diff --git a/.github/scripts/tests/test_recipe_canary_matrix.py b/.github/scripts/tests/test_recipe_canary_matrix.py index 961db0ae4b..872db9fd4c 100644 --- a/.github/scripts/tests/test_recipe_canary_matrix.py +++ b/.github/scripts/tests/test_recipe_canary_matrix.py @@ -191,6 +191,7 @@ def test_matrix_is_one_entry_per_recipe_and_version(tmp_path): "core/python/rag-agent-search", "core/python/rag-vector-search", "core/python/safety-plugins", + "skills/retail/product-search", } diff --git a/skills/retail/product-search/.env.example b/skills/retail/product-search/.env.example new file mode 100644 index 0000000000..5160ec1f0e --- /dev/null +++ b/skills/retail/product-search/.env.example @@ -0,0 +1,32 @@ +# retail-product-search — all configuration in one place. +# +# Copy this file to `.env` and edit values. Every script reads from `.env` +# via `scripts/config.py` (which calls `load_dotenv()` once at import). +# +# Q-MODE users: after running the skill's Q-MODE interview, `setup.py` writes +# your answers into `.env` automatically — you don't need to edit this file +# by hand. + +# --- GCP project & region --- +GOOGLE_CLOUD_PROJECT= + +# LLM serving region. `global` is recommended for Gemini text models. +GOOGLE_CLOUD_LOCATION=global + +# --- Vertex AI Vector Search --- +# Vector Search 2.0 currently only works in us-central1. Do not change unless +# your project has been enabled for another region. +VECTOR_SEARCH_LOCATION=us-central1 + +# Full collection resource path. Leave blank to let scripts.config derive the +# default: projects/$GOOGLE_CLOUD_PROJECT/locations/$VECTOR_SEARCH_LOCATION +# /collections/retail-skill-products-collection +VECTOR_SEARCH_COLLECTION= + +# --- Models --- +# ADK agent LLM. Any Gemini text model published at $GOOGLE_CLOUD_LOCATION. +GEMINI_MODEL=gemini-3.5-flash + +# Embedding model used by ingest_vertex_search.py. gemini-embedding-001 is +# currently the only supported option for Vector Search 2.0 auto-embed. +EMBEDDING_MODEL=gemini-embedding-001 diff --git a/skills/retail/product-search/EVAL.yaml b/skills/retail/product-search/EVAL.yaml new file mode 100644 index 0000000000..552f49928a --- /dev/null +++ b/skills/retail/product-search/EVAL.yaml @@ -0,0 +1,209 @@ +# uses_catalog: true +cases: +- id: headphones_under_200 + prompt: "I'm looking for wireless headphones under $200, what do you have?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST return product results that include wireless headphones from the catalog." + - "The response MUST only include products priced under $200." + - "The response MUST include key product details: name, price, and description for each result." + - "The response MUST NOT hallucinate product names, brands, or prices not present in the catalog." + - "The response SHOULD use semantic search to match the user intent, not just keyword matching." + assertions: + - type: contains_product + field: name + value: Wireless Headphones + - type: price_under + value: 200 + - type: tool_called + tool: retrieve_docs + - type: no_hallucination + forbidden: + - Bose + - AirPods + - Sennheiser + +- id: cheapest_item + prompt: "What is the cheapest product you have available right now?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST identify the lowest-priced product in the catalog." + - "The response MUST include the exact price as a dollar amount." + - "The response MUST include the product name and a brief description." + - "The response MUST NOT fabricate prices or products not present in the catalog." + assertions: + - type: tool_called + tool: retrieve_docs + - type: mentions_price + value: true + - type: response_not_empty + +- id: keyboard_search + prompt: "I need a mechanical keyboard for coding, what do you recommend?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST return at least one mechanical keyboard product from the catalog." + - "The response MUST include the brand and price for each returned product." + - "The response MUST provide details relevant to coding use (e.g., key switch type, ergonomics)." + - "The response MUST NOT recommend products outside the catalog inventory." + - "The response SHOULD explain why the recommended product suits coding workflows." + assertions: + - type: contains_product + field: name + value: Mechanical Keyboard + - type: mentions_field + field: brand + - type: mentions_field + field: price + - type: tool_called + tool: retrieve_docs + +- id: category_filter + prompt: "Show me all audio products you carry." + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST return products categorized under Audio from the catalog." + - "The response MUST include the name and price for each audio product listed." + - "The response MUST NOT include products from non-audio categories in the results." + - "The response MUST NOT hallucinate audio products not present in the catalog (e.g., speaker systems, turntables)." + assertions: + - type: tool_called + tool: retrieve_docs + - type: contains_product + field: category + value: Audio + - type: no_hallucination + forbidden: + - speaker system + - turntable + +- id: brand_search + prompt: "Do you have any Sony products in your store?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST return products from the Sony brand present in the catalog." + - "The response MUST include the product name, category, and price for each Sony item." + - "The response MUST NOT fabricate Sony products that are not in the catalog." + - "The response MUST NOT include products from other brands unless explicitly relevant for comparison." + assertions: + - type: tool_called + tool: retrieve_docs + - type: contains_product + field: brand + value: Sony + - type: response_not_empty + +- id: no_results_handling + prompt: "Do you have any cars for sale?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST clearly state that no matching products were found in the catalog." + - "The response MUST NOT fabricate or hallucinate car products." + - "The response SHOULD suggest alternative actions (e.g., browsing other categories or refining the query)." + - "The response MUST remain helpful and polite even when no results are available." + assertions: + - type: tool_called + tool: retrieve_docs + - type: graceful_no_results + +- id: comparison_query + prompt: "Can you compare the keyboard and the headphones you have?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST include a side-by-side comparison covering at least name, price, and category." + - "The response MUST reference actual products from the catalog for both the keyboard and headphones." + - "The response MUST NOT invent specifications or features not listed in the catalog." + - "The response SHOULD highlight key differences that help the user make a purchase decision." + assertions: + - type: tool_called + tool: retrieve_docs + - type: mentions_field + field: price + - type: response_min_length + value: 50 + +- id: price_range + prompt: "Show me products between $50 and $150." + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST only include products with prices between $50 and $150 inclusive." + - "The response MUST display the price for each returned product." + - "The response MUST NOT return products priced outside the specified range." + - "The response MUST NOT hallucinate products or prices not present in the catalog." + assertions: + - type: tool_called + tool: retrieve_docs + - type: mentions_price + value: true + - type: response_not_empty + +- id: vague_query_handling + prompt: "I need something for my desk, any suggestions?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + rubric: + - "The response MUST return relevant desk-related products from the catalog (e.g., keyboards, monitors)." + - "The response MUST include product name and price for each suggestion." + - "The response MUST NOT hallucinate products not in the catalog." + - "The response SHOULD interpret the vague query using semantic search rather than exact keyword matching." + assertions: + - type: tool_called + tool: retrieve_docs + - type: response_not_empty + - type: response_min_length + value: 30 + +- id: skill_covers_vector_search + prompt: "How does the product search system work under the hood?" + metadata: + skill_name: "retail-product-search" + products: + - "Vertex AI Vector Search" + - "BigQuery" + - "Cloud Storage" + rubric: + - "The response MUST mention vector search as part of the product retrieval mechanism." + - "The response MUST reference BigQuery as the data storage or query layer." + - "The response MUST NOT claim the system uses technologies or services it does not employ." + - "The response SHOULD explain how semantic embeddings power product matching." + assertions: + - type: skill_covers + topic: vector search + - type: skill_covers + topic: bigquery + - type: response_not_empty diff --git a/skills/retail/product-search/README.md b/skills/retail/product-search/README.md new file mode 100644 index 0000000000..d4261bf357 --- /dev/null +++ b/skills/retail/product-search/README.md @@ -0,0 +1,170 @@ +# Retail Product Search + +Semantic product search agent on Google Cloud (Vertex AI Vector Search, +BigQuery, embeddings). Use to build e-commerce search, catalog discovery, or +shopping assistant agents. + +## Install + +Install directly into your AI coding assistant (Claude Code, Antigravity, +Codex, ...) via `npx skills add`. The tool discovers `SKILL.md` from this +recipe and registers `/retail-product-search` as an invocable skill: + +```bash +npx skills add google/adk-samples --skill retail-product-search +``` + +Installs to `~/.claude/skills/` or `~/.agents/skills/` depending on host. +Antigravity discovers from `~/.agents/skills/` automatically. + +**Developer install** (if you're contributing to the recipe rather than +consuming it): + +```bash +git clone https://github.com/google/adk-samples.git +cd adk-samples/skills/retail/product-search +uv sync +``` + +## Prerequisites + +- Python 3.11+ +- [`gcloud` CLI](https://cloud.google.com/sdk/docs/install) with ADC + configured (`gcloud auth application-default login`) +- A GCP project with billing enabled and BigQuery + Vertex AI APIs on: + ```bash + gcloud services enable bigquery.googleapis.com aiplatform.googleapis.com + ``` + +## Run + +In a fresh workspace, launch your AI coding agent and trigger the skill. + +**Claude Code:** + +``` +/retail-product-search +``` + +**Antigravity:** + +``` +Use the retail-product-search skill to set up a product search agent on Google Cloud. +``` + +The agent walks Q-MODE, runs `scripts/bootstrap.sh` to create the venv, then +`scripts/setup.py` to validate the catalog, ingest to BigQuery, and create +the Vector Search collection. Once setup finishes, the agent (or you, at a +terminal) launches the ADK web UI as a separate step: + +```bash +.venv/bin/adk web "$SKILL_DIR/scripts" --port 8765 +``` + +Then open [http://localhost:8765](http://localhost:8765). + +### Which mode? + +- **Quick (2 questions):** GCP project + catalog source. Silently defaults to + the `us-central1` region and the `Extended` fields preset. Right choice for + a first run or a demo. +- **Full (4 questions):** adds a fields-level question (`Basic` / `Standard` / + `Extended` / `Full`) and a region confirmation. Note: `us-central1` is + currently the only region where Vector Search 2.0 works, so the region + question is effectively fixed — Full is really about choosing which columns + from your CSV get indexed. + +Both modes accept a custom CSV path or `gs://` URI at Q-B — you don't need +Full to use your own catalog. + +## Use your own catalog + +Point Q-B at a local CSV or `gs://` URI. Column requirements depend on the +fields level (Quick uses `Extended` by default): + +| Level | Required | Optional | +|---|---|---| +| `Basic` | `product_id, name, price, description` | — | +| `Standard` | same required | `category, brand, image_url` | +| `Extended` (Quick default) | same required | `category, brand, image_url, rating, stock, manufacturer` | +| `Full` | same required | Extended's optional set + `variants, tags, specifications, reviews` | + +Extra columns outside the chosen level's schema are rejected by +`validate_schema.py` — pick the level that matches (or exceeds) your CSV. + +## Cleanup + +In the agent chat: + +``` +clean up the GCP resources +``` + +Runs `cleanup.py --confirm` to delete the BigQuery dataset and Vector Search +collection. + +## Troubleshooting + +| Error | Fix | +|---|---| +| `MethodNotImplemented: 501` from Vector Search | `VECTOR_SEARCH_COLLECTION` has a newline. Re-export on one line | +| `ModuleNotFoundError: google.adk` | `pip install -e "$SKILL_DIR"` — google-adk is an unconditional dependency, no `[adk]` extra needed | +| `Package requires Python: 3.9.X` | Recreate venv with `python3.12 -m venv .venv` | +| `BILLING_DISABLED` / `PERMISSION_DENIED` | GCP project setup — see [references/troubleshooting.md](references/troubleshooting.md) | + +Full table: [references/troubleshooting.md](references/troubleshooting.md). + +## What gets built + +- BigQuery dataset `retail_skill_products.products` +- Vertex AI Vector Search collection `retail-skill-products-collection` in + `us-central1`, with auto-embeddings via `gemini-embedding-001` +- Workspace venv with the skill installed editable + a `design-spec.md` + +The skill's source code stays in the install directory — nothing is copied +to your workspace. + +## Try it + +Open [http://localhost:8765](http://localhost:8765) and paste the queries below. + +**Sanity check first — confirm the right catalog got ingested:** + +``` +List all products in the catalog, just product_id and name. +``` + +The IDs you see here tell you which CSV setup actually used. If you passed +your own CSV at Q-B and see different IDs than expected, setup silently +fell back to the bundled `assets/sample-products.csv`. + +**Then exercise semantic search — these queries never appear verbatim in any product description:** + +| What you're testing | Query | +|---|---| +| Intent → product (concept bridge) | `what should I get to reduce wrist strain while working?` | +| Constraint + intent | `budget-friendly desk accessories under $50` | +| Multi-product bundle | `setting up a home podcast studio, what do I need under $600?` | +| Pure semantic (no keyword overlap) | `what would help me switch between sitting and standing throughout the workday?` | +| Ask the agent to explain itself | `Why did you pick those results? What matched?` | + +If the "explain yourself" prompt cites specific product features from the +descriptions (not just names), Vector Search retrieval + the LLM's +reasoning are both working. If it hallucinates products that don't exist +in your catalog, that's a bug worth filing. + +**Failure modes to watch for:** + +- Query returns 0 hits when the answer clearly exists → embeddings didn't + index. Check the `ingest_vertex_search.py` output in the setup log for a + `501 MethodNotImplemented` (wrong region) or an aborted indexing wait. +- Query returns only literal keyword matches, misses concept-adjacent + products → your Vector Search collection may still be indexing. Wait + 5-10 min after setup completes and try again. +- Agent invents products with IDs you didn't ingest → LLM hallucination. + Sharpen the `INSTRUCTION` in `scripts/agent.py` to require citing tool + output verbatim. + +## License + +Apache 2.0 diff --git a/skills/retail/product-search/SKILL.md b/skills/retail/product-search/SKILL.md new file mode 100644 index 0000000000..b4ea6c7a2f --- /dev/null +++ b/skills/retail/product-search/SKILL.md @@ -0,0 +1,270 @@ +--- +name: retail-product-search +description: >- + Creates product search agents with semantic search and RAG on Google Cloud + (Vertex AI Vector Search, BigQuery, embeddings). Use when the user wants to + "build a product search agent", "create an e-commerce search", "make a + shopping assistant", "set up semantic catalog discovery", "ingest products + into Vector Search", or "deploy a retail RAG agent". Handles the full + pipeline: catalog data ingestion to BigQuery, Vertex AI Vector Search + collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment. +metadata: + author: Google + license: Apache-2.0 + version: 0.2.0 +--- + +# Product Search Agent + +Creates product search agents with semantic search and RAG on Google Cloud. + +## STOP — Q-MODE FIRST + +**If a catalog is already loaded** (system context says "DEPLOYED search agent" +or provides a `` block), skip Q-MODE and answer product queries +directly using the catalog. + +**Otherwise**, your first message MUST be exactly this: + +``` +[skill: retail-product-search] active. +Q-MODE: Pick a setup mode? [default: 1] + 1. Quick start -- 2 questions, smart defaults, ~60s. Best for demos and first-timers. + 2. Full setup -- 4 questions, ~2 min. Best for real builds. +``` + +Then stop and wait. Accept `1`, `quick`, empty/Enter (Quick), or `2`, `full` (Full). + +## Execution Rules + +1. Q-MODE first. No preamble, no plan-proposing. +2. One question at a time. Show `[default: ...]