diff --git a/ai4rag/assets_generator/__init__.py b/ai4rag/assets_generator/__init__.py index abfb0fb9..5ce7ea7b 100644 --- a/ai4rag/assets_generator/__init__.py +++ b/ai4rag/assets_generator/__init__.py @@ -4,10 +4,12 @@ # ----------------------------------------------------------------------------- from ai4rag.assets_generator.leaderboard import build_leaderboard_html from ai4rag.assets_generator.notebook import Notebook +from ai4rag.assets_generator.starter_kit import generate_starter_kit from ai4rag.assets_generator.templates import generate_notebook_from_template __all__ = [ "Notebook", "build_leaderboard_html", "generate_notebook_from_template", + "generate_starter_kit", ] diff --git a/ai4rag/assets_generator/starter_kit.py b/ai4rag/assets_generator/starter_kit.py new file mode 100644 index 00000000..1c8df93c --- /dev/null +++ b/ai4rag/assets_generator/starter_kit.py @@ -0,0 +1,166 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +import base64 +import importlib.resources +import re +import shutil +import tempfile +import zipfile +from pathlib import Path +from typing import Any + +_CONFIGURABLE_FILES = {".env.example", "values.yaml", "agent.yaml"} +_IGNORED_TEMPLATE_NAMES = {".DS_Store", ".env", ".venv", "__pycache__"} + +_PROVIDER_BLOCK_PATTERN = re.compile( + r"^# <<< BEGIN (?P\w+) >>>\n(?P.*?)^# <<< END \1 >>>\n", + re.MULTILINE | re.DOTALL, +) + + +def _create_starter_kit_mapping(output_data: dict[str, Any]) -> dict[str, str]: + """Build a flat placeholder mapping from pattern.json data. + + All values are stringified so they can be used with ``str.replace()``. + """ + mapping: dict[str, str] = {} + + def _value(value: Any, default: str = "") -> str: + return default if value is None else str(value) + + mapping["__PATTERN_NAME__"] = _value(output_data.get("name")) + + settings = output_data.get("settings", {}) + + fm = settings.get("generation", {}) + mapping["__FM_MODEL_ID__"] = _value(fm.get("model_id")) + mapping["__TEMPERATURE__"] = _value(fm.get("temperature"), "0.0") + mapping["__MAX_COMPLETION_TOKENS__"] = _value(fm.get("max_completion_tokens")) + mapping["__SYSTEM_MESSAGE__"] = _value(fm.get("system_message_text")) + mapping["__USER_MESSAGE__"] = _value(fm.get("user_message_text")) + mapping["__CONTEXT_TEMPLATE__"] = _value(fm.get("context_template_text")) + mapping["__USER_MESSAGE_B64__"] = base64.b64encode(mapping["__USER_MESSAGE__"].encode()).decode() + mapping["__CONTEXT_TEMPLATE_B64__"] = base64.b64encode(mapping["__CONTEXT_TEMPLATE__"].encode()).decode() + mapping["__SYSTEM_MESSAGE_B64__"] = base64.b64encode(mapping["__SYSTEM_MESSAGE__"].encode()).decode() + + language = fm.get("language", {}) + if isinstance(language, dict): + mapping["__LANGUAGE_CODE__"] = _value(language.get("code")) + mapping["__LANGUAGE_NAME__"] = _value(language.get("name"), "auto") + else: + mapping["__LANGUAGE_CODE__"] = "" + mapping["__LANGUAGE_NAME__"] = _value(language, "auto") + + em = settings.get("embedding", {}) + mapping["__EMBEDDING_MODEL_ID__"] = _value(em.get("model_id")) + em_params = em.get("embedding_params", {}) + mapping["__EMBEDDING_DIMENSION__"] = _value(em_params.get("embedding_dimension"), "768") + + ret = settings.get("retrieval", {}) + mapping["__RETRIEVAL_METHOD__"] = _value(ret.get("method"), "simple") + mapping["__NUMBER_OF_CHUNKS__"] = _value(ret.get("number_of_chunks"), "5") + mapping["__SEARCH_MODE__"] = _value(ret.get("search_mode")) + mapping["__RANKER_STRATEGY__"] = _value(ret.get("ranker_strategy")) + mapping["__RANKER_ALPHA__"] = _value(ret.get("ranker_alpha")) + + vs = settings.get("vector_store_binding", {}) + mapping["__PROVIDER_TYPE__"] = _value(vs.get("provider_type"), "milvus") + mapping["__COLLECTION_NAME__"] = _value(vs.get("collection_name")) + + indexing_params = output_data.get("indexing", {}).get("pipeline_spec", {}).get("parameters", {}) + mapping["__MAAS_SECRET_NAME__"] = _value(indexing_params.get("maas_secret_name")) + mapping["__VECTOR_DB_SECRET_NAME__"] = _value(indexing_params.get("vector_db_secret_name")) + + return mapping + + +def _copy_template_tree(src: Path, dst: Path) -> None: + """Recursively copy a template directory tree to *dst*.""" + if src.name in _IGNORED_TEMPLATE_NAMES or src.suffix == ".pyc": + return + + if src.is_file(): + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + return + + for child in src.iterdir(): + if child.name in _IGNORED_TEMPLATE_NAMES or child.suffix == ".pyc": + continue + _copy_template_tree(child, dst / child.name) + + +def _replace_placeholders(file_path: Path, mapping: dict[str, str]) -> None: + """Replace ``__PLACEHOLDER__`` markers in a text file.""" + content = file_path.read_text(encoding="utf-8") + for marker, value in mapping.items(): + content = content.replace(marker, value) + file_path.write_text(content, encoding="utf-8") + + +def _apply_provider_conditionals(file_path: Path, active_provider: str) -> None: + """Keep blocks for *active_provider* and strip blocks for others.""" + content = file_path.read_text(encoding="utf-8") + + def _replacer(match: re.Match) -> str: + provider = match.group("provider").lower() + body = match.group("body") + if provider == active_provider.lower(): + return body + return "" + + content = _PROVIDER_BLOCK_PATTERN.sub(_replacer, content) + file_path.write_text(content, encoding="utf-8") + + +def generate_starter_kit( + output_data: dict[str, Any], + output_dir: str | Path, +) -> Path: + """Generate a ``starter_kit.zip`` from pattern optimisation data. + + Parameters + ---------- + output_data : dict[str, Any] + The parsed ``pattern.json`` data produced by the optimisation pipeline. + output_dir : str | Path + Directory where ``starter_kit.zip`` will be written (typically the + pattern directory alongside ``pattern.json``). + + Returns + ------- + Path + Absolute path to the generated ``starter_kit.zip``. + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + mapping = _create_starter_kit_mapping(output_data) + active_provider = mapping.get("__PROVIDER_TYPE__", "milvus") + + template_root = importlib.resources.files("ai4rag.assets_generator").joinpath( + "starter_kit_templates", "agentic_rag" + ) + + with tempfile.TemporaryDirectory() as tmpdir: + kit_dir = Path(tmpdir) / "starter_kit" + + with importlib.resources.as_file(template_root) as resolved_root: + _copy_template_tree(resolved_root, kit_dir) + + for fname in _CONFIGURABLE_FILES: + target = kit_dir / fname + if target.exists(): + _replace_placeholders(target, mapping) + _apply_provider_conditionals(target, active_provider) + + zip_path = output_dir / "starter_kit.zip" + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf: + for file_path in sorted(kit_dir.rglob("*")): + if file_path.is_file(): + arcname = str(Path("starter_kit") / file_path.relative_to(kit_dir)) + zf.write(file_path, arcname) + + return zip_path diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.env.example b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.env.example new file mode 100644 index 00000000..9f679535 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.env.example @@ -0,0 +1,31 @@ +# === LLM (MaaS) === +# OpenShift deployment loads MaaS credentials from maas_secret_name in values.yaml. +# For local development, export MAAS_BASE_URL and MAAS_API_KEY before make run-app. +# Generated generation settings; do not edit. +TEMPERATURE=__TEMPERATURE__ +MAX_COMPLETION_TOKENS=__MAX_COMPLETION_TOKENS__ + +# === Generation Prompts === +# Prompt templates are base64 encoded so multiline values remain valid dotenv entries. +SYSTEM_MESSAGE_B64=__SYSTEM_MESSAGE_B64__ +USER_MESSAGE_B64=__USER_MESSAGE_B64__ +CONTEXT_TEMPLATE_B64=__CONTEXT_TEMPLATE_B64__ +LANGUAGE_CODE=__LANGUAGE_CODE__ +LANGUAGE_NAME=__LANGUAGE_NAME__ + +# MODEL_ID, embedding and retrieval settings are generated in values.yaml. + +# <<< BEGIN MILVUS >>> +MILVUS_COLLECTION_NAME=__COLLECTION_NAME__ +# <<< END MILVUS >>> +# <<< BEGIN PGVECTOR >>> +PGVECTOR_COLLECTION_NAME=__COLLECTION_NAME__ +# <<< END PGVECTOR >>> + +# === Local deployment === +PORT=8000 + +# === Optional — MLflow Tracing === +# MLFLOW_TRACKING_URI= +# MLFLOW_EXPERIMENT_NAME= +# MLFLOW_HEALTH_CHECK_TIMEOUT=5 diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.gitignore b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.gitignore new file mode 100644 index 00000000..954f9667 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/.gitignore @@ -0,0 +1,5 @@ +.env +.venv/ +__pycache__/ +*.py[cod] +.DS_Store diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Containerfile.openshell b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Containerfile.openshell new file mode 100644 index 00000000..9833e11d --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Containerfile.openshell @@ -0,0 +1,46 @@ +# openShell sandbox flavor for agentic_rag +# +# Usage: +# podman build -f Containerfile.openshell -t quay.io//openshell-agentic-rag:latest . +# podman push quay.io//openshell-agentic-rag:latest +# openshell sandbox create --name rag-sandbox --from quay.io//openshell-agentic-rag:latest + +ARG BASE_IMAGE=ghcr.io/nvidia/openshell-community/sandboxes/base@sha256:aeef1c63f00e2913ea002ccb3aaf925f338b5c5d70e63576f0d95c16a138044e +FROM ${BASE_IMAGE} + +USER 0 + +WORKDIR /sandbox + +# Install uv for fast dependency installation +COPY --from=ghcr.io/astral-sh/uv@sha256:fc93e9ecd7218e9ec8fba117af89348eef8fd2463c50c13347478769aaedd0ce /uv /usr/local/bin/uv + +COPY --chown=1001:0 pyproject.toml uv.lock ./ +COPY --chown=1001:0 src/ ./src/ + +# Use system Python 3.12 for compatibility (base image has both 3.12 and 3.14) +RUN uv export --frozen --no-dev --extra tracing --no-emit-project --no-hashes > requirements.txt && \ + uv pip install --python /usr/bin/python3.12 --prefix /sandbox/.local --no-cache -r requirements.txt && \ + uv pip install --python /usr/bin/python3.12 --prefix /sandbox/.local --no-cache --no-deps . + +# Make Python find packages in /sandbox/.local +ENV PYTHONPATH="/sandbox/.local/lib/python3.12/site-packages:/sandbox/.local/lib64/python3.12/site-packages:$PYTHONPATH" + +# Application code and assets +COPY --chown=1001:0 main.py auth_wrapper.py sqlite_shim.py playground_sandbox.py . +COPY --chown=1001:0 playground-sandbox/ ./playground-sandbox/ + +# Set granular permissions +RUN chmod -R 755 /sandbox && \ + chmod -R 775 /sandbox/.local && \ + find /sandbox -type f -name "*.py" -exec chmod 644 {} \; + +# Default user (OpenShell will override this) +USER 1001 + +ENV PORT=8080 + +EXPOSE 8080 + +# NOTE: OpenShell's sandbox supervisor replaces CMD at runtime. +CMD ["uvicorn", "auth_wrapper:app", "--host", "127.0.0.1", "--port", "8080"] diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Makefile b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Makefile new file mode 100644 index 00000000..c38e4cdc --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/Makefile @@ -0,0 +1,312 @@ +SHELL := bash +OPENSHELL_SANDBOX ?= $(shell . ./.env 2>/dev/null && echo $${OPENSHELL_SANDBOX_NAME:-rag-sandbox}) +OPENSHELL_IMAGE := openshell-agentic-rag + +.PHONY: init env run-app run-cli refresh-certs setup-gateway teardown-gateway build-openshell create-sandbox wait-sandbox setup-egress start-agent expose-agent deploy-openshell undeploy-openshell playground-sandbox test help + +help: ## Show this help + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf " %-22s %s\n", $$1, $$2}' + +init: ## Copy .env.example to .env for configuration + @if [ ! -f .env ]; then cp .env.example .env && echo "Created .env from .env.example — edit it with your configuration"; else echo ".env already exists — skipping"; fi + +env: ## Create venv and install dependencies + @echo "==> Creating virtual environment and installing dependencies..." && \ + rm -rf .venv && \ + uv sync --python 3.12 && \ + echo "" && \ + echo "Done. Activate with: source .venv/bin/activate" && \ + echo "Then edit .env with your MaaS credentials and run: make run-app" + +run-app: ## Run agent locally with hot-reload + @source .venv/bin/activate && set -a && source .env && set +a && \ + yaml_env() { sed -n "s/^[[:space:]]*$$1:[[:space:]]*\"\(.