Mued api adopted

.dockerignore

@@ -147,11 +147,4 @@ data/
 reports/
 
 # Synthetic data conversations
-src/agents/utils/example_inputs/
-src/agents/utils/synthetic_conversations/
-src/agents/utils/synthetic_conversation_generation.py
-src/agents/utils/testbench_prompts.py
-src/agents/utils/langgraph_viz.py
-
-# development agents
-src/agents/student_agent/
+src/agents/utils/example_inputs/

.github/workflows/dev.yml

@@ -50,6 +50,7 @@ jobs:
         if: always()
         run: |
           source .venv/bin/activate
+          export PYTHONPATH=$PYTHONPATH:.
           pytest --junit-xml=./reports/pytest.xml --tb=auto -v
 
       - name: Upload test results

.github/workflows/main.yml

@@ -50,6 +50,7 @@ jobs:
         if: always()
         run: |
           source .venv/bin/activate
+          export PYTHONPATH=$PYTHONPATH:.
           pytest --junit-xml=./reports/pytest.xml --tb=auto -v
 
       - name: Upload test results

.gitignore

@@ -50,6 +50,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+reports/
 
 # Translations
 *.mo

AGENTS.md

@@ -0,0 +1,88 @@
+# AGENTS.md
+
+This file provides guidance to AI agents when working with code in this repository.
+
+## Project Overview
+
+This is a boilerplate for creating AI educational chatbots that integrate 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.
+
+## Commands
+
+**Testing:**
+```bash
+pytest                              # Run all unit tests
+python tests/manual_agent_run.py   # Test agent locally with example inputs
+python tests/manual_agent_requests.py  # Test running Docker container
+```
+
+**Docker:**
+```bash
+docker build -t llm_chat .
+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 \
+  -H 'Content-Type: application/json' \
+  -d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
+```
+
+**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
+```
+
+## 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)
+```
+
+### 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 |
+| `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) |
+| `src/agent/context.py` | Converts muEd question/submission context dicts to LLM prompt text |
+| `tests/utils.py` | Shared test helpers: `assert_valid_chat_request`, `assert_valid_chat_response` |
+| `tests/example_inputs/` | Real muEd payloads used for end-to-end tests |
+
+### Agent Logic (LangGraph)
+
+`BaseAgent` maintains a state graph with two nodes:
+- **`call_llm`**: Invokes the LLM with system prompt + conversation summary + conversational style preference
+- **`summarize_conversation`**: Triggered when message count exceeds ~11; summarizes history and also extracts the student's preferred conversational style
+
+Messages are trimmed after summarization to keep context window manageable. The `summary` and `conversationalStyle` fields persist across calls via the `ChatRequest` metadata.
+
+### muEd API Format
+
+`src/module.py` handles the muEd request format (https://mued.org/). The `context` field in `ChatRequest` contains nested educational data (question parts, student submissions, task info) that gets parsed into a tutoring prompt via `src/agent/context.py`.
+
+### LLM Configuration
+
+LLM provider and model are set via environment variables (see `.env.example`). The `llm_factory.py` selects the provider at runtime. The Lambda function name/identity is set in `config.json`.
+
+The agent uses **two separate LLM instances** — `self.llm` for chat responses and `self.summarisation_llm` for conversation summarisation and style analysis. By default both use the same provider, but you can point them at different models (e.g. a cheaper model for summarisation) by changing the class in `agent.py`.
+
+## Deployment
+
+- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
+- Pushing to `main` triggers staging deployment, with manual approval required for production
+- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them

CLAUDE.md

@@ -0,0 +1,88 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a boilerplate for creating AI educational chatbots that integrate 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.
+
+## Commands
+
+**Testing:**
+```bash
+pytest                              # Run all unit tests
+python tests/manual_agent_run.py   # Test agent locally with example inputs
+python tests/manual_agent_requests.py  # Test running Docker container
+```
+
+**Docker:**
+```bash
+docker build -t llm_chat .
+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 \
+  -H 'Content-Type: application/json' \
+  -d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
+```
+
+**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
+```
+
+## 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)
+```
+
+### 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 |
+| `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) |
+| `src/agent/context.py` | Converts muEd question/submission context dicts to LLM prompt text |
+| `tests/utils.py` | Shared test helpers: `assert_valid_chat_request`, `assert_valid_chat_response` |
+| `tests/example_inputs/` | Real muEd payloads used for end-to-end tests |
+
+### Agent Logic (LangGraph)
+
+`BaseAgent` maintains a state graph with two nodes:
+- **`call_llm`**: Invokes the LLM with system prompt + conversation summary + conversational style preference
+- **`summarize_conversation`**: Triggered when message count exceeds ~11; summarizes history and also extracts the student's preferred conversational style
+
+Messages are trimmed after summarization to keep context window manageable. The `summary` and `conversationalStyle` fields persist across calls via the `ChatRequest` metadata.
+
+### muEd API Format
+
+`src/module.py` handles the muEd request format (https://mued.org/). The `context` field in `ChatRequest` contains nested educational data (question parts, student submissions, task info) and the `user` field contains user-specific information (e.g., user type, preferences, task progress) that gets parsed into a tutoring prompt via `src/agent/context.py`.
+
+### LLM Configuration
+
+LLM provider and model are set via environment variables (see `.env.example`). The `llm_factory.py` selects the provider at runtime. The Lambda function name/identity is set in `config.json`.
+
+The agent uses **two separate LLM instances** — `self.llm` for chat responses and `self.summarisation_llm` for conversation summarisation and style analysis. By default both use the same provider, but you can point them at different models (e.g. a cheaper model for summarisation) by changing the class in `agent.py`.
+
+## Deployment
+
+- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
+- Pushing to `main` triggers staging deployment, with manual approval required for production
+- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them

Dockerfile

@@ -25,7 +25,7 @@ COPY src ./src
 
 COPY index.py .
 
-COPY index_test.py .
+COPY tests ./tests
 
 # Set the Lambda function handler
 CMD ["index.handler"]