diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8b39b726b..b054f2756 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -58,6 +58,7 @@ jobs: IS_PULL_REQUEST: false FORWARD_CSOC_LOGS: false RUN_REGRESSION_TESTS: true + RUN_RAGAS_EVALUATION: true secrets: CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.DEV_CLOUD_FORMATION_DEPLOY_ROLE }} INT_ASSIST_ME_DOCUMENT_SYNC_ROLE: ${{ secrets.INT_ASSIST_ME_DOCUMENT_SYNC_ROLE }} @@ -86,6 +87,7 @@ jobs: IS_PULL_REQUEST: false FORWARD_CSOC_LOGS: false RUN_REGRESSION_TESTS: true + RUN_RAGAS_EVALUATION: true secrets: CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.QA_CLOUD_FORMATION_DEPLOY_ROLE }} INT_ASSIST_ME_DOCUMENT_SYNC_ROLE: ${{ secrets.INT_ASSIST_ME_DOCUMENT_SYNC_ROLE }} diff --git a/.github/workflows/pull_request.yml b/.github/workflows/pull_request.yml index 14db62bcb..f6b14257c 100644 --- a/.github/workflows/pull_request.yml +++ b/.github/workflows/pull_request.yml @@ -169,6 +169,7 @@ jobs: IS_PULL_REQUEST: true FORWARD_CSOC_LOGS: false RUN_REGRESSION_TESTS: true + RUN_RAGAS_EVALUATION: true secrets: CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.DEV_CLOUD_FORMATION_DEPLOY_ROLE }} INT_ASSIST_ME_DOCUMENT_SYNC_ROLE: ${{ secrets.INT_ASSIST_ME_DOCUMENT_SYNC_ROLE }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6d439fcb7..99941bf3d 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -35,7 +35,6 @@ jobs: jira_release_prefix: "epsam" secrets: EXECUTE_JIRA_LAMBDA_ROLE: ${{ secrets.DEV_CLOUD_FORMATION_EXECUTE_LAMBDA_ROLE }} - package_code: needs: [tag_release, get_config_values] @@ -62,6 +61,7 @@ jobs: IS_PULL_REQUEST: false FORWARD_CSOC_LOGS: false RUN_REGRESSION_TESTS: true + RUN_RAGAS_EVALUATION: true secrets: CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.DEV_CLOUD_FORMATION_DEPLOY_ROLE }} INT_ASSIST_ME_DOCUMENT_SYNC_ROLE: ${{ secrets.INT_ASSIST_ME_DOCUMENT_SYNC_ROLE }} @@ -90,6 +90,7 @@ jobs: IS_PULL_REQUEST: false FORWARD_CSOC_LOGS: false RUN_REGRESSION_TESTS: true + RUN_RAGAS_EVALUATION: true secrets: CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.QA_CLOUD_FORMATION_DEPLOY_ROLE }} INT_ASSIST_ME_DOCUMENT_SYNC_ROLE: ${{ secrets.INT_ASSIST_ME_DOCUMENT_SYNC_ROLE }} diff --git a/.github/workflows/release_all_stacks.yml b/.github/workflows/release_all_stacks.yml index 1843ca3f2..9e14f9215 100644 --- a/.github/workflows/release_all_stacks.yml +++ b/.github/workflows/release_all_stacks.yml @@ -30,6 +30,9 @@ on: RUN_REGRESSION_TESTS: type: boolean default: true + RUN_RAGAS_EVALUATION: + type: boolean + default: true IS_PULL_REQUEST: type: boolean default: false @@ -125,7 +128,6 @@ jobs: IS_PULL_REQUEST: ${{ inputs.IS_PULL_REQUEST }} RUN_REGRESSION_TESTS: ${{ inputs.RUN_REGRESSION_TESTS }} - - name: Deploy code for stack if: ${{ inputs.DEPLOY_CODE == true }} run: | @@ -199,3 +201,16 @@ jobs: pinned_image: ${{ inputs.pinned_image }} secrets: REGRESSION_TESTS_PEM: ${{ secrets.REGRESSION_TESTS_PEM }} + + ragas_evaluation: + name: Ragas Quality Evaluation + uses: ./.github/workflows/run_ragas_evaluation.yml + if: ${{ always() && !failure() && !cancelled() && inputs.RUN_RAGAS_EVALUATION == true }} + needs: [release_all_code] + with: + ENVIRONMENT: ${{ inputs.TARGET_ENVIRONMENT }} + VERSION_NUMBER: ${{ inputs.VERSION_NUMBER }} + STACK_NAME: ${{ inputs.STACK_NAME }} + pinned_image: ${{ inputs.pinned_image }} + secrets: + CLOUD_FORMATION_DEPLOY_ROLE: ${{ secrets.CLOUD_FORMATION_DEPLOY_ROLE }} diff --git a/.github/workflows/run_ragas_evaluation.yml b/.github/workflows/run_ragas_evaluation.yml new file mode 100644 index 000000000..a7fca453f --- /dev/null +++ b/.github/workflows/run_ragas_evaluation.yml @@ -0,0 +1,105 @@ +name: Run Ragas Quality Evaluation + +on: + workflow_call: + inputs: + ENVIRONMENT: + required: true + type: string + VERSION_NUMBER: + required: true + type: string + STACK_NAME: + required: true + type: string + pinned_image: + type: string + required: true + secrets: + CLOUD_FORMATION_DEPLOY_ROLE: + required: true + +jobs: + ragas_evaluation: + runs-on: ubuntu-22.04 + container: + image: ${{ inputs.pinned_image }} + options: --user 1001:1001 --group-add 128 + defaults: + run: + shell: bash + permissions: + id-token: write + contents: read + + steps: + - name: copy .tool-versions + run: | + cp /home/vscode/.tool-versions "$HOME/.tool-versions" + + - name: Checkout repository + uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd + with: + ref: ${{ github.ref }} + fetch-depth: 0 + + - name: Configure AWS Credentials + uses: aws-actions/configure-aws-credentials@8df5847569e6427dd6c4fb1cf565c83acfa8afa7 + with: + aws-region: eu-west-2 + role-to-assume: ${{ secrets.CLOUD_FORMATION_DEPLOY_ROLE }} + role-session-name: eps-assist-me-ragas-evaluation + + - name: Resolve Lambda function name + id: resolve_lambda + run: | + # Discover the SlackBot Lambda name from CloudFormation stack outputs + STACK_NAME="${{ inputs.STACK_NAME }}-${{ inputs.ENVIRONMENT }}" + FUNCTION_NAME=$(aws cloudformation describe-stacks \ + --stack-name "$STACK_NAME" \ + --query "Stacks[0].Outputs[?OutputKey=='SlackBotFunctionName'].OutputValue" \ + --output text 2>/dev/null || echo "") + + if [[ -z "$FUNCTION_NAME" || "$FUNCTION_NAME" == "None" ]]; then + # Fallback: list Lambda functions matching the expected naming pattern + FUNCTION_NAME=$(aws lambda list-functions \ + --query "Functions[?contains(FunctionName, '${{ inputs.STACK_NAME }}') && contains(FunctionName, 'SlackBot')].FunctionName | [0]" \ + --output text 2>/dev/null || echo "") + fi + + if [[ -z "$FUNCTION_NAME" || "$FUNCTION_NAME" == "None" ]]; then + echo "::error::Could not resolve SlackBot Lambda function name for stack $STACK_NAME" + exit 1 + fi + + echo "lambda_function_name=$FUNCTION_NAME" >> "$GITHUB_OUTPUT" + echo "Resolved Lambda function: $FUNCTION_NAME" + + - name: Install dependencies + run: | + poetry install --with ragasEvaluation,dev + + - name: Run Ragas evaluation + working-directory: packages/ragasEvaluation + env: + RAGAS_LAMBDA_FUNCTION_NAME: ${{ steps.resolve_lambda.outputs.lambda_function_name }} + RAGAS_AWS_REGION: eu-west-2 + RAGAS_EVALUATOR_MODEL_ID: eu.anthropic.claude-3-5-sonnet-20241022-v2:0 + RAGAS_RESULTS_DIR: results + run: | + echo "Running Ragas evaluation against: $RAGAS_LAMBDA_FUNCTION_NAME" + PYTHONPATH=. poetry run python -m pytest tests/ \ + -m ragas \ + -v \ + --tb=short \ + --junitxml=results/ragas_junit.xml \ + 2>&1 | tee results/ragas_output.log + + - name: Upload evaluation results + if: always() + uses: actions/upload-artifact@v4 + with: + name: ragas-evaluation-${{ inputs.ENVIRONMENT }}-${{ inputs.VERSION_NUMBER }} + path: | + packages/ragasEvaluation/results/ + retention-days: 30 diff --git a/.gitignore b/.gitignore index 63f88b679..9a9ca6435 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,5 @@ cdk.out .dependencies/ .poetry/ .trivy_out/ +# Ragas evaluation results (generated at runtime) +**/results/ diff --git a/Makefile b/Makefile index c36b9331a..3d504a8dd 100644 --- a/Makefile +++ b/Makefile @@ -42,6 +42,9 @@ test: cd packages/preprocessingFunction && PYTHONPATH=. COVERAGE_FILE=coverage/.coverage poetry run python -m pytest cd packages/bedrockLoggingConfigFunction && PYTHONPATH=. COVERAGE_FILE=coverage/.coverage poetry run python -m pytest +ragas-eval: + cd packages/ragasEvaluation && PYTHONPATH=. poetry run python -m pytest tests/ -m ragas -v --tb=short + clean: rm -rf packages/cdk/coverage rm -rf packages/cdk/lib diff --git a/packages/ragasEvaluation/README.md b/packages/ragasEvaluation/README.md new file mode 100644 index 000000000..8738dd437 --- /dev/null +++ b/packages/ragasEvaluation/README.md @@ -0,0 +1,54 @@ +# Ragas Quality Evaluation for EPS Assist Me + +Automated post-deployment quality evaluation of the EPS Assist Me AI bot using the [Ragas](https://docs.ragas.io/) framework. + +## Overview + +After each deployment, this evaluation suite invokes the deployed Slack Bot Lambda directly (bypassing Slack) and evaluates the AI responses against a curated dataset of EPS onboarding questions using LLM-as-a-judge metrics. + +## Metrics + +| Metric | Description | +|--------|-------------| +| **Faithfulness** | Is the response grounded in the retrieved knowledge base source context? | +| **Answer Relevancy** | Does the response actually answer the question that was asked? | +| **Semantic Similarity** | How close is the response to the expected reference answer? | +| **Answer Correctness** | Is the response factually correct? (combines faithfulness + similarity) | + +## Running Locally + +```bash +# Requires AWS credentials with Lambda invoke permissions +export RAGAS_LAMBDA_FUNCTION_NAME="epsam-dev-SlackBotFunction" +export RAGAS_AWS_REGION="eu-west-2" +export RAGAS_EVALUATOR_MODEL_ID="eu.anthropic.claude-3-5-sonnet-20241022-v2:0" + +cd /workspaces/eps-assist-me +poetry run pytest packages/ragasEvaluation -m ragas -v +``` + +## Test Dataset + +The evaluation dataset is defined in `evaluation/test_dataset.py` and contains representative EPS onboarding questions covering: + +- Prescription ID generation and structure +- CIS2 authentication requirements +- RBAC controls for prescribers +- Repeat dispensing cancellation scenarios +- Controlled drug prescribing rules +- FHIR API schema requirements +- Nomination management +- Error handling guidance + +## CI/CD Integration + +The evaluation runs automatically in the `release_all_stacks.yml` workflow after successful deployment, gated by the `RUN_RAGAS_EVALUATION` input. + +## Thresholds + +The evaluation enforces minimum score thresholds (configurable in `evaluation/config.py`): + +- Faithfulness: >= 0.7 +- Answer Relevancy: >= 0.7 +- Semantic Similarity: >= 0.7 +- Answer Correctness: >= 0.7 diff --git a/packages/ragasEvaluation/__init__.py b/packages/ragasEvaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/ragasEvaluation/evaluation/__init__.py b/packages/ragasEvaluation/evaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/ragasEvaluation/evaluation/config.py b/packages/ragasEvaluation/evaluation/config.py new file mode 100644 index 000000000..d8e761f81 --- /dev/null +++ b/packages/ragasEvaluation/evaluation/config.py @@ -0,0 +1,30 @@ +""" +Configuration for Ragas quality evaluation. + +All settings can be overridden via environment variables. +""" + +import os + + +# --- AWS / Lambda Configuration --- +LAMBDA_FUNCTION_NAME = os.environ.get("RAGAS_LAMBDA_FUNCTION_NAME", "") +AWS_REGION = os.environ.get("RAGAS_AWS_REGION", "eu-west-2") + +# --- Evaluator LLM --- +# Model used by Ragas to judge responses (not the bot's own model) +EVALUATOR_MODEL_ID = os.environ.get("RAGAS_EVALUATOR_MODEL_ID", "eu.anthropic.claude-3-5-sonnet-20241022-v2:0") +EVALUATOR_EMBEDDING_MODEL_ID = os.environ.get("RAGAS_EVALUATOR_EMBEDDING_MODEL_ID", "amazon.titan-embed-text-v2:0") + +# --- Score Thresholds --- +# Minimum acceptable scores for each metric. A test fails if the +# aggregate (mean) score for any metric falls below its threshold. +THRESHOLDS = { + "faithfulness": float(os.environ.get("RAGAS_THRESHOLD_FAITHFULNESS", "0.7")), + "answer_relevancy": float(os.environ.get("RAGAS_THRESHOLD_ANSWER_RELEVANCY", "0.7")), + "semantic_similarity": float(os.environ.get("RAGAS_THRESHOLD_SEMANTIC_SIMILARITY", "0.7")), + "answer_correctness": float(os.environ.get("RAGAS_THRESHOLD_ANSWER_CORRECTNESS", "0.7")), +} + +# --- Output --- +RESULTS_OUTPUT_DIR = os.environ.get("RAGAS_RESULTS_DIR", "packages/ragasEvaluation/results") diff --git a/packages/ragasEvaluation/evaluation/lambda_invoker.py b/packages/ragasEvaluation/evaluation/lambda_invoker.py new file mode 100644 index 000000000..1ecb8e019 --- /dev/null +++ b/packages/ragasEvaluation/evaluation/lambda_invoker.py @@ -0,0 +1,58 @@ +""" +Lambda invoker for Ragas evaluation. + +Calls the deployed Slack Bot Lambda via direct invocation (bypasses Slack) +and returns the AI response with citations for evaluation. +""" + +import json +import logging + +import boto3 + +from evaluation.config import LAMBDA_FUNCTION_NAME, AWS_REGION + +logger = logging.getLogger(__name__) + + +def invoke_bot(query: str, session_id: str | None = None) -> dict: + """ + Invoke the deployed EPS Assist Me Lambda with a direct query. + + Args: + query: The user question to send to the bot. + session_id: Optional session ID for conversation continuity. + + Returns: + dict with keys: text, session_id, citations + """ + client = boto3.client("lambda", region_name=AWS_REGION) + + payload = { + "invocation_type": "direct", + "query": query, + } + if session_id: + payload["session_id"] = session_id + + logger.info("Invoking Lambda %s with query: %s", LAMBDA_FUNCTION_NAME, query[:80]) + + response = client.invoke( + FunctionName=LAMBDA_FUNCTION_NAME, + InvocationType="RequestResponse", + Payload=json.dumps(payload), + ) + + response_payload = json.loads(response["Payload"].read()) + + if response_payload.get("statusCode") != 200: + raise RuntimeError(f"Lambda invocation failed: {response_payload}") + + data = response_payload["response"] + logger.info("Got response (session=%s, %d citations)", data.get("session_id"), len(data.get("citations", []))) + + return { + "text": data["text"], + "session_id": data.get("session_id"), + "citations": data.get("citations", []), + } diff --git a/packages/ragasEvaluation/evaluation/metrics.py b/packages/ragasEvaluation/evaluation/metrics.py new file mode 100644 index 000000000..e54942cd2 --- /dev/null +++ b/packages/ragasEvaluation/evaluation/metrics.py @@ -0,0 +1,63 @@ +"""Ragas metrics factory. + +Configures and returns the 4 core evaluation metrics: + - Faithfulness: Is the response grounded in the retrieved source context? + - Answer Relevancy: Does the response actually answer the question asked? + - Semantic Similarity: How close is the response to the expected reference answer? + - Answer Correctness: Is the response factually correct? +""" + +from langchain_aws import ChatBedrock +from ragas.llms import LangchainLLMWrapper +from ragas.embeddings import LangchainEmbeddingsWrapper +from langchain_aws import BedrockEmbeddings +from ragas.metrics import ( + Faithfulness, + ResponseRelevancy, + SemanticSimilarity, + AnswerCorrectness, +) + +from evaluation.config import EVALUATOR_MODEL_ID, EVALUATOR_EMBEDDING_MODEL_ID, AWS_REGION + + +def get_evaluator_llm() -> LangchainLLMWrapper: + """Create the LLM wrapper used by Ragas to judge responses.""" + bedrock_llm = ChatBedrock( + model_id=EVALUATOR_MODEL_ID, + region_name=AWS_REGION, + ) + return LangchainLLMWrapper(bedrock_llm) + + +def get_evaluator_embeddings() -> LangchainEmbeddingsWrapper: + """Create the embeddings wrapper used by Ragas for similarity metrics.""" + bedrock_embeddings = BedrockEmbeddings( + model_id=EVALUATOR_EMBEDDING_MODEL_ID, + region_name=AWS_REGION, + ) + return LangchainEmbeddingsWrapper(bedrock_embeddings) + + +def get_metrics() -> list: + """ + Return the 4 evaluation metrics. + + All are single-turn metrics that evaluate individual question->answer pairs. + """ + llm = get_evaluator_llm() + embeddings = get_evaluator_embeddings() + + # Is the response grounded in the retrieved source context? + faithfulness = Faithfulness(llm=llm) + + # Does the response actually answer the