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Copy file name to clipboardExpand all lines: docs/scens/data_agent_fin.rst
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📖 Background
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~~~~~~~~~~~~~~
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In the dynamic world of quantitative trading, **factors** are the secret weapons that traders use to harness market inefficiencies.
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These powerful tools—ranging from straightforward metrics like price-to-earnings ratios to intricate discounted cash flow models—unlock the potential to predict stock prices with remarkable precision.
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By tapping into this rich vein of data, quantitative traders craft sophisticated strategies that not only capitalize on market patterns but also drastically enhance trading efficiency and accuracy.
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Embrace the power of factors, and you're not just trading; you're strategically outsmarting the market.
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In the dynamic world of quantitative trading, **factors** serve as the strategic tools that enable traders to exploit market inefficiencies.
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These factors—ranging from simple metrics like price-to-earnings ratios to complex models like discounted cash flows—are the key to predicting stock prices with a high degree of accuracy.
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By leveraging these factors, quantitative traders can develop sophisticated strategies that not only identify market patterns but also significantly enhance trading efficiency and precision.
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The ability to systematically analyze and apply these factors is what separates ordinary trading from truly strategic market outmaneuvering.
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And this is where the **Finance Model Agent** comes into play.
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🎥 Demo
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~~~~~~~~~~
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- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
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.. code-block:: sh
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conda create -n rdagent python=3.10
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- Activate the environment:
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conda activate rdagent
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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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make dev
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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- **Path to the folder containing private data (default fundamental data in Qlib):**
Copy file name to clipboardExpand all lines: docs/scens/data_copilot_fin.rst
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Does the factor capture the essential market dynamics? How unique is it compared to the factors already in your library?
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Therefore, there is an urgent need for a systematic approach to design a framework that can effectively manage this process.
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This is where our RDAgent comes into play.
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And this is where the **Finance Data Copilot** steps in.
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🎥 Demo
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conda activate rdagent
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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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make dev
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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- If you want to change the default environment variables, you can refer to `Env Config`_ below
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- 🚀 Run the Application
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.. code-block:: sh
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- Store the factors you want to extract from the financial reports in your desired folder. Then, save the paths of the reports in the `report_result_json_file_path`. The format should be as follows:
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rdagent fin_factor_report
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.. code-block:: json
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[
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"git_ignore_folder/report/fin_report1.pdf",
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"git_ignore_folder/report/fin_report2.pdf",
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"git_ignore_folder/report/fin_report3.pdf"
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]
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- Run the application using the following command:
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.. code-block:: sh
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rdagent fin_factor_report
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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- **Path to the folder containing research reports:**
Copy file name to clipboardExpand all lines: docs/scens/model_agent_fin.rst
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📖 Background
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~~~~~~~~~~~~~~
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TODO
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In the realm of quantitative finance, both factor discovery and model development play crucial roles in driving performance.
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While much attention is often given to the discovery of new financial factors, the **models** that leverage these factors are equally important.
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The effectiveness of a quantitative strategy depends not only on the factors used but also on how well these factors are integrated into robust, predictive models.
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However, the process of developing and optimizing these models can be labor-intensive and complex, requiring continuous refinement and adaptation to ever-changing market conditions.
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And this is where the **Finance Model Agent** steps in.
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🎥 Demo
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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In this scenario, our automated system proposes hypothesis, constructs model, implements code, receives back-testing, and uses feedbacks.
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Hypothesis is iterated in this continuous process.
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The system aims to automatically optimise performance metrics from Qlib library thereby finding the optimised code through autonomous research and development.
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In this scenario, our automated system proposes hypothesis, constructs model, implements code, conducts back-testing, and utilizes feedback in a continuous, iterative process.
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The goal is to automatically optimize performance metrics within the Qlib library, ultimately discovering the most efficient code through autonomous research and development.
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Here's an enhanced outline of the steps:
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conda activate rdagent
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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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make dev
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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- The `.env.example` file contains the environment variables required for users using the OpenAI API (Please note that `.env.example` is an example file. `.env` is the one that will be finally used.)
