Leveraging Your Own Documents in a Langchain Pipeline
This project highlights how to leverage a ChromaDB vectorstore in a Langchain pipeline to create drumroll please a GPT Investment Banker (ergh, I cringed as I wrote that, but alas it's actually pretty practical). You can load in a pdf based document and use it alongside an LLM without the need for fine tuning.
See it live and in action 📺
Startup 🚀
- Create a virtual environment
python -m venv langchainenv - Activate it:
- Windows:
.\langchainenv\Scripts\activate - Mac:
source langchain/bin/activate
- Windows:
- Clone this repo
git clone https://github.com/nicknochnack/LangchainDocuments - Go into the directory
cd LangchainDocuments - Install the required dependencies
pip install -r requirements.txt - Add your OpenAI APIKey to line 22 of
app.py - Start the app
streamlit run app.py - Go back to my YouTube channel and like and subscribe
😉 ...no seriously...please! lol
Other References 🔗
The main LG Agent used:Langchain VectorStore Agents
Who, When, Why?
👨🏾💻 Author: Nick Renotte