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SASKATOON, SK, Oct. 11, 2023 /CNW/ - Vendasta Technologies today announced a CAD $20 million financing. At the same time, a CAD $52.5 million debenture converted to equity. This financing provides ...

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Foundry partner Brad Feld joins board, sees Vendasta as a “natural consolidator” of B2B SaaS firms. After buying four companies in the past two years, Saskatoon-based Vendasta has secured $20 million CAD in equity Series E funding to continue its ...

Vendasta closes $20-million Series E to fund acquisitions, converts existing debt into equity

SASKATOON, SK, Oct. 11, 2023 /PRNewswire/ - Vendasta Technologies today announced a CAD $20 million financing. At the same time, a CAD $52.5 million debenture converted to equity. This financing ...

Saskatoon-based software company welcomes Foundry partner Brad Feld to board of directors SASKATOON, SK, Oct. 11, 2023 /CNW/ - Vendasta Technologies today announced a CAD $20 million financing. At the same time, a CAD $52.5 million debenture converted to ...

Get the most recent info and news about Vendasta Technologies on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

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Tech giant Apple has come under fire in recent weeks following underwhelming iPhone sales and a perceived lack of innovation among its smartphone releases. But is the reality that Apple is preparing for a future bigger than the iPhone? Sales issues surrounding the iPhone have led to Apple’s stock receiving two rating downgrades within a week, sending share values tumbling at the start of January 2024. Following an initial Barclays downgrade, Harsh Kumar from Piper Sandler & Co. opted to downgrade Apple’s rating from ‘Overweight’ to ‘Neutral’, underlining a troubled period for the stock on Wall Street. “We are concerned about handset inventories entering into 1H24 and also feel that growth rates have peaked for unit sales,” in a note. explained Kumar Barclays analysts led by Tim Long also shared the perspective that iPhone sales were insufficient. “Our checks remain negative on volumes and mix for iPhone 15, and we see no features or upgrades that are likely to make the iPhone 16 more compelling,” . the analysts confirmed has long struggled to capture any meaningful momentum since Q3 2023, and the iPhone’s perceived shortcomings, along with geopolitical tensions leading to on Apple products in China, have paved the way for growing negative sentiment towards the innovative tech firm. Apple’s stock reported bans Interest in iPhones Waning? On the surface, iPhone specifications have begun to appear relatively familiar from model to model as technological limitations prevent the implementation of powerful new features packed into handsets. Taking a deeper glance at how recent iPhone models compare, we can see that the , front camera, and connectivity as well as very similar features in terms of battery life and physical appearance as the iPhone 14. iPhone 15 features the same display This, accompanied by growing consumer wariness about the drawbacks of through fears of slowing handset speeds, battery drainage, and a lack of meaningful features, illustrates that have become limited in the level of innovation available to consumers in new models. iOS system updates Apple’s iPhones As wider economic headwinds are caused by high inflation, lingering high interest rates, and geopolitical conflict, consumer spending has been in the US and beyond, creating greater hesitation over upgrading products with fewer tangible benefits. dealt a sizeable blow The widespread consumer spending downturn has seen Taiwanese firm Foxconn report a in December 2023 sales, with forecasts remaining gloomy in the new year–adding to the concern that iPhone sales aren’t set to recover any time soon. heavy 27% drop The Financial Times’ flagship opinion column, Lex, suggests that news of slowing iPhone sales is over the long term, however. unlikely to trouble Apple While the column offers a reminder that Apple still dominates the growing smartphone premium market with a 71% global share, the innovative tech firm also has its attention firmly fixed on groundbreaking innovations elsewhere in the near future, all with the potential to outlast the smartphone as we know it today. 2024 Will See Apple Embrace Generative AI So far, Apple has been slow to embrace the generative AI boom, which was sparked by OpenAI’s launch of ChatGPT in late 2022. However, this is set to change quickly as the firm has begun to the development of generative artificial intelligence products. committing $1 billion per year “But you can bet that we’re investing, we’re investing quite a bit, we’re going to do it responsibly and it will — you will see product advancements over time where those technologies are at the heart of them,” , Apple CEO. confirmed Tim Cook In December 2023, , a machine learning framework that allows developers to build models that run on Apple Silicon and deep learning model library MLX Data. Apple launched MLX In addition to this, Craig Federighi, Apple’s senior vice president in charge of AI and software engineering, has been working on adding AI to upcoming rollouts of iOS. This would introduce large language models to Apple devices that could help bring to a range of core Apple services. greater artificial intelligence For instance, Siri could benefit from vast improvements in terms of comprehension and the quality of query responses, while Apple’s Messages app would gain the ability to field questions itself and auto-complete sentences intuitively. Apple has been cautious with its adoption of generative AI, but as the iPhone approaches a technological ceiling in its current form, the Wall Street speculators of today may be sorely missing the bigger picture: the future of Apple won’t feature the iPhone. Innovating Beyond iPhone In a world where generative AI can intuitively write messages on your behalf and generate a conversational level of comprehension with consumers, the future of the smartphone could face an upcoming challenge for relevance. Apple has long been seeking out what a looks like, and its lack of major iPhone developments in recent years portrays a company that’s finally beginning to believe that the end is in sight for the heyday of the smartphone. future without smartphone dependency While Apple’s generative AI push may be at its fledgling stage, 2024 could yet prove to be a transformative year for the company as dives into the world of spatial computing with the launch of the Vision Pro. “Today marks the beginning of a new era for computing,” when announcing the Vision Pro last year. “Just as the Mac introduced us to personal computing, and iPhone introduced us to mobile computing, Apple Vision Pro introduces us to spatial computing." declared Cook Crucially, the Vision Pro operates as an ‘infinite canvas’ that delivers a fully interactive and three-dimensional user interface that’s , hands, and voice. controlled primarily by the user’s eyes This offers an indication of Apple’s future intentions. We’re looking at a life beyond hardware, and control delivered through biometrics. Although the Vision Pro’s launch may be initially impacted by the same consumer spending issues that have hampered iPhone sales in recent months, it’s an innovation that has the potential to unite a range of technologies for entertainment, productivity, and social purposes. Building a Sustainable Future Apple’s future will ultimately lie beyond the iPhone, but its success could be hampered by the sheer scale of the technological revolution on the horizon. With firms like Meta and Microsoft still betting big on the metaverse, Apple’s spatial computing ambitions with the Vision Pro could face rivals that carry similar purposes. This technological space race will depend on many emerging technologies and their long-term sustainability. However, it’s important to keep the bigger picture in mind when looking at Apple’s recent iPhone sales struggles. In the future that Apple is preparing for, the iPhone will be obsolete.

