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Tools: Building an AI-Native Retail Platform on GCP: Personalization + Multi-Agent Ops + Agentic RAG as One Unified Stack

2026-03-23 0 views admin
Tools: Building an AI-Native Retail Platform on GCP: Personalization + Multi-Agent Ops + Agentic RAG as One Unified Stack

🏗️ The Three Layers of an AI-Native Retail Platform

📐 Unified Architecture Overview

🎯 Layer 1: Real-Time Personalization Engine

The Core Problem

The Six-Stage Pipeline

Handling Cold Start

🤖 Layer 2: Multi-Agent Operations

The Core Problem

Agent Architecture

A Reorder Request — Traced End-to-End

The Pub/Sub Design — Why It Matters

Shared Memory: The agent_decision_log Table

📚 Layer 3: Agentic RAG for Retail Knowledge

The Core Problem

Three Retrieval Sources

Query Decomposition in Action

The Self-Correction Loop

🔗 How the Three Layers Connect

📊 Observability — One Dashboard, Three Layers

🚀 Where to Start

💡 Key Takeaways A shopper searches for rain boots on your storefront. Within 120ms, your personalization engine surfaces the right products. A stock alert fires, and three AI agents coordinate a reorder without a human touching a keyboard. The customer asks a question in chat — the answer comes back grounded in live inventory and your return policy, cited and accurate. This is not three separate AI projects. It is one unified platform — and this article shows you how to build it on GCP. Most retail AI initiatives start with one use case and stop there. What makes a platform is when these three capabilities are designed together, sharing infrastructure and data: The key insight: all three layers share the same data backbone — BigQuery as the source of truth, Pub/Sub as the event spine, and Vertex AI as the intelligence layer. Daily batch recommendations ignore the most powerful signal available: what the user is doing right now. A shopper who just added rain boots to their cart does not want yesterday's trending sneakers. Design principle: Personalization is a retrieval problem. Given a user and their context right now, find the items most likely to convert — in under 120ms. Stage 1 — Event Capture (Pub/Sub) Every user interaction fires a structured event to Pub/Sub. The client SDK is fire-and-forget — it does not wait for a response. Stage 2 — Stream Enrichment (Dataflow) A Dataflow streaming job picks up events, joins with item metadata from BigQuery, and writes two outputs: Stage 3 — Feature Assembly (Vertex AI Feature Store) At query time, three feature groups are fetched in a single low-latency call: Stage 4 — ANN Retrieval (Vertex AI Matching Engine) The assembled user context vector is submitted to Matching Engine — Google's managed ANN index. It returns the top 50 candidate SKUs from a catalog of millions in under 10ms. Under the hood: Google's ScaNN algorithm, pre-filtered by in-stock status so the re-ranker never sees unavailable items. Stage 5 — Re-Ranking (Vertex AI Prediction) A lightweight model re-scores the 50 candidates using signals the embedding index cannot capture: Stage 6 — Serve (Cloud Run) Top 10 results + display metadata returned to the frontend. End-to-end: < 120ms at p99. A single LLM handling all retail operations hits three walls: context overload, sequential latency, and unmaintainable prompts. When the inventory rule, pricing model, supplier contract, and customer policy all need to fit in one context — reasoning quality degrades. Design principle: Treat operations like a well-run team. One orchestrator receives requests and coordinates specialists. Each specialist does one thing well. Notice: the Customer Agent IS Layer 3 — Agentic RAG is not separate, it is the intelligence layer of the Customer Agent. This is where the three layers connect. Input: "Should we reorder SKU-991?" Step 1 — Decompose: Orchestrator identifies three parallel sub-tasks. Step 2 — Dispatch: All three tasks published to Pub/Sub simultaneously. Step 3 — Execute in Parallel: Each Cloud Run agent handles its task independently: Total time = max(slowest agent) — not the sum of all three. Three properties you get for free: Every orchestrated request is fully logged: This table powers weekly evaluation reports and feeds back into model fine-tuning — your audit trail is also your training dataset. Standard RAG (embed query → retrieve chunks → generate) fails retail because: Design principle: RAG should reason, not just retrieve. The agent decides which source to query, validates the result, and cites its sources. 1. Policy & Compliance Index (Vertex AI Search) Return policies, warranty terms, BOPIS rules, hazmat shipping. Indexed as documents with hybrid retrieval (dense semantic + sparse BM25 keyword). BM25 matters here: product part numbers and model codes are not well-served by pure vector search. Hybrid retrieval handles both. 2. Product Catalog Index (Vertex AI Search) Product descriptions, specs, compatibility notes, sizing guides. Indexed with multimodal embeddings (text + image) so "waterproof jacket similar to this one" works. 3. Live Operational Data (BigQuery as a Tool) Inventory levels, order status, real-time pricing — not indexed as documents but called as a live tool. This is the key architectural decision that prevents stale answers. Customer query: "Can I return the 40V battery I bought online at a store, and is it in stock at the Cumming, GA location?" Agent validates Sub-query A: relevance score > 0.82 threshold ✅ Agent validates Sub-query B: live data, timestamp 2 minutes ago ✅ Every fact is cited. No hallucination. No "please check the website." This loop means your system knows what it does not know — and routes accordingly. The platform is unified, not assembled. Here is how data and events flow across all three layers in a single customer session: The feedback loop is the platform. Every interaction trains the next version of every model. All three layers write to BigQuery. One Looker Studio dashboard covers the full platform: When retrieval precision drops, you know before customers notice. Don't try to ship all three layers at once. Here is a proven sequencing: Week 1–4: Lay the data foundation Week 5–8: Ship Personalization (Layer 1) Week 9–12: Add Multi-Agent Ops (Layer 2) Week 13–16: Add Agentic RAG (Layer 3) The Pub/Sub bus means each new layer plugs in without touching what already works. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to ? It will become hidden in your post, but will still be visible via the comment's permalink. as well , this person and/or

