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Snabbstart: Vektorsökning med Python i Azure DocumentDB

Använd vektorsökning i Azure DocumentDB med Python-klientbiblioteket. Lagra och fråga vektordata effektivt.

Den här kom igång-guiden använder ett exempelhotell-dataset i en JSON-fil med vektorer från text-embedding-ada-002 modellen. Datamängden innehåller hotellnamn, platser, beskrivningar och vektorinbäddningar.

Hitta exempelkoden på GitHub.

Förutsättningar

  • En prenumeration på Azure

  • Ett befintligt Azure DocumentDB-kluster

Skapa ett Python-projekt

  1. Skapa en ny katalog för projektet och öppna den i Visual Studio Code:

    mkdir vector-search-quickstart
code vector-search-quickstart

  2. Skapa och aktivera en virtuell miljö i terminalen:

    För Windows:

    python -m venv venv
venv\\Scripts\\activate

    För macOS/Linux:

    python -m venv venv
source venv/bin/activate

  3. Installera de paket som krävs:

    pip install pymongo azure-identity openai python-dotenv
    • pymongo: MongoDB-drivrutin för Python
    • azure-identity: Azure Identity-bibliotek för lösenordslös autentisering
    • openai: OpenAI-klientbibliotek för att skapa vektorer
    • python-dotenv: Miljövariabelhantering från .env-filer

  4. Skapa en .env fil i projektroten för miljövariabler:

    # Azure OpenAI configuration
AZURE_OPENAI_EMBEDDING_ENDPOINT= 
AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
AZURE_OPENAI_EMBEDDING_API_VERSION=2024-02-01

# Azure DocumentDB configuration
MONGO_CLUSTER_NAME=

# Data Configuration (defaults should work)
DATA_FILE_WITH_VECTORS=data/HotelsData_with_vectors.json
EMBEDDED_FIELD=text_embedding_ada_002
EMBEDDING_DIMENSIONS=1536
EMBEDDING_SIZE_BATCH=16
LOAD_SIZE_BATCH=100

    För lösenordslös autentisering som används i den här artikeln ersätter du platshållarvärdena .env i filen med din egen information:

    • AZURE_OPENAI_EMBEDDING_ENDPOINT: Url för Din Azure OpenAI-resursslutpunkt
    • MONGO_CLUSTER_NAME: Ditt Azure DocumentDB-resursnamn

    Du bör alltid föredra lösenordslös autentisering, men det kräver ytterligare konfiguration. Mer information om hur du konfigurerar hanterad identitet och alla dina autentiseringsalternativ finns i Autentisera Python-appar till Azure-tjänster med hjälp av Azure SDK för Python.

  5. Skapa en ny underkatalog från roten med namnet data.

  6. Kopiera rådatafilen med vektorer till en ny HotelsData_with_vectors.json fil i underkatalogen data .

  7. Projektstrukturen bör se ut så här:

    vector-search-quickstart
├── .env
├── data
│   └── HotelsData_with_vectors.json
└── venv (or your virtual environment folder)

Fortsätt projektet genom att skapa kodfiler för vektorsökning. När du är klar bör projektstrukturen se ut så här:

vector-search-quickstart
├── .env
├── data
│   └── HotelsData_with_vectors.json
├── src
│   ├── diskann.py
│   ├── ivf.py
│   └── hnsw.py
│   └── utils.py
└── venv (or your virtual environment folder)

Skapa en src katalog för dina Python-filer. Lägg till två filer: diskann.py och utils.py för DiskANN-indeximplementeringen:

mkdir src    
touch src/diskann.py
touch src/utils.py

Klistra in följande kod i diskann.py filen.

import os
from typing import List, Dict, Any
from utils import get_clients, get_clients_passwordless, read_file_return_json, insert_data, print_search_results, drop_vector_indexes
from dotenv import load_dotenv

# Load environment variables
load_dotenv()


def create_diskann_vector_index(collection, vector_field: str, dimensions: int) -> None:

    print(f"Creating DiskANN vector index on field '{vector_field}'...")

