MongoDB Atlas + Fireworks AI RAG 示例¶

In [ ]

!pip install -q llama-index llama-index-vector-stores-mongodb llama-index-embeddings-fireworks==0.1.2 llama-index-llms-fireworks
!pip install -q pymongo datasets pandas

!pip install -q llama-index llama-index-vector-stores-mongodb llama-index-embeddings-fireworks==0.1.2 llama-index-llms-fireworks !pip install -q pymongo datasets pandas

In [ ]

# set up Fireworks.ai Key
import os
import getpass

fw_api_key = getpass.getpass("Fireworks API Key:")
os.environ["FIREWORKS_API_KEY"] = fw_api_key

# set up Fireworks.ai Key import os import getpass fw_api_key = getpass.getpass("Fireworks API Key:") os.environ["FIREWORKS_API_KEY"] = fw_api_key

In [ ]

from datasets import load_dataset
import pandas as pd

# https://hugging-face.cn/datasets/AIatMongoDB/whatscooking.restaurants
dataset = load_dataset("AIatMongoDB/whatscooking.restaurants")

# Convert the dataset to a pandas dataframe
dataset_df = pd.DataFrame(dataset["train"])

dataset_df.head(5)

from datasets import load_dataset import pandas as pd # https://hugging-face.cn/datasets/AIatMongoDB/whatscooking.restaurants dataset = load_dataset("AIatMongoDB/whatscooking.restaurants") # Convert the dataset to a pandas dataframe dataset_df = pd.DataFrame(dataset["train"]) dataset_df.head(5)

/mnt/disks/data/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Out[ ]

	restaurant_id	attributes	cuisine	DogsAllowed	embedding	OutdoorSeating	borough	address	_id	name	menu	TakeOut	location	PriceRange	HappyHour	review_count	sponsored	stars
0	40366661	{'Alcohol': ''none'', 'Ambience': '{'romantic'...	Tex-Mex	无	[-0.14520384, 0.018315623, -0.018330636, -0.10...	True	Manhattan	{'building': '627', 'coord': [-73.975980999999...	{'$oid': '6095a34a7c34416a90d3206b'}	Baby Bo'S Burritos	无	True	{'coordinates': [-73.97598099999999, 40.745132...	1.0	无	10	NaN	2.5
1	40367442	{'Alcohol': ''beer_and_wine'', 'Ambience': '{'...	American	True	[-0.11977468, -0.02157107, 0.0038846824, -0.09...	True	Staten Island	{'building': '17', 'coord': [-74.1350211, 40.6...	{'$oid': '6095a34a7c34416a90d3209e'}	Buddy'S Wonder Bar	[Grilled cheese sandwich, Baked potato, Lasagn...	True	{'coordinates': [-74.1350211, 40.6369042], 'ty...	2.0	无	62	NaN	3.5
2	40364610	{'Alcohol': ''none'', 'Ambience': '{'touristy'...	American	无	[-0.1004329, -0.014882699, -0.033005167, -0.09...	True	Staten Island	{'building': '37', 'coord': [-74.138263, 40.54...	{'$oid': '6095a34a7c34416a90d31ff6'}	Great Kills Yacht Club	[Mozzarella sticks, Mushroom swiss burger, Spi...	True	{'coordinates': [-74.138263, 40.546681], 'type...	1.0	无	72	NaN	4.0
3	40365288	{'Alcohol': None, 'Ambience': '{'touristy': Fa...	American	无	[-0.11735515, -0.0397448, -0.0072645755, -0.09...	True	Manhattan	{'building': '842', 'coord': [-73.970637000000...	{'$oid': '6095a34a7c34416a90d32017'}	Keats Restaurant	[French fries, Chicken pot pie, Mac & cheese, ...	True	{'coordinates': [-73.97063700000001, 40.751495...	2.0	True	149	NaN	4.0
4	40363151	{'Alcohol': None, 'Ambience': None, 'BYOB': No...	Bakery	无	[-0.096541286, -0.009661355, 0.04402167, -0.12...	True	Manhattan	{'building': '120', 'coord': [-73.9998042, 40....	{'$oid': '6095a34a7c34416a90d31fbd'}	Olive'S	[doughnuts, chocolate chip cookies, chocolate ...	True	{'coordinates': [-73.9998042, 40.7251256], 'ty...	1.0	无	7	NaN	5.0

