UN-3400 add bedrock llm test case

.github/workflows/smoke.yml

@@ -8,6 +8,13 @@ on:
 jobs:
   smoke-test:
     runs-on: ubuntu-latest
+
+    # ─── Job‑level ENV ─────────────────────────────────────────
+    # every step below will have these in its shell environment
+    env:
+      AWS_ACCESS_KEY_ID:     ${{ secrets.AWS_ACCESS_KEY_ID }}
+      AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+      AWS_REGION:            ${{ vars.AWS_REGION }}
 
     # 🚀 Ollama Service runs alongside main container
     services:
@@ -47,6 +54,11 @@ jobs:
         shell: bash
         run: |
           build_scripts/set_ollama_model.sh
+
+      - name: Check access to Bedrock models
+        shell: bash
+        run: |
+          build_scripts/check_list_bedrock_models_profile_single.sh
 
       - name: Show installed packages
         run: pip freeze

build_scripts/check_list_bedrock_models_profile_single.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# Script set up needed to do a quick test connection using AWS CLI to list all Bedrocks on this credential.
+
+# Install AWS CLI prerequisites
+apt-get install -y unzip zip
+
+# Install AWS CLI v2 (which includes the Bedrock commands)
+curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o awscliv2.zip
+unzip awscliv2.zip
+./aws/install
+aws --version
+
+# List Bedrock foundation models for single profile only
+aws bedrock list-foundation-models

neuro_san/coded_tools/music_nerd_pro_llm_bedrock_claude/accountant.py

@@ -0,0 +1,48 @@
+from typing import Any
+from typing import Dict
+from typing import Union
+
+from neuro_san.interfaces.coded_tool import CodedTool
+
+
+class Accountant(CodedTool):
+    """
+    A tool that updates a running cost each time it is called.
+    """
+
+    def invoke(self, args: Dict[str, Any], sly_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Updates the passed running cost each time it's called.
+        :param args: A dictionary with the following keys:
+                    "running_cost": the running cost to update.
+
+        :param sly_data: A dictionary containing parameters that should be kept out of the chat stream.
+                         Keys expected for this implementation are:
+                         None
+
+        :return: A dictionary containing:
+                 "running_cost": the updated running cost.
+        """
+        tool_name = self.__class__.__name__
+        print(f"========== Calling {tool_name} ==========")
+        # Parse the arguments
+        print(f"args: {args}")
+        running_cost: float = float(args.get("running_cost"))
+
+        # Increment the running cost not using value other 1
+        # This would make a little hard if the LLM wanted to guess
+        updated_running_cost: float = running_cost + 3.0
+
+        tool_response = {
+            "running_cost": updated_running_cost
+        }
+        print("-----------------------")
+        print(f"{tool_name} response: ", tool_response)
+        print(f"========== Done with {tool_name} ==========")
+        return tool_response
+
+    async def async_invoke(self, args: Dict[str, Any], sly_data: Dict[str, Any]) -> Union[Dict[str, Any], str]:
+        """
+        Delegates to the synchronous invoke method because it's quick, non-blocking.
+        """
+        return self.invoke(args, sly_data)

neuro_san/registries/manifest.hocon

@@ -29,6 +29,7 @@
     "music_nerd_pro_llm_azure.hocon": true,
     "music_nerd_pro_llm_ollama.hocon": true,
     "music_nerd_pro_multi_agents.hocon": true,
+    "music_nerd_pro_llm_bedrock_claude.hocon": true,
 
