MongoDB Atlas + Fireworks AI RAG 示例

!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

# 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

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

.dataframe tbody tr th {
 垂直对齐：顶部;
}

.dataframe thead th {
 文本对齐：右对齐;
}

	restaurant_id	属性	菜系	DogsAllowed	嵌入	OutdoorSeating	行政区	地址	_id	名称	菜单	TakeOut	位置	PriceRange	HappyHour	review_count	赞助内容	星标数
0	40366661	{'酒精': ''无'', '氛围': '{'浪漫'...	德墨风味	无	[-0.14520384, 0.018315623, -0.018330636, -0.10...	真	曼哈顿	{'building': '627', 'coord': [-73.975980999999...	{'$oid': '6095a34a7c34416a90d3206b'}	宝贝波波卷饼店	无	真	{'coordinates': [-73.97598099999999, 40.745132...	1.0	无	10	NaN	2.5
1	40367442	{'酒精饮品': ''beer_and_wine'', '环境氛围': '{'...	美国人	真	[-0.11977468, -0.02157107, 0.0038846824, -0.09...	True	斯塔滕岛	{'building': '17', 'coord': [-74.1350211, 40.6...	{'$oid': '6095a34a7c34416a90d3209e'}	巴迪的神奇酒吧	[烤芝士三明治，烤土豆，千层面...	真	{'coordinates': [-74.1350211, 40.6369042], '类型'...	2.0	无	62	NaN	3.5
2	40364610	{'酒精': ''无'', '氛围': '{'旅游景点'...	美国人	无	[-0.1004329, -0.014882699, -0.033005167, -0.09...	True	斯塔滕岛	{'building': '37', 'coord': [-74.138263, 40.54...	{'$oid': '6095a34a7c34416a90d31ff6'}	大基尔斯游艇俱乐部	[马苏里拉奶酪条, 蘑菇瑞士汉堡, 辣味...]	True	{'coordinates': [-74.138263, 40.546681], '类型...	1.0	无	72	NaN	4.0
3	40365288	{'酒精': 无, '氛围': '{'旅游景点': 假...	美国人	无	[-0.11735515, -0.0397448, -0.0072645755, -0.09...	真	曼哈顿	{'building': '842', 'coord': [-73.970637000000...	{'$oid': '6095a34a7c34416a90d32017'}	济慈餐厅	[炸薯条，鸡肉馅饼，通心粉和奶酪，...	真	{'coordinates': [-73.97063700000001, 40.751495...	2.0	真	149	NaN	4.0
4	40363151	{'酒精饮品': 无, '环境氛围': 无, '自带酒水': 否...	面包店	无	[-0.096541286, -0.009661355, 0.04402167, -0.12...	真	曼哈顿	{'building': '120', 'coord': [-73.9998042, 40....	{'$oid': '6095a34a7c34416a90d31fbd'}	奥利弗的	[甜甜圈, 巧克力曲奇饼, 巧克力 ...	真	{'coordinates': [-73.9998042, 40.7251256], 'ty...	1.0	无	7	NaN	5.0

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

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}

llama_documents[0]

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')

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)

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

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

如需获取数据库集群设置及URI的帮助，请参考此指南设置MongoDB集群，并查看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从MongoDB高效检索文档到我们的开发环境至关重要。要实现这一点，请参考关于向量搜索索引创建的官方指南。

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

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

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)

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

from llama_index.core import VectorStoreIndex, StorageContext

index = VectorStoreIndex.from_vector_store(vector_store)

%pip install -q matplotlib

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

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: 根据提供的上下文，两家不提供酒精饮品的餐厅选择是：

位于布鲁克林的“学院餐厅”，供应美式料理，提供多种菜品，如马苏里拉奶酪条、芝士汉堡、烤土豆、面包棒、凯撒沙拉、帕尔马干酪鸡、毯子裹香肠、鸡汤、通心粉和奶酪、蘑菇瑞士汉堡、肉丸意大利面和土豆泥。
位于曼哈顿的“Gabriel’S Bar & Grill”餐厅，主打意大利美食，提供诸如奶酪意式饺子、那不勒斯披萨、什锦意式冰淇淋、素食烤千层面、素食西兰花披萨、千层面、布卡三重拼盘、菠菜意式饺子、乳清干酪意面、意大利面、炸鱿鱼和阿尔弗雷多披萨等菜肴。

两家餐厅均提供户外座位，适合家庭用餐，着装要求休闲。它们还提供外卖服务并设有欢乐时光促销活动。

[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": "{\'dj\': False, \'background_music\': False, \'no_music\': False, \'jukebox\': False, \'live\': False, \'video\': False, \'karaoke\': False}", "NoiseLevel": "u\'average\'", "RestaurantsAttire": "u\'casual\'", "RestaurantsDelivery": "None", "RestaurantsGoodForGroups": "True", "RestaurantsReservations": "True", "RestaurantsTableService": "True", "WheelchairAccessible": "True", "WiFi": "u\'free\'"}', 'cuisine': '"American"', 'DogsAllowed': True, 'OutdoorSeating': True, 'borough': '"Brooklyn"', 'address': '{"building": "69", "coord": [-73.9757464, 40.687295], "street": "Lafayette Avenue", "zipcode": "11217"}', 'name': '"Academy Restauraunt"', 'menu': '["Mozzarella sticks", "Cheeseburger", "Baked potato", "Breadsticks", "Caesar salad", "Chicken parmesan", "Pigs in a blanket", "Chicken soup", "Mac & cheese", "Mushroom swiss burger", "Spaghetti with meatballs", "Mashed potatoes"]', 'TakeOut': 'true', 'PriceRange': '2.0', 'HappyHour': 'true', 'review_count': '173', 'sponsored': None, 'stars': 4.5}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='bbfc4bf5-d9c3-4f3b-8c1f-ddcf94f3b5df', node_type=<ObjectType.DOCUMENT: '4'>, 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\': 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