# AlphaNeural Documentation ## API Documentation - [Introduction](https://docs.alphaneural.io/introduction.md): AlphaNeural provides a single, OpenAI-compatible API for chatting, vision, embeddings, and image generation. If you have already integrated the OpenAI API, you can usually switch by updating the base - [Getting Started](https://docs.alphaneural.io/getting-started.md) - [Authentication](https://docs.alphaneural.io/authentication.md): AlphaNeural uses API keys for all requests. Send your key in an HTTP header over HTTPS. - [Core Concepts](https://docs.alphaneural.io/core-concepts.md) - [Chat Completions](https://docs.alphaneural.io/chat-completions.md): Create a model response for a conversation. This endpoint is OpenAI-compatible, including message roles, tool calling, and streaming. - [Image Generation](https://docs.alphaneural.io/image-generation.md): AlphaNeural supports OpenAI-compatible image generation for GPT-style image models (and any provider-backed image model you expose through the OpenAI Images interface). The API shape matches OpenAI’s - [Image generation (Azure-style deployments)](https://docs.alphaneural.io/image-generation-azure-style-deployments.md): Some teams prefer the Azure OpenAI-style URL shape where the deployment name is part of the path. AlphaNeural supports that format for image generation too. - [Image generation (Passthrough. Diffusion and custom APIs)](https://docs.alphaneural.io/image-generation-passthrough.-diffusion-and-custom-apis.md): AlphaNeural also deploys non-LLM image models, like Stable Diffusion-style pipelines and other diffusion systems. - [Embeddings](https://docs.alphaneural.io/embeddings.md): Embeddings turn text into vectors you can use for semantic search, clustering, recommendations, and RAG. The AlphaNeural proxy follows the same API shape as OpenAI’s Embeddings endpoint. - [Models](https://docs.alphaneural.io/models.md): List the models available to your AlphaNeural API key. This endpoint is OpenAI-compatible, so tools and SDKs that expect GET /v1/models will work out of the box. - [Usage & Billing](https://docs.alphaneural.io/usage-and-billing.md): Retrieve token usage and cost. - [Error Codes](https://docs.alphaneural.io/error-codes.md): Standard HTTP error codes are used. - [Best Practices](https://docs.alphaneural.io/best-practices.md) - [Use AlphaNeural with OpenCode](https://docs.alphaneural.io/tutorials/use-alphaneural-with-opencode.md): OpenCode can talk to any OpenAI-compatible API by setting a provider baseURL and giving it an API key. AlphaNeural fits that shape, so the integration is mostly config and credentials. - [Use AlphaNeural with Continue (VS Code and JetBrains)](https://docs.alphaneural.io/tutorials/use-alphaneural-with-continue-vs-code-and-jetbrains.md): Continue supports OpenAI API compatible providers by letting you set a custom apiBase. AlphaNeural is OpenAI-compatible, so the integration is mostly configuration. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on a page URL with the `ask` query parameter: ``` GET https://docs.alphaneural.io/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.