Upgrade Readme.md

CHANGELOG.md

@@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## [0.1.24]
 
 ### Added
 

README.md

@@ -1,12 +1,160 @@
 # Prompt-Admin
 
+Prompt-Admin is a system designed for integrating prompt-based classes into your Python application. It allows you to
+conveniently moderate your prompts through an admin panel.
+
+## How Install
+To get Prompt-Admin up and running, follow these installation steps:
+
+### 1. Database Setup
+
+First, you need to set up your database. Run the SQL script located at `server/promptadmin_server/data/init.sql`. This script will initialize your database, which is vital for the backend to function properly. Automation for this process is planned for future releases.
+
+### 2. Docker Installation
+
+Prompt-Admin uses Docker for easy deployment. Ensure Docker and Docker Compose are installed on your system. If not, please install them from [Docker's official site](https://docker.com).
+
+Once Docker is set up, you can proceed with the deployment of Prompt-Admin using Docker Compose. Create a `docker-compose.yml` file in your project directory with the following configuration:
+
+```yaml
+services:
+  prompt-admin:
+    container_name: prompt-admin
+    image: phoenixnazarov/prompt-admin:dev
+    restart: always
+    ports:
+      - "80:80"
+    environment:
+      PA_DATABASE_USERNAME: postgres
+      PA_DATABASE_PASSWORD: password
+      PA_DATABASE_HOST: 192.168.0.1
+      PA_DATABASE_PORT: 5432
+      PA_DATABASE_DATABASE: prompt-admin
+      PA_CONNECTION_PROJECT1: postgresql://postgres:[email protected]:5432/totalis
+      PA_CONNECTION_PROJECT2: postgresql://postgres:[email protected]:5432/totalis
+      PA_SYNC_ENDPOINT_PROJECT1: https://project1.ai/api/prompt-admin
+      PA_SYNC_SECRET_PROJECT1: 1234
+      PA_SYNC_ENDPOINT_PROJECT2: https://project2.ai/api/prompt-admin
+      PA_SYNC_SECRET_PROJECT2: qwer
+      # Optional
+      PA_OPENAI_KEY: qwe
+      PA_ANTHROPIC_KEY: qwe
+      AWS_SECRET_KEY: qwe
+      AWS_ACCESS_KEY: qwe
+      AWS_REGION: qwe
+```
+
 ## Projects
 
-#TODO
+Prompt-Admin supports the integration of multiple projects, each of which can be configured within the environment variables using the format: `PA_CONNECTION_{PROJECT}`. For instance, a database connection can be established using a connection string like `postgresql://postgres:[email protected]:5432/totalis`. Currently, only PostgreSQL databases are supported.
+
+Within the admin panel, users have the capability to manually specify where in the database the prompts are located. This is managed under the `Table` section of the admin panel, where users can simply fill out the Mapping to associate prompt data with specific database tables.
+
+Additionally, for certain projects, it might be necessary to define synchronization and testing functionalities. This can be done using the following environment variables:
+- `PA_SYNC_ENDPOINT_{PROJECT}`: Specifies the endpoint for automatic synchronization of prompts from the hosted application into the database.
+- `PA_SYNC_SECRET_{PROJECT}`: Defines a secret key required for secure communication between the application and the synchronization endpoint.
+
+These configurations allow for advanced operations such as:
+1. Automated synchronization of prompts into the database from the application.
+2. Automated testing processes to ensure prompt and application integrity and functionality.
+
+Through this setup, Prompt-Admin offers a robust system for managing and maintaining multiple projects efficiently from a central admin panel, enhancing productivity and operational control.
+
+
+## Library
+
+
+The Prompt-Admin provides a Python library that facilitates automated synchronization between the admin panel and applications. This library is available on PyPI and can be installed using:
+
+```
+pip3 install prompt-admin
+```
+
+### Using the Library
+
+Once you have installed the `prompt-admin` package, you can easily integrate prompt management into your applications by using decorators and service models provided by the library. This setup simplifies the process of using prompts in your application code.
+
+Here’s how to set up and use the library:
+
+#### Importing Necessary Modules
+
+Start by importing the necessary classes and decorators from the library:
+
+```python
+from promptadmin.prompt_service.decorator import bind_prompt
+from promptadmin.prompt_service.models import OpenaiModelService
+from promptadmin.prompt_service.models.openai_model import OpenaiModelResponse
+```
+
+#### Setting Up the Model Service
+
+Initialize your model service by specifying the type of prompt position and the model you are going to use. Here is an example using `gpt-4o-mini`:
+
+```python
+model_service = OpenaiModelService(prompt_position='system', model='gpt-4o-mini')
+```
+
+#### Creating a Service Class
+
+You can define a service class that utilizes the `bind_prompt` decorator to associate class methods with specific database prompts. This structure allows for dynamic interaction with different prompts based on your requirements:
+
+```python
+class PublicPromptService:
+    @bind_prompt(
+        'table',
+        'field',
+        'field_name',
+        'name',
+        model_service=model_service,
+    )
+    async def demo(self) -> OpenaiModelResponse:
+        """ 
+        This method serves as a prompt handler in the system. It retrieves user inputs and utilizes model behavior to provide responses.
+        """
+```
+
+#### Utilizing the Service
+
+Finally, you can use instances of your service class to interact with the system and generate responses based on user input and historical data:
+
+```python
+public_prompt_service = PublicPromptService()
+
+def find_prompt_from_database():
+    ...
+
+response = await public_prompt_service.demo(
+    prompt=find_prompt_from_database(),
+    history=history
+)
+```
+
+The provided framework simplifies the integration of machine learning models and prompt management into your application, streamlining the development and deployment of AI-driven services.
+
 
 ## Prompts
 
-#TODO
+### Overview
+
+In the Prompt-Admin system, prompts act as customizable query templates that facilitate dynamic interactions between your application and its users. After endpoints for prompts are created, they are synchronized and displayed within the admin panel client, where they can be fully managed.
+
+### Editing and Management
+
+The admin panel provides a user-friendly interface for prompt management where you can edit and configure each prompt to meet specific needs of your project. It allows for versatile modifications including the addition of contextual information or specific conditions under which the prompts should be triggered.
+
+### Contextual Execution
+
+Prompts in Prompt-Admin can be executed based on specific contexts, enhancing their flexibility and application across different scenarios. Users can configure prompts to respond according to the varying conditions or queries presented by end users, ensuring a tailored interaction that improves user experience and engagement.
+
+### Variable Insertion
+
+To add an additional layer of customization, prompts support the insertion of variables using Jinja templating. This feature enables the dynamic insertion of environment-specific variables directly into the prompts. It allows prompts to be more adaptive and responsive to the changing conditions or data in the environment.
+
+### Testing and Validation
+
+Within the Prompt-Admin panel, you can also test your prompts to ensure they meet the required format and function as expected. This feature is crucial for maintaining the integrity and reliability of interactions driven by these prompts. The system allows you to validate the format correctness and functional behavior of the prompts before deploying them into a live environment.
+
+Through these capabilities, Prompt-Admin ensures that managing and deploying prompts is a seamless and robust process, significantly boosting the efficiency and effectiveness of your application's interactions with its users.
 
 ## Blog
 

library/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "prompt-admin"
-version = "0.1.24"
+version = "0.1.25"
 description = ""
 authors = ["phoenix <[email protected]>"]
 readme = "README.md"