> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/llm-module/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/llm-module/components/models/api.md).

# API

The API runtime of our LLM Module provides a gateway for different LLM providers, starting with OpenAI and Gemini. This runtime provides a wrapper around the OpenAI and Gemini suite of models and allows you to easily deploy and integrate models like GPT-3.5-turbo, GPT-4, Gemini 1.5 Flash, etc. into [Seldon Core 2 pipelines](https://docs.seldon.io/projects/seldon-core/en/v2/contents/pipelines/index.html). This runtime also includes support for OpenAI Azure Deployments.

Typical LLM applications are split up into a series of components aside from your Large Language Model. These may include an additional model for creating embeddings, a vector database to store these embeddings, memory stores and prompt templates, among others. The language model is the component responsible for acting on text that is typically provided by a user or from the interactions of your system with other applications. As straightforward example of a chat app is illustrated in the diagram below:

{% @mermaid/diagram content="flowchart LR
input(\[input])
output(\[output])
filesys\[(FILE SYSTEM)]
memory\_1
memory\_2
OAI\["OpenAI"]

```
input --> memory_1 --> OAI --> output
filesys <--> memory_1
memory_2 --> filesys
memory_2 --> output
OAI --> memory_2

%% Styling for OpenAI node
style OAI fill:#407,stroke:#333,stroke-width:2px,color:#fff" %}
```

Here, we combine different components of an LLM-based application into a pipeline. The component of interest in this diagram is the OpenAI model in the centre of the diagram. The pipeline is a question-answer chat application where a user's questions will be answered by the LLM. The memory components keep a history of the conversation so that the user can ask follow-up questions to the LLM (see [Conversational Memory](/llm-module/components/memory.md)).

The API runtime is designed to slot into a [Seldon Core 2 pipeline](https://docs.seldon.io/projects/seldon-core/en/v2/contents/pipelines/index.html) with minimal set-up and configuration. It can also be run as a stand-alone runtime with a selection of the available OpenAI and Gemini models in order to provide natural language output for chat and text completions applications, embeddings to use in combination with a vector database, and image generation from models such as Dalle-3. For a list of compatible models for those endpoints see the [OpenAI documentation](https://platform.openai.com/docs/models/overview) and [Gemini documentation](https://ai.google.dev/gemini-api/docs/models/gemini).