*\)\"[[:space:]]*$$/\1/p" values.yaml; } && \ + export MODEL_ID="$$(yaml_env MODEL_ID)" EMBEDDING_MODEL_ID="$$(yaml_env EMBEDDING_MODEL_ID)" \ + EMBEDDING_DIMENSION="$$(yaml_env EMBEDDING_DIMENSION)" PROVIDER_TYPE="$$(yaml_env PROVIDER_TYPE)" \ + RETRIEVAL_METHOD="$$(yaml_env RETRIEVAL_METHOD)" NUMBER_OF_CHUNKS="$$(yaml_env NUMBER_OF_CHUNKS)" \ + SEARCH_MODE="$$(yaml_env SEARCH_MODE)" RANKER_STRATEGY="$$(yaml_env RANKER_STRATEGY)" \ + RANKER_ALPHA="$$(yaml_env RANKER_ALPHA)" && \ + if lsof -ti:$${PORT:-8000} >/dev/null 2>&1; then \ + echo "ERROR: Port $${PORT:-8000} is already in use." && \ + exit 1; \ + fi && \ + uv run $${MLFLOW_TRACKING_URI:+--extra tracing} uvicorn main:app --host 127.0.0.1 --port $${PORT:-8000} --reload --reload-exclude .venv + +run-cli: ## Run interactive CLI chat (no web server) + @source .venv/bin/activate && set -a && source .env && set +a && \ + echo "CLI mode not yet configured. Use: make run-app" + +test: ## Run unit tests + uv run --extra dev python -m pytest tests/ $(PYTEST_ARGS) + +## ── openShell sandbox targets ───────────────────────────────────────────────── + +OPENSHELL_VERSION ?= 0.0.99 + +refresh-certs: ## Refresh openShell client certificates from cluster + @echo "==> Refreshing openShell client certificates..." && \ + MTLS_DIR=~/.config/openshell/gateways/openshift/mtls && mkdir -p $$MTLS_DIR && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.ca\.crt}' | base64 -d > $$MTLS_DIR/ca.crt && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.tls\.crt}' | base64 -d > $$MTLS_DIR/tls.crt && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.tls\.key}' | base64 -d > $$MTLS_DIR/tls.key && \ + echo " Certificates refreshed." + +setup-gateway: ## Install openShell gateway, create Route, register CLI (one-time) + @set -e && \ + APPS_DOMAIN=$$(oc get ingresses.config cluster -o jsonpath='{.spec.domain}') && \ + echo "==> Detected apps domain: $$APPS_DOMAIN" && \ + echo "==> Ensuring openshell namespace and SCCs..." && \ + oc create ns openshell 2>/dev/null || true && \ + oc adm policy add-scc-to-user privileged -z openshell-sandbox -n openshell 2>/dev/null || true && \ + oc adm policy add-scc-to-user privileged -z openshell -n openshell 2>/dev/null || true && \ + echo "==> Granting TokenReview RBAC to sandbox SA..." && \ + oc create clusterrolebinding openshell-tokenreview \ + --clusterrole=system:auth-delegator \ + --serviceaccount=openshell:openshell-sandbox 2>/dev/null || true && \ + echo "==> Installing openShell gateway $(OPENSHELL_VERSION)..." && \ + helm upgrade --install openshell oci://ghcr.io/nvidia/openshell/helm-chart \ + --version $(OPENSHELL_VERSION) \ + --namespace openshell \ + --set securityContext.runAsUser=null \ + --set securityContext.runAsNonRoot=false \ + --set securityContext.allowPrivilegeEscalation=true \ + --set securityContext.capabilities=null \ + --set server.auth.allowUnauthenticatedUsers=true \ + --set "pkiInitJob.serverDnsNames={*.openshell.$$APPS_DOMAIN,openshell.$$APPS_DOMAIN,openshell-gateway-openshell.$$APPS_DOMAIN}" && \ + echo "==> Waiting for gateway to be ready..." && \ + oc -n openshell rollout status statefulset/openshell --timeout=120s && \ + echo "==> Disabling mTLS client cert requirement..." && \ + TOML=$$(oc -n openshell get configmap openshell-config -o jsonpath='{.data.gateway\.toml}' | sed '/^client_ca_path/d') && \ + oc -n openshell patch configmap openshell-config --type merge \ + -p "$$(printf '{"data":{"gateway.toml":%s}}' "$$(printf '%s' "$$TOML" | jq -Rs .)")" && \ + oc -n openshell delete pod openshell-0 && \ + oc -n openshell rollout status statefulset/openshell --timeout=120s && \ + echo "==> Creating gateway Route..." && \ + oc -n openshell create route passthrough openshell-gateway \ + --service=openshell --port=8080 2>/dev/null || true && \ + GATEWAY_URL="https://openshell-gateway-openshell.$$APPS_DOMAIN" && \ + echo "==> Registering gateway in CLI..." && \ + openshell gateway remove openshift 2>/dev/null || true && \ + MTLS_DIR=~/.config/openshell/gateways/openshift/mtls && mkdir -p $$MTLS_DIR && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.ca\.crt}' | base64 -d > $$MTLS_DIR/ca.crt && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.tls\.crt}' | base64 -d > $$MTLS_DIR/tls.crt && \ + oc -n openshell get secret openshell-client-tls -o jsonpath='{.data.tls\.key}' | base64 -d > $$MTLS_DIR/tls.key && \ + openshell gateway add "$$GATEWAY_URL" --local --name openshift && \ + echo "==> Verifying connection..." && \ + export OPENSHELL_GATEWAY_INSECURE=true && \ + openshell status && \ + echo "" && \ + echo "Gateway ready. Next: make build-openshell" + +teardown-gateway: ## Uninstall openShell gateway and clean up + @openshell gateway remove openshift 2>/dev/null || true && \ + helm uninstall openshell -n openshell 2>/dev/null || true && \ + oc -n openshell delete pvc --all 2>/dev/null || true && \ + oc -n openshell delete secret openshell-server-tls openshell-client-tls openshell-jwt-keys 2>/dev/null || true && \ + oc -n openshell delete route --all 2>/dev/null || true && \ + oc delete clusterrolebinding openshell-tokenreview 2>/dev/null || true && \ + oc adm policy remove-scc-from-user privileged -z openshell-sandbox -n openshell 2>/dev/null || true && \ + oc adm policy remove-scc-from-user privileged -z openshell -n openshell 2>/dev/null || true && \ + echo "Gateway removed." + +_openshell-env = set -a && . ./.env && set +a && export OPENSHELL_GATEWAY_INSECURE=true && \ + SANDBOX=$${OPENSHELL_SANDBOX_NAME:-rag-sandbox} && \ + NS=$$(oc project -q) && \ + IMAGE="image-registry.openshift-image-registry.svc:5000/$$NS/$(OPENSHELL_IMAGE):latest" && \ + APPS_DOMAIN=$$(oc get ingresses.config cluster -o jsonpath='{.spec.domain}') && \ + yaml_env() { sed -n "s/^[[:space:]]*$$1:[[:space:]]*\"\(.*\)\"[[:space:]]*$$/\1/p" values.yaml; } && \ + MODEL_ID=$$(yaml_env MODEL_ID) && \ + EMBEDDING_MODEL_ID=$$(yaml_env EMBEDDING_MODEL_ID) && \ + EMBEDDING_DIMENSION=$$(yaml_env EMBEDDING_DIMENSION) && \ + PROVIDER_TYPE=$$(yaml_env PROVIDER_TYPE) && \ + RETRIEVAL_METHOD=$$(yaml_env RETRIEVAL_METHOD) && \ + NUMBER_OF_CHUNKS=$$(yaml_env NUMBER_OF_CHUNKS) && \ + SEARCH_MODE=$$(yaml_env SEARCH_MODE) && \ + RANKER_STRATEGY=$$(yaml_env RANKER_STRATEGY) && \ + RANKER_ALPHA=$$(yaml_env RANKER_ALPHA) && \ + MAAS_SECRET_NAME=$$(sed -n 's/^[[:space:]]*maas_secret_name:[[:space:]]*"\([^"\]*\)".*/\1/p' values.yaml) && \ + VECTOR_DB_SECRET_NAME=$${VECTOR_DB_SECRET_NAME:-$$(sed -n 's/^[[:space:]]*vector_db_secret_name:[[:space:]]*"\([^"\]*\)".*/\1/p' values.yaml)} && \ + secret_value() { oc get secret "$$1" -n "$$NS" -o jsonpath="{.data.$$2}" 2>/dev/null | base64 -d; } && \ + if [ -n "$$MAAS_SECRET_NAME" ] && [ "$$MAAS_SECRET_NAME" != "__MAAS_SECRET_NAME__" ]; then \ + value=$$(secret_value "$$MAAS_SECRET_NAME" MAAS_BASE_URL); [ -z "$$value" ] || MAAS_BASE_URL="$$value"; \ + value=$$(secret_value "$$MAAS_SECRET_NAME" MAAS_API_KEY); [ -z "$$value" ] || MAAS_API_KEY="$$value"; \ + fi && \ + if [ -n "$$VECTOR_DB_SECRET_NAME" ] && [ "$$VECTOR_DB_SECRET_NAME" != "__VECTOR_DB_SECRET_NAME__" ]; then \ + for key in MILVUS_URI MILVUS_TOKEN MILVUS_SERVER_CERT MILVUS_SERVER_NAME PGVECTOR_HOST PGVECTOR_PORT PGVECTOR_DB PGVECTOR_DATABASE PGVECTOR_USER PGVECTOR_PASSWORD; do \ + value=$$(secret_value "$$VECTOR_DB_SECRET_NAME" "$$key"); current=$$(printenv "$$key"); [ -n "$$current" ] || [ -z "$$value" ] || export "$$key=$$value"; \ + done; \ + fi && export MAAS_BASE_URL MAAS_API_KEY MILVUS_URI MILVUS_TOKEN MILVUS_SERVER_CERT MILVUS_SERVER_NAME PGVECTOR_HOST PGVECTOR_PORT PGVECTOR_DB PGVECTOR_DATABASE PGVECTOR_USER PGVECTOR_PASSWORD + +build-openshell: ## Build openShell sandbox image via OpenShift BuildConfig + @oc new-build --strategy=docker --binary \ + --name=$(OPENSHELL_IMAGE) --to=$(OPENSHELL_IMAGE):latest 2>/dev/null || true && \ + oc patch bc $(OPENSHELL_IMAGE) \ + -p '{"spec":{"strategy":{"dockerStrategy":{"dockerfilePath":"Containerfile.openshell"}}}}' && \ + oc start-build $(OPENSHELL_IMAGE) --from-dir=. --follow && \ + NS=$$(oc project -q) && \ + echo "" && \ + echo "Image ready: image-registry.openshift-image-registry.svc:5000/$$NS/$(OPENSHELL_IMAGE):latest" && \ + echo "Next: make deploy-openshell" + +create-sandbox: ## Delete existing sandbox and create a new one (with 120s timeout) + @$(_openshell-env) && \ + echo "==> Granting image-pull to openshell namespace..." && \ + oc policy add-role-to-user system:image-puller "system:serviceaccount:openshell:openshell-sandbox" -n "$$NS" 2>/dev/null || true + @$(_openshell-env) && \ + oc policy add-role-to-user system:image-puller "system:serviceaccount:openshell:default" -n "$$NS" 2>/dev/null || true + @$(_openshell-env) && \ + echo "==> Deleting old sandbox $$SANDBOX..." && \ + openshell sandbox delete "$$SANDBOX" 2>/dev/null || true + @$(_openshell-env) && \ + echo "==> Creating sandbox $$SANDBOX from $$IMAGE..." && \ + echo " (timeout 120s — this may take a minute while the image is pulled)..." && \ + openshell sandbox create --name "$$SANDBOX" --from "$$IMAGE" \ + --env "MAAS_BASE_URL=$$MAAS_BASE_URL" \ + --env "CHAT_BASE_URL=$${CHAT_BASE_URL:-$$BASE_URL}" \ + --env "MAAS_API_KEY=$$MAAS_API_KEY" \ + --env "MODEL_ID=$$MODEL_ID" \ + --env "TEMPERATURE=$$TEMPERATURE" \ + --env "MAX_COMPLETION_TOKENS=$$MAX_COMPLETION_TOKENS" \ + --env "SYSTEM_MESSAGE_B64=$$SYSTEM_MESSAGE_B64" \ + --env "USER_MESSAGE_B64=$$USER_MESSAGE_B64" \ + --env "CONTEXT_TEMPLATE_B64=$$CONTEXT_TEMPLATE_B64" \ + --env "LANGUAGE_CODE=$$LANGUAGE_CODE" \ + --env "LANGUAGE_NAME=$$LANGUAGE_NAME" \ + --env "EMBEDDING_MODEL_ID=$$EMBEDDING_MODEL_ID" \ + --env "EMBEDDING_DIMENSION=$${EMBEDDING_DIMENSION:-768}" \ + --env "PROVIDER_TYPE=$${PROVIDER_TYPE:-milvus}" \ + --env "RETRIEVAL_METHOD=$${RETRIEVAL_METHOD:-simple}" \ + --env "NUMBER_OF_CHUNKS=$${NUMBER_OF_CHUNKS:-5}" \ + --env "SEARCH_MODE=$${SEARCH_MODE:-vector}" \ + --env "RANKER_STRATEGY=$$RANKER_STRATEGY" \ + --env "RANKER_ALPHA=$$RANKER_ALPHA" \ + --env "MILVUS_URI=$$MILVUS_URI" \ + --env "MILVUS_TOKEN=$$MILVUS_TOKEN" \ + --env "PORT=8080" /dev/null ) & TPID=$$!; \ + wait $$CPID 2>/dev/null; RC=$$?; kill $$TPID 2>/dev/null; wait $$TPID 2>/dev/null; \ + if [ $$RC -ne 0 ]; then echo " sandbox create exited with $$RC (may still be starting — wait-sandbox will poll)"; fi; true + +wait-sandbox: ## Poll until sandbox phase is Ready (max 150s) + @$(_openshell-env) && \ + echo "==> Waiting for sandbox $$SANDBOX to be ready..." && \ + i=0; while [ $$i -lt 30 ]; do \ + i=$$((i+1)); \ + phase=$$(openshell sandbox list 2>/dev/null | grep "$$SANDBOX" | awk '{print $$NF}' | sed 's/\x1b\[[0-9;]*m//g'); \ + if [ "$$phase" = "Ready" ]; then echo " Sandbox ready."; exit 0; fi; \ + echo " status: $$phase (attempt $$i/30, waiting 5s...)"; \ + sleep 5; \ + done; \ + echo "ERROR: sandbox not ready after 150s"; exit 1 + +setup-egress: ## Add MaaS + Milvus egress policies for agent Python binary + @$(_openshell-env) && \ + echo "==> Using agent Python binary: /usr/bin/python3.12" && \ + PYTHON_BIN="/usr/bin/python3.12" && \ + echo "==> Adding egress policy for MaaS..." && \ + MAAS_HOST=$$(echo "$${CHAT_BASE_URL:-$${BASE_URL:-$$MAAS_BASE_URL}}" | sed -E 's|^https?://||;s|/.*||') && \ + openshell policy update "$$SANDBOX" --add-endpoint "$$MAAS_HOST:443" --binary "$$PYTHON_BIN" --wait && \ + echo "==> Adding egress policies for K8s API + Milvus..." && \ + MILVUS_HOSTPORT=$$(echo "$$MILVUS_URI" | sed -E 's|^https?://||;s|/.