Azure Cosmos DB NoSQL
此笔记本向您展示了如何利用此集成向量数据库在集合中存储文档、创建索引并使用近似最近邻算法（如 COS（余弦距离）、L2（欧几里得距离）和 IP（内积）来执行向量搜索查询，以查找靠近查询向量的文档。
Azure Cosmos DB 是为 OpenAI 的 ChatGPT 服务提供支持的数据库。它提供个位数毫秒的响应时间、自动且即时的可扩展性，以及在任何规模下保证的速度。
适用于 NoSQL 的 Azure Cosmos DB 目前提供预览版中的向量索引和搜索功能。此功能旨在处理高维向量，从而能够在任何规模下实现高效且准确的向量搜索。您现在可以将向量与数据一起直接存储在文档中。这意味着数据库中的每个文档不仅可以包含传统的无模式数据，还可以包含高维向量作为文档的其他属性。数据和向量的这种共存允许高效的索引和搜索，因为向量与它们表示的数据存储在同一个逻辑单元中。这简化了数据管理、AI 应用架构以及基于向量的操作的效率。
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%pip install --upgrade --quiet azure-cosmos langchain-openai langchain-community
Note: you may need to restart the kernel to use updated packages.
OPENAI_API_KEY = "YOUR_KEY"
OPENAI_API_TYPE = "azure"
OPENAI_API_VERSION = "2023-05-15"
OPENAI_API_BASE = "YOUR_ENDPOINT"
OPENAI_EMBEDDINGS_MODEL_NAME = "text-embedding-ada-002"
OPENAI_EMBEDDINGS_MODEL_DEPLOYMENT = "text-embedding-ada-002"
插入数据
from langchain_community.document_loaders import PyPDFLoader
# Load the PDF
loader = PyPDFLoader("https://arxiv.org/pdf/2303.08774.pdf")
data = loader.load()
API 参考：PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
docs = text_splitter.split_documents(data)
print(docs[0])
page_content='GPT-4 Technical Report\nOpenAI∗\nAbstract\nWe report the development of GPT-4, a large-scale, multimodal model which can\naccept image and text inputs and produce text outputs. While less capable than\nhumans in many real-world scenarios, GPT-4 exhibits human-level performance\non various professional and academic benchmarks, including passing a simulated\nbar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-\nbased model pre-trained to predict the next token in a document. The post-training\nalignment process results in improved performance on measures of factuality and\nadherence to desired behavior. A core component of this project was developing\ninfrastructure and optimization methods that behave predictably across a wide\nrange of scales. This allowed us to accurately predict some aspects of GPT-4’s\nperformance based on models trained with no more than 1/1,000th the compute of\nGPT-4.\n1 Introduction' metadata={'source': 'https://arxiv.org/pdf/2303.08774.pdf', 'page': 0}
创建 AzureCosmosDB NoSQL 向量搜索
indexing_policy = {
"indexingMode": "consistent",
"includedPaths": [{"path": "/*"}],
"excludedPaths": [{"path": '/"_etag"/?'}],
"vectorIndexes": [{"path": "/embedding", "type": "quantizedFlat"}],
}
vector_embedding_policy = {
"vectorEmbeddings": [
{
"path": "/embedding",
"dataType": "float32",
"distanceFunction": "cosine",
"dimensions": 1536,
}
]
}
from azure.cosmos import CosmosClient, PartitionKey
from langchain_community.vectorstores.azure_cosmos_db_no_sql import (
AzureCosmosDBNoSqlVectorSearch,
)
from langchain_openai import AzureOpenAIEmbeddings
HOST = "AZURE_COSMOS_DB_ENDPOINT"
KEY = "AZURE_COSMOS_DB_KEY"
cosmos_client = CosmosClient(HOST, KEY)
database_name = "langchain_python_db"
container_name = "langchain_python_container"
partition_key = PartitionKey(path="/id")
cosmos_container_properties = {"partition_key": partition_key}
openai_embeddings = AzureOpenAIEmbeddings(
azure_deployment=OPENAI_EMBEDDINGS_MODEL_DEPLOYMENT,
api_version=OPENAI_API_VERSION,
azure_endpoint=OPENAI_API_BASE,
openai_api_key=OPENAI_API_KEY,
)
# insert the documents in AzureCosmosDBNoSql with their embedding
vector_search = AzureCosmosDBNoSqlVectorSearch.from_documents(
documents=docs,
embedding=openai_embeddings,
cosmos_client=cosmos_client,
database_name=database_name,
container_name=container_name,
vector_embedding_policy=vector_embedding_policy,
indexing_policy=indexing_policy,
cosmos_container_properties=cosmos_container_properties,
)
查询数据
# Perform a similarity search between the embedding of the query and the embeddings of the documents
query = "What were the compute requirements for training GPT 4"
results = vector_search.similarity_search(query)
print(results[0].page_content)
performance based on models trained with no more than 1/1,000th the compute of
GPT-4.