In [ ]

from llama_index.core.settings import Settings
from llama_index.llms.fireworks import Fireworks
from llama_index.embeddings.fireworks import FireworksEmbedding

embed_model = FireworksEmbedding(
    embed_batch_size=512,
    model_name="nomic-ai/nomic-embed-text-v1.5",
    api_key=fw_api_key,
)
llm = Fireworks(
    temperature=0,
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    api_key=fw_api_key,
)

Settings.llm = llm
Settings.embed_model = embed_model

from llama_index.core.settings import Settings from llama_index.llms.fireworks import Fireworks from llama_index.embeddings.fireworks import FireworksEmbedding embed_model = FireworksEmbedding( embed_batch_size=512, model_name="nomic-ai/nomic-embed-text-v1.5", api_key=fw_api_key, ) llm = Fireworks( temperature=0, model="accounts/fireworks/models/mixtral-8x7b-instruct", api_key=fw_api_key, ) Settings.llm = llm Settings.embed_model = embed_model

In [ ]

import json
from llama_index.core import Document
from llama_index.core.schema import MetadataMode

# Convert the DataFrame to a JSON string representation
documents_json = dataset_df.to_json(orient="records")
# Load the JSON string into a Python list of dictionaries
documents_list = json.loads(documents_json)

llama_documents = []

for document in documents_list:
    # Value for metadata must be one of (str, int, float, None)
    document["name"] = json.dumps(document["name"])
    document["cuisine"] = json.dumps(document["cuisine"])
    document["attributes"] = json.dumps(document["attributes"])
    document["menu"] = json.dumps(document["menu"])
    document["borough"] = json.dumps(document["borough"])
    document["address"] = json.dumps(document["address"])
    document["PriceRange"] = json.dumps(document["PriceRange"])
    document["HappyHour"] = json.dumps(document["HappyHour"])
    document["review_count"] = json.dumps(document["review_count"])
    document["TakeOut"] = json.dumps(document["TakeOut"])
    # these two fields are not relevant to the question we want to answer,
    # so I will skip it for now
    del document["embedding"]
    del document["location"]

    # Create a Document object with the text and excluded metadata for llm and embedding models
    llama_document = Document(
        text=json.dumps(document),
        metadata=document,
        metadata_template="{key}=>{value}",
        text_template="Metadata: {metadata_str}\n-----\nContent: {content}",
    )

    llama_documents.append(llama_document)

# Observing an example of what the LLM and Embedding model receive as input
print(
    "\nThe LLM sees this: \n",
    llama_documents[0].get_content(metadata_mode=MetadataMode.LLM),
)
print(
    "\nThe Embedding model sees this: \n",
    llama_documents[0].get_content(metadata_mode=MetadataMode.EMBED),
)

import json from llama_index.core import Document from llama_index.core.schema import MetadataMode # Convert the DataFrame to a JSON string representation documents_json = dataset_df.to_json(orient="records") # Load the JSON string into a Python list of dictionaries documents_list = json.loads(documents_json) llama_documents = [] for document in documents_list: # Value for metadata must be one of (str, int, float, None) document["name"] = json.dumps(document["name"]) document["cuisine"] = json.dumps(document["cuisine"]) document["attributes"] = json.dumps(document["attributes"]) document["menu"] = json.dumps(document["menu"]) document["borough"] = json.dumps(document["borough"]) document["address"] = json.dumps(document["address"]) document["PriceRange"] = json.dumps(document["PriceRange"]) document["HappyHour"] = json.dumps(document["HappyHour"]) document["review_count"] = json.dumps(document["review_count"]) document["TakeOut"] = json.dumps(document["TakeOut"]) # these two fields are not relevant to the question we want to answer, # so I will skip it for now del document["embedding"] del document["location"] # Create a Document object with the text and excluded metadata for llm and embedding models llama_document = Document( text=json.dumps(document), metadata=document, metadata_template="{key}=>{value}", text_template="Metadata: {metadata_str}\n-----\nContent: {content}", ) llama_documents.append(llama_document) # Observing an example of what the LLM and Embedding model receive as input print( "\nThe LLM sees this: \n", llama_documents[0].get_content(metadata_mode=MetadataMode.LLM), ) print( "\nThe Embedding model sees this: \n", llama_documents[0].get_content(metadata_mode=MetadataMode.EMBED), )

