This notebook demonstrates the use of Retrieval-Augmented Generation (RAG) techniques using various libraries and APIs such as HuggingFace, LangChain, Pinecone, and OpenAI. The goal is to process a dataset of resumes, embed the text data, store it in a vector database, and perform queries to retrieve relevant information.
Skills:
- HuggingFace
- LangChain
- Pinecone
- OpenAi
Other Resources:
To get started with this project, follow these steps:
-
Clone the repository to your local machine.
-
Set up a virtual environment:
python -m venv .env source .env/bin/activate # On Windows, use `.env\Scripts\activate` -
Install the required dependencies:
pip install -r requirements.txt -
Set up your environment variables: Create a
.envfile in the root directory and add your API keys:GROQ_API_KEY=your_groq_api_key OPENAI_API_KEY=your_openai_api_key PINECONE_API_KEY=your_pinecone_api_key -
Make sure you have the necessary permissions and access to the APIs mentioned in the
.envfile.
Project Structure
.env: Contains environment variables and API keys (not tracked by git)
.gitignore: Specifies files and directories that should not be tracked by git
requirements.txt: Lists all Python dependencies for the project