*||') && \ + MILVUS_HOST_ONLY=$$(echo "$$MILVUS_HOSTPORT" | cut -d: -f1) && \ + MILVUS_PORT_ONLY=$$(echo "$$MILVUS_HOSTPORT" | cut -d: -f2) && \ + POLICY_FILE=$$(mktemp /tmp/openshell-policy.XXXXXX) && \ + export MILVUS_H="$$MILVUS_HOST_ONLY" MILVUS_P="$$MILVUS_PORT_ONLY" PY_BIN="$$PYTHON_BIN" && \ + openshell policy get "$$SANDBOX" --full -o json 2>/dev/null | sed -n '/^{/,$$p' | \ + python3 -c 'import json,sys,os;d=json.load(sys.stdin);p=d["policy"];p["network_policies"]["k8s_api"]={"name":"k8s-api","endpoints":[{"host":"kubernetes.default.svc","port":443,"tls":"skip"}],"binaries":[{"path":os.environ["PY_BIN"]}]};p["network_policies"]["milvus_grpc"]={"name":"milvus-grpc","endpoints":[{"host":os.environ["MILVUS_H"],"port":int(os.environ["MILVUS_P"]),"tls":"skip"}],"binaries":[{"path":os.environ["PY_BIN"]}]};json.dump(p,sys.stdout,indent=2)' > "$$POLICY_FILE" && \ + openshell policy set "$$SANDBOX" --policy "$$POLICY_FILE" --wait && \ + rm -f "$$POLICY_FILE" && \ + echo "Done!" + +start-agent: refresh-certs ## Start agent with K8s ServiceAccount auth inside sandbox + @$(_openshell-env) && \ + echo "==> Creating agent-client ServiceAccount and token..." && \ + oc create sa agent-client 2>/dev/null || true && \ + CLIENT_TOKEN=$$(oc create token agent-client --duration=168h) && \ + oc delete secret agent-client-token 2>/dev/null || true && \ + oc create secret generic agent-client-token --from-literal=token="$$CLIENT_TOKEN" && \ + echo "==> Generating sandbox reviewer token..." && \ + K8S_API_URL="https://kubernetes.default.svc" && \ + K8S_REVIEWER_TOKEN=$$(oc create token openshell-sandbox -n openshell --duration=168h) && \ + ALLOWED_SA_USERNAME="system:serviceaccount:$${NS}:agent-client" && \ + echo "==> Stopping any running agent..." && \ + openshell sandbox exec -n "$$SANDBOX" -- bash -c "pkill -f '[/]usr/bin/python3.12 -m uvicorn auth_wrapper:app'" 2>/dev/null || true && \ + sleep 5 && \ + echo "==> Starting agent (auth: enabled)..." && \ + openshell sandbox exec -n "$$SANDBOX" -- bash -c '\ + export MAAS_API_KEY="'"$$MAAS_API_KEY"'" && \ + export MAAS_BASE_URL="'"$$MAAS_BASE_URL"'" && \ + export CHAT_BASE_URL="'"$${CHAT_BASE_URL:-$$BASE_URL}"'" && \ + export MODEL_ID="'"$$MODEL_ID"'" && \ + export TEMPERATURE="'"$$TEMPERATURE"'" && \ + export MAX_COMPLETION_TOKENS="'"$$MAX_COMPLETION_TOKENS"'" && \ + export SYSTEM_MESSAGE_B64="'"$$SYSTEM_MESSAGE_B64"'" && \ + export USER_MESSAGE_B64="'"$$USER_MESSAGE_B64"'" && \ + export CONTEXT_TEMPLATE_B64="'"$$CONTEXT_TEMPLATE_B64"'" && \ + export LANGUAGE_CODE="'"$$LANGUAGE_CODE"'" && \ + export LANGUAGE_NAME="'"$$LANGUAGE_NAME"'" && \ + export EMBEDDING_MODEL_ID="'"$$EMBEDDING_MODEL_ID"'" && \ + export EMBEDDING_DIMENSION="'"$$EMBEDDING_DIMENSION"'" && \ + export PROVIDER_TYPE="'"$${PROVIDER_TYPE:-milvus}"'" && \ + export RETRIEVAL_METHOD="'"$${RETRIEVAL_METHOD:-simple}"'" && \ + export NUMBER_OF_CHUNKS="'"$${NUMBER_OF_CHUNKS:-5}"'" && \ + export SEARCH_MODE="'"$${SEARCH_MODE:-vector}"'" && \ + export RANKER_STRATEGY="'"$$RANKER_STRATEGY"'" && \ + export RANKER_ALPHA="'"$$RANKER_ALPHA"'" && \ + export MILVUS_URI="'"$$MILVUS_URI"'" && \ + export MILVUS_TOKEN="'"$$MILVUS_TOKEN"'" && \ + export MILVUS_COLLECTION_NAME="'"$$MILVUS_COLLECTION_NAME"'" && \ + export MILVUS_SERVER_CERT="'"$$MILVUS_SERVER_CERT"'" && \ + export MILVUS_SERVER_NAME="'"$$MILVUS_SERVER_NAME"'" && \ + export K8S_API_URL="'"$$K8S_API_URL"'" && \ + export K8S_REVIEWER_TOKEN="'"$$K8S_REVIEWER_TOKEN"'" && \ + export K8S_API_INSECURE="true" && \ + export PLAYGROUND_TOKEN="'"$$CLIENT_TOKEN"'" && \ + export ALLOWED_SA_USERNAME="'"$$ALLOWED_SA_USERNAME"'" && \ + export PYTHONPATH=/sandbox/.local/lib/python3.12/site-packages:/sandbox/.local/lib64/python3.12/site-packages:/sandbox && \ + cd /sandbox && \ + nohup /usr/bin/python3.12 -m uvicorn auth_wrapper:app --host 0.0.0.0 --port 8080 > /sandbox/agent.log 2>&1 &\ + echo $$! > /sandbox/agent.pid && \ + sleep 3 && \ + if kill -0 $$! 2>/dev/null; then echo "Agent process alive"; else echo "ERROR: Agent died on startup"; cat /sandbox/agent.log; exit 1; fi' && \ + echo " Agent started in background." + +expose-agent: ## Expose agent service and create Route + @$(_openshell-env) && \ + echo "==> Exposing service URL..." && \ + openshell service expose "$$SANDBOX" 8080 agent 2>/dev/null || true && \ + sleep 2 && \ + SVC_HOST="default--$$SANDBOX--agent.openshell.$$APPS_DOMAIN" && \ + printf 'apiVersion: route.openshift.io/v1\nkind: Route\nmetadata:\n name: openshell-rag-agent\n namespace: openshell\nspec:\n host: "%s"\n to:\n kind: Service\n name: openshell\n port:\n targetPort: grpc\n tls:\n termination: passthrough\n' "$$SVC_HOST" | oc apply -f - && \ + echo "==> Waiting for agent to initialize (10s)..." && \ + sleep 10 && \ + echo "==> Checking agent health..." && \ + curl -sk -m 3 "https://$$SVC_HOST/health" 2>/dev/null || echo "Agent not ready yet (check logs)" && \ + echo "" && \ + echo "Agent URL: https://$$SVC_HOST" && \ + echo "" && \ + echo "Get token:" && \ + echo " TOKEN=\$$(oc get secret agent-client-token -o jsonpath='{.data.token}' | base64 -d)" && \ + echo "" && \ + echo "Query:" && \ + echo " curl -sk https://$$SVC_HOST/chat/completions \\" && \ + echo " -H \"X-Api-Key: \$$TOKEN\" \\" && \ + echo " -H 'Content-Type: application/json' \\" && \ + echo " -d '{\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'" + +deploy-openshell: create-sandbox wait-sandbox setup-egress start-agent expose-agent ## Full sandbox deploy: create, start, expose + +undeploy-openshell: ## Remove openShell sandbox and route + @export OPENSHELL_GATEWAY_INSECURE=true && \ + (openshell sandbox delete $(OPENSHELL_SANDBOX) 2>/dev/null || true) && \ + oc -n openshell delete route openshell-rag-agent 2>/dev/null || true && \ + echo "Sandbox $(OPENSHELL_SANDBOX) and route removed." && \ + sleep 10 2>/dev/null || true + +playground-sandbox: ## Start sandbox playground web UI (auto-fetches agent URL and SA token) + @. .venv/bin/activate && \ + echo "Starting sandbox playground on http://localhost:5002" && \ + echo "Auto-fetching agent URL from OpenShift Route..." && \ + echo "Auto-fetching SA token from Secret agent-client-token..." && \ + echo "" && \ + python playground-sandbox/app.py diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/README.md b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/README.md new file mode 100644 index 00000000..0260b526 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/README.md @@ -0,0 +1,38 @@ +# Agentic RAG starter kit + +This project is generated from an optimized RAG pattern. Configure the empty +credentials in `.env` before running the application. + +The indexed production collection is an input to this starter kit. Do not run +`make load-docs` against the production collection. Re-indexing belongs to the +separate documents-indexing pipeline and can overwrite or corrupt the shared +collection. + +The application exposes `POST /chat/completions`, `GET /health`, and a local +playground at `GET /`. + +## OpenShell deployment + +### Prerequisites + +- An OpenShift cluster with `oc` logged in and permission to create the required resources. +- The `openshell` CLI and Helm 3 installed locally. +- An OpenShift AI MaaS deployment exposing chat and embedding models. +- A reachable Milvus instance and Kubernetes secrets referenced by `values.yaml` (`maas_secret_name` and `vector_db_secret_name`). The vector database secret must contain the Milvus CA certificate as `MILVUS_SERVER_CERT` when TLS is enabled. +- The Red Hat build of the Agent Sandbox operator installed in the cluster. + +### Commands + +Run the following commands from the starter-kit root directory: + +```bash +make init + +# Run once per cluster. +make setup-gateway + +make build-openshell +make deploy-openshell +``` + +`make deploy-openshell` creates and configures the sandbox, starts the agent, and exposes it through an OpenShift route. diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/agent.yaml b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/agent.yaml new file mode 100644 index 00000000..c8d6fc05 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/agent.yaml @@ -0,0 +1,16 @@ +name: __PATTERN_NAME__ +displayName: "Agentic RAG Agent" +framework: langgraph +description: "RAG agent that retrieves relevant chunks from a vector store to augment the LLM's answers." +labels: ["rag", "tool-calling", "vector-store"] +env: + required: + - MAAS_API_KEY + - MAAS_BASE_URL + - MODEL_ID + optional: + - PORT + - CONTAINER_IMAGE + - EMBEDDING_MODEL_ID + - EMBEDDING_DIMENSION + - PROVIDER_TYPE diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/auth_wrapper.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/auth_wrapper.py new file mode 100644 index 00000000..cff29315 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/auth_wrapper.py @@ -0,0 +1,152 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +"""Thin auth layer that wraps main:app without modifying agent code. + +Authenticates requests using K8s ServiceAccount tokens via TokenReview API. +Set ``K8S_API_URL`` and ``K8S_REVIEWER_TOKEN`` to enable auth. +When not configured, every request passes through unchanged. + +Usage: + K8S_API_URL=https://... K8S_REVIEWER_TOKEN=... uvicorn auth_wrapper:app --host 0.0.0.0 --port 8080 +""" + +import logging +from os import getenv +from pathlib import Path + +import httpx +from main import app # noqa: F401 — re-exported for uvicorn +from starlette.requests import Request +from starlette.responses import JSONResponse +from starlette.types import ASGIApp, Receive, Scope, Send + +log = logging.getLogger("auth_wrapper") + +_K8S_API_URL = getenv("K8S_API_URL", "").strip().rstrip("/") +_K8S_REVIEWER_TOKEN = getenv("K8S_REVIEWER_TOKEN", "").strip() +_ALLOWED_SA_USERNAME = getenv("ALLOWED_SA_USERNAME", "").strip() +_K8S_CA_PATH = getenv("K8S_CA_PATH", "/var/run/secrets/kubernetes.io/serviceaccount/ca.crt") +_K8S_API_INSECURE = getenv("K8S_API_INSECURE", "").strip().lower() == "true" +# The playground proxy must not be usable as an unauthenticated way to spend +# the server-side PLAYGROUND_TOKEN. Kubernetes token authentication is the +# actual caller authentication for both direct chat and proxied chat requests. +_PROTECTED_PATHS = frozenset( + { + "/chat/completions", + "/chat/completions/", + "/api/chat", + "/api/chat/", + } +) + +_AUTH_ENABLED = bool(_K8S_API_URL and _K8S_REVIEWER_TOKEN) + +_ca_path_exists = Path(_K8S_CA_PATH).is_file() +if _ca_path_exists: + _tls_verify: str | bool = _K8S_CA_PATH +elif _K8S_API_INSECURE: + if _AUTH_ENABLED: + log.warning( + "K8S_API_INSECURE=true: Using insecure TLS verification (verify=False). " + "This is ONLY acceptable in isolated test environments like OpenShell sandbox. " + "NEVER use this in production." + ) + _tls_verify: str | bool = False +else: + if _AUTH_ENABLED: + raise RuntimeError( + f"K8s CA certificate not found at {_K8S_CA_PATH} and K8S_API_INSECURE is not set. " + "Either provide a valid CA certificate via K8S_CA_PATH, " + "or set K8S_API_INSECURE=true for test environments (NOT production)." + ) + _tls_verify: str | bool = True + + +_http_client: httpx.AsyncClient | None = None + + +def _get_http_client() -> httpx.AsyncClient: + """Get or create the cached HTTP client for K8s TokenReview calls.""" + global _http_client + if _http_client is None: + _http_client = httpx.AsyncClient(verify=_tls_verify, timeout=10.0) + return _http_client + + +async def _validate_k8s_token(token: str) -> bool: + if not (_K8S_API_URL and _K8S_REVIEWER_TOKEN): + return False + try: + client = _get_http_client() + resp = await client.post( + f"{_K8S_API_URL}/apis/authentication.k8s.io/v1/tokenreviews", + json={ + "apiVersion": "authentication.k8s.io/v1", + "kind": "TokenReview", + "spec": {"token": token}, + }, + headers={ + "Authorization": f"Bearer {_K8S_REVIEWER_TOKEN}", + "Content-Type": "application/json", + }, + ) + if resp.status_code == 201: + status = resp.json().get("status", {}) + if status.get("authenticated"): + user = status.get("user", {}).get("username", "unknown") + if _ALLOWED_SA_USERNAME and user != _ALLOWED_SA_USERNAME: + log.warning( + "K8s token authenticated but username mismatch: got %s, expected %s", + user, + _ALLOWED_SA_USERNAME, + ) + return False + log.info("K8s token authenticated: %s", user) + return True + return False + except Exception as e: + log.exception("K8s TokenReview failed: %s", str(e)) + return False + + +class _BearerAuthMiddleware: + def __init__(self, app: ASGIApp) -> None: + self.app = app + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if scope["type"] != "http": + await self.app(scope, receive, send) + return + + request = Request(scope) + if request.url.path not in _PROTECTED_PATHS: + await self.app(scope, receive, send) + return + + token = "" + auth = request.headers.get("authorization", "") + if auth.lower().startswith("bearer "): + token = auth[7:] + else: + token = request.headers.get("x-api-key", "") + + if not token: + response = JSONResponse( + {"error": "Missing API key (use X-Api-Key)"}, + status_code=401, + ) + await response(scope, receive, send) + return + + if await _validate_k8s_token(token): + await self.app(scope, receive, send) + return + + response = JSONResponse({"error": "Invalid API key"}, status_code=401) + await response(scope, receive, send) + + +if _AUTH_ENABLED: + app.add_middleware(_BearerAuthMiddleware) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/main.