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https://github.com/codecrunchers/agent-fw/blob/49f2ce280c21877beb2a539a24e972038cd759e5/README.md#L59-L66 https://github.com/codecrunchers/agent-fw/blob/49f2ce280c21877beb2a539a24e972038cd759e5/README.md#L95-L102 https://github.com/codecrunchers/agent-fw/blob/49f2ce280c21877beb2a539a24e972038cd759e5/README.md#L40-L55

Step 2: ⌨️ Coding

[X] Modify README.md ✓ https://github.com/codecrunchers/agent-fw/commit/7e40195b8a65c14efdcb648eee7dc0068bde900f Edit
Modify README.md with contents:
• Add a brief introduction about the repository at the beginning of the README.md file. This should include a description of the repository, its purpose, and its main features. Mention that it is a lightweight beta model deployment framework that provides wrapped access to chat history, databases, sessions, authentication, an LLM, and a file parsing model. Also mention that it works out of the box with an OpenAI key and relies heavily on langchain for quick enhancements with new GPTs and LLMs.

--- 
+++ 
@@ -1,4 +1,6 @@
 #  LLM-API-starterkit
+
+This repository serves as a lightweight beta model deployment framework designed to facilitate easy and efficient deployment of machine learning models. It provides wrapped access to features such as chat history, databases, sessions, authentication, an LLM (Large Language Model), and a file parsing model. Developed with a focus on agility and integrability, the framework works out of the box with an OpenAI key and integrates seamlessly with langchain, allowing for rapid enhancements with new GPTs and LLMs. Whether you are looking to deploy a model quickly or build a custom solution with various utilities, this framework offers a solid starting point.

 ##  Quick-start

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[X] Modify README.md ✓ https://github.com/codecrunchers/agent-fw/commit/bc0458590dcb97bce9aa681c46d1c708751efa01 Edit
Modify README.md with contents:
• Expand the "Installation of dependencies" section to provide more detailed instructions. Explain what a virtual environment is and why it is recommended. Also explain what the requirements.txt file is and what it does. Provide step-by-step instructions for creating a virtual environment, activating it, and installing the requirements.

--- 
+++ 
@@ -1,4 +1,6 @@
 #  LLM-API-starterkit
+
+This repository serves as a lightweight beta model deployment framework designed to facilitate easy and efficient deployment of machine learning models. It provides wrapped access to features such as chat history, databases, sessions, authentication, an LLM (Large Language Model), and a file parsing model. Developed with a focus on agility and integrability, the framework works out of the box with an OpenAI key and integrates seamlessly with langchain, allowing for rapid enhancements with new GPTs and LLMs. Whether you are looking to deploy a model quickly or build a custom solution with various utilities, this framework offers a solid starting point.

 ##  Quick-start

@@ -40,7 +42,11 @@

 ## # 1. Installation of dependencies

-We use the most common way of installing dependencies, which is using `pip install` with a requirements.txt.
+Installing dependencies in a Python project is typically done using the `pip install` command along with a requirements.txt file. This file contains a list of packages needed for the project, specifying versions to ensure compatibility.
+
+It is highly recommended to install these dependencies within a virtual environment. A virtual environment is an isolated Python runtime environment that allows you to manage dependencies for different projects separately without running into version conflicts. It's one of the best practices in Python development because it helps maintain your system's integrity and ensures reproducible builds.
+
+To set up a virtual environment and install the required packages, follow these steps:

 Tutorial was created using `Python 3.10`.

@@ -48,7 +54,32 @@
 pip install -r requirements.txt
 ```

-It is advised to install these requirements in a virtual environment. To create a virtual environment and install the requirements there, use the following:
+1. Install the `virtualenv` package globally (if not already installed):
+```bash
+pip install virtualenv
+```
+
+2. Navigate to the project directory where you want to create the virtual environment.
+
+3. Create a virtual environment named 'venv' (you can choose any name):
+```bash
+python3 -m venv venv
+```
+
+4. Activate the virtual environment:
+On macOS and Linux:
+```bash
+source venv/bin/activate
+```
+On Windows:
+```bash
+call venv\Scripts\activate
+```
+
+5. With the virtual environment activated, install the dependencies from requirements.txt:
+```bash
+pip install -r requirements.txt
+```
 ```bash
 python3 -m venv venv
 . venv/bin/activate

[X] Running GitHub Actions for README.md ✓ Edit
Check README.md with contents:

Ran GitHub Actions for bc0458590dcb97bce9aa681c46d1c708751efa01:

[X] Modify README.md ✓ https://github.com/codecrunchers/agent-fw/commit/fd7c7e764380ae4e6a7e503ea9dc233503c6372c Edit
Modify README.md with contents:
• Expand the "LLM model preparation" section to provide more detailed instructions. Explain what an LLM is and why it is needed. Also explain what the .env file is and what it does. Provide step-by-step instructions for renaming .env.example to .env and adding the OpenAI API key.