question asked? + relevancy = ResponseRelevancy(llm=llm, embeddings=embeddings) + + # How close is the response to the expected reference answer? + similarity = SemanticSimilarity(embeddings=embeddings) + + # Is the response factually correct? (combines faithfulness + similarity) + correctness = AnswerCorrectness(llm=llm, embeddings=embeddings) + + return [faithfulness, relevancy, similarity, correctness] diff --git a/packages/ragasEvaluation/evaluation/runner.py b/packages/ragasEvaluation/evaluation/runner.py new file mode 100644 index 000000000..371d565f8 --- /dev/null +++ b/packages/ragasEvaluation/evaluation/runner.py @@ -0,0 +1,201 @@ +""" +Core evaluation runner. + +Orchestrates: +1. Invoking the deployed bot for each question in the dataset +2. Building Ragas SingleTurnSample objects with response + retrieved contexts + reference +3. Running Ragas evaluate() with 4 metrics: faithfulness, relevancy, similarity, correctness +4. Persisting results to JSON for CI reporting +""" + +import asyncio +import json +import logging +import sys +from datetime import datetime, timezone +from pathlib import Path + +# --------------------------------------------------------------------------- +# Python 3.14 compatibility patch +# --------------------------------------------------------------------------- +# nest_asyncio (used internally by Ragas) patches asyncio's _run_once to pop +# the current task from _current_tasks before executing each handle. On Python +# 3.14+, asyncio.wait_for() -> asyncio.timeout() now raises +# RuntimeError("Timeout should be used inside a task") +# because the current task has been temporarily removed by nest_asyncio. +# +# We monkey-patch asyncio.wait_for *before* importing Ragas so the patched +# version is picked up everywhere. The patch simply falls back to awaiting +# the coroutine without a timeout when the RuntimeError is raised. +# --------------------------------------------------------------------------- +if sys.version_info >= (3, 14): + _original_wait_for = asyncio.wait_for + + async def _patched_wait_for(fut, timeout, **kwargs): + try: + return await _original_wait_for(fut, timeout, **kwargs) + except RuntimeError as exc: + if "Timeout should be used inside a task" in str(exc): + return await fut + raise + + asyncio.wait_for = _patched_wait_for + +import nest_asyncio # noqa: E402 – must come after the patch above +from ragas import evaluate # noqa: E402 +from ragas.dataset_schema import EvaluationDataset, SingleTurnSample # noqa: E402 + +from evaluation.config import RESULTS_OUTPUT_DIR # noqa: E402 +from evaluation.lambda_invoker import invoke_bot # noqa: E402 +from evaluation.metrics import get_metrics # noqa: E402 +from evaluation.test_dataset import EVALUATION_DATASET # noqa: E402 + +nest_asyncio.apply() + +logger = logging.getLogger(__name__) + + +def _extract_citation_texts(citations: list[dict]) -> list[str]: + """ + Extract retrieved context strings from Bedrock citation format. + + Bedrock returns citations as a list of objects with retrievedReferences + containing content->text. We flatten to a list of strings. + """ + contexts: list[str] = [] + for citation_group in citations: + refs = citation_group.get("retrievedReferences", []) + for ref in refs: + text = ref.get("content", {}).get("text", "") + if text: + contexts.append(text) + return contexts + + +def collect_responses() -> list[dict]: + """ + Invoke the bot for every question in the dataset and collect responses. + + Returns a list of dicts with keys: + user_input, reference, category, response_text, retrieved_contexts, session_id + """ + results = [] + for item in EVALUATION_DATASET: + query = item["user_input"] + try: + response = invoke_bot(query) + retrieved_contexts = _extract_citation_texts(response.get("citations", [])) + results.append( + { + "user_input": query, + "reference": item["reference"], + "category": item["category"], + "response_text": response["text"], + "retrieved_contexts": retrieved_contexts, + "session_id": response.get("session_id"), + } + ) + except Exception: + logger.exception("Failed to get response for query: %s", query[:80]) + results.append( + { + "user_input": query, + "reference": item["reference"], + "category": item["category"], + "response_text": "", + "retrieved_contexts": [], + "session_id": None, + "error": True, + } + ) + return results + + +def build_single_turn_samples(responses: list[dict]) -> list[SingleTurnSample]: + """Build Ragas SingleTurnSample objects for RAG metric evaluation.""" + samples = [] + for r in responses: + if r.get("error"): + continue + samples.append( + SingleTurnSample( + user_input=r["user_input"], + response=r["response_text"], + retrieved_contexts=r["retrieved_contexts"] if r["retrieved_contexts"] else ["No context retrieved."], + reference=r["reference"], + ) + ) + return samples + + +def run_evaluation(responses: list[dict]) -> dict: + """Run all 4 metrics: faithfulness, relevancy, similarity, correctness.""" + samples = build_single_turn_samples(responses) + if not samples: + logger.warning("No valid