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Add comprehensive documentation for DeepSeek Wrapper
- Created multiple new documentation files including API Reference, Deployment Guide, FAQ, Features, Getting Started, and Web UI Guide. - Added README.md for an overview of the documentation structure. - Included images directory with guidelines for required images to enhance documentation clarity. - Provided detailed instructions for local and cloud deployment options, environment variable configurations, and security recommendations. - Documented core features, advanced features, and usage instructions to assist developers and users in effectively utilizing the DeepSeek Wrapper.
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‎docs/README.md‎

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# DeepSeek Wrapper Documentation
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This directory contains comprehensive documentation for the DeepSeek Wrapper project.
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## Contents
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- [Getting Started](getting-started.md) - Quick setup guide
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- [Web UI Guide](web-ui-guide.md) - Guide to using the web interface
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- [Features](features.md) - Overview of all features
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- [API Reference](api-reference.md) - API documentation for developers
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- [Deployment](deployment.md) - Deployment options and configurations
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- [Contributing](CONTRIBUTING.md) - How to contribute to this project
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- [FAQ](faq.md) - Frequently asked questions
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For a detailed overview of the DeepSeek AI capabilities, see [deepseek-docs.md](deepseek-docs.md).

‎docs/api-reference.md‎

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# API Reference
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This document provides details about the DeepSeek Wrapper API for developers who want to integrate or extend the functionality.
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## REST API Endpoints
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The DeepSeek Wrapper provides a FastAPI-based REST API with the following endpoints:
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### Chat Endpoints
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#### `POST /api/chat`
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Create a new chat message and get a response from the DeepSeek AI.
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**Request Body:**
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```json
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{
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"message": "Your message here",
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"conversation_id": "optional-conversation-id",
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"system_prompt": "Optional system prompt to guide the AI's behavior"
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}
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```
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**Response:**
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```json
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{
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"id": "response-id",
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"content": "AI response content",
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"conversation_id": "conversation-id"
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}
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```
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#### `GET /api/chat/stream`
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Stream a chat response using Server-Sent Events (SSE).
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**Query Parameters:**
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- `message` (string): The user's message
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- `conversation_id` (string, optional): Conversation identifier
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- `system_prompt` (string, optional): System prompt
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**Response:**
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Server-sent events with the following data format:
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```json
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{
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"id": "chunk-id",
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"content": "Partial content chunk",
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"is_complete": false
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}
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```
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Final chunk will have `is_complete` set to `true`.
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### Conversation Endpoints
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#### `GET /api/conversations`
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Get a list of all conversations.
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**Response:**
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```json
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[
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{
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"id": "conversation-id",
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"title": "Conversation title",
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"created_at": "2023-06-15T14:30:00Z",
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"updated_at": "2023-06-15T14:35:00Z"
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}
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]
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```
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#### `GET /api/conversations/{conversation_id}`
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Get details and messages for a specific conversation.
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**Response:**
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```json
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{
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"id": "conversation-id",
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"title": "Conversation title",
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"created_at": "2023-06-15T14:30:00Z",
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"updated_at": "2023-06-15T14:35:00Z",
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"messages": [
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{
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"id": "message-id",
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"role": "user",
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"content": "User message",
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"created_at": "2023-06-15T14:30:00Z"
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},
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{
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"id": "response-id",
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"role": "assistant",
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"content": "AI response",
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"created_at": "2023-06-15T14:31:00Z"
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}
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]
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}
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```
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#### `DELETE /api/conversations/{conversation_id}`
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Delete a specific conversation.
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**Response:** Status 204 No Content
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### Document Endpoints
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#### `POST /api/documents/upload`
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Upload a document for processing.
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**Request:** Multipart form data with a `file` field containing the document.
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**Response:**
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```json
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{
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"id": "document-id",
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"filename": "document.pdf",
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"content": "Extracted text from the document",
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"content_type": "application/pdf",
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"size": 1024
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}
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```
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## Python Client API
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The DeepSeek Wrapper provides a Python client for programmatic access to the DeepSeek API.
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### Basic Usage
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```python
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from deepseek_wrapper.client import DeepSeekClient
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# Initialize the client
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client = DeepSeekClient(api_key="your-api-key")
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# Simple chat completion
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response = client.chat_completion(
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messages=[
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{"role": "user", "content": "Hello, how are you?"}
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]
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)
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print(response.content)
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# Streaming response
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for chunk in client.chat_completion_stream(
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messages=[
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{"role": "user", "content": "Write a short story about a robot."}
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]
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):
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print(chunk.content, end="", flush=True)
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```
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### Client Methods
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#### `chat_completion(messages, system_prompt=None, **kwargs)`
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Get a complete response for the given messages.
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**Parameters:**
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- `messages` (List[Dict]): List of message objects with `role` and `content`
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- `system_prompt` (str, optional): System prompt to guide the AI
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- `**kwargs`: Additional parameters to pass to the API
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**Returns:** Response object with attributes like `id`, `content`, etc.
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#### `chat_completion_stream(messages, system_prompt=None, **kwargs)`
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Stream the response for the given messages.
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**Parameters:**
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- `messages` (List[Dict]): List of message objects with `role` and `content`
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- `system_prompt` (str, optional): System prompt to guide the AI
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- `**kwargs`: Additional parameters to pass to the API
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**Returns:** Generator yielding response chunks
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#### `clean_up()`
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Clean up resources used by the client, including closing the HTTP session.
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## Error Handling
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The API returns standard HTTP status codes:
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- `200 OK`: Successful operation
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- `400 Bad Request`: Invalid input
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- `401 Unauthorized`: Missing or invalid API key
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- `404 Not Found`: Resource not found
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- `500 Internal Server Error`: Server error
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Error responses include a JSON body with `error` and `message` fields.

