azure-search-openai-javascript

azure-search-openai-javascript

A TypeScript sample app for the Retrieval Augmented Generation pattern running on Azure, using Azure AI Search for retrieval and Azure OpenAI and LangChain large language models (LLMs) to power ChatGPT-style and Q&A experiences.

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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 the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

README:

ChatGPT + Enterprise data with Azure OpenAI and Azure AI Search

Table of Contents

Open in GitHub Codespaces Open in Remote - Containers

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 the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

Retrieval Augmented Generation Architecture

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 Real Estate, and the experience allows its customers to ask support questions about the usage of its products. The sample data includes a set of documents that describe its terms of service, privacy policy and a support guide.

The application is made from multiple components, including:

  • Search service: the backend service that provides the search and retrieval capabilities.
  • Indexer service: the service that indexes the data and creates the search indexes.
  • Web app: the frontend web application that provides the user interface and orchestrates the interaction between the user and the backend services.

App Architecture

Features

  • Chat and Q&A interfaces
  • Explores various options to help users evaluate the trustworthiness of responses with citations, tracking of source content, etc.
  • Shows possible approaches for data preparation, prompt construction, and orchestration of interaction between model (ChatGPT) and retriever (Azure AI Search)
  • Settings directly in the UX to tweak the behavior and experiment with options
  • Optional performance tracing and monitoring with Application Insights

Chat screen

📺 Watch a video overview of the app

Getting started

Azure account prerequisites

IMPORTANT: In order to deploy and run this sample, you'll need:

Azure deployment

Cost estimation

Pricing may vary per region and usage. Exact costs cannot be estimated. You may try the Azure pricing calculator for the resources below.

  • Azure Container Apps: Pay-as-you-go tier. Costs based on vCPU and memory used. Pricing
  • Azure Static Web Apps: Free Tier. Pricing
  • Azure OpenAI: Standard tier, ChatGPT and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. Pricing
  • Azure AI Search: Standard tier, 1 replica, free level of semantic search*. Pricing per hour.Pricing (The pricing may vary or reflect an outdated tier model. Please visit the linked page for more accurate information)
  • Azure Blob Storage: Standard tier with ZRS (Zone-redundant storage). Pricing per storage and read operations. Pricing
  • Azure Monitor: Pay-as-you-go tier. Costs based on data ingested. Pricing

⚠️ To avoid unnecessary costs, remember to take down your app if it's no longer in use, either by deleting the resource group in the Portal or running azd down --purge.

Project setup

There are multiple ways to successfully setup this project.

The easiest way to get started is with GitHub Codespaces that provides preconfigurations to setup all the tools for you. Read more below. Alternatively you can set up your local environment follwing the instructions below.

GitHub Codespaces

You can run this repo virtually by using GitHub Codespaces, which will open a web-based VS Code in your browser:

Open in GitHub Codespaces

VS Code Remote Containers

A similar option to Codespaces is VS Code Remote Containers, that will open the project in your local VS Code instance using the Dev Containers extension:

Open in Remote - Containers

Local environment

Then get the project code:

  1. Create a new folder and switch to it in the terminal
  2. Run azd auth login
  3. Run azd init -t azure-search-openai-javascript
    • note that this command will initialize a git repository and you do not need to clone this repository

Deploying from scratch

Execute the following command, if you don't have any pre-existing Azure services and want to start from a fresh deployment.

  1. Run azd up - This will provision Azure resources and deploy this sample to those resources, including building the search index based on the files found in the ./data folder.
    • You will be prompted to select a location for the majority of resources, except for the OpenAI and Static Web App resources.
    • By default, the OpenAI resource will be deployed to eastus2. You can set a different location with azd env set AZURE_OPENAI_RESOURCE_GROUP_LOCATION {location}. Currently only a short list of locations is accepted. That location list is based on the OpenAI model availability table and may become outdated as availability changes.
    • By default, the Staic Web App resource will be deployed to eastus2. You can set a different location with azd env set AZURE_WEBAPP_LOCATION {location}. Currently only a short list of locations is accepted. Note that Static Web App is a global service, and the location you choose will only affect the managed Functions App which is not used in this sample.
  2. After the application has been successfully deployed you will see a URL printed to the console. Click that URL to interact with the application in your browser.

