Zelfstudie: Deel 2: een aangepaste RAG-app (Knowledge Retrieval) bouwen met de Azure AI Foundry SDK

Artikel
12/19/2024

In deze zelfstudie gebruikt u de Azure AI Foundry SDK (en andere bibliotheken) om een chat-app te bouwen, configureren en evalueren voor uw retailbedrijf met de naam Contoso Trek. Uw detailhandel is gespecialiseerd in outdoor camping gear en kleding. De chat-app moet vragen beantwoorden over uw producten en services. De chat-app kan bijvoorbeeld vragen beantwoorden zoals 'welke tent is de meest waterdicht?' of 'wat is de beste slaapzak voor koud weer?'.

In deze deel twee ziet u hoe u een eenvoudige chattoepassing kunt verbeteren door rag (retrieval augmented generation) toe te voegen om de antwoorden in uw aangepaste gegevens te gronden. Het ophalen van Augmented Generation (RAG) is een patroon dat gebruikmaakt van uw gegevens met een groot taalmodel (LLM) om antwoorden te genereren die specifiek zijn voor uw gegevens. In dit deel twee leert u het volgende:

Voorbeeldgegevens ophalen
Een zoekindex maken van de gegevens voor de chat-app die moet worden gebruikt
Aangepaste RAG-code ontwikkelen

Deze zelfstudie is deel twee van een driedelige zelfstudie.

Vereisten

Volledige zelfstudie: Deel 1: resources maken voor het bouwen van een aangepaste chattoepassing met de Azure AI SDK voor:
- Een project maken met een verbonden Azure AI Search-index
- De Azure CLI, Python en de vereiste pakketten installeren
- Uw omgevingsvariabelen configureren

Voorbeeldgegevens voor uw chat-app maken

Het doel van deze RAG-toepassing is om de modelreacties in uw aangepaste gegevens te baseren. U gebruikt een Azure AI Search-index waarmee vectorgegevens uit het insluitingsmodel worden opgeslagen. De zoekindex wordt gebruikt om relevante documenten op te halen op basis van de vraag van de gebruiker.

Als u al een zoekindex met gegevens hebt, kunt u doorgaan naar Productdocumenten ophalen. Anders kunt u een eenvoudige voorbeeldgegevensset maken voor gebruik in uw chat-app.

Maak een assetsmap en voeg deze voorbeeldgegevens toe aan een products.csv-bestand :

