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Updated Documentation

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Michael Ikehi
2025-04-18 20:21:11 +01:00
parent 71ad7b4d26
commit 6d07556b85
3 changed files with 104 additions and 70 deletions
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README.md
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@@ -12,6 +12,9 @@ A comprehensive AI-powered tool designed to streamline the process of ideation,
- **Brand Style Management**: Configure and update brand guidelines through a user-friendly interface
- **Training Data Management**: Add successful marketing content to improve the AI over time
- **Content Performance Analysis**: Get insights into the potential effectiveness of your content
- **History Tracking**: Keep track of all generated content and user interactions
- **Pagination Support**: Efficiently browse through large datasets of historical content
- **Error Handling**: Robust error handling and user feedback systems
## 📋 Project Overview
@@ -23,7 +26,10 @@ The Marketing Assistant AI combines a powerful backend built with FastAPI and a
- **Frontend**: Vanilla HTML, CSS, and JavaScript
- **AI Models**: Cohere's generation and embedding models
- **Vector Database**: FAISS for efficient content similarity search
- **Storage**: Local JSON files for historical marketing data
- **Storage**:
- JSON files for historical marketing data
- SQLite for training data
- FAISS vector store for content similarity search
## 🚀 Getting Started
@@ -66,8 +72,7 @@ API_HOST=localhost
API_PORT=8000
# Database Configuration
VECTOR_DB_PATH=./data/vector_store
HISTORY_DB_PATH=./data/history.db
DATABASE_URL=sqlite:///./data/training_data.db
# Brand Configuration
BRAND_NAME=Adriana James
@@ -244,4 +249,4 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
---
Made with ❤️ for Adriana James
Made with ❤️ for Adriana James
data/README.md
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@@ -8,6 +8,7 @@ This directory contains the data used by the Marketing Assistant AI system.
- **user_queries/**: Stores user queries and requests for analytics and model improvement
- **style_guidelines/**: Contains brand tone and voice guidelines
- **vector_store/**: Generated vector database for content retrieval (created automatically)
- **db/**: Contains SQLite database files for structured data storage
## File Formats
@@ -22,15 +23,16 @@ Past campaign files are stored as JSON with the following structure:
"metadata": {
"campaign_name": "Name of the campaign",
"performance_metrics": {
"metric1": value,
"metric2": value
"open_rate": 0.42,
"click_rate": 0.15,
"conversion_rate": 0.08
},
"content_type": "Same as above",
"added_at": "ISO timestamp",
"added_at": "2024-01-01T12:00:00Z",
"training_data": true
},
"document_id": 0,
"timestamp": "ISO timestamp"
"document_id": "unique-identifier",
"timestamp": "2024-01-01T12:00:00Z"
}
```
@@ -44,10 +46,13 @@ User query files store information about requests made to the AI:
"parameters": {
"content_type": "Type of content requested",
"tone": "Requested tone",
"length": "Requested length",
"length": "short|medium|long",
"include_cta": true|false
},
"timestamp": "ISO timestamp"
"timestamp": "2024-01-01T12:00:00Z",
"generated_content": "The AI-generated content",
"feedback": "Optional user feedback",
"performance_score": 0.95
}
```
@@ -63,12 +68,15 @@ Brand style is stored as a JSON file with the following structure:
"taboo_words": ["cheap", "discount", "bargain"],
"preferred_terms": {
"customers": "clients",
"products": "solutions"
}
"products": "solutions",
"problems": "challenges"
},
"last_updated": "2024-01-01T12:00:00Z",
"version": "1.0"
}
```
## Adding New Data
## Data Management
### Adding Past Campaigns
@@ -78,4 +86,23 @@ Brand style is stored as a JSON file with the following structure:
### Updating Brand Style
1. Use the API endpoint `PUT /brand-style` with the updated style guidelines
2. The system will automatically update the style file
2. The system will automatically update the style file and create a backup
### Managing User Queries
1. User queries are automatically stored when using the generation API
2. Each query is stored with its parameters, generated content, and any feedback
3. Use the `GET /user-queries` endpoint to retrieve historical data with pagination
### Vector Store Management
The vector store is automatically maintained by the system:
1. New content is automatically embedded and added to the store
2. Similar content can be retrieved using the `POST /find-similar` endpoint
3. The store is periodically optimized for performance
## Backup and Maintenance
1. All JSON files are versioned and can be restored if needed
2. The SQLite database is automatically backed up daily
3. The vector store can be rebuilt from the source content if necessary
docs/API_DOCUMENTATION.md
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@@ -4,7 +4,7 @@
### Generate Copy
Generates marketing copy based on the provided prompt and optional parameters.
Generates marketing copy based on the provided prompt and parameters.
**Endpoint**: `/generate-copy`
**Method**: POST
@@ -15,52 +15,55 @@ Generates marketing copy based on the provided prompt and optional parameters.
