https://github.com/subhangisati/langchat-explorer
"LangChat Explorer: Your intuitive document companion. Effortlessly explore vast information with natural language conversations. Simplify queries, gain insights, and embark on a seamless journey of knowledge discovery. Unleash the power of language with LangChat Explorer."
https://github.com/subhangisati/langchat-explorer
api deep-learning document-retrieval generative-ai llms machine-learning pdf-document-processor python3 q-and-a-bot
Last synced: 3 months ago
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"LangChat Explorer: Your intuitive document companion. Effortlessly explore vast information with natural language conversations. Simplify queries, gain insights, and embark on a seamless journey of knowledge discovery. Unleash the power of language with LangChat Explorer."
- Host: GitHub
- URL: https://github.com/subhangisati/langchat-explorer
- Owner: SubhangiSati
- Created: 2024-02-05T17:50:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-10T06:18:15.000Z (over 1 year ago)
- Last Synced: 2025-01-01T06:14:11.908Z (5 months ago)
- Topics: api, deep-learning, document-retrieval, generative-ai, llms, machine-learning, pdf-document-processor, python3, q-and-a-bot
- Language: Python
- Homepage:
- Size: 471 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# LangChain Explorer
## Overview
This Streamlit web application, LangChain Explorer, leverages LangChain's powerful capabilities for question answering and information retrieval. It enables users to input queries and receive relevant answers from a collection of PDF documents. The application employs various language models and embedding techniques to provide accurate and context-aware responses.
## Prerequisites
- Python 3.x
- Streamlit
- langchain library
- Hugging Face Transformers
- PyTorch (if using GPU)
## Installation
Ensure you have the required dependencies installed using:
```bash
pip install streamlit langchain torch
```
Additionally, you may need to install the Hugging Face Transformers library:
```bash
pip install transformers
```
## Usage
1. Download the PDF documents and place them in a directory (replace 'data/' with the actual directory path).
2. Run the Streamlit app using the following command:
```bash
streamlit run langchain_explorer.py
```
3. Enter your query in the input box and click the "Submit" button to receive relevant answers.
## Code Structure
- **Document Loading and Splitting:**
- PDF documents are loaded from a specified directory using LangChain's `PyPDFLoader` and split into text chunks.
- **Embedding and Vector Store:**
- LangChain utilizes Hugging Face embeddings to convert text chunks into embeddings.
- A FAISS Vector Store is created from the embeddings.
- **Question Answering Chain:**
- LangChain's `RetrievalQA` is configured to retrieve relevant information from the vector store.
- A language model (LLM) is used to generate answers based on the retrieved information.
- **Streamlit App:**
- A Streamlit web application allows users to input queries and receive real-time answers.
## Steps to Run
1. Configure the data path, model paths, and other parameters according to your setup.
2. Run the Streamlit app script:
```bash
streamlit run langchain_explorer.py
```
3. Open the provided local URL in your web browser.
## Customization
- Adjust the data path, model paths, and other configurations based on your document collection and language model choices.
## License
This code is licensed under the [MIT License](LICENSE).
Feel free to customize and use this code for your question answering and information retrieval tasks. If you find it helpful, consider providing attribution to the original source.