samples for evaluation") + return {} + + metrics = get_metrics() + dataset = EvaluationDataset(samples=samples) + + logger.info("Running evaluation with %d samples and %d metrics", len(samples), len(metrics)) + result = evaluate(dataset=dataset, metrics=metrics) + + df = result.to_pandas() + return { + "scores": result.scores.to_dict() if hasattr(result.scores, "to_dict") else {}, + "summary": { + col: float(df[col].mean()) + for col in df.columns + if col not in ("user_input", "reference", "response", "retrieved_contexts") + }, + } + + +def run_full_evaluation() -> dict: + """ + Run the complete evaluation pipeline: + 1. Collect bot responses + 2. Evaluate with all 4 metrics (faithfulness, relevancy, similarity, correctness) + 3. Persist results + + Returns combined results dict. + """ + logger.info("Starting Ragas evaluation with %d questions", len(EVALUATION_DATASET)) + + responses = collect_responses() + error_count = sum(1 for r in responses if r.get("error")) + if error_count: + logger.warning("%d/%d queries failed", error_count, len(responses)) + + evaluation_results = run_evaluation(responses) + + combined = { + "timestamp": datetime.now(timezone.utc).isoformat(), + "total_questions": len(EVALUATION_DATASET), + "successful_queries": len(responses) - error_count, + "failed_queries": error_count, + "results": evaluation_results, + } + + _persist_results(combined, responses) + + return combined + + +def _persist_results(results: dict, responses: list[dict]) -> None: + """Write evaluation results and raw responses to JSON files.""" + output_dir = Path(RESULTS_OUTPUT_DIR) + output_dir.mkdir(parents=True, exist_ok=True) + + run_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") + + results_path = output_dir / f"evaluation_{run_id}.json" + with open(results_path, "w") as f: + json.dump(results, f, indent=2, default=str) + logger.info("Results written to %s", results_path) + + responses_path = output_dir / f"responses_{run_id}.json" + with open(responses_path, "w") as f: + json.dump(responses, f, indent=2, default=str) + logger.info("Responses written to %s", responses_path) diff --git a/packages/ragasEvaluation/evaluation/test_dataset.jsonl b/packages/ragasEvaluation/evaluation/test_dataset.jsonl new file mode 100644 index 000000000..c57a97ddb --- /dev/null +++ b/packages/ragasEvaluation/evaluation/test_dataset.jsonl @@ -0,0 +1,12 @@ +{"user_input": "If a user prescribes 6 items in a session for a patient, the GP system should separate these as 2 forms with 4 items on one and 2 items on the other. Do both prescription forms have the same Prescription ID or different IDs?", "reference": "A prescription can have a maximum of 4 line items. Therefore, these will need to be prescribed over a minimum of 2 prescriptions. These prescriptions will have their own unique identifiers.", "category": "prescription_ids"} +{"user_input": "How do prescribers authenticate themselves to access Spine services like EPS?", "reference": "Prescribers authenticate through the Care Identity Service (CIS2) using their NHS provisioned CIS2 authenticator to access Spine services like EPS.", "category": "authentication"} +{"user_input": "What are the RBAC requirements for prescribing systems?", "reference": "Role-based access controls (RBAC) must be used to limit what users of the prescribing system can and cannot do. RBAC rules are in the National RBAC Database.", "category": "rbac"} +{"user_input": "I have a query related to cancelling Repeat Dispensing courses. A patient has an ongoing RD course with 6 issues. Issue 1 is dispensed, issue 2 is with the dispenser, and issues 3-6 are still on Spine. Can the doctor cancel all issues 1-6? Can the doctor cancel only issues 3-6?", "reference": "The Spine allows cancellation of repeat dispensing issues. Issues that are already dispensed or with the dispenser may have different cancellation outcomes compared to issues still on Spine. The cancel request can target specific issues.", "category": "repeat_dispensing"} +{"user_input": "Can Schedule 1 controlled drugs be prescribed using EPS?", "reference": "Schedule 1 controlled drugs must not be prescribed using EPS.", "category": "controlled_drugs"} +{"user_input": "How can a prescriber change a patient's nominated dispensing site?", "reference": "Prescribers can add, update, or remove a patient's nomination on PDS at the patient's request. They can change the dispenser's ODS code and the type of dispenser.", "category": "nominations"} +{"user_input": "What patient details must be included on an EPS prescription according to the FHIR API schema?", "reference": "The prescription must include the patient's NHS number, usual name, gender, and date of birth.", "category": "fhir_api"} +{"user_input": "What prescription treatment types can prescribers choose from in Secondary Care?", "reference": "Prescribers in Secondary Care can choose: 0001 Acute (Mandatory), 0002 Repeat Prescribing (optional), and 0003 Electronic Repeat Dispensing (eRD) (optional depending on use case).", "category": "prescription_types"} +{"user_input": "Can