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In the era of rapid cloud technology advancement, where Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) are integral to numerous projects, the demand for seamless integration of diverse cloud services is undeniable. Microsoft, a major player in this landscape, invested considerable time in crafting the user-friendly Microsoft Entra ID service. However, despite meticulously laid-out instructions from vendors, specialists often encounter issues when configuring integrations with platforms such as Slack, Salesforce, or Datadog. Documentation falls short in providing answers to specific questions. The Social Discovery Group team faced these challenges as well, and in this article, we will offer insights on how to navigate and resolve them. In its portfolio of 50+ dating platforms, the Social Discovery Group utilizes various technologies and cloud services to guarantee their security and reliability. We have extensive experience in integrating and configuring different systems, including peer-to-peer connections between various clouds. Microsoft Entra ID is a cloud solution for identity and access management, serving as a directory and authentication service operating in the cloud. Microsoft Entra ID offers authentication and authorization services for various Microsoft and non-Microsoft services. Within this article, we will explore the integration of the following cloud systems: Slack, MongoAtlas, ElasticCloud, Salesforce, and Datadog. Slack Due to the absence of a precise guide, we decided to create our own, detailing all the headaches that arose along the way. So, let's navigate to the "Enterprise Application" section in Azure. It's necessary to create a new corporate application (this can be done in a few clicks). Next, access the application we've just created and configure Single Sign-On – SAML. Make changes from top to bottom: Identifier: https://slack.com Reply URL: https://<DOMAIN NAME>.slack.com/sso/saml (in case you have ent Grid – https://<DOMAIN NAME>.enterprise.slack.com/sso/saml ) Sign-on URL: https://<DOMAIN NAME>.slack.com/sso/saml/start Logout URL: https:// <DOMAIN NAME>.slack.com/sso/saml/logout Create a "Member" role (or choose another name). Next, create a test user in Microsoft Entra ID and add them to our application. Then, go to the Admin Center in Slack. Follow the steps: Slack Admin - Authentication - SAML configuration - Configure The next step is to configure SAML. We recommend paying attention to the "Display Name" setting. Further, in the settings, uncheck "Update Profile on login," disallow changing the email address and Display Name. There is also an option to customize the text on the login button and prohibit users from logging in using any other method except SSO. After this, you should be able to sign in to Slack using your Microsoft account. If any additional fields or settings are needed, this can be done through Azure AD Provision. To add a new attribute, go to User Attributes and select Add New Mapping. Choose the desired source attribute that you want to be displayed in Slack and associate it with the corresponding Slack attribute. After adding the necessary attributes and saving the settings, proceed to Provision on Demand to test the functionality. Upon a positive outcome, you will receive the following: Next, you need to go to Slack and specify which attributes to display in the complete user profile and where to retrieve them. Go to Profiles and choose the information to be displayed and where it will be pulled from. In this case, SCIM corresponds to Azure AD. Next, we recommend adding the necessary users from Active Directory to Slack to experience the seamless integration. We also want to bring attention to an issue encountered when removing users: if a user is manually deleted from Slack (via the Slack admin center), an error will persist, indicating that the user cannot be transferred to Slack due to insufficient permissions. This occurs because Entra ID retains information about users within the scope and assigns them a unique ID. Deleting and re-adding a user in Slack is perceived as a new user by Entra ID, leading to permission issues during updates. Check the documentation . here MongoAtlas For MongoAtlas, the process follows a similar path. You'll also need to create a new corporate application; for ease, enter "MongoDB Atlas — SSO" in the search field. More detailed instructions can be found here. Next, proceed to the Single Sign-On settings, where you'll need to input the following: In the "Identifier" text field, enter the URL using the following template: https://www.okta.com/saml2/service-provider/<Customer_Unique> In the "Response URL" text field, enter the URL using the template: https://auth.mongodb.com/sso/saml2/<Customer_Unique> In the "Login URL" text field, enter the URL using the template: https://cloud.mongodb.com/sso/<Customer_Unique> We recommend paying attention to the Attributes section and configuring it as shown in the screenshot below (it was essential to add "memberOf"). Next, on the "Setting up Single Sign-On with SAML" page, go to the "SAML Signing Certificate" section and find the XML metadata for federation. Select "Download" to download the certificate and save it locally (Federation Metadata XML). We will need it later in MongoDB Atlas. In the "Setting up MongoDB Atlas — SSO" section, copy the respective URLs. Next, navigate to . MongoDB Atlas -> Organization -> Settings -> Federated Authentication Settings Begin by adding the Microsoft Entra ID credential provider in Atlas. Input the respective URLs mentioned in the previous step from the Microsoft portal , upload . (Issuer URI, Login URL, Single Sign-On URL) the Identity Provider Signature Certificate, and configure the remaining settings as shown in the screenshot below Click "Next" and save the metadata file locally, as you will need to upload it later in Azure on the Single Sign-On configuration page. Next, add, map, and validate the domain, for example, using the corresponding TXT records generated in MongoDB Atlas. After