The LLM sees this: 
 Metadata: restaurant_id=>40366661
attributes=>{"Alcohol": "'none'", "Ambience": "{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}", "BYOB": null, "BestNights": null, "BikeParking": null, "BusinessAcceptsBitcoin": null, "BusinessAcceptsCreditCards": null, "BusinessParking": "None", "Caters": "True", "DriveThru": null, "GoodForDancing": null, "GoodForKids": "True", "GoodForMeal": null, "HasTV": "True", "Music": null, "NoiseLevel": "'average'", "RestaurantsAttire": "'casual'", "RestaurantsDelivery": "True", "RestaurantsGoodForGroups": "True", "RestaurantsReservations": "True", "RestaurantsTableService": "False", "WheelchairAccessible": "True", "WiFi": "'free'"}
cuisine=>"Tex-Mex"
DogsAllowed=>None
OutdoorSeating=>True
borough=>"Manhattan"
address=>{"building": "627", "coord": [-73.975981, 40.745132], "street": "2 Avenue", "zipcode": "10016"}
_id=>{'$oid': '6095a34a7c34416a90d3206b'}
name=>"Baby Bo'S Burritos"
menu=>null
TakeOut=>true
PriceRange=>1.0
HappyHour=>null
review_count=>10
sponsored=>None
stars=>2.5
-----
Content: {"restaurant_id": "40366661", "attributes": "{\"Alcohol\": \"'none'\", \"Ambience\": \"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\", \"BYOB\": null, \"BestNights\": null, \"BikeParking\": null, \"BusinessAcceptsBitcoin\": null, \"BusinessAcceptsCreditCards\": null, \"BusinessParking\": \"None\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": null, \"GoodForKids\": \"True\", \"GoodForMeal\": null, \"HasTV\": \"True\", \"Music\": null, \"NoiseLevel\": \"'average'\", \"RestaurantsAttire\": \"'casual'\", \"RestaurantsDelivery\": \"True\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"False\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"'free'\"}", "cuisine": "\"Tex-Mex\"", "DogsAllowed": null, "OutdoorSeating": true, "borough": "\"Manhattan\"", "address": "{\"building\": \"627\", \"coord\": [-73.975981, 40.745132], \"street\": \"2 Avenue\", \"zipcode\": \"10016\"}", "_id": {"$oid": "6095a34a7c34416a90d3206b"}, "name": "\"Baby Bo'S Burritos\"", "menu": "null", "TakeOut": "true", "PriceRange": "1.0", "HappyHour": "null", "review_count": "10", "sponsored": null, "stars": 2.5}