`. Empty input = default. +3. Save answers to `./design-spec.md` in the workspace as you collect them. +4. After interview, run `scripts/setup.py` (see Workspace Setup below). +5. User can say "configure more" mid-Quick-Start to switch to Full. + +## Workspace Setup + +The skill has two locations: +- **Install dir** — where SKILL.md and scripts live (varies by host) +- **Workspace** — the agent's cwd; design-spec.md, .venv, and per-run state live here + +By the end of this section the workspace must have `.venv/` (with the skill +installed editable), `design-spec.md`, and `SKILL_DIR` exported in the shell. + +Run this as ONE shell command — splitting it across tool calls loses state: + +```bash +SKILL_DIR=$(for d in ~/.claude/skills ~/.agents/skills ~/.gemini/skills ~/.cursor/skills; do + [ -f "$d/retail-product-search/SKILL.md" ] && echo "$d/retail-product-search" && break +done) +bash "$SKILL_DIR/scripts/bootstrap.sh" +``` + +`bootstrap.sh` finds a Python 3.10+ interpreter (with absolute-path fallback +for sandboxed shells), creates `.venv`, installs the skill editable, and +copies `design-spec.md` into the workspace. + +All scripts run from the install dir against the workspace config. **Use +`.venv/bin/python`, not bare `python`** — bare `python` may resolve to a +Python without the skill's editable install on sys.path. + +```bash +.venv/bin/python "$SKILL_DIR/scripts/setup.py" --config ./design-spec.md +.venv/bin/python "$SKILL_DIR/scripts/cleanup.py" --config ./design-spec.md --confirm +``` + +Details in [references/install-paths.md](references/install-paths.md). + +## Skill Dependencies + +This skill works in conjunction with the following core Google Cloud skills: +- `bigquery-basics` (for database configuration guidelines) +- `gemini-api` (for Vertex AI API / Google Gen AI SDK best practices) + +Verify if these skills are installed in your active skills directory. If they are missing, recommend the developer to install them by running: +`npx skills add google/skills --skill bigquery-basics gemini-api` + +## Mode 1: Quick Start (2 questions) + +| Q | Question | Default | +|---|---|---| +| Q-A | GCP project ID? | `$GOOGLE_CLOUD_PROJECT` or `gcloud config get-value project` | +| Q-B | Where's your product data? | `assets/sample-products.csv` (bundled) | + +Accepted for Q-B: empty / `default` (bundled), `/path/to/file.csv`, or `gs://...`. + +Silent defaults: Extended fields, `us-central1`. + +After Q-A and Q-B, do this automatically (don't ask the user to copy/paste). +**Run these steps SEQUENTIALLY — do not parallelize.** Steps 2-3 modify the +file bootstrap copies in step 1; running them concurrently is a race. + +1. **Run bootstrap first and wait for completion.** `bash "$SKILL_DIR/scripts/bootstrap.sh"` + copies the YAML-frontmatter design-spec template into the workspace at + `./design-spec.md`. Do NOT touch `./design-spec.md` until bootstrap exits. +2. **Mutate the existing `./design-spec.md`** — do NOT rewrite it from scratch. + `setup.py` parses YAML frontmatter via `_setup_utils.py`. A Markdown-only + file fails with `'NoneType' object has no attribute 'get'`. Use Edit / sed + to replace specific lines: + - `gcp_project_id: ""` → `gcp_project_id: ""` + - `data_source: assets/sample-products.csv` → `data_source: ` (only if user gave a non-default) +3. Say: "Taking defaults for the rest. Running setup — this takes 2-5 min to + create a BigQuery dataset and Vector Search collection. Say 'configure + more' to switch to Full setup." +4. Run `.venv/bin/python "$SKILL_DIR/scripts/setup.py" --config ./design-spec.md` +5. Stream output. On non-zero exit, surface the error and check + [references/troubleshooting.md](references/troubleshooting.md) +6. On success, set `VECTOR_SEARCH_COLLECTION` and proceed to Test + +## Mode 2: Full Setup + +Adds two more questions: product fields level and GCP region. + +| Q | Question | Default | Notes | +|---|---|---|---| +| Q-fields | Product fields level | `Extended` | `Basic` / `Standard` / `Extended` / `Full`. Match this to your CSV's columns. Don't offer "Custom" — `validate_schema.py` rejects it. | +| Q-region | GCP region | `us-central1` | **Only confirmed-working region for Vector Search 2.0.** Other regions return `501 MethodNotImplemented`. | + +Otherwise identical to Quick Start. + +## When to Use + +- E-commerce product search, shopping assistants, semantic catalog discovery + +Don't use for generic document search, simple keyword search, or non-retail. + +## Project Tree + +``` +retail-product-search/ + assets/ + design-spec.md # Source of truth -- filled by Q-MODE + sample-products.csv # Bundled 5-product demo catalog + references/ # Deep-dive docs (load on demand) + scripts/ + agent.py # Reference ADK agent + retrievers.py # Vector Search retrieval logic + setup.py # Pipeline driver (reads design-spec.md) + bootstrap.sh # Workspace bootstrap (called from Workspace Setup) + validate_schema.py + ingest_bigquery.py + ingest_vertex_search.py + cleanup.py +``` + +Customize: rewrite `scripts/agent.py` (see +[references/agent-example.md](references/agent-example.md)) and +`scripts/retrievers.py` with your product-specific fields. + +## Test + +After `setup.py` succeeds, set the collection env var (one line, no newlines): + +```bash +export VECTOR_SEARCH_COLLECTION="projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/collections/retail-skill-products-collection" +``` + +Then either: + +**With ADK** (interactive UI): +```bash +# Use the WORKSPACE VENV's adk (not bare `adk`) so the skill's editable +# install is on sys.path. Bare `adk` may resolve to a global Python (pyenv, +# brew, etc.) whose ADK can't find the skill and reports an empty app list. +.venv/bin/adk web "$SKILL_DIR/scripts" --port 8765 +``` +Open http://127.0.0.1:8765, click `scripts`, query. + +⚠️ Two things must be right: +- **Point `adk web` at `$SKILL_DIR/scripts`, not at `.`** — agent code lives + in the install dir, not the workspace. `adk web .` fails with "No agents + found in current folder". +- **Use `.venv/bin/adk`, not bare `adk`** — bare `adk` may launch the wrong + Python and silently fail to load the agent (UI loads, but `/list-apps` + returns `[]` and queries time out). + +**Without ADK** (direct smoke test): +```bash +.venv/bin/python -c "from scripts.retrievers import search; print(search('laptop for video editing', top_k=3))" +``` + +Semantic-only retrieval — no structured filters on price, stock, or rating. +For demo queries and how to add structured filtering, see +[references/architecture.md](references/architecture.md). + +## Evaluate + +```bash +cd +./vs eval retail-product-search --project-id $PROJECT +``` + +`EVAL.yaml` declares `rubric` (LLM-as-judge) + `assertions` (deterministic +checks). Target: 80%+ passing. + +## Deploy + +**Never deploy without explicit human approval.** + +Cloud Run service account needs `roles/bigquery.dataViewer` on the dataset and +`roles/aiplatform.user` on the project. Deploy via `gcloud run deploy` or your +org's existing tooling. + +## Gotchas + +- **No results**: collection empty or `VECTOR_SEARCH_COLLECTION` not set +- **Slow search**: check region and `top_k` +- **No structured filters**: `search()` is pure semantic similarity. Price / + stock / currency filters happen client-side in the LLM, so results may + include items outside the constraint. Don't promise hard filters +- **ADK session memory**: if the retriever errored in earlier turns, the + model "learns" the tool is broken. Click "New Session" in `adk web` after + fixing the underlying issue + +## Troubleshooting + +Most-common failures inline; full table in +[references/troubleshooting.md](references/troubleshooting.md). + +| Error | Fix | +|---|---| +| `setup.py` exits with `'NoneType' object has no attribute 'get'` | `design-spec.md` was rewritten as plain Markdown instead of mutating the YAML-frontmatter template bootstrap copied. Wait for bootstrap to finish, then **edit** (not rewrite) `./design-spec.md` — only change the field values inside the existing `---...---` frontmatter | +| `adk web` starts but `/list-apps` returns `[]` / browser shows "No agents found" | Bare `adk` resolved to a global Python that lacks the editable install. Kill it and restart with `.venv/bin/adk web "$SKILL_DIR/scripts" --port 8765` | +| `MethodNotImplemented: 501` from Vector Search | `VECTOR_SEARCH_COLLECTION` has a newline. Re-export on one line | +| `ModuleNotFoundError: google.adk` | `pip install -e "$SKILL_DIR"` — google-adk is an unconditional dependency, no `[adk]` extra needed | +| `Package requires Python: 3.9.X` | venv used system Python 3.9. Recreate with `python3.12 -m venv .venv` | +| `BILLING_DISABLED` / `PERMISSION_DENIED` / `API has not been used` | GCP project setup — see troubleshooting.md | + +## MCP Migration + +This skill uses `gcloud` CLI + Python SDKs (`google-genai`, +`google-cloud-bigquery`, `google-cloud-aiplatform`). Per +[Phase 2 Skills guidelines](https://github.com/google/skills), 1p skills +should prefer remote MCP tools when available. Migration map: + +| Service | Where | Future MCP | +|---|---|---| +| BigQuery | `ingest_bigquery.py`, `validate_schema.py` | BigQuery MCP | +| Vertex AI Vector Search | `ingest_vertex_search.py`, `setup.py` | Vertex AI MCP | +| Vertex AI Embeddings | `retrievers.py` | Vertex AI MCP | +| Cloud Run | `gcloud run deploy` | Cloud Run MCP | + +## Completion Checklist + +- [ ] Product fields level and data source confirmed +- [ ] Data ingestion ran; Vector Search populated +- [ ] `retrieve_docs` returns results in ADK web UI +- [ ] Evaluation passes success criteria +- [ ] Deployed (if beyond prototype) + +## References + +Load on demand: + +- [references/install-paths.md](references/install-paths.md) — host install dirs, Python fallback, `bash -c` rationale +- [references/dependencies.md](references/dependencies.md) — pip deps and install quirks +- [references/architecture.md](references/architecture.md) — what retrieval does (semantic-only), demo queries, structured-filter strategies +- [references/troubleshooting.md](references/troubleshooting.md) — full error table +- [references/agent-example.md](references/agent-example.md) — how agent.py + retrievers.py fit together +- [references/ingestion-scripts.md](references/ingestion-scripts.md) — per-script CLI reference diff --git a/skills/retail/product-search/assets/design-spec.md b/skills/retail/product-search/assets/design-spec.md new file mode 100644 index 0000000000..57542ed5f5 --- /dev/null +++ b/skills/retail/product-search/assets/design-spec.md @@ -0,0 +1,37 @@ +--- +# Product Search Agent - Design Spec +# Fill in your values and use this as input for project generation. + +# --- Required --- +gcp_project_id: "" # REQUIRED: your GCP project ID + +# --- Data --- +data_source: assets/sample-products.csv # local CSV path or gs://bucket/path/products.csv +product_fields: Extended # Basic | Standard | Extended | Full + +# --- GCP --- +gcp_region: us-central1 # Vector Search 2.0 region (us-central1 is the only confirmed-working region today) +dataset_id: retail_skill_products +table_id: products + +# --- Optional warnings --- +catalog_size: "1K-50K" # only used to trigger a >500K Dataflow hint +--- + +# Product Search Agent + +## Overview + +This design spec captures the configuration decisions for a retail product search +agent built on Google Cloud. It is generated during the SKILL.md interview and +used by ingestion scripts and agent scaffolding. + +## How to Use + +1. Fill in `gcp_project_id` above (or let the coding agent do it conversationally). +2. Pass this file to any script: `python scripts/ingest_bigquery.py --config assets/design-spec.md`. +3. CLI args always override values from this file. + +## Design Decisions + +Document any non-obvious choices here so future contributors understand the "why". diff --git a/skills/retail/product-search/assets/sample-products.csv b/skills/retail/product-search/assets/sample-products.csv new file mode 100644 index 0000000000..f75d833758 --- /dev/null +++ b/skills/retail/product-search/assets/sample-products.csv @@ -0,0 +1,6 @@ +product_id,name,price,description,category,brand,image_url,rating,stock +prod-001,Wireless Headphones,199.99,Premium noise-cancelling over-ear headphones with 30-hour battery,Audio,Sony,,4.5,50 +prod-002,Laptop Stand,49.99,Aluminum adjustable laptop stand with cable management,Accessories,Generic,,4.2,100 +prod-003,USB-C Cable,19.99,6ft braided USB-C to USB-C cable with 100W charging,Cables,Anker,,4.7,200 +prod-004,Mechanical Keyboard,129.99,Compact 75% mechanical keyboard with hot-swappable switches,Peripherals,Keychron,,4.6,30 +prod-005,Monitor Light Bar,59.99,Screen-mounted LED light bar with adjustable color temperature,Lighting,BenQ,,4.4,75 diff --git a/skills/retail/product-search/manifest.yaml b/skills/retail/product-search/manifest.yaml new file mode 100644 index 0000000000..f6eb5e790d --- /dev/null +++ b/skills/retail/product-search/manifest.yaml @@ -0,0 +1,31 @@ +type: standalone +status: active +language: python +description: Semantic product search agent using Vertex AI Vector Search and BigQuery, with ADK agent scaffolding and Cloud Run deployment. +license: Apache-2.0 +architecture: + agent: single + stateful: false + datasources: + - external +dependencies: + libraries: + - google-adk + - google-cloud-aiplatform + - google-cloud-bigquery + - google-cloud-storage + - google-cloud-vectorsearch + - google-genai + services: + - vertex-ai + - vector-search + - bigquery + - cloud-storage +ownership: + team: FDE/Blackbelt + poc: tanvisinghal-0105 +tags: + - retail + - search + - rag + - vector-search diff --git a/skills/retail/product-search/pyproject.toml b/skills/retail/product-search/pyproject.toml new file mode 100644 index 0000000000..0672fe546e --- /dev/null +++ b/skills/retail/product-search/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "retail-product-search" +version = "0.2.0" +requires-python = ">=3.11" +license = {text = "Apache-2.0"} + +dependencies = [ + "google-adk>=2.2.0", # agent runtime for `adk web` and the ADK framework wrapper + "google-cloud-bigquery>=3.0", + "google-cloud-storage>=2.0", + "google-cloud-vectorsearch>=0.5,<1.0", # preview/beta -- pin to known-working range + "google-cloud-aiplatform>=1.30", + "google-genai>=1.0", + "pyOpenSSL>=23.0", # mTLS during BigQuery / Vector Search auth in some envs + "python-dotenv>=1.0", + "pyyaml>=6.0", + "requests>=2.28", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[[tool.uv.index]] +url = "https://pypi.org/simple/" +default = true + +[tool.hatch.build.targets.wheel] +packages = ["scripts"] + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pytest-mock>=3.14", + "pytest-cov>=4.1", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-s -v" +log_cli = true +log_cli_level = "INFO" +markers = [ + "live: integration test that hits real GCP. Requires LIVE_EVAL=1 and a fresh GCP project to ingest into.", +] diff --git a/skills/retail/product-search/references/agent-example.md b/skills/retail/product-search/references/agent-example.md new file mode 100644 index 0000000000..3fe74238f8 --- /dev/null +++ b/skills/retail/product-search/references/agent-example.md @@ -0,0 +1,71 @@ +# Example Agent Code + +The shipping implementation is in `scripts/agent.py` and `scripts/retrievers.py`. +Read those files directly -- this doc summarizes the shape so you know what +to look at when adapting it. + +## Files + +- **`scripts/agent.py`** -- defines `root_agent`, a single ADK `Agent` with one + tool (`retrieve_docs`) and a static instruction string focused on product + search. +- **`scripts/retrievers.py`** -- defines `search_collection`, the Vector Search + 2.0 query that backs `retrieve_docs`, plus a `search()` convenience + wrapper used by the Step 9 smoke test. + +## Shape + +``` +User query + | +root_agent (Gemini, ADK) + | instruction = "search the catalog for every query, present results..." + | +retrieve_docs(query) + | +search_collection(query, collection_path, top_k=10) + | +vectorsearch.DataObjectSearchServiceClient.search_data_objects( + SemanticSearch(search_text=query, search_field="text_embedding", + task_type="QUESTION_ANSWERING", top_k=10, + output_fields=[product_id, name, price, description, + category, brand, rating, stock]) +) + | +Formatted "Product 1: name, $price, by brand, rated X/5, " string +``` + +## Configuration + +The agent reads these env vars at runtime: + +| Variable | Default | Purpose | +|---|---|---| +| `GEMINI_MODEL` | `gemini-3.5-flash` | LLM used by `root_agent` | +| `GOOGLE_CLOUD_PROJECT` | from ADC | GCP project for Vertex AI | +| `GOOGLE_CLOUD_LOCATION` | `global` | LLM region | +| `VECTOR_SEARCH_LOCATION` | `us-central1` | Vector Search region (used to build the default collection path) | +| `VECTOR_SEARCH_COLLECTION` | `projects//locations//collections/retail-skill-products-collection` | Full collection resource path | + +`VECTOR_SEARCH_COLLECTION` must match the regex +`projects/