‎docs/deployment.md‎

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# Deployment Guide
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This guide covers different options for deploying the DeepSeek Wrapper application in various environments.
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## Local Deployment
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### Running for Development
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For local development, you can run the application with:
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```bash
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python -m src.deepseek_wrapper.main
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```
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This will start the application with auto-reload enabled for development purposes.
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### Running for Production
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For a more production-ready local deployment:
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```bash
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uvicorn src.deepseek_wrapper.main:app --host 0.0.0.0 --port 8000 --workers 4
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```
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This uses the Uvicorn ASGI server with multiple worker processes for better performance.
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## Docker Deployment
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### Building the Docker Image
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1. Ensure Docker is installed on your system
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2. Build the image:
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```bash
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docker build -t deepseek-wrapper .
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```
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### Running with Docker
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```bash
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docker run -d -p 8000:8000 --env-file .env --name deepseek-wrapper deepseek-wrapper
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```
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This will:
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- Run the container in detached mode (`-d`)
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- Map port 8000 from the container to your host
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- Use your local `.env` file for environment variables
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- Name the container "deepseek-wrapper"
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### Docker Compose
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For easier management, you can use Docker Compose:
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```yaml
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# docker-compose.yml
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version: '3'
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services:
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deepseek-wrapper:
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build: .
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ports:
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- "8000:8000"
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environment:
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- DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}
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volumes:
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- ./uploads:/app/uploads
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restart: unless-stopped
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```
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Run with:
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```bash
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docker-compose up -d
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```
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## Cloud Deployment
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### Deploying to Heroku
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1. Install the Heroku CLI and login
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2. Initialize a Git repository if not already done:
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```bash
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git init
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git add .
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git commit -m "Initial commit"
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```
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3. Create a Heroku app and deploy:
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```bash
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heroku create deepseek-wrapper
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heroku config:set DEEPSEEK_API_KEY=your_api_key_here
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git push heroku main
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```
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### Deploying to AWS Elastic Beanstalk
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1. Install the EB CLI and initialize your project:
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```bash
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pip install awsebcli
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eb init
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```
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2. Create and deploy to an environment:
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```bash
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eb create deepseek-wrapper-env
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```
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3. Set environment variables:
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```bash
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eb setenv DEEPSEEK_API_KEY=your_api_key_here
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```
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### Deploying to Azure App Service
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1. Install the Azure CLI and login:
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```bash
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az login
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```
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2. Create an App Service and deploy:
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```bash
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az webapp up --name deepseek-wrapper --resource-group your-resource-group --runtime "PYTHON:3.9"
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```
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3. Set environment variables:
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```bash
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az webapp config appsettings set --name deepseek-wrapper --resource-group your-resource-group --settings DEEPSEEK_API_KEY=your_api_key_here
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```
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## Environment Variables
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The following environment variables can be configured for deployment:
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `DEEPSEEK_API_KEY` | Your DeepSeek API key (required) | None |
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| `HOST` | Host to bind the server to | 0.0.0.0 |
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| `PORT` | Port to run the server on | 8000 |
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| `LOG_LEVEL` | Logging level (DEBUG, INFO, WARNING, ERROR) | INFO |
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| `UPLOAD_DIR` | Directory for file uploads | ./uploads |
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| `MAX_UPLOAD_SIZE` | Maximum file upload size in MB | 10 |
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## Scaling Considerations
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For high-traffic deployments, consider:
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1. Using a reverse proxy like Nginx in front of the application
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2. Implementing Redis for session storage
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3. Deploying with multiple workers/instances
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4. Setting up a load balancer for horizontal scaling
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## Security Recommendations
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1. Always use HTTPS in production
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2. Set up proper authentication if exposing to public internet
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3. Keep your API key secure and use environment variables
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4. Regularly update dependencies
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5. Consider using a WAF (Web Application Firewall)
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## Monitoring
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For production deployments, set up monitoring:
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1. Application logs
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2. Server metrics (CPU, memory, disk usage)
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3. Request/response times
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4. Error rates
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Popular monitoring tools include Prometheus, Grafana, or cloud provider monitoring services.

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