## This shows how to load your pre-indexed data from mongo and query it
## Note that you MUST manually create a vector search index before this will work
## and you must pass in the name of that index when connecting to Mongodb below 
from dotenv import load_dotenv
load_dotenv()

# Turns on really noisy logging
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import os
from pymongo.mongo_client import MongoClient
from llama_index.vector_stores.azurecosmosmongo import AzureCosmosDBMongoDBVectorSearch
from llama_index.indices.vector_store.base import VectorStoreIndex

# Create a new client and connect to the server
client = MongoClient(os.getenv("MONGODB_URI"))

# connect to Atlas as a vector store
store = AzureCosmosDBMongoDBVectorSearch(
    client,
    db_name=os.getenv('MONGODB_DATABASE'), # this is the database where you stored your embeddings
    collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings were stored in 2_load_and_index.py
    index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you created after loading your data
)
index = VectorStoreIndex.from_vector_store(store)

# query your data!
# here we have customized the number of documents returned per query to 20, because tweets are really short
query_engine = index.as_query_engine(similarity_top_k=20)
response = query_engine.query("What does the author think of web frameworks?")
print(response)