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- Export each variable in the .env file:
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.. code-block:: sh
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export$(grep -v '^#' .env | xargs)
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- If you want to change the default environment variables, you can refer to `Env Config`_ below
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- 🚀 Run the Application
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.. code-block:: sh
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rdagent fin_model
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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TODO: Show some examples:
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.. _Env Config:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
- The `config.yaml` file located in the `model_template` folder contains the relevant configurations for running the developed model in Qlib. The default settings include key information such as:
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- **market**: Specifies the market, which is set to `csi300`.
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- **fields_group**: Defines the fields group, with the value `feature`.
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- **col_list**: A list of columns used, including various indicators such as `RESI5`, `WVMA5`, `RSQR5`, and others.
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- **start_time**: The start date for the data, set to `2008-01-01`.
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- **end_time**: The end date for the data, set to `2020-08-01`.
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- **fit_start_time**: The start date for fitting the model, set to `2008-01-01`.
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- **fit_end_time**: The end date for fitting the model, set to `2014-12-31`.
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- The default hyperparameters used in the configuration are as follows:
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- **n_epochs**: The number of epochs, set to `100`.
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- **lr**: The learning rate, set to `1e-3`.
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- **early_stop**: The early stopping criterion, set to `10`.
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- **batch_size**: The batch size, set to `2000`.
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- **metric**: The evaluation metric, set to `loss`.
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- **loss**: The loss function, set to `mse`.
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- **n_jobs**: The number of parallel jobs, set to `20`.
In this scenario, we consider the problem of risk prediction from patients' ICU monitoring data. We use the a public EHR dataset - MIMIC-III and extract a binary classification task for evaluating the framework.
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In this task, we aim at predicting the whether the patients will suffer from Acute Respiratory Failure (ARF) based their first 12 hours ICU monitoring data.
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🎥 Demo
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~~~~~~~~~~
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TODO: Here should put a video of the demo.
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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In this scenario, our automated system proposes hypothesis, constructs model, implements code, receives back-testing, and uses feedbacks.
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Hypothesis is iterated in this continuous process.
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The system aims to automatically optimise performance metrics of medical prediction thereby finding the optimised code through autonomous research and development.
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Here's an enhanced outline of the steps:
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**Step 1 : Hypothesis Generation 🔍**
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- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and justification.
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**Step 2 : Model Creation ✨**
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- Transform the hypothesis into a model.
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- Develop, define, and implement a machine learning model, including its name, description, and formulation.
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**Step 3 : Model Implementation 👨💻**
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- Implement the model code based on the detailed description.
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- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
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**Step 4 : Backtesting with MIMIC-III 📉**
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- Conduct backtesting using the newly developed model on the extracted task from MIMIC-III.
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- Evaluate the model's effectiveness and performance in terms of AUROC score.
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**Step 5 : Feedback Analysis 🔍**
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- Analyze backtest results to assess performance.
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- Incorporate feedback to refine hypotheses and improve the model.
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**Step 6 :Hypothesis Refinement ♻️**
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- Refine hypotheses based on feedback from backtesting.
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- Repeat the process to continuously improve the model.
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⚡ Quick Start
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~~~~~~~~~~~~~~~~~
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You can try our demo by running the following command:
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- 🐍 Create a Conda Environment
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- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
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.. code-block:: sh
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conda create -n rdagent python=3.10
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- Activate the environment:
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.. code-block:: sh
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conda activate rdagent
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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- The `.env.example` file contains the environment variables required for users using the OpenAI API (Please note that `.env.example` is an example file. `.env` is the one that will be finally used.)
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- Export each variable in the .env file:
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.. code-block:: sh
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export$(grep -v '^#' .env | xargs)
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- If you want to change the default environment variables, you can refer to `Env Config`_ below
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- 🚀 Run the Application
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.. code-block:: sh
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rdagent med_model
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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.. _Env Config:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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