It will look like the following:

'Output from running azd up'

NOTE: It can take 15+ minutes for the application to be fully deployed.

Deploying with existing resources

If you already have existing Azure resources, you can re-use those by setting azd environment values.

Existing resource group

  1. Run azd env set AZURE_RESOURCE_GROUP {Name of existing resource group}
  2. Run azd env set AZURE_LOCATION {Location of existing resource group}

Existing OpenAI resource

  1. Run azd env set AZURE_OPENAI_SERVICE {Name of existing OpenAI service}
  2. Run azd env set AZURE_OPENAI_RESOURCE_GROUP {Name of existing resource group that OpenAI service is provisioned to}
  3. Run azd env set AZURE_OPENAI_CHATGPT_DEPLOYMENT {Name of existing ChatGPT deployment}. Only needed if your ChatGPT deployment is not the default 'chat'.
  4. Run azd env set AZURE_OPENAI_EMBEDDING_DEPLOYMENT {Name of existing GPT embedding deployment}. Only needed if your embeddings deployment is not the default 'embedding'.

Existing Azure AI Search resource

  1. Run azd env set AZURE_SEARCH_SERVICE {Name of existing Azure AI Search service}
  2. Run azd env set AZURE_SEARCH_SERVICE_RESOURCE_GROUP {Name of existing resource group with ACS service}
  3. If that resource group is in a different location than the one you'll pick for the azd up step, then run azd env set AZURE_SEARCH_SERVICE_LOCATION {Location of existing service}
  4. If the search service's SKU is not standard, then run azd env set AZURE_SEARCH_SERVICE_SKU {Name of SKU}. The free tier won't work as it doesn't support managed identity. (See other possible values)

Other existing Azure resources

You can also use an existing Storage Account. See ./infra/main.parameters.json for list of environment variables to pass to azd env set to configure those existing resources.

Provision remaining resources

Now you can run azd up, following the steps in Deploying from scratch above. That will both provision resources and deploy the code.

Deploying again

If you've only changed the backend/frontend code in the app folder, then you don't need to re-provision the Azure resources. You can just run:

azd deploy

If you've changed the infrastructure files (infra folder or azure.yaml), then you'll need to re-provision the Azure resources. You can do that by running:

azd up

Sharing environments

To give someone else access to a completely deployed and existing environment, either you or they can follow these steps:

  1. Install the Azure Developer CLI
  2. Run azd init -t azure-search-openai-javascript or clone this repository.
  3. Run azd env refresh -e {environment name} They will need the azd environment name, subscription ID, and location to run this command. You can find those values in your .azure/{env name}/.env file. This will populate their azd environment's .env file with all the settings needed to run the app locally.
  4. Set the environment variable AZURE_PRINCIPAL_ID either in that .env file or in the active shell to their Azure ID, which they can get with az ad signed-in-user show.
  5. Run ./scripts/roles.ps1 or ./scripts/roles.sh to assign all of the necessary roles to the user. If they do not have the necessary permission to create roles in the subscription, then you may need to run this script for them. Once the script runs, they should be able to run the app locally.

Clean up

To clean up all the resources created by this sample:

  1. Run azd down --purge
  2. When asked if you are sure you want to continue, enter y
  3. When asked if you want to permanently delete the resources, enter y

The resource group and all the resources will be deleted.

Enabling optional features

Enabling authentication

By default, the deployed Azure web app will have no authentication or access restrictions enabled, meaning anyone with routable network access to the web app can chat with your indexed data. You can require authentication to your Azure Entra ID by following the Add app authentication tutorial and set it up against the deployed web app.

