add render file check

.gitignore

@@ -104,7 +104,7 @@ venv.bak/
 # MacOS Metadata
 *.DS_Store
 
-
+*.yaml_rendered
 
 # =========================================
 

arch/validate_arch_config.sh

@@ -3,11 +3,22 @@
 failed_files=()
 
 for file in $(find . -name arch_config.yaml -o -name arch_config_full_reference.yaml); do
-  echo "Validating $file..."
-  if ! docker run --rm -v "$(pwd)/$file:/app/arch_config.yaml:ro" --entrypoint /bin/sh katanemo/archgw:latest -c "python config_generator.py" 2>&1 > /dev/null ; then
+  echo "Validating ${file}..."
+  touch $(pwd)/${file}_rendered
+  if ! docker run --rm -v "$(pwd)/${file}:/app/arch_config.yaml:ro" -v "$(pwd)/${file}_rendered:/app/arch_config_rendered.yaml:rw" --entrypoint /bin/sh katanemo/archgw:latest -c "python config_generator.py" 2>&1 > /dev/null ; then
     echo "Validation failed for $file"
     failed_files+=("$file")
   fi
+  RENDERED_CHECKED_IN_FILE=$(echo $file | sed 's/\.yaml$/_rendered.yaml/')
+  if [ -f "$RENDERED_CHECKED_IN_FILE" ]; then
+    echo "Checking rendered file against checked-in version..."
+    if ! diff -q "${file}_rendered" "$RENDERED_CHECKED_IN_FILE" > /dev/null; then
+      echo "Rendered file ${file}_rendered does not match checked-in version ${RENDERED_CHECKED_IN_FILE}"
+      failed_files+=("${file}_rendered")
+    else
+      echo "Rendered file matches checked-in version."
+    fi
+  fi
 done
 
 # Print summary of failed files

demos/use_cases/ollama/arch_config.yaml

@@ -9,7 +9,8 @@ listeners:
 
 llm_providers:
 
-  - model: openai/llama3.2
+  - model: my_llm_provider/llama3.2
+    provider_interface: openai
     base_url: http://host.docker.internal:11434
     default: true
 

demos/use_cases/preference_based_routing/arch_config_rendered.yaml

@@ -1,29 +0,0 @@
-listeners:
-  egress_traffic:
-    address: 0.0.0.0
-    message_format: openai
-    port: 12000
-    timeout: 30s
-llm_providers:
-- access_key: $OPENAI_API_KEY
-  default: true
-  model: gpt-4o-mini
-  name: openai/gpt-4o-mini
-  provider_interface: openai
-- access_key: $OPENAI_API_KEY
-  model: gpt-4o
-  name: openai/gpt-4o
-  provider_interface: openai
-  routing_preferences:
-  - description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
-    name: code generation
-- access_key: $OPENAI_API_KEY
-  model: gpt-4.1
-  name: openai/gpt-4.1
-  provider_interface: openai
-  routing_preferences:
-  - description: understand and explain existing code snippets, functions, or libraries
-    name: code understanding
-tracing:
-  random_sampling: 100
-version: v0.1.0