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/main.py new file mode 100644 index 00000000..459736bb --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/main.py @@ -0,0 +1,215 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- + +import asyncio +import json +import logging +import time +import uuid +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from os import getenv +from typing import Any + +from sqlite_shim import patch_sqlite3 + +patch_sqlite3() + +import openai # noqa: E402 +from agentic_rag.agent import create_rag # noqa: E402 +from agentic_rag.tracing import enable_tracing # noqa: E402 +from fastapi import FastAPI, HTTPException # noqa: E402 +from fastapi.responses import JSONResponse, StreamingResponse # noqa: E402 +from pydantic import BaseModel, Field # noqa: E402 + +logger = logging.getLogger(__name__) + +_MAX_INVOKE_ATTEMPTS = 3 +_GRACEFUL_ERROR_MESSAGE = "I was unable to process this request due to repeated internal errors." +_RETRYABLE_EXCEPTIONS = ( + openai.InternalServerError, + openai.APITimeoutError, + openai.APIConnectionError, + openai.RateLimitError, +) + + +class ChatMessage(BaseModel): + role: str = Field(..., examples=["user", "assistant", "system"]) + content: str + + +class ChatCompletionRequest(BaseModel): + messages: list[ChatMessage] = Field(..., min_length=1) + model: str | None = None + stream: bool = False + + +class ChoiceMessage(BaseModel): + role: str = "assistant" + content: str + + +class Choice(BaseModel): + index: int + message: ChoiceMessage + finish_reason: str + + +class ChatCompletionResponse(BaseModel): + id: str + object: str = "chat.completion" + created: int + model: str + choices: list[Choice] + usage: dict | None = None + + +class HealthResponse(BaseModel): + status: str + agent_initialized: bool + + +rag = None + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncIterator[None]: + global rag + enable_tracing() + rag = create_rag() + app.state.rag = rag + yield + rag = None + app.state.rag = None + + +app = FastAPI( + title="Agentic RAG API", + description=( + "FastAPI service for Agentic RAG Agent with " + "OpenAI-compatible chat completions API. " + "To access the sandbox playground, click " + "[Sandbox Playground](/playground)." + ), + lifespan=lifespan, + openapi_tags=[ + {"name": "Health", "description": "Service health monitoring"}, + {"name": "Chat", "description": "Chat completion operations"}, + ], +) + + +def _make_completion_id() -> str: + return f"chatcmpl-{uuid.uuid4().hex[:12]}" + + +async def _invoke_with_retry(messages: list[dict[str, str]]) -> dict[str, Any]: + if rag is None: + raise HTTPException(status_code=503, detail="Agent not initialized") + + last_exception: Exception = RuntimeError("no invocation attempts were made") + for attempt in range(1, _MAX_INVOKE_ATTEMPTS + 1): + try: + result = await asyncio.to_thread(rag.chat, messages) + choice = result[0] + return { + "content": choice.message.content or "", + "context": [ + {"role": "assistant", "content": choice.message.content or ""}, + ], + } + except _RETRYABLE_EXCEPTIONS as exc: + last_exception = exc + if attempt < _MAX_INVOKE_ATTEMPTS: + logger.warning( + "RAG invocation failed (attempt %d/%d): %s", attempt, _MAX_INVOKE_ATTEMPTS, type(exc).__name__ + ) + await asyncio.sleep(0.5 * attempt) + + raise last_exception + + +@app.post( + "/chat/completions", + response_model=ChatCompletionResponse, + summary="Create chat completion", + tags=["Chat"], +) +async def chat_completions(request: ChatCompletionRequest): + messages = [message.model_dump() for message in request.messages] + model_id = request.model or getenv("MODEL_ID") or "model" + + try: + result = await _invoke_with_retry(messages) + except _RETRYABLE_EXCEPTIONS as exc: + logger.exception("RAG invocation failed", exc_info=exc) + result = {"content": _GRACEFUL_ERROR_MESSAGE, "context": []} + except Exception as exc: + raise HTTPException(status_code=500, detail=f"Error processing request: {exc}") from exc + + completion_id = _make_completion_id() + if request.stream: + return _stream_response(completion_id, model_id, result["content"]) + + return { + "id": completion_id, + "object": "chat.completion", + "created": int(time.time()), + "model": model_id, + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": result["content"]}, + "finish_reason": "stop", + } + ], + "context": result["context"], + "usage": None, + } + + +def _stream_response(completion_id: str, model_id: str, content: str) -> StreamingResponse: + created = int(time.time()) + + async def event_generator() -> AsyncIterator[str]: + data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [{"index": 0, "delta": {"content": content}, "finish_reason": None}], + } + yield f"data: {json.dumps(data)}\n\n" + yield f"data: {json.dumps({'id': completion_id, 'object': 'chat.completion.chunk', 'created': created, 'model': model_id, 'choices': [{'index': 0, 'delta': {}, 'finish_reason': 'stop'}]})}\n\n" + yield "data: [DONE]\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, + ) + + +@app.get("/health", response_model=HealthResponse, summary="Health check", tags=["Health"]) +async def health(): + initialized = rag is not None + body = {"status": "healthy" if initialized else "not_ready", "agent_initialized": initialized} + if not initialized: + return JSONResponse(status_code=503, content=body) + return body + + +_SANDBOX_MODE = bool(getenv("K8S_REVIEWER_TOKEN", "").strip()) +if _SANDBOX_MODE: + from playground_sandbox import router as sandbox_router + + app.include_router(sandbox_router) + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run("main:app", host="0.0.0.0", port=int(getenv("PORT", 8000))) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/app.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/app.py new file mode 100644 index 00000000..74edb03e --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/app.py @@ -0,0 +1,273 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +r""" +Playground UI for the LangGraph Agentic RAG Agent running in openShell sandbox. + +A Flask chat interface that proxies requests to the sandbox agent's +/chat/completions endpoint with K8s SA token authentication and streaming support. + +Usage: + # Start the playground (auto-fetches agent URL and SA token from OpenShift): + python playground-sandbox/app.py + + # Or manually specify: + AGENT_URL=https://rag-sandbox--agent.openshell.apps.example.com \ + AGENT_TOKEN=$(oc get secret agent-client-token -o jsonpath='{.data.token}' | base64 -d) \ + AGENT_CA_BUNDLE=/path/to/ca.crt \ + flask --app playground-sandbox/app run --port 5002 + +The app will: +1. Auto-detect AGENT_URL from the openshell Route (if not set) +2. Auto-fetch AGENT_TOKEN from the agent-client-token Secret (if not set) +3. Inject X-Api-Key header on all /chat/completions requests +""" + +import json +import logging +import subprocess +from os import getenv +from pathlib import Path + +import requests as http_requests +from flask import ( + Flask, + Response, + jsonify, + render_template, + request, + stream_with_context, +) + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + +app = Flask(__name__) + + +def get_current_namespace(): + """Get current OpenShift namespace/project.""" + try: + result = subprocess.run( + ["oc", "project", "-q"], + capture_output=True, + text=True, + timeout=5, + ) + if result.returncode == 0 and result.stdout.strip(): + return result.stdout.strip() + except Exception: + pass + return None + + +def get_agent_url(): + """Auto-detect agent URL from OpenShift Route.""" + url = getenv("AGENT_URL") + if url: + return url + + try: + result = subprocess.run( + [ + "oc", + "get", + "route", + "openshell-rag-agent", + "-n", + "openshell", + "-o", + "jsonpath={.spec.host}", + ], + capture_output=True, + text=True, + timeout=10, + ) + if result.returncode == 0 and result.stdout.strip(): + host = result.stdout.strip() + logger.info(f"Auto-detected agent URL from route: https://{host}") + return f"https://{host}" + except Exception: + logger.exception("Failed to auto-detect agent URL from route") + + logger.warning("AGENT_URL not set and route detection failed, using fallback") + return "http://localhost:8000" + + +def get_agent_token(): + """Auto-fetch agent token from OpenShift Secret in current namespace.""" + token = getenv("AGENT_TOKEN") + if token: + return token + + namespace = get_current_namespace() + if not namespace: + logger.warning("Could not determine current namespace") + return None + + try: + result = subprocess.run( + [ + "oc", + "get", + "secret", + "agent-client-token", + "-n", + namespace, + "-o", + "jsonpath={.data.token}", + ], + capture_output=True, + text=True, + timeout=10, + ) + if result.returncode == 0 and result.stdout.strip(): + import base64 + + token_b64 = result.stdout.strip() + token = base64.b64decode(token_b64).decode("utf-8") + logger.info(f"Auto-fetched agent token from Secret agent-client-token in namespace {namespace}") + return token + except Exception: + logger.exception("Failed to auto-fetch agent token from Secret") + + logger.warning(f"AGENT_TOKEN not set and Secret fetch failed (namespace: {namespace})") + return None + + +CURRENT_NAMESPACE = get_current_namespace() +AGENT_URL = get_agent_url() +AGENT_TOKEN = get_agent_token() +AGENT_CA_BUNDLE = getenv("AGENT_CA_BUNDLE", "") +if AGENT_CA_BUNDLE and Path(AGENT_CA_BUNDLE).is_file(): + VERIFY_TLS = AGENT_CA_BUNDLE +else: + VERIFY_TLS = False + if AGENT_CA_BUNDLE: + logger.warning("AGENT_CA_BUNDLE=%s not found, TLS verification disabled", AGENT_CA_BUNDLE) + else: + logger.warning("AGENT_CA_BUNDLE not set, TLS verification disabled (set to a CA path to enable)") + +logger.info(f"Current namespace: {CURRENT_NAMESPACE or 'UNKNOWN'}") +logger.info(f"Agent URL: {AGENT_URL}") +logger.info(f"Agent token: {'***' + AGENT_TOKEN[-8:] if AGENT_TOKEN else 'NOT SET'}") +logger.info(f"TLS verify: {VERIFY_TLS}") + + +@app.route("/") +def index(): + return render_template("index.html") + + +@app.route("/api/health", methods=["GET"]) +def health(): + """Check if the agent is reachable (health endpoint does not require auth).""" + try: + resp = http_requests.get(f"{AGENT_URL}/health", timeout=10, verify=VERIFY_TLS) + return jsonify(resp.json()), resp.status_code + except Exception: + logger.exception("Error checking agent health") + return ( + jsonify( + { + "status": "unreachable", + "error": "Agent is unreachable. Please try again later.", + } + ), + 503, + ) + + +@app.route("/api/chat", methods=["POST"]) +def chat(): + """Proxy chat requests to the agent with K8s SA token auth and streaming.""" + if not AGENT_TOKEN: + return ( + jsonify( + { + "error": { + "message": "Agent token not configured. Set AGENT_TOKEN or ensure agent-client-token Secret exists." + } + } + ), + 500, + ) + + data = request.get_json() or {} + messages = data.get("messages", []) + + payload = { + "messages": messages, + "stream": True, + } + + headers = { + "Content-Type": "application/json", + "X-Api-Key": AGENT_TOKEN, + } + + logger.info( + f"Sending request to {AGENT_URL}/chat/completions (messages={len(payload.get('messages', []))}, stream={payload.get('stream')})" + ) + + def generate(): + try: + with http_requests.post( + f"{AGENT_URL}/chat/completions", + json=payload, + headers=headers, + stream=True, + timeout=(10, 300), + verify=VERIFY_TLS, + ) as resp: + logger.info(f"Agent response status: {resp.status_code}") + + if resp.status_code != 200: + error_msg = resp.text[:500] + logger.error(f"Agent error: {error_msg}") + error = json.dumps({"error": {"message": f"Agent returned {resp.status_code}: {error_msg}"}}) + yield f"data: {error}\n\n" + return + + for chunk in resp.iter_content(chunk_size=None, decode_unicode=True): + if chunk: + yield chunk + + except http_requests.exceptions.ConnectionError: + logger.error(f"Cannot connect to agent at {AGENT_URL}") + error = json.dumps({"error": {"message": f"Cannot connect to agent at {AGENT_URL}. Is it running?"