To then limit access to a specific set of users or groups, you can follow the steps from Restrict your Azure Entra app to a set of users by changing "Assignment Required?" option under the Enterprise Application, and then assigning users/groups access. Users not granted explicit access will receive the error message -AADSTS50105: Your administrator has configured the application <app_name> to block users unless they are specifically granted ('assigned') access to the application.-

Additional security considerations

We recommend deploying additional security mechanisms. When applicable, consider setting up a VNet or setting up a Proxy Policy.

Enabling CORS for an alternate frontend

By default, the deployed search API will only allow requests from the same origin as the deployed web app origin. To enable CORS for a frontend hosted on a different origin, run:

  1. Run azd env set ALLOWED_ORIGIN https://<your-domain.com>
  2. Run azd up

Running locally

You can only run locally after having successfully run the azd up command.

  1. Run azd auth login
  2. Run azd env get-values > .env to get the environment variables for the app
  3. Run az login
  4. Run npm start or run the "VS Code Task: Start App" to start the project locally.

Using the app

  • In Azure: navigate to the Azure Static Web App deployed by azd. The URL is printed out when azd completes (as "Endpoint"), or you can find it in the Azure portal.
  • Running locally: navigate to http://127.0.0.1:5173

Once in the web app:

  • Try different topics in chat or Q&A context. For chat, try follow up questions, clarifications, ask to simplify or elaborate on answer, etc.
  • Explore citations and sources
  • Click on "settings" to try different options, tweak prompts, etc.

Guidance

Using a different backend

The Search API service implements the HTTP protocol for AI chat apps. It can be swapped with any service that implements the same protocol, like the Python backend client in this repository instead of the Node.js implementation featured in this repo.

To do so, follow these steps:

  1. Deploy this repository, following the steps above.
  2. Get the frontend URL:
  • If you want to use the deployed web app, run azd env get-values | grep WEBAPP_URI to get the URL.
  • If you want to use the local web app, use http://localhost:5173.
  • If you want to use the Codespaces local web app, use https://<your_codespace_base_url>-5173.app.github.dev.
  1. Open the alternative backend repository your want to use, for example: https://github.com/Azure-Samples/azure-search-openai-demo
  2. Set the frontend URL as an allowed origin with azd env set ALLOWED_ORIGIN <your_frontend_url>.
  3. Follow the steps to deploy the Python backend.
  4. Once the Python backend is fully deployed, get the backend URL with azd env get-values | grep BACKEND_URI.
  5. Set the backend URL in this repo, running azd env set BACKEND_URI <your_backend_url>.
  6. Depending on whether you want to use the deployed web app or the local web app:

  • If you want to use the deployed web app, run azd up to redeploy.

  • If you want to use the local web app on your machine or in Codespaces, run:

    # Export the environment variable.
# The syntax may be different depending on your shell or if you're using Windows.
export BACKEND_URI=<your_backend_url>

# Start the app
npm start --workspace=webapp

Enabling Authentication

This sample is composed by two applications: a backend service and API, deployed to Azure Container Apps, and a frontend application, deployed to Azure Static Web Apps. By default, the deployed Azure Container App will have no authentication or access restrictions enabled, meaning anyone with routable network access to the container app can chat with your indexed data. You can require authentication to your Azure Entra ID by following the Add container app authentication tutorial and set it up against the deployed Azure Container App.

To limit access to a specific set of users or groups, you can follow the steps from Restrict your Azure Entra app to a set of users by changing "Assignment Required?" option under the Enterprise Application, and then assigning users/groups access. Users not granted explicit access will receive the error message -AADSTS50105: Your administrator has configured the application <app_name> to block users unless they are specifically granted ('assigned') access to the application.-

Productionizing

This sample is designed to be a starting point for your own production application, but you should do a thorough review of the security and performance before deploying to production. Here are some things to consider:

