Shimmy boilerplate

.github/workflows/production-deploy.yml

@@ -31,7 +31,7 @@ jobs:
       environment: "production"
       version-bump: ${{ inputs.version-bump }}
       branch: ${{ inputs.branch }}
-      deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\"]'
+      deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\",\"OPENROUTER_API_KEY\",\"OPENROUTER_MODEL\",\"OPENROUTER_BASE_URL\"]'
     secrets:
       aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
       aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}

.github/workflows/staging-deploy.yml

@@ -23,6 +23,9 @@ jobs:
       OPENAI_MODEL: ${{ vars.OPENAI_MODEL }}
       GOOGLE_AI_API_KEY: ${{ secrets.GOOGLE_AI_API_KEY }}
       GOOGLE_AI_MODEL: ${{ vars.GOOGLE_AI_MODEL }}
+      OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
+      OPENROUTER_MODEL: ${{ vars.OPENROUTER_MODEL }}
+      OPENROUTER_BASE_URL: ${{ vars.OPENROUTER_BASE_URL }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -67,7 +70,7 @@ jobs:
     with:
       template-repository-name: "lambda-feedback/chat-function-boilerplate"
       environment: "staging"
-      deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\"]'
+      deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\",\"OPENROUTER_API_KEY\",\"OPENROUTER_MODEL\",\"OPENROUTER_BASE_URL\"]'
     secrets:
       aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
       aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}

.github/workflows/test-lint.yml

@@ -21,6 +21,9 @@ jobs:
       OPENAI_MODEL: ${{ vars.OPENAI_MODEL }}
       GOOGLE_AI_API_KEY: ${{ secrets.GOOGLE_AI_API_KEY }}
       GOOGLE_AI_MODEL: ${{ vars.GOOGLE_AI_MODEL }}
+      OPENROUTER_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
+      OPENROUTER_MODEL: ${{ vars.OPENROUTER_MODEL }}
+      OPENROUTER_BASE_URL: ${{ vars.OPENROUTER_BASE_URL }}
     steps:
       - name: Checkout
         uses: actions/checkout@v4

AGENTS.md

@@ -4,13 +4,13 @@ This file provides guidance to AI agents when working with code in this reposito
 
 ## Project Overview
 
-This is a chat function connecting students to an AI educational chatbot that is integrated with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses. Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
+This is a chat function connecting students to an AI educational chatbot that is integrated with the **Lambda-Feedback** educational platform. It's containerized via Docker and deployed behind [shimmy](https://github.com/lambda-feedback/shimmy), a shim that spawns this function as a persistent JSON-RPC worker process and exposes it as the muEd `/chat` / `/chat/health` HTTP API (both locally and as an AWS Lambda container). It receives student chat messages with educational context and returns LLM-powered chatbot responses. Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
 
 ## Commands
 
 **Testing:**
 ```bash
-pytest                              # Run all unit tests
+PYTHONPATH=. pytest                 # Run all unit tests (CI sets PYTHONPATH=. too)
 python tests/manual_agent_run.py   # Test agent locally with example inputs
 python tests/manual_agent_requests.py  # Test running Docker container
 ```
@@ -23,39 +23,45 @@ docker run --env-file .env -p 8080:8080 llm_chat
 
 **Manual API test (while Docker is running):**
 ```bash
-curl -X POST http://localhost:8080/2015-03-31/functions/function/invocations \
+curl -X POST http://localhost:8080/chat \
   -H 'Content-Type: application/json' \
-  -d '{"body":"{\"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}]}"}'
+  -H 'X-Api-Version: 0.1.0' \
+  -d '{"messages": [{"role": "USER", "content": "hi"}]}'
+
+curl http://localhost:8080/chat/health -H 'X-Api-Version: 0.1.0'
 ```
 
 **Run a single test:**
 ```bash
 pytest tests/test_module.py        # Run specific test file
-pytest tests/test_index.py::test_function_name  # Run specific test
+pytest tests/test_module.py::TestChatModuleFunction::test_response_format  # Run specific test
 ```
 
 ## Architecture
 
 ### Request Flow
 
 ```
-Lambda event → index.py (handler)
-  → validates via lf_toolkit ChatRequest schema
-  → src/module.py (chat_module)
-    → extracts muEd API context (messages, conversationId, question context, user type)
-    → parses educational context to prompt text via src/agent/context.py
-    → src/agent/agent.py (BaseAgent / LangGraph)
-      → routes to call_llm or summarize_conversation node
-      → calls LLM provider (OpenAI / Google / Azure / Ollama)
-  → returns ChatResponse (output, summary, conversationalStyle, processingTime)
+shimmy (shim, container entrypoint)
+  → spawns index.py as a persistent worker subprocess (lf_toolkit RPC server)
+  → forwards POST /chat / GET /chat/health as JSON-RPC "chat" / "chat/health" calls
+  → index.py registers src/module.py's chat_module / chat_health_module as handlers
+    → lf_toolkit validates the request body against the muEd ChatRequest schema
+    → src/module.py (chat_module)
+      → extracts muEd API context (messages, conversationId, question context, user type)
+      → parses educational context to prompt text via src/agent/context.py
+      → src/agent/agent.py (BaseAgent / LangGraph)
+        → routes to call_llm or summarize_conversation node
+        → calls LLM provider (OpenAI / Google / Azure / Ollama)
+    → returns ChatResponse (output, summary, conversationalStyle, processingTime)
 ```
 