1 Introduction
This technical report presents GPT-4, a large multimodal model capable of processing image and
text inputs and producing text outputs. Such models are an important area of study as they have the
potential to be used in a wide range of applications, such as dialogue systems, text summarization,
and machine translation. As such, they have been the subject of substantial interest and progress in
recent years [1–34].
One of the main goals of developing such models is to improve their ability to understand and generate
natural language text, particularly in more complex and nuanced scenarios. To test its capabilities
in such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In
these evaluations it performs quite well and often outscores the vast majority of human test takers.
带分数的相似性搜索
query = "What were the compute requirements for training GPT 4"
results = vector_search.similarity_search_with_score(
query=query,
k=5,
)
# Display results
for result in results:
print(result)
(Document(page_content='performance based on models trained with no more than 1/1,000th the compute of\nGPT-4.\n1 Introduction\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\ntext inputs and producing text outputs. Such models are an important area of study as they have the\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\nand machine translation. As such, they have been the subject of substantial interest and progress in\nrecent years [1–34].\nOne of the main goals of developing such models is to improve their ability to understand and generate\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', metadata={'id': '95cc87c2-27f5-40d2-afc2-a354e8f339e4', 'embedding': [-0.025839418172836304, -0.004486586898565292, -0.003380518639460206, -0.014719882979989052, 0.01754477061331272, 0.01754477061331272, -0.011216467246413231, 0.006923745386302471, -0.018334077671170235, -0.027348794043064117, 0.011860375292599201, 0.030990684404969215, -0.03522801771759987, -0.020854320377111435, -0.008370808325707912, 0.022931445389986038, 0.016727767884731293, -0.007706128526479006, 0.0006555921281687915, -0.017406295984983444, 0.014020584523677826, 0.00848851166665554, -0.018237145617604256, -0.018320230767130852, 0.010136363096535206, 0.011811909265816212, 0.031156854704022408, -0.037000495940446854, -0.014719882979989052, 0.0030897213146090508, 0.015052222646772861, -0.016893938183784485, -0.022045204415917397, -0.0068510458804667, 0.0028629687149077654, -0.007408407516777515, 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'performance based on models trained with no more than 1/1,000th the compute of\nGPT-4.\n1 Introduction\nThis technical report presents GPT-4, a large multimodal model capable of processing image and\ntext inputs and producing text outputs. Such models are an important area of study as they have the\npotential to be used in a wide range of applications, such as dialogue systems, text summarization,\nand machine translation. As such, they have been the subject of substantial interest and progress in\nrecent years [1–34].\nOne of the main goals of developing such models is to improve their ability to understand and generate\nnatural language text, particularly in more complex and nuanced scenarios. To test its capabilities\nin such scenarios, GPT-4 was evaluated on a variety of exams originally designed for humans. In\nthese evaluations it performs quite well and often outscores the vast majority of human test takers.', 'SimilarityScore': 0.8395394297757695}), 0.8395394297757695)
(Document(page_content='2 GPT-4 Observed Safety Challenges\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\nalso present new safety challenges, which we highlight in this section.\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\nmitigation efforts; and iteratively test and build safer versions of the model. Some of the specific\nrisks we explored are:6\n•Hallucinations\n•Harmful content\n•Harms of representation, allocation, and quality of service\n•Disinformation and influence operations\n•Proliferation of conventional and unconventional weapons\n•Privacy\n•Cybersecurity\n•Potential for risky emergent behaviors\n•Interactions with other systems\n•Economic impacts\n•Acceleration\n•Overreliance', metadata={'id': '4a3bcc96-5927-477b-bc8c-f499ffceb310', 'embedding': [3.201593426638283e-05, -0.01149508636444807, -0.006550004705786705, -0.034101177006959915, -0.004657018929719925, 0.020493626594543457, -0.018833834677934647, 0.004362097475677729, -0.02326451987028122, -0.022660959511995316, 0.025678761303424835, 