}}) + yield f"data: {error}\n\n" + except http_requests.exceptions.ReadTimeout: + logger.error("Agent request timed out") + error = json.dumps({"error": {"message": "Agent request timed out (300s)"}}) + yield f"data: {error}\n\n" + except Exception: + logger.exception("Unexpected error in proxy") + error = json.dumps({"error": {"message": "Internal server error"}}) + yield f"data: {error}\n\n" + + return Response( + stream_with_context(generate()), + content_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + }, + ) + + +if __name__ == "__main__": + if not AGENT_TOKEN: + logger.error( + f"AGENT_TOKEN not found. Set it manually or ensure:\n" + f" 1. You're logged into OpenShift (oc whoami)\n" + f" 2. You're in the correct namespace (current: {CURRENT_NAMESPACE or 'UNKNOWN'})\n" + f" 3. Secret 'agent-client-token' exists in your namespace (oc get secret agent-client-token)\n" + f" 4. If missing, run: make start-agent" + ) + exit(1) + + debug_mode = getenv("FLASK_DEBUG", "false").lower() == "true" + app.run(debug=debug_mode, host=getenv("FLASK_HOST", "127.0.0.1"), port=5002) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/templates/index.html b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/templates/index.html new file mode 100644 index 00000000..0a1138d6 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground-sandbox/templates/index.html @@ -0,0 +1,657 @@ + + + + + + LangGraph Agentic RAG — Sandbox Playground + + + + +
+
+ LangGraph Agentic RAG +
Sandbox
+
+ +
+
+ +
+
+ +
+ + + Get it with oc get secret agent-client-token -o jsonpath='{.data.token}' | base64 -d +
+ +
+ + +
+ + + + + diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground_sandbox.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground_sandbox.py new file mode 100644 index 00000000..5ee3a1bf --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/playground_sandbox.py @@ -0,0 +1,191 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +""" +OpenShell sandbox playground integration. + +Provides routes for the embedded playground UI when running in sandbox mode. +This module is imported conditionally when K8S_REVIEWER_TOKEN is set. +""" + +import json +import logging +from os import getenv +from pathlib import Path + +import requests as http_requests +from fastapi import APIRouter, HTTPException, Request +from fastapi.openapi.docs import get_swagger_ui_html +from fastapi.responses import ( + FileResponse, + HTMLResponse, + JSONResponse, + RedirectResponse, + StreamingResponse, +) +from pydantic import BaseModel, Field + +logger = logging.getLogger(__name__) + +router = APIRouter(include_in_schema=False) + +_BASE_DIR = Path(__file__).resolve().parent +_SANDBOX_PLAYGROUND_HTML = _BASE_DIR / "playground-sandbox" / "templates" / "index.html" + +_PLAYGROUND_URL = getenv("PLAYGROUND_URL", "http://localhost:5002").rstrip("/") +_SANDBOX_MODE = bool(getenv("K8S_REVIEWER_TOKEN", "").strip()) +_PLAYGROUND_TOKEN = getenv("PLAYGROUND_TOKEN", "").strip() + + +class ChatMessage(BaseModel): + """A message in the conversation.""" + + role: str = Field( + ..., + description="The role of the message author.", + examples=["user", "assistant", "system", "tool"], + ) + content: str = Field( + ..., + description="The contents of the message.", + examples=["What is LangChain?"], + ) + + +class ChatCompletionRequest(BaseModel): + """Creates a model response for the given chat conversation.""" + + messages: list[ChatMessage] = Field( + ..., + min_length=1, + description="A list of messages comprising the conversation so far.", + ) + model: str | None = Field( + None, + description="ID of the model to use. Defaults to the server's configured MODEL_ID.", + ) + stream: bool = Field( + False, + description="If true, partial message deltas will be sent as SSE events.", + ) + + +def _playground_page_response() -> FileResponse: + """Return the playground page.""" + return FileResponse(_SANDBOX_PLAYGROUND_HTML) + + +@router.get("/docs", response_class=HTMLResponse) +async def custom_swagger_ui(request: Request): + """Serve Swagger UI with a shortcut to the sandbox playground.""" + swagger_html = get_swagger_ui_html( + openapi_url=request.app.openapi_url, + title=f"{request.app.title} - Swagger UI", + swagger_favicon_url="https://fastapi.tiangolo.com/img/favicon.png", + ) + html = swagger_html.body.decode("utf-8") + shortcut = ( + 'Sandbox Playground' + ) + styles = """ + + """ + html = html.replace("", f"{styles}{shortcut}", 1) + return HTMLResponse(html) + + +@router.get( + "/playground", + summary="Open the sandbox playground", + description=( + "Redirects to the local sandbox playground UI. Start it with " + "`make playground-sandbox`; it obtains the ServiceAccount token " + "automatically through `oc`." + ), + responses={307: {"description": "Redirect to the sandbox playground UI"}}, +) +async def playground_redirect(): + """Serve the sandbox playground or redirect to the local fallback UI.""" + if _SANDBOX_MODE: + return _playground_page_response() + return RedirectResponse(url=_PLAYGROUND_URL) + + +@router.get("/", response_class=HTMLResponse) +async def playground(): + """Serve the playground chat UI.""" + if _SANDBOX_MODE: + return _playground_page_response() + raise HTTPException(status_code=404, detail="Not found") + + +@router.get("/api/health") +async def playground_health(request: Request): + """Expose the agent health response expected by the sandbox UI.""" + initialized = getattr(request.app.state, "agent_graph", None) is not None + body = { + "status": "healthy" if initialized else "not_ready", + "agent_initialized": initialized, + } + if not initialized: + return JSONResponse(status_code=503, content=body) + return body + + +@router.post("/api/chat") +async def playground_chat(chat_request: ChatCompletionRequest, request: Request): + """Proxy sandbox UI requests with the server-side ServiceAccount token. + + Security: ``auth_wrapper.py`` authenticates the caller with a Kubernetes + ServiceAccount token before this route is reached. + """ + if not (_SANDBOX_MODE and _PLAYGROUND_TOKEN): + raise HTTPException(status_code=503, detail="Sandbox playground is not configured") + + payload = chat_request.model_dump(exclude_none=True) + payload["stream"] = True + + def event_generator(): + try: + with http_requests.post( + "http://127.0.0.1:8080/chat/completions", + json=payload, + headers={"X-Api-Key": _PLAYGROUND_TOKEN}, + stream=True, + timeout=(10, 300), + ) as response: + if response.status_code != 200: + error = json.dumps( + {"error": {"message": f"Agent returned {response.status_code}: {response.text[:500]}"}} + ) + yield f"data: {error}\n\n" + return + for chunk in response.iter_content(chunk_size=None, decode_unicode=True): + if chunk: + yield chunk + except http_requests.RequestException as exc: + logger.exception("Sandbox playground proxy request failed") + error = json.dumps({"error": {"message": str(exc)}}) + yield f"data: {error}\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, + ) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/pyproject.toml b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/pyproject.toml new file mode 100644 index 00000000..3a9d2c16 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/pyproject.toml @@ -0,0 +1,27 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "agentic-rag" +version = "0.1.0" +description = "Agentic RAG starter kit" +requires-python = ">=3.12,<3.15" + +dependencies = [ + "fastapi>=0.115", + "uvicorn>=0.34", + "pymilvus>=3.0,<4", + "ai4rag>=0.15", + "flask>=3.1", + "requests>=2.32", + "python-dotenv>=1.1", + "openai>=2.0", +] + +[project.optional-dependencies] +dev = ["pytest"] +tracing = ["mlflow>=2.21"] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/sqlite_shim.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/sqlite_shim.py new file mode 100644 index 00000000..bb50f473 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/sqlite_shim.py @@ -0,0 +1,22 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +""" +Patch sqlite3 to use pysqlite3-binary if available. + +Required for chromadb (used by ai4rag) when the system sqlite3 is too old. +Must be called BEFORE importing any code that uses sqlite3. +""" + +import sys + + +def patch_sqlite3() -> None: + """Replace sys.modules['sqlite3'] with pysqlite3 if available.""" + try: + import pysqlite3 # noqa: F401 + + sys.modules["sqlite3"] = sys.modules.pop("pysqlite3") + except ImportError: + pass diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/__init__.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/__init__.py new file mode 100644 index 00000000..7f9f5568 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/__init__.py @@ -0,0 +1,4 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/agent.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/agent.py new file mode 100644 index 00000000..1290d49f --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/agent.py @@ -0,0 +1,62 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- + +from os import getenv + +from openai import OpenAI + +from ai4rag.rag.foundation_models.base_model import Language +from ai4rag.rag.foundation_models.openai_model import OpenAIFoundationModel +from ai4rag.rag.template.agentic_rag_template import AgenticRAG + +from .config import AgentConfig, get_chat_base_url +from .tools import _initialize_retriever + + +def create_rag( + model_id: str | None = None, + base_url: str | None = None, + api_key: str | None = None, +) -> AgenticRAG: + """Create the configured RAG template from environment settings.""" + config = AgentConfig.from_env() + model_id = model_id or config.model_id or getenv("MODEL_ID") + base_url = (base_url or get_chat_base_url() or "").rstrip("/") + api_key = api_key or getenv("MAAS_API_KEY") + + if not model_id: + raise ValueError("MODEL_ID is required for the chat model.") + if not base_url: + raise ValueError("CHAT_BASE_URL or BASE_URL is required for the chat model.") + if not base_url.endswith("/v1"): + base_url += "/v1" + + is_local = any(host in base_url for host in ["localhost", "127.0.0.1"]) + if not is_local and not api_key: + raise ValueError("MAAS_API_KEY is required for non-local environments.") + + client = OpenAI(api_key=api_key or "not-needed-for-local-development", base_url=base_url) + foundation_model = OpenAIFoundationModel( + client=client, + model_id=model_id, + params={ + "temperature": config.temperature, + "max_completion_tokens": config.max_completion_tokens, + }, + system_message_text=config.system_message, + user_message_text=config.user_message_template, + context_template_text=config.context_template, + language=Language(code=config.language_code, name=config.language_name), + ) + + return AgenticRAG( + foundation_model=foundation_model, + retriever=_initialize_retriever( + maas_api_key=api_key, + maas_base_url=base_url, + embedding_model_id=getenv("EMBEDDING_MODEL_ID"), + embedding_dimension=int(getenv("EMBEDDING_DIMENSION", "768")), + ), + ) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/config.