Aperol Spritz Summer With MongoDB Geospatial Queries & Vector Search

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!pip install googlemaps
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!pip install openai==0.28

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import googlemaps
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import getpass
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import openai

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# google API Key
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google_api_key = getpass.getpass(prompt= "Put in Google API Key here")
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map_client = googlemaps.Client(key=google_api_key)
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# openAI API Key
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openai_api_key = getpass.getpass(prompt= "Put in OpenAI API Key here")

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# set your key
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openai.api_key = openai_api_key
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# embedding model we are using
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EMBEDDING_MODEL = "text-embedding-3-small"
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# our embedding function
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def get_embedding(text):
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 response = openai.Embedding.create(input=text, model=EMBEDDING_MODEL)
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 return response['data'][0]['embedding']

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# for Google Maps API we need to use a radius in meters. Let's first change our miles to meters
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def miles_to_meters(miles):
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   return miles * 1609.344

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middle_of_west_village = (40.73490473393682, -74.00521094160642)
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search_radius = miles_to_meters(0.4) # West Village is small so just do less than half a mile.
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spritz_finder = 'aperol spritz'

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# making the API call using our places_nearby method and our parameters
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response = map_client.places_nearby(
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   location=middle_of_west_village,
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   radius=search_radius,
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   keyword=spritz_finder
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)

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{
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"html_attributions": [],
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"results":
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 [
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   {
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     "business_status": "OPERATIONAL",
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     "geometry":
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       {
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         "location": { "lat": -33.8587323, "lng": 151.2100055 },
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         "viewport":
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           {
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             "northeast":
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               { "lat": -33.85739847010727, "lng": 151.2112436298927 },
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             "southwest":
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               { "lat": -33.86009812989271, "lng": 151.2085439701072 },
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           },

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# find information we want: use the Nearby Places documentation to figure out which fields you want
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spritz_locations = []
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for location in response.get('results', []):
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   location_detail = map_client.place(
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       place_id=location['place_id'], fields=['name', 'formatted_address', 'geometry', 'reviews', 'rating']
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   )
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   # these are the specific details we want to be saved as fields in our documents
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   details = location_detail.get('result', {})
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   # we want to embed the five reviews so lets extract and join together
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   location_reviews = details.get('reviews', [])
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   store_reviews = [review['text'] for review in location_reviews[:5]]
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   joined_reviews = " ".join(store_reviews)
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   # generate embedding on your reviews
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   embedding_reviews = get_embedding(joined_reviews)
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   # we know that the longitude and latitude is nested inside Geometry and Location.
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   # so let's grab it using .get and then format it how we want.
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   geometry = details.get('geometry', {})
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   location = geometry.get('location', {})
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   # both are nested under location so open it up
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   longitude = location.get('lng')
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   latitude = location.get('lat')
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   location_info = {
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       'name': details.get('name'),
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       'address': details.get('formatted_address'),
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       # MongoDB geospatial queries require GeoJSON formatting
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       'location': {
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           'type': 'Point',
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           'coordinates': [longitude, latitude]
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       },
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       'rating': details.get('rating'),
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       'reviews': store_reviews,
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       'embedding': embedding_reviews
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   }
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   spritz_locations.append(location_info)

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# print our spritz information
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for location in spritz_locations:
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   print(f"Name: {location['name']}, Address: {location['address']}, Coordinates: {location['location']}, Rating: {location['rating']}, Reviews: {location['reviews']}, Embedding: {location['embedding']}")

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# install pymongo
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!pip install pymongo

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from pymongo import MongoClient
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# set up your MongoDB connection
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connection_string = getpass.getpass(prompt= "Enter connection string WITH USER + PASS here")
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client = MongoClient(connection_string)
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# name your database and collection anything you want since it will be created when you enter your data
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database = client['spritz_summer']
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collection = database['spritz_locations_WV']
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# insert our spritz locations
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collection.insert_many(spritz_locations)

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# create a Vector Search Index so we can use it
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{
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"fields": [
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  {
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    "numDimensions": 1536,
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    "path": "embedding",
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    "similarity": "cosine",
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    "type": "vector"
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  }
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]
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}

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# You have to embed your queries just the same way you embedded your documents.
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# my query
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query_description = "outdoor seating quick service"
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# we need to embed the query as well, since our documents are embedded
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query_vector = get_embedding(query_description)