that, link your domain with the credential provider and move on to the most interesting part: role configuration. We've created the following roles as an example, each with specific permissions granted to projects. Then, you need to map these roles to Azure roles. Go to "App roles" for your application, create, and add roles with values exactly matching the names you just assigned to these roles in MongoAtlas. Afterward, add users to the application in the Microsoft Entra ID Enterprise Application and assign them the appropriate roles. Activate "Sign in with Azure SSO" in the Federation Management section in MongoDB Atlas and verify the correct functioning of the configured settings. Refer to the documentation for more detailed instructions. here Elastic Cloud Let's move on to the integration with Elastic Cloud. The fundamental steps for creating an application in the Enterprise Application Entra ID are similar to the previous ones. There, you can find information on where to obtain the Identifier or Logout URL. However, we encountered an issue with attributes, so we'll provide a screenshot of our working configuration. We created two roles - a reader and a superuser. Here, you can create as many roles as you need. Role settings in Elasticsearch are configured in Role Mappings and look as follows: Now comes the interesting part. Two things need to be done: add Azure SSO configuration for Kibana and Elasticsearch. To achieve this, follow these steps: Navigate to the editing section of your Elasticsearch Cloud deployment to update the elasticsearch.yml configuration. Add the following lines: “ xpack: security: authc: realms: saml: saml-azure-ad: order: 2 attributes.principal: "http://schemas.xmlsoap.org/ws/2005/05/identity/claims/name" attributes.groups: "http://schemas.xmlsoap.org/ws/2005/05/identity/claims/role" attributes.name: "http://schemas.xmlsoap.org/ws/2005/05/identity/claims/givenname" attributes.mail: "http://schemas.xmlsoap.org/ws/2005/05/identity/claims/emailaddress" idp.metadata.path: "APP Federation metadata URL" idp.entity_id: "Azure Entra ID Identifier" sp.entity_id: "Kibana endpoint" sp.acs: "Kibana endpoint/api/security/v1/saml" sp.logout: "Kibana endpoint/logout" “ After adding and modifying the values highlighted in red to match your configuration, click "Save." Elastic Cloud will validate all changes and initiate the cluster update. This process may take a few minutes. Following the successful completion of the previous step, you need to edit the kibana.yml configuration to allow logins from Microsoft Entra ID in addition to the default login. To do this, add the following lines to the settings: “ xpack.security.authc.providers: saml.saml1: order: 0 realm: saml-azure-ad description: "Log in with Azure Entra ID" basic.basic1: order: 1 ” After completing the previous two steps, another login option should be available on your Kibana login page. Next, proceed with adding users to the application in the Microsoft Entra ID Enterprise Application and assigning them the necessary roles. Refer to the documentation here for more information. Salesforce Integration with Salesforce was a bit more complex than the previous ones. The initial steps are similar, but attention should be given to the Provisioning section. There are nuances regarding specifying admin credentials under which account synchronization will occur, and enabling Attribute Mapping is necessary. Here is an example of our configuration settings in the screenshot. We didn't encounter any significant issues with attributes, but we'll also provide a screenshot of the ones we used. In Salesforce itself, there weren't any particular nuances, and we proceeded with the following steps as outlined in this . guide Datadog During the integration with Datadog, we also encountered some issues. The primary documentation that guided us can be found . For troubleshooting nuances, we referred to guide. here this Everything started smoothly, and initially, it seemed like there wouldn't be any issues. As usual, we created a new Enterprise Application in Microsoft Entra ID. In the "Set up Single Sign-On with SAML" section, pay attention to the location you selected for your Datadog account. In our case, it was the Europe region. As mentioned earlier, for MongoAtlasDB, we add the additional attribute "memberOf" for role mapping. Save all the settings and download the Federation Metadata XML. Upload this file into the SAML settings in Datadog. Here is a convenient for reference. Datadog SAML Settings (replace "EU" with the appropriate domain zone if your location differs). It should look like the screenshot below. link In the organization's Login Methods settings, keep only what is necessary (in my case, I disabled password login and Google Authenticate). Add roles in Azure in the application registration for Microsoft Entra ID. Here comes the most interesting part. Navigate to SAML Group Mappings -> Role Mappings in Datadog and add the key, value, and role. It seems straightforward. See the screenshot below. But it doesn’t work, and it keeps giving the error "There are no AuthNMappings for this user". For quite a while, we tried to understand what we were doing wrong, reviewing all the documentation, logs, and troubleshooting pages. Unfortunately, there was no mention of this anywhere. In the end, the solution is simple: follow the configuration shown in the screenshot below. In the KEY field, enter the string: http://schemas.microsoft.com/ws/2008/06/identity/claims/role After doing this, everything started working successfully. "This is just a small part of the systems that we integrated with Microsoft Entra ID. We hope that the insights shared here will serve as a helpful resource, streamlining your integration processes and saving valuable time. In conclusion, the following advantages of using Microsoft Entra ID can be highlighted: Single Sign-On, which doesn’t require entering the password multiple times; no additional components required; ease of configuration and administration; group policies and groups; device registration; and high-level security.