--- 
+++ 
@@ -1,4 +1,6 @@
 #  LLM-API-starterkit
+
+This repository serves as a lightweight beta model deployment framework designed to facilitate easy and efficient deployment of machine learning models. It provides wrapped access to features such as chat history, databases, sessions, authentication, an LLM (Large Language Model), and a file parsing model. Developed with a focus on agility and integrability, the framework works out of the box with an OpenAI key and integrates seamlessly with langchain, allowing for rapid enhancements with new GPTs and LLMs. Whether you are looking to deploy a model quickly or build a custom solution with various utilities, this framework offers a solid starting point.

 ##  Quick-start

@@ -40,7 +42,11 @@

 ## # 1. Installation of dependencies

-We use the most common way of installing dependencies, which is using `pip install` with a requirements.txt.
+Installing dependencies in a Python project is typically done using the `pip install` command along with a requirements.txt file. This file contains a list of packages needed for the project, specifying versions to ensure compatibility.
+
+It is highly recommended to install these dependencies within a virtual environment. A virtual environment is an isolated Python runtime environment that allows you to manage dependencies for different projects separately without running into version conflicts. It's one of the best practices in Python development because it helps maintain your system's integrity and ensures reproducible builds.
+
+To set up a virtual environment and install the required packages, follow these steps:

 Tutorial was created using `Python 3.10`.

@@ -48,7 +54,32 @@
 pip install -r requirements.txt
 ```

-It is advised to install these requirements in a virtual environment. To create a virtual environment and install the requirements there, use the following:
+1. Install the `virtualenv` package globally (if not already installed):
+```bash
+pip install virtualenv
+```
+
+2. Navigate to the project directory where you want to create the virtual environment.
+
+3. Create a virtual environment named 'venv' (you can choose any name):
+```bash
+python3 -m venv venv
+```
+
+4. Activate the virtual environment:
+On macOS and Linux:
+```bash
+source venv/bin/activate
+```
+On Windows:
+```bash
+call venv\Scripts\activate
+```
+
+5. With the virtual environment activated, install the dependencies from requirements.txt:
+```bash
+pip install -r requirements.txt
+```
 ```bash
 python3 -m venv venv
 . venv/bin/activate
@@ -59,10 +90,29 @@

 ## # 2. LLM model preparation

+LLM stands for Large Language Model, which is an advanced Artificial Intelligence model capable of understanding and generating natural language. LLMs are essential for a variety of Natural Language Processing (NLP) tasks such as translation, question-answering, and conversation simulations. In the context of our deployment framework, we use LLMs to process and interact with user inputs, providing intelligent and contextually relevant responses.
+
+Before using an LLM, you must ensure the model is properly set up with the necessary API keys and configurations. This typically involves the following steps:
+
 ## ## 2.1 **With an OpenAI key**

-1. Change the filename of .env.example to .env
-2. Add your OpenAI API key to .env
+1. Rename the `.env.example` file to `.env`. This can be done with the following command on UNIX-based systems (including Linux and macOS):
+```bash
+mv .env.example .env
+```
+On Windows, you can use:
+```cmd
+rename .env.example .env
+```
+
+2. Open the newly renamed `.env` file in a text editor of your choice.
+
+3. Locate the line that reads `OPENAI_API_KEY=` and add your OpenAI API key immediately after the equals sign so that it looks like this:
+```plaintext
+OPENAI_API_KEY=your_api_key_here
+```
+
+The `.env` file is used to store environment variables, which are a set of dynamic named values that can affect the way running processes will behave on a computer. In this case, it is being used to securely store the API key which is sensitive information and should not be hard-coded or checked into version control systems. Adding your OpenAI API key to the `.env` file allows the application to authenticate with the OpenAI API and use the LLM for processing requests.`

 Done.