/locations//collections/` with no whitespace. A +newline embedded mid-path (common from multi-line shell pastes) causes the +SDK to return a confusing 501 -- `scripts/agent.py` validates this up front. + +## Extending + +To add a tool (e.g. cart actions, inventory checks, price-history lookups): + +1. Define a function in `scripts/agent.py` with a docstring describing when to + use it -- ADK feeds the docstring to the LLM as the tool description. +2. Append it to the `tools=[...]` list on `root_agent`. +3. Update the `instruction` string so the LLM knows when to pick the new + tool over `retrieve_docs`. + +To change the LLM, set `GEMINI_MODEL` -- no code changes needed. + +To change the retrieval contract (different fields, different `top_k`, +filtering), edit `search_collection` in `scripts/retrievers.py`. Note that VS +2.0 semantic search has no built-in structured filters; any +price/stock/category gating must happen in the agent's prompt or in a +client-side post-filter. diff --git a/skills/retail/product-search/references/architecture.md b/skills/retail/product-search/references/architecture.md new file mode 100644 index 0000000000..7ff0de9a2f --- /dev/null +++ b/skills/retail/product-search/references/architecture.md @@ -0,0 +1,49 @@ +# What the retrieval layer actually does + +Read this when demoing the skill or extending it — covers what's +semantic vs what isn't, and where structured filtering would have to +live. + +## Semantic search, not filtered search + +`search(query, top_k)` runs Vertex AI Vector Search semantic similarity +over the embedding fields configured in `design-spec.md` (default: +`name, description, category, brand`). **It does NOT apply structured +filters on price, currency, stock, or rating** -- those words in a +query are just text the embedder sees. + +Implications when you demo: + +- `"headphones under 100 EUR"` returns headphones ranked by semantic + match; any price filtering happens in the agent's LLM, not the + retriever, so results may include items above the threshold (the LLM + may then narrate "no matches" if it filters client-side). +- The bundled `sample-products.csv` has USD prices. If the user asks in + EUR, the agent silently treats them as equivalent. For a real + multi-currency demo, add a `price_eur` or `currency` column to the + catalog and prompt the agent to use it. + +## Test queries + +| Query | What you're testing | +|---|---| +| `"laptop for video editing"` | Pure semantic match (works as documented) | +| `"I need a gift"` | Vague-query clarification flow (agent prompt-driven) | +| `"Which one has the best battery life?"` | RAG-style follow-up | +| `"wireless headphones under $100"` | Semantic + LLM-side price filtering; agent will narrate the price filter even though the retriever doesn't apply it | + +## Where to add structured filtering + +If a real catalog needs `price < X` to actually constrain results +(not just narrate around them), there are three places to add it: + +1. **Inside the retriever** — pre-filter the BigQuery dataset + before embedding ingest, or post-filter the Vector Search results + in `scripts/retrievers.py:search()`. +2. **As a separate tool on the agent** — add a `filter_by_price` tool + alongside `retrieve_docs` and let the LLM compose them. +3. **In the agent's system prompt** — give the LLM filter rules and + trust it to apply them. Cheapest, least reliable. + +Option 1 is the right answer for production; options 2 and 3 are +useful for demos that need quick wins. diff --git a/skills/retail/product-search/references/dependencies.md b/skills/retail/product-search/references/dependencies.md new file mode 100644 index 0000000000..e4d223fc6f --- /dev/null +++ b/skills/retail/product-search/references/dependencies.md @@ -0,0 +1,27 @@ +# Dependencies + +`pip install -e .` from the install dir resolves everything from +`pyproject.toml`. You don't install these manually. + +## Required + +- `google-adk>=2.2.0` — ADK web UI and agent runtime +- `google-cloud-aiplatform>=1.30` +- `google-cloud-bigquery>=3.0` +- `google-cloud-storage>=2.0` +- `google-cloud-vectorsearch>=0.5,<1.0` (preview, pinned) +- `google-genai>=1.0` +- `pyOpenSSL>=23.0` (mTLS for BigQuery in some envs) +- `python-dotenv>=1.0` — loads `.env` at import via `scripts/config.py` +- `pyyaml>=6.0` +- `requests>=2.28` + +## Install quirks (handled by bootstrap.sh) + +1. **Paths with spaces.** `bash -c "pip install -e '$SKILL_DIR'"` quotes the + whole argument so pip sees one path. +2. **Agent shell tools reset state between calls.** Run the workspace setup + block as a single shell invocation. +3. **Stripped PATH** in sandboxed terminals hides brew/pyenv Python. The + bootstrap script falls back to absolute paths + (`/opt/homebrew/bin/python3.13`, `~/.pyenv/shims/python3.12`, etc.). diff --git a/skills/retail/product-search/references/ingestion-scripts.md b/skills/retail/product-search/references/ingestion-scripts.md new file mode 100644 index 0000000000..84e7e9793e --- /dev/null +++ b/skills/retail/product-search/references/ingestion-scripts.md @@ -0,0 +1,167 @@ +# Product Search - Data Ingestion Scripts Reference + +Reference documentation for the scripts in `scripts/`. These are standalone +Python scripts that handle product data ingestion and vector search indexing. + +`setup.py`, `ingest_bigquery.py`, and `ingest_vertex_search.py` accept +`--config design-spec.md` to load defaults from your project +configuration. `validate_schema.py` takes `--file` and `--fields-level` +directly — it does not read design-spec.md. + +--- + +## Script 1: ingest_bigquery.py + +**Purpose**: Load product catalog from CSV/JSON into BigQuery +**Location**: `scripts/ingest_bigquery.py` + +### Usage + +```bash +# From GCS +python scripts/ingest_bigquery.py \ + --project-id my-project \ + --gcs-bucket my-project-products \ + --gcs-path products.csv + +# From local file +python scripts/ingest_bigquery.py \ + --project-id my-project \ + --local-file data/products.json \ + --format json + +# Using design-spec.md for defaults +python scripts/ingest_bigquery.py \ + --config design-spec.md \ + --local-file data/products.csv +``` + +### Schema + +The script uses a fixed schema matching the Extended product field level: + +| Field | Type | Mode | Notes | +|-------|------|------|-------| +| product_id | STRING | REQUIRED | Unique identifier | +| name | STRING | REQUIRED | Product name | +| price | FLOAT64 | REQUIRED | Price in configured currency | +| description | STRING | REQUIRED | Product description | +| category | STRING | NULLABLE | Product category | +| brand | STRING | NULLABLE | Brand name | +| image_url | STRING | NULLABLE | Product image URL | +| rating | FLOAT64 | NULLABLE | Rating (0-5) | +| stock | INT64 | NULLABLE | Stock quantity | + +To customize the schema (e.g. for Basic or Full field levels), edit +`REQUIRED_FIELDS`, `OPTIONAL_FIELDS`, and `SCHEMA` at the top of the script. + +### Validation + +The script validates each row before loading: +- Required fields must be present and non-empty +- `price` must be numeric +- `stock` must be an integer +- Invalid rows are skipped with warnings (not fatal) + +--- + +## Script 2: ingest_vertex_search.py + +**Purpose**: Create a Vector Search 2.0 Collection and ingest products +**Location**: `scripts/ingest_vertex_search.py` + +Uses Vector Search 2.0 Collections with auto-embeddings. No manual +embedding generation or GCS bucket needed -- the Collection's configured +embedding model generates embeddings automatically when data objects +are inserted. + +### Usage + +```bash +# Basic usage +python scripts/ingest_vertex_search.py \ + --project-id my-project \ + --collection-id retail-skill-products-collection + +# Using design-spec.md for defaults +python scripts/ingest_vertex_search.py --config design-spec.md +``` + +### Pipeline + +1. Fetch products from BigQuery (`retail_skill_products.products`) +2. Create Vector Search 2.0 Collection if it doesn't exist (with auto-embedding config) +3. Insert products as data objects one at a time, catching `AlreadyExists` for idempotent re-runs +4. VS 2.0 auto-generates embeddings from the configured text template + +### Configuration + +| Flag | Default | Config key | +|------|---------|------------| +| `--collection-id` | retail-skill-products-collection | `collection_id` | +| `--embedding-model` | gemini-embedding-001 | `embedding_model` | +| `--embedding-fields` | name,description,category,brand | `embedding_fields` | +| `--location` | us-central1 | `gcp_region` | + +### Output + +After ingestion, set the `VECTOR_SEARCH_COLLECTION` environment variable +in your agent to the collection path printed by the script: + +```bash +export VECTOR_SEARCH_COLLECTION="projects/PROJECT_ID/locations/us-central1/collections/retail-skill-products-collection" +``` + +--- + +## Script 3: validate_schema.py + +**Purpose**: Validate product data files before ingestion +**Location**: `scripts/validate_schema.py` + +### Usage + +```bash +# Validate CSV at Extended level (matches sample-products.csv) +python scripts/validate_schema.py \ + --file data/products.csv \ + --fields-level Extended + +# Validate JSON at Standard level (format auto-detected from .json/.jsonl suffix) +python scripts/validate_schema.py \ + --file data/products.json \ + --fields-level Standard +``` + +### Field Levels + +| Level | Required | Optional | +|-------|----------|----------| +| Basic | product_id, name, price, description | (none) | +| Standard | Basic | category, brand, image_url | +| Extended | Basic | Standard + rating, stock, manufacturer | +| Full | Basic | Extended + variants, tags, specifications, reviews | + +--- + +## Ingestion Order + +Run scripts in this order: + +1. `validate_schema.py` -- validate your data file +2. `ingest_bigquery.py` -- load to BigQuery +3. `ingest_vertex_search.py` -- create collection + ingest products + +**Always use these retail skill scripts** -- generic document ingestion +pipelines are incompatible with product catalogs. + +--- + +## Customizing the Schema + +To change the product schema: + +1. Edit `REQUIRED_FIELDS`, `OPTIONAL_FIELDS`, and `SCHEMA` in `ingest_bigquery.py` +2. Edit `DEFAULT_EMBEDDING_FIELDS` and `PRODUCT_DATA_FIELDS` in `ingest_vertex_search.py` +3. Update `validate_schema.py` field level definitions if needed +4. Update `design-spec.md` with the new field level and embedding fields diff --git a/skills/retail/product-search/references/install-paths.md b/skills/retail/product-search/references/install-paths.md new file mode 100644 index 0000000000..e6482a8fb8 --- /dev/null +++ b/skills/retail/product-search/references/install-paths.md @@ -0,0 +1,48 @@ +# Install Paths and Environment Quirks + +## Known install dirs per host + +| Host | Install dir | +|---|---| +| Claude Code | `~/.claude/skills//` | +| `npx skills add` (Codex, Gemini CLI, GitHub Copilot, others) | `~/.agents/skills//` | +| Gemini CLI standalone | `~/.gemini/skills//` | +| Cursor | `~/.cursor/skills//` | + +`bootstrap.sh` tries these paths in order. If your host installs elsewhere, +set `SKILL_DIR` manually: + +```bash +export SKILL_DIR=/path/to/your/install/dir +``` + +## Python interpreter fallback + +`bootstrap.sh` tries `command -v python3.{13,12,11,10}` first, then falls +back to absolute paths (`/opt/homebrew/bin/`, `/usr/local/bin/`, +`~/.pyenv/shims/`). The fallback handles sandboxed shells with stripped PATH. + +For conda/asdf/other layouts, set `PYTHON_BIN` and skip the loop. + +## Why `bash -c` wraps the pip command + +`SKILL_DIR` may contain spaces (`/Users/name with space/.claude/skills/...`). +`bash -c "pip install -e '$SKILL_DIR'"` quotes the whole argument so pip +sees one path, not tokens split at spaces. + +(Historical note: earlier versions installed `pip install -e "$SKILL_DIR[adk]"` +to pull an optional ADK extra. `google-adk` is now an unconditional dependency +in `pyproject.toml`, so the `[adk]` selector is no longer needed. If you see +it in a stale doc, drop the `[adk]` suffix and the install still works.) + +## Why the workspace setup must run as one shell command + +Agent shell tools reset cwd and clear variables between calls. If you set +`SKILL_DIR` in call 1 and run pip in call 2, `$SKILL_DIR` is empty in call 2 +and the install becomes `pip install -e ` (invalid). + +## Description tuning for triggering + +If your skill is registered but the agent doesn't pick it for relevant +prompts, the `description` field is what hosts' routers match against. +Include concrete trigger phrases alongside the abstract description. diff --git a/skills/retail/product-search/references/troubleshooting.md b/skills/retail/product-search/references/troubleshooting.md new file mode 100644 index 0000000000..746b537ffe --- /dev/null +++ b/skills/retail/product-search/references/troubleshooting.md @@ -0,0 +1,39 @@ +# Troubleshooting + +Read this when `setup.py` or a per-step script exits non-zero. Match +the error message against the table below before guessing. + +## Error table + +| Error pattern | Likely cause | Fix | +|---|---|---| +| `BILLING_DISABLED` / `Billing must be enabled` | GCP project has no billing account | Link a billing account in Cloud Console, then re-run | +| `PERMISSION_DENIED` on BigQuery | Service account missing IAM | `gcloud projects add-iam-policy-binding $PROJECT_ID --member=user:$EMAIL --role=roles/bigquery.dataEditor` | +| `PERMISSION_DENIED` on Vertex AI | Missing `aiplatform.user` role | `gcloud projects add-iam-policy-binding $PROJECT_ID --member=user:$EMAIL --role=roles/aiplatform.user` | +| `API has not been used` / `is disabled` | Required API not enabled | `gcloud services enable bigquery.googleapis.com aiplatform.googleapis.com` | +| `Collection not found` at query time | Vector Search collection never created or wrong path | Re-run `ingest_vertex_search.py`; verify `VECTOR_SEARCH_COLLECTION` env var | +| `setup.py` exits with `AttributeError: 'NoneType' object has no attribute 'get'` from `_setup_utils.py` | `design-spec.md` was rewritten as plain Markdown instead of editing the YAML template bootstrap copied. `_setup_utils.load_config()` returns an empty dict for non-frontmatter files, but downstream code then indexes into a nested dict that isn't there and the error surfaces one call deeper | Wait for `bootstrap.sh` to finish before touching `./design-spec.md`. Then **edit** the file (don't rewrite) — modify only the values inside the existing `---...---` frontmatter block. The canonical template is at `/assets/design-spec.md` | +| `adk web` boots but `/list-apps` returns `[]`, browser shows "No agents found in current folder" | Bare `adk` resolved through `PATH` to a global Python (pyenv, brew, etc.) that doesn't have the skill's editable install on `sys.path`. The server runs but can't import the agent module | Kill it (`lsof -ti :8765 \| xargs kill -9`) and restart with the workspace venv's adk explicitly: `.venv/bin/adk web "$SKILL_DIR/scripts" --port 8765`. Same root cause if `python "$SKILL_DIR/scripts/setup.py"` succeeds but the smoke test fails — use `.venv/bin/python` instead of bare `python` | +| `VECTOR_SEARCH_COLLECTION is malformed` | env var has embedded whitespace (usually a newline from a wrapped paste) | Re-`export` the value on a single line; prefer the `$PROJECT_ID` short form | +| `MethodNotImplemented: 501` from Vector Search in `us-central1` | Almost always a malformed `VECTOR_SEARCH_COLLECTION` (see above) -- not a real region/service issue | Echo `$VECTOR_SEARCH_COLLECTION` and confirm it has no `\n` or whitespace | +| `ModuleNotFoundError: google.cloud.aiplatform` | Python deps not installed | `pip install -e .