  • OpenAI Capacity: The default TPM (tokens per minute) is set to 30K. That is equivalent to approximately 30 conversations per minute (assuming 1K per user message/response). You can increase the capacity by changing the chatGptDeploymentCapacity and embeddingDeploymentCapacity parameters in infra/main.bicep to your account's maximum capacity. You can also view the Quotas tab in Azure OpenAI studio to understand how much capacity you have.
  • Azure Storage: The default storage account uses the Standard_LRS SKU. To improve your resiliency, we recommend using Standard_ZRS for production deployments, which you can specify using the sku property under the storage module in infra/main.bicep.
  • Azure AI Search: The default search service uses the Standard SKU with the free semantic search option, which gives you 1000 free queries a month. Assuming your app will experience more than 1000 questions, you should either change semanticSearch to "standard" or disable semantic search entirely in the request options. If you see errors about search service capacity being exceeded, you may find it helpful to increase the number of replicas by changing replicaCount in infra/core/search/search-services.bicep or manually scaling it from the Azure Portal.
  • Azure Container Apps: The default container app setup uses 1 vCPU core and 2 GB RAM per container, with autoscaling enabled. The minimum number of replicas is set to 1, and the maximum to 10. You can change vCPU and RAM capacity in the template, and define your own auto-scaling rules based on load. For more details, read Set scaling rules in Azure Container Apps.
  • Authentication: By default, the deployed app is publicly accessible. We recommend restricting access to authenticated users. See Enabling authentication above for how to enable authentication.
  • Networking: We recommend deploying inside a Virtual Network. If the app is only for internal enterprise use, use a private DNS zone. Also consider using Azure API Management (APIM) for firewalls and other forms of protection. For more details, read Azure OpenAI Landing Zone reference architecture.

Resources

FAQ

Why do we need to break up the documents into chunks when Azure AI Search supports searching large documents?

Chunking allows us to limit the amount of information we send to OpenAI due to token limits. By breaking up the content, it allows us to easily find potential chunks of text that we can inject into OpenAI. The method of chunking we use leverages a sliding window of text such that sentences that end one chunk will start the next. This allows us to reduce the chance of losing the context of the text.

How can we upload additional documents without redeploying everything?

To upload more documents, put them in the data/ folder and run ./scripts/index-data.sh or ./scripts/index-data.ps1.

How does this sample compare to other Chat with Your Data samples?

Another popular repository for this use case is here: https://github.com/Microsoft/sample-app-aoai-chatGPT/

That repository is designed for use by customers using Azure OpenAI studio and Azure Portal for setup. It also includes azd support for folks who want to deploy it completely from scratch.

The primary differences:

  • This repository includes multiple RAG (retrieval-augmented generation) approaches that chain the results of multiple API calls (to Azure OpenAI and ACS) together in different ways. The other repository uses only the built-in data sources option for the ChatCompletions API, which uses a RAG approach on the specified ACS index. That should work for most uses, but if you needed more flexibility, this sample may be a better option.
  • This repository is also a bit more experimental in other ways, since it's not tied to the Azure OpenAI Studio like the other repository.

Feature comparison:

Feature azure-search-openai-javascript sample-app-aoai-chatGPT
RAG approach Multiple approaches Only via ChatCompletion API data_sources
Vector support ✅ Yes ✅ Yes
Data ingestion ✅ Yes (MD) ✅ Yes (PDF, TXT, MD, HTML)
Persistent chat history ❌ No (browser tab only) ✅ Yes, in CosmosDB

Technology comparison:

Tech azure-search-openai-javascript sample-app-aoai-chatGPT
Frontend React/Lit React
Backend Node.js (Fastify) Python (Flask)
Vector DB Azure AI Search Azure AI Search
Deployment Azure Developer CLI (azd) Azure Portal, az, azd
How do you use GPT-4 with this sample?

Run these commands:

azd env set AZURE_OPENAI_CHATGPT_MODEL gpt-4

You may also need to adjust the capacity in infra/main.bicep file, depending on how much TPM your account is allowed.

What is the difference between the Chat and Ask tabs?

