https://github.com/lalanikarim/ai-chatbot-conversational
AI Chatbot with Streamlit, Langchain, and Mistral7b with conversational memory
https://github.com/lalanikarim/ai-chatbot-conversational
ai chatbot langchain llamacpp llm mistral-7b streamlit
Last synced: 5 months ago
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AI Chatbot with Streamlit, Langchain, and Mistral7b with conversational memory
- Host: GitHub
- URL: https://github.com/lalanikarim/ai-chatbot-conversational
- Owner: lalanikarim
- License: mit
- Created: 2023-10-24T03:24:52.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-29T17:04:26.000Z (over 2 years ago)
- Last Synced: 2023-10-29T18:31:12.649Z (over 2 years ago)
- Topics: ai, chatbot, langchain, llamacpp, llm, mistral-7b, streamlit
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
title: Ai Chatbot with Conversational Memory
emoji: 🔥
colorFrom: indigo
colorTo: gray
sdk: streamlit
sdk_version: 1.28.0
app_file: main.py
pinned: false
license: mit
---
# Streamlit + Langchain + LLama.cpp w/ Mistral + Conversational Memory
Run your own AI Chatbot locally without a GPU.
To make that possible, we use the [Mistral 7b](https://mistral.ai/news/announcing-mistral-7b/) model.
However, you can use any quantized model that is supported by [llama.cpp](https://github.com/ggerganov/llama.cpp).
This model will chatbot will allow you to define it's personality and respond to the questions accordingly.
This example remembers the chat history allowing you to ask follow up questions.
# TL;DR instructions
1. Install llama-cpp-python
2. Install langchain
3. Install streamlit
4. Run streamlit
# Step by Step instructions
The setup assumes you have `python` already installed and `venv` module available.
1. Download the code or clone the repository.
2. Inside the root folder of the repository, initialize a python virtual environment:
```bash
python -m venv venv
```
3. Activate the python envitonment:
```bash
source venv/bin/activate
```
4. Install required packages (`langchain`, `llama.cpp`, and `streamlit`):
```bash
pip install -r requirements.txt
```
5. Start `streamlit`:
```bash
streamlit run main.py
```
6. The `models` directory will be created and the app will download the `Mistral7b` quantized model from `huggingface` from the following link:
[mistral-7b-instruct-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_0.gguf)
# Screenshot