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/config.py new file mode 100644 index 00000000..1788558d --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/config.py @@ -0,0 +1,70 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +import base64 +from dataclasses import dataclass +from os import getenv + + +def _decode_template(name: str) -> str: + """Decode a base64-encoded multiline template from the environment.""" + encoded = getenv(name, "") + if not encoded: + return "" + try: + return base64.b64decode(encoded).decode("utf-8") + except (ValueError, UnicodeDecodeError) as exc: + raise ValueError(f"{name} must contain valid base64-encoded UTF-8 text") from exc + + +def get_chat_base_url() -> str | None: + """Return the shared OpenAI-compatible MaaS endpoint. + + MaaS serves foundation and embedding models through the same ``/v1`` + endpoint. The model identifier belongs in the request body, so it must not + be rewritten into a model-specific URL. + """ + explicit_url = getenv("CHAT_BASE_URL", "").strip() + if explicit_url: + return explicit_url + + maas_url = getenv("MAAS_BASE_URL", "").strip().rstrip("/") + if not maas_url: + return getenv("BASE_URL") or None + return maas_url + + +@dataclass(frozen=True) +class AgentConfig: + """Runtime configuration generated from an optimized RAG pattern.""" + + model_id: str + temperature: float + max_completion_tokens: int + system_message: str + user_message_template: str + context_template: str + language_code: str + language_name: str + + @classmethod + def from_env(cls) -> "AgentConfig": + """Load and validate agent configuration from environment variables.""" + try: + temperature = float(getenv("TEMPERATURE", "0.0")) + max_completion_tokens = int(getenv("MAX_COMPLETION_TOKENS", "1024")) + except ValueError as exc: + raise ValueError("TEMPERATURE and MAX_COMPLETION_TOKENS must be numeric") from exc + + return cls( + model_id=getenv("MODEL_ID", ""), + temperature=temperature, + max_completion_tokens=max_completion_tokens, + system_message=_decode_template("SYSTEM_MESSAGE_B64") or getenv("SYSTEM_MESSAGE", ""), + user_message_template=_decode_template("USER_MESSAGE_B64") + or "Context:\n{reference_documents}\n\nQuestion: {question}", + context_template=_decode_template("CONTEXT_TEMPLATE_B64") or "{document}", + language_code=getenv("LANGUAGE_CODE", ""), + language_name=getenv("LANGUAGE_NAME", "auto"), + ) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tools.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tools.py new file mode 100644 index 00000000..87583dc0 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tools.py @@ -0,0 +1,72 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- + +import sys +from os import getenv +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) + +from sqlite_shim import patch_sqlite3 # noqa: E402 + +patch_sqlite3() + +from openai import OpenAI # noqa: E402 + +from ai4rag.rag.embedding.openai_model import ( # noqa: E402 + OpenAIEmbeddingModel, + OpenAIEmbeddingParams, +) +from ai4rag.rag.retrieval.retriever import Retriever # noqa: E402 +from ai4rag.rag.vector_store import ( # noqa: E402 + get_vector_store, + get_vector_store_config, +) + + +def _initialize_retriever( + maas_api_key: str | None = None, + maas_base_url: str | None = None, + embedding_model_id: str | None = None, + embedding_dimension: int | None = None, + collection_name: str | None = None, +) -> Retriever: + """Initialize the ai4rag retriever with MaaS embeddings and vector store.""" + + maas_api_key = maas_api_key or getenv("MAAS_API_KEY") + maas_base_url = maas_base_url or getenv("MAAS_BASE_URL") + embedding_model_id = embedding_model_id or getenv("EMBEDDING_MODEL_ID", "") + embedding_dimension = embedding_dimension or int(getenv("EMBEDDING_DIMENSION", "768")) + collection_name = collection_name or getenv("MILVUS_COLLECTION_NAME") or getenv("PGVECTOR_COLLECTION_NAME") + + if not maas_api_key or not maas_base_url: + raise ValueError("MAAS_API_KEY and MAAS_BASE_URL must be set") + if not collection_name: + raise RuntimeError("Collection name env var is not set (MILVUS_COLLECTION_NAME or PGVECTOR_COLLECTION_NAME).") + if not maas_base_url.startswith("https://"): + raise ValueError(f"MaaS base URL must use HTTPS to protect API key transmission. Got: {maas_base_url}") + + client = OpenAI(base_url=maas_base_url, api_key=maas_api_key) + embedding_model = OpenAIEmbeddingModel( + client=client, + model_id=embedding_model_id, + params=OpenAIEmbeddingParams(embedding_dimension=embedding_dimension, context_length=1015), + ) + + vector_store = get_vector_store( + embedding_model=embedding_model, + config=get_vector_store_config(getenv("PROVIDER_TYPE", "milvus")), + collection_name=collection_name, + ) + + ranker_alpha_raw = getenv("RANKER_ALPHA") + return Retriever( + vector_store=vector_store, + method=getenv("RETRIEVAL_METHOD", "simple"), + number_of_chunks=int(getenv("NUMBER_OF_CHUNKS", "5")), + search_mode=getenv("SEARCH_MODE") or "vector", + ranker_strategy=getenv("RANKER_STRATEGY") or None, + ranker_alpha=float(ranker_alpha_raw) if ranker_alpha_raw else None, + ) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tracing.py b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tracing.py new file mode 100644 index 00000000..bf9cd112 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/src/agentic_rag/tracing.py @@ -0,0 +1,96 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +import logging +import time +from os import getenv +from typing import Optional + +from dotenv import load_dotenv + +logger = logging.getLogger("tracing") +logger.setLevel(logging.INFO) +if not logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] %(message)s") + handler.setFormatter(formatter) + logger.addHandler(handler) + + +def check_mlflow_health(mlflow_tracking_uri: str, max_wait_time: int = 5, retry_interval: int = 1) -> None: + """Check MLflow health by trying the /health endpoint.""" + import requests + + mlflow_health_endpoint = "/health" + mlflow_url = f"{mlflow_tracking_uri.rstrip('/')}{mlflow_health_endpoint}" + start_time = time.time() + + while True: + remaining = max_wait_time - (time.time() - start_time) + if remaining <= 0: + logger.error(f"MLflow server is unavailable after {max_wait_time} seconds of checking.") + raise RuntimeError("MLflow server is unavailable. Please start the server or check the URI.") + + try: + response = requests.get(mlflow_url, timeout=min(5, remaining)) + if response.status_code == 200: + logger.info(f"MLflow health check passed at {mlflow_url} with status code {response.status_code}.") + return + else: + logger.warning( + f"MLflow returned status code {response.status_code} at {mlflow_url}\n" + f" Status Code: {response.status_code}\n" + f" Reason: {response.reason}" + ) + except requests.exceptions.RequestException as e: + logger.warning(f"Failed to connect to MLflow at {mlflow_url}: {e}") + + logger.warning(f"Retrying in {retry_interval} seconds...") + time.sleep(retry_interval) + + +def enable_tracing() -> None: + """Enable MLflow tracing if MLFLOW_TRACKING_URI is set.""" + load_dotenv() + tracking_uri: Optional[str] = getenv("MLFLOW_TRACKING_URI") + if not tracking_uri: + logger.info("[Tracing] MLFLOW_TRACKING_URI not set. Tracing is disabled.") + return + + try: + import mlflow + import mlflow.langchain + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "MLFLOW_TRACKING_URI is set but mlflow is not installed. " "Install it with: uv sync --extra tracing" + ) from e + + try: + try: + health_check_timeout = int(getenv("MLFLOW_HEALTH_CHECK_TIMEOUT", "5")) + except ValueError: + health_check_timeout = 5 + check_mlflow_health(mlflow_tracking_uri=tracking_uri, max_wait_time=health_check_timeout) + logger.info(f"[Tracing] MLflow server is reachable at {tracking_uri}") + except RuntimeError as e: + logger.warning( + f"[Tracing] MLflow server is unreachable at {tracking_uri}. " + f"Tried connecting for {health_check_timeout}s. Continuing without tracing. Error: {e}" + ) + return + + try: + mlflow.set_tracking_uri(tracking_uri) + experiment_name: str = getenv("MLFLOW_EXPERIMENT_NAME", "default-agent-experiment") + mlflow.set_experiment(experiment_name) + mlflow.config.enable_async_logging() + + mlflow.langchain.autolog() + + logger.info(f"[Tracing Enabled] MLflow -> {tracking_uri}, Experiment: {experiment_name}") + except Exception as e: + logger.warning( + f"[Tracing] Failed to configure MLflow tracing at {tracking_uri}. " + f"Continuing without tracing. Error: {e}" + ) diff --git a/ai4rag/assets_generator/starter_kit_templates/agentic_rag/uv.lock b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/uv.lock new file mode 100644 index 00000000..5d4a9ca7 --- /dev/null +++ b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/uv.lock @@ -0,0 +1,5018 @@ +version = 1 +revision = 3 +requires-python = ">=3.12, <3.15" +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version == '3.13.*'", + "python_full_version < '3.13'", +] + +[[package]] +name = "agentic-rag" +version = "0.1.0" +source = { editable = "." } +dependencies = [ + { name = "ai4rag" }, + { name = "fastapi" }, + { name = "flask" }, + { name = "openai" }, + { name = "pymilvus" }, + { name = "python-dotenv" }, + { name = "requests" }, + { name = "uvicorn" }, +] + +[package.optional-dependencies] +dev = [ + { name = "pytest" }, +] +tracing = [ + { name = "mlflow" }, +] + +[package.metadata] +requires-dist = [ + { name = "ai4rag", specifier = 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b/ai4rag/assets_generator/starter_kit_templates/agentic_rag/values.yaml @@ -0,0 +1,17 @@ +nameOverride: "__PATTERN_NAME__" + +env: + MODEL_ID: "__FM_MODEL_ID__" + MAAS_BASE_URL: "" + EMBEDDING_MODEL_ID: "__EMBEDDING_MODEL_ID__" + EMBEDDING_DIMENSION: "__EMBEDDING_DIMENSION__" + PROVIDER_TYPE: "__PROVIDER_TYPE__" + RETRIEVAL_METHOD: "__RETRIEVAL_METHOD__" + NUMBER_OF_CHUNKS: "__NUMBER_OF_CHUNKS__" + SEARCH_MODE: "__SEARCH_MODE__" + RANKER_STRATEGY: "__RANKER_STRATEGY__" + RANKER_ALPHA: "__RANKER_ALPHA__" + +secrets: + maas_secret_name: "__MAAS_SECRET_NAME__" + vector_db_secret_name: "__VECTOR_DB_SECRET_NAME__" diff --git a/ai4rag/core/experiment/experiment.py b/ai4rag/core/experiment/experiment.py index 64613a75..e8a2bdc1 100644 --- a/ai4rag/core/experiment/experiment.py +++ b/ai4rag/core/experiment/experiment.py @@ -39,7 +39,8 @@ from ai4rag.rag.embedding.base_model import BaseEmbeddingModel from ai4rag.rag.foundation_models.base_model import BaseFoundationModel from ai4rag.rag.retrieval.retriever import Retriever -from ai4rag.rag.template.simple_rag_template import SimpleRAG +from ai4rag.rag.template.agentic_rag_template import AgenticRAG +from ai4rag.rag.template.base_template import BaseRAGTemplate from ai4rag.rag.vector_store.config import BaseVectorStoreConfig, PGVectorConfig from ai4rag.rag.vector_store.get_vector_store import get_vector_store from ai4rag.search_space.src.parameter import Parameter @@ -92,6 +93,11 @@ class AI4RAGExperiment: Other Parameters ---------------- + rag_template : type[BaseRAGTemplate], default=AgenticRAG + RAG template class used to evaluate each pattern. ``SimpleRAG`` is the + legacy alternative; custom templates must implement the + ``BaseRAGTemplate`` interface. + metrics : Sequence[RAGMetric] Metrics evaluated during the AutoRAG experiment, each a ``RAGMetric`` instance selected from :class:`Metrics`. Not all of these metrics are @@ -153,6 +159,9 @@ def __init__( ) self.known_observations: list[dict] | None = kwargs.pop("known_observations", None) self.inference_max_threads: int = kwargs.pop("inference_max_threads", 10) + self.rag_template: type[BaseRAGTemplate] = kwargs.pop("rag_template", AgenticRAG) + if not isinstance(self.rag_template, type) or not issubclass(self.rag_template, BaseRAGTemplate): + raise TypeError("rag_template must be a BaseRAGTemplate subclass.") self.results: ExperimentResults = ExperimentResults() self._exception_handler = ExperimentExceptionHandler(self.event_handler) @@ -552,7 +561,7 @@ def run_single_evaluation(self, rag_params: RAGParamsType) -> float: ranker_alpha=retrieval_params.get(AI4RAGParamNames.RANKER_ALPHA), ) - rag_pattern = SimpleRAG( + rag_pattern = self.rag_template( foundation_model=foundation_model, retriever=retriever, ) @@ -815,6 +824,7 @@ def _stream_finished_pattern( "evaluation": {"metrics": metrics_payload}, "duration_seconds": int(evaluation_result.execution_time), "settings": { + "rag_template": self.rag_template.