Navigating Microsoft Entra ID Integrations for Slack, MongoAtlas, Salesforce, and More

স্ল্যাক, মঙ্গোআটলাস, সেলসফোর্স এবং আরও অনেক কিছুর জন্য মাইক্রোসফ্ট এন্ট্রা আইডি ইন্টিগ্রেশন নেভিগেট করা

Navigieren durch Microsoft Entra ID-Integrationen für Slack, MongoAtlas, Salesforce und mehr

Navegando por las integraciones de Microsoft Entra ID para Slack, MongoAtlas, Salesforce y más

Navigation dans les intégrations Microsoft Entra ID pour Slack, MongoAtlas, Salesforce et plus encore

स्लैक, मोंगोएटलस, सेल्सफोर्स और अन्य के लिए माइक्रोसॉफ्ट एंट्रा आईडी एकीकरण को नेविगेट करना

Slack、MongoAtlas、Salesforce などの Microsoft Entra ID 統合のナビゲート

Slack, MongoAtlas, Salesforce 등을 위한 Microsoft Entra ID 통합 탐색

Navegando nas integrações do Microsoft Entra ID para Slack, MongoAtlas, Salesforce e muito mais

Навигация по интеграции Microsoft Entra ID для Slack, MongoAtlas, Salesforce и других

Slack, MongoAtlas, Salesforce ve Daha Fazlası için Microsoft Entra ID Entegrasyonlarında Gezinme

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导航 Slack、MongoAtlas、Salesforce 等的 Microsoft Entra ID 集成