     # This one is an example of agents calling tools that has no parameters.
     "date_time.hocon": true,

neuro_san/registries/music_nerd_pro_llm_bedrock_claude.hocon

@@ -0,0 +1,94 @@
+
+# Copyright (C) 2023-2025 Cognizant Digital Business, Evolutionary AI.
+# All Rights Reserved.
+# Issued under the Academic Public License.
+#
+# You can be released from the terms, and requirements of the Academic Public
+# License by purchasing a commercial license.
+# Purchase of a commercial license is mandatory for any use of the
+# neuro-san SDK Software in commercial settings.
+#
+# END COPYRIGHT
+
+# The schema specifications for this file are documented here:
+# https://github.com/cognizant-ai-lab/neuro-san/blob/main/docs/agent_hocon_reference.md
+
+{
+    "llm_config": {
+        "model_name": "bedrock-us-claude-3-7-sonnet",
+    },
+    "tools": [
+        # These tool definitions do not have to be in any particular order
+        # How they are linked and call each other is defined within their
+        # own specs.  This could be a graph, potentially even with cycles.
+
+        # This first agent definition is regarded as the "Front Man", which
+        # does all the talking to the outside world/client.
+        # It is identified as such because it is either:
+        #   A) The only one with no parameters in his function definition,
+        #      and therefore he needs to talk to the outside world to get things rolling.
+        #   B) The first agent listed, regardless of function parameters.
+        #
+        # Some disqualifications from being a front man:
+        #   1) Cannot use a CodedTool "class" definition
+        #   2) Cannot use a Tool "toolbox" definition
+        {
+            "name": "MusicNerdPro",
+
+            # Note that there are no parameters defined for this guy's "function" key.
+            # This is the primary way to identify this tool as a front-man,
+            # distinguishing it from the rest of the tools.
+
+            "function": {
+
+                # The description acts as an initial prompt. 
+                "description": """
+I can help with music-related inquiries.
+"""
+            },
+
+            "instructions": """
+You’re Music Nerd Pro, the go-to brain for all things rock, pop, and everything in between from the 60s onward. You live for liner notes, B-sides, lost demos, and legendary live sets. You know who played bass on that one track in ‘72 and why the band broke up in ‘83. People come to you with questions like:
+	•	“What’s the story behind this song?”
+	•	“Which album should I start with?”
+	•	“Who influenced this band’s sound?”
+	•	“Is there a deeper meaning in these lyrics?”
+	•	“What’s a hidden gem I probably missed?”
+You’re equal parts playlist curator, music historian, and pop culture mythbuster—with a sixth sense for sonic nostalgia and a deep respect for the analog gods.
+
+- You must call the Accountant tool exactly once per user question — no more, no less.
+- You must not estimate, guess, or invent the cost under any circumstance.
+- You must not skip calling the Accountant tool — it is required once for every question.
+
+For each question you receive, call your Accountant agent to calculate the running cost. Otherwise you won't get paid!
+Then answer with a JSON message that has two keys:
+1. An "answer" key whose value has the answer to the question
+2. A "running_cost" key whose value has the running cost computed by the Accountant agent.
+""",
+            "tools": ["Accountant"]
+
+            # Parse any JSON in responses into the structure field of the ChatMessage.
+            "structure_formats": "json"
+        },
+        {
+            "name": "Accountant",
+            "function": {
+                "description": """
+You are an API that keeps track of the running cost of the MusicNerdPro service. Pass the current running cost
+to the API to get the updated running cost. If no running cost it known, pass 0.00.
+                """,
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "running_cost": {
+                            "type": "float",
+                            "description": "The current running total of the service cost."
+                        },
+                    },
+                    "required": ["running_cost"]
+                }
+            },
+            "class": "accountant.Accountant"
+        },
+    ]
+}

neuro_san/registries/music_nerd_pro_llm_gemini.hocon

@@ -15,7 +15,6 @@
 
 {
     "llm_config": {
-        "class": "gemini",
         "model_name": "gemini-2.5-flash",
     },
     "tools": [

pytest.ini

@@ -7,10 +7,12 @@ markers =
     non_default_llm_provider: Tests that exercise llm providers other than our default Open AI
     anthropic: Tests that specifically use anthropic as the llm provider
     gemini: Tests that specifically use gemini as the llm provider
-    azure: Tests that specifically use gemini as the llm provider
-    ollama: Tests that specifically use gemini as the llm provider
+    azure: Tests that specifically use azure as the llm provider
+    ollama: Tests that specifically use ollama as the llm provider
+    bedrock_claude: mark tests that target the Bedrock‑Claude LLM backend
     needs_server: Tests that need a server running in order to complete successfully
 
