> ## Documentation Index
> Fetch the complete documentation index at: https://acem-52171079.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Components

> Key services and internal modules

The server logic is organized into modular services found in `app/services/`. This design pattern separates business logic from API routes, making the code testable and reusable.

## Key Services

### Chat Service

**File**: `app/services/chat_service.py`

This is the heart of the conversational capability. It handles:

* **Session Management**: Creating and retrieving chat sessions.
* **Prompt Engineering**: Constructing the system prompt with context.
* **Token Management**: Ensuring prompts stay within model limits.
* **Provider Resolution**: Determining which LLM model to use for a specific workspace.

### Embedding Service

**File**: `app/services/embedding_service.py`

Responsible for everything related to RAG (Retrieval-Augmented Generation):

* **Chunking**: Splitting large documents into manageable text chunks.
* **Embedding**: Calling embedding models (e.g., OpenAI `text-embedding-3-small`) to vectorize text.
* **Indexing**: Storing vectors in the configured Vector DB.
* **Retrieval**: Performing cosine similarity searches to find relevant context.

### Agent Service

**File**: `app/services/agent_service.py`

Manages autonomous behaviors and tool use. It allows the LLM to "act" rather than just "speak" by executing defined tools (like web search or file operations).

## Pluggable Layers

The server is designed to be agnostic to specific vendors for key components.

### LLM Providers

**Location**: `app/services/llm/`

The server uses an adapter pattern to support multiple LLM providers.

* **Factory**: `app/services/llm/factory.py` instantiates the correct provider based on configuration.
* **Base Class**: All providers inherit from a common base class, ensuring a consistent interface.
* **Supported**: OpenAI, Anthropic, Ollama, Google Gemini, Groq, Azure.

### Vector Databases

**Location**: `app/services/vector_db/`

Similar to LLMs, vector database support is modular.

* **Factory**: `app/services/vector_db/factory.py`.
* **Supported**:
  * **LanceDB**: Embedded, serverless vector DB (default).
  * **ChromaDB**: Open-source embedding database.
  * **Pinecone**: Managed cloud vector database.
  * **Qdrant**: High-performance vector search engine.
  * **Weaviate**: AI-native vector database.

## Authentication System

**Location**: `app/core/security.py`

Authentication is flexible and controlled by `MULTI_USER_MODE`.

* **Single User**: Validates a simple static `AUTH_TOKEN`. Ideal for personal use.
* **Multi User**: Full JWT implementation.
  * **Login**: `/api/v1/auth/login` returns an access token.
  * **Protection**: Routes are protected by the `get_current_user` dependency.
  * **Hashing**: Passwords are hashed using `bcrypt`.