap chart files from deploy/bootstrap to deploy/helm/bootstrap while preserving the ApplicationSet and values structure used to configure the RAG Helm deployment.

Made-with: Cursor
---
 .gitignore                                                    | 2 ++
 deploy/{ => helm}/bootstrap/Chart.yaml                        | 0
 deploy/{ => helm}/bootstrap/templates/applicationset-rag.yaml | 0
 deploy/{ => helm}/bootstrap/values.yaml                       | 0
 4 files changed, 2 insertions(+)
 rename deploy/{ => helm}/bootstrap/Chart.yaml (100%)
 rename deploy/{ => helm}/bootstrap/templates/applicationset-rag.yaml (100%)
 rename deploy/{ => helm}/bootstrap/values.yaml (100%)

diff --git a/.gitignore b/.gitignore
index aeb61d02..6ff87be5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -184,3 +184,5 @@ deploy/helm/secrets
 # User-specific configuration file (contains sensitive data)
 deploy/helm/rag-values.yaml
 deploy/helm/rag/Chart.lock
+deploy/helm/values.yaml
+deploy/helm/bootstrap-values.yaml
diff --git a/deploy/bootstrap/Chart.yaml b/deploy/helm/bootstrap/Chart.yaml
similarity index 100%
rename from deploy/bootstrap/Chart.yaml
rename to deploy/helm/bootstrap/Chart.yaml
diff --git a/deploy/bootstrap/templates/applicationset-rag.yaml b/deploy/helm/bootstrap/templates/applicationset-rag.yaml
similarity index 100%
rename from deploy/bootstrap/templates/applicationset-rag.yaml
rename to deploy/helm/bootstrap/templates/applicationset-rag.yaml
diff --git a/deploy/bootstrap/values.yaml b/deploy/helm/bootstrap/values.yaml
similarity index 100%
rename from deploy/bootstrap/values.yaml
rename to deploy/helm/bootstrap/values.yaml

From 3f6ee93958005abd4e235dfe21a43b1ade172256 Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 21:03:25 -0400
Subject: [PATCH 13/15] fix: enable namespace auto-creation in bootstrap
 applicationset

Configure ApplicationSet sync options to create destination namespaces automatically and explicitly disable llm-service in bootstrap values to avoid unsupported ServingRuntime resources.

Made-with: Cursor
---
 deploy/helm/bootstrap/templates/applicationset-rag.yaml | 2 ++
 deploy/helm/bootstrap/values.yaml                       | 1 +
 2 files changed, 3 insertions(+)

diff --git a/deploy/helm/bootstrap/templates/applicationset-rag.yaml b/deploy/helm/bootstrap/templates/applicationset-rag.yaml
index 55b8c5b5..e66e42bd 100644
--- a/deploy/helm/bootstrap/templates/applicationset-rag.yaml
+++ b/deploy/helm/bootstrap/templates/applicationset-rag.yaml
@@ -33,6 +33,8 @@ spec:
         automated:
           prune: false
           selfHeal: false
+        syncOptions:
+          - CreateNamespace=true
         retry:
           limit: 30
           backoff:
diff --git a/deploy/helm/bootstrap/values.yaml b/deploy/helm/bootstrap/values.yaml
index 7e287fc2..a0cddad8 100644
--- a/deploy/helm/bootstrap/values.yaml
+++ b/deploy/helm/bootstrap/values.yaml
@@ -17,6 +17,7 @@ rag:
     path: deploy/helm/rag
   values:
     llm-service:
+      enabled: false
       secret:
         hf_token: ""
     llama-stack:

From 249d435e060dc1bb7f52b09e9990f78cbcfeb0cb Mon Sep 17 00:00:00 2001
From: sauagarwa <sauagarw@redhat.com>
Date: Tue, 10 Mar 2026 21:18:28 -0400
Subject: [PATCH 14/15] feat: add bootstrap-managed RBAC for Argo controller

Add configurable GitOps namespace settings and provision ClusterRole/ClusterRoleBinding resources from the bootstrap chart so the Argo CD application controller can create target namespaces and reconcile RAG resources.

