Best AI tools for< Cache Call Responses >

DataGPT is a conversational AI data analyst that provides instant analysis and answers to any data-related question in everyday language. It connects to any data source and automatically defines and suggests the most relevant metrics and dimensions. DataGPT's core analytics engine carries out intricate analysis against all data, checking every segment, identifying anomalies, detecting outliers, diving into funnel analytics, or conducting robust comparative analysis to reveal accurate results. The AI-powered onboarding agent guides users through the setup process, and the Lightning Cache boosts query speeds 100x over current data warehouses. The Data Navigator allows users to freely explore any part of their data with just a few clicks. DataGPT empowers decision-makers by replacing specialized dashboards with an 'ask me anything' interface, enabling them to access essential insights on demand.

imgix is an end-to-end visual media solution that enables users to create, transform, and optimize captivating images and videos for an unparalleled visual experience. It simplifies the complex visual media technology, improves web performance, and delivers responsive design. Trusted by innovative companies worldwide, imgix offers features such as easy cloud storage connection, intelligent compression, fast loading with a globally distributed CDN, over 150 image operations, video streaming, asset management, intuitive analytics, and powerful SDKs & tools.

Bosquet is a tool designed for LLMOps in large language model-based applications. It simplifies building AI applications by managing LLM and tool services, integrating with Selmer templating library for prompt templating, enabling prompt chaining and composition with Pathom graph processing, defining agents and tools for external API interactions, handling LLM memory, and providing features like call response caching. The tool aims to streamline the development process for AI applications that require complex prompt templates, memory management, and interaction with external systems.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features like Virtual API System, Solvable Queries, and Stable Evaluation System. The benchmark ensures consistency through a caching system and API simulators, filters queries based on solvability using LLMs, and evaluates model performance using GPT-4 with metrics like Solvable Pass Rate and Solvable Win Rate.

Call Center AI is an AI-powered call center solution that leverages Azure and OpenAI GPT. It is a proof of concept demonstrating the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI to build an automated call center solution. The project showcases features like accessing claims on a public website, customer conversation history, language change during conversation, bot interaction via phone number, multiple voice tones, lexicon understanding, todo list creation, customizable prompts, content filtering, GPT-4 Turbo for customer requests, specific data schema for claims, documentation database access, SMS report sending, conversation resumption, and more. The system architecture includes components like RAG AI Search, SMS gateway, call gateway, moderation, Cosmos DB, event broker, GPT-4 Turbo, Redis cache, translation service, and more. The tool can be deployed remotely using GitHub Actions and locally with prerequisites like Azure environment setup, configuration file creation, and resource hosting. Advanced usage includes custom training data with AI Search, prompt customization, language customization, moderation level customization, claim data schema customization, OpenAI compatible model usage for the LLM, and Twilio integration for SMS.

A lightweight, functional, and composable framework for building AI agents that work together to solve complex tasks. Built with TypeScript and designed to be serverless-ready. Fabrice embraces functional programming principles, remains stateless, and stays focused on composability. It provides core concepts like easy teamwork creation, infrastructure-agnosticism, statelessness, and includes all tools and features needed to build AI teams. Agents are specialized workers with specific roles and capabilities, able to call tools and complete tasks. Workflows define how agents collaborate to achieve a goal, with workflow states representing the current state of the workflow. Providers handle requests to the LLM and responses. Tools extend agent capabilities by providing concrete actions they can perform. Execution involves running the workflow to completion, with options for custom execution and BDD testing.

Bolna is an open-source platform for building voice-driven conversational applications using large language models (LLMs). It provides a comprehensive set of tools and integrations to handle various aspects of voice-based interactions, including telephony, transcription, LLM-based conversation handling, and text-to-speech synthesis. Bolna simplifies the process of creating voice agents that can perform tasks such as initiating phone calls, transcribing conversations, generating LLM-powered responses, and synthesizing speech. It supports multiple providers for each component, allowing users to customize their setup based on their specific needs. Bolna is designed to be easy to use, with a straightforward local setup process and well-documented APIs. It is also extensible, enabling users to integrate with other telephony providers or add custom functionality.

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.

LiteLLM is a tool that allows you to call all LLM APIs using the OpenAI format. This includes Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, and more. LiteLLM manages translating inputs to provider's `completion`, `embedding`, and `image_generation` endpoints, providing consistent output, and retry/fallback logic across multiple deployments. It also supports setting budgets and rate limits per project, api key, and model.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

GPTel is a simple Large Language Model chat client for Emacs, with support for multiple models and backends. It's async and fast, streams responses, and interacts with LLMs from anywhere in Emacs. LLM responses are in Markdown or Org markup. Supports conversations and multiple independent sessions. Chats can be saved as regular Markdown/Org/Text files and resumed later. You can go back and edit your previous prompts or LLM responses when continuing a conversation. These will be fed back to the model. Don't like gptel's workflow? Use it to create your own for any supported model/backend with a simple API.

Empower Functions is a family of large language models (LLMs) that provide GPT-4 level capabilities for real-world 'tool using' use cases. These models offer compatibility support to be used as drop-in replacements, enabling interactions with external APIs by recognizing when a function needs to be called and generating JSON containing necessary arguments based on user inputs. This capability is crucial for building conversational agents and applications that convert natural language into API calls, facilitating tasks such as weather inquiries, data extraction, and interactions with knowledge bases. The models can handle multi-turn conversations, choose between tools or standard dialogue, ask for clarification on missing parameters, integrate responses with tool outputs in a streaming fashion, and efficiently execute multiple functions either in parallel or sequentially with dependencies.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

Call Center AI is an AI-powered call center solution leveraging Azure and OpenAI GPT. It allows for AI agent-initiated phone calls or direct calls to the bot from a configured phone number. The bot is customizable for various industries like insurance, IT support, and customer service, with features such as accessing claim information, conversation history, language change, SMS sending, and more. The project is a proof of concept showcasing the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI for an automated call center solution.

Toolio is an OpenAI-like HTTP server API implementation that supports structured LLM response generation, making it conform to a JSON schema. It is useful for reliable tool calling and agentic workflows based on schema-driven output. Toolio is based on the MLX framework for Apple Silicon, specifically M1/M2/M3/M4 Macs. It allows users to host MLX-format LLMs for structured output queries and provides a command line client for easier usage of tools. The tool also supports multiple tool calls and the creation of custom tools for specific tasks.

Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and follows a process of embedding docs and queries, searching for top passages, creating summaries, scoring and selecting relevant summaries, putting summaries into prompt, and generating answers. Users can customize prompts and use various models for embeddings and LLMs. The tool can be used asynchronously and supports adding documents from paths, files, or URLs.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

The AWS Lex Web UI is a sample Amazon Lex web interface that provides a chatbot UI component for integration into websites. It supports voice and text interactions, Lex response cards, and programmable configuration using JavaScript. The interface can be used as a full-page chatbot UI or embedded as a widget. It offers mobile-ready responsive UI, seamless voice-text switching, and interactive messaging support. The project includes CloudFormation templates for easy deployment and customization. Users can modify configurations, integrate the UI into existing sites, and deploy using various methods like CloudFormation, pre-built libraries, or npm installation.

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