__name__, "vector_store_binding": vector_store_payload, **indexing_payload, "retrieval": retrieval_payload, diff --git a/ai4rag/rag/template/__init__.py b/ai4rag/rag/template/__init__.py index 7e0e403a..248d57fd 100644 --- a/ai4rag/rag/template/__init__.py +++ b/ai4rag/rag/template/__init__.py @@ -2,3 +2,9 @@ # Copyright IBM Corp. 2025-2026 # SPDX-License-Identifier: Apache-2.0 # ----------------------------------------------------------------------------- + +from .agentic_rag_template import AgenticRAG +from .base_template import BaseRAGTemplate +from .simple_rag_template import SimpleRAG + +__all__ = ["AgenticRAG", "BaseRAGTemplate", "SimpleRAG"] diff --git a/ai4rag/rag/template/agentic_rag_template.py b/ai4rag/rag/template/agentic_rag_template.py new file mode 100644 index 00000000..9d8e4924 --- /dev/null +++ b/ai4rag/rag/template/agentic_rag_template.py @@ -0,0 +1,44 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- + +from typing import Any + +from ..foundation_models.base_model import MessageTyped +from .base_template import BaseRAGTemplate + + +class AgenticRAG(BaseRAGTemplate): + """Single-step agentic RAG template. + + The first version intentionally performs one retrieval for the current + user turn and one foundation-model call. It is kept as a separate + template so the retrieval/generation policy can later grow to include + query rewriting or additional retrieval steps without changing the + experiment and starter-kit interfaces. + """ + + def generate(self, question: str, **kwargs) -> dict[str, Any]: + """Generate an answer after one retrieval step.""" + reference_documents, user_message = self._build_enriched_user_message(question, **kwargs) + response = self.foundation_model.chat( + messages=[ + {"role": "system", "content": self.foundation_model.system_message_text}, + {"role": "user", "content": user_message}, + ] + ) + return { + "answer": response[0].message.content, + "reference_documents": reference_documents, + "question": question, + } + + def generate_stream(self, question: str, **kwargs): + """Yield the complete answer as one chunk until streaming is supported.""" + yield self.generate(question, **kwargs)["answer"] + + def chat(self, messages: list[MessageTyped], **kwargs) -> list[Any]: + """Run one-step retrieval for the last user message in the conversation.""" + rag_messages = self._build_rag_messages(messages, **kwargs) + return self.foundation_model.chat(messages=rag_messages, **kwargs) diff --git a/ai4rag/rag/template/base_template.py b/ai4rag/rag/template/base_template.py index 048d2093..3375b9d4 100644 --- a/ai4rag/rag/template/base_template.py +++ b/ai4rag/rag/template/base_template.py @@ -4,7 +4,9 @@ # ----------------------------------------------------------------------------- from abc import ABC, abstractmethod -from ..foundation_models.base_model import BaseFoundationModel +from ai4rag.rag.chunking.chunk import AI4RAGChunk + +from ..foundation_models.base_model import BaseFoundationModel, MessageTyped from ..retrieval.retriever import Retriever @@ -44,6 +46,32 @@ def __init__( self.foundation_model: BaseFoundationModel = foundation_model self.retriever: Retriever = retriever + def _build_enriched_user_message(self, question: str, **kwargs) -> tuple[list[AI4RAGChunk], str]: + """Retrieve context for a question and render the user message.""" + reference_documents = self.retriever.retrieve(question, **kwargs) + context = "\n\n".join( + self.foundation_model.context_template_text.format(document=chunk.text, doc_number=doc_number) + for doc_number, chunk in enumerate(reference_documents, start=1) + ) + user_message = self.foundation_model.user_message_text.format( + reference_documents=context, + question=question, + ) + return reference_documents, user_message + + def _build_rag_messages(self, messages: list[MessageTyped], **kwargs) -> list[MessageTyped]: + """Add retrieved context to the last user message in a conversation.""" + if not messages: + raise ValueError("`messages` must contain at least one message.") + + *history, last_message = messages + _, enriched_content = self._build_enriched_user_message(last_message["content"], **kwargs) + return [ + {"role": "system", "content": self.foundation_model.system_message_text}, + *history, + {**last_message, "content": enriched_content}, + ] + @abstractmethod def generate( self, diff --git a/ai4rag/rag/template/simple_rag_template.py b/ai4rag/rag/template/simple_rag_template.py index 00dd9bac..2bd875bb 100644 --- a/ai4rag/rag/template/simple_rag_template.py +++ b/ai4rag/rag/template/simple_rag_template.py @@ -5,8 +5,6 @@ from typing import Any -from ai4rag.rag.chunking.chunk import AI4RAGChunk - from ..foundation_models.base_model import MessageTyped from .base_template import BaseRAGTemplate @@ -25,37 +23,6 @@ class SimpleRAG(BaseRAGTemplate): Initialized retriever for document retrieval. """ - def _build_enriched_user_message(self, question: str, **kwargs) -> tuple[list[AI4RAGChunk], str]: - """ - Retrieve context for `question` and render the RAG-enriched user message. - - Parameters - ---------- - question : str - The question used as the retrieval query. - - **kwargs - Additional parameters forwarded to the retriever (e.g. number_of_chunks). - - Returns - ------- - tuple[list[AI4RAGChunk], str] - The retrieved chunks and the rendered user message containing them. - """ - reference_documents = self.retriever.retrieve(question, **kwargs) - - context = "\n\n".join( - self.foundation_model.context_template_text.format(document=chunk.text, doc_number=doc_number) - for doc_number, chunk in enumerate(reference_documents, start=1) - ) - - user_message = self.foundation_model.user_message_text.format( - reference_documents=context, - question=question, - ) - - return reference_documents, user_message - def generate(self, question: str, **kwargs) -> dict[str, Any]: """ Generate an answer for a question using RAG pipeline. @@ -138,16 +105,5 @@ def chat(self, messages: list[MessageTyped], **kwargs) -> list[Any]: list[Any] Chat response choices from the foundation model. """ - if not messages: - raise ValueError("`messages` must contain at least one message.") - - *history, last_message = messages - _, enriched_content = self._build_enriched_user_message(last_message["content"], **kwargs) - - rag_messages = [ - {"role": "system", "content": self.foundation_model.system_message_text}, - *history, - {**last_message, "content": enriched_content}, - ] - + rag_messages = self._build_rag_messages(messages, **kwargs) return self.foundation_model.chat(messages=rag_messages, **kwargs) diff --git a/pyproject.toml b/pyproject.toml index 9b98b05f..bd1ebd59 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -90,7 +90,11 @@ docs = [ version = { attr = "ai4rag.__version__" } [tool.setuptools.package-data] -"ai4rag.assets_generator" = ["notebook_templates/*.ipynb"] +"ai4rag.assets_generator" = [ + "notebook_templates/*.ipynb", + "starter_kit_templates/**/*", + "starter_kit_templates/**/.*", +] [tool.setuptools.packages.find] exclude = ["tests*", "dev_utils*"] diff --git a/tests/unit/ai4rag/assets_generator/test_starter_kit.py b/tests/unit/ai4rag/assets_generator/test_starter_kit.py new file mode 100644 index 00000000..4116f62c --- /dev/null +++ b/tests/unit/ai4rag/assets_generator/test_starter_kit.py @@ -0,0 +1,333 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- +import base64 +import zipfile +from pathlib import Path + +import pytest + +from ai4rag.assets_generator.starter_kit import ( + _apply_provider_conditionals, + _create_starter_kit_mapping, + generate_starter_kit, +) + +_SAMPLE_PATTERN_DATA: dict = { + "name": "pattern_001", + "settings": { + "generation": { + "model_id": "publishers/ibm/models/granite-3.1-8b-instruct", + "system_message_text": "Answer the question.", + "user_message_text": "Question: {question}", + "context_template_text": "Context: {context}", + "language": {"code": "en", "name": "English"}, + "temperature": 0.2, + "max_completion_tokens": 1024, + }, + "embedding": { + "model_id": "publishers/ibm/models/slate-125m-english-rtrvr", + "embedding_params": {"embedding_dimension": 768}, + }, + "vector_store_binding": { + "provider_type": "milvus", + "collection_name": "test_collection", + }, + "retrieval": { + "method": "simple", + "number_of_chunks": 5, + "search_mode": "hybrid", + "ranker_strategy": "weighted", + "ranker_alpha": 0.5, + }, + "rag_template": "AgenticRAG", + }, + "indexing": { + "pipeline_spec": { + "parameters": { + "maas_secret_name": "maas-connection", + "vector_db_secret_name": "vector-db-connection", + } + } + }, +} + + +# --------------------------------------------------------------------------- +# _create_starter_kit_mapping +# --------------------------------------------------------------------------- + + +class TestCreateStarterKitMapping: + def test_extracts_generation_fields(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__FM_MODEL_ID__"] == "publishers/ibm/models/granite-3.1-8b-instruct" + assert mapping["__TEMPERATURE__"] == "0.2" + assert mapping["__MAX_COMPLETION_TOKENS__"] == "1024" + assert mapping["__SYSTEM_MESSAGE__"] == "Answer the question." + assert mapping["__USER_MESSAGE__"] == "Question: {question}" + assert mapping["__CONTEXT_TEMPLATE__"] == "Context: {context}" + assert mapping["__LANGUAGE_CODE__"] == "en" + assert mapping["__LANGUAGE_NAME__"] == "English" + + def test_extracts_embedding_fields(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__EMBEDDING_MODEL_ID__"] == "publishers/ibm/models/slate-125m-english-rtrvr" + assert mapping["__EMBEDDING_DIMENSION__"] == "768" + + def test_extracts_retrieval_fields(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__RETRIEVAL_METHOD__"] == "simple" + assert mapping["__NUMBER_OF_CHUNKS__"] == "5" + assert mapping["__SEARCH_MODE__"] == "hybrid" + assert mapping["__RANKER_STRATEGY__"] == "weighted" + assert mapping["__RANKER_ALPHA__"] == "0.5" + + def test_extracts_vector_store_fields(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__PROVIDER_TYPE__"] == "milvus" + assert mapping["__COLLECTION_NAME__"] == "test_collection" + + def test_extracts_indexing_secret_names(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__MAAS_SECRET_NAME__"] == "maas-connection" + assert