LangChain Workflows

LangChain Workflows is a Python-based project that leverages advanced language models to generate concise workflow prompts from input text. It provides a streamlined process from reading and processing input to generating and saving workflow prompts, making it a powerful tool for text analysis and information extraction.

Project Structure

The project is structured as follows:

• langchain_env/: Main project directory
  • configs.py: Contains the API key setup and the dictionary of prompts.
  • models.py: Contains the ResponseModel Pydantic model.
  • input_processing.py: Contains functions for reading input and processing tasks.
  • output_generation.py: Contains the function for generating output using language models.
  • file_operations.py: Contains functions for saving results to a Markdown file.
  • main.py: The main script to orchestrate the process.
• tests/: Contains test files for the project.
• Input-Output/: Contains input and output files.

Usage

To use this project, follow these steps:

1. Make sure you have conda installed on your system.

2. Clone the repository and navigate to the project directory.

3. Set up the environment using the environment.yml file:

   conda env create -f environment.yml
   

4. Activate the newly created environment:

   conda activate langchain-env
   

5. Set up API keys for various providers by setting environment variables:

   • For macOS/Linux (add to .bashrc, .zshrc, or equivalent):

     export OPENAI_API_KEY='your_openai_api_key'
     export GROQ_API_KEY='your_groq_api_key'
     export COHERE_API_KEY='your_cohere_api_key'
     export MISTRAL_API_KEY='your_mistral_api_key'
     # Add more providers as needed
     

   • For Windows (set environment variables in the command prompt or PowerShell):

     setx OPENAI_API_KEY "your_openai_api_key"
     setx GROQ_API_KEY "your_groq_api_key"
     setx COHERE_API_KEY "your_cohere_api_key"
     setx MISTRAL_API_KEY "your_mistral_api_key"
     REM Add more providers as needed

6. Install all the dependencies using poetry. All dependencies are listed in the pyproject.toml file:

   poetry install
   

7. Prepare your input text file and place it in the Input-Output/ directory.

8. Run the main.py script. This will read the input file, process the tasks, generate the output, and save it to an output file in the Input-Output/ directory.

Contributing

Contributions are welcome. Please submit a pull request or open an issue to discuss your changes.

License

This project is licensed under the terms of the MIT license.