` from the skill dir, or `pip install google-cloud-aiplatform google-cloud-bigquery google-genai` | +| `Package 'retail-product-search' requires a different Python: 3.10.X not in '>=3.11'` | venv was created with a Python older than 3.11 | Recreate the venv with a 3.11+ interpreter: `rm -rf .venv && python3.12 -m venv .venv && source .venv/bin/activate && bash -c "pip install -e '$SKILL_DIR'"` (substitute the Python version you have installed). Use the interpreter-detection loop from the Workspace Setup section to auto-find one. | +| `ModuleNotFoundError: google.adk` | Editable install never ran, or venv is stale | `bash -c "pip install -e '$SKILL_DIR'"` — google-adk is an unconditional dependency, no `[adk]` extra needed | +| `Schema mismatch` from `validate_schema.py` | CSV missing required fields (`product_id`, `name`, `price`) | Add the missing columns and re-run; or pass `--fields-level Standard` if your data is sparse | +| `Quota exceeded` on embedding requests | Free-tier embedding quota hit | Wait an hour, re-run with a smaller catalog slice, or request a quota increase. The script catches per-row failures and continues, so a partial run is fine to resume | +| Setup script hangs on Vector Search create | Collection creation is async and takes 2-5 min | Wait. If >10 min with no progress, check Cloud Console > Vertex AI > Vector Search for collection status | +| `projects//locations/...` in error path | `$GOOGLE_CLOUD_PROJECT` was empty when you ran the `export VECTOR_SEARCH_COLLECTION=...` -- substitution produced an empty project ID | `export GOOGLE_CLOUD_PROJECT=` first, then re-run the `export VECTOR_SEARCH_COLLECTION` line, then restart `adk web` | +| Agent in `adk web` keeps saying "tool encountered an error" / "I am still unable to search" even after env fixed | ADK session memory -- model learned the tool is broken from earlier turns | Click "New Session" in the ADK web UI; the fresh session will retry the tool | +| Agent ignores price/currency filters in user queries | Retriever is pure semantic similarity; no structured filters at retrieval time | Expected behavior. Document the limit in your demo; or add a `currency`/`price_eur` column to the catalog and prompt the agent to filter client-side | + +## ADK web UI: "tool encountered an error" loop + +If `adk web` returns "tool encountered an error" repeatedly even after +the env var is correct: ADK persists session history in +`scripts/.adk/session.db`. If the first few queries in a session got a +tool error (typically a malformed env var, since fixed), the model +"learns" the tool is broken and starts skipping it, apologizing without +retrying. + +Click "New Session" in the ADK web UI after fixing the underlying +issue -- a fresh session will retry the tool from scratch. diff --git a/skills/retail/product-search/scripts/_paths.py b/skills/retail/product-search/scripts/_paths.py new file mode 100644 index 0000000000..1640839cdb --- /dev/null +++ b/skills/retail/product-search/scripts/_paths.py @@ -0,0 +1,35 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Install-dir resolution for the retail-product-search skill. + +INSTALL_DIR is the directory containing SKILL.md -- i.e. the root of the +installed skill, regardless of where the user invokes a script from. +Computed from this file's own location at import time, so it stays stable +even when the user's cwd changes mid-session. + +Use this for any "where do I find my bundled assets?" lookup. +Do NOT use this for "where should I write user state?" -- user-mutable +state goes to the agent's current working directory. +""" + +from __future__ import annotations + +import pathlib + +INSTALL_DIR: pathlib.Path = pathlib.Path(__file__).resolve().parent.parent + +ASSETS_DIR: pathlib.Path = INSTALL_DIR / "assets" +SAMPLE_PRODUCTS_CSV: pathlib.Path = ASSETS_DIR / "sample-products.csv" +DEFAULT_DESIGN_SPEC: pathlib.Path = ASSETS_DIR / "design-spec.md" diff --git a/skills/retail/product-search/scripts/_setup_utils.py b/skills/retail/product-search/scripts/_setup_utils.py new file mode 100644 index 0000000000..c7847edecc --- /dev/null +++ b/skills/retail/product-search/scripts/_setup_utils.py @@ -0,0 +1,59 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Shared utilities for skill setup scripts. + +Provides load_config() for reading YAML frontmatter from design-spec.md. + +Vendored locally so this skill ships as a self-contained package -- no +parent-directory _shared/ folder required to run setup. +""" + +import logging +import pathlib +from typing import Any + +import yaml # type: ignore[import-untyped] + +logger = logging.getLogger(__name__) + + +def load_config(config_path: str) -> dict[str, Any]: + """Load design-spec.md YAML frontmatter. + + Args: + config_path: Path to the design-spec markdown file. + + Returns: + Parsed YAML frontmatter as a dict. Empty dict if the file is + missing, has no frontmatter, or the frontmatter is empty. + """ + if not pathlib.Path(config_path).exists(): + return {} + text = pathlib.Path(config_path).read_text(encoding="utf-8") + if text.startswith("---"): + lines = text.split("\n") + yaml_lines = [] + in_frontmatter = False + for line in lines: + if line.strip() == "---": + if not in_frontmatter: + in_frontmatter = True + continue + break + if in_frontmatter: + yaml_lines.append(line) + yaml_text = "\n".join(yaml_lines) + return yaml.safe_load(yaml_text) or {} + return yaml.safe_load(text) or {} diff --git a/skills/retail/product-search/scripts/agent.py b/skills/retail/product-search/scripts/agent.py new file mode 100644 index 0000000000..570a0332df --- /dev/null +++ b/skills/retail/product-search/scripts/agent.py @@ -0,0 +1,165 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""ADK agent definition for the retail product search skill. + +Exposes a :data:`root_agent` (and :data:`app`) that uses Vertex AI Vector +Search 2.0 for semantic product retrieval via the :func:`retrieve_docs` tool. +""" + +# pylint: disable=line-too-long +# (error messages and example URLs are intentionally long; breaking them hurts copy-paste UX.) + +import os +import re + +import google +import vertexai +from google.adk import agents, apps, models + +from scripts.config import config +from scripts.retrievers import search_collection + +_COLLECTION_PATH_RE = re.compile( + r"^projects/[^/\s]+/locations/[^/\s]+/collections/[^/\s]+$" +) + +# Cache for lazy Vertex init. Populated on first tool call, not at import, +# so \`from scripts.agent import root_agent\` works in CI where ADC is not +# configured (e.g. the runnability test). +_resolved_project_id: str | None = None + + +def _resolve_project_id() -> str: + """Resolve the effective GCP project id and init Vertex AI on first call. + + Prefers the value in .env (via config), falls back to ADC's default + project, then writes back into os.environ so any code that reads + GOOGLE_CLOUD_PROJECT directly sees the same value. Idempotent — + subsequent calls return the cached result. + """ + global _resolved_project_id + if _resolved_project_id is not None: + return _resolved_project_id + project_id: str | None = config.GOOGLE_CLOUD_PROJECT or None + if not project_id: + _, adc_default = google.auth.default() + project_id = adc_default + if not project_id: + raise RuntimeError( + "GOOGLE_CLOUD_PROJECT is not set and ADC has no default project. " + "Copy .env.example to .env and set GOOGLE_CLOUD_PROJECT, or run " + "`gcloud auth application-default set-quota-project `." + ) + os.environ.setdefault("GOOGLE_CLOUD_PROJECT", project_id) + os.environ.setdefault("GOOGLE_CLOUD_LOCATION", config.GOOGLE_CLOUD_LOCATION) + vertexai.init(project=project_id, location=config.GOOGLE_CLOUD_LOCATION) + _resolved_project_id = project_id + return project_id + + +def _get_vector_search_collection() -> str: + """Return the Vector Search collection resource path. + + Reads from ``config.VECTOR_SEARCH_COLLECTION`` when set; otherwise + builds the default path from ``config.GOOGLE_CLOUD_PROJECT`` and + ``config.VECTOR_SEARCH_LOCATION``. + + Returns: + The fully qualified Vector Search collection path. + + Raises: + ValueError: If ``VECTOR_SEARCH_COLLECTION`` is set to a malformed + path (e.g. contains a newline from a wrapped shell paste, which + silently causes the Vector Search SDK to return a 501). + """ + raw = config.VECTOR_SEARCH_COLLECTION + if not raw: + project_id = _resolve_project_id() + return ( + f"projects/{project_id}/locations/{config.VECTOR_SEARCH_LOCATION}" + "/collections/retail-skill-products-collection" + ) + # Strip whitespace -- multi-line shell pastes can embed a newline mid-path, + # which the Vector Search SDK silently maps to a 501 from the wrong endpoint. + cleaned = raw.strip() + if not _COLLECTION_PATH_RE.match(cleaned): + raise ValueError( + "VECTOR_SEARCH_COLLECTION is malformed -- expected " + "'projects//locations//collections/' with no whitespace. " + f"Got: {raw!r}. " + "Tip: type the export on a single line, e.g. " + "export VECTOR_SEARCH_COLLECTION=" + '"projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/' + 'collections/retail-skill-products-collection"' + ) + return cleaned + + +def retrieve_docs(query: str) -> str: + """Search the product catalog using semantic similarity. + + Call this for every user query about products -- by name, by attributes + (price/brand/category), by intent ('something for my desk'), or by + comparison ('X vs Y'). Returns a formatted list of matching products + with name, price, brand, rating, and a description snippet. + + Args: + query: The product search query in natural language. + + Returns: + Formatted product list, or a "no matches" message. + """ + try: + return search_collection( + query=query, + collection_path=_get_vector_search_collection(), + ) + except Exception as e: # pylint: disable=broad-exception-caught + return ( + f"Calling retrieval tool with query:\n\n{query}\n\n" + f"raised the following error:\n\n{type(e)}: {e}" + ) + + +INSTRUCTION = """You are a retail product search assistant. +Use the retrieve_docs tool to search the product catalog for every user query. + +IMPORTANT: After receiving tool results, you MUST immediately present the results to the user. +Do not stop after saying "let me search" -- always continue to present the full results. + +When presenting search results: +- Say each product's name, price, brand, and a short description. +- Keep it conversational and concise. +- Never make up products -- only mention products returned by the tool. +- If no products match, say so and suggest broadening the search.""" + + +root_agent = agents.Agent( + name="root_agent", + model=models.Gemini(model=config.GEMINI_MODEL), + instruction=INSTRUCTION, + tools=[retrieve_docs], +) + +app = apps.App( + root_agent=root_agent, + # ADK's `adk web` auto-names the app from the agent module's parent + # directory. The agent lives at scripts/agent.py, so ADK names it + # "scripts". The App name MUST match the auto-discovered name or + # session creation fails with "Session not found: . The runner + # is configured with app name '', but the root agent was loaded + # from '/.../scripts'." + name="scripts", +) diff --git a/skills/retail/product-search/scripts/bootstrap.sh b/skills/retail/product-search/scripts/bootstrap.sh new file mode 100755 index 0000000000..05f3cce4a9 --- /dev/null +++ b/skills/retail/product-search/scripts/bootstrap.sh @@ -0,0 +1,125 @@ +#!/usr/bin/env bash +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Workspace bootstrap for retail-product-search. +# +# Discovers the skill's install dir, finds a Python 3.11+ interpreter, +# creates .venv in the current directory, installs the skill editable +# (google-adk is now an unconditional dependency), and copies +# design-spec.md into the workspace. +# +# Run from the workspace directory as a single shell invocation: +# bash /path/to/scripts/bootstrap.sh +# +# All defensive logic (interpreter fallback, bash -c around pip, +# stripped-PATH workarounds) is documented in +# references/install-paths.md. + +set -e + +# 1. Locate the install dir. Try common spec-compliant locations. +SKILL_DIR="" +for candidate in \ + ~/.claude/skills/retail-product-search \ + ~/.agents/skills/retail-product-search \ + ~/.gemini/skills/retail-product-search \ + ~/.cursor/skills/retail-product-search; do + if [ -f "$candidate/SKILL.md" ]; then + SKILL_DIR="$candidate" + break + fi +done + +# Bootstrap may itself be invoked via absolute path; if so, derive +# SKILL_DIR from $0 as a final fallback. +if [ -z "$SKILL_DIR" ]; then + script_dir="$(cd "$(dirname "$0")" && pwd)" + if [ -f "$script_dir/../SKILL.md" ]; then + SKILL_DIR="$(cd "$script_dir/.." && pwd)" + fi +fi + +if [ -z "$SKILL_DIR" ]; then + echo "ERROR: retail-product-search skill not installed. Install it first." >&2 + exit 1 +fi + +# 2. Pick a Python 3.11+ interpreter (pyproject's requires-python floor). +# Honor a pre-exported PYTHON_BIN first (conda / asdf / other layouts +# documented in references/install-paths.md), then try PATH lookup, +# then fall back to absolute paths for sandboxed shells with a +# stripped PATH. +PYTHON_BIN="${PYTHON_BIN:-}" +if [ -n "$PYTHON_BIN" ]; then + ver=$("$PYTHON_BIN" -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")' 2>/dev/null || echo "") + case "$ver" in + 3.11|3.12|3.13) ;; # keep the exported one + *) + echo "WARNING: exported PYTHON_BIN=$PYTHON_BIN reports version '$ver' (want 3.11+). Falling back to auto-detect." >&2 + PYTHON_BIN="" + ;; + esac +fi + +if [ -z "$PYTHON_BIN" ]; then + for py in python3.13 python3.12 python3.11 python3; do + if command -v "$py" >/dev/null 2>&1; then + ver=$("$py" -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")' 2>/dev/null) + case "$ver" in 3.11|3.12|3.13) PYTHON_BIN="$py"; break ;; esac + fi + done +fi + +if [ -z "$PYTHON_BIN" ]; then + for path in \ + /opt/homebrew/bin/python3.13 \ + /opt/homebrew/bin/python3.12 \ + /opt/homebrew/bin/python3.11 \ + /usr/local/bin/python3.13 \ + /usr/local/bin/python3.12 \ + /usr/local/bin/python3.11 \ + "$HOME/.pyenv/shims/python3.13" \ + "$HOME/.pyenv/shims/python3.12" \ + "$HOME/.pyenv/shims/python3.11"; do + if [ -x "$path" ]; then + ver=$("$path" -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")' 2>/dev/null) + case "$ver" in 3.11|3.12|3.13) PYTHON_BIN="$path"; break ;; esac + fi + done +fi + +if [ -z "$PYTHON_BIN" ]; then + echo "ERROR: need Python 3.11+ (pyproject.toml floor). Install one (brew install python@3.12) and retry." >&2 + exit 1 +fi + +echo "Using PYTHON_BIN=$PYTHON_BIN" +echo "Using SKILL_DIR=$SKILL_DIR" + +# 3. Create venv, activate, install the skill. +# google-adk is now an unconditional dependency in pyproject.toml, so no +# extras selector is needed. bash -c preserves quoting so a SKILL_DIR +# containing spaces still works. +if [ ! -d .venv ]; then + "$PYTHON_BIN" -m venv .venv +fi +source .venv/bin/activate +bash -c "pip install -e '$SKILL_DIR'" + +# 4. Copy the design-spec template into the workspace. +if [ ! -f ./design-spec.md ]; then + cp "$SKILL_DIR/assets/design-spec.md" ./design-spec.md +fi + +echo "" +echo "READY" +echo " SKILL_DIR=$SKILL_DIR" +echo " workspace=$(pwd)" +echo " venv=$(pwd)/.venv" +echo " design-spec=$(pwd)/design-spec.md" diff --git a/skills/retail/product-search/scripts/cleanup.py b/skills/retail/product-search/scripts/cleanup.py new file mode 100644 index 0000000000..36158c4737 --- /dev/null +++ b/skills/retail/product-search/scripts/cleanup.py @@ -0,0 +1,516 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Clean up all GCP resources created by the product search agent. + +Deletes BigQuery datasets, Vector Search 2.0 collections, and Cloud Run +services. + +Usage: + # Dry run (show what would be deleted, don't delete) + python scripts/cleanup.py --config design-spec.md --dry-run + + # Delete everything + python scripts/cleanup.py --config design-spec.md --confirm + + # Delete only specific resources + python scripts/cleanup.py --config design-spec.md --confirm \\ + --only bigquery,vectorsearch +""" + +# pylint: disable=line-too-long +# (error messages with resource paths and gcloud commands are intentionally long.) + +import argparse +import logging +import pathlib +import subprocess +import sys +from typing import Any + +# Allow imports from the script's own directory before pip install -e is run. +# MUST run before `from _setup_utils import ...