[X] Running GitHub Actions for README.md ✓ Edit
Check README.md with contents:

Ran GitHub Actions for fd7c7e764380ae4e6a7e503ea9dc233503c6372c:

[X] Modify README.md ✓ https://github.com/codecrunchers/agent-fw/commit/f2f234090a004624d1f46100009fc4afeb447935 Edit
Modify README.md with contents:
• Expand the "Running the FastAPI application" section to provide more detailed instructions. Explain what FastAPI is and why it is used. Also explain what the OpenAI API is and what it does. Provide step-by-step instructions for running the application with the OpenAI API.

--- 
+++ 
@@ -1,4 +1,6 @@
 #  LLM-API-starterkit
+
+This repository serves as a lightweight beta model deployment framework designed to facilitate easy and efficient deployment of machine learning models. It provides wrapped access to features such as chat history, databases, sessions, authentication, an LLM (Large Language Model), and a file parsing model. Developed with a focus on agility and integrability, the framework works out of the box with an OpenAI key and integrates seamlessly with langchain, allowing for rapid enhancements with new GPTs and LLMs. Whether you are looking to deploy a model quickly or build a custom solution with various utilities, this framework offers a solid starting point.

 ##  Quick-start

@@ -40,7 +42,11 @@

 ## # 1. Installation of dependencies

-We use the most common way of installing dependencies, which is using `pip install` with a requirements.txt.
+Installing dependencies in a Python project is typically done using the `pip install` command along with a requirements.txt file. This file contains a list of packages needed for the project, specifying versions to ensure compatibility.
+
+It is highly recommended to install these dependencies within a virtual environment. A virtual environment is an isolated Python runtime environment that allows you to manage dependencies for different projects separately without running into version conflicts. It's one of the best practices in Python development because it helps maintain your system's integrity and ensures reproducible builds.
+
+To set up a virtual environment and install the required packages, follow these steps:

 Tutorial was created using `Python 3.10`.

@@ -48,7 +54,32 @@
 pip install -r requirements.txt
 ```

-It is advised to install these requirements in a virtual environment. To create a virtual environment and install the requirements there, use the following:
+1. Install the `virtualenv` package globally (if not already installed):
+```bash
+pip install virtualenv
+```
+
+2. Navigate to the project directory where you want to create the virtual environment.
+
+3. Create a virtual environment named 'venv' (you can choose any name):
+```bash
+python3 -m venv venv
+```
+
+4. Activate the virtual environment:
+On macOS and Linux:
+```bash
+source venv/bin/activate
+```
+On Windows:
+```bash
+call venv\Scripts\activate
+```
+
+5. With the virtual environment activated, install the dependencies from requirements.txt:
+```bash
+pip install -r requirements.txt
+```
 ```bash
 python3 -m venv venv
 . venv/bin/activate
@@ -59,10 +90,29 @@

 ## # 2. LLM model preparation

+LLM stands for Large Language Model, which is an advanced Artificial Intelligence model capable of understanding and generating natural language. LLMs are essential for a variety of Natural Language Processing (NLP) tasks such as translation, question-answering, and conversation simulations. In the context of our deployment framework, we use LLMs to process and interact with user inputs, providing intelligent and contextually relevant responses.
+
+Before using an LLM, you must ensure the model is properly set up with the necessary API keys and configurations. This typically involves the following steps:
+
 ## ## 2.1 **With an OpenAI key**

-1. Change the filename of .env.example to .env
-2. Add your OpenAI API key to .env
+1. Rename the `.env.example` file to `.env`. This can be done with the following command on UNIX-based systems (including Linux and macOS):
+```bash
+mv .env.example .env
+```
+On Windows, you can use:
+```cmd
+rename .env.example .env
+```
+
+2. Open the newly renamed `.env` file in a text editor of your choice.
+
+3. Locate the line that reads `OPENAI_API_KEY=` and add your OpenAI API key immediately after the equals sign so that it looks like this:
+```plaintext
+OPENAI_API_KEY=your_api_key_here
+```
+
+The `.env` file is used to store environment variables, which are a set of dynamic named values that can affect the way running processes will behave on a computer. In this case, it is being used to securely store the API key which is sensitive information and should not be hard-coded or checked into version control systems. Adding your OpenAI API key to the `.env` file allows the application to authenticate with the OpenAI API and use the LLM for processing requests.`

 Done.

