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Slite is an AI-powered knowledge base application that helps users create, organize, and search for information efficiently. It offers features such as live editor, AI-driven search, templates, integrations with existing tools, and multi-platform availability. Slite aims to streamline documentation processes, facilitate team collaboration, and provide personalized onboarding and support services. The application is trusted by over 200,000 leading companies for its user-friendly interface, quick setup, and continuous updates.

Tettra is an AI-powered knowledge management system designed to help organizations curate company information into an internal knowledge base, instantly answer team questions with AI in Slack or the app, save reusable answers with automation, and streamline onboarding processes. It offers features like internal Q&A, knowledge management system, Slack integration, CSAT notifications, and integrations with SupportMan. Tettra is trusted by various teams to organize scattered knowledge and improve efficiency by eliminating bottlenecks and saving time from repetitive questions.

Tettra is an AI-powered knowledge management system designed to help companies curate internal information into a knowledge base, instantly answer team questions using AI in Slack or the app, save reusable answers with automation, and facilitate knowledge verification and approval. It streamlines knowledge sharing, reduces repetitive questions, and enhances team productivity.

Competely is an AI-powered competitive analysis tool that provides comprehensive insights into competitors across various industries. It saves users hours of manual research by generating detailed side-by-side comparisons, including information on marketing, product features, pricing, audience, customer sentiment, company info, and SWOT analysis. The tool is suitable for founders, executives, marketers, product managers, agencies, and consultants, helping them make data-driven decisions and strategies to outsmart the competition. Competely offers cost-effective plans starting from $9 per month, making it accessible to individuals, freelancers, startups, agencies, and businesses of all sizes.

Threado is an AI application designed for customer service, offering AI agents trained on internal knowledge to provide instant, automated support to customers and internal teams. It integrates effortlessly into daily tools like Slack and Microsoft Teams, allowing users to access company information instantly. Threado AI helps resolve customer queries, automate chat support, personalize customer engagement, accelerate sales processes, and transform how customer-facing teams find information and get work done.

The website is an AI tool called Price Prediction AI Robo Advisor, developed by Regulus Technologies. It provides cryptocurrency trading predictions and news updates. Users can access the platform in multiple languages and find information about various cryptocurrencies. The tool offers price predictions for popular cryptocurrencies like Bitcoin, Ethereum, and Ripple, along with customer support and company details.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

THE POLICY CHATBOT is an AI-powered tool that transforms Standard Operating Procedures into a dynamic chatbot, providing instant answers and guidance to users within a company. It allows authorized employees to access and interact with company policies in real-time, enhancing efficiency and accuracy in policy-related queries. The chatbot leverages AI technology to extract information from uploaded PDF SOPs, offering a seamless user experience and freeing up employees to focus on more critical tasks.

mjslackbot.com is a website that provides resources and information related to mjslackbot. Users can find valuable content and details about mjslackbot on this platform. The website aims to offer a comprehensive source of information for individuals interested in mjslackbot and its functionalities.

SimplyPut is an AI-powered data analytics platform that allows users to ask questions about their data in natural language and get instant answers. It is designed to be intuitive and easy to use, and it can be integrated with a variety of data sources. SimplyPut is used by businesses of all sizes to improve their data literacy and make better decisions.

Paka AI is a cloud-based phone system that uses artificial intelligence (AI) to automate customer interactions. It offers a range of services, including custom AI bot creation, interactive voice response (IVR) system, and message center. Paka AI's AI-powered bots can handle common queries round the clock, so businesses can focus on running their operations. The company's technology is designed to offer a seamless and interactive customer experience.

The website offers an AI tool called KnowledgeBot that helps businesses save time by providing expert-level responses to repetitive questions. It uses AI to quote directly from experts and content, auto-escalates to experts when unsure, and learns reusable information from replies. KnowledgeBot can resolve help requests, find collateral quickly, discover popular queries, and absorb informal chats to capture insights. It aims to streamline sales enablement, customer support, and knowledge management processes, ultimately saving time and improving efficiency for businesses.