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┌─────────────────────────────────────────────────────────────────┐ │ FRONTEND / API GATEWAY │ └───────────┬──────────────────┬───────────────────┬─────────────┘ │ │ │ ┌───────▼──────┐ ┌────────▼───────┐ ┌───────▼──────────┐ │ PERSONALI- │ │ MULTI-AGENT │ │ AGENTIC RAG │ │ ZATION │ │ ORCHESTRATOR │ │ (Customer Q&A) │ │ ENGINE │ │ (Gemini 1.5) │ │ (Gemini + │ │ (Cloud Run) │ │ (Vertex AI │ │ Vertex Search) │ └───────┬──────┘ │ Reasoning) │ └───────┬──────────┘ │ └────────┬───────┘ │ │ │ │ └──────────────────┼──────────────────┘ │ ┌────────────────▼────────────────┐ │ GOOGLE CLOUD PUB/SUB │ │ (Shared Event Spine) │ └───┬──────────┬──────────┬────────┘ │ │ │ ┌───────▼──┐ ┌─────▼────┐ ┌──▼────────────┐ │ Dataflow │ │Specialist│ │ Vertex AI │ │ Streaming │ │ Agents │ │ Search Index │ └───────┬──┘ └─────┬────┘ └──┬────────────┘ │ │ │ ┌───▼──────────▼──────────▼───┐ │ BIGQUERY │ │ (Shared Operational Store) │ └─────────────────────────────┘ CODE_BLOCK: ┌─────────────────────────────────────────────────────────────────┐ │ FRONTEND / API GATEWAY │ └───────────┬──────────────────┬───────────────────┬─────────────┘ │ │ │ ┌───────▼──────┐ ┌────────▼───────┐ ┌───────▼──────────┐ │ PERSONALI- │ │ MULTI-AGENT │ │ AGENTIC RAG │ │ ZATION │ │ ORCHESTRATOR │ │ (Customer Q&A) │ │ ENGINE │ │ (Gemini 1.5) │ │ (Gemini + │ │ (Cloud Run) │ │ (Vertex AI │ │ Vertex Search) │ └───────┬──────┘ │ Reasoning) │ └───────┬──────────┘ │ └────────┬───────┘ │ │ │ │ └──────────────────┼──────────────────┘ │ ┌────────────────▼────────────────┐ │ GOOGLE CLOUD PUB/SUB │ │ (Shared Event Spine) │ └───┬──────────┬──────────┬────────┘ │ │ │ ┌───────▼──┐ ┌─────▼────┐ ┌──▼────────────┐ │ Dataflow │ │Specialist│ │ Vertex AI │ │ Streaming │ │ Agents │ │ Search Index │ └───────┬──┘ └─────┬────┘ └──┬────────────┘ │ │ │ ┌───▼──────────▼──────────▼───┐ │ BIGQUERY │ │ (Shared Operational Store) │ └─────────────────────────────┘ CODE_BLOCK: ┌─────────────────────────────────────────────────────────────────┐ │ FRONTEND / API GATEWAY │ └───────────┬──────────────────┬───────────────────┬─────────────┘ │ │ │ ┌───────▼──────┐ ┌────────▼───────┐ ┌───────▼──────────┐ │ PERSONALI- │ │ MULTI-AGENT │ │ AGENTIC RAG │ │ ZATION │ │ ORCHESTRATOR │ │ (Customer Q&A) │ │ ENGINE │ │ (Gemini 1.5) │ │ (Gemini + │ │ (Cloud Run) │ │ (Vertex AI │ │ Vertex Search) │ └───────┬──────┘ │ Reasoning) │ └───────┬──────────┘ │ └────────┬───────┘ │ │ │ │ └──────────────────┼──────────────────┘ │ ┌────────────────▼────────────────┐ │ GOOGLE CLOUD PUB/SUB │ │ (Shared Event Spine) │ └───┬──────────┬──────────┬────────┘ │ │ │ ┌───────▼──┐ ┌─────▼────┐ ┌──▼────────────┐ │ Dataflow │ │Specialist│ │ Vertex AI │ │ Streaming │ │ Agents │ │ Search Index │ └───────┬──┘ └─────┬────┘ └──┬────────────┘ │ │ │ ┌───▼──────────▼──────────▼───┐ │ BIGQUERY │ │ (Shared Operational Store) │ └─────────────────────────────┘ CODE_BLOCK: { "event_type": "CART_ADD", "user_id": "u_8821", "sku_id": "SKU-4471", "session_id": "s_992abc", "ts": "2026-03-22T14:03:11Z", "context": { "device": "mobile", "location": "Atlanta, GA" } } CODE_BLOCK: { "event_type": "CART_ADD", "user_id": "u_8821", "sku_id": "SKU-4471", "session_id": "s_992abc", "ts": "2026-03-22T14:03:11Z", "context": { "device": "mobile", "location": "Atlanta, GA" } } CODE_BLOCK: { "event_type": "CART_ADD", "user_id": "u_8821", "sku_id": "SKU-4471", "session_id": "s_992abc", "ts": "2026-03-22T14:03:11Z", "context": { "device": "mobile", "location": "Atlanta, GA" } } CODE_BLOCK: feature_store_client.read_feature_values( entity_type="user", entity_ids=[user_id], feature_selector={ "id_matcher": { "ids": ["purchase_history", "session_clicks", "device_type", "location"] } } ) CODE_BLOCK: feature_store_client.read_feature_values( entity_type="user", entity_ids=[user_id], feature_selector={ "id_matcher": { "ids": ["purchase_history", "session_clicks", "device_type", "location"] } } ) CODE_BLOCK: feature_store_client.read_feature_values( entity_type="user", entity_ids=[user_id], feature_selector={ "id_matcher": { "ids": ["purchase_history", "session_clicks", "device_type", "location"] } } ) CODE_BLOCK: response = index_endpoint.find_neighbors( deployed_index_id="retail_item_embeddings", queries=[user_context_vector], num_neighbors=50 ) CODE_BLOCK: response = index_endpoint.find_neighbors( deployed_index_id="retail_item_embeddings", queries=[user_context_vector], num_neighbors=50 ) CODE_BLOCK: response = index_endpoint.find_neighbors( deployed_index_id="retail_item_embeddings", queries=[user_context_vector], num_neighbors=50 ) CODE_BLOCK: Operator / System Trigger │ ▼ ┌─────────────────────────────────┐ │ ORCHESTRATOR AGENT │ │ Gemini 1.5 Pro │ │ Vertex AI Reasoning Engine │ │ - Decomposes tasks │ │ - Routes to specialists │ │ - Synthesizes final response │ └────┬──────────┬──────────┬──────┘ │ Pub/Sub │ │ ▼ ▼ ▼ ┌─────────┐ ┌────────┐ ┌──────────┐ ┌──────────┐ │Inventory│ │Pricing │ │Supplier │ │Customer │ │Agent │ │Agent │ │Agent │ │Agent │ │BigQuery │ │BQ ML │ │Vertex AI │ │Agentic │ │ │ │ │ │Search │ │RAG ←────── Layer 3 └─────────┘ └────────┘ └──────────┘ └──────────┘ CODE_BLOCK: Operator / System Trigger │ ▼ ┌─────────────────────────────────┐ │ ORCHESTRATOR AGENT │ │ Gemini 1.5 Pro │ │ Vertex AI Reasoning Engine │ │ - Decomposes tasks │ │ - Routes to specialists │ │ - Synthesizes final response │ └────┬──────────┬──────────┬──────┘ │ Pub/Sub │ │ ▼ ▼ ▼ ┌─────────┐ ┌────────┐ ┌──────────┐ ┌──────────┐ │Inventory│ │Pricing │ │Supplier │ │Customer │ │Agent │ │Agent │ │Agent │ │Agent │ │BigQuery │ │BQ ML │ │Vertex AI │ │Agentic │ │ │ │ │ │Search │ │RAG ←────── Layer 3 └─────────┘ └────────┘ └──────────┘ └──────────┘ CODE_BLOCK: Operator / System Trigger │ ▼ ┌─────────────────────────────────┐ │ ORCHESTRATOR AGENT │ │ Gemini 1.5 Pro │ │ Vertex AI Reasoning Engine │ │ - Decomposes tasks │ │ - Routes to specialists │ │ - Synthesizes final response │ └────┬──────────┬──────────┬──────┘ │ Pub/Sub │ │ ▼ ▼ ▼ ┌─────────┐ ┌────────┐ ┌──────────┐ ┌──────────┐ │Inventory│ │Pricing │ │Supplier │ │Customer │ │Agent │ │Agent │ │Agent │ │Agent │ │BigQuery │ │BQ ML │ │Vertex AI │ │Agentic │ │ │ │ │ │Search │ │RAG ←────── Layer 3 └─────────┘ └────────┘ └──────────┘ └──────────┘ COMMAND_BLOCK: tasks = orchestrator.decompose(query) # → [ # {"agent": "inventory", "task": "get_stock_level", "sku": "SKU-991"}, # {"agent": "supplier", "task": "get_eta_and_cost", "sku": "SKU-991"}, # {"agent": "pricing", "task": "get_reorder_cost", "sku": "SKU-991"} # ] COMMAND_BLOCK: tasks = orchestrator.decompose(query) # → [ # {"agent": "inventory", "task": "get_stock_level", "sku": "SKU-991"}, # {"agent": "supplier", "task": "get_eta_and_cost", "sku": "SKU-991"}, # {"agent": "pricing", "task": "get_reorder_cost", "sku": "SKU-991"} # ] COMMAND_BLOCK: tasks = orchestrator.decompose(query) # → [ # {"agent": "inventory", "task": "get_stock_level", "sku": "SKU-991"}, # {"agent": "supplier", "task": "get_eta_and_cost", "sku": "SKU-991"}, # {"agent": "pricing", "task": "get_reorder_cost", "sku": "SKU-991"} # ] COMMAND_BLOCK: # Inventory Agent stock = bq_client.query(""" SELECT units_available FROM inventory_snapshot WHERE sku_id = 'SKU-991' AND store_id = 'DC-ATL' """).result() # Pricing Agent (BigQuery ML) reorder_cost = bq_client.query(""" SELECT ML.PREDICT(MODEL `retail.pricing_model`, (SELECT * FROM pricing_signals WHERE sku_id = 'SKU-991')) """).result() COMMAND_BLOCK: # Inventory Agent stock = bq_client.query(""" SELECT units_available FROM inventory_snapshot WHERE sku_id = 'SKU-991' AND store_id = 'DC-ATL' """).result() # Pricing Agent (BigQuery ML) reorder_cost = bq_client.query(""" SELECT ML.PREDICT(MODEL `retail.pricing_model`, (SELECT * FROM pricing_signals WHERE sku_id = 'SKU-991')) """).result() COMMAND_BLOCK: # Inventory Agent stock = bq_client.query(""" SELECT units_available FROM inventory_snapshot WHERE sku_id = 'SKU-991' AND store_id = 'DC-ATL' """).result() # Pricing Agent (BigQuery ML) reorder_cost = bq_client.query(""" SELECT ML.PREDICT(MODEL `retail.pricing_model`, (SELECT * FROM pricing_signals WHERE sku_id = 'SKU-991')) """).result() CODE_BLOCK: Orchestrator → "Reorder 50 units from Vendor A at $4.20/unit, ETA 3 days. Current stock: 8 units (below reorder threshold of 15)." ✅ CODE_BLOCK: Orchestrator → "Reorder 50 units from Vendor A at $4.20/unit, ETA 3 days. Current stock: 8 units (below reorder threshold of 15)." ✅ CODE_BLOCK: Orchestrator → "Reorder 50 units from Vendor A at $4.20/unit, ETA 3 days. Current stock: 8 units (below reorder threshold of 15)." ✅ CODE_BLOCK: CREATE TABLE retail.agent_decision_log ( request_id STRING, ts TIMESTAMP, agent_called STRING, tools_used ARRAY<STRING>, input_payload JSON, output_payload JSON, latency_ms INT64, confidence FLOAT64 ); CODE_BLOCK: CREATE TABLE retail.agent_decision_log ( request_id STRING, ts TIMESTAMP, agent_called STRING, tools_used ARRAY<STRING>, input_payload JSON, output_payload JSON, latency_ms INT64, confidence FLOAT64 ); CODE_BLOCK: CREATE TABLE retail.agent_decision_log ( request_id STRING, ts TIMESTAMP, agent_called STRING, tools_used ARRAY<STRING>, input_payload JSON, output_payload JSON, latency_ms INT64, confidence FLOAT64 ); COMMAND_BLOCK: tools = [ VertexAISearchTool(index="retail_policy_index"), VertexAISearchTool(index="retail_product_index"), BigQueryTool(query_template=INVENTORY_QUERY) # live call, not indexed ] COMMAND_BLOCK: tools = [ VertexAISearchTool(index="retail_policy_index"), VertexAISearchTool(index="retail_product_index"), BigQueryTool(query_template=INVENTORY_QUERY) # live