mirror of
https://github.com/run-llama/azure-cosmos-db-demo.git
synced 2026-06-30 21:08:04 -04:00
Laurie Voss
36 lines
1.5 KiB
Python
36 lines
1.5 KiB
Python
## This shows how to load your pre-indexed data from mongo and query it
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## Note that you MUST manually create a vector search index before this will work
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## and you must pass in the name of that index when connecting to Mongodb below
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from dotenv import load_dotenv
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load_dotenv()
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# Turns on really noisy logging
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import logging
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import sys
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logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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import os
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from pymongo.mongo_client import MongoClient
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from llama_index.vector_stores.azurecosmosmongo import AzureCosmosDBMongoDBVectorSearch
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from llama_index.indices.vector_store.base import VectorStoreIndex
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# Create a new client and connect to the server
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client = MongoClient(os.getenv("MONGODB_URI"))
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# connect to Atlas as a vector store
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store = AzureCosmosDBMongoDBVectorSearch(
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client,
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db_name=os.getenv('MONGODB_DATABASE'), # this is the database where you stored your embeddings
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collection_name=os.getenv('MONGODB_VECTORS'), # this is where your embeddings were stored in 2_load_and_index.py
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index_name=os.getenv('MONGODB_VECTOR_INDEX') # this is the name of the index you created after loading your data
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)
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index = VectorStoreIndex.from_vector_store(store)
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# query your data!
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# here we have customized the number of documents returned per query to 20, because tweets are really short
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query_engine = index.as_query_engine(similarity_top_k=20)
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response = query_engine.query("What does the author think of web frameworks?")
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print(response)
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