    # Drop any existing vector indexes on this field first
    drop_vector_indexes(collection, vector_field)

    # Use the native MongoDB command for Cosmos DB vector indexes
    index_command = {
        "createIndexes": collection.name,
        "indexes": [
            {
                "name": f"diskann_index_{vector_field}",
                "key": {
                    vector_field: "cosmosSearch"  # Cosmos DB vector search index type
                },
                "cosmosSearchOptions": {
                    # DiskANN algorithm configuration
                    "kind": "vector-diskann",

                    # Vector dimensions must match the embedding model
                    "dimensions": dimensions,

                    # Vector similarity metric - cosine is good for text embeddings
                    "similarity": "COS",

                    # Maximum degree: number of edges per node in the graph
                    # Higher values improve accuracy but increase memory usage
                    "maxDegree": 20,

                    # Build parameter: candidates evaluated during index construction
                    # Higher values improve index quality but increase build time
                    "lBuild": 10
                }
            }
        ]
    }

    try:
        # Execute the createIndexes command directly
        result = collection.database.command(index_command)
        print("DiskANN vector index created successfully")

    except Exception as e:
        print(f"Error creating DiskANN vector index: {e}")

        # Check if it's a tier limitation and suggest alternatives
        if "not enabled for this cluster tier" in str(e):
            print("\nDiskANN indexes require a higher cluster tier.")
            print("Try one of these alternatives:")
            print("  • Upgrade your Cosmos DB cluster to a higher tier")
            print("  • Use HNSW instead: python src/hnsw.py")
            print("  • Use IVF instead: python src/ivf.py")
        raise


def perform_diskann_vector_search(collection,
                                 azure_openai_client,
                                 query_text: str,
                                 vector_field: str,
                                 model_name: str,
                                 top_k: int = 5) -> List[Dict[str, Any]]:

    print(f"Performing DiskANN vector search for: '{query_text}'")

    try:
        # Generate embedding for the query text
        embedding_response = azure_openai_client.embeddings.create(
            input=[query_text],
            model=model_name
        )

        query_embedding = embedding_response.data[0].embedding

        # Construct the aggregation pipeline for vector search
        # Cosmos DB for MongoDB vCore uses $search with cosmosSearch
        pipeline = [
            {
                "$search": {
                    # Use cosmosSearch for vector operations in Cosmos DB
                    "cosmosSearch": {
                        # The query vector to search for
                        "vector": query_embedding,

                        # Field containing the document vectors to compare against
                        "path": vector_field,

                        # Number of final results to return
                        "k": top_k
                    }
                }
            },
            {
                # Add similarity score to the results
                "$project": {
                    "document": "$$ROOT",
                    # Add search score from metadata
                    "score": {"$meta": "searchScore"}
                }
            }
        ]

        # Execute the aggregation pipeline
        results = list(collection.aggregate(pipeline))

        return results

    except Exception as e:
        print(f"Error performing DiskANN vector search: {e}")
        raise


def main():

    # Load configuration from environment variables
    config = {
        'cluster_name': os.getenv('MONGO_CLUSTER_NAME', 'vectorSearch'),
        'database_name': 'vectorSearchDB',
        'collection_name': 'vectorSearchCollection',
        'data_file': os.getenv('DATA_FILE_WITH_VECTORS', 'data/HotelsData_with_vectors.json'),
        'vector_field': os.getenv('EMBEDDED_FIELD', 'DescriptionVector'),
        'model_name': os.getenv('AZURE_OPENAI_EMBEDDING_MODEL', 'text-embedding-ada-002'),
        'dimensions': int(os.getenv('EMBEDDING_DIMENSIONS', '1536')),
        'batch_size': int(os.getenv('LOAD_SIZE_BATCH', '100'))
    }

    try:
        # Initialize clients
        print("\nInitializing MongoDB and Azure OpenAI clients...")
        mongo_client, azure_openai_client = get_clients_passwordless()

        # Get database and collection
        database = mongo_client[config['database_name']]
        collection = database[config['collection_name']]

        # Load data with embeddings
        print(f"\nLoading data from {config['data_file']}...")
        data = read_file_return_json(config['data_file'])
        print(f"Loaded {len(data)} documents")

        # Verify embeddings are present
        documents_with_embeddings = [doc for doc in data if config['vector_field'] in doc]
        if not documents_with_embeddings:
            raise ValueError(f"No documents found with embeddings in field '{config['vector_field']}'. "
                           "Please run create_embeddings.py first.")