call, not indexed ] COMMAND_BLOCK: tools = [ VertexAISearchTool(index="retail_policy_index"), VertexAISearchTool(index="retail_product_index"), BigQueryTool(query_template=INVENTORY_QUERY) # live call, not indexed ] CODE_BLOCK: Agent Plan: Sub-query A → Policy Index: "online purchase battery return policy in-store" Sub-query B → BigQuery Tool: SELECT units_available FROM inventory_snapshot WHERE sku_id='SKU-4471' AND store='GA-CUMMING' CODE_BLOCK: Agent Plan: Sub-query A → Policy Index: "online purchase battery return policy in-store" Sub-query B → BigQuery Tool: SELECT units_available FROM inventory_snapshot WHERE sku_id='SKU-4471' AND store='GA-CUMMING' CODE_BLOCK: Agent Plan: Sub-query A → Policy Index: "online purchase battery return policy in-store" Sub-query B → BigQuery Tool: SELECT units_available FROM inventory_snapshot WHERE sku_id='SKU-4471' AND store='GA-CUMMING' CODE_BLOCK: "Yes — online purchases can be returned in-store within 90 days (Policy §3.2). The 40V battery (SKU-4471) shows 3 units in stock at Cumming, GA as of 14:07 EST today." CODE_BLOCK: "Yes — online purchases can be returned in-store within 90 days (Policy §3.2). The 40V battery (SKU-4471) shows 3 units in stock at Cumming, GA as of 14:07 EST today." CODE_BLOCK: "Yes — online purchases can be returned in-store within 90 days (Policy §3.2). The 40V battery (SKU-4471) shows 3 units in stock at Cumming, GA as of 14:07 EST today." COMMAND_BLOCK: MAX_RETRIES = 3 for attempt in range(MAX_RETRIES): result = vertex_search.retrieve(query, index=index_id) if result.confidence_score >= THRESHOLD: return result # Reformulate: broaden scope, try synonyms, switch retrieval mode query = agent.reformulate(query, attempt) # After max retries: escalate to human agent queue escalate_to_human(original_query) COMMAND_BLOCK: MAX_RETRIES = 3 for attempt in range(MAX_RETRIES): result = vertex_search.retrieve(query, index=index_id) if result.confidence_score >= THRESHOLD: return result # Reformulate: broaden scope, try synonyms, switch retrieval mode query = agent.reformulate(query, attempt) # After max retries: escalate to human agent queue escalate_to_human(original_query) COMMAND_BLOCK: MAX_RETRIES = 3 for attempt in range(MAX_RETRIES): result = vertex_search.retrieve(query, index=index_id) if result.confidence_score >= THRESHOLD: return result # Reformulate: broaden scope, try synonyms, switch retrieval mode query = agent.reformulate(query, attempt) # After max retries: escalate to human agent queue escalate_to_human(original_query) CODE_BLOCK: 1. Customer browses → Pub/Sub event → Personalization Engine surfaces relevant products (Layer 1) 2. Inventory drops below threshold → Pub/Sub alert → Orchestrator Agent dispatches reorder across 3 specialist agents in parallel (Layer 2) 3. Customer asks: "Is this in stock?" → Customer Agent (Layer 2) → Agentic RAG (Layer 3) queries BigQuery live + policy index → grounded, cited answer in < 2s 4. All events → BigQuery agent_decision_log + interaction_log → weekly eval reports + model retraining for Layers 1 & 3 CODE_BLOCK: 1. Customer browses → Pub/Sub event → Personalization Engine surfaces