The chat tab uses the approach programmed in chat-read-retrieve-read.ts. The ask tab uses the approach programmed in ask-retrieve-then-read.ts. There is also another one /ask approach available, [using an agent](https://github.com/Azure-Samples/azure-search-openai-javascript/blob/main/packages/search/src/lib/approaches/ask-read-retrieve-read.ts.

What does the `azd up` command do?

The azd up command comes from the Azure Developer CLI, and takes care of both provisioning the Azure resources and deploying code to the selected Azure hosts.

The azd up command uses the azure.yaml file combined with the infrastructure-as-code .bicep files in the infra/ folder. The azure.yaml file for this project declares several "hooks" for the prepackage step and postprovision steps. The up command first runs the prepackage hook which installs Node dependencies and builds the React.JS-based JavaScript files. It then packages all the code (both frontend and backend services) into a zip file which it will deploy later.

Next, it provisions the resources based on main.bicep and main.parameters.json. At that point, since there is no default value for the OpenAI resource location, it asks you to pick a location from a short list of available regions. Then it will send requests to Azure to provision all the required resources. With everything provisioned, it runs the postprovision hook to process the local data and add it to an Azure AI Search index.

Finally, it looks at azure.yaml to determine the Azure host (Container Apps and Static Web Apps, in this case) and uploads the zip to Azure App Service. The azd up command is now complete, but it may take a few minutes for the app to be fully available and working after the initial deploy.

Related commands are azd provision for just provisioning (if infra files change) and azd deploy for just deploying updated app code.

Troubleshooting

Here are the most common failure scenarios and solutions:

  1. The subscription (AZURE_SUBSCRIPTION_ID) doesn't have access to the Azure OpenAI service. Please ensure AZURE_SUBSCRIPTION_ID matches the ID specified in the OpenAI access request process.

  2. You're attempting to create resources in regions not enabled for Azure OpenAI (e.g. East US 2 instead of East US), or where the model you're trying to use isn't enabled. See this matrix of model availability.

  3. You've exceeded a quota, most often number of resources per region. See this article on quotas and limits.

  4. You're getting "same resource name not allowed" conflicts. That's likely because you've run the sample multiple times and deleted the resources you've been creating each time, but are forgetting to purge them. Azure keeps resources for 48 hours unless you purge from soft delete. See this article on purging resources.

  5. After running azd up and visiting the website, you see a '404 Not Found' in the browser. Wait 10 minutes and try again, as it might be still starting up. Then try running azd deploy and wait again. If you still encounter errors with the deployed app, consult these tips for debugging App Service app deployments and file an issue if the error logs don't help you resolve the issue.

  6. You're getting an error 401 Principal does not have access to API/Operation while running the project locally or trying to deploy. That's likely because your environment variables include AZURE_TENANT_ID, AZURE_CLIENT_ID and AZURE_CLIENT_SECRET. You should either grant permissions to the related Service Principal or remove these variables from your environment to ensure normal access. For more details, please refer to Azure identity SDK.

Note

Note: The documents used in this demo contain information generated using a language model (Azure OpenAI Service). The information contained in these documents is only for demonstration purposes and does not reflect the opinions or beliefs of Microsoft. Microsoft makes no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the information contained in this document. All rights reserved to Microsoft.

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ChatGPT Telegram Bot is a Telegram bot that provides a smooth AI experience. It supports both Azure OpenAI and native OpenAI, and offers real-time (streaming) response to AI, with a faster and smoother experience. The bot also has 15 preset bot identities that can be quickly switched, and supports custom bot identities to meet personalized needs. Additionally, it supports clearing the contents of the chat with a single click, and restarting the conversation at any time. The bot also supports native Telegram bot button support, making it easy and intuitive to implement required functions. User level division is also supported, with different levels enjoying different single session token numbers, context numbers, and session frequencies. The bot supports English and Chinese on UI, and is containerized for easy deployment.