The Embedding model sees this: 
 Metadata: restaurant_id=>40366661
attributes=>{"Alcohol": "'none'", "Ambience": "{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}", "BYOB": null, "BestNights": null, "BikeParking": null, "BusinessAcceptsBitcoin": null, "BusinessAcceptsCreditCards": null, "BusinessParking": "None", "Caters": "True", "DriveThru": null, "GoodForDancing": null, "GoodForKids": "True", "GoodForMeal": null, "HasTV": "True", "Music": null, "NoiseLevel": "'average'", "RestaurantsAttire": "'casual'", "RestaurantsDelivery": "True", "RestaurantsGoodForGroups": "True", "RestaurantsReservations": "True", "RestaurantsTableService": "False", "WheelchairAccessible": "True", "WiFi": "'free'"}
cuisine=>"Tex-Mex"
DogsAllowed=>None
OutdoorSeating=>True
borough=>"Manhattan"
address=>{"building": "627", "coord": [-73.975981, 40.745132], "street": "2 Avenue", "zipcode": "10016"}
_id=>{'$oid': '6095a34a7c34416a90d3206b'}
name=>"Baby Bo'S Burritos"
menu=>null
TakeOut=>true
PriceRange=>1.0
HappyHour=>null
review_count=>10
sponsored=>None
stars=>2.5
-----
Content: {"restaurant_id": "40366661", "attributes": "{\"Alcohol\": \"'none'\", \"Ambience\": \"{'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}\", \"BYOB\": null, \"BestNights\": null, \"BikeParking\": null, \"BusinessAcceptsBitcoin\": null, \"BusinessAcceptsCreditCards\": null, \"BusinessParking\": \"None\", \"Caters\": \"True\", \"DriveThru\": null, \"GoodForDancing\": null, \"GoodForKids\": \"True\", \"GoodForMeal\": null, \"HasTV\": \"True\", \"Music\": null, \"NoiseLevel\": \"'average'\", \"RestaurantsAttire\": \"'casual'\", \"RestaurantsDelivery\": \"True\", \"RestaurantsGoodForGroups\": \"True\", \"RestaurantsReservations\": \"True\", \"RestaurantsTableService\": \"False\", \"WheelchairAccessible\": \"True\", \"WiFi\": \"'free'\"}", "cuisine": "\"Tex-Mex\"", "DogsAllowed": null, "OutdoorSeating": true, "borough": "\"Manhattan\"", "address": "{\"building\": \"627\", \"coord\": [-73.975981, 40.745132], \"street\": \"2 Avenue\", \"zipcode\": \"10016\"}", "_id": {"$oid": "6095a34a7c34416a90d3206b"}, "name": "\"Baby Bo'S Burritos\"", "menu": "null", "TakeOut": "true", "PriceRange": "1.0", "HappyHour": "null", "review_count": "10", "sponsored": null, "stars": 2.5}

In [ ]

llama_documents[0]

llama_documents[0]

Out[ ]

Document(id_='93d3f08d-85f3-494d-a057-19bc834abc29', embedding=None, metadata={'restaurant_id': '40366661', 'attributes': '{"Alcohol": "\'none\'", "Ambience": "{\'romantic\': False, \'intimate\': False, \'classy\': False, \'hipster\': False, \'divey\': False, \'touristy\': False, \'trendy\': False, \'upscale\': False, \'casual\': False}", "BYOB": null, "BestNights": null, "BikeParking": null, "BusinessAcceptsBitcoin": null, "BusinessAcceptsCreditCards": null, "BusinessParking": "None", "Caters": "True", "DriveThru": null, "GoodForDancing": null, "GoodForKids": "True", "GoodForMeal": null, "HasTV": "True", "Music": null, "NoiseLevel": "\'average\'", "RestaurantsAttire": "\'casual\'", "RestaurantsDelivery": "True", "RestaurantsGoodForGroups": "True", "RestaurantsReservations": "True", "RestaurantsTableService": "False", "WheelchairAccessible": "True", "WiFi": "\'free\'"}', 'cuisine': '"Tex-Mex"', 'DogsAllowed': None, 'OutdoorSeating': True, 'borough': '"Manhattan"', 'address': '{"building": "627", "coord": [-73.975981, 40.745132], "street": "2 Avenue", "zipcode": "10016"}', '_id': {'$oid': '6095a34a7c34416a90d3206b'}, 'name': '"Baby Bo\'S Burritos"', 'menu': 'null', 'TakeOut': 'true', 'PriceRange': '1.0', 'HappyHour': 'null', 'review_count': '10', 'sponsored': None, 'stars': 2.5}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='{"restaurant_id": "40366661", "attributes": "{\\"Alcohol\\": \\"\'none\'\\", \\"Ambience\\": \\"{\'romantic\': False, \'intimate\': False, \'classy\': False, \'hipster\': False, \'divey\': False, \'touristy\': False, \'trendy\': False, \'upscale\': False, \'casual\': False}\\", \\"BYOB\\": null, \\"BestNights\\": null, \\"BikeParking\\": null, \\"BusinessAcceptsBitcoin\\": null, \\"BusinessAcceptsCreditCards\\": null, \\"BusinessParking\\": \\"None\\", \\"Caters\\": \\"True\\", \\"DriveThru\\": null, \\"GoodForDancing\\": null, \\"GoodForKids\\": \\"True\\", \\"GoodForMeal\\": null, \\"HasTV\\": \\"True\\", \\"Music\\": null, \\"NoiseLevel\\": \\"\'average\'\\", \\"RestaurantsAttire\\": \\"\'casual\'\\", \\"RestaurantsDelivery\\": \\"True\\", \\"RestaurantsGoodForGroups\\": \\"True\\", \\"RestaurantsReservations\\": \\"True\\", \\"RestaurantsTableService\\": \\"False\\", \\"WheelchairAccessible\\": \\"True\\", \\"WiFi\\": \\"\'free\'\\"}", "cuisine": "\\"Tex-Mex\\"", "DogsAllowed": null, "OutdoorSeating": true, "borough": "\\"Manhattan\\"", "address": "{\\"building\\": \\"627\\", \\"coord\\": [-73.975981, 40.745132], \\"street\\": \\"2 Avenue\\", \\"zipcode\\": \\"10016\\"}", "_id": {"$oid": "6095a34a7c34416a90d3206b"}, "name": "\\"Baby Bo\'S Burritos\\"", "menu": "null", "TakeOut": "true", "PriceRange": "1.0", "HappyHour": "null", "review_count": "10", "sponsored": null, "stars": 2.5}', start_char_idx=None, end_char_idx=None, text_template='Metadata: {metadata_str}\n-----\nContent: {content}', metadata_template='{key}=>{value}', metadata_seperator='\n')

