https://github.com/ravirch/chat-interfaces-with-langchain
Build interactive chatbots with LangChain, including Q&A bots, RAG-based chat, SQL chatbots, and search engine assistants using Streamlit.
https://github.com/ravirch/chat-interfaces-with-langchain
agentic-ai generative-ai langchain-python
Last synced: 4 months ago
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Build interactive chatbots with LangChain, including Q&A bots, RAG-based chat, SQL chatbots, and search engine assistants using Streamlit.
- Host: GitHub
- URL: https://github.com/ravirch/chat-interfaces-with-langchain
- Owner: ravirch
- Created: 2025-02-19T12:29:32.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-19T12:33:45.000Z (8 months ago)
- Last Synced: 2025-02-19T13:31:41.605Z (8 months ago)
- Topics: agentic-ai, generative-ai, langchain-python
- Language: Jupyter Notebook
- Homepage:
- Size: 28.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Chat Interfaces with LangChain**
This section covers multiple chat-based projects using **LangChain**, demonstrating different techniques for building interactive AI-powered chat applications.
---
## **📌 Features**
✅ **Conversational AI with OpenAI & Ollama**
✅ **RAG-based chatbots for document interaction**
✅ **Conversational memory for contextual responses**
✅ **SQL-integrated chatbot for structured queries**
✅ **Search engine chatbot using LangChain agents**
---
## **📂 Folder Structure**
```
│── 1-Q&A Chatbot Using OpenAI/
│ ├── app.py # Chatbot powered by OpenAI LLM
│── 2-Q&A Chatbot Using Ollama/
│ ├── app.py # Chatbot using Ollama models
│── 3-RAG Document Q&A/
│ ├── research_papers/ # Sample documents for testing
│ ├── app_huggingfaceembedding.py # Chatbot using Hugging Face embeddings
│ ├── main.py # Main script for RAG-based interaction
│── 4-RAG Q&A Conversation/
│ ├── app.py # Chatbot with memory-enhanced Q&A
│ ├── temp.pdf # Example document for testing
│── 5-Search Engine/
│ ├── app.py # Chatbot acting as a search engine
│ ├── tools_agents.ipynb # LangChain tools & agents integration
│── 6-Chat SQL/
│ ├── app.py # SQL chatbot for structured queries
│ ├── sqlite.py # SQLite database management
│ ├── student.db # Sample database
```
---
## **🚀 Project Descriptions & Usage**
### **1️⃣ Q&A Chatbot Using OpenAI**
- Uses **OpenAI LLMs** to answer queries.
- Run the chatbot:
```bash
streamlit run 1-Q&A Chatbot Using OpenAI/app.py
```
### **2️⃣ Q&A Chatbot Using Ollama**
- Uses **Ollama models** for answering queries.
- Run the chatbot:
```bash
streamlit run 2-Q&A Chatbot Using Ollama/app.py
```
### **3️⃣ RAG Document Q&A**
- Implements **Retrieval-Augmented Generation (RAG)** with **Hugging Face embeddings**.
- Run the chatbot:
```bash
streamlit run 3-RAG Document Q&A/main.py
```
### **4️⃣ RAG Q&A Conversation**
- Chatbot with **memory-enabled document interaction**.
- Run the chatbot:
```bash
streamlit run 4-RAG Q&A Conversation/app.py
```
### **5️⃣ Search Engine with LangChain Agents**
- Uses **LangChain tools & agents** to perform intelligent search.
- Run the chatbot:
```bash
streamlit run 5-Search Engine/app.py
```
### **6️⃣ Chat SQL – SQL-Based Chatbot**
- Chatbot capable of querying an **SQLite database**.
- Run the chatbot:
```bash
streamlit run 6-Chat SQL/app.py
```
---
This **Chat Interfaces** section is part of the **[Generative AI with LangChain Python](https://github.com/ravirch/Generative-AI-with-LangChain-Python)** repository. 🚀