#  Opper AI

> Opper is a developer platform and API for software engineers to build custom generative AI features and applications, offering tools for interacting with models, user feedback, logging, and compliance, designed to streamline the development of production-grade AI solutions.

# Important notes

- Interacting with Opper is best through our Python and Node/Typescript SDKs
- Using the SDKs it is possible to issue calls to models, index documents and retrieve form them, implement tracing and feedback.

# Docs

## SDKs
- [Opper.ai Node SDK](https://docs.opper.ai/sdks/node): To provide installation and usage instructions for the Opper.ai Node SDK.
- [Python SDK Documentation](https://docs.opper.ai/sdks/python): To provide documentation for installing and using the Opper AI Python SDK.
- [HTTP API Documentation](https://docs.opper.ai/sdks/http): Provides documentation for the HTTP API of Opper AI.

## Introduction
- [Opper API Overview](https://docs.opper.ai/introduction/overview): Provide an overview of Opper's structured API for interacting with generative models and related concepts.
- [Getting Started with Opper](https://docs.opper.ai/introduction/get-started): Guide users to set up and use the Opper API effectively.
- [Opper Platform Security Overview](https://docs.opper.ai/introduction/security): To inform users about the security features and architecture of the Opper platform.
- [Opper Administration Overview](https://docs.opper.ai/introduction/administration): Guide for managing users, organizations, and projects in the Opper platform for AI application collaboration.

## Capabilities
- [Opper Model Capabilities](https://docs.opper.ai/capabilities/models): Detailing available language and multimodal models on the Opper platform with their respective providers and regions.
- [Opper API Call Instructions](https://docs.opper.ai/capabilities/calls): Explains how to make calls to the Opper AI API for generating responses using various configurations.
- [Tracing in LLMs](https://docs.opper.ai/capabilities/tracing): Explains tracing functionality for improving debugging in LLM applications through structured logging.
- [LLM Evaluation Overview](https://docs.opper.ai/capabilities/evaluations): To explain the evaluation process for LLM calls in the Opper platform.
- [Document Indexing Guide](https://docs.opper.ai/capabilities/indexes): Detailed guide on creating and managing document indexes for semantic querying using Opper's API.

## Patterns & Use Cases
- [Text Processing Use Cases](https://docs.opper.ai/patterns/text): Explaining various text processing use cases for LLMs.
- [Image Processing Guide](https://docs.opper.ai/patterns/images): Guide on utilizing image processing capabilities in language models for generating and analyzing images.
- [Code Generation Patterns](https://docs.opper.ai/patterns/code): Guide on using LLMs for code generation and processing strategies.
- [Audio Processing Guide](https://docs.opper.ai/patterns/audio): Guide for processing audio using language models.

## Guides
- [Customer Service Chatbot Guide](https://docs.opper.ai/guides/customer-service-bot): Guide for building a customer service chatbot using Opper with features like intent classification and conversation tracing.
- [Streamlit Chat App Guide](https://docs.opper.ai/guides/streamlit): Guide for building a Streamlit app using Opper AI for company-specific inquiries.
- [Indexing Support Tickets](https://docs.opper.ai/guides/rag-on-tickets): This guide explains indexing and querying customer support tickets using Opper AI.

## General Resources
- [Generative AI Toolkit](https://docs.opper.ai): This webpage offers a toolkit for developers to create generative AI applications and features easily.