In [ ]

from llama_index.core.node_parser import SentenceSplitter

parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(llama_documents)
# 25k nodes takes about 10 minutes, will trim it down to 2.5k
new_nodes = nodes[:2500]

# There are 25k documents, so we need to do batching. Fortunately LlamaIndex provides good batching
# for embedding models, and we are going to rely on the __call__ method for the model to handle this
node_embeddings = embed_model(new_nodes)

from llama_index.core.node_parser import SentenceSplitter parser = SentenceSplitter() nodes = parser.get_nodes_from_documents(llama_documents) # 25k nodes takes about 10 minutes, will trim it down to 2.5k new_nodes = nodes[:2500] # There are 25k documents, so we need to do batching. Fortunately LlamaIndex provides good batching # for embedding models, and we are going to rely on the __call__ method for the model to handle this node_embeddings = embed_model(new_nodes)

In [ ]

for idx, n in enumerate(new_nodes):
    n.embedding = node_embeddings[idx].embedding
    if "_id" in n.metadata:
        del n.metadata["_id"]

for idx, n in enumerate(new_nodes): n.embedding = node_embeddings[idx].embedding if "_id" in n.metadata: del n.metadata["_id"]

请确保您已在 MongoDB Atlas 上为集合设置了数据库、集合和向量存储索引，否则后续步骤将无法正常工作。

• 有关数据库集群设置和获取 URI 的帮助，请参阅此指南以设置 MongoDB 集群，以及此指南以获取您的连接字符串。

• 成功创建集群后，通过单击“+ Create Database”（+ 创建数据库）在 MongoDB Atlas 集群中创建数据库和集合。数据库将命名为 movies，集合将命名为 movies_records。

• 在 movies_records 集合中创建向量搜索索引对于将文档从 MongoDB 高效检索到我们的开发环境中至关重要。为此，请参阅有关向量搜索索引创建的官方指南。

In [ ]

import pymongo


def get_mongo_client(mongo_uri):
    """Establish connection to the MongoDB."""
    try:
        client = pymongo.MongoClient(mongo_uri)
        print("Connection to MongoDB successful")
        return client
    except pymongo.errors.ConnectionFailure as e:
        print(f"Connection failed: {e}")
        return None


# set up Fireworks.ai Key
import os
import getpass

mongo_uri = getpass.getpass("MONGO_URI:")
if not mongo_uri:
    print("MONGO_URI not set")

mongo_client = get_mongo_client(mongo_uri)

