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import openai, os, time
from datasets import load_dataset
from pymongo.collection import Collection
from pymongo.errors import OperationFailure
from pymongo.mongo_client import MongoClient
from pymongo.operations import SearchIndexModel
DB_NAME = "airbnb_dataset"
COLLECTION_NAME = "listings_reviews"
def connect_to_database():
MONGODB_ATLAS_CLUSTER_URI = os.environ["MONGODB_ATLAS_CLUSTER_URI"]
mongo_client = MongoClient(MONGODB_ATLAS_CLUSTER_URI, appname="advanced-rag")
db = mongo_client.get_database(DB_NAME)
collection = db.get_collection(COLLECTION_NAME)
return db, collection
def rag_ingestion(collection):
dataset = load_dataset("bstraehle/airbnb-san-francisco-202403-embed", streaming=True, split="train")
collection.delete_many({})
collection.insert_many(dataset)
return "Manually create a vector search index (in free tier, this feature is not available via SDK)"
def rag_retrieval_naive(openai_api_key,
prompt,
db,
collection,
vector_index="vector_index"):
# Naive RAG: Semantic search
retrieval_result = vector_search_naive(
openai_api_key,
prompt,
db,
collection,
vector_index
)
if not retrieval_result:
return "No results found."
#print("###")
#print(retrieval_result)
#print("###")
return retrieval_result
def rag_retrieval_advanced(openai_api_key,
prompt,
accomodates,
bedrooms,
db,
collection,
vector_index="vector_index"):
# Advanced RAG: Semantic search plus...
# 1a) Pre-retrieval processing: index filter (accomodates, bedrooms) plus...
# 1b) Post-retrieval processing: result filter (accomodates, bedrooms) plus...
#match_stage = {
# "$match": {
# "accommodates": { "$eq": 2},
# "bedrooms": { "$eq": 1}
# }
#}
#additional_stages = [match_stage]
# 2) Average review score and review count boost, sorted in descending order
review_average_stage = {
"$addFields": {
"averageReviewScore": {
"$divide": [
{
"$add": [
"$review_scores_rating",
"$review_scores_accuracy",
"$review_scores_cleanliness",
"$review_scores_checkin",
"$review_scores_communication",
"$review_scores_location",
"$review_scores_value",
]
},
7
]
},
"reviewCountBoost": "$number_of_reviews"
}
}
weighting_stage = {
"$addFields": {
"combinedScore": {
"$add": [
{"$multiply": ["$averageReviewScore", 0.9]},
{"$multiply": ["$reviewCountBoost", 0.1]},
]
}
}
}
sorting_stage_sort = {
"$sort": {"combinedScore": -1}
}
additional_stages = [review_average_stage, weighting_stage, sorting_stage_sort]
retrieval_result = vector_search_advanced(
openai_api_key,
prompt,
accomodates,
bedrooms,
db,
collection,
additional_stages,
vector_index
)
if not retrieval_result:
return "No results found."
#print("###")
#print(retrieval_result)
#print("###")
return retrieval_result
def inference(openai_api_key, prompt):
content = f"Answer this user question: {prompt}"
return invoke_llm(openai_api_key, content)
def rag_inference(openai_api_key, prompt, retrieval_result):
content = f"Answer this user question: {prompt} with the following context:\n{retrieval_result}"
return invoke_llm(openai_api_key, content)
def invoke_llm(openai_api_key, content):
openai.api_key = openai_api_key
completion = openai.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": "You are an AirBnB listing recommendation system."},
{
"role": "user",
"content": content
}
]
)
return completion.choices[0].message.content
def vector_search_naive(openai_api_key,
user_query,
db,
collection,
vector_index="vector_index"):
query_embedding = get_text_embedding(openai_api_key, user_query)
if query_embedding is None:
return "Invalid query or embedding generation failed."
vector_search_stage = {
"$vectorSearch": {
"index": vector_index,
"queryVector": query_embedding,
"path": "description_embedding",
"numCandidates": 150,
"limit": 25,
}
}
remove_embedding_stage = {
"$unset": "description_embedding"
}
pipeline = [vector_search_stage, remove_embedding_stage]
return invoke_search(collection, pipeline)
def vector_search_advanced(openai_api_key,
user_query,
accommodates,
bedrooms,
db,
collection,
additional_stages=[],
vector_index="vector_index"):
query_embedding = get_text_embedding(openai_api_key, user_query)
if query_embedding is None:
return "Invalid query or embedding generation failed."
vector_search_stage = {
"$vectorSearch": {
"index": vector_index,
"queryVector": query_embedding,
"path": "description_embedding",
"numCandidates": 150,
"limit": 25,
"filter": {
"$and": [
{"accommodates": {"$eq": accommodates}},
{"bedrooms": {"$eq": bedrooms}}
]
},
}
}
remove_embedding_stage = {
"$unset": "description_embedding"
}
pipeline = [vector_search_stage, remove_embedding_stage] + additional_stages
return invoke_search(collection, pipeline)
def invoke_search(collection, pipeline):
results = collection.aggregate(pipeline)
return list(results)
def get_text_embedding(openai_api_key, text):
if not text or not isinstance(text, str):
return None
openai.api_key = openai_api_key
try:
return openai.embeddings.create(
input=text,
model="text-embedding-3-small", dimensions=1536
).data[0].embedding
except Exception as e:
print(f"Error in get_embedding: {e}")
return None |