AltIndex is an AI-powered investment analysis platform that provides unique AI stock picks, stock alerts, and alternative insights to help users make better investment decisions. The platform goes beyond traditional financial data by integrating various alternative data points such as job postings, website traffic, customer satisfaction, app downloads, and social media trends. AltIndex offers impactful insights and alerts, cutting-edge solutions to stay informed about companies in your portfolio, and advanced algorithms for real-time investment decision-making.

The Keyword is a blog by Google that covers the latest news and stories about Google products and technology. The blog is written by Google employees and covers a wide range of topics, including product updates, company news, and technology trends. The Keyword is a great resource for anyone who wants to stay up-to-date on the latest from Google.

MarketGPT is an artificial intelligence model trained to predict stock movements based on news items. It evaluates the news and decides how the company stock is going to be affected by it. Users can access the model through the MarketGPT website or mobile app to get stock predictions and picks. The model's performance can be viewed for different time frames such as 1 week, 1 month, and 1 year. However, users are advised that investing in stocks and derivatives carries a risk of financial loss, and past performance is not a guarantee of future performance. MarketGPT is designed to assist users in making informed decisions in the stock market.

AskDocs is an AI-powered document assistant designed to help users read faster and create better work content. It offers cross-document analysis, quick answers linked to documents, one-click summaries of key concepts, and the ability to understand confusing information. With a focus on enhancing productivity, AskDocs is trusted by students, knowledge workers, and small businesses to streamline research, meeting notes, emails, and more. The tool supports various document types and provides instant answers directly linked to sources within the uploaded documents.

The Keyword is a website that provides Google product and technology news and stories. It covers a wide range of topics including product updates, company news, technology, AI, and more. The site offers insights into Google's latest developments, initiatives, and events, keeping users informed about the tech giant's advancements and innovations.

Dashworks is an AI-powered knowledge assistant that provides accurate answers to internal employee questions in seconds. It unifies all your company knowledge bases to provide comprehensive and secure search capabilities. With Dashworks, you can reduce the time spent responding to repetitive questions, empower every team member with instant access to collective knowledge, and make quick and informed decisions across your organization.

Letter AI is a revenue acceleration platform powered by Generative AI, designed to empower sales teams to find information faster and support revenue growth. The platform offers AI-powered training and coaching, content creation and management, real-time assistance from an AI co-pilot, and tools for deal pursuit automation. Letter AI combines generative AI technology with company-specific data to deliver a unified revenue enablement platform, enhancing user experience and driving cost savings. The platform ensures security and privacy by adhering to industry-leading standards and responsible data practices.

Harriet is an AI-powered employee experience platform designed to engage teams and provide seamless support. It offers instant AI support, HR ticketing system, knowledge management, automated employee lifecycle, people calendar planner, analytics, and insights. Harriet aims to make employees' lives easier by providing quick responses, seamless automation, and accurate information through various communication channels like Slack, Teams, GChat, and SMS. The platform is integrated with HR systems, trained on company policies, and offers 24/7 support. Harriet helps organizations improve productivity, employee satisfaction, and operational efficiency.

Phidata is a framework for building AI Assistants with memory, knowledge, and tools. It enables LLMs to have long-term conversations by storing chat history in a database, provides them with business context by storing information in a vector database, and enables them to take actions like pulling data from an API, sending emails, or querying a database. Memory and knowledge make LLMs smarter, while tools make them autonomous.