@@ -95,12 +145,21 @@

 ## # 3. Running the FastAPI application

-You should be ready to run the most basic example.
+FastAPI is a modern, high-performance web framework for building APIs with Python 3.7+ based on standard Python type hints. It's known for its speed, ease of use, and ability to create RESTful APIs quickly with automatic interactive documentation. FastAPI is particularly suited for this Light Language Model (LLM) API as it supports asynchronous request handling and is designed for scalability, making it a good choice for machine learning applications where concurrent handling of multiple requests is commonplace.

-With OpenAI API
+The OpenAI API provides access to OpenAI's powerful language models, including GPT-3 and others. By interacting with this API, users can perform natural language tasks such as completion, translation, summarization, and question-answering. Its strength lies in its ability to generate human-like text and understand complex queries.
+
+To run the application using the OpenAI API, follow these steps:
+1. Make sure you have created and activated your virtual environment (as described in the installation section) and that the `.env` file contains your OpenAI API key.
+
+2. Run the following command in the terminal from the root directory of the project:
 ```bash
 uvicorn app.main_openai:app --port 80 --env-file .env
 ```
+
+3. Open a web browser and navigate to `http://localhost:80/docs` to view the automatically generated API documentation courtesy of FastAPI and Swagger UI. Here, you have an interactive UI to send requests to the API and observe the responses.
+
+4. Use the interactive API documentation to send requests to your LLM API. To do this, click on the endpoint you wish to test, then click 'Try it out', enter your request data, and finally hit the 'Execute' button to run the query and see the response.

 With local LLM using Vicuna, compatible with X86_64 architecture
 ```bash

[X] Running GitHub Actions for README.md ✓ Edit
Check README.md with contents:

Ran GitHub Actions for f2f234090a004624d1f46100009fc4afeb447935:

[X] Modify README.md ✓ https://github.com/codecrunchers/agent-fw/commit/c208329ea8b5069354e31de159e32487fb31b62a Edit
Modify README.md with contents:
• Add a new section at the end of the README.md file called "Additional Information". In this section, provide any additional information that users might find useful. This could include tips and tricks, common issues and solutions, links to relevant resources, etc.

--- 
+++ 
@@ -1,4 +1,6 @@
 #  LLM-API-starterkit
+
+This repository serves as a lightweight beta model deployment framework designed to facilitate easy and efficient deployment of machine learning models. It provides wrapped access to features such as chat history, databases, sessions, authentication, an LLM (Large Language Model), and a file parsing model. Developed with a focus on agility and integrability, the framework works out of the box with an OpenAI key and integrates seamlessly with langchain, allowing for rapid enhancements with new GPTs and LLMs. Whether you are looking to deploy a model quickly or build a custom solution with various utilities, this framework offers a solid starting point.

 ##  Quick-start

@@ -40,7 +42,11 @@

 ## # 1. Installation of dependencies

-We use the most common way of installing dependencies, which is using `pip install` with a requirements.txt.
+Installing dependencies in a Python project is typically done using the `pip install` command along with a requirements.txt file. This file contains a list of packages needed for the project, specifying versions to ensure compatibility.
+
+It is highly recommended to install these dependencies within a virtual environment. A virtual environment is an isolated Python runtime environment that allows you to manage dependencies for different projects separately without running into version conflicts. It's one of the best practices in Python development because it helps maintain your system's integrity and ensures reproducible builds.
+
+To set up a virtual environment and install the required packages, follow these steps:

 Tutorial was created using `Python 3.10`.

@@ -48,7 +54,32 @@
 pip install -r requirements.txt
 ```