0.029574472457170486, -0.047050297260284424, -0.020493626594543457, 0.01711917482316494, 0.03264714404940605, 0.038161493837833405, -0.008086340501904488, -0.0072015756741166115, -0.0050582499243319035, 0.0031618347857147455, 0.01932765729725361, -0.02500661462545395, 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-0.0036419397220015526, 0.004557568579912186, -0.009855871088802814, -0.0027623188216239214, -0.007215293124318123, -0.026405777782201767, -0.006587727461010218, -0.005150841549038887], 'text': '2 GPT-4 Observed Safety Challenges\nGPT-4 demonstrates increased performance in areas such as reasoning, knowledge retention, and\ncoding, compared to earlier models such as GPT-2[ 22] and GPT-3.[ 10] Many of these improvements\nalso present new safety challenges, which we highlight in this section.\nWe conducted a range of qualitative and quantitative evaluations of GPT-4. These evaluations\nhelped us gain an understanding of GPT-4’s capabilities, limitations, and risks; prioritize our\nmitigation efforts; and iteratively test and build safer versions of the model. Some of the specific\nrisks we explored are:6\n•Hallucinations\n•Harmful content\n•Harms of representation, allocation, and quality of service\n•Disinformation and influence operations\n•Proliferation of conventional and unconventional weapons\n•Privacy\n•Cybersecurity\n•Potential for risky emergent behaviors\n•Interactions with other systems\n•Economic impacts\n•Acceleration\n•Overreliance', 'SimilarityScore': 0.8297811051676229}), 0.8297811051676229)
(Document(page_content='GPT-4 Technical Report\nOpenAI∗\nAbstract\nWe report the development of GPT-4, a large-scale, multimodal model which can\naccept image and text inputs and produce text outputs. While less capable than\nhumans in many real-world scenarios, GPT-4 exhibits human-level performance\non various professional and academic benchmarks, including passing a simulated\nbar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-\nbased model pre-trained to predict the next token in a document. The post-training\nalignment process results in improved performance on measures of factuality and\nadherence to desired behavior. A core component of this project was developing\ninfrastructure and optimization methods that behave predictably across a wide\nrange of scales. This allowed us to accurately predict some aspects of GPT-4’s\nperformance based on models trained with no more than 1/1,000th the compute of\nGPT-4.\n1 Introduction', metadata={'id': '89def1ae-3e87-4a7b-8e00-3d5a340b83ae', 'embedding': [-0.007440360262989998, -0.016012800857424736, -0.015371742658317089, -0.010420596227049828, 0.025601385161280632, 0.012255111709237099, -0.01030466053634882, 0.014921639114618301, -0.016231033951044083, -0.01673569530248642, 0.031043555587530136, 0.037372294813394547, -0.03641752898693085, -0.01844063587486744, -0.0032530263997614384, 0.02834293060004711, 0.022627970203757286, -0.006424215622246265, -0.001998190302401781, -0.01643562503159046, 0.0033928314223885536, -0.00016473987489007413, -0.028670279309153557, -0.014471534639596939, -0.00011455068306531757, 0.002762003568932414, 0.027483640238642693, -0.037590526044368744, -0.0028267912566661835, -0.0037338195834308863, 0.01684481091797352, -0.005680861417204142, -0.0324893444776535, 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Report\nOpenAI∗\nAbstract\nWe report the development of GPT-4, a large-scale, multimodal model which can\naccept image and text inputs and produce text outputs. While less capable than\nhumans in many real-world scenarios, GPT-4 exhibits human-level performance\non various professional and academic benchmarks, including passing a simulated\nbar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-\nbased model pre-trained to predict the next token in a document. The post-training\nalignment process results in improved performance on measures of factuality and\nadherence to desired behavior. A core component of this project was developing\ninfrastructure and optimization methods that behave predictably across a wide\nrange of scales. This allowed us to accurately predict some aspects of GPT-4’s\nperformance based on models trained with no more than 1/1,000th the compute of\nGPT-4.\n1 Introduction', 'SimilarityScore': 0.8287368247866265}), 0.8287368247866265)