Enhancing LLM Applications with Context-Aware Technologies The applications of Large Language Models have been transformative across various sectors, shaping a new frontier in natural language processing and understanding. LLMs, renowned for generating human-like text, have revolutionized chatbots, content creation, and even complex problem-solving tasks. However, despite their impressive capabilities, LLMs face notable challenges, particularly in and maintaining accuracy over extended interactions. A common pitfall is their tendency towards " ," where the generated content, though fluent, may drift into inaccuracy or irrelevance. context awareness hallucinations This is where technologies like and become pivotal. By integrating LLMs with RAG, which dynamically retrieves relevant information from vast datasets, we can significantly mitigate these limitations. The synergy between LLMs and vector databases, capable of efficiently handling and retrieving structured vector data, promises to bring a new level of depth, context, and reliability to LLM applications. Retrieval-Augmented Generation (RAG) vector databases In this blog, readers can expect: : Understanding the limitations of LLMs, such as context awareness and accuracy issues. Comprehensive Insights into LLM Challenges : Exploring how these techniques address the drawbacks of traditional LLMs. Introduction to RAG and Vector Databases : Hands-on examples showcasing the integration of RAG with vector databases to enhance LLM applications. Practical Demonstrations and Tutorials : Exploring how these integrations can be applied effectively in enterprise settings. Real-World Applications : Whether you're a tech enthusiast, an AI practitioner, or a business professional, the blog aims to provide valuable insights and practical knowledge. Actionable Knowledge for Various Audiences Understanding Retrieval-Augmented Generation (RAG) is an innovative paradigm in the field of AI and natural language processing. It marks a significant shift from conventional language models by integrating information retrieval into the language generation process. This hybrid approach enhances the ability of AI models to generate responses that are not only contextually accurate but also infused with up-to-date knowledge from external data sources. Retrieval-Augmented Generation (RAG) The inception of RAG can be traced back to the quest for overcoming the limitations of standard language models, such as GPT (Generative Pre-trained Transformer). Traditional models, despite their proficiency in generating coherent text, often struggle with providing accurate, fact-based responses, particularly for queries requiring specific, real-time knowledge. Here's a description of how Retrieval-Augmented Generation (RAG) works, integrating with a vector database: : Workflow begins with a comprehensive , which is the foundation of the system's intelligence. This Knowledge Base is typically a large corpus of documents containing the information that might be queried by users. These documents could be anything from FAQ sheets and articles to databases of structured information. Before these documents can be utilized, they undergo an ingestion process where they are pre-processed and transformed into embeddings. An —often a sophisticated machine learning algorithm—is employed to convert the textual information into vector embeddings. These embeddings numerically represent the semantic content of the documents in a multi-dimensional space, which is optimal for similarity comparisons. Ingestion and Indexing Knowledge Base Embedding Model : It all begins with a customer interacting with an application and posing a query. This query is a request for information or a question that the customer expects the AI to answer. Customer Interaction : The raw customer query is then processed by an Embedding Model. This model converts the text query into a vector, which is a numeric representation that captures the semantic meaning of the query in a high-dimensional space. Query Embedding : The query vector is sent to a Vector Database, a specialized database designed to handle high-dimensional vector data. The database performs a similarity search to retrieve the most relevant document embeddings. These embeddings represent pre-processed knowledge from a Knowledge Base that has been ingested into the system. Vector Database Search : The retrieved document embeddings are combined with the original query to form a prompt that includes both the query and the relevant context. Context Retrieval : This enriched prompt is then fed into a Language Learning Model (LLM). The LLM uses the context from the retrieved documents to generate a response that is accurate, informative, and contextually relevant to the customer's query. Language Model Response Generation : Finally, the LLM produces a generative response, which is delivered back to the customer via the app. This response is not only based on the model's pre-trained knowledge but also augmented with specific information retrieved from the knowledge base, making it highly relevant to the user's request. Generative Response Vector Databases Vector databases store and manage data that has been converted into numerical vector form, often through processes like embedding models in machine learning.  Embeddings are numerical representations of data, often high-dimensional vectors, that capture the semantic or contextual features of the original input. In the case of text data, embeddings convert words, sentences, or entire documents into a form that a computer can process. Machine learning models, particularly neural networks, are used to generate these embeddings so that similar meanings are close in the vector space. These databases are designed to efficiently perform similarity searches, which locate data points that are closest to a given query vector within the vector space. Here's a deeper look into the process: : When documents are ingested, an embedding model (such as a neural network) transforms the text into a high-dimensional vector. Each vector represents the semantic meaning of the document in a numerical form. These vectors are then stored in the vector database. Data Storage : To facilitate fast retrieval, the database builds an index on these vectors using algorithms suited for high-dimensional spaces, such as Inverted File Index (IVF) or Hierarchical Navigable Small World (HNSW). The choice of index type balances between the speed and accuracy of the search. Indexing : When a query is made, it is also converted into a vector using the same embedding model. The vector database then uses the index to quickly find the vectors most similar to the query vector. Similarity is determined by distance metrics like Euclidean distance or cosine similarity. Similarity Search : Advantages of Embeddings : Embeddings are designed so that semantically similar items are closer in the vector space, enabling systems to understand context and meaning. For example, in the field of genomics, gene expression data can be encoded as embeddings to reveal patterns that indicate relationships between different genes and phenotypes. This can assist in identifying biomarkers for diseases that may not be apparent through traditional analysis. Semantic Similarity : They can capture complex relationships and nuances in the data that might be missed with traditional representations. A practical application is seen in recommendation systems, such as those used by streaming services like Netflix or Spotify. These platforms use embeddings to understand user preferences and content features, thereby recommending movies or songs that share similarities with a user's previous choices. Despite the diversity in content, embeddings allow for nuanced recommendations that go beyond genre or artist, considering deeper patterns in user consumption. Complex Relationships : Embeddings convert varied data types into a uniform vector format, simplifying operations like comparison and retrieval. Uniformity Getting Started with Vector DB Creating a local development environment for RAG and Vector DB ( ) involves several key steps. Milvus Here's a structured guide: : Prerequisites Ensure Python 3.6+ is installed on your system. Docker is required for running Milvus. : Virtual Environment Create a new virtual environment and use that: python3 -m venv rag-milvus-env
source rag-milvus-env/bin/activate

# Install supporting dependencies
pip install transformers datasets faiss-cpu torch sentence-transformers pymilvus : Milvus using docker Pull and run the Milvus Docker image: (you can also use other vector db’s) You can use the below steps or follow the getting started guide provided . here docker pull milvusdb/milvus:latest
docker run -d --name milvus_cpu -p 19530:19530 -p 19121:19121 milvusdb/milvus:latest : Setup Data Now let’s try to download some sample data, create embeddings, and insert them into a collection. import requests
import csv

url = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv"

# Download the file
response = requests.get(url)
response.raise_for_status()  # This will raise an error if the download failed

# Decode the content and split into lines
lines = response.content.decode('utf-8').splitlines()

questions = []

# Process the lines
reader = csv.reader(lines, delimiter='\t')
next(reader)  # Skip the header row
for row in reader:
    questions.extend([row[1], row[4]])  # Assuming the questions are in the 2nd and 3rd columns


questions = questions[:10000] Create Embeddings from sentence_transformers import SentenceTransformer

# transformer to create embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')

embeddings = model.encode(questions) Insert into vector DB. # connect to db
connections.connect()

embedding_size = 384

# Prepare the collection schema
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=embedding_size)
]


schema = CollectionSchema(fields, "questions")
collection = Collection("questions", schema)

