> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pawa-ai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> The Pawa AI chat endpoint provides a unified interface for conversation, reasoning, multimodal input, retrieval-augmented generation (RAG), agentic workflows, tools, and structured outputs — all powered by our Pawa model family.

## What You Can Do

The endpoint supports a wide range of capabilities to power intelligent applications:

* **Basic Chat** → Natural conversation in Swahili, English, and other African languages.
* **Multimodal** → Combine text and images as input for richer interactions.
* **RAG (Retrieval-Augmented Generation)** → Connect external knowledge sources for contextual, grounded responses.
* **Agentic RAG** → Extend RAG with agent workflows for planning, reasoning, and task execution.
* **Agents** → Run multi-step autonomous workflows where the model coordinates actions.
* **Reasoning** → Handle structured problem solving, logic, and advanced query resolution.
* **Tools Calling** → Dynamically call external APIs or functions from within a conversation.
* **Structured Output** → Generate reliable, typed JSON for downstream processing.

## Chat Models includes

The chat endpoint runs on **Pawa small language models (SLMs)**, optimized for efficiency, long context, and multilingual use.

* **Pawa Ember** → Lightweight, efficient model for real-time and low-cost deployments.
* **Pawa Blaze** → Advanced model for reasoning, multimodal tasks, and agentic workflows.

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With chat endpoint, you can start simple and scale up to advanced multimodal and agent-based applications — without changing the core interface.