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https://github.com/seven-33/langchain-chat
langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.
https://github.com/seven-33/langchain-chat
chatbot chatbots embedding-model embedding-models embedding-python embedding-similarity faiss faiss-backend gpt-3 gpt-4 gpt-j gpt35turbo langchain language-python pinecone
Last synced: 3 months ago
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langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.
- Host: GitHub
- URL: https://github.com/seven-33/langchain-chat
- Owner: Seven-33
- Created: 2023-04-23T16:17:19.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-23T16:18:01.000Z (almost 2 years ago)
- Last Synced: 2024-07-29T21:20:08.769Z (6 months ago)
- Topics: chatbot, chatbots, embedding-model, embedding-models, embedding-python, embedding-similarity, faiss, faiss-backend, gpt-3, gpt-4, gpt-j, gpt35turbo, langchain, language-python, pinecone
- Language: Python
- Homepage:
- Size: 433 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# langchain-chat
langchain-chat is a powerful AI-driven Q&A system that leverages OpenAI's GPT-4 model to provide relevant and accurate
answers to user queries. The system indexes documents from websites or PDF files using FAISS (Facebook AI Similarity
Search) and offers a convenient interface for interacting with the data.
## Features
- Load and split documents from websites or PDF files
- Index documents using FAISS for efficient similarity search
- Utilize OpenAI's GPT-4 to generate human-like responses
- Remember previous conversations and provide context-aware answers
- Easy to set up and extend
## Installation
1. Clone the repository
2. Create a virtual environment
```bash
python -m venv venv
```
3. Activate the virtual environment
```bash
source venv/Scripts/activate
```
4. Install the dependencies
```bash
pip install -r requirements.txt
```
5. Copy the `.env.example` file to `.env` and fill in the required values
```bash
cp .env.example .env
```
```bash
OPEN_AI_KEY = "sk-"
WEBSITE_URLS="https://website1, https://website2"
```
6. Run the application
```bash
python with_faiss.py
```
## Example Images