The Linkedin API for Python allows users to programmatically search profiles, send messages, and find jobs using a regular Linkedin user account. It does not require 'official' API access, just a valid Linkedin account. However, it is important to note that this library is not officially supported by LinkedIn and using it may violate LinkedIn's Terms of Service. Users can authenticate using any Linkedin account credentials and access features like getting profiles, profile contact info, and connections. The library also provides commercial alternatives for extracting data, scraping public profiles, and accessing a full LinkedIn API. It is not endorsed or supported by LinkedIn and is intended for educational purposes and personal use only.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in. Ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

BraveGPT is a userscript that brings the power of ChatGPT to Brave Search. It allows users to engage with a conversational AI assistant directly within their search results, providing instant and personalized responses to their queries. BraveGPT is powered by GPT-4, the latest and most advanced language model from OpenAI, ensuring accurate and comprehensive answers. With BraveGPT, users can ask questions, get summaries, generate creative content, and more, all without leaving the Brave Search interface. The tool is easy to install and use, making it accessible to users of all levels. BraveGPT is a valuable addition to the Brave Search experience, enhancing its capabilities and providing users with a more efficient and informative search experience.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

Cyber is a superintelligence protocol that aims to create a decentralized and censorship-resistant internet. It uses a novel consensus mechanism called CometBFT and a knowledge graph to store and process information. Cyber is designed to be scalable, secure, and efficient, and it has the potential to revolutionize the way we interact with the internet.

ChatDev is a virtual software company powered by intelligent agents like CEO, CPO, CTO, programmer, reviewer, tester, and art designer. These agents collaborate to revolutionize the digital world through programming. The platform offers an easy-to-use, highly customizable, and extendable framework based on large language models, ideal for studying collective intelligence. ChatDev introduces innovative methods like Iterative Experience Refinement and Experiential Co-Learning to enhance software development efficiency. It supports features like incremental development, Docker integration, Git mode, and Human-Agent-Interaction mode. Users can customize ChatChain, Phase, and Role settings, and share their software creations easily. The project is open-source under the Apache 2.0 License and utilizes data licensed under CC BY-NC 4.0.

Embedchain is an Open Source Framework for personalizing LLM responses. It simplifies the creation and deployment of personalized AI applications by efficiently managing unstructured data, generating relevant embeddings, and storing them in a vector database. With diverse APIs, users can extract contextual information, find precise answers, and engage in interactive chat conversations tailored to their data. The framework follows the design principle of being 'Conventional but Configurable' to cater to both software engineers and machine learning engineers.

AI-powered call center solution with Azure and OpenAI GPT. The bot can answer calls, understand the customer's request, and provide relevant information or assistance. It can also create a todo list of tasks to complete the claim, and send a report after the call. The bot is customizable, and can be used in multiple languages.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

This repository is a sample chatbot using the Anthropic company's LLM Claude, one of the foundational models provided by Amazon Bedrock for generative AI. It allows users to have basic conversations with the chatbot, personalize it with their own instructions and external knowledge, and analyze usage for each user/bot on the administrator dashboard. The chatbot supports various languages, including English, Japanese, Korean, Chinese, French, German, and Spanish. Deployment is straightforward and can be done via the command line or by using AWS CDK. The architecture is built on AWS managed services, eliminating the need for infrastructure management and ensuring scalability, reliability, and security.

Cohere's Build-Your-Own-Connector framework allows integration of Cohere's Command LLM via the Chat API endpoint to any datastore/software holding text information with a search endpoint. Enables user queries grounded in proprietary information. Use-cases include question/answering, knowledge working, comms summary, and research. Repository provides code for popular datastores and a template connector. Requires Python 3.11+ and Poetry. Connectors can be built and deployed using Docker. Environment variables set authorization values. Pre-commits for linting. Connectors tailored to integrate with Cohere's Chat API for creating chatbots. Connectors return documents as JSON objects for Cohere's API to generate answers with citations.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

This repository provides a list of the most starred and forked repositories on GitHub. It is updated automatically and includes information such as the project name, number of stars, number of forks, language, number of open issues, description, and last commit date. The repository is divided into two sections: LLM and chatGPT. The LLM section includes repositories related to large language models, while the chatGPT section includes repositories related to the chatGPT chatbot.

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"LikeImFive" is a specialized GPT designed to provide simplified explanations and clear answers to a wide range of everyday questions. It is tailored for those seeking to understand complex topics in simple, straightforward terms. No Fluff.

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