from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(reponse)

from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(reponse)

from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(reponse)

from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(reponse)

from llama_index import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(reponse)

json_file = 'tinytweets.json'

# Load environment variables from local .env file
from dotenv import load_dotenv
load_dotenv()

import os
import json
from pymongo.mongo_client import MongoClient

# Load the tweets from a local file
with open(json_file, 'r') as f:
    tweets = json.load(f)

# Create a new client and connect to the server
client = MongoClient(os.getenv('MONGODB_URI'))
db = client[os.getenv("MONGODB_DATABASE")]
collection = db[os.getenv("MONGODB_COLLECTION")]

# Insert the tweets into mongo
collection.insert_many(tweets)

query_dict = {}
reader = SimpleMongoReader(uri=os.getenv("MONGODB_URI"))
documents = reader.load_data(
    os.getenv("MONGODB_DATABASE"),
    os.getenv("MONGODB_COLLECTION"),
    field_names=["full_text"],
    query_dict=query_dict
)

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

# create Azure Cosmos as a vector store
store = AzureCosmosDBMongoDBVectorSearch(
    client,
    db_name=os.getenv('MONGODB_DATABASE'),
    collection_name=os.getenv('MONGODB_VECTORS'),
    index_name=os.getenv('MONGODB_VECTOR_INDEX')
)

storage_context = StorageContext.from_defaults(vector_store=store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context,
    show_progress=True
)

query_engine = index.as_query_engine(similarity_top_k=20)
response = query_engine.query("What does the author think of web frameworks?")
print(response)