id,name,price,category,brand,description
1,TrailMaster X4 Tent,250.0,Tents,OutdoorLiving,"Unveiling the TrailMaster X4 Tent from OutdoorLiving, your home away from home for your next camping adventure. Crafted from durable polyester, this tent boasts a spacious interior perfect for four occupants. It ensures your dryness under drizzly skies thanks to its water-resistant construction, and the accompanying rainfly adds an extra layer of weather protection. It offers refreshing airflow and bug defence, courtesy of its mesh panels. Accessibility is not an issue with its multiple doors and interior pockets that keep small items tidy. Reflective guy lines grant better visibility at night, and the freestanding design simplifies setup and relocation. With the included carry bag, transporting this convenient abode becomes a breeze. Be it an overnight getaway or a week-long nature escapade, the TrailMaster X4 Tent provides comfort, convenience, and concord with the great outdoors. Comes with a two-year limited warranty to ensure customer satisfaction."
2,Adventurer Pro Backpack,90.0,Backpacks,HikeMate,"Venture into the wilderness with the HikeMate's Adventurer Pro Backpack! Uniquely designed with ergonomic comfort in mind, this backpack ensures a steadfast journey no matter the mileage. It boasts a generous 40L capacity wrapped up in durable nylon fabric ensuring its long-lasting performance on even the most rugged pursuits. It's meticulously fashioned with multiple compartments and pockets for organized storage, hydration system compatibility, and adjustable padded shoulder straps all in a lightweight construction. The added features of a sternum strap and hip belt enhance stability without compromising on comfort. The Adventurer Pro Backpack also prioritizes your safety with its reflective accents for when night falls. This buoyant beauty does more than carry your essentials; it carries the promise of a stress-free adventure!"
3,Summit Breeze Jacket,120.0,Hiking Clothing,MountainStyle,"Discover the joy of hiking with MountainStyle's Summit Breeze Jacket. This lightweight jacket is your perfect companion for outdoor adventures. Sporting a trail-ready, windproof design and a water-resistant fabric, it's ready to withstand any weather. The breathable polyester material and adjustable cuffs keep you comfortable, whether you're ascending a mountain or strolling through a park. And its sleek black color adds style to function. The jacket features a full-zip front closure, adjustable hood, and secure zippered pockets. Experience the comfort of its inner lining and the convenience of its packable design. Crafted for night trekkers too, the jacket has reflective accents for enhanced visibility. Rugged yet chic, the Summit Breeze Jacket is more than a hiking essential, it's the gear that inspires you to reach new heights. Choose adventure, choose the Summit Breeze Jacket."
4,TrekReady Hiking Boots,140.0,Hiking Footwear,TrekReady,"Introducing the TrekReady Hiking Boots - stepping up your hiking game, one footprint at a time! Crafted from leather, these stylistic Trailmates are made to last. TrekReady infuses durability with its reinforced stitching and toe protection, making sure your journey is never stopped short. Comfort? They have that covered too! The boots are a haven with their breathable materials, cushioned insole, with padded collar and tongue; all nestled neatly within their lightweight design. As they say, it's what's inside that counts - so inside you'll find a moisture-wicking lining that quarantines stank and keeps your feet fresh as that mountaintop breeze. Remember the fear of slippery surfaces? With these boots, you can finally tell it to 'take a hike'! Their shock-absorbing midsoles and excellent traction capabilities promise stability at your every step. Beautifully finished in a traditional lace-up system, every adventurer deserves a pair of TrekReady Hiking Boots. Hike more, worry less!"
5,BaseCamp Folding Table,60.0,Camping Tables,CampBuddy,"CampBuddy's BaseCamp Folding Table is an adventurer's best friend. Lightweight yet powerful, the table is a testament to fun-meets-function and will elevate any outing to new heights. Crafted from resilient, rust-resistant aluminum, the table boasts a generously sized 48 x 24 inches tabletop, perfect for meal times, games and more. The foldable design is a godsend for on-the-go explorers. Adjustable legs rise to the occasion to conquer uneven terrains and offer height versatility, while the built-in handle simplifies transportation. Additional features like non-slip feet, integrated cup holders and mesh pockets add a pinch of finesse. Quick to set up without the need for extra tools, this table is a silent yet indispensable sidekick during camping, picnics, and other outdoor events. Don't miss out on the opportunity to take your outdoor experiences to a new level with the BaseCamp Folding Table. Get yours today and embark on new adventures tomorrow! "
6,EcoFire Camping Stove,80.0,Camping Stoves,EcoFire,"Introducing EcoFire's Camping Stove, your ultimate companion for every outdoor adventure! This portable wonder is precision-engineered with a lightweight and compact design, perfect for capturing that spirit of wanderlust. Made from high-quality stainless steel, it promises durability and steadfast performance. This stove is not only fuel-efficient but also offers an easy, intuitive operation that ensures hassle-free cooking. Plus, it's flexible, accommodating a variety of cooking methods whether you're boiling, grilling, or simmering under the starry sky. Its stable construction, quick setup, and adjustable flame control make cooking a breeze, while safety features protect you from any potential mishaps. And did we mention it also includes an effective wind protector and a carry case for easy transportation? But that's not all! The EcoFire Camping Stove is eco-friendly, designed to minimize environmental impact. So get ready to enhance your camping experience and enjoy delicious outdoor feasts with this unique, versatile stove!"
7,CozyNights Sleeping Bag,100.0,Sleeping Bags,CozyNights,"Embrace the great outdoors in any season with the lightweight CozyNights Sleeping Bag! This durable three-season bag is superbly designed to give hikers, campers, and backpackers comfort and warmth during spring, summer, and fall. With a compact design that folds down into a convenient stuff sack, you can whisk it away on any adventure without a hitch. The sleeping bag takes comfort seriously, featuring a handy hood, ample room and padding, and a reliable temperature rating. Crafted from high-quality polyester, it ensures long-lasting use and can even be zipped together with another bag for shared comfort. Whether you're gazing at stars or catching a quick nap between trails, the CozyNights Sleeping Bag makes it a treat. Don't just sleep— dream with CozyNights."
