132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
import os
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import logging
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import numpy as np
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from repositories.client_trips_repository import client_trips_repository
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logger = logging.getLogger(__name__)
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OPENROUTER_API_KEY = os.getenv('OPENROUTER_API_KEY')
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EMBEDDING_MODEL = 'openai/text-embedding-3-small'
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SIMILARITY_THRESHOLD = 0.75
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def save_client_trip(tenant_id, destination, duration, travel_style, product_name=None):
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"""
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Upserts a client_trips record from a PM's trip-intent description
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(CLAUDE.md §8's TripIntentForm — destination/duration/travel_style,
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optionally a product name). Every /research submission calls this,
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which is the only place client_trips ever gets populated — without
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it, find_comparable_trips() has nothing on the client side to match
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competitor products against.
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Dedupes on (tenant_id, trip_name) so re-running analysis for the
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same product doesn't create duplicate rows — it just keeps the
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existing one current.
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Data flow:
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destination/duration/travel_style/product_name →
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trip_name resolved (product_name if given, else a
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destination+style fallback) →
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client_trips_repository.get_by_tenant_and_name() →
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found: update in place → not found: create →
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client_trips record returned
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"""
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trip_name = product_name or f'{destination} {travel_style}'.strip()
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payload = {
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'tenant_id': tenant_id,
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'trip_name': trip_name,
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'destination': destination,
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'duration_days': duration,
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}
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existing = client_trips_repository.get_by_tenant_and_name(tenant_id, trip_name)
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if existing:
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# A stale embedding would silently never get regenerated —
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# /internal/embed-products only embeds trips missing a vector —
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# so clear it whenever the text embed_trip() hashes actually changed.
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if existing.get('destination') != destination or existing.get('duration_days') != duration:
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payload['embedding'] = None
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return client_trips_repository.update(existing['id'], payload)
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return client_trips_repository.create(payload)
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def embed_trip(trip):
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"""
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Generates a semantic embedding for a trip using OpenRouter's
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text-embedding-3-small, combining trip name, destination, duration,
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and activities into a single text representation first.
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Data flow:
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trip dict → formatted text string → OpenRouter embeddings API →
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vector list returned for storage in products.embedding /
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client_trips.embedding
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"""
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# Imported lazily so this module has no hard dependency on the
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# `openai` package at import time for callers that only need
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# find_comparable_trips() (pure math, no API calls).
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from openai import OpenAI
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text = (
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f"{trip.get('trip_name', '')}\n"
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f"Destination: {trip.get('destination', '')}\n"
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f"Duration: {trip.get('duration_days', '')} days\n"
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f"Activities: {trip.get('activities', '')}\n"
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f"Start: {trip.get('start_location', '')}"
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)
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client = OpenAI(api_key=OPENROUTER_API_KEY, base_url='https://openrouter.ai/api/v1')
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response = client.embeddings.create(model=EMBEDDING_MODEL, input=text)
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return response.data[0].embedding
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def _cosine_similarity(a, b):
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"""Standard cosine similarity between two equal-length embedding vectors."""
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a, b = np.array(a), np.array(b)
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denom = (np.linalg.norm(a) * np.linalg.norm(b))
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if denom == 0:
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return 0.0
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return float(np.dot(a, b) / denom)
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def find_comparable_trips(client_trips, competitor_trips, threshold=SIMILARITY_THRESHOLD):
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"""
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Calculates cosine similarity between every client trip and every
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competitor trip, returning matches above threshold sorted by
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similarity score descending.
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Data flow:
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client_trips + competitor_trips (each with .embedding) →
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pairwise cosine similarity → filter by threshold →
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sort by score descending → list of match dicts returned
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"""
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matches = []
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for client_trip in client_trips:
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client_embedding = client_trip.get('embedding')
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if not client_embedding:
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continue
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for competitor_trip in competitor_trips:
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competitor_embedding = competitor_trip.get('embedding')
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if not competitor_embedding:
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continue
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score = _cosine_similarity(client_embedding, competitor_embedding)
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if score >= threshold:
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matches.append({
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'client_product': client_trip.get('trip_name'),
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'competitor_product': competitor_trip.get('id'),
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'similarity_score': round(score, 4),
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'price_difference': _safe_diff(
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competitor_trip.get('majority_price_usd'), client_trip.get('price_usd')
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),
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'duration_difference': _safe_diff(
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competitor_trip.get('duration_days'), client_trip.get('duration_days')
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),
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})
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matches.sort(key=lambda m: m['similarity_score'], reverse=True)
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return matches
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def _safe_diff(a, b):
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"""Returns a - b if both are present, else None — keeps the diff fields nullable per schema."""
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if a is None or b is None:
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return None
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return a - b
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