        # Insert data into collection
        print(f"\nInserting data into collection '{config['collection_name']}'...")

        # Clear existing data to ensure clean state
        collection.delete_many({})
        print("Cleared existing data from collection")

        # Insert the hotel data
        stats = insert_data(
            collection,
            documents_with_embeddings,
            batch_size=config['batch_size']
        )

        if stats['inserted'] == 0:
            raise ValueError("No documents were inserted successfully")

        # Create DiskANN vector index
        create_diskann_vector_index(
            collection,
            config['vector_field'],
            config['dimensions']
        )

        # Wait briefly for index to be ready
        import time
        print("Waiting for index to be ready...")
        time.sleep(2)

        # Perform sample vector search
        query = "quintessential lodging near running trails, eateries, retail"

        results = perform_diskann_vector_search(
            collection,
            azure_openai_client,
            query,
            config['vector_field'],
            config['model_name'],
            top_k=5
        )

        # Display results
        print_search_results(results, max_results=5, show_score=True)


    except Exception as e:
        print(f"\nError during DiskANN demonstration: {e}")
        raise

    finally:
        # Close the MongoDB client
        if 'mongo_client' in locals():
            mongo_client.close()


if __name__ == "__main__":
    main()

Den här huvudmodulen innehåller följande funktioner:

  • Innehåller verktygsfunktioner

  • Skapar ett konfigurationsobjekt för miljövariabler

  • Skapar klienter för Azure OpenAI och Azure DocumentDB

  • Ansluter till MongoDB, skapar en databas och samling, infogar data och skapar standardindex

  • Skapar ett vektorindex med IVF, HNSW eller DiskANN

  • Skapar en inbäddning för en exempelfrågetext med hjälp av OpenAI-klienten. Du kan ändra frågan överst i filen

  • Kör en vektorsökning med inbäddningen och skriver ut resultatet

Skapa verktygsfunktioner

Klistra in följande kod i utils.py:

import json
import os
import time
from typing import Dict, List, Any, Optional, Tuple
from pymongo import MongoClient, InsertOne
from pymongo.collection import Collection
from pymongo.errors import BulkWriteError
from azure.identity import DefaultAzureCredential
from pymongo.auth_oidc import OIDCCallback, OIDCCallbackContext, OIDCCallbackResult
from openai import AzureOpenAI
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

class AzureIdentityTokenCallback(OIDCCallback):
    def __init__(self, credential):
        self.credential = credential

    def fetch(self, context: OIDCCallbackContext) -> OIDCCallbackResult:
        token = self.credential.get_token(
            "https://ossrdbms-aad.database.windows.net/.default").token
        return OIDCCallbackResult(access_token=token)

def get_clients() -> Tuple[MongoClient, AzureOpenAI]:

    # Get MongoDB connection string - required for Cosmos DB access
    mongo_connection_string = os.getenv("MONGO_CONNECTION_STRING")
    if not mongo_connection_string:
        raise ValueError("MONGO_CONNECTION_STRING environment variable is required")

    # Create MongoDB client with optimized settings for Cosmos DB
    mongo_client = MongoClient(
        mongo_connection_string,
        maxPoolSize=50,  # Allow up to 50 connections for better performance
        minPoolSize=5,   # Keep minimum 5 connections open
        maxIdleTimeMS=30000,  # Close idle connections after 30 seconds
        serverSelectionTimeoutMS=5000,  # 5 second timeout for server selection
        socketTimeoutMS=20000  # 20 second socket timeout
    )

    # Get Azure OpenAI configuration
    azure_openai_endpoint = os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
    azure_openai_key = os.getenv("AZURE_OPENAI_EMBEDDING_KEY")

    if not azure_openai_endpoint or not azure_openai_key:
        raise ValueError("Azure OpenAI endpoint and key are required")