8,Alpine Explorer Tent,350.0,Tents,AlpineGear,"Welcome to the joy of camping with the Alpine Explorer Tent! This robust, 8-person, 3-season marvel is from the responsible hands of the AlpineGear brand. Promising an enviable setup that is as straightforward as counting sheep, your camping experience is transformed into a breezy pastime. Looking for privacy? The detachable divider provides separate spaces at a moment's notice. Love a tent that breathes? The numerous mesh windows and adjustable vents fend off any condensation dragon trying to dampen your adventure fun. The waterproof assurance keeps you worry-free during unexpected rain dances. With a built-in gear loft to stash away your outdoor essentials, the Alpine Explorer Tent emerges as a smooth balance of privacy, comfort, and convenience. Simply put, this tent isn't just a shelter - it's your second home in the heart of nature! Whether you're a seasoned camper or a nature-loving novice, this tent makes exploring the outdoors a joyous journey."
9,SummitClimber Backpack,120.0,Backpacks,HikeMate,"Adventure waits for no one! Introducing the HikeMate SummitClimber Backpack, your reliable partner for every exhilarating journey. With a generous 60-liter capacity and multiple compartments and pockets, packing is a breeze. Every feature points to comfort and convenience; the ergonomic design and adjustable hip belt ensure a pleasantly personalized fit, while padded shoulder straps protect you from the burden of carrying. Venturing into wet weather? Fear not! The integrated rain cover has your back, literally. Stay hydrated thanks to the backpack's hydration system compatibility. Travelling during twilight? Reflective accents keep you visible in low-light conditions. The SummitClimber Backpack isn't merely a carrier; it's a wearable base camp constructed from ruggedly durable nylon and thoughtfully designed for the great outdoors adventurer, promising to withstand tough conditions and provide years of service. So, set off on that quest - the wild beckons! The SummitClimber Backpack - your hearty companion on every expedition!"
10,TrailBlaze Hiking Pants,75.0,Hiking Clothing,MountainStyle,"Meet the TrailBlaze Hiking Pants from MountainStyle, the stylish khaki champions of the trails. These are not just pants; they're your passport to outdoor adventure. Crafted from high-quality nylon fabric, these dapper troopers are lightweight and fast-drying, with a water-resistant armor that laughs off light rain. Their breathable design whisks away sweat while their articulated knees grant you the flexibility of a mountain goat. Zippered pockets guard your essentials, making them a hiker's best ally. Designed with durability for all your trekking trials, these pants come with a comfortable, ergonomic fit that will make you forget you're wearing them. Sneak a peek, and you are sure to be tempted by the sleek allure that is the TrailBlaze Hiking Pants. Your outdoors wardrobe wouldn't be quite complete without them."
11,TrailWalker Hiking Shoes,110.0,Hiking Footwear,TrekReady,"Meet the TrekReady TrailWalker Hiking Shoes, the ideal companion for all your outdoor adventures. Constructed with synthetic leather and breathable mesh, these shoes are tough as nails yet surprisingly airy. Their cushioned insoles offer fabulous comfort for long hikes, while the supportive midsoles and traction outsoles with multidirectional lugs ensure stability and excellent grip. A quick-lace system, padded collar and tongue, and reflective accents make these shoes a dream to wear. From combating rough terrain with the reinforced toe cap and heel, to keeping off trail debris with the protective mudguard, the TrailWalker Hiking Shoes have you covered. These waterproof warriors are made to endure all weather conditions. But they're not just about being rugged, they're light as a feather too, minimizing fatigue during epic hikes. Each pair can be customized for a perfect fit with removable insoles and availability in multiple sizes and widths. Navigate hikes comfortably and confidently with the TrailWalker Hiking Shoes. Adventure, here you come!"
12,TrekMaster Camping Chair,50.0,Camping Tables,CampBuddy,"Gravitate towards comfort with the TrekMaster Camping Chair from CampBuddy. This trusty outdoor companion boasts sturdy construction using high-quality materials that promise durability and enjoyment for seasons to come. Impeccably lightweight and portable, it's designed to be your go-to seat whether you're camping, at a picnic, cheering at a sporting event, or simply relishing in your backyard pleasures. Beyond its foldable design ensuring compact storage and easy transportation, its ergonomic magic is in the details. An adjustable recline, padded seat and backrest, integrated cup holder, and side pockets ensure the greatest outdoor comfort. Weather resistant, easy to clean, and capable of supporting diverse body types, this versatile chair also comes with a carry bag, ready for your next adventure."
13,PowerBurner Camping Stove,100.0,Camping Stoves,PowerBurner,"Unleash your inner explorer with the PowerBurner Dual Burner Camping Stove. It's designed for the adventurous heart, with sturdy construction and a high heat output that makes boiling and cooking a breeze. This stove isn't just about strength—it's got finesse too. With adjustable flame control, you can simmer, sauté, or sizzle with absolute precision. Its compact design and integrated carrying handle make transportation effortless. Moreover, it's crafted to defy the elements, boasting a wind-resistant exterior and piezo ignition system for quick, reliable starts. And when the cooking's done, its removable grates make cleanup swift and easy. Rugged, versatile and reliable, the PowerBurner marks a perfect blend of practicality and performance. So, why wait? Let's turn up the heat on your outdoor culinary adventures today."
14,MountainDream Sleeping Bag,130.0,Sleeping Bags,MountainDream,"Meet the MountainDream Sleeping Bag: your new must-have companion for every outdoor adventure. Designed to handle 3-season camping with ease, it comes equipped with a premium synthetic insulation that will keep you cozy even when temperatures fall down to 15°F! Sporting a durable water-resistant nylon shell and soft breathable polyester lining, this bag doesn't sacrifice comfort for toughness. The star of the show is the contoured mummy shape that not only provides optimal heat retention but also cuts down on the weight. A smooth, snag-free YKK zipper with a unique anti-snag design allows for hassle-free operation, while the adjustable hood and full-length zipper baffle work together to ensure you stay warm all night long. Need to bring along some essentials? Not to worry! There's an interior pocket just for that. And when it's time to pack up? Just slip it into the included compression sack for easy storage and transport. Whether you're a backpacking pro or a camping novice, the MountainDream Sleeping Bag is the perfect blend of durability, warmth, and comfort that you've been looking for."
