Chatbot using Langchain and Retrieval-Augmented Generation (RAG)

Langchain-RAG-Chatbot

Overview

Langchain-RAG-Chatbot is an advanced AI chatbot that utilizes Retrieval-Augmented Generation (RAG) with LangChain to provide highly relevant, context-aware responses. By combining large language models (LLMs) with external knowledge retrieval, the chatbot can answer domain-specific queries, support users with documentation, and maintain coherent multi-turn conversations.

Key Concepts

What is Retrieval-Augmented Generation (RAG)?

RAG is an architecture that combines traditional language models with a retrieval system. Instead of relying solely on the LLM's training data, the model retrieves relevant information from external sources (like documents, databases, or APIs) at runtime. This enables the chatbot to:

Provide up-to-date and factual answers
Handle domain-specific knowledge
Reduce hallucinations by grounding responses in real data

How LangChain Helps

LangChain is a framework for developing applications powered by language models. It offers out-of-the-box tools for chaining LLM prompts with retrieval, memory, and more.

Architecture: How the RAG Chatbot Works

Here’s a high-level flow of a typical RAG-based chatbot using LangChain:

User Query → Retriever → Relevant Documents → LLM (with context) → Response

Step-by-step:

Receive Query: The user asks a question.
Retrieve Documents: The retriever searches your vector database for relevant documents.
Augment Context: The chatbot passes both the user’s question and the retrieved context to the LLM.
Generate Response: The LLM crafts a grounded, accurate reply.

Example: Code Walkthrough

Below is a simplified example of how you might implement a RAG chatbot using LangChain in Python.

1. Load Documents & Build Vector Store (e.g., using FAISS)

from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Load and split your documents
loader = TextLoader("data/knowledge_base.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = text_splitter.split_documents(documents)

# Create embeddings and build the vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)

2. Create a Retriever

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

3. Set Up the Language Model

from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

4. Build the RAG Chain

from langchain.chains import RetrievalQA

rag_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",  # "stuff" is a simple chain that puts all context into the prompt
    retriever=retriever,
    return_source_documents=True,
)

5. Ask a Question

query = "Explain how vector search improves chatbot accuracy."
result = rag_chain({"query": query})

print("Answer:", result["result"])
print("Sources:", [doc.metadata for doc in result["source_documents"]])

Advanced Features

Conversational Memory: Use ConversationBufferMemory from LangChain to maintain chat history.
Multi-Document Support: Index multiple files or even web pages.
Custom Prompt Engineering: Craft prompt templates to guide the LLM’s responses.

Example: Adding Conversational Memory

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

conversational_rag = ConversationalRetrievalChain.from_llm(
    llm,
    retriever=retriever,
    memory=memory,
)

# Example chat
response = conversational_rag({"question": "What is RAG in chatbots?"})
print(response["answer"])

Project Structure

Langchain-RAG-chatbot/
├── data/              # Knowledge base and document storage
├── src/               # Source code for the chatbot
├── requirements.txt   # Python dependencies
├── app.py             # Main application entry point
└── README.md

Use Cases

Customer support chatbots that fetch answers from documentation
Internal knowledge assistants for teams
Educational tutoring bots grounded in textbooks
Automated FAQ bots with up-to-date information

Technologies Used

Python
LangChain
Vector Databases: FAISS, Pinecone, ChromaDB, etc.
Large Language Models: OpenAI GPT, Hugging Face models

Getting Started

Clone the Repository

git clone https://github.com/erenyeager101/Langchain-RAG-chatbot.git
cd Langchain-RAG-chatbot

Install Requirements
```
pip install -r requirements.txt
```
Configure Environment
- Set API keys for your LLM provider.
- Place your documents in the data/ directory or configure your document loader.
Run the Chatbot
```
python app.py
```

Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements or new features.

License

This project is licensed under the MIT License.