    # Create Azure OpenAI client for generating embeddings
    azure_openai_client = AzureOpenAI(
        azure_endpoint=azure_openai_endpoint,
        api_key=azure_openai_key,
        api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION", "2024-02-01")
    )

    return mongo_client, azure_openai_client


def get_clients_passwordless() -> Tuple[MongoClient, AzureOpenAI]:

    # Get MongoDB cluster name for passwordless authentication
    cluster_name = os.getenv("MONGO_CLUSTER_NAME")
    if not cluster_name:
        raise ValueError("MONGO_CLUSTER_NAME environment variable is required")

    # Create credential object for Azure authentication
    credential = DefaultAzureCredential()

    authProperties = {"OIDC_CALLBACK": AzureIdentityTokenCallback(credential)}

    # Create MongoDB client with Azure AD token callback
    mongo_client = MongoClient(
        f"mongodb+srv://{cluster_name}.global.mongocluster.cosmos.azure.com/",
        connectTimeoutMS=120000,
        tls=True,
        retryWrites=True,
        authMechanism="MONGODB-OIDC",
        authMechanismProperties=authProperties
    )

    # Get Azure OpenAI endpoint
    azure_openai_endpoint = os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
    if not azure_openai_endpoint:
        raise ValueError("AZURE_OPENAI_EMBEDDING_ENDPOINT environment variable is required")

    # Create Azure OpenAI client with credential-based authentication
    azure_openai_client = AzureOpenAI(
        azure_endpoint=azure_openai_endpoint,
        azure_ad_token_provider=lambda: credential.get_token("https://cognitiveservices.azure.com/.default").token,
        api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION", "2024-02-01")
    )

    return mongo_client, azure_openai_client


def azure_identity_token_callback(credential: DefaultAzureCredential) -> str:

    # Cosmos DB for MongoDB requires this specific scope
    token_scope = "https://cosmos.azure.com/.default"

    # Get token from Azure AD
    token = credential.get_token(token_scope)

    return token.token


def read_file_return_json(file_path: str) -> List[Dict[str, Any]]:

    try:
        with open(file_path, 'r', encoding='utf-8') as file:
            return json.load(file)
    except FileNotFoundError:
        print(f"Error: File '{file_path}' not found")
        raise
    except json.JSONDecodeError as e:
        print(f"Error: Invalid JSON in file '{file_path}': {e}")
        raise


def write_file_json(data: List[Dict[str, Any]], file_path: str) -> None:

    try:
        with open(file_path, 'w', encoding='utf-8') as file:
            json.dump(data, file, indent=2, ensure_ascii=False)
        print(f"Data successfully written to '{file_path}'")
    except IOError as e:
        print(f"Error writing to file '{file_path}': {e}")
        raise


def insert_data(collection: Collection, data: List[Dict[str, Any]],
                batch_size: int = 100, index_fields: Optional[List[str]] = None) -> Dict[str, int]:

    total_documents = len(data)
    inserted_count = 0
    failed_count = 0

    print(f"Starting batch insertion of {total_documents} documents...")

    # Create indexes if specified
    if index_fields:
        for field in index_fields:
            try:
                collection.create_index(field)
                print(f"Created index on field: {field}")
            except Exception as e:
                print(f"Warning: Could not create index on {field}: {e}")

    # Process data in batches to manage memory and error recovery
    for i in range(0, total_documents, batch_size):
        batch = data[i:i + batch_size]
        batch_num = (i // batch_size) + 1
        total_batches = (total_documents + batch_size - 1) // batch_size

        try:
            # Prepare bulk insert operations
            operations = [InsertOne(document) for document in batch]

            # Execute bulk insert
            result = collection.bulk_write(operations, ordered=False)
            inserted_count += result.inserted_count

            print(f"Batch {batch_num} completed: {result.inserted_count} documents inserted")

        except BulkWriteError as e:
            # Handle partial failures in bulk operations
            inserted_count += e.details.get('nInserted', 0)
            failed_count += len(batch) - e.details.get('nInserted', 0)

            print(f"Batch {batch_num} had errors: {e.details.get('nInserted', 0)} inserted, "
                  f"{failed_count} failed")

