https://github.com/sachs7/chat_llm
A RAG pipeline using LangChain, OpenAI API, Gradio to chat with your PDFs!
https://github.com/sachs7/chat_llm
chat gradio langchain llm pdf-chatbot rag
Last synced: 2 months ago
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A RAG pipeline using LangChain, OpenAI API, Gradio to chat with your PDFs!
- Host: GitHub
- URL: https://github.com/sachs7/chat_llm
- Owner: sachs7
- License: apache-2.0
- Created: 2023-08-17T22:09:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-23T04:23:05.000Z (over 1 year ago)
- Last Synced: 2025-02-04T18:42:12.000Z (4 months ago)
- Topics: chat, gradio, langchain, llm, pdf-chatbot, rag
- Language: Python
- Homepage:
- Size: 1.83 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Chat with your own data!
An app that lets you chat with your documents (PDFs); the app makes use of the OpenAI LLM using LangChain as the framework.
```
.
├── README.md
├── chat.py --> Main app
├── config.conf --> Configuration file that allows one to change the models etc
├── input_docs --> Document directory to store your PDFs
│ └── Python-ML.pdf
├── logs --> Log directory; app logs as well as user logs
├── requirements.txt
├── restart_service_on_changes.py --> Restarts the `chat.py` if there are changes in the `input_docs` folder
└── translator_langchain.py --> Used for supporting the translation of the queries
```
# How it works?
`chat.py` is the main app that connects to the LLM (OpenAI) based on the configuration that you give in the `config.conf` file.
First, we split the document based on the configurations and then create a database index which will be used later to query. The database that we use is a vector database called, 'ChromaDB'. This gets created when the script is executed for the first time. Also, for 'ChromaDB' to work, one needs to have a `Sqlite3 DB`, so make sure to install one.
Create a `.env` file and add your OpenAI API key as:
`OPENAI_API_KEY=sk-XYZ`
The app makes use of 'Gradio' framework; so make sure to run the following command to install the required libraries:
`pip install -r requirements.txt`
Once the required libraries are installed, you can start the application by:
`python3 chat.py`, will output the link which you can paste into your browser to access the app.
The `chat.py` in turn makes use of `translator_langchain.py` to provide a translation feature.
In case, the user wants the indexing to be updated whenever there are changes to the `input_docs` like when a new PDF file is added/removed; the user can make use of `restart_service_on_changes.py` -- this will start the `chat.py` internally, so you need to make sure to close any previous running instance of `chat.py`.
Note: If you are using `restart_service_on_changes.py` --> then expect brief service disruption whenever there are changes to the `input_docs` folder as the script will stop the `chat.py` momentarily to index the new files.