docs/source/resources/includes/arch_config_full_reference_rendered.yaml

@@ -1,95 +1,81 @@
-version: v0.1
-
-listeners:
-  ingress_traffic:
-    address: 0.0.0.0
-    port: 10000
-    message_format: openai
-    timeout: 5s
-  egress_traffic:
-    address: 0.0.0.0
-    port: 12000
-    message_format: openai
-    timeout: 5s
-
-# Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
 endpoints:
   app_server:
-    # value could be ip address or a hostname with port
-    # this could also be a list of endpoints for load balancing
-    # for example endpoint: [ ip1:port, ip2:port ]
-    endpoint: 127.0.0.1:80
-    # max time to wait for a connection to be established
     connect_timeout: 0.005s
-
-  mistral_local:
-    endpoint: 127.0.0.1:8001
-
+    endpoint: 127.0.0.1
+    port: 80
   error_target:
     endpoint: error_target_1
-
-# Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
+    port: 80
+  mistral_local:
+    endpoint: 127.0.0.1
+    port: 8001
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    message_format: openai
+    port: 12000
+    timeout: 5s
+  ingress_traffic:
+    address: 0.0.0.0
+    message_format: openai
+    port: 10000
+    timeout: 5s
 llm_providers:
-  - name: openai/gpt-4o
-    provider_interface: openai
-    access_key: $OPENAI_API_KEY
-    model: gpt-4o
-    default: true
-
-  - name: mistral/mistral-8x7b
-    provider_interface: mistral
-    access_key: $MISTRAL_API_KEY
-    model: mistral-8x7b
-
-  - name: mistral/mistral-7b-instruct
-    provider_interface: mistral
-    model: mistral-7b-instruct
-    base_url: http://mistral_local
-
-# provides a way to override default settings for the arch system
+- access_key: $OPENAI_API_KEY
+  default: true
+  model: gpt-4o
+  name: openai/gpt-4o
+  provider_interface: openai
+- access_key: $MISTRAL_API_KEY
+  model: mistral-8x7b
+  name: mistral/mistral-8x7b
+  provider_interface: mistral
+- base_url: http://mistral_local
+  endpoint: mistral_local
+  model: mistral-7b-instruct
+  name: mistral/mistral-7b-instruct
+  port: 80
+  protocol: http
+  provider_interface: mistral
 overrides:
-  # By default Arch uses an NLI + embedding approach to match an incoming prompt to a prompt target.
-  # The intent matching threshold is kept at 0.80, you can override this behavior if you would like
-  prompt_target_intent_matching_threshold: 0.60
-
-# default system prompt used by all prompt targets
-system_prompt: You are a network assistant that just offers facts; not advice on manufacturers or purchasing decisions.
-
+  prompt_target_intent_matching_threshold: 0.6
 prompt_guards:
   input_guards:
     jailbreak:
       on_exception:
-        message: Looks like you're curious about my abilities, but I can only provide assistance within my programmed parameters.
-
+        message: Looks like you're curious about my abilities, but I can only provide
+          assistance within my programmed parameters.
 prompt_targets:
-  - name: information_extraction
-    default: true
-    description: handel all scenarios that are question and answer in nature. Like summarization, information extraction, etc.
-    endpoint:
-      name: app_server
-      path: /agent/summary
-      http_method: POST
-    # Arch uses the default LLM and treats the response from the endpoint as the prompt to send to the LLM
-    auto_llm_dispatch_on_response: true
-    # override system prompt for this prompt target
-    system_prompt: You are a helpful information extraction assistant. Use the information that is provided to you.
-
-  - name: reboot_network_device
-    description: Reboot a specific network device
-    endpoint:
-      name: app_server
-      path: /agent/action
-    parameters:
-      - name: device_id
-        type: str
-        description: Identifier of the network device to reboot.
-        required: true
-      - name: confirmation
-        type: bool
-        description: Confirmation flag to proceed with reboot.
-        default: false
-        enum: [true, false]
-
+- auto_llm_dispatch_on_response: true
+  default: true
+  description: handel all scenarios that are question and answer in nature. Like summarization,
+    information extraction, etc.
+  endpoint:
+    http_method: POST
+    name: app_server
+    path: /agent/summary
+  name: information_extraction
+  system_prompt: You are a helpful information extraction assistant. Use the information
+    that is provided to you.
+- description: Reboot a specific network device
+  endpoint:
+    name: app_server
+    path: /agent/action
+  name: reboot_network_device
+  parameters:
+  - description: Identifier of the network device to reboot.
+    name: device_id
+    required: true
+    type: str
+  - default: false
+    description: Confirmation flag to proceed with reboot.
+    enum:
+    - true
+    - false
+    name: confirmation
+    type: bool
+system_prompt: You are a network assistant that just offers facts; not advice on manufacturers
+  or purchasing decisions.
 tracing:
-  # sampling rate. Note by default Arch works on OpenTelemetry compatible tracing.
   sampling_rate: 0.1
+version: v0.1