15,SkyView 2-Person Tent,200.0,Tents,OutdoorLiving,"Introducing the OutdoorLiving SkyView 2-Person Tent, a perfect companion for your camping and hiking adventures. This tent offers a spacious interior that houses two people comfortably, with room to spare. Crafted from durable waterproof materials to shield you from the elements, it is the fortress you need in the wild. Setup is a breeze thanks to its intuitive design and color-coded poles, while two large doors allow for easy access. Stay organized with interior pockets, and store additional gear in its two vestibules. The tent also features mesh panels for effective ventilation, and it comes with a rainfly for extra weather protection. Light enough for on-the-go adventurers, it packs compactly into a carrying bag for seamless transportation. Reflective guy lines ensure visibility at night for added safety, and the tent stands freely for versatile placement. Experience the reliability of double-stitched seams that guarantee increased durability, and rest easy under the stars with OutdoorLiving's SkyView 2-Person Tent. It's not just a tent; it's your home away from home."
16,TrailLite Daypack,60.0,Backpacks,HikeMate,"Step up your hiking game with HikeMate's TrailLite Daypack. Built for comfort and efficiency, this lightweight and durable backpack offers a spacious main compartment, multiple pockets, and organization-friendly features all in one sleek package. The adjustable shoulder straps and padded back panel ensure optimal comfort during those long exhilarating treks. Course through nature without worry as the daypack's water-resistant fabric protects your essentials from unexpected showers. Plus, never run dry with the integrated hydration system. And did we mention it comes in a plethora of colors and designs? So you can choose one that truly speaks to your outdoorsy soul! Keeping your visibility in mind, we've added reflective accents that light up in low-light conditions. Don't just carry a backpack, adorn a companion that takes you a step ahead in your adventures. Trust the TrailLite Daypack for a hassle-free, enjoyable hiking experience."
17,RainGuard Hiking Jacket,110.0,Hiking Clothing,MountainStyle,"Introducing the MountainStyle RainGuard Hiking Jacket - the ultimate solution for weatherproof comfort during your outdoor undertakings! Designed with waterproof, breathable fabric, this jacket promises an outdoor experience that's as dry as it is comfortable. The rugged construction assures durability, while the adjustable hood provides a customizable fit against wind and rain. Featuring multiple pockets for safe, convenient storage and adjustable cuffs and hem, you can tailor the jacket to suit your needs on-the-go. And, don't worry about overheating during intense activities - it's equipped with ventilation zippers for increased airflow. Reflective details ensure visibility even during low-light conditions, making it perfect for evening treks. With its lightweight, packable design, carrying it inside your backpack requires minimal effort. With options for men and women, the RainGuard Hiking Jacket is perfect for hiking, camping, trekking and countless other outdoor adventures. Don't let the weather stand in your way - embrace the outdoors with MountainStyle RainGuard Hiking Jacket!"
18,TrekStar Hiking Sandals,70.0,Hiking Footwear,TrekReady,"Meet the TrekStar Hiking Sandals from TrekReady - the ultimate trail companion for your feet. Designed for comfort and durability, these lightweight sandals are perfect for those who prefer to see the world from a hiking trail. They feature adjustable straps for a snug, secure fit, perfect for adapting to the contours of your feet. With a breathable design, your feet will stay cool and dry, escaping the discomfort of sweaty hiking boots on long summer treks. The deep tread rubber outsole ensures excellent traction on any terrain, while the cushioned footbed promises enhanced comfort with every step. For those wild and unpredictable trails, the added toe protection and shock-absorbing midsole protect your feet from rocky surprises. Ingeniously, the removable insole makes for easy cleaning and maintenance, extending the lifespan of your sandals. Available in various sizes and a handsome brown color, the versatile TrekStar Hiking Sandals are just as comfortable on a casual walk in the park as they are navigating rocky slopes. Explore more with TrekReady!"
19,Adventure Dining Table,90.0,Camping Tables,CampBuddy,"Discover the joy of outdoor adventures with the CampBuddy Adventure Dining Table. This feature-packed camping essential brings both comfort and convenience to your memorable trips. Made from high-quality aluminum, it promises long-lasting performance, weather resistance, and easy maintenance - all key for the great outdoors! It's light, portable, and comes with adjustable height settings to suit various seating arrangements and the spacious surface comfortably accommodates meals, drinks, and other essentials. The sturdy yet lightweight frame holds food, dishes, and utensils with ease. When it's time to pack up, it fold and stows away with no fuss, ready for the next adventure!  Perfect for camping, picnics, barbecues, and beach outings - its versatility shines as brightly as the summer sun! Durable, sturdy and a breeze to set up, the Adventure Dining Table will be a loyal companion on every trip. Embark on your next adventure and make lifetime memories with CampBuddy. As with all good experiences, it'll leave you wanting more! "
20,CompactCook Camping Stove,60.0,Camping Stoves,CompactCook,"Step into the great outdoors with the CompactCook Camping Stove, a convenient, lightweight companion perfect for all your culinary camping needs. Boasting a robust design built for harsh environments, you can whip up meals anytime, anywhere. Its wind-resistant and fuel-versatile features coupled with an efficient cooking performance, ensures you won't have to worry about the elements or helpless taste buds while on adventures. The easy ignition technology and adjustable flame control make cooking as easy as a walk in the park, while its compact, foldable design makes packing a breeze. Whether you're camping with family or hiking solo, this reliable, portable stove is an essential addition to your gear. With its sturdy construction and safety-focused design, the CompactCook Camping Stove is a step above the rest, providing durability, quality, and peace of mind. Be wild, be free, be cooked for with the CompactCook Camping Stove!"