(Document(page_content='plan to refine these methods and register performance predictions across various capabilities before\nlarge model training begins, and we hope this becomes a common goal in the field.\n4 Capabilities\nWe tested GPT-4 on a diverse set of benchmarks, including simulating exams that were originally\ndesigned for humans.4We did no specific training for these exams. A minority of the problems in the\nexams were seen by the model during training; for each exam we run a variant with these questions\nremoved and report the lower score of the two. We believe the results to be representative. For further\ndetails on contamination (methodology and per-exam statistics), see Appendix C.\nExams were sourced from publicly-available materials. Exam questions included both multiple-\nchoice and free-response questions; we designed separate prompts for each format, and images were\nincluded in the input for questions which required it. 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training begins, and we hope this becomes a common goal in the field.\n4 Capabilities\nWe tested GPT-4 on a diverse set of benchmarks, including simulating exams that were originally\ndesigned for humans.4We did no specific training for these exams. A minority of the problems in the\nexams were seen by the model during training; for each exam we run a variant with these questions\nremoved and report the lower score of the two. We believe the results to be representative. For further\ndetails on contamination (methodology and per-exam statistics), see Appendix C.\nExams were sourced from publicly-available materials. Exam questions included both multiple-\nchoice and free-response questions; we designed separate prompts for each format, and images were\nincluded in the input for questions which required it. The evaluation setup was designed based', 'SimilarityScore': 0.8285566330929187}), 0.8285566330929187)
(Document(page_content='overall GPT-4 training budget. When mixing in data from these math benchmarks, a portion of the\ntraining data was held back, so each individual training example may or may not have been seen by\nGPT-4 during training.\nWe conducted contamination checking to verify the test set for GSM-8K is not included in the training\nset (see Appendix D). We recommend interpreting the performance results reported for GPT-4\nGSM-8K in Table 2 as something in-between true few-shot transfer and full benchmark-specific\ntuning.\nF Multilingual MMLU\nWe translated all questions and answers from MMLU [ 49] using Azure Translate. We used an\nexternal model to perform the translation, instead of relying on GPT-4 itself, in case the model had\nunrepresentative performance for its own translations. We selected a range of languages that cover\ndifferent geographic regions and scripts, we show an example question taken from the astronomy', metadata={'id': 'a7e86563-592a-4304-962b-25315f0d3f46', 'embedding': [-0.005677336826920509, -0.007514122407883406, 0.013824593275785446, -0.029277246445417404, -0.005625155754387379, 0.020705582574009895, 0.00037940230686217546, -0.006303513888269663, -0.042524367570877075, -0.03512156382203102, 0.025394950062036514, 0.05716298893094063, -0.031086202710866928, -0.008022021502256393, 0.0013036654563620687, 0.03437015414237976, 0.015584846027195454, 0.013984616845846176, 0.0026055914349853992, -0.02005157433450222, -0.01544569618999958, 0.008731688372790813, -0.029945168644189835, -0.005879105068743229, 0.007695017848163843, 0.0021742251701653004, 0.034147512167692184, -0.02949988842010498, -0.019981998950242996, 0.01817304454743862, 0.008279449306428432, -0.0009810103802010417, -0.037681933492422104, 0.006557463202625513, -0.01828436367213726, 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-0.024420898407697678], 'text': 'overall GPT-4 training budget. When mixing in data from these math benchmarks, a portion of the\ntraining data was held back, so each individual training example may or may not have been seen by\nGPT-4 during training.\nWe conducted contamination checking to verify the test set for GSM-8K is not included in the training\nset (see Appendix D). We recommend interpreting the performance results reported for GPT-4\nGSM-8K in Table 2 as something in-between true few-shot transfer and full benchmark-specific\ntuning.\nF Multilingual MMLU\nWe translated all questions and answers from MMLU [ 49] using Azure Translate. We used an\nexternal model to perform the translation, instead of relying on GPT-4 itself, in case the model had\nunrepresentative performance for its own translations. We selected a range of languages that cover\ndifferent geographic regions and scripts, we show an example question taken from the astronomy', 'SimilarityScore': 0.8271415481962364}), 0.8271415481962364)