` below, or the local import +# resolves via whatever fallback happens to be on sys.path (which is not +# guaranteed to be this script's dir when run as `python -m scripts.cleanup`). +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent)) + +from _setup_utils import load_config +from google.api_core import exceptions +from google.cloud import bigquery, vectorsearch + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +ALL_RESOURCE_TYPES = ["bigquery", "vectorsearch", "cloudrun"] +# Quick Start only creates BigQuery + Vector Search. Cloud Run is only used by +# the deploy extension, so we skip it by default to avoid slow `gcloud describe` +# roundtrips. Users who deployed to Cloud Run can opt in with --only cloudrun. +DEFAULT_RESOURCE_TYPES = ["bigquery", "vectorsearch"] + +# Bound how long we'll wait for a single `gcloud describe` to answer. Without +# this, an unreachable network / wrong project can hang cleanup indefinitely. +GCLOUD_DESCRIBE_TIMEOUT_SEC = 15 + + +def delete_bigquery(project_id: str, dataset_id: str, dry_run: bool) -> bool: + """Delete a BigQuery dataset and all of its tables. + + Args: + project_id: GCP project ID. + dataset_id: Bare dataset name (no project prefix). + dry_run: If True, log what would be deleted but don't delete. + + Returns: + True on successful delete or when the dataset is already absent. + False if the delete call raised an unexpected exception. + """ + client = bigquery.Client(project=project_id) + dataset_ref = f"{project_id}.{dataset_id}" + + try: + client.get_dataset(dataset_ref) + except exceptions.NotFound: + logger.info("BigQuery dataset %s does not exist, skipping", dataset_ref) + return True + except Exception as e: # pylint: disable=broad-exception-caught + # Permission denied / auth / network / etc. Return False so the + # caller doesn't mistake this for "already deleted". + logger.error("Failed to check BigQuery dataset %s: %s", dataset_ref, e) + return False + + if dry_run: + logger.info( + "[DRY RUN] Would delete BigQuery dataset: %s (and all tables)", + dataset_ref, + ) + return True + + try: + client.delete_dataset( + dataset_ref, delete_contents=True, not_found_ok=True + ) + logger.info("Deleted BigQuery dataset: %s", dataset_ref) + return True + except Exception as e: # pylint: disable=broad-exception-caught + logger.error("Failed to delete BigQuery dataset %s: %s", dataset_ref, e) + return False + + +def _drain_data_objects(collection_path: str) -> int: + """Delete all data objects inside a Vector Search collection. + + The API rejects collection deletion while data objects exist, so we + list them via semantic search (broad query) and batch-delete in chunks. + + Args: + collection_path: Fully qualified Vector Search collection path + (``projects/.../locations/.../collections/...``). + + Returns: + The total number of data objects deleted. + """ + search_client = vectorsearch.DataObjectSearchServiceClient() + data_client = vectorsearch.DataObjectServiceClient() + + # Broad-stroke search to enumerate IDs server-side. top_k is capped by the + # API; we loop until a search returns no new IDs (or we hit a sane ceiling). + seen: set[str] = set() + deleted_total = 0 + batch_size = 100 + max_iterations = ( + 50 # 50 * 100 = 5000 objects; raise if you have a bigger collection + ) + + for _ in range(max_iterations): + request = vectorsearch.SearchDataObjectsRequest( + parent=collection_path, + semantic_search=vectorsearch.SemanticSearch( + search_text="product", # broad query -- collection is product catalog + search_field="text_embedding", + task_type="QUESTION_ANSWERING", # required by the API; matches scripts/retrievers.py + top_k=batch_size, + output_fields=vectorsearch.OutputFields( + data_fields=["product_id"] + ), + ), + ) + + batch_ids: list[str] = [] + for result in search_client.search_data_objects(request): + object_name = result.data_object.name + if object_name and object_name not in seen: + seen.add(object_name) + batch_ids.append(object_name) + + if not batch_ids: + break + + delete_requests = [ + vectorsearch.DeleteDataObjectRequest(name=name) + for name in batch_ids + ] + data_client.batch_delete_data_objects( + request=vectorsearch.BatchDeleteDataObjectsRequest( + parent=collection_path, + requests=delete_requests, + ) + ) + deleted_total += len(batch_ids) + logger.info( + " Drained %d data objects (%d total)", + len(batch_ids), + deleted_total, + ) + + return deleted_total + + +def delete_vectorsearch_collection( + project_id: str, location: str, collection_id: str, dry_run: bool +) -> bool: + """Delete a Vector Search 2.0 Collection. + + The API requires the collection to be empty before deletion, so we + first drain all contained data objects. + + Args: + project_id: GCP project ID. + location: GCP region of the collection. + collection_id: Bare collection ID (no project/location prefix). + dry_run: If True, log what would be deleted but don't delete. + + Returns: + True on successful delete, when the collection is already absent, + or when Vector Search isn't available in this region. False if the + drain or delete call raised an unexpected exception. + """ + client = vectorsearch.VectorSearchServiceClient() + collection_name = f"projects/{project_id}/locations/{location}/collections/{collection_id}" + + try: + client.get_collection( + request=vectorsearch.GetCollectionRequest(name=collection_name) + ) + except exceptions.NotFound: + logger.info( + "Vector Search collection %s does not exist, skipping", + collection_id, + ) + return True + except exceptions.MethodNotImplemented: + # Vector Search 2.0 isn't available in this region. Nothing to delete + # because nothing could have been created here in the first place. + logger.info( + "Vector Search not available in %s; no collection to clean up.", + location, + ) + return True + + if dry_run: + logger.info( + "[DRY RUN] Would drain data objects and delete Vector Search collection: %s", + collection_name, + ) + return True + + try: + logger.info( + "Draining data objects from %s before deletion...", collection_id + ) + drained = _drain_data_objects(collection_name) + logger.info("Drained %d data object(s) from %s", drained, collection_id) + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + "Failed to drain data objects from %s: %s\n" + " Collection deletion will fail until it is empty. Try the manual path:\n" + " gcloud ai vector-search-collections delete %s " + "--region=%s --project=%s --force", + collection_id, + e, + collection_id, + location, + project_id, + ) + return False + + try: + operation = client.delete_collection( + request=vectorsearch.DeleteCollectionRequest(name=collection_name) + ) + operation.result() + logger.info("Deleted Vector Search collection: %s", collection_name) + return True + except Exception as e: # pylint: disable=broad-exception-caught + logger.error( + "Failed to delete Vector Search collection %s: %s\n" + " Data objects were drained, but the delete still failed. Possible causes:\n" + " - An async embedding/index operation is still in flight (wait 2-3 min and retry)\n" + " - Stragglers remain that the drain pass missed; verify in Cloud Console.\n" + " Manual cleanup:\n" + " gcloud ai vector-search-collections delete %s " + "--region=%s --project=%s --force", + collection_id, + e, + collection_id, + location, + project_id, + ) + return False + + +def delete_cloudrun( + project_id: str, location: str, service_name: str, dry_run: bool +) -> bool: + """Delete a Cloud Run service. + + Args: + project_id: GCP project ID. + location: GCP region of the service. + service_name: Cloud Run service name. + dry_run: If True, log what would be deleted but don't delete. + + Returns: + True on successful delete, when the service is already absent, or + when ``gcloud describe`` times out (treated as expected-absent). + False if the delete call raised an unexpected error. + """ + try: + result = subprocess.run( + [ + "gcloud", + "run", + "services", + "describe", + service_name, + "--region", + location, + "--project", + project_id, + "--format", + "value(name)", + ], + capture_output=True, + text=True, + timeout=GCLOUD_DESCRIBE_TIMEOUT_SEC, + check=False, + ) + except subprocess.TimeoutExpired: + logger.warning( + "gcloud timed out checking Cloud Run service %s after %ds. Skipping. " + "If you did deploy to Cloud Run, delete the service manually: " + "gcloud run services delete %s --region=%s --project=%s", + service_name, + GCLOUD_DESCRIBE_TIMEOUT_SEC, + service_name, + location, + project_id, + ) + return True + + if result.returncode != 0: + logger.info( + "Cloud Run service %s does not exist, skipping", service_name + ) + return True + + if dry_run: + logger.info( + "[DRY RUN] Would delete Cloud Run service: %s", service_name + ) + return True + + try: + subprocess.run( + [ + "gcloud", + "run", + "services", + "delete", + service_name, + "--region", + location, + "--project", + project_id, + "--quiet", + ], + check=True, + ) + logger.info("Deleted Cloud Run service: %s", service_name) + return True + except subprocess.CalledProcessError as e: + logger.error( + "Failed to delete Cloud Run service %s: %s", service_name, e + ) + return False + + +def cleanup( + config: dict[str, Any], + dry_run: bool, + only: list[str], + dataset_id: str = "retail_skill_products", +) -> bool: + """Run cleanup for all or selected resource types. + + Args: + config: Parsed design-spec config dict. + dry_run: If True, log what would be deleted but don't delete. + only: Resource types to delete; subset of + ``["bigquery", "vectorsearch", "cloudrun"]``. + dataset_id: BigQuery dataset name to delete. + + Returns: + True if every step (across the resource types in ``only``) + succeeded; False if any step returned failure. + """ + project_id = config.get("gcp_project_id", "") + if not project_id: + logger.error("gcp_project_id not set in config") + sys.exit(1) + + location = config.get("gcp_region", "us-central1") + project_name = config.get("project_name", "product-search") + collection_id = config.get( + "collection_id", "retail-skill-products-collection" + ) + + mode = "[DRY RUN] " if dry_run else "" + logger.info("%sCleaning up resources for project: %s", mode, project_id) + logger.info("%sResource types: %s", mode, ", ".join(only)) + logger.info("%sBigQuery dataset: %s", mode, dataset_id) + logger.info("") + + failures: list[str] = [] + + def _track(label: str, success: bool) -> None: + if not success: + failures.append(label) + + if "bigquery" in only: + _track("bigquery", delete_bigquery(project_id, dataset_id, dry_run)) + + if "vectorsearch" in only: + _track( + "vectorsearch (collection)", + delete_vectorsearch_collection( + project_id, location, collection_id, dry_run + ), + ) + + if "cloudrun" in only: + _track( + "cloudrun", + delete_cloudrun(project_id, location, project_name, dry_run), + ) + + logger.info("") + if dry_run: + logger.info("Dry run complete. No resources were deleted.") + logger.info("Run with --confirm to actually delete.") + return True + + if failures: + logger.error( + "Cleanup finished with errors. Failed steps: %s", + ", ".join(failures), + ) + logger.error("See messages above for the manual cleanup commands.") + return False + + logger.info("Cleanup complete. All selected resources deleted.") + return True + + +def main(): + """Parse CLI arguments and run cleanup.""" + parser = argparse.ArgumentParser( + description="Clean up all GCP resources created by the product search agent" + ) + parser.add_argument( + "--config", required=True, help="Path to design-spec.md" + ) + parser.add_argument( + "--confirm", + action="store_true", + help="Actually delete resources (without this flag, runs in dry-run mode)", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Show what would be deleted without deleting", + ) + parser.add_argument( + "--only", + default="", + help=( + f"Comma-separated resource types to delete. Valid: {','.join(ALL_RESOURCE_TYPES)}. " + f"Default if omitted: {','.join(DEFAULT_RESOURCE_TYPES)} " + "(Cloud Run excluded by default; opt in with --only cloudrun if you deployed to Cloud Run)." + ), + ) + parser.add_argument( + "--dataset-id", + default="retail_skill_products", + help="BigQuery dataset name to delete (default: retail_skill_products)", + ) + + args = parser.parse_args() + + config = load_config(args.config) + if not config: + logger.error("Config file not found or empty: %s", args.config) + sys.exit(1) + + # Honor dataset_id from design-spec.md unless overridden on the CLI. + # Otherwise per-case suffixes get ignored and the wrong dataset is "deleted" + # (i.e. cleanup looks for the default and reports a false success). + dataset_id = args.dataset_id + if dataset_id == "retail_skill_products" and config.get("dataset_id"): + dataset_id = config["dataset_id"] + + dry_run = not args.confirm or args.dry_run + + if args.only: + only = [r.strip() for r in args.only.split(",")] + invalid = [r for r in only if r not in ALL_RESOURCE_TYPES] + if invalid: + parser.error( + f"Invalid resource types: {invalid}. Valid: {ALL_RESOURCE_TYPES}" + ) + else: + only = DEFAULT_RESOURCE_TYPES + + if not dry_run: + project_id = config.get("gcp_project_id", "unknown") + print( + f"\nYou are about to permanently delete GCP resources in project: {project_id}" + ) + print(f"Resource types: {', '.join(only)}") + try: + answer = ( + input("\nAre you sure? Type 'yes' to confirm: ").strip().lower() + ) + except (EOFError, KeyboardInterrupt): + answer = "" + if answer != "yes": + print("Aborted. No resources were deleted.") + sys.exit(0) + + ok = cleanup(config, dry_run, only, dataset_id=dataset_id) + sys.exit(0 if ok else 1) + + +if __name__ == "__main__": + main() diff --git a/skills/retail/product-search/scripts/config.py b/skills/retail/product-search/scripts/config.py new file mode 100644 index 0000000000..31aeb2a5fa --- /dev/null +++ b/skills/retail/product-search/scripts/config.py @@ -0,0 +1,70 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Single source of truth for retail-product-search configuration. + +Loads `.env` via `python-dotenv` at import, then exposes every configurable +value via the module-level `config` object. Reads are lazy — each attribute +access calls `os.getenv()` — so that scripts which mutate `os.environ` (for +example `setup._design_spec_to_env`) see their changes reflected on the +very next read. + +`.env.example` at the recipe root documents every key below. When adding +a new value, add it in three places: `.env.example`, this module, and +the code that consumes it. +""" + +from __future__ import annotations + +import os + +from dotenv import load_dotenv + +load_dotenv() + +# Vertex AI genai client bootstrap. Centralized so downstream helpers can +# rely on it being set before the first genai call. +os.environ.setdefault("GOOGLE_GENAI_USE_VERTEXAI", "True") + + +class _Config: + """Lazy env-var accessor. Each read hits os.getenv() fresh.""" + + @property + def GOOGLE_CLOUD_PROJECT(self) -> str: + return os.getenv("GOOGLE_CLOUD_PROJECT", "") + + @property + def GOOGLE_CLOUD_LOCATION(self) -> str: + return os.getenv("GOOGLE_CLOUD_LOCATION", "global") + + @property + def VECTOR_SEARCH_LOCATION(self) -> str: + return os.getenv("VECTOR_SEARCH_LOCATION", "us-central1") + + @property + def VECTOR_SEARCH_COLLECTION(self) -> str: + """Explicit collection path if set; empty string means 'derive from other config'.""" + return os.getenv("VECTOR_SEARCH_COLLECTION", "") + + @property + def GEMINI_MODEL(self) -> str: + return os.getenv("GEMINI_MODEL", "gemini-3.5-flash") + + @property + def EMBEDDING_MODEL(self) -> str: + return os.getenv("EMBEDDING_MODEL", "gemini-embedding-001") + + +config = _Config() diff --git a/skills/retail/product-search/scripts/ingest_bigquery.py b/skills/retail/product-search/scripts/ingest_bigquery.py new file mode 100644 index 0000000000..46e8bcf2a7 --- /dev/null +++ b/skills/retail/product-search/scripts/ingest_bigquery.py @@ -0,0 +1,487 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Ingest product catalog data into BigQuery. + +Supports CSV and JSON source formats from GCS or local files. +Validates products against the configured schema before loading. + +Usage: + python ingest_bigquery.py \\ + --project-id my-project \\ + --gcs-bucket my-project-products \\ + --gcs-path products.csv + + python ingest_bigquery.py \\ + --project-id my-project \\ + --local-file data/products.json \\ + --format json + + # Or use design-spec.md for defaults: + python ingest_bigquery.py --config design-spec.md \\ + --local-file data/products.csv +""" + +# pylint: disable=line-too-long +# (validation error messages and argparse help strings are intentionally long.) + +import argparse +import csv +import io +import json +import logging +import pathlib +import sys +from typing import Any + +from google.cloud import bigquery, storage + +# Allow imports from the script's own directory before pip install -e is run. +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent)) +from _setup_utils import ( + load_config, # pylint: disable=wrong-import-position +) + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Configure these for your product schema. +# Basic: product_id, name, price, description +# Standard: + category, brand, image_url +# Extended: + rating, stock, manufacturer +# Full: + variants, tags, specifications, reviews +REQUIRED_FIELDS = ["product_id", "name", "price", "description"] + +SCHEMA = [ + bigquery.SchemaField("product_id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("name", "STRING", mode="REQUIRED"), + bigquery.SchemaField("price", "FLOAT64", mode="REQUIRED"), + bigquery.SchemaField("description", "STRING", mode="REQUIRED"), + bigquery.SchemaField("category", "STRING", mode="NULLABLE"), + bigquery.SchemaField("brand", "STRING", mode="NULLABLE"), + bigquery.SchemaField("image_url", "STRING", mode="NULLABLE"), + bigquery.SchemaField("rating", "FLOAT64", mode="NULLABLE"), + bigquery.SchemaField("stock", "INT64", mode="NULLABLE"), +] + + +def validate_product(product: dict[str, Any], row_num: int) -> list[str]: + """Validate a single product record. + + Args: + product: One product as a dict from the source file. + row_num: 1-based row number for error messages. + + Returns: + List of error strings for this row (empty if valid). + """ + errors = [] + for field in REQUIRED_FIELDS: + if field not in product or not product[field]: + errors.append(f"Row {row_num}: missing required field '{field}'") + + if product.get("price"): + try: + float(product["price"]) + except (ValueError, TypeError): + errors.append( + f"Row {row_num}: 'price' must be numeric, got '{product['price']}'" + ) + + if product.get("stock"): + try: + int(product["stock"]) + except (ValueError, TypeError): + errors.append( + f"Row {row_num}: 'stock' must be an integer, got '{product['stock']}'" + ) + + return errors + + +def convert_types(product: dict[str, Any]) -> dict[str, Any]: + """Convert string values to proper types for BigQuery. + + Args: + product: Raw product dict (typically with string-typed values + from CSV parsing). + + Returns: + New dict with ``price``/``rating`` cast to float, ``stock`` cast + to int, and string fields passed through. + """ + converted = {} + converted["product_id"] = product.get("product_id", "") + converted["name"] = product.get("name", "") + converted["description"] = product.get("description", "") + + if product.get("price"): + converted["price"] = float(product["price"]) + + if product.get("rating"): + converted["rating"] = float(product["rating"]) + + # `stock: 0` is a legit value (out-of-stock product); only skip when + # the field is missing or an empty string from CSV. + stock = product.get("stock") + if stock is not None and stock != "": + converted["stock"] = int(stock) + + for field in ["category", "brand", "image_url"]: + if field in product: + converted[field] = product[field] + + return converted + + +def load_from_csv(source: str) -> list[dict[str, Any]]: + """Load products from CSV (GCS URI or local path). + + Args: + source: Either a ``gs://bucket/path.csv`` URI or a local filesystem + path to a CSV file. + + Returns: + List of validated, type-converted product dicts. + """ + if source.startswith("gs://"): + parts = source.replace("gs://", "").split("/", 1) + client = storage.Client() + blob = client.bucket(parts[0]).blob(parts[1]) + content = blob.download_as_text() + reader = csv.DictReader(content.splitlines()) + else: + with open(source, encoding="utf-8") as f: + reader = csv.DictReader(f) + return _validate_and_convert(list(reader)) + + return _validate_and_convert(list(reader)) + + +def load_from_json(source: str) -> list[dict[str, Any]]: + """Load products from JSON or JSONL (GCS URI or local path). + + Args: + source: Either a ``gs://bucket/path.json[l]`` URI or a local + filesystem path. JSONL is detected from the suffix. + + Returns: + List of validated, type-converted product dicts. + """ + if source.startswith("gs://"): + parts = source.replace("gs://", "").split("/", 1) + client = storage.Client() + blob = client.bucket(parts[0]).blob(parts[1]) + content = blob.download_as_text() + else: + content = pathlib.Path(source).read_text(encoding="utf-8") + + if source.endswith(".jsonl"): + raw = [ + json.loads(line) + for line in content.strip().splitlines() + if line.strip() + ] + else: + parsed = json.loads(content) + raw = parsed if isinstance(parsed, list) else parsed.get("products", []) + + return _validate_and_convert(raw) + + +def _validate_and_convert( + raw_products: list[dict[str, Any]], +) -> list[dict[str, Any]]: + """Validate and type-convert a list of product dicts. + + Logs a summary of skipped rows when validation produces errors. + + Args: + raw_products: Raw dicts straight from the source loader. + + Returns: + List of cleaned, type-converted product dicts (skipped rows are + excluded). + """ + products = [] + all_errors = [] + + for i, product in enumerate(raw_products, start=1): + errors = validate_product(product, i) + if errors: + all_errors.extend(errors) + continue + products.append(convert_types(product)) + + if all_errors: + logger.warning( + "Skipped rows with %d validation errors:", len(all_errors) + ) + for err in all_errors[:10]: + logger.warning(" %s", err) + if len(all_errors) > 10: + logger.warning(" ... and %d more", len(all_errors) - 10) + + logger.info("Loaded %d valid products", len(products)) + return products + + +def _ensure_dataset_and_table( + client: bigquery.Client, + dataset_ref: str, + table_ref: str, + table_id: str, + if_exists: str, +) -> bool: + """Ensure the BigQuery dataset and table exist based on if_exists policy. + + Args: + client: An initialized BigQuery client. + dataset_ref: Fully qualified dataset reference (``project.dataset``). + table_ref: Fully qualified table reference (``project.dataset.table``). + table_id: Bare table name (used when creating the table). + if_exists: Behavior when the dataset already exists: ``"error"``, + ``"skip"``, or ``"rename"`` (interactive only). + + Returns: + ``True`` if ingestion should continue (dataset/table ready), + ``False`` if the existing data should be left alone (skip path). + + Raises: + ValueError: When ``if_exists="rename"`` and the user supplied a new + dataset name; the new name is the exception's argument. The + caller must re-derive refs from this name and re-invoke. + """ + try: + client.get_dataset(dataset_ref) + dataset_exists = True + except Exception: # pylint: disable=broad-exception-caught + dataset_exists = False + + if dataset_exists: + if if_exists == "skip": + try: + client.get_table(table_ref) + count_query = f"SELECT COUNT(*) AS n FROM `{table_ref}`" + row_count = next(iter(client.query(count_query).result())).n + if row_count > 0: + logger.info( + "Dataset %s and table %s already exist with %d rows " + "-- skipping ingestion.", + dataset_ref, + table_ref, + row_count, + ) + return False + logger.info( + "Table %s exists but is empty -- loading rows.", table_ref + ) + except Exception: # pylint: disable=broad-exception-caught + logger.info( + "Dataset %s exists but table %s missing -- creating table.", + dataset_ref, + table_ref, + ) + table = bigquery.Table(table_ref, schema=SCHEMA) + client.create_table(table) + elif if_exists == "rename" and sys.stdin.isatty(): + logger.warning("Dataset %s already exists.", dataset_ref) + try: + new_name = input( + " Enter a different dataset name (or Ctrl+C to cancel): " + ).strip() + except (EOFError, KeyboardInterrupt): + print() + logger.info("Cancelled.") + sys.exit(0) + if not new_name: + logger.error("No name provided. Exiting.") + sys.exit(1) + # Caller must re-derive refs from new_name; signal by returning None + # We return the new dataset ID via sys.exit to keep signature simple. + # Raise so the caller can re-try with new name. + raise ValueError(new_name) + else: + logger.error( + "Dataset %s already exists. " + "Re-run with --if-exists skip to reuse it, or --dataset-id .", + dataset_ref, + ) + sys.exit(1) + + if not dataset_exists: + logger.info("Creating dataset %s", dataset_ref) + dataset = bigquery.Dataset(dataset_ref) + dataset.location = "US" + client.create_dataset(dataset) + + logger.info("Creating table %s", table_ref) + table = bigquery.Table(table_ref, schema=SCHEMA) + client.create_table(table) + + return True + + +def ingest( + project_id: str, + dataset_id: str, + table_id: str, + source: str, + source_format: str = "csv", + if_exists: str = "error", +): + """Ingest products into BigQuery. + + if_exists controls behavior when the target dataset already exists: + - "error" (default): fail with a clear message + - "skip": no-op if the table already has rows (idempotent re-runs) + - "rename": prompt for a new name (interactive only) + """ + client = bigquery.Client(project=project_id) + + dataset_ref = f"{project_id}.{dataset_id}" + table_ref = f"{project_id}.{dataset_id}.{table_id}" + + try: + should_continue = _ensure_dataset_and_table( + client, dataset_ref, table_ref, table_id, if_exists + ) + except ValueError as new_name: + # "rename" path: recurse with the new dataset name. + dataset_id = str(new_name) + dataset_ref = f"{project_id}.{dataset_id}" + table_ref = f"{project_id}.{dataset_id}.{table_id}" + try: + client.get_dataset(dataset_ref) + logger.error( + "Dataset %s also exists. Re-run with --dataset-id .", + dataset_ref, + ) + sys.exit(1) + except Exception: # pylint: disable=broad-exception-caught + pass + should_continue = _ensure_dataset_and_table( + client, dataset_ref, table_ref, table_id, "error" + ) + + if not should_continue: + return + + # Load products + if source_format == "json": + products = load_from_json(source) + else: + products = load_from_csv(source) + + if not products: + logger.error("No valid products to ingest") + sys.exit(1) + + # Use a load job (batch) instead of streaming insert. Streaming inserts go + # through a buffer that can keep stale dataset-uuid references for ~5-10 min + # after a delete, which causes "Dataset is deleted" errors when the same + # dataset name is recreated quickly (e.g. test re-runs). Load jobs route + # through a different path with no such race. + job_config = bigquery.LoadJobConfig( + schema=SCHEMA, + write_disposition=bigquery.WriteDisposition.WRITE_TRUNCATE, + source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON, + ) + ndjson = "\n".join(json.dumps(p) for p in products).encode("utf-8") + load_job = client.load_table_from_file( + io.BytesIO(ndjson), + table_ref, + job_config=job_config, + ) + load_job.result() # Wait for completion; raises on error. + if load_job.errors: + logger.error("Load job errors: %s", load_job.errors) + sys.exit(1) + + logger.info( + "Successfully ingested %d products to %s", len(products), table_ref + ) + + +def main(): + """Parse CLI arguments and ingest products to BigQuery.""" + parser = argparse.ArgumentParser( + description="Ingest product catalog to BigQuery" + ) + parser.add_argument( + "--config", + default="", + help="Path to design-spec.md (provides defaults for other args)", + ) + parser.add_argument("--project-id", help="GCP project ID") + parser.add_argument( + "--dataset-id", + default="retail_skill_products", + help="BigQuery dataset ID", + ) + parser.add_argument( + "--table-id", default="products", help="BigQuery table ID" + ) + parser.add_argument( + "--gcs-bucket", help="GCS bucket name (used with --gcs-path)" + ) + parser.add_argument("--gcs-path", help="Path to data file in GCS bucket") + parser.add_argument("--local-file", help="Path to local data file") + parser.add_argument( + "--format", choices=["csv", "json"], default="csv", help="Source format" + ) + parser.add_argument( + "--if-exists", + choices=["error", "skip", "rename"], + default="error", + help=( + "Behavior when dataset already exists: error (default), " + "skip (idempotent re-run), rename (interactive)" + ), + ) + + args = parser.parse_args() + + # Load design-spec.md defaults -- CLI args override config values + if args.config: + cfg = load_config(args.config) + if not args.project_id: + args.project_id = cfg.get("gcp_project_id", "") + + if not args.project_id: + parser.error( + "--project-id is required (or set gcp_project_id in design-spec.md)" + ) + if args.local_file: + source = args.local_file + elif args.gcs_bucket and args.gcs_path: + source = f"gs://{args.gcs_bucket}/{args.gcs_path}" + else: + parser.error( + "Provide either --local-file or both --gcs-bucket and --gcs-path" + ) + + ingest( + project_id=args.project_id, + dataset_id=args.dataset_id, + table_id=args.table_id, + source=source, + source_format=args.format, + if_exists=args.if_exists, + ) + + +if __name__ == "__main__": + main() diff --git a/skills/retail/product-search/scripts/ingest_vertex_search.py b/skills/retail/product-search/scripts/ingest_vertex_search.py new file mode 100644 index 0000000000..7a0ae02a27 --- /dev/null +++ b/skills/retail/product-search/scripts/ingest_vertex_search.py @@ -0,0 +1,502 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +r"""Ingest products into Vertex AI Vector Search 2.0 Collection. + +Reads products from BigQuery, creates a Vector Search 2.0 Collection +(if it doesn't exist), and inserts products one at a time. `AlreadyExists` +errors are caught so re-runs are idempotent. Embeddings are auto-generated +by the Collection's configured model. + +Usage: + python ingest_vertex_search.py \\ + --project-id my-project \\ + --collection-id my-products + + # Or use design-spec.md for defaults: + python ingest_vertex_search.py --config design-spec.md +""" + +# pylint: disable=line-too-long +# (error messages with collection_id hints and gcloud commands are intentionally long.) + +import argparse +import logging +import pathlib +import sys +from typing import Any + +from google.api_core import exceptions +from google.cloud import bigquery, vectorsearch +from google.protobuf import struct_pb2 + +# Allow imports from the script's own directory before pip install -e is run. +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent)) +from _setup_utils import ( + load_config, # pylint: disable=wrong-import-position +) + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +DEFAULT_EMBEDDING_MODEL = "gemini-embedding-001" +DEFAULT_EMBEDDING_FIELDS = ["name", "description", "category", "brand"] + +# Vector dimensions each embedding model emits. Vector Search collections +# must be created with the correct dimension count or vectors are rejected +# / index results are wrong. gemini-embedding-001's native output is 3072, +# but we request the 768-dim projection for parity with text-embedding-* +# and lower storage/query cost. Add new entries as models are qualified. +EMBEDDING_MODEL_DIMENSIONS: dict[str, int] = { + "gemini-embedding-001": 768, + "text-embedding-004": 768, + "text-embedding-005": 768, + "text-multilingual-embedding-002": 768, +} +DEFAULT_EMBEDDING_DIMENSIONS = 768 + +# All product fields stored as data object fields +PRODUCT_DATA_FIELDS = { + "product_id": "string", + "name": "string", + "price": "number", + "description": "string", + "category": "string", + "brand": "string", + "image_url": "string", + "rating": "number", + "stock": "integer", +} + + +def fetch_products( + project_id: str, dataset_id: str, table_id: str +) -> list[dict[str, Any]]: + """Fetch all products from a BigQuery table. + + Args: + project_id: GCP project ID. + dataset_id: BigQuery dataset name. + table_id: BigQuery table name. + + Returns: + List of product dicts (one per BQ row). + """ + client = bigquery.Client(project=project_id) + query = f"SELECT * FROM `{project_id}.{dataset_id}.{table_id}`" + + logger.info( + "Fetching products from %s.%s.%s", project_id, dataset_id, table_id + ) + results = client.query(query).result() + products = [dict(row.items()) for row in results] + logger.info("Fetched %d products", len(products)) + return products + + +def build_text_template(embedding_fields: list[str]) -> str: + """Build the ``text_template`` for auto-embedding from field names. + + The template uses ``{field_name}`` placeholders that VS 2.0 resolves + from the data object's data fields. + + Args: + embedding_fields: Field names to include in the template. + + Returns: + Pipe-separated template string, e.g. + ``"name: {name} | description: {description}"``. + """ + parts = [f"{field}: {{{field}}}" for field in embedding_fields] + return " | ".join(parts) + + +def get_data_schema(products: list[dict[str, Any]]) -> dict[str, Any]: + """Build the data schema from known product fields. + + Only includes fields that exist in the actual data (sampled from the + first 10 products). + + Args: + products: Product dicts loaded from BigQuery. + + Returns: + JSON-Schema-style dict ``{"type": "object", "properties": {...