Een zoekindex maken

De zoekindex wordt gebruikt voor het opslaan van vectorgegevens uit het insluitingsmodel. De zoekindex wordt gebruikt om relevante documenten op te halen op basis van de vraag van de gebruiker.

Maak het bestand create_search_index.py in de hoofdmap (dat wil gezegd dezelfde map waarin u de map assets hebt geplaatst, niet in de map assets).
Kopieer en plak de volgende code in uw create_search_index.py bestand.

Voeg de code toe om de vereiste bibliotheken te importeren, een projectclient te maken en enkele instellingen te configureren:

import os
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import ConnectionType
from azure.identity import DefaultAzureCredential
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
from config import get_logger

# initialize logging object
logger = get_logger(__name__)

# create a project client using environment variables loaded from the .env file
project = AIProjectClient.from_connection_string(
    conn_str=os.environ["AIPROJECT_CONNECTION_STRING"], credential=DefaultAzureCredential()
)

# create a vector embeddings client that will be used to generate vector embeddings
embeddings = project.inference.get_embeddings_client()

# use the project client to get the default search connection
search_connection = project.connections.get_default(
    connection_type=ConnectionType.AZURE_AI_SEARCH, include_credentials=True
)

# Create a search index client using the search connection
# This client will be used to create and delete search indexes
index_client = SearchIndexClient(
    endpoint=search_connection.endpoint_url, credential=AzureKeyCredential(key=search_connection.key)
)

Voeg nu de functie toe om een zoekindex te definiëren:

import pandas as pd
from azure.search.documents.indexes.models import (
    SemanticSearch,
    SearchField,
    SimpleField,
    SearchableField,
    SearchFieldDataType,
    SemanticConfiguration,
    SemanticPrioritizedFields,
    SemanticField,
    VectorSearch,
    HnswAlgorithmConfiguration,
    VectorSearchAlgorithmKind,
    HnswParameters,
    VectorSearchAlgorithmMetric,
    ExhaustiveKnnAlgorithmConfiguration,
    ExhaustiveKnnParameters,
    VectorSearchProfile,
    SearchIndex,
)


def create_index_definition(index_name: str, model: str) -> SearchIndex:
    dimensions = 1536  # text-embedding-ada-002
    if model == "text-embedding-3-large":
        dimensions = 3072

    # The fields we want to index. The "embedding" field is a vector field that will
    # be used for vector search.
    fields = [
        SimpleField(name="id", type=SearchFieldDataType.String, key=True),
        SearchableField(name="content", type=SearchFieldDataType.String),
        SimpleField(name="filepath", type=SearchFieldDataType.String),
        SearchableField(name="title", type=SearchFieldDataType.String),
        SimpleField(name="url", type=SearchFieldDataType.String),
        SearchField(
            name="contentVector",
            type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
            searchable=True,
            # Size of the vector created by the text-embedding-ada-002 model.
            vector_search_dimensions=dimensions,
            vector_search_profile_name="myHnswProfile",
        ),
    ]

    # The "content" field should be prioritized for semantic ranking.
    semantic_config = SemanticConfiguration(
        name="default",
        prioritized_fields=SemanticPrioritizedFields(
            title_field=SemanticField(field_name="title"),
            keywords_fields=[],
            content_fields=[SemanticField(field_name="content")],
        ),
    )

    # For vector search, we want to use the HNSW (Hierarchical Navigable Small World)
    # algorithm (a type of approximate nearest neighbor search algorithm) with cosine
    # distance.
    vector_search = VectorSearch(
        algorithms=[
            HnswAlgorithmConfiguration(
                name="myHnsw",
                kind=VectorSearchAlgorithmKind.HNSW,
                parameters=HnswParameters(
                    m=4,
                    ef_construction=1000,
                    ef_search=1000,
                    metric=VectorSearchAlgorithmMetric.COSINE,
                ),
            ),
            ExhaustiveKnnAlgorithmConfiguration(
                name="myExhaustiveKnn",
                kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
                parameters=ExhaustiveKnnParameters(metric=VectorSearchAlgorithmMetric.COSINE),
            ),
        ],
        profiles=[
            VectorSearchProfile(
                name="myHnswProfile",
                algorithm_configuration_name="myHnsw",
            ),
            VectorSearchProfile(
                name="myExhaustiveKnnProfile",
                algorithm_configuration_name="myExhaustiveKnn",
            ),
        ],
    )

    # Create the semantic settings with the configuration
    semantic_search = SemanticSearch(configurations=[semantic_config])

    # Create the search index definition
    return SearchIndex(
        name=index_name,
        fields=fields,
        semantic_search=semantic_search,
        vector_search=vector_search,
    )

Maak de functie om een CSV-bestand toe te voegen aan de index:

