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Runtime error
Runtime error
Pavel Duchovny
commited on
Commit
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df3f326
1
Parent(s):
73bb5ee
Stability fixes
Browse files
app.py
CHANGED
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@@ -28,56 +28,60 @@ def get_restaurants(search, location, meters):
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# Pre aggregate restaurants collection based on chosen location and radius, the output is stored into
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# trips_collection
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third_restaurant = restaurant_docs[2]['restaurant_id']
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restaurant_string = f"'{first_restaurant}', '{second_restaurant}', '{third_restaurant}'"
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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\':{$in:[' + restaurant_string + ']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>'
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client.close()
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return chat_response.choices[0].message.content, iframe,str(pre_agg), str(vectorQuery)
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def pre_aggregate_meters(restaurants_collection, location, meters):
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# Pre aggregate restaurants collection based on chosen location and radius, the output is stored into
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# trips_collection
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try:
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newTrip, pre_agg = pre_aggregate_meters(restaurants_collection, location, meters)
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## Get openai embeddings
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response = openai_client.embeddings.create(
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input=search,
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model="text-embedding-3-small",
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dimensions=256
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)
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## prepare the similarity search on current trip
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vectorQuery = {
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"$vectorSearch": {
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"index" : "vector_index",
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"queryVector": response.data[0].embedding,
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"path" : "embedding",
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"numCandidates": 10,
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"limit": 3,
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"filter": {"searchTrip": newTrip}
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}}
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## Run the retrieved documents through a RAG system.
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restaurant_docs = list(trips_collection.aggregate([vectorQuery,
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{"$project": {"_id" : 0, "embedding": 0}}]))
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chat_response = openai_client.chat.completions.create(
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model="gpt-3.5-turbo-0125",
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messages=[
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{"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."},
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{ "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."}
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]
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)
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## Removed the temporary documents
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trips_collection.delete_many({"searchTrip": newTrip})
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if len(restaurant_docs) == 0:
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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)
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## Build the map filter
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first_restaurant = restaurant_docs[0]['restaurant_id']
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second_restaurant = restaurant_docs[1]['restaurant_id']
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third_restaurant = restaurant_docs[2]['restaurant_id']
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restaurant_string = f"'{first_restaurant}', '{second_restaurant}', '{third_restaurant}'"
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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>'
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client.close()
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return chat_response.choices[0].message.content, iframe,str(pre_agg), str(vectorQuery)
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except Exception as e:
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print(e)
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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)
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def pre_aggregate_meters(restaurants_collection, location, meters):
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