Welcome to the Docs-to-Code RAG application repository! This project demonstrates how to build a Reasoning and Answer Generation (RAG) system using LangChain, LangGraph, and Streamlit. The application ingests documentation, allows users to query it, and generates code solutions based on the provided documentation.
This application consists of several key components:
- Data Ingestion: Ingests documentation and stores it in a vector store.
- Query Transformation: Transforms user queries to optimize for similarity search.
- Knowledge Retrieval: Performs semantic search to retrieve relevant documentation.
- Knowledge Grading: Grades the retrieved knowledge for relevance.
- Code Generation: Generates code based on the refined query and relevant documentation.
- Code Evaluation: Evaluates the generated code for correctness.
- Code Fixing: Fixes any errors in the generated code.
Ensure you have the following installed on your system:
- Python 3.8+
- Virtualenv (optional but recommended)
-
Clone the repository
git clone https://github.com/BellaBe/ai-docs-to-code cd docs-to-code-rag -
Create and activate a virtual environment
python3 -m venv .venv source .venv/bin/activate -
Install the required dependencies
pip install -r requirements.txt
To start the application, run the following command:
streamlit run main.pyThis will start the Streamlit server and open the application in your default web browser.
Usage Set the Model and API Key: Enter the necessary details to configure the model. Ingest Documentation: Provide the URL and other details of the documentation to be ingested. Ask Questions: Enter your query and let the application generate code solutions based on the ingested documentation.
For a detailed tutorial on how to build this application, check out our Medium article: Building a Docs-to-Code RAG Application
Contributing Contributions are welcome! Please feel free to submit a Pull Request.
License This project is licensed under the MIT License.