github
: 476
supersonic Screenshot
supersonic

SuperSonic is a next-generation BI platform that integrates Chat BI (powered by LLM) and Headless BI (powered by semantic layer) paradigms. This integration ensures that Chat BI has access to the same curated and governed semantic data models as traditional BI. Furthermore, the implementation of both paradigms benefits from the integration: * Chat BI's Text2SQL gets augmented with context-retrieval from semantic models. * Headless BI's query interface gets extended with natural language API. SuperSonic provides a Chat BI interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of metric/dimension/tag, along with their meaning and relationships) through a Headless BI interface. Meanwhile, SuperSonic is designed to be extensible and composable, allowing custom implementations to be added and configured with Java SPI. The integration of Chat BI and Headless BI has the potential to enhance the Text2SQL generation in two dimensions: 1. Incorporate data semantics (such as business terms, column values, etc.) into the prompt, enabling LLM to better understand the semantics and reduce hallucination. 2. Offload the generation of advanced SQL syntax (such as join, formula, etc.) from LLM to the semantic layer to reduce complexity. With these ideas in mind, we develop SuperSonic as a practical reference implementation and use it to power our real-world products. Additionally, to facilitate further development we decide to open source SuperSonic as an extensible framework.

github
: 2.2k
chat-ollama Screenshot
chat-ollama

ChatOllama is an open-source chatbot based on LLMs (Large Language Models). It supports a wide range of language models, including Ollama served models, OpenAI, Azure OpenAI, and Anthropic. ChatOllama supports multiple types of chat, including free chat with LLMs and chat with LLMs based on a knowledge base. Key features of ChatOllama include Ollama models management, knowledge bases management, chat, and commercial LLMs API keys management.

github
: 2.6k
ChatIDE Screenshot
ChatIDE

ChatIDE is an AI assistant that integrates with your IDE, allowing you to converse with OpenAI's ChatGPT or Anthropic's Claude within your development environment. It provides a seamless way to access AI-powered assistance while coding, enabling you to get real-time help, generate code snippets, debug errors, and brainstorm ideas without leaving your IDE.

github
: 214
azure-search-openai-javascript Screenshot
azure-search-openai-javascript

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 the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

github
: 228
xiaogpt Screenshot
xiaogpt

xiaogpt is a tool that allows you to play ChatGPT and other LLMs with Xiaomi AI Speaker. It supports ChatGPT, New Bing, ChatGLM, Gemini, Doubao, and Tongyi Qianwen. You can use it to ask questions, get answers, and have conversations with AI assistants. xiaogpt is easy to use and can be set up in a few minutes. It is a great way to experience the power of AI and have fun with your Xiaomi AI Speaker.

github
: 6.2k
googlegpt Screenshot
googlegpt

GoogleGPT is a browser extension that brings the power of ChatGPT to Google Search. With GoogleGPT, you can ask ChatGPT questions and get answers directly in your search results. You can also use GoogleGPT to generate text, translate languages, and more. GoogleGPT is compatible with all major browsers, including Chrome, Firefox, Edge, and Safari.

github
: 122

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ChatFAQ

ChatFAQ is an open-source comprehensive platform for creating a wide variety of chatbots: generic ones, business-trained, or even capable of redirecting requests to human operators. It includes a specialized NLP/NLG engine based on a RAG architecture and customized chat widgets, ensuring a tailored experience for users and avoiding vendor lock-in.

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agentcloud Screenshot
agentcloud

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

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anything-llm Screenshot
anything-llm

AnythingLLM is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.

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ai-guide Screenshot
ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

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Magick Screenshot
Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

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glide Screenshot
glide

Glide is a cloud-native LLM gateway that provides a unified REST API for accessing various large language models (LLMs) from different providers. It handles LLMOps tasks such as model failover, caching, key management, and more, making it easy to integrate LLMs into applications. Glide supports popular LLM providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock (Titan), Cohere, Google Gemini, OctoML, and Ollama. It offers high availability, performance, and observability, and provides SDKs for Python and NodeJS to simplify integration.

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chatbot-ui Screenshot
chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

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onnxruntime-genai Screenshot
onnxruntime-genai

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

github
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