pymongo-search-utils

import os import pymongo from pymongo_search_utils import AtlasSearch from pymongo_search_utils.embeddings import OpenAIEmbeddings # Replace with your MongoDB Atlas connection string CONNECTION_STRING = os.environ.get("MONGO_URI", "mongodb://localhost:27017/") # Replace with your OpenAI API key OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "sk-YOUR_OPENAI_API_KEY") # Connect to MongoDB Atlas client = pymongo.MongoClient(CONNECTION_STRING) db = client["mydatabase"] collection = db["mycollection"] # Initialize OpenAI Embeddings embedding_service = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) # Initialize AtlasSearch atlas_search = AtlasSearch( collection=collection, index_name="default", # Your Atlas Search index name embedding_function=embedding_service, vector_search_field="plot_embedding", # The field in your collection containing vector embeddings text_search_field="plot" # The field in your collection for text search ) # Example: Insert dummy data (if collection is empty) if collection.count_documents({}) == 0: print("Inserting dummy data...") collection.insert_one({"plot": "A dog goes on an adventure.", "plot_embedding": embedding_service.embed_query("A dog goes on an adventure.")}) collection.insert_one({"plot": "Two friends discover a magical portal.", "plot_embedding": embedding_service.embed_query("Two friends discover a magical portal.")}) print("Dummy data inserted.") # Perform a vector search query = "a furry companion's journey" results_vector = atlas_search.vector_search(query_string=query, limit=1) print(f"\nVector Search Results for '{query}':") for doc in results_vector: print(f" - Plot: {doc.get('plot')}") # Perform a text search query_text = "magical portal" results_text = atlas_search.text_search(query_string=query_text, limit=1) print(f"\nText Search Results for '{query_text}':") for doc in results_text: print(f" - Plot: {doc.get('plot')}") client.close()