PyMongo is the official Python driver for connecting applications to MongoDB and performing database operations. It is the preferred choice for synchronous Python applications, where database calls proceed in a straightforward, sequential flow. section 3.6 section 3.6.3
A typical PyMongo workflow is:
- Install the driver with pip. section 3.6.1
- Create a MongoClient with a connection string and, when supported, specify Stable API version 1. section 3.6.1
- Select a database and collection, define a query, and execute operations such as find_one. section 3.6.1
- Handle exceptions, inspect returned documents, and close the connection. section 3.6.1
Find a route from JFK
Connect with MongoClient, select the sample_training database and routes collection, then run {"src_airport": "JFK", "airline.id": 3201}. A matching document contains airline, destination, codeshare, stops, and airplane fields. Run the saved script with python3 mongodb-pymongo.py. section 3.6.1
python3 -m pip install pymongo
Install PyMongo before importing MongoClient or using the driver. section 3.6.1
from pymongo import MongoClient
def vector_search():
# MongoDB connection string
uri = 'your_mongodb_connection_string'
# Create a new MongoClient
client = MongoClient(uri)
try:
# Connect to the MongoDB server
client = MongoClient(uri)
# Select the database and collection
database = client['your_database_name']
collection = database['your_collection_name']
# Define the pre-filters and vector search query
query = {
"$and": [
{"category": "electronics"}, # Example pre-filter
{"price": {"$lt": 500}}, # Another example pre-filter
{
"$vectorSearch": {
"queryVector": [/* your query vector here */],
"numResults": 10,
"path": "your_vector_field_name"
}
}
]
}
try:
# Execute the vector search query
➥ # and convert the results to a list
results = list(collection.find(query))
# Print the results
for result in results:
print(result)
except Exception as query_error:
# Handle errors that occur during the query execution
print('Error executing vector search query:', query_error)
except Exception as connection_error:
# Handle errors that occur while connecting to MongoDB
print('Error connecting to MongoDB:', connection_error)
finally:
try:
# Ensure the MongoDB client is closed
client.close()
except Exception as close_error:
# Handle errors that occur while closing the MongoDB connection
print('Error closing MongoDB connection:', close_error)
# Call the vector_search function
vector_search()
Use the Python client to combine category and price prefilters with $vectorSearch, then collect and print similarity results. section 12.4.2