# define a function for indexing a csv file, that adds each row as a document
# and generates vector embeddings for the specified content_column
def create_docs_from_csv(path: str, content_column: str, model: str) -> list[dict[str, any]]:
    products = pd.read_csv(path)
    items = []
    for product in products.to_dict("records"):
        content = product[content_column]
        id = str(product["id"])
        title = product["name"]
        url = f"/products/{title.lower().replace(' ', '-')}"
        emb = embeddings.embed(input=content, model=model)
        rec = {
            "id": id,
            "content": content,
            "filepath": f"{title.lower().replace(' ', '-')}",
            "title": title,
            "url": url,
            "contentVector": emb.data[0].embedding,
        }
        items.append(rec)

    return items


def create_index_from_csv(index_name, csv_file):
    # If a search index already exists, delete it:
    try:
        index_definition = index_client.get_index(index_name)
        index_client.delete_index(index_name)
        logger.info(f"🗑️  Found existing index named '{index_name}', and deleted it")
    except Exception:
        pass

    # create an empty search index
    index_definition = create_index_definition(index_name, model=os.environ["EMBEDDINGS_MODEL"])
    index_client.create_index(index_definition)

    # create documents from the products.csv file, generating vector embeddings for the "description" column
    docs = create_docs_from_csv(path=csv_file, content_column="description", model=os.environ["EMBEDDINGS_MODEL"])

    # Add the documents to the index using the Azure AI Search client
    search_client = SearchClient(
        endpoint=search_connection.endpoint_url,
        index_name=index_name,
        credential=AzureKeyCredential(key=search_connection.key),
    )

    search_client.upload_documents(docs)
    logger.info(f"➕ Uploaded {len(docs)} documents to '{index_name}' index")

Voer ten slotte de functies uit om de index te bouwen en te registreren bij het cloudproject:

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--index-name",
        type=str,
        help="index name to use when creating the AI Search index",
        default=os.environ["AISEARCH_INDEX_NAME"],
    )
    parser.add_argument(
        "--csv-file", type=str, help="path to data for creating search index", default="assets/products.csv"
    )
    args = parser.parse_args()
    index_name = args.index_name
    csv_file = args.csv_file

    create_index_from_csv(index_name, csv_file)

Meld u vanuit uw console aan bij uw Azure-account en volg de instructies voor het verifiëren van uw account:
```
az login
```
Voer de code uit om uw index lokaal te bouwen en te registreren bij het cloudproject:
```
python create_search_index.py
```
Zodra het script is uitgevoerd, kunt u de zojuist gemaakte index bekijken op de pagina Gegevens en indexen van uw Azure AI Foundry-project. Zie Vectorindexen bouwen en gebruiken in de Azure AI Foundry-portal voor meer informatie.
Als u het script opnieuw uitvoert met dezelfde indexnaam, wordt er een nieuwe versie van dezelfde index gemaakt.

Productdocumenten ophalen

Vervolgens maakt u een script om productdocumenten op te halen uit de zoekindex. Met het script wordt een query uitgevoerd op de zoekindex naar documenten die overeenkomen met de vraag van een gebruiker.

Script maken om productdocumenten op te halen

Wanneer de chat een aanvraag ontvangt, worden uw gegevens doorzocht om relevante informatie te vinden. Dit script maakt gebruik van de Azure AI SDK om een query uit te voeren op de zoekindex naar documenten die overeenkomen met de vraag van een gebruiker. Vervolgens worden de documenten geretourneerd naar de chat-app.

Maak het get_product_documents.py-bestand in de hoofdmap. Kopieer en plak de volgende code in het bestand.

Begin met code om de vereiste bibliotheken te importeren, een projectclient te maken en instellingen te configureren:

import os
from pathlib import Path
from opentelemetry import trace
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import ConnectionType
from azure.identity import DefaultAzureCredential
from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient
from config import ASSET_PATH, get_logger

# initialize logging and tracing objects
logger = get_logger(__name__)
tracer = trace.get_tracer(__name__)

# create a project client using environment variables loaded from the .env file
project = AIProjectClient.from_connection_string(
    conn_str=os.environ["AIPROJECT_CONNECTION_STRING"], credential=DefaultAzureCredential()
)

# create a vector embeddings client that will be used to generate vector embeddings
chat = project.inference.get_chat_completions_client()
embeddings = project.inference.get_embeddings_client()

# use the project client to get the default search connection
search_connection = project.connections.get_default(
    connection_type=ConnectionType.AZURE_AI_SEARCH, include_credentials=True
)

# Create a search index client using the search connection
# This client will be used to create and delete search indexes
search_client = SearchClient(
    index_name=os.environ["AISEARCH_INDEX_NAME"],
    endpoint=search_connection.endpoint_url,
    credential=AzureKeyCredential(key=search_connection.key),
)

Voeg de functie toe om productdocumenten op te halen:

from azure.ai.inference.prompts import PromptTemplate
from azure.search.documents.models import VectorizedQuery


@tracer.start_as_current_span(name="get_product_documents")
def get_product_documents(messages: list, context: dict = None) -> dict:
    if context is None:
        context = {}

    overrides = context.get("overrides", {})
    top = overrides.get("top", 5)