DB_NAME = "whatscooking"
COLLECTION_NAME = "restaurants"

db = mongo_client[DB_NAME]
collection = db[COLLECTION_NAME]

import pymongo def get_mongo_client(mongo_uri): """Establish connection to the MongoDB.""" try: client = pymongo.MongoClient(mongo_uri) print("Connection to MongoDB successful") return client except pymongo.errors.ConnectionFailure as e: print(f"Connection failed: {e}") return None # set up Fireworks.ai Key import os import getpass mongo_uri = getpass.getpass("MONGO_URI:") if not mongo_uri: print("MONGO_URI not set") mongo_client = get_mongo_client(mongo_uri) DB_NAME = "whatscooking" COLLECTION_NAME = "restaurants" db = mongo_client[DB_NAME] collection = db[COLLECTION_NAME]

Connection to MongoDB successful

In [ ]

# To ensure we are working with a fresh collection
# delete any existing records in the collection
collection.delete_many({})

# To ensure we are working with a fresh collection # delete any existing records in the collection collection.delete_many({})

Out[ ]

DeleteResult({'n': 0, 'electionId': ObjectId('7fffffff00000000000001ce'), 'opTime': {'ts': Timestamp(1708970193, 3), 't': 462}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1708970193, 3), 'signature': {'hash': b'\x9a3H8\xa1\x1b\xb6\xbb\xa9\xc3x\x17\x1c\xeb\xe9\x03\xaa\xf8\xf17', 'keyId': 7294687148333072386}}, 'operationTime': Timestamp(1708970193, 3)}, acknowledged=True)

In [ ]

from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch

vector_store = MongoDBAtlasVectorSearch(
    mongo_client,
    db_name=DB_NAME,
    collection_name=COLLECTION_NAME,
    index_name="vector_index",
)
vector_store.add(new_nodes)

from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch vector_store = MongoDBAtlasVectorSearch( mongo_client, db_name=DB_NAME, collection_name=COLLECTION_NAME, index_name="vector_index", ) vector_store.add(new_nodes)

现在请确保您在这里使用正确的名称创建了搜索索引

In [ ]

from llama_index.core import VectorStoreIndex, StorageContext

index = VectorStoreIndex.from_vector_store(vector_store)

from llama_index.core import VectorStoreIndex, StorageContext index = VectorStoreIndex.from_vector_store(vector_store)

In [ ]

%pip install -q matplotlib

%pip install -q matplotlib

Note: you may need to restart the kernel to use updated packages.

In [ ]

import pprint
from llama_index.core.response.notebook_utils import display_response

query_engine = index.as_query_engine()

query = "search query: Anything that doesn't have alcohol in it"

response = query_engine.query(query)
display_response(response)
pprint.pprint(response.source_nodes)

import pprint from llama_index.core.response.notebook_utils import display_response query_engine = index.as_query_engine() query = "search query: Anything that doesn't have alcohol in it" response = query_engine.query(query) display_response(response) pprint.pprint(response.source_nodes)

最终响应：根据提供的上下文，不提供酒精饮品的两家餐厅选项是

1. 位于布鲁克林的“Academy Restaurant”，提供美式菜肴和各种菜品，例如马苏里拉芝士条、芝士汉堡、烤土豆、面包棒、凯撒沙拉、帕玛森鸡肉、培根卷香肠、鸡汤、通心粉加芝士、蘑菇瑞士汉堡、肉丸意面和土豆泥。

2. 位于曼哈顿的“Gabriel's Bar & Grill”，专门提供意大利菜，提供芝士馄饨、那不勒斯披萨、什锦冰淇淋、素烤通心粉、素花椰菜披萨、烤宽面条、Buca Trio 拼盘、菠菜馄饨、意面配意大利乳清干酪、意大利面条、炸鱿鱼和阿尔弗雷多披萨等菜肴。

这两家餐厅都提供户外座位，适合儿童，并且着装要求随意。它们还提供外卖服务，并有欢乐时光促销活动。

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