MultiRouteChatbot

A Streamlit-powered chatbot using LangChain and Groq LLMs, capable of routing user questions to different expert personas (Math Professor, Philosopher, Medical Doctor, or General Assistant) based on the question's content.

---

Features

• Expert Routing: Directs user questions to the most relevant expert.
• Two Routing Modes:
  • Keyword-based routing (app.py): Fast, transparent, and simple.
  • LLM-based smart routing (app2.py): Uses the LLM to classify questions for more flexible and robust routing.
• Streamlit UI: Simple web interface for chatting with the routed experts.
• LangSmith Tracing: Integrated tracing for debugging and monitoring via LangSmith.

---

Files

app.py (Keyword-based Routing)

• How it works:
  Uses a Python function to check for keywords in the user's question and selects the appropriate expert chain.
• Pros:
  • Fast and cost-effective (only one LLM call per question).
  • Easy to understand and modify routing logic.
• Cons:
  • Limited to the keywords defined in the code.
  • May misclassify questions that use unexpected wording.

app2.py (LLM-based Smart Routing)

• How it works:
  Uses the LLM itself to classify the user's question into one of four categories: math, philosophy, doctor, or default. The selected expert then answers the question.
• Pros:
  • More flexible and robust to varied question phrasing.
  • Can handle ambiguous or complex queries better.
• Cons:
  • Slightly slower (two LLM calls per question: one for routing, one for answering).
  • Higher API usage/cost.

---

Setup

1. Clone the repository:

   git clone https://github.com/Dhru3/multi-route-chatbot-using-langchain-framework.git
   cd MultiRouteChatbot

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Create a .env file in the root directory with the following:

     GROQ_API_KEY=your_groq_api_key
     LANGCHAIN_API_KEY=your_langchain_api_key
     

4. Run the app:

   • For keyword-based routing:

     streamlit run app.py

   • For LLM-based smart routing:

     streamlit run app2.py

---

Usage

• Enter your question in the input box.
• The chatbot will route your question to the most relevant expert and display the answer, indicating which expert responded.

---

Example Questions

• Math:
  "How do I solve this integral?"
• Philosophy:
  "What is the meaning of life?"
• Doctor:
  "What should I do if I have a fever?"
• Default:
  "How are potatoes grown?"# MultiRouteChatbot

A Streamlit-powered chatbot using LangChain and Groq LLMs, capable of routing user questions to different expert personas (Math Professor, Philosopher, Medical Doctor, or General Assistant) based on the question's content.

---

Features

• Expert Routing: Directs user questions to the most relevant expert.
• Two Routing Modes:
  • Keyword-based routing (app.py): Fast, transparent, and simple.
  • LLM-based smart routing (app2.py): Uses the LLM to classify questions for more flexible and robust routing.
• Streamlit UI: Simple web interface for chatting with the routed experts.
• LangSmith Tracing: Integrated tracing for debugging and monitoring via LangSmith.

---

Files

app.py (Keyword-based Routing)

• How it works:
  Uses a Python function to check for keywords in the user's question and selects the appropriate expert chain.
• Pros:
  • Fast and cost-effective (only one LLM call per question).
  • Easy to understand and modify routing logic.
• Cons:
  • Limited to the keywords defined in the code.
  • May misclassify questions that use unexpected wording.

app2.py (LLM-based Smart Routing)

• How it works:
  Uses the LLM itself to classify the user's question into one of four categories: math, philosophy, doctor, or default. The selected expert then answers the question.
• Pros:
  • More flexible and robust to varied question phrasing.
  • Can handle ambiguous or complex queries better.
• Cons:
  • Slightly slower (two LLM calls per question: one for routing, one for answering).
  • Higher API usage/cost.

---

Setup

1. Clone the repository:

   git clone https://github.com/Dhru3/MultiRouteChatbot.git
   cd MultiRouteChatbot

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Create a .env file in the root directory with the following:

     GROQ_API_KEY=your_groq_api_key
     LANGCHAIN_API_KEY=your_langchain_api_key
     

4. Run the app:

   • For keyword-based routing:

     streamlit run app.py

   • For LLM-based smart routing:

     streamlit run app2.py

---

Usage

• Enter your question in the input box.
• The chatbot will route your question to the most relevant expert and display the answer, indicating which expert responded.

---

Example Questions

• Math:
  "How do I solve this integral?"
• Philosophy:
  "What is the meaning of life?"
• Doctor:
  "What should I do if I have a fever?"
• Default:
  "How are potatoes grown?"