I create an EPS prescription for a patient who has been marked as deceased on PDS?", "reference": "No. You must not create a prescription using EPS for patients that have been marked as deceased on PDS.", "category": "patient_eligibility"} +{"user_input": "What are the recommended error handling strategies when calling NHS England APIs?", "reference": "Applications should examine the return status code, implement retry strategies such as exponential backoff, and incorporate wait/loop code with appropriate delays before retrying. For end users, a lower initial retry interval of 200ms with a maximum of 3 seconds is recommended.", "category": "error_handling"} +{"user_input": "What should happen when prescribing for a patient with a sensitive status on PDS?", "reference": "A prescription must not be created using EPS for patients that have a sensitive status on PDS.", "category": "patient_eligibility"} +{"user_input": "What happens if a medication is not listed in the dm+d? Can it still be prescribed via EPS?", "reference": "Medication not listed within dm+d must not be prescribed via EPS. FP10s must be used for items not in dm+d and not mapped from a local proprietary database. Medication may be split across electronic and FP10 prescriptions.", "category": "dmd"} diff --git a/packages/ragasEvaluation/evaluation/test_dataset.py b/packages/ragasEvaluation/evaluation/test_dataset.py new file mode 100644 index 000000000..fa107710b --- /dev/null +++ b/packages/ragasEvaluation/evaluation/test_dataset.py @@ -0,0 +1,137 @@ +""" +EPS Assist Me evaluation dataset. + +Curated questions that exercise the knowledge base across the key EPS domains: +prescribing, dispensing, FHIR APIs, CIS2 auth, SCAL requirements, and general +onboarding guidance. + +Each entry contains: + - user_input: The question sent to the bot. + - reference: An ideal / expected answer summary for reference-based metrics. + - category: A tag for grouping results in reports. +""" + +EVALUATION_DATASET: list[dict] = [ + # ---- Prescription IDs ---- + { + "user_input": ( + "If a user prescribes 6 items in a session for a patient, the GP system should " + "separate these as 2 forms with 4 items on one and 2 items on the other. " + "Do both prescription forms have the same Prescription ID or different IDs?" + ), + "reference": ( + "A prescription can have a maximum of 4 line items. Therefore, these will " + "need to be prescribed over a minimum of 2 prescriptions. These prescriptions " + "will have their own unique identifiers." + ), + "category": "prescription_ids", + }, + # ---- CIS2 Authentication ---- + { + "user_input": ("How do prescribers authenticate themselves to access Spine services like EPS?"), + "reference": ( + "Prescribers authenticate through the Care Identity Service (CIS2) using their " + "NHS provisioned CIS2 authenticator to access Spine services like EPS." + ), + "category": "authentication", + }, + # ---- RBAC Controls ---- + { + "user_input": "What are the RBAC requirements for prescribing systems?", + "reference": ( + "Role-based access controls (RBAC) must be used to limit what users of the " + "prescribing system can and cannot do. RBAC rules are in the National RBAC Database." + ), + "category": "rbac", + }, + # ---- Repeat Dispensing Cancellation ---- + { + "user_input": ( + "I have a query related to cancelling Repeat Dispensing courses. A patient has an " + "ongoing RD course with 6 issues. Issue 1 is dispensed, issue 2 is with the dispenser, " + "and issues 3-6 are still on Spine. Can the doctor cancel all issues 1-6? " + "Can the doctor cancel only issues 3-6?" + ), + "reference": ( + "The Spine allows cancellation of repeat dispensing issues. Issues that are already " + "dispensed or with the dispenser may have different cancellation outcomes compared " + "to issues still on Spine. The cancel request can target specific issues." + ), + "category": "repeat_dispensing", + }, + # ---- Controlled Drugs ---- + { + "user_input": "Can Schedule 1 controlled drugs be prescribed using EPS?", + "reference": ("Schedule 1 controlled drugs must not be prescribed using EPS."), + "category": "controlled_drugs", + }, + # ---- Nomination Management ---- + { + "user_input": ("How can a prescriber change a patient's nominated dispensing site?"), + "reference": ( + "Prescribers can add, update, or remove a patient's nomination on PDS at the " + "patient's request. They can change the dispenser's ODS code and the type of dispenser." + ), + "category": "nominations", + }, + # ---- FHIR API / Prescription Structure ---- + { + "user_input": ( + "What patient details must be included on an EPS prescription according " "to the FHIR API schema?" + ), + "reference": ( + "The prescription must include the patient's NHS number, usual name, gender, " "and date of birth." + ), + "category": "fhir_api", + }, + # ---- Prescription Types ---- + { + "user_input": ("What prescription treatment types can prescribers choose from in " "Secondary Care?"), + "reference": ( + "Prescribers in Secondary Care can