mapping["__VECTOR_DB_SECRET_NAME__"] == "vector-db-connection" + + def test_extracts_pattern_name(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + assert mapping["__PATTERN_NAME__"] == "pattern_001" + + def test_defaults_on_empty_data(self): + mapping = _create_starter_kit_mapping({}) + assert mapping["__FM_MODEL_ID__"] == "" + assert mapping["__TEMPERATURE__"] == "0.0" + assert mapping["__RETRIEVAL_METHOD__"] == "simple" + assert mapping["__PROVIDER_TYPE__"] == "milvus" + assert mapping["__EMBEDDING_DIMENSION__"] == "768" + + def test_all_values_are_strings(self): + mapping = _create_starter_kit_mapping(_SAMPLE_PATTERN_DATA) + for key, value in mapping.items(): + assert isinstance(value, str), f"{key} value is {type(value)}, not str" + + +# --------------------------------------------------------------------------- +# _apply_provider_conditionals +# --------------------------------------------------------------------------- + + +class TestApplyProviderConditionals: + def test_keeps_milvus_strips_pgvector(self, tmp_path): + content = ( + "# common\n" + "# <<< BEGIN MILVUS >>>\n" + "MILVUS_HOST=\n" + "# <<< END MILVUS >>>\n" + "# <<< BEGIN PGVECTOR >>>\n" + "PGVECTOR_HOST=\n" + "# <<< END PGVECTOR >>>\n" + "# footer\n" + ) + f = tmp_path / "test.env" + f.write_text(content) + _apply_provider_conditionals(f, "milvus") + result = f.read_text() + assert "MILVUS_HOST=" in result + assert "PGVECTOR_HOST=" not in result + assert "# common" in result + assert "# footer" in result + assert "<<< BEGIN" not in result + + def test_keeps_pgvector_strips_milvus(self, tmp_path): + content = ( + "# <<< BEGIN MILVUS >>>\n" + "MILVUS_HOST=\n" + "# <<< END MILVUS >>>\n" + "# <<< BEGIN PGVECTOR >>>\n" + "PGVECTOR_HOST=\n" + "# <<< END PGVECTOR >>>\n" + ) + f = tmp_path / "test.env" + f.write_text(content) + _apply_provider_conditionals(f, "pgvector") + result = f.read_text() + assert "PGVECTOR_HOST=" in result + assert "MILVUS_HOST=" not in result + + def test_case_insensitive(self, tmp_path): + content = "# <<< BEGIN MILVUS >>>\n" "MILVUS_HOST=\n" "# <<< END MILVUS >>>\n" + f = tmp_path / "test.env" + f.write_text(content) + _apply_provider_conditionals(f, "Milvus") + result = f.read_text() + assert "MILVUS_HOST=" in result + + +# --------------------------------------------------------------------------- +# generate_starter_kit +# --------------------------------------------------------------------------- + + +class TestGenerateStarterKit: + def test_produces_zip(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + assert zip_path.exists() + assert zip_path.name == "starter_kit.zip" + + def test_zip_has_top_level_directory(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + top_dirs = {name.split("/")[0] for name in zf.namelist()} + assert top_dirs == {"starter_kit"} + + def test_zip_contains_expected_files(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + names = set(zf.namelist()) + assert "starter_kit/.env.example" in names + assert "starter_kit/main.py" in names + assert "starter_kit/Makefile" in names + assert "starter_kit/Containerfile.openshell" in names + assert "starter_kit/values.yaml" in names + assert "starter_kit/agent.yaml" in names + assert "starter_kit/src/agentic_rag/agent.py" in names + assert "starter_kit/src/agentic_rag/tools.py" in names + assert "starter_kit/src/agentic_rag/tracing.py" in names + assert "starter_kit/playground-sandbox/app.py" in names + assert "starter_kit/playground_sandbox.py" in names + assert "starter_kit/auth_wrapper.py" in names + + def test_env_example_has_filled_values(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + env_content = zf.read("starter_kit/.env.example").decode("utf-8") + values_content = zf.read("starter_kit/values.yaml").decode("utf-8") + assert 'MODEL_ID: "publishers/ibm/models/granite-3.1-8b-instruct"' in values_content + assert "MODEL_ID=" not in env_content + assert "TEMPERATURE=0.2" in env_content + assert "MAX_COMPLETION_TOKENS=1024" in env_content + assert ( + base64.b64decode( + next( + line.split("=", 1)[1] + for line in env_content.splitlines() + if line.startswith("USER_MESSAGE_B64=") + ) + ).decode() + == "Question: {question}" + ) + assert 'RETRIEVAL_METHOD: "simple"' in values_content + assert 'SEARCH_MODE: "hybrid"' in values_content + assert 'EMBEDDING_DIMENSION: "768"' in values_content + + def test_credentials_not_baked(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + env_content = zf.read("starter_kit/.env.example").decode("utf-8") + for line in env_content.splitlines(): + stripped = line.strip() + if stripped.startswith("#") or not stripped: + continue + if "=" not in stripped: + continue + key, value = stripped.split("=", 1) + if key in ( + "MAAS_BASE_URL", + "MAAS_API_KEY", + "MILVUS_URI", + "MILVUS_TOKEN", + "MILVUS_SERVER_CERT", + "MILVUS_SERVER_NAME", + ): + assert value == "", f"Credential {key} should be empty, got: {value}" + + def test_no_placeholders_remain(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + for name in zf.namelist(): + if name.endswith((".py", ".yaml", ".yml", ".env.example", ".md", ".toml")): + content = zf.read(name).decode("utf-8") + assert "__FM_MODEL_ID__" not in content, f"Unreplaced placeholder in {name}" + assert "__PROVIDER_TYPE__" not in content, f"Unreplaced placeholder in {name}" + + def test_zip_uses_agentic_rag_template(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + + with zipfile.ZipFile(zip_path, "r") as zf: + agent_content = zf.read("starter_kit/src/agentic_rag/agent.py").decode("utf-8") + + assert "from ai4rag.rag.template.agentic_rag_template import AgenticRAG" in agent_content + + def test_milvus_provider_keeps_milvus_block(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + env_content = zf.read("starter_kit/.env.example").decode("utf-8") + assert "MILVUS_COLLECTION_NAME=test_collection" in env_content + assert "MILVUS_URI=" not in env_content + assert "PGVECTOR_HOST=" not in env_content + + def test_pgvector_provider_keeps_pgvector_block(self, tmp_path): + data = {**_SAMPLE_PATTERN_DATA} + data["settings"] = {**data["settings"]} + data["settings"]["vector_store_binding"] = { + "provider_type": "pgvector", + "collection_name": "pg_collection", + } + zip_path = generate_starter_kit(data, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + env_content = zf.read("starter_kit/.env.example").decode("utf-8") + assert "PGVECTOR_COLLECTION_NAME=pg_collection" in env_content + assert "PGVECTOR_HOST=" not in env_content + assert "MILVUS_URI=" not in env_content + + def test_values_yaml_has_filled_values(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + values_content = zf.read("starter_kit/values.yaml").decode("utf-8") + assert '"publishers/ibm/models/granite-3.1-8b-instruct"' in values_content + assert '"milvus"' in values_content + assert 'maas_secret_name: "maas-connection"' in values_content + assert 'vector_db_secret_name: "vector-db-connection"' in values_content + + def test_creates_output_dir_if_needed(self, tmp_path): + out = tmp_path / "nested" / "dir" + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, out) + assert zip_path.exists() + + def test_collection_name_in_milvus_env(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + env_content = zf.read("starter_kit/.env.example").decode("utf-8") + assert "MILVUS_COLLECTION_NAME=test_collection" in env_content + + def test_no_data_directory(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + data_files = [n for n in zf.namelist() if n.startswith("starter_kit/data/")] + assert data_files == [], f"data/ directory should not exist: {data_files}" + + def test_no_regular_playground(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + playground_files = [n for n in zf.namelist() if n.startswith("starter_kit/playground/")] + assert playground_files == [], f"playground/ directory should not exist: {playground_files}" + + def test_has_sandbox_playground(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + names = set(zf.namelist()) + assert "starter_kit/playground-sandbox/app.py" in names + assert "starter_kit/playground-sandbox/templates/index.html" in names + assert "starter_kit/playground_sandbox.py" in names + assert "starter_kit/auth_wrapper.py" in names + assert "starter_kit/Containerfile.openshell" in names + + def test_excludes_local_development_artifacts(self, tmp_path): + zip_path = generate_starter_kit(_SAMPLE_PATTERN_DATA, tmp_path) + with zipfile.ZipFile(zip_path, "r") as zf: + names = zf.namelist() + assert not any("__pycache__/" in name for name in names) + assert not any(name.endswith(".pyc") for name in names) + assert not any(name.startswith("starter_kit/.venv/") for name in names) + assert "starter_kit/.env" not in names diff --git a/tests/unit/ai4rag/core/experiment/test_experiment.py b/tests/unit/ai4rag/core/experiment/test_experiment.py index 83abec60..214ced5e 100644 --- a/tests/unit/ai4rag/core/experiment/test_experiment.py +++ b/tests/unit/ai4rag/core/experiment/test_experiment.py @@ -19,6 +19,7 @@ from ai4rag.evaluator.llmaj_evaluator import LLMaJEvaluator from ai4rag.evaluator.metric import Metrics, RAGMetric from ai4rag.evaluator.unitxt_evaluator import UnitxtEvaluator +from ai4rag.rag.template.agentic_rag_template import AgenticRAG from ai4rag.rag.vector_store.config import MilvusLiteConfig # --------------------------------------------------------------------------- @@ -60,7 +61,7 @@ def _make_result( ) -def _build_experiment(evaluators=None, optimization_metric=Metrics.FAITHFULNESS, metrics=None): +def _build_experiment(evaluators=None, optimization_metric=Metrics.FAITHFULNESS, metrics=None, rag_template=None): """Construct an AI4RAGExperiment with all heavy deps mocked out.""" from ai4rag.core.experiment.experiment import AI4RAGExperiment @@ -69,6 +70,8 @@ def _build_experiment(evaluators=None, optimization_metric=Metrics.FAITHFULNESS, kwargs["evaluators"] = evaluators if metrics is not None: kwargs["metrics"] = metrics + if rag_template is not None: + kwargs["rag_template"] = rag_template return AI4RAGExperiment( documents=[], @@ -105,6 +108,22 @@ def test_llmaj_evaluator_type(self): assert LLMaJEvaluator.EVALUATOR_TYPE == "judge" +class TestRAGTemplate: + def test_agentic_rag_is_the_default_template(self): + experiment = _build_experiment() + + assert experiment.rag_template is AgenticRAG + + def test_custom_rag_template_is_stored(self): + experiment = _build_experiment(rag_template=AgenticRAG) + + assert experiment.rag_template is AgenticRAG + + def test_rag_template_must_extend_base_template(self): + with pytest.raises(TypeError, match="BaseRAGTemplate"): + _build_experiment(rag_template=object) + + class TestEvaluatorsSetter: def test_default_evaluators_is_unitxt_only(self): exp = _build_experiment() diff --git a/tests/unit/ai4rag/rag/template/test_agentic_rag_template.py b/tests/unit/ai4rag/rag/template/test_agentic_rag_template.py new file mode 100644 index 00000000..00a632e8 --- /dev/null +++ b/tests/unit/ai4rag/rag/template/test_agentic_rag_template.py @@ -0,0 +1,19 @@ +# ----------------------------------------------------------------------------- +# Copyright IBM Corp. 2026 +# SPDX-License-Identifier: Apache-2.0 +# ----------------------------------------------------------------------------- + +from ai4rag.rag.template.agentic_rag_template import AgenticRAG +from ai4rag.rag.template.base_template import BaseRAGTemplate + + +def test_agentic_rag_is_a_base_template(mocker): + """AgenticRAG exposes the common RAG template contract.""" + foundation_model = mocker.MagicMock() + retriever = mocker.MagicMock() + + rag = AgenticRAG(foundation_model=foundation_model, retriever=retriever) + + assert isinstance(rag, BaseRAGTemplate) + assert rag.foundation_model is foundation_model + assert rag.retriever is retriever