            # Print specific error details for debugging
            for error in e.details.get('writeErrors', []):
                print(f"  Error: {error.get('errmsg', 'Unknown error')}")

        except Exception as e:
            # Handle unexpected errors
            failed_count += len(batch)
            print(f"Batch {batch_num} failed completely: {e}")

        # Small delay between batches to avoid overwhelming the database
        time.sleep(0.1)

    # Return summary statistics
    stats = {
        'total': total_documents,
        'inserted': inserted_count,
        'failed': failed_count
    }

    return stats


def drop_vector_indexes(collection, vector_field: str) -> None:

    try:
        # Get all indexes for the collection
        indexes = list(collection.list_indexes())

        # Find vector indexes on the specified field
        vector_indexes = []
        for index in indexes:
            if 'key' in index and vector_field in index['key']:
                if index['key'][vector_field] == 'cosmosSearch':
                    vector_indexes.append(index['name'])

        # Drop each vector index found
        for index_name in vector_indexes:
            print(f"Dropping existing vector index: {index_name}")
            collection.drop_index(index_name)

        if vector_indexes:
            print(f"Dropped {len(vector_indexes)} existing vector index(es)")
        else:
            print("No existing vector indexes found to drop")

    except Exception as e:
        print(f"Warning: Could not drop existing vector indexes: {e}")
        # Continue anyway - the error might be that no indexes exist


def print_search_resultsx(results: List[Dict[str, Any]],
                        max_results: int = 5,
                        show_score: bool = True) -> None:

    if not results:
        print("No search results found.")
        return

    print(f"\nSearch Results (showing top {min(len(results), max_results)}):")
    print("=" * 80)

    for i, result in enumerate(results[:max_results], 1):

        # Display hotel name and ID
        print(f"HotelName: {result['HotelName']}, Score: {result['score']:.4f}")

def print_search_results(results: List[Dict[str, Any]],
                        max_results: int = 5,
                        show_score: bool = True) -> None:

    if not results:
        print("No search results found.")
        return

    print(f"\nSearch Results (showing top {min(len(results), max_results)}):")
    print("=" * 80)

    for i, result in enumerate(results[:max_results], 1):

        # Check if results are nested under 'document' (when using $$ROOT)
        if 'document' in result:
            doc = result['document']
        else:
            doc = result

        # Display hotel name and ID
        print(f"HotelName: {doc['HotelName']}, Score: {result['score']:.4f}")


    if len(results) > max_results:
        print(f"\n... and {len(results) - max_results} more results")

Den här verktygsmodulen innehåller följande funktioner:

  • JsonData: Gränssnitt för datastrukturen

  • scoreProperty: Plats för poängen i frågeresultat baserat på vektorsökningsmetod

  • getClients: Skapar och returnerar klienter för Azure OpenAI och Azure DocumentDB

  • getClientsPasswordless: Skapar och returnerar klienter för Azure OpenAI och Azure DocumentDB med lösenordslös autentisering. Aktivera RBAC på båda resurserna och logga in på Azure CLI

  • readFileReturnJson: Läser en JSON-fil och returnerar dess innehåll som en matris med JsonData objekt

  • writeFileJson: Skriver en matris med JsonData objekt till en JSON-fil

  • insertData: Infogar data i batchar i en MongoDB-samling och skapar standardindex för angivna fält

  • printSearchResults: Skriver ut resultatet av en vektorsökning, inklusive poäng och hotellnamn

Autentisera med Azure CLI

Logga in på Azure CLI innan du kör programmet så att det kan komma åt Azure-resurser på ett säkert sätt.

az login

Starta programmet

Så här kör du Python-skripten:

python src/diskann.py

Du ser de fem bästa hotellen som matchar vektorsökningsfrågan och deras likhetspoäng.

Visa och hantera data i Visual Studio Code

  1. Välj DocumentDB-tillägget i Visual Studio Code för att ansluta till ditt Azure DocumentDB-konto.

  2. Visa data och index i databasen Hotell.

    Skärmbild av DocumentDB-tillägget som visar Azure DocumentDB-samlingen.

Rensa resurser

Ta bort resursgruppen, Azure DocumentDB-kontot och Azure OpenAI-resursen när du inte behöver dem för att undvika extra kostnader.

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