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spritz_near_me_vector = [
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   {
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       '$vectorSearch': {
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           'index': 'vector_index',
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           'path': 'embedding',
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           'queryVector': query_vector,
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           'numCandidates': 15,
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           'limit': 5
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       }
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   },
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   {
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       "$project": {
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           "_id": 1,  # we want to keep this in place so we can search again using GeoNear
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           "name": 1,
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           "rating": 1,
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           "reviews": 1
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           #"address": 1,
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           #"location": 1,
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           #"embedding": 1
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       }
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   }
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]

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spritz_near_me_vector_results = list(collection.aggregate(spritz_near_me_vector))
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for result in spritz_near_me_vector_results:
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   print(result)

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# now, we want to take the _ids from our above pipeline so we can use it to geo search
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spritz_near_me_ids = [result['_id'] for result in spritz_near_me_vector_results]
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print(spritz_near_me_ids)

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collection.create_index( { "location" : "2dsphere" } )

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# use the $geoNear operator to return documents that are at least 100 meters and at most 1000 meters from our specified GeoJSON point.
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spritz_near_me_geo = [
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   {
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       "$geoNear": {
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           "near": {
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               "type": "Point",
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               "coordinates": [-74.0059456749148, 40.73781277366724]
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           },
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           # here we are saying that we only want to use the sample size from above
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           "query": {"_id": {"$in": spritz_near_me_ids}},
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           "minDistance": 100,
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           "maxDistance": 1000,
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           "spherical": True,
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           "distanceField": "dist.calculated"
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       }
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   },
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   {
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       "$project": {
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           "_id": 0,
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           "name": 1,
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           "address": 1,
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           "rating": 1,
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           "dist.calculated": 1,
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           #"location": 1,
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           #"embedding": 1
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       }
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   },
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   {
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       "$limit": 3
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   },
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   {
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       "$sort": {
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           "dist.calculated": 1
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       }
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   }
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]

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spritz_near_me_geo_results = collection.aggregate(spritz_near_me_geo)
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for result in spritz_near_me_geo_results:
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   print(result)

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# create a 2dsphere index on our location field
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collection.create_index({"location": "2dsphere"})
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# our $geoNear pipeline
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spritz_near_me_geo = [
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   {
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       "$geoNear": {
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           "near": {
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               "type": "Point",
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               "coordinates": [-74.0059456749148, 40.73781277366724]
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           },
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           "minDistance": 100,
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           "maxDistance": 1000,
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           "spherical": True,
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           "distanceField": "dist.calculated"
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       }
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   },
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   {
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       "$project": {
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           "_id": 1,
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           "dist.calculated": 1
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       }
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   }
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]
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# list of ID's and distances so we can use them as our sample size
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places_ids = list(collection.aggregate(spritz_near_me_geo))
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distances = {result['_id']: result['dist']['calculated'] for result in places_ids} # have to create a new dictionary to keep our distances
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spritz_near_me_ids = [result['_id'] for result in places_ids]
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# print(spritz_near_me_ids)

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# our vector search index that was created inside of MongoDB Atlas
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vector_search_index = {
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   "fields": [
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       {
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           "numDimensions": 1536,
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           "path": "embedding",
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           "similarity": "cosine",
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           "type": "vector"
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       },
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       {
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           "type": "filter",
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           "path": "_id"
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       }
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   ]
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}

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# vector search pipeline
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spritz_near_me_vector = [
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   {
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       '$vectorSearch': {
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           'index': 'vector_index',
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           'path': 'embedding',
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           'queryVector': query_vector,
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           'numCandidates': 15,
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           'limit': 3,
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           'filter': {"_id": {'$in': spritz_near_me_ids}}
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       }
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   },
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   {
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       "$project": {
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           "_id": 1,  # we want to keep this in place
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           "name": 1,
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           "rating": 1,
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           "dist.calculated": 1
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           #"reviews": 1
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           # "address": 1,
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           # "location": 1,
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           # "embedding": 1
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       }
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   }
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]
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spritz_near_me_vector_results = collection.aggregate(spritz_near_me_vector)
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for result in spritz_near_me_vector_results:
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   result['dist.calculated'] = distances.get(result['_id'])
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   print(result)