choose: 0001 Acute (Mandatory), " + "0002 Repeat Prescribing (optional), and 0003 Electronic Repeat Dispensing " + "(eRD) (optional depending on use case)." + ), + "category": "prescription_types", + }, + # ---- Deceased Patients ---- + { + "user_input": ("Can I create an EPS prescription for a patient who has been marked " "as deceased on PDS?"), + "reference": ( + "No. You must not create a prescription using EPS for patients that have " "been marked as deceased on PDS." + ), + "category": "patient_eligibility", + }, + # ---- Error Handling ---- + { + "user_input": ("What are the recommended error handling strategies when calling " "NHS England APIs?"), + "reference": ( + "Applications should examine the return status code, implement retry strategies " + "such as exponential backoff, and incorporate wait/loop code with appropriate " + "delays before retrying. For end users, a lower initial retry interval of 200ms " + "with a maximum of 3 seconds is recommended." + ), + "category": "error_handling", + }, + # ---- Sensitive Patients ---- + { + "user_input": ("What should happen when prescribing for a patient with a sensitive " "status on PDS?"), + "reference": ( + "A prescription must not be created using EPS for patients that have " "a sensitive status on PDS." + ), + "category": "patient_eligibility", + }, + # ---- dm+d Requirements ---- + { + "user_input": ( + "What happens if a medication is not listed in the dm+d? Can it still " "be prescribed via EPS?" + ), + "reference": ( + "Medication not listed within dm+d must not be prescribed via EPS. " + "FP10s must be used for items not in dm+d and not mapped from a local " + "proprietary database. Medication may be split across electronic and " + "FP10 prescriptions." + ), + "category": "dmd", + }, +] diff --git a/packages/ragasEvaluation/pytest.ini b/packages/ragasEvaluation/pytest.ini new file mode 100644 index 000000000..ab7221e85 --- /dev/null +++ b/packages/ragasEvaluation/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + ragas: Ragas quality evaluation tests (require deployed infrastructure) +testpaths = tests diff --git a/packages/ragasEvaluation/tests/__init__.py b/packages/ragasEvaluation/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/ragasEvaluation/tests/conftest.py b/packages/ragasEvaluation/tests/conftest.py new file mode 100644 index 000000000..eb497f51b --- /dev/null +++ b/packages/ragasEvaluation/tests/conftest.py @@ -0,0 +1,26 @@ +""" +Shared fixtures for Ragas evaluation tests. +""" + +import logging + +import pytest + +from evaluation.config import LAMBDA_FUNCTION_NAME + +logger = logging.getLogger(__name__) + + +def pytest_configure(config): + """Register custom markers.""" + config.addinivalue_line("markers", "ragas: Ragas quality evaluation tests (require deployed infrastructure)") + + +@pytest.fixture(scope="session", autouse=True) +def check_environment(): + """Ensure required environment variables are set before running any ragas tests.""" + if not LAMBDA_FUNCTION_NAME: + pytest.skip( + "RAGAS_LAMBDA_FUNCTION_NAME not set — skipping Ragas evaluation. " + "Set this environment variable to the deployed Lambda function name." + ) diff --git a/packages/ragasEvaluation/tests/test_ragas_evaluation.py b/packages/ragasEvaluation/tests/test_ragas_evaluation.py new file mode 100644 index 000000000..4c876f307 --- /dev/null +++ b/packages/ragasEvaluation/tests/test_ragas_evaluation.py @@ -0,0 +1,88 @@ +""" +Ragas quality evaluation tests for EPS Assist Me. + +Runs the full evaluation pipeline against the deployed bot and asserts +that aggregate metric scores meet the configured thresholds. + +Metrics: + - faithfulness: Is the response grounded in the retrieved source context? + - answer_relevancy: Does the response answer the question asked? + - semantic_similarity: How close is the response to the expected reference answer? + - answer_correctness: Is the response factually correct? + +Usage: + RAGAS_LAMBDA_FUNCTION_NAME=epsam-dev-SlackBotFunction \ + poetry run pytest packages/ragasEvaluation -m ragas -v +""" + +import logging + +import pytest + +from evaluation.config import THRESHOLDS +from evaluation.runner import run_full_evaluation + +logger = logging.getLogger(__name__) + + +@pytest.fixture(scope="module") +def evaluation_results(): + """Run the full evaluation once per module and cache results.""" + return run_full_evaluation() + + +@pytest.mark.ragas +class TestQualityMetrics: + """Tests for the 4 core quality metrics.""" + + def test_faithfulness_above_threshold(self, evaluation_results): + """Responses should be grounded in the retrieved knowledge base context.""" + summary = evaluation_results.get("results", {}).get("summary", {}) + score = summary.get("faithfulness") + if score is None: + pytest.skip("Faithfulness metric not available in results") + threshold = THRESHOLDS["faithfulness"] + assert score >= threshold, f"Faithfulness score {score:.2f} below threshold {threshold:.2f}" + + def test_answer_relevancy_above_threshold(self, evaluation_results): + """Responses should directly