    # generate a search query from the chat messages
    intent_prompty = PromptTemplate.from_prompty(Path(ASSET_PATH) / "intent_mapping.prompty")

    intent_mapping_response = chat.complete(
        model=os.environ["INTENT_MAPPING_MODEL"],
        messages=intent_prompty.create_messages(conversation=messages),
        **intent_prompty.parameters,
    )

    search_query = intent_mapping_response.choices[0].message.content
    logger.debug(f"🧠 Intent mapping: {search_query}")

    # generate a vector representation of the search query
    embedding = embeddings.embed(model=os.environ["EMBEDDINGS_MODEL"], input=search_query)
    search_vector = embedding.data[0].embedding

    # search the index for products matching the search query
    vector_query = VectorizedQuery(vector=search_vector, k_nearest_neighbors=top, fields="contentVector")

    search_results = search_client.search(
        search_text=search_query, vector_queries=[vector_query], select=["id", "content", "filepath", "title", "url"]
    )

    documents = [
        {
            "id": result["id"],
            "content": result["content"],
            "filepath": result["filepath"],
            "title": result["title"],
            "url": result["url"],
        }
        for result in search_results
    ]

    # add results to the provided context
    if "thoughts" not in context:
        context["thoughts"] = []

    # add thoughts and documents to the context object so it can be returned to the caller
    context["thoughts"].append(
        {
            "title": "Generated search query",
            "description": search_query,
        }
    )

    if "grounding_data" not in context:
        context["grounding_data"] = []
    context["grounding_data"].append(documents)

    logger.debug(f"📄 {len(documents)} documents retrieved: {documents}")
    return documents

Voeg ten slotte code toe om de functie te testen wanneer u het script rechtstreeks uitvoert:

if __name__ == "__main__":
    import logging
    import argparse

    # set logging level to debug when running this module directly
    logger.setLevel(logging.DEBUG)

    # load command line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--query",
        type=str,
        help="Query to use to search product",
        default="I need a new tent for 4 people, what would you recommend?",
    )

    args = parser.parse_args()
    query = args.query

    result = get_product_documents(messages=[{"role": "user", "content": query}])

Promptsjabloon maken voor intentietoewijzing

Het get_product_documents.py script gebruikt een promptsjabloon om het gesprek te converteren naar een zoekquery. De sjabloon geeft aan hoe de intentie van de gebruiker uit het gesprek kan worden geëxtraheerd.

Voordat u het script uitvoert, maakt u de promptsjabloon. Voeg het bestand intent_mapping.prompty toe aan de map assets :

---
name: Chat Prompt
description: A prompty that extract users query intent based on the current_query and chat_history of the conversation
model:
    api: chat
    configuration:
        azure_deployment: gpt-4o
inputs:
    conversation:
        type: array
---
system:
# Instructions
- You are an AI assistant reading a current user query and chat_history.
- Given the chat_history, and current user's query, infer the user's intent expressed in the current user query.
- Once you infer the intent, respond with a search query that can be used to retrieve relevant documents for the current user's query based on the intent
- Be specific in what the user is asking about, but disregard parts of the chat history that are not relevant to the user's intent.
- Provide responses in json format

# Examples
Example 1:
With a conversation like below:
```
 - user: are the trailwalker shoes waterproof?
 - assistant: Yes, the TrailWalker Hiking Shoes are waterproof. They are designed with a durable and waterproof construction to withstand various terrains and weather conditions.
 - user: how much do they cost?
```
Respond with:
{
    "intent": "The user wants to know how much the Trailwalker Hiking Shoes cost.",
    "search_query": "price of Trailwalker Hiking Shoes"
}

Example 2:
With a conversation like below:
```
 - user: are the trailwalker shoes waterproof?
 - assistant: Yes, the TrailWalker Hiking Shoes are waterproof. They are designed with a durable and waterproof construction to withstand various terrains and weather conditions.
 - user: how much do they cost?
 - assistant: The TrailWalker Hiking Shoes are priced at $110.
 - user: do you have waterproof tents?
 - assistant: Yes, we have waterproof tents available. Can you please provide more information about the type or size of tent you are looking for?
 - user: which is your most waterproof tent?
 - assistant: Our most waterproof tent is the Alpine Explorer Tent. It is designed with a waterproof material and has a rainfly with a waterproof rating of 3000mm. This tent provides reliable protection against rain and moisture.
 - user: how much does it cost?
```
Respond with:
{
    "intent": "The user would like to know how much the Alpine Explorer Tent costs.",
    "search_query": "price of Alpine Explorer Tent"
}

user:
Return the search query for the messages in the following conversation:
{{#conversation}}
 - {{role}}: {{content}}
{{/conversation}}

Het script voor het ophalen van het productdocument testen

Nu u zowel het script als de sjabloon hebt, voert u het script uit om te testen welke documenten de zoekindex retourneert uit een query. Voer in een terminalvenster het volgende uit:

python get_product_documents.py --query "I need a new tent for 4 people, what would you recommend?"

Rag-code (Custom Knowledge Retrieval) ontwikkelen

Vervolgens maakt u aangepaste code om mogelijkheden voor het ophalen van augmented generation (RAG) toe te voegen aan een eenvoudige chattoepassing.