# Insert the document embeddings
mr = collection.insert([embeddings])

# to get document, in practice this would be some external DB.
id_to_question = {str(mr.primary_keys[i]): questions[i] for i in range(len(questions))}


# List all collections
collections = list_collections()
print(collections) Index the collection. from pymilvus import Collection

index_params = {
    "metric_type": "L2",
    "index_type": "HNSW", # Index of type HNSW, refer to docs for other types.
    "params": {
        "M": 16,  # Example value, adjust as needed
        "efConstruction": 200  
    }
}
collection = Collection("questions")    
collection.create_index(
  field_name="embedding", 
  index_params=index_params
) Query Documents query = "What is artificial intelligence?"
query_embedding = model.encode(query)

collection.load()

# Define search parameters
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}

# Perform the search
results = collection.search(
    data=[query_vector], 
    anns_field="embedding", 
    param=search_params, 
    limit=10, 
    expr=None,
    consistency_level="Strong"
)

# Process results
for result in results:
    milvus_id = str(result.id)  # Convert ID to string for dictionary lookup
    original_question = id_to_question[milvus_id]
    print(f"Milvus ID: {milvus_id}, Similar Question: {original_question}") Once we get semantically similar documents from vector db., we could pass this context + input query to LLMs which will have much better results as LLMs have context now. Conclusion In conclusion, the integration of RAG with vector databases like Milvus offers a potent solution to some of the most pressing challenges in LLM applications—particularly those requiring deep contextual understanding and dynamic information retrieval. By combining the generative prowess of LLMs with the precision and efficiency of vector databases, enterprises can vastly improve the relevance and accuracy of AI-driven responses, providing users with valuable and contextually rich interactions. As AI continues to advance, the fusion of these technologies represents not just a step, but a leap forward, heralding a future where AI can support more sophisticated, varied, and nuanced applications across all sectors. This blog has set the stage for innovators and practitioners to begin experimenting with these tools, pushing the boundaries of what's possible in the realm of enterprise AI applications.

Elevating Enterprise LLMs with Retrieval-Augmented Generation (RAG) and Vector Database Integration

পুনরুদ্ধার-অগমেন্টেড জেনারেশন (RAG) এবং ভেক্টর ডেটাবেস ইন্টিগ্রেশন সহ এলিভেটিং এন্টারপ্রাইজ এলএলএম

Verbesserung von Unternehmens-LLMs mit Retrieval-Augmented Generation (RAG) und Vector-Datenbankintegration

Mejora de los LLM empresariales con recuperación de generación aumentada (RAG) e integración de bases de datos vectoriales

Améliorer les LLM d'entreprise grâce à la génération augmentée par récupération (RAG) et à l'intégration de bases de données vectorielles

पुनर्प्राप्ति-संवर्धित पीढ़ी (आरएजी) और वेक्टर डेटाबेस एकीकरण के साथ एंटरप्राइज़ एलएलएम को उन्नत करना

検索拡張生成 (RAG) とベクトル データベースの統合によるエンタープライズ LLM の強化

검색 증강 생성(RAG) 및 벡터 데이터베이스 통합을 통해 엔터프라이즈 LLM 향상

Elevando LLMs empresariais com geração aumentada de recuperação (RAG) e integração de banco de dados vetorial

Повышение уровня корпоративного уровня LLM за счет интеграции расширенной генерации данных (RAG) и векторной базы данных

Alma-Artırılmış Üretim (RAG) ve Vektör Veritabanı Entegrasyonu ile Kurumsal LLM'leri Yükseltme

Nâng cao LLM doanh nghiệp với thế hệ tăng cường truy xuất (RAG) và tích hợp cơ sở dữ liệu vectơ

