Spaces:
Build error
Build error
Pavel Duchovny
commited on
Commit
·
df3f326
1
Parent(s):
73bb5ee
Stability fixes
Browse files
app.py
CHANGED
|
@@ -28,56 +28,60 @@ def get_restaurants(search, location, meters):
|
|
| 28 |
|
| 29 |
# Pre aggregate restaurants collection based on chosen location and radius, the output is stored into
|
| 30 |
# trips_collection
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
"
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
|
|
|
|
| 54 |
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 63 |
|
| 64 |
-
|
| 65 |
-
|
|
|
|
|
|
|
|
|
|
| 66 |
|
| 67 |
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
third_restaurant = restaurant_docs[2]['restaurant_id']
|
| 75 |
-
restaurant_string = f"'{first_restaurant}', '{second_restaurant}', '{third_restaurant}'"
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
iframe = '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':{$in:[' + restaurant_string + ']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>'
|
| 79 |
-
client.close()
|
| 80 |
-
return chat_response.choices[0].message.content, iframe,str(pre_agg), str(vectorQuery)
|
| 81 |
|
| 82 |
|
| 83 |
def pre_aggregate_meters(restaurants_collection, location, meters):
|
|
|
|
| 28 |
|
| 29 |
# Pre aggregate restaurants collection based on chosen location and radius, the output is stored into
|
| 30 |
# trips_collection
|
| 31 |
+
try:
|
| 32 |
+
newTrip, pre_agg = pre_aggregate_meters(restaurants_collection, location, meters)
|
| 33 |
+
|
| 34 |
+
## Get openai embeddings
|
| 35 |
+
response = openai_client.embeddings.create(
|
| 36 |
+
input=search,
|
| 37 |
+
model="text-embedding-3-small",
|
| 38 |
+
dimensions=256
|
| 39 |
+
)
|
| 40 |
+
|
| 41 |
+
## prepare the similarity search on current trip
|
| 42 |
+
vectorQuery = {
|
| 43 |
+
"$vectorSearch": {
|
| 44 |
+
"index" : "vector_index",
|
| 45 |
+
"queryVector": response.data[0].embedding,
|
| 46 |
+
"path" : "embedding",
|
| 47 |
+
"numCandidates": 10,
|
| 48 |
+
"limit": 3,
|
| 49 |
+
"filter": {"searchTrip": newTrip}
|
| 50 |
+
}}
|
| 51 |
+
|
| 52 |
+
## Run the retrieved documents through a RAG system.
|
| 53 |
+
restaurant_docs = list(trips_collection.aggregate([vectorQuery,
|
| 54 |
+
{"$project": {"_id" : 0, "embedding": 0}}]))
|
| 55 |
|
| 56 |
+
|
| 57 |
+
chat_response = openai_client.chat.completions.create(
|
| 58 |
+
model="gpt-3.5-turbo-0125",
|
| 59 |
+
messages=[
|
| 60 |
+
{"role": "system", "content": "You are a helpful restaurant assistant. You will get a context if the context is not relevat to the user query please address that and not provide by default the restaurants as is."},
|
| 61 |
+
{ "role": "user", "content": f"Find me the 2 best restaurant and why based on {search} and {restaurant_docs}. explain trades offs and why I should go to each one. You can mention the third option as a possible alternative."}
|
| 62 |
+
]
|
| 63 |
+
)
|
| 64 |
+
|
| 65 |
+
## Removed the temporary documents
|
| 66 |
+
trips_collection.delete_many({"searchTrip": newTrip})
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
if len(restaurant_docs) == 0:
|
| 70 |
+
return "No restaurants found", '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':\'\'}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>', str(pre_agg), str(vectorQuery)
|
| 71 |
|
| 72 |
+
## Build the map filter
|
| 73 |
+
first_restaurant = restaurant_docs[0]['restaurant_id']
|
| 74 |
+
second_restaurant = restaurant_docs[1]['restaurant_id']
|
| 75 |
+
third_restaurant = restaurant_docs[2]['restaurant_id']
|
| 76 |
+
restaurant_string = f"'{first_restaurant}', '{second_restaurant}', '{third_restaurant}'"
|
| 77 |
|
| 78 |
|
| 79 |
+
iframe = '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':{$in:[' + restaurant_string + ']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>'
|
| 80 |
+
client.close()
|
| 81 |
+
return chat_response.choices[0].message.content, iframe,str(pre_agg), str(vectorQuery)
|
| 82 |
+
except Exception as e:
|
| 83 |
+
print(e)
|
| 84 |
+
return "Your query caused an error, please retry with allowed input only ...", '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':\'\'}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>', str(pre_agg), str(vectorQuery)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 85 |
|
| 86 |
|
| 87 |
def pre_aggregate_meters(restaurants_collection, location, meters):
|