relevant products (Layer 1) 2. Inventory drops below threshold → Pub/Sub alert → Orchestrator Agent dispatches reorder across 3 specialist agents in parallel (Layer 2) 3. Customer asks: "Is this in stock?" → Customer Agent (Layer 2) → Agentic RAG (Layer 3) queries BigQuery live + policy index → grounded, cited answer in < 2s 4. All events → BigQuery agent_decision_log + interaction_log → weekly eval reports + model retraining for Layers 1 & 3 CODE_BLOCK: 1. Customer browses → Pub/Sub event → Personalization Engine surfaces relevant products (Layer 1) 2. Inventory drops below threshold → Pub/Sub alert → Orchestrator Agent dispatches reorder across 3 specialist agents in parallel (Layer 2) 3. Customer asks: "Is this in stock?" → Customer Agent (Layer 2) → Agentic RAG (Layer 3) queries BigQuery live + policy index → grounded, cited answer in < 2s 4. All events → BigQuery agent_decision_log + interaction_log → weekly eval reports + model retraining for Layers 1 & 3 - Session feature update → Vertex AI Feature Store (< 5s latency) - Interaction log → BigQuery (for offline model training) - Current inventory level - Promotional pricing flag - User's price sensitivity segment - Real-time trend score - Loose coupling: agents have no direct dependency on each other, only on topic names - Fault tolerance: if an agent crashes, the message is retained and redelivered on recovery - Independent scaling: each Cloud Run agent scales on its own Pub/Sub queue depth - A single customer question often spans multiple knowledge domains (policy + inventory + product specs) - Inventory data goes stale in minutes — you cannot index it as static documents - Retrieval confidence varies — a system that cannot detect low-confidence answers will hallucinate - Set up BigQuery tables: inventory_snapshot, interaction_log, agent_decision_log - Stand up Pub/Sub topics and Dataflow streaming job - This infrastructure is shared by all three layers — do it once, use it everywhere - Train a two-tower model on BigQuery interaction history - Index item embeddings into Vertex AI Matching Engine - Wire up Cloud Run serving API - Measure: recommendation CTR vs. batch baseline - Start with two agents: Inventory + Pricing - Orchestrator on Vertex AI Reasoning Engine - Add Supplier Agent once the first two are stable - Index return policy + product catalog into Vertex AI Search - Wire the BigQuery inventory tool into the agent - Deploy as the Customer Agent inside your multi-agent system - Share infrastructure, not code. BigQuery and Pub/Sub serve all three layers. Build them once. - The Customer Agent IS Agentic RAG. Don't build these as separate projects. - The agent_decision_log is your most valuable table. It is your audit trail, your eval dataset, and your retraining signal. - Personalization cold start is solved by context, not history. Device + time + location gets you 80% of the way there for new users. - Hybrid retrieval beats pure vector search for retail. BM25 handles part numbers and model codes that semantic search misses.

🏷️ Tags

toolsutilitiessecurity toolsbuildingnativeretailplatformpersonalizationmultiagent

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