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://huggingface.co/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://huggingface.co/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	None	[-0.14520384, 0.018315623, -0.018330636, -0.10...	True	Manhattan	{'building': '627', 'coord': [-73.975980999999...	{'$oid': '6095a34a7c34416a90d3206b'}	Baby Bo'S Burritos	None	True	{'coordinates': [-73.97598099999999, 40.745132...	1.0	None	10	NaN	2.5
1	40367442	{'Alcohol': ''beer_and_wine'', 'Ambience': '{'...	American	True	[-0.11977468, -0.02157107, 0.0038846824, -0.09...	True	Staten Island	{'building': '17', 'coord': [-74.1350211, 40.6...	{'$oid': '6095a34a7c34416a90d3209e'}	Buddy'S Wonder Bar	[Grilled cheese sandwich, Baked potato, Lasagn...	True	{'coordinates': [-74.1350211, 40.6369042], 'ty...	2.0	None	62	NaN	3.5
2	40364610	{'Alcohol': ''none'', 'Ambience': '{'touristy'...	American	None	[-0.1004329, -0.014882699, -0.033005167, -0.09...	True	Staten Island	{'building': '37', 'coord': [-74.138263, 40.54...	{'$oid': '6095a34a7c34416a90d31ff6'}	Great Kills Yacht Club	[Mozzarella sticks, Mushroom swiss burger, Spi...	True	{'coordinates': [-74.138263, 40.546681], 'type...	1.0	None	72	NaN	4.0
3	40365288	{'Alcohol': None, 'Ambience': '{'touristy': Fa...	American	None	[-0.11735515, -0.0397448, -0.0072645755, -0.09...	True	Manhattan	{'building': '842', 'coord': [-73.970637000000...	{'$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	None	[-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	None	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"]

请确保您的数据库、集合和向量存储索引已在 MongoDB Atlas 上为指定集合完成设置，否则后续步骤在 MongoDB 上将无法正常运行。

• 如需数据库集群设置和获取连接 URI 的帮助，请参考此指南配置 MongoDB 集群，以及此指南获取连接字符串。

• 成功创建集群后，通过点击“+ 创建数据库”在 MongoDB Atlas 集群内创建数据库和集合。数据库应命名为 movies，集合应命名为 movies_records。

• 在 movies_records 集合中创建向量搜索索引对于从 MongoDB 高效检索文档到开发环境至关重要。具体操作请参阅官方指南创建向量搜索索引。

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)

现在请确保在此处使用正确的名称创建搜索索引

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)

Final Response: Based on the context provided, two restaurant options that don't serve alcohol are:

1. "Academy Restauraunt" in Brooklyn, which serves American cuisine and has a variety of dishes such as Mozzarella sticks, Cheeseburger, Baked potato, Breadsticks, Caesar salad, Chicken parmesan, Pigs in a blanket, Chicken soup, Mac & cheese, Mushroom swiss burger, Spaghetti with meatballs, and Mashed potatoes.

2. "Gabriel'S Bar & Grill" in Manhattan, which specializes in Italian cuisine and offers dishes like Cheese Ravioli, Neapolitan Pizza, assorted gelato, Vegetarian Baked Ziti, Vegetarian Broccoli Pizza, Lasagna, Buca Trio Platter, Spinach Ravioli, Pasta with ricotta cheese, Spaghetti, Fried calamari, and Alfredo Pizza.

Both restaurants offer outdoor seating, are kid-friendly, and have a casual dress code. They also provide take-out service and have happy hour promotions.

[NodeWithScore(node=TextNode(id_='5405e68c-19f2-4a65-95d7-f880fa6a8deb', embedding=None, metadata={'restaurant_id': '40385767', 'attributes': '{"Alcohol": "u\'beer_and_wine\'", "Ambience": "{\'touristy\': False, \'hipster\': False, \'romantic\': False, \'divey\': False, \'intimate\': None, \'trendy\': None, \'upscale\': False, \'classy\': False, \'casual\': True}", "BYOB": null, "BestNights": "{\'monday\': False, \'tuesday\': False, \'friday\': True, \'wednesday\': False, \'thursday\': False, \'sunday\': False, \'saturday\': True}", "BikeParking": "True", "BusinessAcceptsBitcoin": "False", "BusinessAcceptsCreditCards": "True", "BusinessParking": "{\'garage\': False, \'street\': False, \'validated\': False, \'lot\': True, \'valet\': False}", "Caters": "True", "DriveThru": null, "GoodForDancing": "False", "GoodForKids": "True", "GoodForMeal": "{\'dessert\': False, \'latenight\': False, \'lunch\': True, \'dinner\': True, \'brunch\': False, \'breakfast\': False}", "HasTV": "True", "Music": 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