通过检索增强生成 (RAG) 和矢量数据库集成提升企业法学硕士

The Future of NFTs and Metaverse According to Punk 6529

El futuro de las NFT y Metaverse según Punk 6529

L'avenir des NFT et du métaverse selon Punk 6529

पंक 6529 के अनुसार एनएफटी और मेटावर्स का भविष्य

パンク6529が語るNFTとメタバースの未来

O futuro dos NFTs e do metaverso de acordo com Punk 6529

Tương lai của NFT và Metaverse Theo Punk 6529

根据朋克 6529 的 NFT 和元界的未来

Here's a crazy stat for you. Did you know that salespeople spend than they do actually selling things? I'm not even joking – 22 percent of the time is spent on sales, 23 percent on admin, 12 percent on order processing, and 10 percent on planning. more time on admin Oh, and only 6 percent on lunch breaks. Too bad about that last one. To me, it feels like all of these things should be taking the backseat to selling – but alas, that's not the world we live in. Or is it? In 2022, we've got software tools and gadgets for just about anything we could possibly ask. Whether it's customer management, scheduling, or even something as small as a digital business card, there's an app for that. (And more recently, there's an AI for that, too. Machine learning is being used more and more in sales to automate the busywork and free up time for selling. But that's a story for later in the article.) The point is, there are ways to be more efficient with your time – you just need to know where to look. So without further ado, here are five tips on how to be a more efficient salesperson. Get ready to spend more time selling and less time shredding! 1. Find Leads Faster Way back in June 2021, I spoke to Yoni Tserruya, co-founder and CEO of Lusha. If you've never heard of Lusha, it's a that allows salespeople to find contact information for leads in seconds. lead-generation tool "Lusha is a crowdsourced B2B data company, which helps salespeople and marketing people find their next customer. We do it by providing them accurate business data and insight about technologies and the buyers so they can easily identify and connect with the ideal profile," Yoni explained in our interview. If you're a salesperson, you know how painstaking it can be to find the right contact information for a lead. It's a process that often takes hours – and even then, you're not always guaranteed to have the most up-to-date information. Imagine if you could find that information in seconds. And not only that but what if you could be sure that the information was accurate? That's exactly why I wanted Yoni on my show – he's created something truly valuable for people like me, and I admire anyone who fills a gap so efficiently. "The main aim of Lusha is to build the largest salespeople community in the world. The more salespeople are using the product, the better the data become; salespeople help us to validate the data and provide the best B2B data quality in the world." Of course, an alternative is to find yourself a good PPC agency that will help you create targeted ad campaigns and get in front of your ideal customer. But cold calling is still a well-used sales tactic – and considering that it takes 6-8 cold calls per prospect to get a conversion, any time you can save with a tool like Lusha is valuable. 2. Clean Your Data If you're running a team of salespeople, chances are, they're spending between 20 and 30 percent of their day selling your product or service. The rest is administrative work. It's simple math – the less time spent on admin, the more time spent on sales, and the more sales you're likely to make. One of the best ways to tip the scales in your favor is to provide your sales reps with clean, organized data. Going back to my interview with Yoni, he was the person who made me think about this more seriously. "If you provide [your team] with a higher quality of data, or you help them prioritize the leads based on criteria like industry, number of employees, or technologies, they can spend more time selling, because you actually help them with automation and data to prioritize the work." There's just one problem with that; you're not decreasing the time spent on data sorting, you're just transferring the work onto yourself. So, how can you speed up the data cleaning process? It may or may not surprise you to hear that artificial intelligence is our best solution to date. There are now AI-powered sales tools specifically designed for lead prioritization – and as author Victor Antonio noted in his book, Sales Ex Machina, tools like this can . erase human error What if you could feed a vast and complex data sheet into a machine, and have it come out the other side as a clean, organized list of leads? That's exactly what these tools are designed to do – and they're only going to become more common (and more necessary) as time goes on. MadKudu is one of the front-runners in this space, and it's also worth checking out h2O.ai, a company that focuses on making AI solutions available across all industries. 3. Eat Your Frogs Early “Top performers organize their day into distinct time blocks dedicated to specific activities, concentrating their focus and eliminating distractions within those blocks." - Jeb Blount I quote Jeb here for a couple of reasons: one, because I've personally found this to be true, and two, because it's such a simple yet overlooked strategy. (Oh, and a third reason – Jeb is the most quotable salesman on the planet. But that's just my opinion.) If you've ever read some of the prominent studies around multitasking, like the one , you'll know that switching between tasks comes at an enormous cost to our productivity. discussed here Here's why: multitasking is a myth. When you 'juggle two things at once', you're really just switching between them at a rapid pace. But all that mental energy takes a toll, and you wind up spending more time overall on both tasks. Switching from a phone call to writing emails to catching up on client paperwork is only going to prolong the amount of time you spend on administrative tasks – which is why I recommend that you eat your frogs early (the frogs in question being your administrative work.) Set aside the first block of your day to handle every single admin-related task you can think of. That way, you'll stay one-track-minded until it's time to switch over to cold calling or meeting with clients. 4. Automate Your Cold Emails Whether we like it or not, emails remain a prominent sales strategy. It's just unfortunate that they're such a time suck. For a while now, we've had access to tools that make the process easier. MailChimp, Constant Contact, and HubSpot Sales are just a few of the more popular ones. But what if I told you there's a way to automate your cold email process even further? Tools like Phrasee and Lyne.ai use split testing data to determine which of your emails have the best chance of getting opened, and using that info, they craft emails for you that are more likely to get responses. Sometimes I can't believe we live in a world where this is possible. It's insane, and it's great news for us salespeople. 5. Keep Your CRM Up-To-Date Customer relationship management tools are super helpful for keeping all your info in one spot. They quickly become a pain in the rear, though, when you're pressed for time and can't continually update client profiles. The answer? Use integrations. Make the movement of data between platforms so seamless that you hardly know it's happening. Some of the best integrations to use, depending on your needs: Call logging. Set up your CRM with a platform like LiiD so that every time a sales call is made or received, it's automatically logged in your CRM. Emails. Integrate your CRM into Outlook or Gmail so that you can automatically log emails and have them appear in your CRM, as well as easily access customer data while you're crafting emails. Social media. Automate posts and outreach by integrating your CRM with Facebook, Instagram, or LinkedIn. Data and surveys. If you use services like Type Form and Survey Monkey to collect customer data and feedback, integrate them with your CRM so you can automatically populate fields. Team communication. You can connect your Slack to most CRMs to prevent switching between tabs and reduce the risk of forgetting to update your CRM with important customer info. These only scratch the surface, by the way. Do some research and see what other integrations might be helpful for you and your team – there's a whole world of possibilities out there. Wrap-Up It was Yoni Tserruya who gave me the idea for today's newsletter with his incredible service, Lusha. I want to wrap up by directing you over to his interview – we spoke about his incredible journey with bootstrapping Lusha, and he's got some great insights into the sales industry as a whole. on my YouTube channel In the meantime, look into your administrative processes and see where you can make some changes. By automating the tedious and time-consuming tasks, you'll be able to focus on selling, which is what we're all here to do in the first place. Oh, and don't forget to leave your tips and tricks in the comments. I'd love to hear how you stay efficient in your sales process!