}}`` + with one property per detected field. + """ + properties: dict[str, Any] = {} + sample_keys: set[str] = set() + for p in products[:10]: + sample_keys.update(p.keys()) + + for field, field_type in PRODUCT_DATA_FIELDS.items(): + if field in sample_keys: + properties[field] = {"type": field_type} + + return {"type": "object", "properties": properties} + + +def create_collection_if_needed( # pylint: disable=too-many-arguments + project_id: str, + location: str, + collection_id: str, + embedding_model: str, + embedding_fields: list[str], + data_schema: dict[str, Any], +) -> str: + """Create a Vector Search 2.0 Collection if it doesn't exist. + + Args: + project_id: GCP project ID. + location: GCP region for the collection. + collection_id: Collection ID (RFC1035-compliant, max 63 chars). + embedding_model: Auto-embedding model ID (e.g. + ``"gemini-embedding-001"``). + embedding_fields: Fields used in the embedding text template. + data_schema: JSON-Schema-style dict from :func:`get_data_schema`. + + Returns: + Fully qualified collection resource path. + """ + client = vectorsearch.VectorSearchServiceClient() + parent = f"projects/{project_id}/locations/{location}" + collection_name = f"{parent}/collections/{collection_id}" + + try: + client.get_collection( + request=vectorsearch.GetCollectionRequest(name=collection_name) + ) + logger.info("Collection '%s' already exists", collection_id) + return collection_name + except exceptions.NotFound: + pass + + text_template = build_text_template(embedding_fields) + logger.info( + "Creating collection '%s' with text_template: %s", + collection_id, + text_template, + ) + + dimensions = EMBEDDING_MODEL_DIMENSIONS.get( + embedding_model, DEFAULT_EMBEDDING_DIMENSIONS + ) + if embedding_model not in EMBEDDING_MODEL_DIMENSIONS: + logger.warning( + "Embedding model '%s' is not in EMBEDDING_MODEL_DIMENSIONS; " + "using default %d dimensions. Add it to the lookup table in " + "ingest_vertex_search.py if this is wrong for your model.", + embedding_model, + DEFAULT_EMBEDDING_DIMENSIONS, + ) + + request = vectorsearch.CreateCollectionRequest( + parent=parent, + collection_id=collection_id, + collection={ + "data_schema": data_schema, + "vector_schema": { + "text_embedding": { + "dense_vector": { + "dimensions": dimensions, + "vertex_embedding_config": { + "model_id": embedding_model, + "text_template": text_template, + "task_type": "RETRIEVAL_DOCUMENT", + }, + }, + }, + }, + }, + ) + + try: + operation = client.create_collection(request=request) + logger.info("Waiting for collection creation to complete...") + operation.result() + logger.info("Collection created successfully") + except Exception as e: # pylint: disable=broad-exception-caught + # The SDK often mangles the underlying API error into a confusing gRPC + # StatusCode mismatch. Most common real causes: + # 1. collection_id > 63 chars (RFC1035 limit) + # 2. collection_id has uppercase or invalid characters + # 3. location/region doesn't support Vector Search + # 4. project lacks aiplatform.user IAM + hints = [] + if len(collection_id) > 63: + hints.append( + f"collection_id is {len(collection_id)} chars (max 63 per RFC1035). " + "Shorten it via the 'collection_id' key in design-spec.md." + ) + if not collection_id.islower() or any( + c.isupper() for c in collection_id + ): + hints.append( + "collection_id must be all lowercase letters, digits, and dashes." + ) + hint_text = ( + ("\n Likely cause: " + " ".join(hints)) + if hints + else ( + "\n Possible causes: collection_id too long (>63), uppercase chars, " + "region doesn't support Vector Search, or missing aiplatform.user IAM." + ) + ) + logger.error( + "Error creating collection '%s': %s%s\n" + " Manual cleanup if the collection was partially created:\n" + " gcloud ai vector-search-collections delete %s " + "--region=%s --project=%s", + collection_id, + e, + hint_text, + collection_id, + location, + project_id, + ) + sys.exit(1) + + return collection_name + + +def ingest_products( + collection_path: str, + products: list[dict[str, Any]], +) -> None: + """Insert products into the Vector Search 2.0 Collection. + + Embeddings are auto-generated by the Collection's configured model. + Uses individual inserts for reliability. + """ + client = vectorsearch.DataObjectServiceClient() + + created = 0 + skipped = 0 + errors = 0 + + for i, product in enumerate(products): + product_id = str(product.get("product_id", "")) + # Ensure ID is RFC1035 compliant (lowercase, starts with letter) + safe_id = product_id.lower().replace("_", "-") + if safe_id and not safe_id[0].isalpha(): + safe_id = "p-" + safe_id + + data_struct = struct_pb2.Struct() # pylint: disable=no-member + for field, field_type in PRODUCT_DATA_FIELDS.items(): + value = product.get(field) + if value is None: + continue + # Preserve proper types for the schema + if field_type == "number": + data_struct.update({field: float(value)}) + elif field_type == "integer": + data_struct.update({field: int(value)}) + else: + data_struct.update({field: str(value)}) + + try: + client.create_data_object( + parent=collection_path, + data_object_id=safe_id, + data_object=vectorsearch.DataObject(data=data_struct), + ) + created += 1 + except exceptions.AlreadyExists: + skipped += 1 + except Exception as e: # pylint: disable=broad-exception-caught + errors += 1 + logger.warning("Failed to insert %s: %s", safe_id, e) + + if (i + 1) % 50 == 0 or (i + 1) == len(products): + logger.info("Processed %d/%d products", i + 1, len(products)) + + logger.info( + "Ingestion complete: %d created, %d skipped, %d errors", + created, + skipped, + errors, + ) + + +def ingest_pipeline( # pylint: disable=too-many-arguments + *, + project_id: str, + location: str, + dataset_id: str, + table_id: str, + collection_id: str, + embedding_model: str, + embedding_fields: list[str], +) -> str | None: + """Run the Vector Search ingestion pipeline programmatically. + + Args: + project_id: GCP project ID. + location: GCP region for the collection. + dataset_id: BigQuery dataset to read products from. + table_id: BigQuery table to read products from. + collection_id: Vector Search collection ID. + embedding_model: Auto-embedding model ID. + embedding_fields: Fields used in the embedding text template. + + Returns: + The collection path on success (whether freshly created or + already populated and skipped). ``None`` on error (e.g. no + products found in BigQuery). + """ + collection_path = f"projects/{project_id}/locations/{location}/collections/{collection_id}" + + # Re-run short-circuit: if the collection already exists AND is populated, + # skip the BQ fetch + per-row insert loop. + try: + vs_client = vectorsearch.VectorSearchServiceClient() + vs_client.get_collection( + request=vectorsearch.GetCollectionRequest(name=collection_path) + ) + search_client = vectorsearch.DataObjectSearchServiceClient() + probe = search_client.search_data_objects( + request=vectorsearch.SearchDataObjectsRequest( + parent=collection_path, + semantic_search=vectorsearch.SemanticSearch( + search_text="probe", + search_field="text_embedding", + task_type="QUESTION_ANSWERING", + top_k=1, + output_fields=vectorsearch.OutputFields( + data_fields=["product_id"] + ), + ), + ) + ) + if any(True for _ in probe): + logger.info( + "Collection '%s' already populated -- " + "skipping ingestion (re-run idempotent path).", + collection_id, + ) + logger.info("Collection path: %s", collection_path) + return collection_path + except exceptions.NotFound: + pass + + products = fetch_products(project_id, dataset_id, table_id) + if not products: + logger.error("No products found in BigQuery") + return None + + data_schema = get_data_schema(products) + collection_path = create_collection_if_needed( + project_id, + location, + collection_id, + embedding_model, + embedding_fields, + data_schema, + ) + + ingest_products(collection_path, products) + + logger.info( + "Successfully ingested %d products into collection '%s'", + len(products), + collection_id, + ) + logger.info("Collection path: %s", collection_path) + return collection_path + + +def main(): + """Parse CLI arguments and run the Vector Search ingestion pipeline.""" + parser = argparse.ArgumentParser( + description="Ingest products to Vertex AI Vector Search 2.0" + ) + parser.add_argument( + "--config", + default="", + help="Path to design-spec.md (provides defaults for other args)", + ) + parser.add_argument("--project-id", help="GCP project ID") + parser.add_argument("--location", default="us-central1", help="GCP region") + parser.add_argument( + "--dataset-id", default="retail_skill_products", help="BigQuery dataset" + ) + parser.add_argument("--table-id", default="products", help="BigQuery table") + parser.add_argument( + "--collection-id", + default="retail-skill-products-collection", + help="Vector Search 2.0 Collection ID", + ) + parser.add_argument( + "--embedding-model", + default=DEFAULT_EMBEDDING_MODEL, + help="Auto-embedding model (default: gemini-embedding-001)", + ) + parser.add_argument( + "--embedding-fields", + default=",".join(DEFAULT_EMBEDDING_FIELDS), + help="Comma-separated fields for embedding text template", + ) + + args = parser.parse_args() + + # Load design-spec.md defaults -- CLI args override config values + if args.config: + cfg = load_config(args.config) + if not args.project_id: + args.project_id = cfg.get("gcp_project_id", "") + if args.location == "us-central1" and cfg.get("gcp_region"): + args.location = cfg["gcp_region"] + if cfg.get("dataset_id") and args.dataset_id == "retail_skill_products": + args.dataset_id = cfg["dataset_id"] + if cfg.get("table_id") and args.table_id == "products": + args.table_id = cfg["table_id"] + if ( + cfg.get("collection_id") + and args.collection_id == "retail-skill-products-collection" + ): + args.collection_id = cfg["collection_id"] + if cfg.get("embedding_model"): + model_name = cfg["embedding_model"].split(" ")[0] + if args.embedding_model == DEFAULT_EMBEDDING_MODEL: + args.embedding_model = model_name + if cfg.get("embedding_fields") and args.embedding_fields == ",".join( + DEFAULT_EMBEDDING_FIELDS + ): + args.embedding_fields = cfg["embedding_fields"] + + if not args.project_id: + parser.error( + "--project-id is required (or set gcp_project_id in design-spec.md)" + ) + + embedding_fields = [f.strip() for f in args.embedding_fields.split(",")] + + result_path = ingest_pipeline( + project_id=args.project_id, + location=args.location, + dataset_id=args.dataset_id, + table_id=args.table_id, + collection_id=args.collection_id, + embedding_model=args.embedding_model, + embedding_fields=embedding_fields, + ) + if result_path: + logger.info( + "Set VECTOR_SEARCH_COLLECTION env var to this path in your agent." + ) + + +if __name__ == "__main__": + main() diff --git a/skills/retail/product-search/scripts/retrievers.py b/skills/retail/product-search/scripts/retrievers.py new file mode 100644 index 0000000000..a1e23035b1 --- /dev/null +++ b/skills/retail/product-search/scripts/retrievers.py @@ -0,0 +1,164 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Retrieval helpers for the retail product search agent. + +Provides :func:`search_collection` for semantic product lookup via +Vertex AI Vector Search 2.0, and :func:`search` as a convenience +wrapper that reads the collection path from the environment. +""" + +# pylint: disable=line-too-long +# (error messages and example URLs are intentionally long; breaking them hurts copy-paste UX.) + +import re + +from google.cloud import vectorsearch + +from scripts.config import config + +_COLLECTION_PATH_RE = re.compile( + r"^projects/[^/\s]+/locations/[^/\s]+/collections/[^/\s]+$" +) + + +def _create_search_client(): + return vectorsearch.DataObjectSearchServiceClient() + + +def _format_result(index: int, result) -> str: + """Format a single search result as a compact one-line string. + + Args: + index: 1-based product index used in the formatted output. + result: A Vector Search ``SearchDataObjectsResponse`` entry. + + Returns: + A one-line string of the form + ``"Product N: name, $price, by brand, rated R out of 5, "``. + """ + data = result.data_object.data + name = data.get("name", "Unknown") + price = data.get("price", "N/A") + brand = data.get("brand", "") + rating = data.get("rating", "") + description = data.get("description", "") + + parts = [f"{name}, ${price}"] + if brand: + parts.append(f"by {brand}") + if rating: + parts.append(f"rated {rating} out of 5") + if description: + parts.append(description[:80]) + + return f"Product {index}: " + ", ".join(parts) + + +def search_collection( + query: str, + collection_path: str, + top_k: int = 10, +) -> str: + """Search a Vector Search 2.0 Collection using semantic search. + + Args: + query: The search query text. + collection_path: Full resource path of the collection. + top_k: Number of results to return. + + Returns: + Formatted string containing relevant document content. + """ + client = _create_search_client() + + request = vectorsearch.SearchDataObjectsRequest( + parent=collection_path, + semantic_search=vectorsearch.SemanticSearch( + search_text=query, + search_field="text_embedding", + task_type="QUESTION_ANSWERING", + top_k=top_k, + output_fields=vectorsearch.OutputFields( + data_fields=[ + "product_id", + "name", + "price", + "description", + "category", + "brand", + "rating", + "stock", + ] + ), + ), + ) + + results = client.search_data_objects(request) + + formatted_parts = [ + _format_result(i + 1, result) for i, result in enumerate(results) + ] + + if not formatted_parts: + return "No matching products found. Suggest broadening the search." + + return ( + "Found " + + str(len(formatted_parts)) + + " products. " + + ". ".join(formatted_parts) + ) + + +def search(query: str, top_k: int = 5) -> str: + """Run the SKILL.md smoke-test query against the configured collection. + + Reads the collection path from the ``VECTOR_SEARCH_COLLECTION`` env var, + which is what ``scripts/setup.py`` prints at the end of a successful run. + + Args: + query: The product search query in natural language. + top_k: Maximum number of matching products to return. + + Returns: + A formatted string with the matching products, or a "no matches" + message if the collection returned nothing. + + Raises: + RuntimeError: If ``VECTOR_SEARCH_COLLECTION`` is not set, or is set + to a malformed path (typically with an embedded newline from a + wrapped multi-line shell paste). + """ + raw = config.VECTOR_SEARCH_COLLECTION + if not raw: + raise RuntimeError( + "VECTOR_SEARCH_COLLECTION env var is not set. Set it to the collection " + "path printed by scripts/setup.py, e.g. " + "projects//locations/us-central1/collections/retail-skill-products-collection" + ) + # Strip whitespace -- a newline embedded mid-path (from a wrapped multi-line + # shell paste) silently causes the SDK to 501 from the wrong endpoint. + collection_path = raw.strip() + if not _COLLECTION_PATH_RE.match(collection_path): + raise RuntimeError( + "VECTOR_SEARCH_COLLECTION is malformed -- expected " + "'projects//locations//collections/' with no whitespace. " + f"Got: {raw!r}. " + "Tip: type the export on a single line, e.g. " + "export VECTOR_SEARCH_COLLECTION=" + '"projects/$GOOGLE_CLOUD_PROJECT/locations/us-central1/' + 'collections/retail-skill-products-collection"' + ) + return search_collection(query, collection_path, top_k=top_k) diff --git a/skills/retail/product-search/scripts/setup.py b/skills/retail/product-search/scripts/setup.py new file mode 100644 index 0000000000..e2e10503cd --- /dev/null +++ b/skills/retail/product-search/scripts/setup.py @@ -0,0 +1,288 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Automated project setup driven by design-spec.md. + +Reads the design-spec.md configuration and runs the appropriate ingestion +steps based on the user's answers. This is the single entry point for both +the `vs` CLI and manual setup. + +Runs scripts in-process via direct Python imports -- does NOT shell out to +`python scripts/...