README.md
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
📅 Version: 1.?
📜 License: This project is licensed under the MIT License
File List | Total items: 5 | ||
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Name | Last Commit | Size | Last Modified |
.gitattributes | |||
README.md | |||
annualreport.pdf | |||
app.py | |||
requirements.txt |