Opik Integrations

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Claude is an artificial intelligence large language model that can process and generate human-like text. Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability.

OpenAI o1 represents a new series of AI models designed by OpenAI, focusing on enhanced reasoning capabilities. These models, including o1-preview and o1-mini, are trained using a novel reinforcement learning approach to spend more time "thinking" through problems before providing answers. This approach allows o1 to excel in complex problem-solving tasks in areas like coding, mathematics, and science, outperforming previous models like GPT-4o in certain benchmarks. The o1 series aims to tackle challenges that require deeper thought processes, marking a significant step towards AI systems that can reason more like humans, although it's still in the preview stage with ongoing improvements and evaluations.

Hugging Face is a leading platform for AI and machine learning, offering a vast hub for models, datasets, and tools for natural language processing (NLP) and beyond. The platform supports a wide range of applications, from text, image, and audio to 3D data analysis. Hugging Face fosters collaboration among researchers, developers, and companies by providing open-source tools like Transformers, Diffusers, and Tokenizers. It enables users to build, share, and access pre-trained models, accelerating AI development for a variety of industries.

Declarative machine learning systems provide the best of flexibility and simplicity to enable the fastest-way to operationalize state-of-the-art models. Users focus on specifying the “what”, and the system figures out the “how”. Start with smart defaults, but iterate on parameters as much as you’d like down to the level of code. Our team pioneered declarative machine learning systems in industry, with Ludwig at Uber and Overton at Apple. Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage. Train state-of-the-art deep learning models without the pain of managing infrastructure. Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion. With a declarative approach, finally train and deploy models as quickly as you want.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Flowise is an open-source, low-code platform that enables developers to create customized Large Language Model (LLM) applications through a user-friendly drag-and-drop interface. It supports integration with various LLMs, including LangChain and LlamaIndex, and offers over 100 integrations to facilitate the development of AI agents and orchestration flows. Flowise provides APIs, SDKs, and embedded widgets for seamless incorporation into existing systems, and is platform-agnostic, allowing deployment in air-gapped environments with local LLMs and vector databases.

Ragas is an open-source framework designed to test and evaluate Large Language Model (LLM) applications. It offers automatic metrics to assess performance and robustness, synthetic test data generation tailored to specific requirements, and workflows to ensure quality during development and production monitoring. Ragas integrates seamlessly with existing stacks, providing insights to enhance LLM applications. The platform is maintained by a team of passionate individuals leveraging cutting-edge research and pragmatic engineering practices to empower visionaries redefining LLM possibilities. Synthetically generate high-quality and diverse evaluation data customized for your requirements. Evaluate and ensure the quality of your LLM application in production. Use insights to improve your application. Automatic metrics that helps you understand the performance and robustness of your LLM application.

DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.

​LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple providers. It ensures robust observability by generating unique call IDs for each request, aiding in precise tracking and logging across systems. Developers can leverage pre-defined callbacks to log data using various tools. For enterprise users, LiteLLM offers advanced features like Single Sign-On (SSO), user management, and professional support through dedicated channels like Discord and Slack.

pytest helps you write better programs. The pytest framework makes it easy to write small tests, yet scales to support complex functional testing for applications and libraries. Due to pytest’s detailed assertion introspection, only plain assert statements are used. Detailed info on failing assert statements. Auto-discovery of test modules and functions. Modular fixtures for managing small or parametrized long-lived test resources. Can run unittest (including trial) and nose test suites out of the box. Supports Python 3.6+ and PyPy 3. Rich plugin architecture, with over 315+ external plugins and thriving community. The maintainers of pytest and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.

​Kong AI Gateway is a semantic AI gateway designed to run and secure Large Language Model (LLM) traffic, enabling faster adoption of Generative AI (GenAI) through new semantic AI plugins for Kong Gateway. It allows users to easily integrate, secure, and monitor popular LLMs. The gateway enhances AI requests with semantic caching and security features, introducing advanced prompt engineering for compliance and governance. Developers can power existing AI applications written using SDKs or AI frameworks by simply changing one line of code, simplifying migration. Kong AI Gateway also offers no-code AI integrations, allowing users to transform, enrich, and augment API responses without writing code, using declarative configuration. It implements advanced prompt security by determining allowed behaviors and enables the creation of better prompts with AI templates compatible with the OpenAI interface.

LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.