{
"prompt": "Write a social media post for our new product launch",
"content_type": "social_media",
"tone": "professional",
"length": "medium",
"include_cta": true
}
```
**Parameters**:
- `prompt` (string, required): The main instruction for generating content
- `content_type` (string, optional): Type of content to generate (social_media, email, blog, website, etc.)
- `length` (string, optional): Content length (short, medium, long)
- `include_cta` (boolean, optional): Whether to include a call to action
**Response**:
```json
{
"status": "success",
"content": "Exciting news! Our revolutionary new product has just landed...",
"suggestions": [
"Alternative headline option 1",
"Alternative headline option 2"
],
"content": "Generated marketing content",
"metadata": {
"content_type": "social_media",
"tone": "excited",
"word_count": 85,
"generated_at": "2025-04-17T10:30:45Z"
"timestamp": "2024-01-01T12:00:00Z",
"alignment_score": 0.95
}
}
```
### Get Brand Style Guidelines
### List User Queries
Retrieves the current brand style guidelines.
Retrieves historical user queries with pagination.
**Endpoint**: `/brand-style`
**Method**: GET
**Endpoint**: `/user-queries`
**Method**: GET
**Query Parameters**:
- `page` (optional, default: 1): Page number
- `limit` (optional, default: 10): Items per page
- `content_type` (optional): Filter by content type
**Response**:
```json
{
"brand_name": "Adriana James",
"tone": ["professional", "friendly", "inspiring"],
"voice_characteristics": ["clear", "direct", "empowering"],
"taboo_words": ["cheap", "discount", "bargain"],
"preferred_terms": {
"customers": "clients",
"products": "solutions"
"items": [
{
"prompt": "Write a social media post...",
"parameters": {
"content_type": "social_media",
"tone": "professional",
"length": "medium",
"include_cta": true
},
"timestamp": "2024-01-01T12:00:00Z"
}
],
"pagination": {
"total": 45,
"page": 1,
"limit": 10,
"pages": 5
}
}
```
@@ -76,9 +79,9 @@ Updates the brand style guidelines.
**Request Body**:
```json
{
"tone": ["professional", "friendly", "inspiring", "innovative"],
"tone": ["professional", "friendly", "inspiring"],
"voice_characteristics": ["clear", "direct", "empowering"],
"taboo_words": ["cheap", "discount", "bargain", "basic"],
"taboo_words": ["cheap", "discount", "bargain"],
"preferred_terms": {
"customers": "clients",
"products": "solutions",
@@ -91,7 +94,8 @@ Updates the brand style guidelines.
```json
{
"status": "success",
"message": "Brand style updated successfully"
"message": "Brand style updated successfully",
"timestamp": "2024-01-01T12:00:00Z"
}
```
@@ -107,9 +111,9 @@ Adds new marketing content for AI training.
```json
{
"content_type": "email_campaign",
"content": "Dear valued client, We're thrilled to announce...",
"content": "Dear valued client...",
"metadata": {
"campaign_name": "Spring Launch 2025",
"campaign_name": "Spring Launch 2024",
"performance_metrics": {
"open_rate": 0.42,
"click_rate": 0.15
@@ -123,37 +127,34 @@ Adds new marketing content for AI training.
{
"status": "success",
"message": "Training data added successfully",
"data_id": "12345"
"data_id": "unique-identifier"
}
```
### List Training Data
Retrieves a list of available training data.
Retrieves available training data with pagination.
**Endpoint**: `/training-data`
**Method**: GET
**Method**: GET
**Query Parameters**:
- `page` (optional, default: 1): Page number
- `limit` (optional, default: 10): Items per page
- `content_type` (optional): Filter by content type
- `page` (optional): Page number for pagination
- `limit` (optional): Number of items per page
**Response**:
```json
{
"items": [
{
"id": "12345",
"id": "unique-identifier",
"content_type": "email_campaign",
"preview": "Dear valued client, We're thrilled to announce...",
"added_at": "2025-04-10T14:30:00Z"
},
{
"id": "12346",
"content_type": "social_media",
"preview": "Exciting news! Our revolutionary new product...",
"added_at": "2025-04-11T09:15:00Z"
"preview": "Dear valued client...",
"added_at": "2024-01-01T12:00:00Z",
"performance_metrics": {
"open_rate": 0.42,
"click_rate": 0.15
}
}
],
"pagination": {
@@ -167,19 +168,20 @@ Retrieves a list of available training data.
## Error Handling
All endpoints return standard HTTP status codes:
All endpoints return appropriate HTTP status codes:
- `200 OK`: Request successful
- `400 Bad Request`: Invalid request parameters
- `401 Unauthorized`: Authentication failed
- `404 Not Found`: Resource not found
- `500 Internal Server Error`: Server-side error
- 200: Success
- 400: Bad Request (invalid parameters)
- 401: Unauthorized
- 404: Not Found
- 500: Internal Server Error
Error responses include detailed messages:
Error response format:
```json
{
"status": "error",
"message": "Detailed error message",
"error_code": "ERROR_CODE"
"code": "ERROR_CODE"
}
```