Made-with: Cursor
---
 .../templates/applicationset-rag.yaml         |  4 +-
 .../templates/rbac-argocd-controller.yaml     | 45 +++++++++++++++++++
 deploy/helm/bootstrap/values.yaml             |  8 ++++
 3 files changed, 55 insertions(+), 2 deletions(-)
 create mode 100644 deploy/helm/bootstrap/templates/rbac-argocd-controller.yaml

diff --git a/deploy/helm/bootstrap/templates/applicationset-rag.yaml b/deploy/helm/bootstrap/templates/applicationset-rag.yaml
index e66e42bd..4566ee41 100644
--- a/deploy/helm/bootstrap/templates/applicationset-rag.yaml
+++ b/deploy/helm/bootstrap/templates/applicationset-rag.yaml
@@ -4,7 +4,7 @@ apiVersion: argoproj.io/v1alpha1
 kind: ApplicationSet
 metadata:
   name: rag
-  namespace: openshift-gitops
+  namespace: {{ .Values.gitops.namespace }}
 spec:
   generators:
   - list:
@@ -16,7 +16,7 @@ spec:
   template:
     metadata:
       name: rag-{{- "{{" }} user {{- " }}" }}
-      namespace: openshift-gitops
+      namespace: {{ .Values.gitops.namespace }}
     spec:
       project: default
       source:
diff --git a/deploy/helm/bootstrap/templates/rbac-argocd-controller.yaml b/deploy/helm/bootstrap/templates/rbac-argocd-controller.yaml
new file mode 100644
index 00000000..55ac4429
--- /dev/null
+++ b/deploy/helm/bootstrap/templates/rbac-argocd-controller.yaml
@@ -0,0 +1,45 @@
+{{- if .Values.rbac.enabled -}}
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: ClusterRole
+metadata:
+  name: {{ .Values.rbac.clusterRoleName }}
+rules:
+  - apiGroups: [""]
+    resources: ["namespaces"]
+    verbs: ["get", "list", "watch", "create"]
+  - apiGroups: [""]
+    resources: ["configmaps", "persistentvolumeclaims", "secrets", "serviceaccounts", "services"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["apps"]
+    resources: ["deployments", "statefulsets"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["batch"]
+    resources: ["jobs"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["rbac.authorization.k8s.io"]
+    resources: ["roles", "rolebindings"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["route.openshift.io"]
+    resources: ["routes"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["kubeflow.org"]
+    resources: ["notebooks"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+  - apiGroups: ["datasciencepipelinesapplications.opendatahub.io"]
+    resources: ["datasciencepipelinesapplications"]
+    verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: ClusterRoleBinding
+metadata:
+  name: {{ .Values.rbac.clusterRoleName }}-binding
+roleRef:
+  apiGroup: rbac.authorization.k8s.io
+  kind: ClusterRole
+  name: {{ .Values.rbac.clusterRoleName }}
+subjects:
+  - kind: ServiceAccount
+    name: {{ .Values.gitops.applicationControllerServiceAccount }}
+    namespace: {{ .Values.gitops.namespace }}
+{{- end }}
diff --git a/deploy/helm/bootstrap/values.yaml b/deploy/helm/bootstrap/values.yaml
index a0cddad8..99690375 100644
--- a/deploy/helm/bootstrap/values.yaml
+++ b/deploy/helm/bootstrap/values.yaml
@@ -8,6 +8,14 @@ user:
 
 guid: abcde
 
+gitops:
+  namespace: openshift-gitops
+  applicationControllerServiceAccount: openshift-gitops-argocd-application-controller
+
+rbac:
+  enabled: true
+  clusterRoleName: argocd-rag-bootstrap-manager
+
 # Set to empty to NOT create workspaces
 rag:
   namespace: rag

From 2df402d2dc835372b2487dbafb22e51e2a5c84f9 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Wed, 11 Mar 2026 14:33:54 +0000
Subject: [PATCH 15/15] chore: bump version to 0.2.34

---
 deploy/helm/rag/Chart.yaml  | 4 ++--
 deploy/helm/rag/values.yaml | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/deploy/helm/rag/Chart.yaml b/deploy/helm/rag/Chart.yaml
index 52394360..4048e991 100644
--- a/deploy/helm/rag/Chart.yaml
+++ b/deploy/helm/rag/Chart.yaml
@@ -2,8 +2,8 @@ apiVersion: v2
 name: rag
 description: A Helm chart for Kubernetes
 type: application
-version: 0.2.33
-appVersion: "0.2.33"
+version: 0.2.34
+appVersion: "0.2.34"
 
 dependencies:
   - name: pgvector
diff --git a/deploy/helm/rag/values.yaml b/deploy/helm/rag/values.yaml
index 889f4e05..7f8921fe 100644
--- a/deploy/helm/rag/values.yaml
+++ b/deploy/helm/rag/values.yaml
@@ -3,7 +3,7 @@ replicaCount: 1
 image:
   repository: quay.io/rh-ai-quickstart/llamastack-dist-ui
   pullPolicy: Always
-  tag: 0.2.33
+  tag: 0.2.34
 
 service:
   type: ClusterIP