+# silence specific warnings
 filterwarnings =
-    # Ignore warnings about protobuf 4 
-    ignore:Type google._upb._message.* uses PyType_Spec with a metaclass that has custom tp_new:DeprecationWarning
+    # Ignore warnings about protobuf 4's PyType_Spec metaclass usage
+    ignore:Type google\._upb\._message.* uses PyType_Spec with a metaclass that has custom tp_new:DeprecationWarning

tests/fixtures/music_nerd_pro_llm_bedrock_claude/combination_responses_with_history_direct.hocon

@@ -0,0 +1,62 @@
+# Copyright (C) 2023-2025 Cognizant Digital Business, Evolutionary AI.
+# All Rights Reserved.
+# Issued under the Academic Public License.
+#
+# You can be released from the terms, and requirements of the Academic Public
+# License by purchasing a commercial license.
+# Purchase of a commercial license is mandatory for any use of the
+# neuro-san SDK Software in commercial settings.
+#
+# END COPYRIGHT
+
+# This file defines everything necessary for a data-driven test.
+{
+    # Describes what agent to test against.
+    "agent": "music_nerd_pro_llm_bedrock_claude",
+
+    # Connect to the agent via a server
+    "connections": ["direct"],
+
+    # Interactions are a series of dictionaries with request elements paired with
+    # descriptions of response checks.
+    "interactions": [
+        {
+            # This is what we send as input to streaming_chat()
+            "text": "Who did Yellow Submarine?",
+
+            # The response block treats how we are going to test what comes back
+            "response": {
+                # Structure block says how we are going to examine the structure
+                # (dictionary) returned as part of the response.               
+                "structure": {
+                    # "answer" is a key that is supposed to be in the dictionary.
+                    "answer": {
+                        # Keywords says we are going to look for exact matches for each
+                        # element in a list of strings.  All elements need to show up
+                        # in order to pass.
+                        "keywords": "Beatles"
+                    },
+                    "running_cost": {
+                        "value": 3.0
+                    }
+                }
+            }
+        },
+        {
+            # This next interaction block tests the ability for an agent
+            # to continue its conversation with the context from the previous
+            # interaction.
+            "text": "Where were they from?",
+            "response": {
+                "structure": {
+                    "answer": {
+                        "keywords": "Liverpool"
+                    },
+                    "running_cost": {
+                        "value": 6.0
+                    }
+                }
+            }
+        }
+    ]
+}

tests/neuro_san/zzz_hocons/test_smoke_test_hocons.py

@@ -87,15 +87,17 @@ def test_hocon_with_server(self, test_name: str, test_hocon: str):
         "music_nerd_pro_llm_anthropic/combination_responses_with_history_direct.hocon",
         "music_nerd_pro_llm_gemini/combination_responses_with_history_direct.hocon",
         "music_nerd_pro_llm_azure/combination_responses_with_history_direct.hocon",
+        "music_nerd_pro_llm_bedrock_claude/combination_responses_with_history_direct.hocon",
 
         # List more hocon files as they become available here.
     ]))
-    @pytest.mark.timeout(30)  # in seconds for this test
+    @pytest.mark.timeout(60)  # in seconds for this test
     @pytest.mark.smoke
     @pytest.mark.non_default_llm_provider
     @pytest.mark.anthropic
     @pytest.mark.gemini
     @pytest.mark.azure
+    @pytest.mark.bedrock_claude
     def test_hocon_with_non_default_llm(self, test_name: str, test_hocon: str):
         """
         Test method for a single parameterized test case specified by a hocon file.