https://github.com/hsleonis/llama3_qa_chatbot_mongodb
Llama3 in MongoDB: Conversational QA App with Langchain + Ollama + MongoDB + Streamlit.
https://github.com/hsleonis/llama3_qa_chatbot_mongodb
chatbot chatgpt deep-learning langchain large-language-models llama2 llama3 machine-learning mongodb natural-language-processing ollama openai python streamlit
Last synced: 3 months ago
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Llama3 in MongoDB: Conversational QA App with Langchain + Ollama + MongoDB + Streamlit.
- Host: GitHub
- URL: https://github.com/hsleonis/llama3_qa_chatbot_mongodb
- Owner: hsleonis
- License: mit
- Created: 2023-12-15T21:00:20.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-09T12:32:33.000Z (11 months ago)
- Last Synced: 2025-12-21T07:42:53.277Z (6 months ago)
- Topics: chatbot, chatgpt, deep-learning, langchain, large-language-models, llama2, llama3, machine-learning, mongodb, natural-language-processing, ollama, openai, python, streamlit
- Language: Python
- Homepage:
- Size: 1.24 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Llama3 in MongoDB: Conversational QA App
A conversational chat interface where users can interact with the `Llama-3` language model, and the conversation history is logged in `MongoDB` for future reference.
## Features:
1. **Language Model Integration**:
The app integrates the `Llama-3` language model (LLM) for natural language processing. The model is initialized with a specified `Ollama` model and a callback manager for handling streaming standard output.
2. **User Interface**:
The app's user interface is created using `Streamlit`. Users can input messages through the chat input interface. User messages are displayed in the chat, and the messages are added to the chat history.
3. **Chat Initialization**:
The app starts with an initial prompt in the chat in `Langchain`, displaying a greeting message. The chat history is initialized if it doesn't exist in the session state.
4. **MongoDB Integration**:
The app connects to `MongoDB`'s `Atlas` server. It retrieves the collection name and displays chat messages from the database on app rerun. The messages are fetched and displayed in the chat history container.
5. **Response Processing**:
The app processes user input by obtaining a response from the Ollama language model. The response is then displayed in the chat interface, and the messages are added to the chat history.
6. **Streaming Simulation**:
The app simulates the streaming of the AI's response with a spinner to indicate processing. It adds a delay to simulate a more dynamic chat experience.
7. **MongoDB Logging**:
All user and AI messages are inserted into the `MongoDB` collection for logging and retrieval.
## Demo:
## How to use
1. Install requirements:
```python
pip install -r requirements.txt
```
2. Install and run Ollama:
https://python.langchain.com/docs/integrations/llms/ollama
3. Fetch a model via `ollama pull `
e.g., for Llama 3:
```python
ollama pull llama3
```
4. Place your Environment variables in the `.env` file.
5. Run the app:
```python
streamlit run app.py
```
5. Visit http://localhost:8501 on your browser.
## Links:
1. **LangChain**: https://python.langchain.com/docs/get_started/introduction
2. **Ollama**: https://ollama.ai/library/codellama
3. **MongoDB**: https://www.mongodb.com
4. **Streamlit**: https://streamlit.io