-It is advised to install these requirements in a virtual environment. To create a virtual environment and install the requirements there, use the following:
+1. Install the `virtualenv` package globally (if not already installed):
+```bash
+pip install virtualenv
+```
+
+2. Navigate to the project directory where you want to create the virtual environment.
+
+3. Create a virtual environment named 'venv' (you can choose any name):
+```bash
+python3 -m venv venv
+```
+
+4. Activate the virtual environment:
+On macOS and Linux:
+```bash
+source venv/bin/activate
+```
+On Windows:
+```bash
+call venv\Scripts\activate
+```
+
+5. With the virtual environment activated, install the dependencies from requirements.txt:
+```bash
+pip install -r requirements.txt
+```
 ```bash
 python3 -m venv venv
 . venv/bin/activate
@@ -59,10 +90,29 @@

 ## # 2. LLM model preparation

+LLM stands for Large Language Model, which is an advanced Artificial Intelligence model capable of understanding and generating natural language. LLMs are essential for a variety of Natural Language Processing (NLP) tasks such as translation, question-answering, and conversation simulations. In the context of our deployment framework, we use LLMs to process and interact with user inputs, providing intelligent and contextually relevant responses.
+
+Before using an LLM, you must ensure the model is properly set up with the necessary API keys and configurations. This typically involves the following steps:
+
 ## ## 2.1 **With an OpenAI key**

-1. Change the filename of .env.example to .env
-2. Add your OpenAI API key to .env
+1. Rename the `.env.example` file to `.env`. This can be done with the following command on UNIX-based systems (including Linux and macOS):
+```bash
+mv .env.example .env
+```
+On Windows, you can use:
+```cmd
+rename .env.example .env
+```
+
+2. Open the newly renamed `.env` file in a text editor of your choice.
+
+3. Locate the line that reads `OPENAI_API_KEY=` and add your OpenAI API key immediately after the equals sign so that it looks like this:
+```plaintext
+OPENAI_API_KEY=your_api_key_here
+```
+
+The `.env` file is used to store environment variables, which are a set of dynamic named values that can affect the way running processes will behave on a computer. In this case, it is being used to securely store the API key which is sensitive information and should not be hard-coded or checked into version control systems. Adding your OpenAI API key to the `.env` file allows the application to authenticate with the OpenAI API and use the LLM for processing requests.`

 Done.

@@ -95,12 +145,21 @@

 ## # 3. Running the FastAPI application

-You should be ready to run the most basic example.
+FastAPI is a modern, high-performance web framework for building APIs with Python 3.7+ based on standard Python type hints. It's known for its speed, ease of use, and ability to create RESTful APIs quickly with automatic interactive documentation. FastAPI is particularly suited for this Light Language Model (LLM) API as it supports asynchronous request handling and is designed for scalability, making it a good choice for machine learning applications where concurrent handling of multiple requests is commonplace.

-With OpenAI API
+The OpenAI API provides access to OpenAI's powerful language models, including GPT-3 and others. By interacting with this API, users can perform natural language tasks such as completion, translation, summarization, and question-answering. Its strength lies in its ability to generate human-like text and understand complex queries.
+
+To run the application using the OpenAI API, follow these steps:
+1. Make sure you have created and activated your virtual environment (as described in the installation section) and that the `.env` file contains your OpenAI API key.
+
+2. Run the following command in the terminal from the root directory of the project:
 ```bash
 uvicorn app.main_openai:app --port 80 --env-file .env
 ```
+
+3. Open a web browser and navigate to `http://localhost:80/docs` to view the automatically generated API documentation courtesy of FastAPI and Swagger UI. Here, you have an interactive UI to send requests to the API and observe the responses.
+
+4. Use the interactive API documentation to send requests to your LLM API. To do this, click on the endpoint you wish to test, then click 'Try it out', enter your request data, and finally hit the 'Execute' button to run the query and see the response.

 With local LLM using Vicuna, compatible with X86_64 architecture
 ```bash
@@ -118,3 +177,13 @@

 ![Showing FastAPI with the Try it out button](docs/try_it_out.png)

+## Additional Information
+
+In this section, we offer more insights, tips, and potential fixes that might be helpful during your experience with the LLM-API-starterkit. If you encounter common issues, you can refer to the following resources or this section for solutions.
+
+- **Tips and Tricks**: Get acquainted with the features of langchain to fully utilize its capabilities with LLMs.
+- **Common Issues and Solutions**: Check the GitHub issues tab for troubleshooting common problems that other users have faced.
+- **Relevant Resources**: Visit the official documentation pages of the tools utilized in this framework, such as FastAPI, OpenAI, and langchain, to gain deeper knowledge and best practices.
+
+More information will be added to this section as the project evolves and more feedback is gathered from users.
+

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Sweep: I need documentation for this repo #3