answer the user's question.""" + summary = evaluation_results.get("results", {}).get("summary", {}) + score = summary.get("answer_relevancy") + if score is None: + pytest.skip("Answer relevancy metric not available in results") + threshold = THRESHOLDS["answer_relevancy"] + assert score >= threshold, f"Answer relevancy score {score:.2f} below threshold {threshold:.2f}" + + def test_semantic_similarity_above_threshold(self, evaluation_results): + """Responses should be semantically close to the expected reference answers.""" + summary = evaluation_results.get("results", {}).get("summary", {}) + score = summary.get("semantic_similarity") + if score is None: + pytest.skip("Semantic similarity metric not available in results") + threshold = THRESHOLDS["semantic_similarity"] + assert score >= threshold, f"Semantic similarity score {score:.2f} below threshold {threshold:.2f}" + + def test_answer_correctness_above_threshold(self, evaluation_results): + """Responses should be factually correct.""" + summary = evaluation_results.get("results", {}).get("summary", {}) + score = summary.get("answer_correctness") + if score is None: + pytest.skip("Answer correctness metric not available in results") + threshold = THRESHOLDS["answer_correctness"] + assert score >= threshold, f"Answer correctness score {score:.2f} below threshold {threshold:.2f}" + + +@pytest.mark.ragas +class TestEvaluationHealth: + """Meta-tests to ensure the evaluation itself ran successfully.""" + + def test_all_queries_succeeded(self, evaluation_results): + """All evaluation queries should get a response from the bot.""" + failed = evaluation_results.get("failed_queries", 0) + total = evaluation_results.get("total_questions", 0) + assert failed == 0, f"{failed}/{total} evaluation queries failed" + + def test_minimum_sample_count(self, evaluation_results): + """Ensure we evaluated a meaningful number of questions.""" + successful = evaluation_results.get("successful_queries", 0) + assert successful >= 5, f"Only {successful} queries succeeded -- need at least 5 for meaningful evaluation" diff --git a/packages/slackBotFunction/app/services/semantic_cache.py b/packages/slackBotFunction/app/services/semantic_cache.py new file mode 100644 index 000000000..e86b36f74 --- /dev/null +++ b/packages/slackBotFunction/app/services/semantic_cache.py @@ -0,0 +1,114 @@ +""" +Semantic caching for RAG responses using DynamoDB. + +Caches query→response pairs keyed by a normalized hash of the user query. +Only first-turn queries (no session) are cached to avoid stale conversation context. +Cache entries have a configurable TTL to ensure freshness as the knowledge base evolves. +""" + +import hashlib +import json +import traceback +from time import time +from typing import Any + +from app.core.config import get_logger, get_slack_bot_state_table + +logger = get_logger() + +# Cache configuration +CACHE_TTL_SECONDS = 86400 # 24 hours — balances freshness with cost savings +CACHE_PREFIX = "cache#" + + +def _normalize_query(query: str) -> str: + """Normalize query for consistent cache keys — lowercase, strip, collapse whitespace.""" + return " ".join(query.lower().strip().split()) + + +def _hash_query(query: str) -> str: + """Generate a short hash key from the normalized query.""" + normalized = _normalize_query(query) + return hashlib.sha256(normalized.encode("utf-8")).hexdigest()[:16] + + +def get_cached_response(query: str) -> dict[str, Any] | None: + """ + Look up a cached RAG response for the given query. + + Returns: + Cached response dict matching AIProcessorResponse shape, or None on miss/expiry. + """ + try: + query_hash = _hash_query(query) + table = get_slack_bot_state_table() + start = time() + + result = table.get_item(Key={"pk": f"{CACHE_PREFIX}{query_hash}", "sk": "response"}) + + duration = time() - start + item = result.get("Item") + + if not item: + logger.info("Cache miss", extra={"query_hash": query_hash, "duration": duration}) + return None + + # Check TTL manually as DynamoDB TTL deletion is eventually consistent + if item.get("ttl", 0) < time(): + logger.info("Cache expired", extra={"query_hash": query_hash}) + return None + + cached_data = json.loads(item["response_data"]) + logger.info( + "Cache hit", + extra={"query_hash": query_hash, "duration": duration}, + ) + return cached_data + + except Exception: + # Cache failures should never break the main flow + logger.warning("Cache lookup failed", extra={"error": traceback.format_exc()}) + return None + + +def store_cached_response(query: str, response_data: dict[str, Any]) -> None: + """ + Store a RAG response in the cache. + + Only the serializable parts of the response are cached (text, session_id, citations). + The raw kb_response is excluded as it contains non-serializable Bedrock metadata. + + Args: + query: Original user query + response_data: AIProcessorResponse dict to cache + """ + try: + query_hash = _hash_query(query) + table = get_slack_bot_state_table() + now = int(time()) + + # Cache only the portable 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