`. That makes setup safe to invoke from any cwd, against +any Python interpreter, regardless of whether the skill files were copied +into the workspace. + +Usage: + python scripts/setup.py --config ./design-spec.md + python scripts/setup.py --config ./design-spec.md --dry-run +""" + +# pylint: disable=line-too-long +# (log lines and error messages with config paths are intentionally long.) + +import argparse +import logging +import pathlib +import sys +from typing import Any + +# Allow imports from the script's own directory before pip install -e is run. +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent)) + +# pylint: disable=wrong-import-position +import ingest_bigquery +import ingest_vertex_search +import validate_schema +from _paths import SAMPLE_PRODUCTS_CSV +from _setup_utils import load_config + +# pylint: enable=wrong-import-position + +logging.basicConfig(level=logging.INFO, format="%(message)s") +logger = logging.getLogger(__name__) + + +# Mapping from design-spec.md YAML keys to the .env variables they populate. +# Each entry is `(yaml_key, env_key, default)`. +_DESIGN_SPEC_TO_ENV = [ + ("gcp_project_id", "GOOGLE_CLOUD_PROJECT", None), + ("gcp_region", "VECTOR_SEARCH_LOCATION", None), + ("gemini_model", "GEMINI_MODEL", None), + ("embedding_model", "EMBEDDING_MODEL", None), +] + + +def _write_env_file( + cfg: dict, env_path: pathlib.Path, collection_path: str | None = None +) -> None: + """Write a `.env` file next to design-spec.md from the parsed config. + + This is what makes design-spec.md a Q-MODE-only artifact: after this + runs, every downstream module reads its config from `.env` via + scripts.config, not from design-spec.md. + """ + lines = [ + "# Generated by scripts/setup.py from design-spec.md. Edit freely.\n" + ] + for yaml_key, env_key, default in _DESIGN_SPEC_TO_ENV: + value = cfg.get(yaml_key) or default + if value: + lines.append(f"{env_key}={value}\n") + if collection_path: + lines.append(f"VECTOR_SEARCH_COLLECTION={collection_path}\n") + env_path.write_text("".join(lines)) + logger.info(" Wrote %s (%d keys)", env_path, len(lines) - 1) + + +# Indirection layer so tests can monkeypatch these without touching the +# underlying scripts. Production: forwards to the real implementations. +def _validate_file(file_path, fields_level): + return validate_schema.validate_file(file_path, fields_level) + + +def _run_bigquery_ingest( # pylint: disable=too-many-arguments + *, project_id, dataset_id, table_id, source, source_format, if_exists +): + return ingest_bigquery.ingest( + project_id=project_id, + dataset_id=dataset_id, + table_id=table_id, + source=source, + source_format=source_format, + if_exists=if_exists, + ) + + +def _run_vertex_ingest(**kwargs): + return ingest_vertex_search.ingest_pipeline(**kwargs) + + +def _resolve_data_source(cfg: dict[str, Any]) -> tuple[str | None, str]: + """Resolve ``data_source`` from the spec into ``(source_path, format)``. + + Args: + cfg: Parsed design-spec config dict. + + Returns: + ``(source_path, format)`` where ``source_path`` is a local path or + ``gs://`` URI and ``format`` is ``"csv"`` or ``"json"``. Returns + ``(None, "")`` if the source is missing or broken so the caller + can error out cleanly. + """ + raw = (cfg.get("data_source") or "").strip() + + if raw.startswith("gs://"): + return raw, _format_from_path(raw) + + if raw and raw not in ("default", "assets/sample-products.csv"): + # User specified an explicit local path. + if pathlib.Path(raw).exists(): + return raw, _format_from_path(raw) + logger.error("\n data_source '%s' does not exist.", raw) + return None, "" + + # Default: the bundled sample inside the install dir. + if SAMPLE_PRODUCTS_CSV.exists(): + return str(SAMPLE_PRODUCTS_CSV), "csv" + + logger.error("\n Bundled sample not found at %s", SAMPLE_PRODUCTS_CSV) + return None, "" + + +def _format_from_path(path: str) -> str: + """Return ``"json"`` for ``.json``/``.jsonl`` paths, else ``"csv"``. + + Args: + path: Data file path (local or gs:// URI). + + Returns: + Either ``"json"`` or ``"csv"`` for the BigQuery loader. + """ + return "json" if path.endswith((".json", ".jsonl")) else "csv" + + +def setup(config_path: str, dry_run: bool = False) -> bool: # pylint: disable=too-many-locals + """Run the full setup pipeline based on design-spec.md. + + Args: + config_path: Path to ``design-spec.md`` with the user's config. + dry_run: If True, log the pipeline shape without running ingest. + + Returns: + True if every step succeeded; False if the data source couldn't + be resolved or validation produced errors. + """ + cfg = load_config(config_path) + + project_id = cfg.get("gcp_project_id", "") + if not project_id: + logger.error("gcp_project_id is required in design-spec.md") + sys.exit(1) + + product_fields = cfg.get("product_fields", "Extended") + dataset_id = cfg.get("dataset_id", "retail_skill_products") + table_id = cfg.get("table_id", "products") + catalog_size = cfg.get("catalog_size", "1K-50K") + gcp_region = cfg.get("gcp_region", "us-central1") + collection_id = cfg.get("collection_id", "retail-skill-products-collection") + embedding_model = cfg.get("embedding_model", "gemini-embedding-001") + raw_fields = cfg.get( + "embedding_fields", "name, description, category, brand" + ) + embedding_fields = [f.strip() for f in raw_fields.split(",")] + + logger.info("=" * 60) + logger.info("SETUP PIPELINE (driven by design-spec.md)") + logger.info("=" * 60) + logger.info(" Project: %s", project_id) + logger.info(" Region: %s", gcp_region) + + source, source_format = _resolve_data_source(cfg) + if source is None: + return False + + logger.info(" Data source: %s", source) + + if dry_run: + logger.info( + "\n[dry-run] Would validate, ingest to BigQuery, then create Vector Search collection." + ) + return True + + # Step A: validate. + # validate_schema.load_records reads from the local filesystem only; + # skip the schema-shape check for gs:// sources and let + # ingest_bigquery.load_from_csv validate inline as it streams the file. + if source.startswith("gs://"): + logger.info( + "\n Skipping schema validation for gs:// source (validated inline during ingest)." + ) + else: + logger.info("\n Validating sample data...") + valid_count, errors = _validate_file(source, product_fields) + if errors: + logger.error( + " Validation produced %d errors. First 5:", len(errors) + ) + for e in errors[:5]: + logger.error(" %s", e) + return False + logger.info(" Validated %d records.", valid_count) + + # Step B: ingest to BigQuery (idempotent re-run via if_exists='skip') + logger.info("\n Ingesting into BigQuery...") + _run_bigquery_ingest( + project_id=project_id, + dataset_id=dataset_id, + table_id=table_id, + source=source, + source_format=source_format, + if_exists="skip", + ) + + # Step C: create Vector Search collection + ingest + logger.info("\n Creating Vector Search collection...") + collection_path = _run_vertex_ingest( + project_id=project_id, + location=gcp_region, + dataset_id=dataset_id, + table_id=table_id, + collection_id=collection_id, + embedding_model=embedding_model, + embedding_fields=embedding_fields, + ) + + if catalog_size == "500K+": + logger.info("\n NOTE: Catalog size 500K+ detected.") + logger.info( + " Consider using Dataflow for batch ingestion instead of direct inserts." + ) + + # Write .env next to design-spec.md so every downstream module + # (agent.py, retrievers.py, ingest scripts) reads the same values via + # scripts.config. Q-MODE users never touch .env by hand. + env_path = pathlib.Path(config_path).resolve().parent / ".env" + _write_env_file(cfg, env_path, collection_path=collection_path) + + logger.info("\n" + "=" * 60) + logger.info("SETUP COMPLETE") + logger.info("=" * 60) + if collection_path: + logger.info(" Collection path: %s", collection_path) + logger.info(" Written to %s as VECTOR_SEARCH_COLLECTION.", env_path) + return True + + +def main(): + """Parse CLI arguments and run the setup pipeline.""" + parser = argparse.ArgumentParser( + description="Run setup pipeline from design-spec.md" + ) + parser.add_argument( + "--config", required=True, help="Path to design-spec.md" + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Show what would run without doing it", + ) + + args = parser.parse_args() + ok = setup(args.config, args.dry_run) + sys.exit(0 if ok else 1) + + +if __name__ == "__main__": + main() diff --git a/skills/retail/product-search/scripts/validate_schema.py b/skills/retail/product-search/scripts/validate_schema.py new file mode 100644 index 0000000000..dba6189334 --- /dev/null +++ b/skills/retail/product-search/scripts/validate_schema.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Validate a product catalog CSV/JSON against the expected schema. + +Usage: + python validate_schema.py --file products.csv --fields-level Standard + python validate_schema.py --file products.json --fields-level Extended +""" + +# pylint: disable=line-too-long +# (validation error messages with field names are intentionally long.) + +import argparse +import csv +import json +import logging +import pathlib +import sys +from typing import Any + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +FIELD_LEVELS = { + "Basic": { + "required": ["product_id", "name", "price", "description"], + "optional": [], + }, + "Standard": { + "required": ["product_id", "name", "price", "description"], + "optional": ["category", "brand", "image_url"], + }, + "Extended": { + "required": ["product_id", "name", "price", "description"], + "optional": [ + "category", + "brand", + "image_url", + "rating", + "stock", + "manufacturer", + ], + }, + "Full": { + "required": ["product_id", "name", "price", "description"], + "optional": [ + "category", + "brand", + "image_url", + "rating", + "stock", + "manufacturer", + "variants", + "tags", + "specifications", + "reviews", + ], + }, +} + + +def load_records(file_path: pathlib.Path) -> list[dict[str, Any]]: + """Load records from a CSV, JSON, or JSONL file. + + Args: + file_path: Path to the product data file. The file type is + inferred from the suffix (``.csv``, ``.json``, or ``.jsonl``). + + Returns: + List of records as dicts. + + Raises: + ValueError: If the suffix isn't supported, or the JSON content is + neither a list nor a ``{"products": [...]}`` object. + """ + suffix = file_path.suffix.lower() + + if suffix == ".csv": + with open(file_path, encoding="utf-8") as f: + return list(csv.DictReader(f)) + elif suffix in (".json", ".jsonl"): + content = file_path.read_text(encoding="utf-8") + if suffix == ".jsonl": + return [ + json.loads(line) + for line in content.strip().splitlines() + if line.strip() + ] + parsed = json.loads(content) + if isinstance(parsed, list): + return parsed + if isinstance(parsed, dict) and "products" in parsed: + return parsed["products"] + raise ValueError('JSON must be an array or {"products": [...]}') + else: + raise ValueError( + f"Unsupported file type: {suffix}. Use .csv, .json, or .jsonl" + ) + + +def _validate_single_record( + record: dict[str, Any], + row_num: int, + required: list[str], + all_fields: set[str], + higher_level_fields: set[str], + seen_warned_fields: set[str], + fields_level: str, +) -> list[str]: + """Validate a single record against the schema. + + Args: + record: One product record as a dict from CSV/JSON. + row_num: 1-based row number for error messages. + required: Required field names at the selected fields-level. + all_fields: Set of fields recognized at the selected fields-level. + higher_level_fields: Fields recognized at any higher level; these + warn (and are ignored) instead of failing the row. + seen_warned_fields: Mutable set tracking which higher-level fields + already produced a warning, so each fires once per run. + fields_level: Name of the selected level, used in warning messages. + + Returns: + List of error strings for this row (empty if the row is valid). + """ + row_errors = [] + + for field in required: + if field not in record or not record[field]: + row_errors.append( + f"Row {row_num}: missing required field '{field}'" + ) + + if record.get("price"): + try: + float(record["price"]) + except (ValueError, TypeError): + row_errors.append( + f"Row {row_num}: 'price' must be numeric, got '{record['price']}'" + ) + + if record.get("rating"): + try: + val = float(record["rating"]) + if not 0 <= val <= 5: + row_errors.append( + f"Row {row_num}: 'rating' should be 0-5, got {val}" + ) + except (ValueError, TypeError): + row_errors.append(f"Row {row_num}: 'rating' must be numeric") + + if record.get("stock"): + try: + int(record["stock"]) + except (ValueError, TypeError): + row_errors.append(f"Row {row_num}: 'stock' must be an integer") + + extra_fields = set(record.keys()) - all_fields + known_extras = extra_fields & higher_level_fields + unknown_extras = extra_fields - higher_level_fields + + for field in known_extras - seen_warned_fields: + logger.warning( + "Field '%s' present in data but outside the '%s' schema. " + "It will be ignored. Pick a higher --fields-level if you want it indexed.", + field, + fields_level, + ) + seen_warned_fields.add(field) + + if unknown_extras: + row_errors.append( + f"Row {row_num}: unrecognized fields: {sorted(unknown_extras)}" + ) + + return row_errors + + +def validate( + records: list[dict[str, Any]], fields_level: str +) -> tuple[int, list[str]]: + """Validate records against the schema for ``fields_level``. + + Args: + records: All product records loaded from the source. + fields_level: One of ``"Basic"``, ``"Standard"``, ``"Extended"``, + ``"Full"``. + + Returns: + ``(valid_count, errors)`` where ``valid_count`` is the number of + rows that passed and ``errors`` is the concatenated list of error + strings across all rows. + """ + schema = FIELD_LEVELS[fields_level] + required = schema["required"] + all_fields = set(required + schema["optional"]) + # Fields that exist at any higher level -- treat as known-but-out-of-scope. + # Warn instead of erroring so users with richer CSVs don't have to strip columns. + higher_level_fields: set[str] = set() + for level_schema in FIELD_LEVELS.values(): + higher_level_fields.update(level_schema["required"]) + higher_level_fields.update(level_schema["optional"]) + + errors = [] + seen_warned_fields: set[str] = set() + valid = 0 + + for i, record in enumerate(records, start=1): + row_errors = _validate_single_record( + record, + i, + required, + all_fields, + higher_level_fields, + seen_warned_fields, + fields_level, + ) + if row_errors: + errors.extend(row_errors) + else: + valid += 1 + + return valid, errors + + +def validate_file(file_path, fields_level: str) -> tuple[int, list[str]]: + """Load a CSV/JSON file and validate it in one call. + + Convenience wrapper for orchestrators that don't need the records. + + Args: + file_path: Path to the product data file (CSV, JSON, or JSONL). + fields_level: One of ``"Basic"``, ``"Standard"``, ``"Extended"``, + ``"Full"``. + + Returns: + ``(valid_count, errors)`` -- same shape as ``validate()``. + """ + records = load_records(pathlib.Path(file_path)) + return validate(records, fields_level) + + +def main(): + """Parse CLI arguments and run schema validation.""" + parser = argparse.ArgumentParser( + description="Validate product catalog schema" + ) + parser.add_argument( + "--file", required=True, help="Path to product data file (CSV/JSON)" + ) + parser.add_argument( + "--fields-level", + choices=["Basic", "Standard", "Extended", "Full"], + default="Standard", + help="Product fields level (default: Standard)", + ) + + args = parser.parse_args() + file_path = pathlib.Path(args.file) + + if not file_path.exists(): + logger.error("File not found: %s", file_path) + sys.exit(1) + + records = load_records(file_path) + logger.info("Loaded %d records from %s", len(records), file_path) + + valid_count, errors = validate(records, args.fields_level) + + if errors: + logger.warning("%d validation errors found:", len(errors)) + for err in errors[:20]: + logger.warning(" %s", err) + if len(errors) > 20: + logger.warning(" ... and %d more", len(errors) - 20) + + logger.info( + "Validation complete: %d/%d records valid", valid_count, len(records) + ) + + if errors: + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/skills/retail/product-search/tests/test_runnability.py b/skills/retail/product-search/tests/test_runnability.py new file mode 100644 index 0000000000..88f2b2045a --- /dev/null +++ b/skills/retail/product-search/tests/test_runnability.py @@ -0,0 +1,24 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Runnability test for the retail-product-search recipe.""" + + +def test_agent_runnability() -> None: + """Verify scripts/agent.py imports and defines root_agent.""" + import importlib + + module = importlib.import_module("scripts.agent") + + assert getattr(module, "root_agent", None) is not None diff --git a/skills/retail/product-search/tests/unit/.gitkeep b/skills/retail/product-search/tests/unit/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/skills/retail/product-search/uv.lock b/skills/retail/product-search/uv.lock new file mode 100644 index 0000000000..0eabbff569 --- /dev/null +++ b/skills/retail/product-search/uv.lock @@ -0,0 +1,2273 @@ +version = 1 +revision = 2 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version == '3.13.*'", + "python_full_version < '3.13'", +] + +[[package]] +name = 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