 ### Key Files
 
 | File | Role |
 |------|------|
-| `index.py` | AWS Lambda entry point; parses event body, validates schema |
-| `src/module.py` | Transforms muEd API request → invokes agent → builds ChatResponse |
+| `index.py` | Worker entrypoint; registers `chat_module`/`chat_health_module` with `lf_toolkit`'s RPC server (`create_server()` + `run()`) |
+| `src/module.py` | Transforms muEd API request → invokes agent → builds ChatResponse; also exposes `chat_health_module()` |
 | `src/agent/agent.py` | LangGraph stateful graph; manages message history and summarization |
 | `src/agent/prompts.py` | System prompts for tutor behavior, summarization, style detection |
 | `src/agent/llm_factory.py` | Factory classes for each LLM provider (OpenAI, Google, Azure, Ollama) |

CLAUDE.md

@@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-This is a chat function connecting students to an AI educational chatbot that is integrated with the **Lambda-Feedback** educational platform. It's containerized via Docker and deployed behind [shimmy](https://github.com/lambda-feedback/shimmy), a shim that spawns this function as a persistent JSON-RPC worker process and exposes it as the muEd `/chat` / `/chat/health` HTTP API (both locally and as an AWS Lambda container). Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
+This is a chat function connecting students to an AI educational chatbot that is integrated with the **Lambda-Feedback** educational platform. It's containerized via Docker and deployed behind [shimmy](https://github.com/lambda-feedback/shimmy), a shim that spawns this function as a persistent JSON-RPC worker process and exposes it as the muEd `/chat` / `/chat/health` HTTP API (both locally and as an AWS Lambda container). It receives student chat messages with educational context and returns LLM-powered chatbot responses. Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
 
 ## Commands
 

Dockerfile

@@ -34,4 +34,4 @@ ENV FUNCTION_RPC_TRANSPORT="ipc"
 
 ENV FUNCTION_WORKER_SEND_TIMEOUT="170s"
 
-ENV LOG_LEVEL="debug"
+ENV LOG_LEVEL="debug"

README.md

@@ -24,19 +24,15 @@ OPENAI_MODEL
 GOOGLE_AI_API_KEY
 GOOGLE_AI_MODEL
 ```
-
-> [!Note]
-> If you decide to use another endpoint such as Azure or Ollama or any other, please update the github workflow files to use the right secrets and variables for testing.
+> If you use OpenRouter:
 ```bash
-> If you use Azure-OpenAI:
-AZURE_OPENAI_API_KEY
-AZURE_OPENAI_ENDPOINT
-AZURE_OPENAI_API_VERSION
-AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
-AZURE_OPENAI_EMBEDDING_3072_DEPLOYMENT
-AZURE_OPENAI_EMBEDDING_1536_DEPLOYMENT
-AZURE_OPENAI_EMBEDDING_3072_MODEL
-AZURE_OPENAI_EMBEDDING_1536_MODEL
+OPENROUTER_API_KEY
+OPENROUTER_MODEL
+OPENROUTER_BASE_URL
+```
+
+> [!NOTE]
+> If you decide to use other providers like Azure OpenAI or Ollama, you will need to update the workflow files and the `llm_factory.py` file to include the necessary environment variables for those providers.
 
 > For monitoring of the LLM calls (follow instructions on how to set up on langsmith online):
 LANGCHAIN_TRACING_V2

docs/dev.md

@@ -94,7 +94,7 @@ POST URL:
 http://localhost:8080/chat
 ```
 
-Input body (requests must include an `X-Api-Version: 0.1.0` header):
+Body (requests may include an `X-Api-Version: 0.1.0` header):
 
 ```JSON
 {"conversationId": "12345Test", "messages": [{"role": "USER", "content": "hi"}], "user": {"type": "LEARNER"}}

src/agent/llm_factory.py

@@ -1,4 +1,5 @@
 import os
+from typing import Optional
 
 from langchain_openai import AzureChatOpenAI
 from langchain_openai import AzureOpenAIEmbeddings
@@ -82,3 +83,19 @@ def __init__(self, temperature: int = 0):
 
     def get_llm(self):
         return self._google_llm
+
+class ChatOpenRouterProvider:
+    def __init__(self, temperature: int = 0, model: Optional[str] = None):
+        model_name = model or os.environ['OPENROUTER_MODEL']
+        key = os.environ['OPENROUTER_API_KEY']
+        base_url = os.environ['OPENROUTER_BASE_URL']
+
+        self._openrouter_llm = ChatOpenAI(
+            model=model_name,
+            temperature=temperature,
+            api_key=key,
+            base_url=base_url,
+        )
+
+    def get_llm(self):
+        return self._openrouter_llm

src/module.py

@@ -70,7 +70,7 @@ def chat_health_module() -> ChatHealthResponse:
         status=HealthStatus.OK,
         capabilities=ChatCapabilities(
             supportsChat=True,
-            supportsUserPreferences=True,
+            supportsUserPreferences=False,
             supportsStreaming=False,
             supportsDataPolicy=DataPolicySupport.NOT_SUPPORTED,
         ),

tests/manual_agent_requests.py

@@ -1,5 +1,4 @@
 import requests
-import json
 
 """
 Script that sends requests straight to shimmy's muEd chat routes on the