Een chatscript maken met RAG-mogelijkheden

Maak in de hoofdmap een nieuw bestand met de naam chat_with_products.py. Met dit script worden productdocumenten opgehaald en wordt een antwoord gegenereerd op de vraag van een gebruiker.

Voeg de code toe om de vereiste bibliotheken te importeren, een projectclient te maken en instellingen te configureren:

import os
from pathlib import Path
from opentelemetry import trace
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from config import ASSET_PATH, get_logger, enable_telemetry
from get_product_documents import get_product_documents


# initialize logging and tracing objects
logger = get_logger(__name__)
tracer = trace.get_tracer(__name__)

# create a project client using environment variables loaded from the .env file
project = AIProjectClient.from_connection_string(
    conn_str=os.environ["AIPROJECT_CONNECTION_STRING"], credential=DefaultAzureCredential()
)

# create a chat client we can use for testing
chat = project.inference.get_chat_completions_client()

Maak de chatfunctie die gebruikmaakt van de RAG-mogelijkheden:

from azure.ai.inference.prompts import PromptTemplate


@tracer.start_as_current_span(name="chat_with_products")
def chat_with_products(messages: list, context: dict = None) -> dict:
    if context is None:
        context = {}

    documents = get_product_documents(messages, context)

    # do a grounded chat call using the search results
    grounded_chat_prompt = PromptTemplate.from_prompty(Path(ASSET_PATH) / "grounded_chat.prompty")

    system_message = grounded_chat_prompt.create_messages(documents=documents, context=context)
    response = chat.complete(
        model=os.environ["CHAT_MODEL"],
        messages=system_message + messages,
        **grounded_chat_prompt.parameters,
    )
    logger.info(f"💬 Response: {response.choices[0].message}")

    # Return a chat protocol compliant response
    return {"message": response.choices[0].message, "context": context}

Voeg ten slotte de code toe om de chatfunctie uit te voeren:

if __name__ == "__main__":
    import argparse

    # load command line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--query",
        type=str,
        help="Query to use to search product",
        default="I need a new tent for 4 people, what would you recommend?",
    )
    parser.add_argument(
        "--enable-telemetry",
        action="store_true",
        help="Enable sending telemetry back to the project",
    )
    args = parser.parse_args()
    if args.enable_telemetry:
        enable_telemetry(True)

    # run chat with products
    response = chat_with_products(messages=[{"role": "user", "content": args.query}])

Een sjabloon voor een geaarde chatprompt maken

Het chat_with_products.py script roept een promptsjabloon aan om een antwoord te genereren op de vraag van de gebruiker. De sjabloon geeft aan hoe u een antwoord genereert op basis van de vraag van de gebruiker en de opgehaalde documenten. Maak deze sjabloon nu.

Voeg in de map assets het bestand grounded_chat.prompty toe:

---
name: Chat with documents
description: Uses a chat completions model to respond to queries grounded in relevant documents
model:
    api: chat
    configuration:
        azure_deployment: gpt-4o
inputs:
    conversation:
        type: array
---
system:
You are an AI assistant helping users with queries related to outdoor outdooor/camping gear and clothing.
If the question is not related to outdoor/camping gear and clothing, just say 'Sorry, I only can answer queries related to outdoor/camping gear and clothing. So, how can I help?'
Don't try to make up any answers.
If the question is related to outdoor/camping gear and clothing but vague, ask for clarifying questions instead of referencing documents. If the question is general, for example it uses "it" or "they", ask the user to specify what product they are asking about.
Use the following pieces of context to answer the questions about outdoor/camping gear and clothing as completely, correctly, and concisely as possible.
Do not add documentation reference in the response.

# Documents

{{#documents}}

## Document {{id}}: {{title}}
{{content}}
{{/documents}}

Het chatscript uitvoeren met RAG-mogelijkheden

Nu u zowel het script als de sjabloon hebt, voert u het script uit om uw chat-app te testen met RAG-mogelijkheden:

python chat_with_products.py --query "I need a new tent for 4 people, what would you recommend?"

Telemetrielogboekregistratie toevoegen

Ga als volgende te werk om logboekregistratie van telemetrie in te schakelen voor uw project:

Voeg een Application Insights-resource toe aan uw project. Ga naar het tabblad Tracering in de Azure AI Foundry-portal en maak een nieuwe resource als u er nog geen hebt.

azure-monitor-opentelemetry installeren:

pip install azure-monitor-opentelemetry

Voeg de --enable-telemetry vlag toe wanneer u het chat_with_products.py script gebruikt:

python chat_with_products.py --query "I need a new tent for 4 people, what would you recommend?" --enable-telemetry

Resources opschonen

Als u onnodige Azure-kosten wilt voorkomen, moet u de resources verwijderen die u in deze zelfstudie hebt gemaakt als ze niet meer nodig zijn. Als u resources wilt beheren, kunt u Azure Portal gebruiken.

Maar verwijder ze nog niet, als u uw chat-app in Azure wilt implementeren in het volgende deel van deze reeks zelfstudies.

Volgende stap

Deel 3: Uw chat-app evalueren en implementeren in Azure

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