Make Sure You're Not Wasting Valuable Sales Time

Asegúrese de no perder un valioso tiempo de ventas

Assurez-vous de ne pas perdre un temps de vente précieux

सुनिश्चित करें कि आप मूल्यवान बिक्री समय बर्बाद नहीं कर रहे हैं

貴重な販売時間を無駄にしないようにする

Certifique-se de que você não está desperdiçando um valioso tempo de vendas

Đảm bảo rằng bạn không lãng phí thời gian bán hàng có giá trị

确保您没有浪费宝贵的销售时间

Companies implementing Salesforce’s built-in security solutions may find their customizations limit visibility into the risks that can impact other processes and applications. These complications often go unaddressed due to the idea that, since the platform includes those measures, security is its exclusive domain. However, the truth is that cybersecurity is a shared responsibility. Many of Salesforce’s vulnerabilities are . The Salesforce platform alone can’t fully protect users from their actions — companies must make ongoing efforts to help secure their cloud applications. a result of user customizations Here are four common Salesforce vulnerabilities to keep in mind. They show why it’s vital to find ways to optimize your company’s security. 1. Unsafe Authorizations Customizing authorizations in Salesforce can result in users with unsafe permission settings. This could leave your company vulnerable to internal or external cybercriminals and cause potential compliance issues. A single user may be able to modify access permissions for other people or even export sensitive data. You can close this security gap by replacing standing authorizations with . Here are two to consider: a more restricted security model Just-in-time model: The JIT model grants users system or application access as they need it. You can implement this method with privileged access management (PAM) software that will grant temporary access at users’ requests. Zero trust model: A zero trust model requires authentication from all users before they can access data or applications. Adopting this method means limiting everyone to the minimum access they need to perform their jobs and regularly validating their security status. It requires implementing technologies like multifactor authentication and identity-based segmentation. 2. Too Many Privileges Users with excess privileges can represent a similar security risk. Many companies delegate their Salesforce upkeep to a single administrator with a basic development staff for support. However, the access this person would need gives them almost unlimited power. They — or someone with their credentials — could purge vital data, create a new user with similarly elevated privileges and access reports with sensitive customer information. It’s crucial to avoid giving a single user too many permissions to prevent a situation like this, even if you think you can trust them. Even the most trustworthy employee can make costly mistakes. You can divide Salesforce management responsibilities across a department or hire a managed services team. 3. Improper Security Configurations Salesforce’s security customization options mean companies can accidentally create holes in their defenses. Misconfiguring may give a cybercriminal an opening to access customer data on your servers or upload malware by hijacking users’ sessions. Prevent this potential vulnerability by configuring your security framework according to best practices. Ensure that you’ve set it with: Multifactor authentication HTTPS encryption Minimum password lengths Appropriate sharing defaults and user permissions 4. Compromised System Integrations Salesforce’s flexibility sometimes makes it challenging to maintain full visibility, which can become a liability during system integrations. Improperly integrated third-party networks can create security gaps, leaving Salesforce vulnerable to external systems and giving hackers the chance to intercept communications. It’s vital to manage integrations according to security best practices to reduce the risk of hackers accessing Salesforce through compromised third-party applications. You should ensure your access and authorization configurations and APIs are secure. You can also use tools like Metazoa Snapshot that . Reviewing reports during the change and testing stages lets you double-check the process when integrating something new. document your change and release process Optimize Your Salesforce Security Four of the most common causes of Salesforce security vulnerabilities — unsafe authorizations, excessive privileges, improper security configurations and compromised system integrations — stem from user customizations. Salesforce offers various services and security options, but it’s up to users to implement them safely by ensuring compliance and following best practices.

How to Optimize Your Salesforce Security

Cómo optimizar su seguridad de Salesforce

Comment optimiser votre sécurité Salesforce

अपनी सेल्सफोर्स सुरक्षा का अनुकूलन कैसे करें

Salesforce セキュリティを最適化する方法

Como otimizar a segurança do Salesforce

Cách tối ưu hóa bảo mật cho lực lượng bán hàng của bạn

如何优化您的 Salesforce 安全性

VENDASTA TECHNOLOGIES

vendasta.com

501-1K employees

Since 2008

COMPANY RANKING

VENDASTA TECHNOLOGIES

EVERGREEN INDEX #11836

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Vendasta Technologies WIKI

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VENDASTA TECHNOLOGIES

vendasta.com

501-1K employees

Since 2008

COMPANY RANKING

VENDASTA TECHNOLOGIES

EVERGREEN INDEX #11836

RELATED COMPANIESRANK

STORIES ABOUT VENDASTA TECHNOLOGIES

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